Biomarkers for age-related macular degeneration and uses thereof

Abstract

Disclosed herein are methods for determining an indicator for assessing the likelihood that an individual will develop age-related macular degeneration (AMD), using a polygenic score calculated based on the results of genome-wide gene association studies.

Description

TITLE OF THE INVENTIONBIOMARKERS FOR AGE-RELATED MACULAR DEGENERATION AND USES THEREOF’

[0001] This application claims priority to Australian Provisional Patent Application No. 2023901944 entitled “Biomarkers For Age-Related Macular Degeneration And Uses Thereof” filed 19 June 2023, the contents of which are incorporated herein by reference in their entirety.FIELD OF THE INVENTION

[0002] This invention relates generally to a method for determining an indicator for assessing the likelihood that an individual will develop age-related macular degeneration (AMD), using a polygenic score calculated based on the results of genome-wide gene association studies, using thousands of single-nucleotide polymorphisms (SNPs).BACKGROUND OF THE INVENTION

[0003] Age-related macular degeneration (AMD) is a progressive degeneration of the macula, which is the leading cause of central vision loss in aged people (Wong et al. 2014). AMD patients experience several physiological changes, such as the loss of central vision, drusen, retinal pigmentary changes and angiogenesis. The disease is characterized by reduced retinal pigment epithelium function and photoreceptor loss in the macula. AMD can be classified into two types: early (dry) AMD and late (wet, exudative) AMD, depending on the angiogenesis or lack thereof in the retina.

[0004] Approximately 196 million people globally were affected by AMD in 2020, with case numbers expected to reach 288 million in 2040 due to the aging population (Wong et al. 2014). Preliminary estimates of the resources required by AMD patients for their diagnosis, treatment, follow-up, and management of conditions associated with AMD in the US are predicted to reach up to $10 billion annually (Venkataraman and Herron 2003).

[0005] Many pathophysiological aspects as well as vascular and environmental risk factors are associated with a progression of the disease. Genetics is also considered one of the major risk factors for AMD (Deangelis et al. 201 1 ). The heritability of AMD was estimated from 0.46 to 0.71 in the United States (Seddon et al. 2005). Understanding the specific genetic effects conferring AMD risk will help unveil the pathogenesis of AMD and may help inform therapy development and enable the prediction of the disease (Black and Clark 2016). The Genome-wide association study (GWAS) design has proven highly effective for identifying AMD risk genes, with over 50 loci identified to date (Han et al. 2020; Fritsche et al. 2013, 2016; Winkler et al. 2020; Kvale et al. 2015).

[0006] It is established that both genetic, demographic (e.g., age and gender) and environmental (e.g., smoking) factors contribute to the development and progression of AMD, where genetic factors include single nucleotide polymorphisms (SNPs), copy number variants(CNVs) and epigenetic variants, associated with DNA methylation or histone modification. However, the relative contributions of these factors, including contribution of each class of genetic variation to disease risk or progression is as of yet unknown.

[0007] Early detection of AMD would reduce the growing societal burden due to AMD, by targeting and emphasizing modifiable habits earlier in life and recommending more frequent surveillance for those high susceptible to the disease. Treatment trials will also benefit from such information when enrolling participants. There remains, therefore, a strong need for improved methods of diagnosing or predicting AMD or a susceptibility to AMD in subjects, as well as for evaluating and developing new methods of treatment.

[0008] It is an object of the invention to address and / or ameliorate at least one of the foregoing problems or at least provide the public with a useful choice.SUMMARY OF THE INVENTION

[0009] The present invention is predicated in part for a method for determining an indicator used in assessing a likelihood of a subject developing age-related macular degeneration (AMD), the method comprising, consisting, or consisting essentially of: a) determining a biomarker value that is measured or derived for at least one group 1 AMD biomarker (e.g., 1 , 2, 3, 4, 5, 6, 7, 8, or 9 AMD biomarkers) in a sample from the subject, wherein the at least one group 1 AMD biomarker is a single nucleotide polymorphism (SNP) selected from rs10797980, rs551911 , rs6661798, rs11674246, rs1871671 , rs7637449, rs12359135, rs7311672, and rs9901671 ; and b) determining the indicator using the biomarker value(s), wherein the indicator is at least partially indicative of the likelihood of the subject developing AMD.

[0010] In some embodiments, biomarker values are measured or derived for each of the group 1 AMD biomarkers.

[0011] Typically, the method further comprises a step of determining a biomarker value that is measured or derived for at least one group 2 AMD biomarker, wherein the group 2 AMD biomarker is selected from the list comprising rs10033900, rs1005819, rs10241492, rs1064583, rs10781177, rs10781180, rs10922109, rs10981380, rs11072508, rs11076175, rs11080055, rs11120691 , rs11142636, rs112635299, rs1142, rs1144, rs11569520, rs11635145, rs11771419, rs11884770, rs121913059, rs12211410, rs12213415, rs12457465, rs12576144, rs12901081 , rs12913832, rs12949956, rs13081855, rs13255394, rs1378940, rs1378942, rs1393350, rs141853578, rs147859257, rs148553336, rs1605677, rs163494, rs16841920, rs17105278, rs17356664, rs17421410, rs17480689, rs17576, rs1800588, rs187328863, rs1926564, rs2011092, rs2011822, rs2043085, rs2070895, rs2170240, rs2232613, rs2280953, rs2367070, rs2414634, rs247616, rs2575876, rs259842, rs2842343, rs2857107, rs3138142, rs3750846, rs3760775, rs3764261 , rs3775220, rs380743, rs3825991 , rs401186, rs4151671 , rs4420638, rs4748976, rs4943289, rs5023028, rs550513, rs570618,rs5754206, rs5754222, rs5754227, rs61760904, rs61818925, rs621736, rs66532523, rs6726589, rs6795735, rs6899205, rs704, rs7182946, rs7266392, rs73036519, rs73045269, rs7405901 , rs7428936, rs760070, rs7624060, rs7625101 , rs7663219, rs77516118, rs7874221 , rs7896471 , rs7939052, rs8017304, rs8051675, rs8056814, rs8135665, rs879180, rs9267576, rs943080, rs9821337, rs9973159, rs485632, rs56123646, rs17826006, rs7439493, rs62358361 , rs116306799, rs7803454, rs79037040, rs11143754, rs334362, rs4570483, rs36212733, rs3138141 , rs61941275, rs9564692, rs1956526, rs2414577, rs17231506, rs72802342, rs12948099, rs1137776, rs12019136, rs11569415, rs429358, rs58847685, rs77280782, rs74406464, rs77968014, rs352920876, rs191281603, rs62247658, rs140647181 , rs55975637, rs114092250, rs116503776, rs144629244, rs114254831 , rs181705462, rs10781182, rs71507014, rs1626340, rs2740488, rs12357257, rs61941274, rs61985136, rs2842339, rs5817082, rs6565597, rs2230199, rs67538026, rs142450006, and rs201459901. In some embodiments, a biomarker value is measured or derived for each of the group 2 AMB biomarkers are measured.

[0012] In some embodiments, the method is performed at least in part using an electronic processing device.

[0013] In some embodiments, the method includes, in at least one processing device, generating a representation of the indicator. By way of an illustrative example, the representation may comprise an alphanumeric indication of the indicator.

[0014] In another aspect, the present invention provides a method for determining a polygenic risk score (PRS), wherein the PRS is based on the indicator mentioned above and at least one genetic risk factor and / or non-genetic risk factor.By way of an example, one or more genetic risk factors may be selected from the group consisting of SNPs from Table 1 , ethnicity, sex, family history of AMD, or other age-related ocular conditions. Similarly, the one or more non-genetic risk factors may be selected from the group consisting of age, alcohol consumption history, smoking history, exercise history, body mass index, or diet.

[0015] In some embodiments the method includes: a) comparing the PRS to a PRS reference; and b) determining a likelihood in accordance with results of the comparison.

[0016] Typically, the PRS reference is based on at least one of: a) an PRS threshold range; b) an PRS threshold; and c) an PRS distribution.

[0017] Typically, the PRS reference is derived from the indicator mentioned above and at least one or more genetic risk factors and / or non-genetic risk factors determined for a number of individuals in a reference population.

[0018] In some embodiments, the reference population consists of individuals diagnosed as having AMD or lacking AMD.

[0019] In some embodiments, the reference population includes: a) a plurality of individuals of different sexes; b) a plurality of individuals of different ethnicities; c) a plurality of healthy individuals; d) a plurality of individuals diagnosed with AMD; e) a plurality of individuals showing clinical signs of AMD; f) first and second groups of individuals, each group of individuals diagnosed with AMD; and g) first and second groups of individuals, the first group of individuals diagnosed with AMD and the second group of individuals lacking AMD.

[0020] In some embodiments, the PRS reference is retrieved from a database.

[0021] Typically, the likelihood of a subject developing AMD is based on a probability generated using the results of a comparison. In some embodiments, a PRS above a predetermined threshold indicates the subject has a likelihood of developing AMD. In some embodiments, a PRS below a predetermined threshold indicates the subject is less likely to develop AMD.

[0022] Typically, the method includes: a) obtaining a sample taken from the subject, the sample including polynucleotide expression products; and b) quantifying at least some of the polynucleotide expression products within the sample to determine the presence or absence of one or more SNP.

[0023] Typically, the method includes: a) quantifying polynucleotide expression products by: b) amplifying at least some polynucleotide expression products in the sample; and c) determining an amplification amount representing a degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products; and d) determining the indicator by determining a difference between the amplification amounts.

[0024] Typically, the amplification amount is at least one of: a) a cycle time; b) a number of cycles; c) a cycle threshold; d) an amplification time; and e) relative to an amplification amount of another amplified product.

[0025] In some embodiments, the sample is derived from saliva or blood.

[0026] In some embodiments, polymerase chain reaction (PCR) is used for amplification.

[0027] In further embodiments, the PCR contains primers that are specific for the SNPs set forth in Table 1 .

[0028] In further embodiments, the primers for amplification are located on a chip.

[0029] Typically, the method further comprises administering an agent suitable for treating AMD in a subject who has been identified as having the likelihood of developing AMD.

[0030] In another broad form the present invention seeks to provide a method of treating or preventing AMD in a subject, comprising: a) analysing the sample obtained from a subject to determine an indicator used in assessing a likelihood of the subject developing AMD based on the presence or absence of one or more SNP from Table 1 ; and b) administering an agent suitable for treating or preventing AMD in a subject who has been identified as likely to develop AMD.

[0031] In some alternative embodiments and in some of the same embodiments, upon the subject being assessed as likely to develop AMD, the subject may make particular lifestyle choices to reduce those known to be associated with an increased likelihood of developing AMD. By way of an illustrative example, the subject may reduce or stop smoking.

[0032] In yet another aspect, the present invention provides a kit comprising reagents suitable for determining the presence, absence, or allele in a single nucleotide polymorphism (SNP), of at least one group 1 AMD biomarker (e.g., 1 , 2, 3, 4, 5, 6, 7, 8, or 9 AMD biomarkers) in a sample from the subject, wherein the at least one group 1 AMD biomarker is a SNP selected from rs10797980, rs551911 , rs6661798, rs11674246, rs1871671 , rs7637449, rs12359135, rs7311672, and rs9901671 . In some embodiments of this type, the reagents suitable for determining the presence, absence, or allele in the sequence of the SNP are oligonucleotide probes, wherein said probes make up at least 1% of the total content of the oligonucleotide probes in the kit.

[0033] In some embodiments, the kit further comprises a reagent suitable for determining the presence, absence, or allele, of at least one group 2 AMD biomarker (e.g., 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, or any integer in between).

[0034] In yet still another aspect, the present invention provides the use of a kit as described above for determining the likelihood of a subject developing AMD.BRIEF DESCRIPTION OF THE FIGURES

[0035] The following figures form part of the present specification and are included to further demonstrate certain aspects of the present disclosure. The disclosure may be better understood by reference to one or more of these figures in combination with the detailed description of specific embodiments presented herein.

[0036] Figure 1 provides a schematic of the biomarker study design.

[0037] Figure 2 illustrates the decile plots (odds ratio (OR)) by three PRS within 10 deciles for 733 cases and 20,487 controls in CLSA. Disease OR from 2ndto 10thdeciles were used as comparison, the 1stdecile is used as reference. The error bars represent 95% Cl or ORs. The PRS were sorted in ascending order from decile 1 to decile 10. the new PRS has larger effect sizes in 9thand 10thdecile than the other two PRS.

[0038] Figure 3 provides comparison of AUC of non-PRS and with-PRS predictive models. The AUC were investigated for the main risk factors of AMD. The AUCs for each model are shown as percentages with 95% confidence intervals.

[0039] Figure 4 shows cumulative incidence of AMD in CLSA (whole cohort, subsets). The ribbon represents 95% confidence intervals. The top 20% and bottom 20% (n = 4244) were extracted from the 21220 individuals in the whole CLSA cohort and compared the cumulative incidence among the top 20%, middle 60% (n = 12732) and bottom 20% groups.

[0040] Figure 5 shows cumulative incidence of AMD in CLSA (whole cohort, subsets). The ribbon represents 95% confidence intervals. The top 20% and bottom 20% (n = 73) were extracted from the 363 individuals with 4 CFH / ARMS2 risk alleles from CLSA and compared the cumulative incidence among the top 20%, middle 60% and bottom 20% groups.

[0041] Figure 6 shows cumulative incidence of AMD in CLSA (whole cohort, subsets). The ribbon represents 95% confidence intervals. Individuals with at least three CFH / ARMS2 risk alleles were selected and compared the cumulative incidence among the top 20% (n = 670), middle 60% (n = 2009) and bottom 20% (n = 670) groups.DETAILED DESCRIPTION OF THE INVENTION1. Definitions

[0042] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which theinvention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, preferred methods and materials are described. For the purposes of the present invention, the following terms are defined below.

[0043] It will be appreciated that the indefinite articles “a” and “an” are not to be read as singular indefinite articles or as otherwise excluding more than one or more than a single subject to which the indefinite article refers. For example, “a SNP” means one SNP or more than one SNP.

[0044] The term “about” as used herein refers to the usual error range for the respective value readily known to the skilled person in this technical field. Reference to “about” a value or parameter herein includes (and describes) embodiments that are directed to that value or parameter per se.

[0045] The term “age-related macular degeneration” or “AMD” is used herein to encompass all stages of AMD, including category 2 (early stage) AMD, category 3 (intermediate) AMD, and category 4 (advanced) AMD.

[0046] The term “allele” refers to one of two or more difference nucleotide sequences that occur or are encoded at a specific locus, or two or more different polypeptide sequences encoded by such a locus. For example, a first allele can occur on one chromosome, while a second allele occurs on a second homologous chromosome, e.g., as occurs for different chromosomes of a heterozygous individual, or between different homozygous or heterozygous individuals in a population. One example of a polymorphism is a “single nucleotide polymorphism” (SNP), which is a polymorphism at a single nucleotide position in a genome (the nucleotide at the specified position varies between individuals or populations). If at a particular chromosomal location, for example, one member of a population has an adenine and another member of the population has a thymine at the same genomic position, then this position is a polymorphic site, and, more specifically, the polymorphic site is a SNP. Polymorphic sites can allow for differences in sequences based on substitutions, insertions or deletions. Each version of the sequence with respect to the polymorphic site is referred to herein as an “allele” of the polymorphic site. Thus, in the previous example, the SNP allows for both an adenine allele and a thymine allele.

[0047] An allele positively correlates with a trait when it is linked to it and when presence of the allele is an indicator that the trait or trait form will occur in an individual comprising the allele. An allele negatively correlates with a trait when it is linked to it and when presence of the allele is an indicator that a trait or trait form will not occur in an individual comprising the allele.

[0048] A marker polymorphism or allele is correlated or associated with a specified phenotype (e.g., AMD susceptibility, etc) when it can be statistically linked (positively or negatively) to the phenotype. That is, the specified polymorphism occurs more commonly in acase population (e.g., AMD patients) than in a control population (e.g., individuals that do not have breast cancer). This correlation is often inferred as being causal in nature, but it need not be simple genetic linkage to (association with) a locus for a trait that underlies the phenotype is sufficient for correlation / association to occur.

[0049] A “favourable allele” is an allele at a particular locus that positively correlates with a desirable phenotype, e.g., resistance to AMD, e.g., an allele that negatively correlates with predisposition to AMD. A favourable allele of a linked marker is a marker allele that segregates with the favourable allele. A favourable allelic form of a chromosome segment is a chromosome segment that includes a nucleotide sequence that positively correlates with the desired phenotype, or that negatively correlates with the unfavourable phenotype at one or more genetic loci physically located on the chromosome segment.

[0050] An “unfavourable allele” is an allele at a particular locus that negatively correlates with a desirable phenotype, or that correlates positively with an undesirable phenotype, e.g., positive correlation to breast cancer susceptibility. An unfavourable allele of a linked marker is a marker allele that segregates with the unfavourable allele. An unfavourable allelic form of chromosome segment is a chromosome segment that includes a nucleotide sequence that negatively correlates with the desired phenotype, or positively correlates with the undesirable phenotype at one or more genetic loci physically located on the chromosome segment.

[0051] As used herein, “and / or” refers to and encompasses any and all possible combinations of one or more of the associated listed items, as well as the lack of combinations when interpreted in the alternative (or).

[0052] The term “biomarker” broadly refers to any detectable compound, such as a protein, a peptide, a proteoglycan, a glycoprotein, a lipoprotein, a carbohydrate, a lipid, a nucleic acid (e.g., DNA, such as cDNA or amplified DNA, or RNA, such as mRNA), an organic or inorganic chemical, a natural or synthetic polymer, a small molecule (e.g. , a metabolite), or a discriminating molecule or discriminating fragment of any of the foregoing, that is present in or derived from a sample. “Derived from” as used in this context refers to a compound that, when detected, is indicative of a particular molecule being present in the sample. For example, detection of a particular cDNA can be indicative of the presence of a particular RNA transcript in the sample. As another example, detection of or binding to a particular antibody can be indicative of the presence of a particular antigen (e.g., protein) in the sample. Here, a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of an above-identified compound. A biomarker can, for example, be isolated from a sample, directly measured in a sample, or detected in or determined to be in a sample. A biomarker can, for example, be functional, partially functional, or non-functional. Inspecific embodiments, the “biomarkers” include “AMD biomarkers”, which are described in more detail below.

[0053] The term “biomarker value” refers to a value measured or derived for at least one corresponding biomarker of a subject and which is typically at least partially indicative of an abundance or concentration of a biomarker in a sample taken from the subject. Thus, the biomarker values could be measured biomarker values, which are values of biomarkers measured for the subject, or alternatively could be derived biomarker values, which are values that have been derived from one or more measured biomarker values, for example by applying a function to the one or more measured biomarker values. Biomarker values can be of any appropriate form depending on the manner in which the values are determined. For example, the biomarker values could be determined using high-throughput technologies such as mass spectrometry, sequencing platforms, array and hybridization platforms, immunoassays, flow cytometry, or any combination of such technologies and in one preferred example, the biomarker values relate to a level of activity or abundance of an expression product or other measurable molecule, quantified using a technique such as PCR, sequencing or the like. In this case, the biomarker values can be in the form of amplification amounts, or cycle times, which are a logarithmic representation of the concentration of the biomarker within a sample, as will be appreciated by persons skilled in the art and as will be described in more detail below.

[0054] The term “biomarker profile” refers to one or a plurality of one or more types of biomarkers (e.g., an mRNA molecule, a cDNA molecule and / or a protein, etc.), or an indication thereof, together with a feature, such as a measurable aspect (e.g., biomarker value) of the biomarker(s). A biomarker profile may comprise a single biomarker whose level, abundance or amount correlates with the presence or absence of a condition (e.g., AMD).

[0055] Alternatively, a biomarker profile may comprise at least two such biomarkers or indications thereof, where the biomarkers can be in the same or different classes, such as, for example, a nucleic acid and a polypeptide. Thus, a biomarker profile may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 or more biomarkers or indications thereof. In some embodiments, a biomarker profile comprises hundreds, or even thousands, of biomarkers or indications thereof. A biomarker profile can further comprise one or more controls or internal standards. In certain embodiments, the biomarker profile comprises at least one biomarker, or indication thereof, that serves as an internal standard. In other embodiments, a biomarker profile comprises an indication of one or more types of biomarkers. The term “indication” as used herein in this context merely refers to a situation where the biomarker profile contains symbols, data, abbreviations or other similar indicia for a biomarker, rather than the biomarker molecular entity itself. The term “biomarker profile” is also used herein to refer to a biomarker value or combination of at least two biomarker values, wherein individual biomarker values correspond to values of biomarkers that can be measured or derived from one or more subjects, which combination is characteristic of a discrete condition, stage of condition, subtype ofcondition or a prognosis for a discrete condition, stage of condition, subtype of condition. The term “profile biomarkers” is used to refer to a subset of the biomarkers that have been identified for use in a biomarker profile that can be used in performing a clinical assessment, such as to rule in or rule out a specific condition, different stages or severity of conditions, subtypes of different conditions or different prognoses. The number of profile biomarkers will vary, but is typically of the order of 5 or more.

[0056] Throughout this specification, unless the context requires otherwise, the words “comprise”, “comprises” and “comprising” will be understood to imply the inclusion of a stated step or element or group of steps or elements but not the exclusion of any other step or element or group of steps or elements. Thus, use of the term “comprising” and the like indicates that the listed elements are required or mandatory, but that other elements are optional and may or may not be present. By “consisting of’ is meant including, and limited to, whatever follows the phrase “consisting of’. Thus, the phrase “consisting of’ indicates that the listed elements are required or mandatory, and that no other elements may be present. By “consisting essentially of’ is meant including any elements listed after the phrase, and limited to other elements that do not interfere with or contribute to the activity or action specified in the disclosure for the listed elements. Thus, the phrase “consisting essentially of’ indicates that the listed elements are required or mandatory, but that other elements are optional and may or may not be present depending upon whether or not they affect the activity or action of the listed elements.

[0057] The term “control subject”, as used in the context of the present invention, may refer to a subject known to be affected with a disease conditions (e.g. AMD) (positive control), or to a subject known to be not affected or diagnosed with the disease condition (negative control), i.e., healthy. It should be noted that a control subject that is known to be healthy, i.e., not suffering from the disease condition, may possibly suffer from another disease not tested / known. It is also understood that control subjects and healthy controls include data obtained and used as a standard, i.e., it can be used over and over again for multiple different subjects. In other words, for example, when comparing a subject sample to a control sample, the data from the control sample could have been obtained in a different set of experiments, for example, it could be an average obtained from a number of healthy subjects and not actually obtained at the time the data for the subject was obtained.

[0058] The term “correlating” generally refers to determining a relationship between one type of data with another or with a state. In various embodiments, correlating SNP profile with the presence or absence of a conditions (e.g., a condition selected from a healthy control or AMD) comprises determining the presence, absence or amount of at least one SNP in a subject that suffers from that condition, or in persons known to be free of that condition. In specific embodiments, a profile of SNP levels, absences or presences is correlated to a global probability or a particular outcome, using receiver operating characteristic (ROC) curves.

[0059] As used here, the terms “diagnosis”, “diagnosing” and the like are used interchangeably herein to encompass determining the likelihood that a subject will develop acondition, or the existence or nature of a condition in a subject. These terms also encompass determining the severity of disease or episode of disease, as well as in the context of rational therapy, in which the diagnosis guides therapy, including initial selection of therapy, modification of therapy (e.g., adjustment of dose or dosage regimen), and the like. By “likelihood” is meant a measure of whether a subject with particular presence, absence or number of biomarkers actually has a condition (or not) based on a given polygenic score. An increased likelihood for example may be relative or absolute and may be expressed qualitatively or quantitatively. For instance, an increased likelihood may be determined simply by determining the subject’s polygenic score and placing the subject in an “increased likelihood” category, based upon previous population studies. The term “likelihood” is also used interchangeably herein with the term “probability”. The term “risk” relates to the possibility or probability of a particular event occurring at some point in the future. “Risk stratification” refers to an arraying of known clinical risk factors to allow physicians to classify patients into a low, moderate, high or highest risk of developing a particular disease or condition (e.g., AMD).

[0060] As used herein, “gene” is a term used to describe a genetic element that gives rise to expression products (e.g., pre-mRNA, mRNA and polypeptides). A gene includes regulatory elements and sequences (e.g., promoters and enhancers) that otherwise appear to have only structural features, e.g., introns and untranslated regions.

[0061] As used herein, a "healthy" subject is a subject that does not have AMD.

[0062] The term “high-density array” refers to a substrate or collection of substrates or surfaces bearing a plurality of array elements (e.g., discrete regions having particular moieties, e.g., proteins (e.g., antibodies), nucleic acids (e.g., oligonucleotide probes), etc., immobilized thereto), where the array elements are present at a density of about 100 elements / cm2or more, about 1 ,000 elements / cm2or more, about 10,000 elements / cm2or more, or about 100,000 elements / cm2or more. In specific embodiments, a “high-density array” is one that comprises a plurality of array elements for detecting about 100 or more different biomarkers, about 1 ,000 or more different biomarkers, about 10,000 or more different biomarkers, or about 100,000 or more different biomarkers. In representative example of these embodiments, a "high-density array” is one that comprises a plurality of array elements for detecting biomarkers of about 100 or more different genes, of about 1 ,000 or more different genes, of about 10,000 or more different genes, or of about 100,000 or more different genes. Generally, the elements of a high-density array are not labeled. The term “low-density array” refers to a substrate or collection of substrates or surfaces bearing a plurality of array elements (e.g., discrete regions having particular moieties, e.g., proteins (e.g., antibodies), nucleic acids (e.g., oligonucleotide probes), etc., immobilized thereto), where the array elements are present at a density of about 100 elements / cm2or less, about 50 elements / cm2or less, about 20 elements / cm2or less, or about 10 elements / cm2or less. In specific embodiments, a “low-density array” is one that comprises a plurality of array elements for detecting about 100 or less different biomarkers, about 50 or less different biomarkers, about 20 or less different biomarkers, orabout 10 or less different biomarkers. In representative example of these embodiments, a “low- density array” is one that comprises a plurality of array elements for detecting biomarkers of about 100 or less different genes, of about 50 or less different genes, of about 20 or less different genes, or of about 10 or less different genes. Generally, the elements of a low-density array are not labeled .

[0063] An individual is “homozygous” if the individual has only one type of allele at a given locus (e.g., a diploid individual has a copy of the same allele at a locus for each of two homologous chromosomes). An individual is “heterozygous” if more than one allele type is present at a given locus (e.g., a diploid individual with one copy each of two different alleles). The term “homogeneity” indicates that members of a group have the same genotype at one or more specific loci. In contrast, the term “heterogeneity” is used to indicate that individuals within the group differ in genotype at one or more specific loci.

[0064] The term “indicator” as used herein refers to a result or representation of a result, including any information, number, ratio, signal, sign, mark, or note by which a skilled artisan can estimate and / or determine a likelihood or risk of whether or not a subject is suffering from a given disease or condition. In the case of the present invention, the “indicator” may optionally be used together with other clinical characteristics, to arrive at a diagnosis (that is, the occurrence or nonoccurrence) of AMD in a subject. That such an indicator is “determined” is not meant to imply that the indicator is 100% accurate. The skilled clinician may use the indicator together with other clinical indicia to arrive at a diagnosis.

[0065] The term “immobilized” means that a molecular species of interest is fixed to a solid support, suitably by covalent linkage. This covalent linkage can be achieved by different means depending on the molecular nature of the molecular species. Moreover, the molecular species may be also fixed on the solid support by electrostatic forces, hydrophobic or hydrophilic interactions or Van der Waals forces. The above-described physicochemical interactions typically occur in interactions between molecules. In particular embodiments, all that is required is that the molecules (e.g., nucleic acids or polypeptides) remain immobilized or attached to a support under conditions in which it is intended to use the support, for example in applications requiring nucleic acid amplification and / or sequencing or in in antibody-binding assays. For example, oligonucleotides or primers are immobilized such that a 3’ end is available for enzymatic extension and / or at least a portion of the sequence is capable of hybridizing to a complementary sequence. In some embodiments, immobilization can occur via hybridization to a surface attached primer, in which case the immobilized primer or oligonucleotide may be in the 3’-5’ orientation. In other embodiments, immobilization can occur by means other than basepairing hybridization, such as the covalent attachment.

[0066] As used herein, the term “label” and grammatical equivalents thereof, refer to any atom or molecule that can be used to provide a detectable and / or quantifiable signal. In particular, the label can be attached, directly or indirectly, to a nucleic acid or protein. Suitable labels that can be attached include, but are not limited to, radioisotopes, fluorophores,quenchers, chromophores, mass labels, electron dense particles, magnetic particles, spin labels, molecules that emit chemiluminescence, electrochemically active molecules, enzymes, cofactors, and enzyme substrates. A label can include an atom or molecule capable of producing a visually detectable signal when reacted with an enzyme. In some embodiments, the label is a “direct” label which is capable of spontaneously producing a detectible signal without the addition of ancillary reagents and is detected by visual means without the aid of instruments. For example, colloidal gold particles can be used as the label. Many labels are well known to those skilled in the art. In specific embodiments, the label is other than a naturally occurring nucleoside. The term “label” also refers to an agent that has been artificially added, linked or attached via chemical manipulation to a molecule.

[0067] The “level” or “amount” of a biomarker is a detectable level or amount in a sample. These can be measured by methods known to one skilled in the art and also disclosed herein. These terms encompass a quantitative amount or level (e.g., weight or moles), a semiquantitative amount or level, a relative amount or level (e.g., weight % or mole % within class), a concentration, and the like. Thus, these terms encompass absolute or relative amounts or levels or concentrations of a biomarker in a sample. The expression level or amount of biomarker assessed can be used to determine the response to treatment. In specific embodiments in which the level of a biomarker is “reduced” relative to a reference or control, the reduced level may refer to an overall reduction of any of at least about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99% or greater, in the level of biomarker (e.g., protein or nucleic acid (e.g., gene or mRNA)), detected by standard art known methods such as those described herein, as compared to a reference sample, reference cell, reference tissue, control sample, control cell, or control tissue. In certain embodiments, reduced level refers to a decrease in level / amount of a biomarker in the sample wherein the decrease is at least about any of 0.9x, 0.8x, 0.7x, 0.6x, 0.5x, 0.4x, 0.3x, 0.2x, 0.1 x, 0.05x, or 0.01 x the level / amount of the respective biomarker in a reference sample, reference cell, reference tissue, control sample, control cell, or control tissue. In certain embodiments in which the level of a biomarker is “about the same” a reference or control, the level of biomarker varies by less than about 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, or even less, as compared to the level of biomarker (e.g., protein or nucleic acid (e.g., gene or mRNA)), detected by standard art known methods such as those described herein, in a reference sample, reference cell, reference tissue, control sample, control cell, or control tissue.

[0068] A “locus” is a chromosomal position or region. For example, a polymorphic locus is a position or region where a polymorphic nucleic acid, trait determinant, gene or marker is located. In a further example, a “gene locus” is a specific chromosome location (region) in the genome of a species where a specific gene can be found. Similarly, the term “quantitative trait locus” or QTL refers to a locus with at least two alleles that differentially affect the expression or alter the variation of a quantitative or continuous phenotypic trait in at least one genetic background, e.g., in at least one population or progeny.

[0069] The term “microarray” refers to an arrangement of hybridisable array elements e.g., probes (including primers), ligands, biomarker nucleic acid sequence or protein sequences on a substrate.

[0070] The term “nucleic acid” or “polynucleotide” as used herein includes RNA, mRNA, miRNA, cRNA, cDNA mtDNA, or DNA. The term typically refers to a polymeric form of nucleotides of at least 10 bases in length, either ribonucleotides or deoxynucleotides or a modified form of either type of nucleotide. The term includes single and double stranded forms of DNA or RNA.

[0071] By “obtained” is meant to come into possession. Samples so obtained include, for example, nucleic acid extracts or polypeptide extracts isolated or derived from a particular source. For instance, the extract may be isolated directly from a biological fluid or tissue of a subject.

[0072] As used herein, the term “positive response” means that the result of a treatment regimen includes some clinically significant benefit, such as the prevention, or reduction of severity, of symptoms, or a slowing of the progression of the condition. By contrast, the term “negative response” means that a treatment regimen provides no clinically significant benefit, such as the prevention, or reduction of severity, of symptoms, or increases the rate of progression of the condition.

[0073] By “primer” is meant an oligonucleotide which, when paired with a strand of DNA, is capable of initiating the synthesis of a primer extension product in the presence of a suitable polymerizing agent. The primer is preferably single-stranded for maximum efficiency in amplification but can alternatively be double-stranded. A primer must be sufficiently long to prime the synthesis of extension products in the presence of the polymerization agent. The length of the primer depends on many factors, including application, temperature to be employed, template reaction conditions, other reagents, and source of primers. For example, depending on the complexity of the target sequence, the primer may be at least about 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 50, 75, 100, 150, 200, 300, 400, 500, to one base shorter in length than the template sequence at the 3’ end of the primer to allow extension of a nucleic acid chain, though the 5’ end of the primer may extend in length beyond the 3’ end of the template sequence. In certain embodiments, primers can be large polynucleotides, such as from about 35 nucleotides to several kilobases or more. Primers can be selected to be “substantially complementary” to the sequence on the template to which it is designed to hybridize and serve as a site for the initiation of synthesis. By “substantially complementary”, it is meant that the primer is sufficiently complementary to hybridize with a target polynucleotide. Desirably, the primer contains no mismatches with the template to which it is designed to hybridize but this is not essential. For example, non-complementary nucleotide residues can be attached to the 5’ end of the primer, with the remainder of the primer sequence being complementary to the template. Alternatively, non-complementary nucleotide residues or a stretch of non-complementary nucleotide residuescan be interspersed into a primer, provided that the primer sequence has sufficient complementarity with the sequence of the template to hybridize therewith and thereby form a template for synthesis of the extension product of the primer.

[0074] As used herein, the term “probe” refers to a molecule that binds to a specific sequence or sub-sequence or other moiety of another molecule. Unless otherwise indicated, the term “probe” typically refers to a nucleic acid probe that binds to another nucleic acid, also referred to herein as a “target polynucleotide”, through complementary base pairing . Probes can bind target polynucleotides lacking complete sequence complementarity with the probe, depending on the stringency of the hybridization conditions. Probes can be labelled directly or indirectly and include primers within their scope.

[0075] The term “prognosis” as used herein refers to a prediction of the probable course and outcome of a clinical condition or disease. A prognosis is usually made by evaluating factors or symptoms of a disease that are indicative of a favourable or unfavourable course or outcome of the disease. The skilled artisan will understand that the term “prognosis” refers to an increased probability that a certain course or outcome will occur; that is, that a course or outcome is more likely to occur in a subject exhibiting a given condition, when compared to those individuals not exhibiting the condition.

[0076] A “risk allele” is an allele that positively correlates with the risk of developing a disease or condition, such as AMD, i.e. indicates that an individual has an increased likelihood to develop AMD, or, progress to a more advanced stage of AMD.

[0077] A reference sequence is typically referred to for a particular genetic element, e.g., a gene. Alleles that differ from the reference are referred to as “variant” alleles. The reference sequence, often chosen as the most frequently occurring allele or as the allele conferring a typical phenotype, is sometimes referred to as the “wild-type” allele.

[0078] Some variant alleles can include changes that affect a polypeptide, e.g., the polypeptide encoded by a complement pathway gene. These sequence differences, when compared to a reference nucleotide sequence, can include the insertion or deletion of a single nucleotide, or of more than one nucleotide, resulting in a frame shift; the change of at least one nucleotide, resulting in a change in the encoded amino acid; the change of at least one nucleotide, resulting in the generation of a premature stop codon; the deletion of several nucleotides, resulting in a deletion of one or more amino acids encoded by the nucleotides; the insertion of one or several nucleotides, such as by unequal recombination or gene conversion, resulting in an interruption of the coding sequence of a reading frame; duplication of all or a part of a sequence; transposition; or a rearrangement of a nucleotide sequence.

[0079] Alternatively, a polymorphism associated with AMD or a susceptibility to AMD can be a synonymous change in one or more nucleotides (i.e., a change that does not result in a change to a codon for a complement pathway gene). Such a polymorphism can, for example, alter splice sites, affect the stability or transport of mRNA, or otherwise after thetranscription or translation of the polypeptide. The polypeptide encoded by the reference nucleotide sequence is the “reference” polypeptide with a particular reference amino acid sequence, and polypeptides encoded by variant alleles are referred to as “variant” polypeptides with variant amino acid sequences.

[0080] A genetic marker is associated with a genetic element of phenotypic trait, for example, if the marker is co-present with the genetic element or phenotypic trait at a frequency that is higher than would be predicted by random assortment of alleles (based on the allele frequencies of the particular population). Association also indicates physical associated, e.g., proximity in the genome or presence in a haplotype block, of a marker and a genetic element.

[0081] Haplotypes are a combination of genetic markers, e.g., particular alleles at polymorphic sites. The haplotypes described herein are associated with AMD and / or susceptibility to AMD, Detection of the presence or absence of the haplotypes here, therefore is indicative of AMD, a susceptibility to AMD or a lack thereof. The haplotypes described herein are a combination of genetic markers, e.g., SNPs and microsatellites. Detecting haplotypes, therefore, can be accomplished by methods known in the art for detecting sequences at polymorphic sites.

[0082] The haplotypes and markers disclosed herein are in “linkage disequilibrium” (LD) with preferred AMD-associated phenotypes. “Linkages” refers to a higher than expected statistical association of genotypes and / or phenotypes with each either. “LD” refers to a nonrandom assortment of two genetic elements. If a particular element (e.g., an allele at a polymorphic site), for example, occurs in a population at a frequency of 0.25 and another occurs at a frequency at 0.25, then the predicted occurrence of a person’s having both elements is 0.123, assuming a random distribution of the elements. If, however it is discovered that the two elements occur together at a frequency statistically significantly higher than 0.125, then the elements are said to be in LD since they tend to be inherited together at a higher frequency than what their independent allele frequencies would predict. Roughly speaking, LD is generally correlated with the frequency of recombination events between the two elements. Allele frequencies can be determined in a population, for example, by genotyping individuals in a population, for example, by genotyping individuals in a population and determining the occurrence of each allele in the population. For populations of diploid individuals, e.g., human populations, individuals will typically have two alleles for each genetic element (e.g., marker or gene).

[0083] The “polygenic score” is used to define an individual’s risk of developing a disease or progressing to a more advanced stage of a disease, based on a large number of common genetic variants each of which might have modest individual effect sizes contribute to the disease or its progression, but in aggregate have significant predicting value. In the present case, the polygenic score used to predict the likelihood that a patient will progress to advanced AMD using common single nucleotide polymorphisms (SNPs) associated with AMD. The log of the odds ratio (OR) from every variant reaching a P<5 x 10-8in the discovery dataset is used tocalculate the polygenic score. Specifically, for each of the variants used in the score, the log of the Odds Ratio is multiplied times the number of reference alleles (0, 1 or 2) carried by the individual. The resulting sum is divided by the number of variants tested in each individual, resulting the final polygenic score.

[0084] The term “PRS reference” is used to refer to PRS which has been quantified for a sample population of one or more individuals having AMD. The term “reference data” refers to data measured for one or more individuals in a sample population, and may include quantification of the PRS measured for each individual, information regarding any conditions of the individuals, and optionally any other information of interest.

[0085] The terms “sample”, “biological sample”, and the like mean a material known or suspected of containing one or more SNP. A test sample can be used directly as obtained from the source or following a pre-treatment to modify the character of the sample. The sample is suitably derived from blood or plasma fractions, including cell fractions (e.g., comprising tumour cells) or lysates thereof, cell-free or cell-depleted fractions, and the like. The sample can be treated prior to use, such as diluting viscous fluids, and the like. Methods of treatment can involve filtration, distillation, extraction, concentration, inactivation of interfering components (e.g., inhibiting nucleases such as RNases and DNases), the addition of reagents, and the like.

[0086] The term “solid support” as used herein refers to a solid inert surface or body to which a molecular species, such as a nucleic acid and polypeptides can be immobilized. Nonlimiting examples of solid supports include glass surfaces, plastic surfaces, latex, dextran, polystyrene surfaces, polypropylene surfaces, polyacrylamide gels, gold surfaces, and silicon wafers. In some embodiments, the solid supports are in the form of membranes, chips or particles. For example, the solid support may be a glass surface (e.g., a planar surface of a flow cell channel). In some embodiments, the solid support may comprise an inert substrate or matrix which has been “functionalized”, such as by applying a layer or coating of an intermediate material comprising reactive groups which permit covalent attachment to molecules such as polynucleotides. By way of non-limiting example, such supports can include polyacrylamide hydrogels supported on an inert substrate such as glass. The molecules (e.g., polynucleotides) can be directly covalently attached to the intermediate material (e.g., a hydrogel) but the intermediate material can itself be non-covalently attached to the substrate or matrix (e.g., a glass substrate). The support can include a plurality of particles or beads each having a different attached molecular species.

[0087] The terms “subject”, “individual” or “patient”, used interchangeably herein, refer to any animal subject, particularly a mammalian subject, more particularly a human subject. In some embodiments, the subject presents with clinical signs of a condition as defined herein. As used herein, the term “clinical sign”, or simply “sign”, refers to objective evidence of a disease present in a subject. Symptoms and / or signs associated with diseases referred to herein and the evaluation of such signs are routine and known in the art. Examples of signs of disease vary depending upon the disease. Signs of AMD may include loss of central vision,drusen, retinal pigmentary changes or angiogenesis. Typically, whether a subject has a disease, and whether a subject is responding to treatment, may be determined by evaluation of signs associated with the disease.

[0088] Those skilled in the art will appreciate that the aspects and embodiments described herein are susceptible to variations and modifications other than those specifically described. It is to be understood that the disclosure includes all such variations and modifications. The disclosure also includes all of the steps, features, compositions and compounds referred to or indicated in this specification, individually or collectively, and any and all combinations of any two or more of said steps or features.

[0089] It will be appreciated that the above-described terms and associated definitions are used for the purpose of explanation only and are not intended to be limiting.2. Biomarkers for AMD and uses thereof

[0090] The present invention concerns methods, apparatus, compositions and kits for identifying the likelihood of a subject developing AMD. In particular, biomarkers are disclosed for use in these modalities to assess the likelihood of the development of AMD in a subject. The methods, apparatus, compositions and kits of the invention are useful for early detection of AMD in a subject, thus allowing better treatment interventions for subjects with AMD.

[0091] The present inventors have determined that certain expression products are commonly, specifically and differentially expressed in a subject who is at risk of developing AMD when compared with a subject that is not at risk of developing AMD. The results presented herein provide clear evidence that a unique biologically relevant biomarker profile predicts whether a subject is likely to develop AMD with a remarkable degree of accuracy. This AMD biomarker profile was validated using human data Overall, these findings provide compelling evidence that the expression products disclosed herein can function as biomarkers for developing AMD and may potentially serve as a useful diagnostic for triaging treatment decisions for subjects that are determined to be at an increased risk of developing AMD. In this regard, it is proposed that the methods, apparatus, compositions and kits disclosed herein that are based on these biomarkers may serve in the point-of-care diagnostics that allow for rapid and inexpensive screening for AMD, which may result in significant cost savings to the medical system.

[0092] Thus, specific SNPs are disclosed herein as AMD biomarkers that provide a means for identifying the likelihood or risk of a subject developing AMD. Evaluation of these AMD biomarkers through analysis of their presence in a subject or in a sample taken from a subject provides a measured or derived biomarker value for determining an indicator that can be used for assessing the likelihood of a subject developing AMD.

[0093] Accordingly, biomarker values can be measured derived biomarker values, which are values that have been derived from one or more measured biomarker values, forexample by applying a function to the one or more measured biomarker values. As used herein, biomarkers to which a function has been applied are referred to as “derived markers”.

[0094] The biomarker values may be determined in any one of a number of ways. In one example, the process of determining biomarker values can include measuring the biomarker values, for example by performing tests on the subject or on sample(s) taken from the subject. More typically however, the step of determining the biomarker values includes having an electronic processing device receive or otherwise obtain biomarker values that have been previously measured or derived. This could include for example, retrieving the biomarker values from a data store such as a remote database, obtaining biomarker values that have been manually input, using an input device, or the like. The indicator is determined using a combination of the plurality of biomarker values, the indicator being at least partially indicative of the likelihood of the subject developing AMD. Assuming the method is performed using an electronic processing device, an indication of the indicator is optionally displayed or otherwise provided to the user. In this regard, the indication could be a graphical or alphanumeric representation of an indicator value. Alternatively, however, the indication could be the result of a comparison of the indicator value to predefined thresholds or ranges, or alternatively could be an indication of the likelihood that the subject will develop AMD, derived using the indicator.

[0095] In some embodiments, biomarker values are combined, for example by adding, multiplying, subtracting, or dividing biomarker values to determine an indicator value. This step is performed so that multiple biomarker values can be combined into a single indicator value, providing a more useful and straightforward mechanism for allowing the indicator to be interpreted and hence used in determining the likelihood of the subject developing AMD.

[0096] The biomarkers of the present invention are independent from one another. In other words, each of the biomarkers have zero, or a low mutual correlation, with one another. This typically suggests that the biomarkers relate to different biological attributes or domains such as, but not limited to different molecular functions, different biological processes and different cellular components. Illustrative examples of molecular function include signaling pathways, including without limitation, receptor signaling pathways and nuclear signaling pathways.

[0097] It will be understood that the use of biomarkers that have different biological attributes or domains provides further information than if the biomarkers were related to the same or common biological attributes or domains. In this regard, it will be appreciated if the at least two biomarkers are highly correlated to each other, the use of both biomarkers would add little diagnostic / prognostic improvement compared to the use of a single one of the biomarkers.

[0098] Accordingly, an indicator-determining method of the present invention in which a plurality of biomarkers and biomarker values are used preferably employ biomarkers that are not well correlated with each other, thereby ensuring that the inclusion of each biomarker in the method adds significantly to the discriminative ability of the indicator.

[0099] Despite this, in order to ensure that the indicator can accurately be used in performing the discrimination between a subject that is likely to develop AMD and a subject that is not likely to develop AMD, the indicator has a performance value that is greater than or equal to a performance threshold. The performance threshold may be of any suitable form but is to be typically indicative of an area under the curve of 0.6 or 0.65 or higher, or an equivalent value of another performance measure.

[0100] It will be understood that in this context, the biomarkers used within the above-described method can define a biomarker profile for the risk of developing AMD which includes a minimal number of biomarkers, whilst maintaining sufficient performance to allow the biomarker profile to be used in making a clinically relevant prognosis or differentiation. Minimizing the number of biomarkers used minimizes the costs associated with performing diagnostic or prognostic tests and in the case of nucleic acid expression products, allows the test to be performed utilizing relatively straightforward techniques such as nucleic acid array, and polymerase chain reaction (PCR) processes, or the like, allowing the test to be performed rapidly in a clinical environment.

[0101] Furthermore, producing a single indicator value allows the results of the test to be easily interpreted by a clinician or other medical practitioner, so that test can be used for reliable determination in a clinical environment.

[0102] Processes for generating suitable biomarker profiles are described for example in WO 2015 / 117204, which uses the term “biomarker signature” in place of “biomarker profile” as defined herein. It will be understood, therefore, that terms “biomarker profile” and “biomarker signature” are equivalent in scope. The biomarker profile-generating processes disclosed in WO 2015 / 117204 provide mechanisms for selecting a combination of biomarkers, and more typically derived biomarkers, that can be used to form a biomarker profile, which in turn can be used in determining the likelihood of a subject developing AMD. In this regard, the biomarker profile defines the biomarkers that should be measured (i.e., the profile biomarkers), how derived biomarker values should be determined for measured biomarker values, and then how biomarker values should be subsequently combined to generate an indicator value. The biomarker profile can also specify defined indicator value ranges that indicate the likelihood that a subject will develop AMD.

[0103] Using the above-described methods a number of biomarkers have been identified that are particularly useful for assessing a likelihood of a subject developing AMD. These biomarkers are referred to herein as “AMD biomarkers”. As used herein, the term “AMD biomarker” refers to a biomarker of the host which is altered, or whose level of expression is altered, in subjects that are predisposed to be likely to develop AMD. The AMD biomarkers are suitably SNPs of genes (also referred to interchangeably herein as “AMD biomarker SNP”).

[0104] AMD biomarkers are suitably selected from the following SNPs: rs10797980, rs551911 , rs6661798, rs11674246, rs1871671 , rs7637449, rs12359135, rs7311672, and rs9901671 (Group 1 AMD biomarkers).

[0105] In some examples, the indicator-determining methods suitably include determining a biomarker value, wherein each biomarker value is a value measured or derived for at least one corresponding AMD biomarker of the subject and is at least partially indicative of the presence or absence of the AMD biomarker in a sample taken from the subject. The derived biomarker value is then used to determine the indicator, either by using a derived biomarker value as an indicator value, or by performing additional processing, such as comparing the derived biomarker value to a reference or the like, as will be described in more detail below.

[0106] The corresponding AMD biomarker could be a biomarker that is highly correlates to the AMD biomarker (“an AMD proxy biomarker”). Two highly correlated biomarkers would generally have a mutual correlation value of 0.9 or greater. For example, the AMD proxy biomarker may be in close proximity to a Group 1 AMD biomarker. In some instances, the corresponding AMD biomarker is selected from Table 1 .TABLE 1PROXY AMD BIOMARKERS

[0107] In some embodiments the AMD biomarkers described above (Group 1 AMD biomarkers) are used with one or more additional AMD biomarker. Typically, the additional AMD biomarker is selected from a Group 2 AMD biomarker. Group 2 AMD biomarkers include the following SNPs: rs10033900, rs1005819, rs10241492, rs1064583, rs10781177, rs10781180, rs10922109, rs10981380, rs11072508, rs 11076175, rs 11080055, rs11120691 , rs11142636, rs112635299, rs1142, rs1144, rs11569520, rs11635145, rs11771419, rs 11884770, rs121913059, rs12211410, rs12213415, rs12457465, rs12576144, rs12901081 , rs12913832, rs12949956, rs13081855, rs13255394, rs1378940, rs1378942, rs1393350, rs141853578,rs147859257, rs148553336, rs1605677, rs163494, rs16841920, rs17105278, rs17356664, rs17421410, rs17480689, rs17576, rs1800588, rs187328863, rs1926564, rs2011092, rs2011822, rs2043085, rs2070895, rs2170240, rs2232613, rs2280953, rs2367070, rs2414634, rs247616, rs2575876, rs259842, rs2842343, rs2857107, rs3138142, rs3750846, rs3760775, rs3764261 , rs3775220, rs380743, rs3825991 , rs401186, rs4151671 , rs4420638, rs4748976, rs4943289, rs5023028, rs550513, rs570618, rs5754206, rs5754222, rs5754227, rs61760904, rs61818925, rs621736, rs66532523, rs6726589, rs6795735, rs6899205, rs704, rs7182946, rs7266392, rs73036519, rs73045269, rs7405901 , rs7428936, rs760070, rs7624060, rs7625101 , rs7663219, rs77516118, rs7874221 , rs7896471 , rs7939052, rs8017304, rs8051675, rs8056814, rs8135665, rs879180, rs9267576, rs943080, rs9821337, rs9973159, rs485632, rs56123646, rs17826006, rs7439493, rs62358361 , rs116306799, rs7803454, rs79037040, rs11143754, rs334362, rs4570483, rs36212733, rs3138141 , rs61941275, rs9564692, rs1956526, rs2414577, rs17231506, rs72802342, rs12948099, rs1137776, rs12019136, rs11569415, rs429358, rs58847685, rs77280782, rs74406464, rs77968014, rs352920876, rs191281603, rs62247658, rs140647181 , rs55975637, rs114092250, rs116503776, rs144629244, rs114254831 , rs 181705462, rs10781182, rs71507014, rs1626340, rs2740488, rs12357257, rs61941274, rs61985136, rs2842339, rs5817082, rs6565597, rs2230199, rs67538026, rs142450006, and rs201459901.

[0108] The derived biomarker values could be combined using a combining function such as an additive model; a linear model; a support vector machine; a neural network model; a tree-learning method (e.g., random forest model); a regression model; a genetic algorithm; an annealing algorithm; a weighted sum; a nearest neighbor model; an ensemble method (e.g., bagging, boosting weighted averaging); and a probabilistic model. In some embodiments, biomarker values are measured or derived for one or more Group 1 AMD biomarkers and for one or more Group 2 AMD biomarkers, and the indicator is determined by combining the biomarker values.

[0109] In some embodiments, the indicator is compared to an indicator reference, with a likelihood being determined in accordance with results of the comparison. The indicator reference may be derived from indicators determined for a number of individuals in a reference population. The reference population typically includes individuals having different characteristics, such as a plurality of individuals of different sexes; and / or ethnicities, with different groups being defined based on different characteristics, with the subject’s indicator being compared to indicator references derived from individuals with similar characteristics. The reference population can also include a plurality of healthy individuals, a plurality of individuals known to develop AMD, a plurality of individuals known not to develop AMD.

[0110] In specific embodiments, the indicator-determining methods of the present invention are performed using at least one electronic processing device, such as a suitably programmed computer system or the like. In this case, the electronic processing device typically obtains at least one measured biomarker value, either by receiving these from a measuring orother quantifying device, or by retrieving these from a database or the like. The processing device then determines a first derived biomarker value indicative of a first AMD biomarker, and optionally, a second derived biomarker value indicative of a second AMD biomarker. In some of the same embodiments and other embodiments, higher numbers of derived biomarker values indicative of respective AMD biomarkers are determined. In instances where more than one biomarker value is derived, the processing device may then determine the indicator by combining the first and second (and optionally third, fourth, fifth, etc.) derived biomarker values, as appropriate.

[0111] The processing device can then generate a representation of the indicator, for example by generating an alphanumeric indication of the indicator, a graphical indication of a comparison of the indicator to one or more indicator references or an alphanumeric indication of a likelihood of the subject having at least one medical condition.

[0112] The indicator-determining methods of the present invention typically include obtaining a sample from a subject, who typically has at least one risk factor for developing AMD, wherein the sample includes one or more AMD biomarkers (e.g., SNPs) and quantifying at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10 or more) of the AMD biomarkers within the sample to determine biomarker values. This can be achieved using any suitable technique and will depend on the nature of the AMD biomarkers. Suitably, an individual measured or derived AMD biomarker value corresponds to the level, abundance or amount of a respective AMD biomarker or to a function that is applied to that level or amount. As used herein the terms “level”, “abundance” and “amount” are used interchangeably herein to refer to a quantitative amount (e.g., weight or moles), a semi-quantitative amount, a relative amount (e.g., weight % or mole % within class), a concentration, and the like. Thus, these terms encompass absolute or relative amounts or concentrations of AMD biomarkers in a sample.

[0113] Risk factors for developing AMD are known in the art and include (bur are not limited to) smoking, old age, and having a family history of developing AMD.

[0114] In some embodiments, the likelihood of a subject developing AMD is established by determining two or more AMD biomarker values, wherein an AMD biomarker value is indicative of a value derived for AMD biomarkers in a subject or in a sample taken from the subject. These biomarkers are referred to herein as “sample AMD biomarkers”. In accordance with the present invention, a sample AMD biomarker corresponds to a reference AMD biomarker (also referred to herein as a “corresponding AMD biomarker”). By “corresponding AMD biomarker” is meant an AMD biomarker that is structurally and / or functionally similar to a reference AMD biomarker.

[0115] Representative corresponding AMD biomarkers include expression products of allelic variants (same locus), homologues (different locus), and orthologues (different organism) of reference AMD biomarker genes. Nucleic acid variants of reference AMD biomarker genes and encoded AMD biomarker polynucleotide expression products can containnucleotide substitutions, deletions, inversions and / or insertions. Variation can occur in either or both the coding and non-coding regions. The variations can produce both conservative and non-conservative amino acid substitutions (as compared in the encoded product). For nucleotide sequences, conservative variants include those sequences that, because of the degeneracy of the genetic code, encode the amino acid sequence of a reference AMD polypeptide.

[0116] Corresponding AMD biomarker polynucleotides also include nucleic acid sequences that hybridize to reference AMD biomarker polynucleotides, or to their complements, under stringency conditions described below. As used herein, the term “hybridizes under low stringency, medium stringency, high stringency, or very high stringency conditions” describes conditions for hybridization and washing. “Hybridization” is used herein to denote the pairing of complementary nucleotide sequences to produce a DNA-DNA hybrid or a DNA-RNA hybrid.

[0117] Complementary base sequences are those sequences that are related by the base-pairing rules. In DNA, A pairs with T and C pairs with G. In RNA, U pairs with A and C pairs with G. In this regard, the terms “match” and “mismatch” as used herein refer to the hybridization potential of paired nucleotides in complementary nucleic acid strands. Matched nucleotides hybridize efficiently, such as the classical A-T and G-C base pair mentioned above. Mismatches are other combinations of nucleotides that do not hybridize efficiently.

[0118] Guidance for performing hybridization reactions can be found in Ausubel et al., (1998, supra), Sections 6.3.1 -6.3.6. Aqueous and non-aqueous methods are described in that reference and either can be used. Reference herein to low stringency conditions include and encompass from at least about 1 % v / v to at least about 15% v / v formamide and from at least about 1 M to at least about 2 M salt for hybridization at 42°C, and at least about 1 M to at least about 2 M salt for washing at 42°C. Low stringency conditions also may include 1% bovine serum albumin (BSA), 1 mM EDTA, 0.5 M NaHPO4 (pH 7.2), 7% SDS for hybridization at 65°C, and (i) 2 x SSC, 0.1% SDS; or (ii) 0.5% BSA, 1 mM EDTA, 40 mM NaHPO4(pH 7.2), 5% SDS for washing at room temperature. One embodiment of low stringency conditions includes hybridization in 6 x sodium chloride / sodium citrate (SSC) at about 45°C, followed by two washes in 0.2 x SSC, 0.1% SDS at least at 50°C (the temperature of the washes can be increased to 55°C for low stringency conditions). Medium stringency conditions include and encompass from at least about 16% v / v to at least about 30% v / v formamide and from at least about 0.5 M to at least about 0.9 M salt for hybridization at 42°C, and at least about 0.1 M to at least about 0.2 M salt for washing at 55°C. Medium stringency conditions also may include 1 % Bovine Serum Albumin (BSA), 1 mM EDTA, 0.5 M NaHPO4 (pH 7.2), 7% SDS for hybridization at 65°C, and (i) 2 x SSC, 0.1% SDS; or (ii) 0.5% BSA, 1 mM EDTA, 40 mM NaHPO4(pH 7.2), 5% SDS for washing at 60-65°C. One embodiment of medium stringency conditions includes hybridizing in 6 x SSC at about 45°C, followed by one or more washes in 0.2 x SSC, 0.1% SDS at 60°C. High stringency conditions include and encompass from at least about 31% v / v to at least about 50% v / v formamide and from about 0.01 M to about 0.15 M salt for hybridization at42°C, and about 0.01 M to about 0.02 M salt for washing at 55°C. High stringency conditions also may include 1% BSA, 1 mM EDTA, 0.5 M NaHPCk (pH 7.2), 7% SDS for hybridization at 65°C, and (i) 0.2 x SSC, 0.1% SDS; or (ii) 0.5% BSA, 1 mM EDTA, 40 mM NaHPC (pH 7.2), 1 % SDS for washing at a temperature in excess of 65°C. One embodiment of high stringency conditions includes hybridizing in 6 x SSC at about 45°C, followed by one or more washes in 0.2 x SSC, 0.1% SDS at 65°C.

[0119] In certain embodiments, a corresponding AMD biomarker polynucleotide is one that hybridizes to a disclosed nucleotide sequence under very high stringency conditions. One embodiment of very high stringency conditions includes hybridizing 0.5 M sodium phosphate, 7% SDS at 65°C, followed by one or more washes at 0.2 x SSC, 1 % SDS at 65°C.

[0120] Other stringency conditions are well known in the art and a skilled addressee will recognize that various factors can be manipulated to optimize the specificity of the hybridization. Optimization of the stringency of the final washes can serve to ensure a high degree of hybridization. For detailed examples, see Ausubel et al, supra at pages 2.10.1 to 2.10.16 and Sambrook et al. (1989, supra) at sections 1 .101 to 1 .104.2.1 Sample preparation

[0121] Generally, a sample is processed prior to AMD biomarker detection or quantification. For example, nucleic acid may be extracted, isolated, and / or purified from a sample prior to analysis. Various DNA and / or mRNA extraction techniques are well known to those skilled in the art. Processing may include centrifugation, ultracentrifugation, ethanol precipitation, filtration, fractionation, resuspension, dilution, concentration, etc. In some embodiments, methods and systems provide analysis (e.g., quantification of RNA or protein biomarkers) from raw sample (e.g., biological fluid such as blood, serum, etc.) without or with limited processing.

[0122] Methods may comprise steps of homogenizing a sample in a suitable buffer, removal of contaminants and / or assay inhibitors, adding an AMD biomarker capture reagent (e.g., a magnetic bead to which is linked an oligonucleotide complementary to a target AMD nucleic acid biomarker), incubated under conditions that promote the association (e.g., by hybridization) of the target biomarker with the capture reagent to produce a target biomarkercapture reagent complex, incubating the target biomarker-capture complex under target biomarker-release conditions. In some embodiments, multiple AMD biomarkers are isolated in each round of isolation by adding multiple AMD biomarkers capture reagents (e.g., specific to the desired biomarkers) to the solution. For example, multiple AMD biomarker capture reagents, each comprising an oligonucleotide specific for a different target AMD biomarker can be added to the sample for isolation of multiple AMD biomarkers. It is contemplated that the methods encompass multiple experimental designs that vary both in the number of capture steps and in the number of target AMD biomarker captured in each capture step. In some embodiments, capture reagents are molecules, moieties, substances, or compositions that preferentially (e.g., specifically and selectively) interact with a particular biomarker sought to be isolated, purified,detected, and / or quantified. Any capture reagent having desired binding affinity and / or specificity to the particular AMD biomarker can be used in the present technology.

[0123] For example, the capture reagent can be a macromolecule such as a peptide, a protein (e.g., an antibody or receptor), an oligonucleotide, a nucleic acid (e.g., nucleic acids capable of hybridizing with the AMD biomarkers), vitamins, oligosaccharides, carbohydrates, lipids, or small molecules, or a complex thereof. As illustrative and non-limiting examples, an oligonucleotide may be used to isolate and purify a complementary oligonucleotide.

[0124] Any nucleic acids, including single-stranded and double-stranded nucleic acids, that are capable of binding, or specifically binding, to a target AMD biomarker can be used as the capture reagent. Examples of such nucleic acids include DNA, RNA, aptamers, peptide nucleic acids, and other modifications to the sugar, phosphate, or nucleoside base. Thus, there are many strategies for capturing a target and accordingly many types of capture reagents are known to those in the art.

[0125] The AMD biomarkers may be quantified or detected using any suitable technique. In specific embodiments, the AMD biomarkers are quantified using reagents that determine the presence of individual AMD biomarkers. Non-limiting reagents of this type include reagents for use in nucleic acid-based assays.2.2 Quantification or detection of nucleic acid biomarkers

[0126] In illustrative nucleic acid-based assays, nucleic acid is isolated from cells contained in the biological sample according to standard methodologies (Sambrook, et al, 1989, supra; and Ausubel et al, 1994, supra). The nucleic acid is typically fractionated (e.g., poly A+RNA) or whole cell RNA. Where RNA is used as the subject of detection, it may be desired to convert the RNA to a complementary DNA. In some embodiments, the nucleic acid is amplified by a template-dependent nucleic acid amplification technique. A number of template dependent processes are available to amplify the AMD biomarker sequences present in a given template sample. An exemplary nucleic acid amplification technique is the polymerase chain reaction (referred to as PCR), which is described in detail in U.S. Pat. Nos. 4,683, 195, 4,683,202 and 4,800,159, Ausubel et al (supra), and in Innis et al, (“PCR Protocols”, Academic Press, Inc., San Diego Calif., 1990). Briefly, in PCR, two primer sequences are prepared that are complementary to regions on opposite complementary strands of the biomarker sequence. An excess of deoxynucleotide triphosphates are added to a reaction mixture along with a DNA polymerase, e.g., Taq polymerase. If a cognate AMD biomarker sequence is present in a sample, the primers will bind to the biomarker and the polymerase will cause the primers to be extended along the biomarker sequence by adding on nucleotides. By raising and lowering the temperature of the reaction mixture, the extended primers will dissociate from the biomarker to form reaction products, excess primers will bind to the biomarker and to the reaction products and the process is repeated. A reverse transcriptase PCR amplification procedure may be performed in order to quantify the amount of mRNA amplified. Methods of reverse transcribingRNA into cDNA are well known and described in Sambrook et al., 1989, supra. Alternative methods for reverse transcription utilize thermostable, RNA-dependent DNA polymerases. These methods are described in International PCT Patent Publication No. WO 90 / 07641 . Polymerase chain reaction methodologies are well known in the art. In specific embodiments in which whole cell RNA is used, cDNA synthesis using whole cell RNA as a sample produces whole cell cDNA.

[0127] In some embodiments, the template-dependent amplification involves quantification of transcripts in real-time. For example, RNA or DNA may be quantified using the real-time PCR technique (Higuchi, 1992, et al., Biotechnology 10: 413-417). By determining the concentration of the amplified products of the target DNA in PCR reactions that have completed the same number of cycles and are in their linear ranges, it is possible to determine the relative concentrations of the specific target sequence in the original DNA mixture. If the DNA mixtures are cDNAs synthesized from RNAs isolated from different tissues or cells, the relative abundance of the specific mRNA from which the target sequence was derived can be determined for the respective tissues or cells. This direct proportionality between the concentration of the PCR products and the relative mRNA abundance is only true in the linear range of the PCR reaction. The final concentration of the target DNA in the plateau portion of the curve is determined by the availability of reagents in the reaction mix and is independent of the original concentration of target DNA. In specific embodiments, multiplexed, tandem PCR (MT-PCR) is employed, which uses a two-step process for gene expression profiling from small quantities of RNA or DNA, as described for example in U.S. Pat. Appl. Pub. No. 2007 / 0190540. In the first step, RNA is converted into cDNA and amplified using multiplexed gene specific primers. In the second step each individual gene is quantitated by real time PCR. Real-time PCR is typically performed using any PCR instrumentation available in the art. Typically, instrumentation used in real-time PCR data collection and analysis comprises a thermal cycler, optics for fluorescence excitation and emission collection, and optionally a computer and data acquisition and analysis software.

[0128] In some embodiments of RT-PCR assays, a TAQMAN® probe is used for quantitating nucleic acid. Such assays may use energy transfer (“ET”), such as fluorescence resonance energy transfer (“FRET”), to detect and quantitate the synthesized PCR product.

[0129] Typically, the TAQMAN® probe comprises a fluorescent label (e.g., a fluorescent dye) coupled to one end (e.g., the 5’-end) and a quencher molecule is coupled to the other end (e.g., the 3’-end), such that the fluorescent label and the quencher are in close proximity, allowing the quencher to suppress the fluorescence signal of the dye via FRET. When a polymerase replicates the chimeric amplicon template to which the fluorescent labeled probe is bound, the 5’-nuclease of the polymerase cleaves the probe, decoupling the fluorescent label and the quencher so that label signal (such as fluorescence) is detected. Signal (such as fluorescence) increases with each PCR cycle proportionally to the amount of probe that is cleaved.

[0130] TAQMAN® probes typically comprise a region of contiguous nucleotides having a sequence that is identically present in or complementary to a region of an AMD biomarker polynucleotide such that the probe is specifically hybridizable to the resulting PCR amplicon. In some embodiments, the probe comprises a region of at least 6 contiguous nucleotides having a sequence that is fully complementary to or identically present in a region of a target AMD biomarker polynucleotide, such as comprising a region of at least 8 contiguous nucleotides, at least 10 contiguous nucleotides, at least 12 contiguous nucleotides, at least 14 contiguous nucleotides, or at least 16 contiguous nucleotides having a sequence that is complementary to or identically present in a region of a target AMD biomarker polynucleotide to be detected and / or quantitated.

[0131] In addition to the TAQMAN® assays, other real-time PCR chemistries useful for detecting PCR products in the methods presented herein include, but are not limited to, Molecular Beacons, Scorpion probes and intercalating dyes, such as SYBR Green, EvaGreen, thiazole orange, YO-PRO, TO-PRO, etc. For example, Molecular Beacons, like TAQMAN® probes, use FRET to detect and quantitate a PCR product via a probe having a fluorescent label (e.g., a fluorescent dye) and a quencher attached at the ends of the probe. Unlike TAQMAN® probes, however, Molecular Beacons remain intact during the PCR cycles. Molecular Beacon probes form a stem-loop structure when free in solution, thereby allowing the fluorescent label and quencher to be in close enough proximity to cause fluorescence quenching. When the Molecular Beacon hybridizes to a target, the stem-loop structure is abolished so that the fluorescent label and the quencher become separated in space and the fluorescent label fluoresces. Molecular Beacons are available, e.g., from Gene Link™ (see, http : / / www.genelink.com / newsite / products / amp&analysis. asp).

[0132] In some embodiments, Scorpion probes can be used as both sequencespecific primers and for PCR product detection and quantitation. Like Molecular Beacons, Scorpion probes form a stem-loop structure when not hybridized to a target nucleic acid. However, unlike Molecular Beacons, a Scorpion probe achieves both sequence-specific priming and PCR product detection. A fluorescent label (e.g., a fluorescent dye molecule) is attached to the 5’-end of the Scorpion probe, and a quencher is attached to the 3’-end. The 3’ portion of the probe is complementary to the extension product of the PCR primer, and this complementary portion is linked to the 5’-end of the probe by a non-amplifiable moiety. After the Scorpion primer is extended, the target-specific sequence of the probe binds to its complement within the extended amplicon, thus opening up the stem-loop structure and allowing the fluorescent label on the 5’-end to fluoresce and generate a signal. Scorpion probes are available from, e.g., Premier Biosoft International (see, www.premierbiosoft.com / tech_notes / Scorpion.html).

[0133] In some embodiments, labels that can be used on the FRET probes include colorimetric and fluorescent dyes such as Alexa Fluor dyes, BODIPY dyes, such as BODIPY FL; Cascade Blue; Cascade Yellow; coumarin and its derivatives, such as 7-amino-4- methylcoumarin, aminocoumarin and hydroxycoumarin; cyanine dyes, such as Cy3 and Cy5;eosins and erythrosins; fluorescein and its derivatives, such as fluorescein isothiocyanate; macrocyclic chelates of lanthanide ions, such as Quantum Dye™; Marina Blue; Oregon Green; rhodamine dyes, such as rhodamine red, tetramethylrhodamine and rhodamine 6G; Texas Red; fluorescent energy transfer dyes, such as thiazole orange-ethidium heterodimer; and, TOTAB.

[0134] Specific examples of dyes include, but are not limited to, those identified above and the following: Alexa Fluor 350, Alexa Fluor 405, Alexa Fluor 430, Alexa Fluor 488, Alexa Fluor 500. Alexa Fluor 514, Alexa Fluor 532, Alexa Fluor 546, Alexa Fluor 555, Alexa Fluor 568, Alexa Fluor 594, Alexa Fluor 610, Alexa Fluor 633, Alexa Fluor 647, Alexa Fluor 660, Alexa Fluor 680, Alexa Fluor 700, and, Alexa Fluor 750; amine-reactive BODIPY dyes, such as BODIPY 493 / 503, BODIPY 530 / 550, BODIPY 558 / 568, BODIPY 564 / 570, BODIPY 576 / 589, BODIPY 581 / 591 , BODIPY 630 / 650, BODIPY 650 / 655, BODIPY FL, BODIPY R6G, BODIPY TMR, and, BODIPY-TR; Cy3, Cy5, 6-FAM, Fluorescein Isothiocyanate, HEX, 6-JOE, Oregon Green 488, Oregon Green 500, Oregon Green 514, Pacific Blue, REG, Rhodamine Green, Rhodamine Red, Renographin, ROX, SYPRO, TAMRA, 2',4',5',7'- Tetrabromosulfonefluorescein, and TET.

[0135] Examples of dye / quencher pairs (i.e. , donor / acceptor pairs) include, but are not limited to, fluorescein / tetramethylrhodamine; lAEDANS / fluorescein; EDANS / dabcyl; fluorescein / fluorescein; BODIPY FL / BODIPY FL; fluorescein / QSY 7 or QSY 9 dyes. When the donor and acceptor are the same, FRET may be detected, in some embodiments, by fluorescence depolarization. Certain specific examples of dye / quencher pairs (i.e., donor / acceptor pairs) include, but are not limited to, Alexa Fluor 350 / Alexa Fluor 488; Alexa Fluor 488 / Alexa Fluor 546; Alexa Fluor 488 / Alexa Fluor 555; Alexa Fluor 488 / Alexa Fluor 568; Alexa Fluor 488 / Alexa Fluor 594; Alexa Fluor 488 / Alexa Fluor 647; Alexa Fluor 546 / Alexa Fluor 568; Alexa Fluor 546 / Alexa Fluor 594; Alexa Fluor 546 / Alexa Fluor 647; Alexa Fluor 555 / Alexa Fluor 594; Alexa Fluor 555 / Alexa Fluor 647; Alexa Fluor 568 / Alexa Fluor 647; Alexa Fluor 594 / Alexa Fluor 647; Alexa Fluor 350 / QSY35; Alexa Fluor 350 / dabcyl ; Alexa Fluor 488 / QSY 35; Alexa Fluor 488 / dabcyl; Alexa Fluor 488 / QSY 7 or QSY 9; Alexa Fluor 555 / QSY 7 or QSY9; Alexa Fluor 568 / QSY 7 or QSY 9; Alexa Fluor 568 / QSY 21 ; Alexa Fluor 594 / QSY 21 ; and Alexa Fluor 647 / QSY 21 . In some embodiments, the same quencher may be used for multiple dyes, for example, a broad spectrum quencher, such as an Iowa Black® quencher (Integrated DNA Technologies, Coralville, Iowa) or a Black Hole Quencher™ (BHQ™; Sigma-Aldrich, St. Louis, Mo.).

[0136] In some embodiments, for example, in a multiplex reaction in which two or more moieties (such as amplicons) are detected simultaneously, each probe comprises a detectably different dye such that the dyes may be distinguished when detected simultaneously in the same reaction. One skilled in the art can select a set of detectably different dyes for use in a multiplex reaction. In some embodiments, multiple target AMD biomarker polynucleotides are detected and / or quantitated in a single multiplex reaction. In some embodiments, each probe that is targeted to a different AMD biomarker polynucleotide is spectrally distinguishable whenreleased from the probe. Thus, each target AMD biomarker polynucleotide is detected by a unique fluorescence signal.

[0137] Specific examples of fluorescently labeled ribonucleotides useful in the preparation of real-time PCR probes for use in some embodiments of the methods described herein are available from Molecular Probes (Invitrogen), and these include, Alexa Fluor 488-5- UTP, Fluorescein-12-UTP, BODIPY FL-14-UTP, BODIPY TMR-14-UTP, Tetramethylrhodamine- 6-UTP, Alexa Fluor 546-14-UTP, Texas Red-5-UTP, and BODIPY TR-14-UTP. Other fluorescent ribonucleotides are available from Amersham Biosciences (GE Healthcare), such as Cy3-UTP and Cy5-UTP.

[0138] Examples of fluorescently labeled deoxyribonucleotides useful in the preparation of real-time PCR probes for use in the methods described herein include Dinitrophenyl (DNP)-l -dUTP, Cascade Blue-7-dUTP, Alexa Fluor 488-5-dUTP, Fluorescein-12- dUTP, Oregon Green 488-5-dUTP, BODIPY FL-14-dUTP, Rhodamine Green-5-dUTP, Alexa Fluor 532-5-dUTP, BODIPY TMR-14-dUTP, Tetramethylrhodamine-6-dUTP, Alexa Fluor 546- 14-dUTP, Alexa Fluor 568-5-dUTP, Texas Red-12-dUTP, Texas Red-5-dUTP, BODIPY TR-14- dUTP, Alexa Fluor 594-5-dUTP, BODIPY 630 / 650-14-dUTP, BODIPY 650 / 665-14-dUTP; Alexa Fluor 488-7-OBEA-dCTP, Alexa Fluor 546-16-OBEA-dCTP, Alexa Fluor 594-7-OBEA-dCTP, Alexa Fluor 647-12-OBEA-dCTP. Fluorescently labeled nucleotides are commercially available and can be purchased from, e.g., Invitrogen.

[0139] In certain embodiments, target nucleic acids are quantified using blotting techniques, which are well known to those of skill in the art. Southern blotting involves the use of DNA as a target, whereas Northern blotting involves the use of RNA as a target. Each provides different types of information, although cDNA blotting is analogous, in many aspects, to blotting of RNA species. Briefly, a probe is used to target a DNA or RNA species that has been immobilized on a suitable matrix, often a filter of nitrocellulose. The different species should be spatially separated to facilitate analysis. This often is accomplished by gel electrophoresis of nucleic acid species followed by “blotting” on to the filter. Subsequently, the blotted target is incubated with a probe (usually labeled) under conditions that promote denaturation and rehybridization. Because the probe is designed to base pair with the target, the probe will bind a portion of the target sequence under renaturing conditions. Unbound probe is then removed, and detection is accomplished as described above. Following detection / quantification, one may compare the results seen in a given subject with a control reaction or a statistically significant reference group or population of control subjects as defined herein. In this way, it is possible to correlate the amount of AMD biomarker nucleic acid detected with the likelihood of a subject developing AMD.

[0140] Also contemplated are biochip-based technologies such as those described by Hacia et al. (1996, Nature Genetics 14: 441 -447) and Shoemaker et al. (1996, Nature Genetics 14: 450-456). Briefly, these techniques involve quantitative methods for analyzing large numbers of genes rapidly and accurately. By tagging genes with oligonucleotides or usingfixed nucleic acid probe arrays, one can employ biochip technology to segregate target molecules as high-density arrays and screen these molecules on the basis of hybridization. See also Pease et al. (1994, Proc. Natl. Acad. Sci. U.S.A. 91 : 5022-5026); Fodor et al. (1991 , Science 251 : 767-773). Briefly, nucleic acid probes to AMD biomarker polynucleotides are made and attached to biochips to be used in screening and diagnostic methods, as outlined herein. The nucleic acid probes attached to the biochip are designed to be substantially complementary to specific expressed AMD biomarker nucleic acids, i.e., the target sequence (either the target sequence of the sample or to other probe sequences, for example in sandwich assays), such that hybridization of the target sequence and the probes of the present invention occur. This complementarity need not be perfect; there may be any number of base pair mismatches, which will interfere with hybridization between the target sequence and the nucleic acid probes of the present invention. However, if the number of mismatches is so great that no hybridization can occur under even the least stringent of hybridization conditions, the sequence is not a complementary target sequence. In certain embodiments, more than one probe per sequence is used, with either overlapping probes or probes to different sections of the target being used. That is, two, three, four or more probes, are used to build in a redundancy for a particular target. The probes can be overlapping (i.e., have some sequence in common), or separate.

[0141] In an illustrative biochip analysis, oligonucleotide probes on the biochip are exposed to or contacted with a nucleic acid sample suspected of containing one or more AMD biomarker polynucleotides under conditions favoring specific hybridization. Sample extracts of DNA or RNA, either single or double-stranded, may be prepared from fluid suspensions of biological materials, or by grinding biological materials, or following a cell lysis step which includes, but is not limited to, lysis effected by treatment with SDS (or other detergents), osmotic shock, guanidinium isothiocyanate and lysozyme. Suitable DNA, which may be used in the method of the invention, includes cDNA. Such DNA may be prepared by any one of a number of commonly used protocols as for example described in Ausubel, et al., 1994, supra, and Sambrook, et al., 1989, supra.

[0142] Suitable RNA, which may be used in the method of the invention, includes messenger RNA, complementary RNA transcribed from DNA (cRNA) or genomic or subgenomic RNA. Such RNA may be prepared using standard protocols as for example described in the relevant sections of Ausubel, et al. 1994, supra and Sambrook, et al. 1989, supra).

[0143] cDNA may be fragmented, for example, by sonication or by treatment with restriction endonucleases. Suitably, cDNA is fragmented such that resultant DNA fragments are of a length greater than the length of the immobilized oligonucleotide probe(s) but small enough to allow rapid access thereto under suitable hybridization conditions. Alternatively, fragments of cDNA may be selected and amplified using a suitable nucleotide amplification technique, as described for example above, involving appropriate random or specific primers.

[0144] Usually the target AMD biomarker polynucleotides are detectably labeled so that their hybridization to individual probes can be determined. The target polynucleotides are typically detectably labeled with a reporter molecule illustrative examples of which include chromogens, catalysts, enzymes, fluorochromes, chemiluminescent molecules, bioluminescent molecules, lanthanide ions (e.g., Eu34), a radioisotope and a direct visual label. In the case of a direct visual label, use may be made of a colloidal metallic or non-metallic particle, a dye particle, an enzyme or a substrate, an organic polymer, a latex particle, a liposome, or other vesicle containing a signal producing substance and the like. Illustrative labels of this type include large colloids, for example, metal colloids such as those from gold, selenium, silver, tin, and titanium oxide. In some embodiments in which an enzyme is used as a direct visual label, biotinylated bases are incorporated into a target polynucleotide.

[0145] The hybrid-forming step can be performed under suitable conditions for hybridizing oligonucleotide probes to test nucleic acid including DNA or RNA. In this regard, reference may be made, for example, to Nucleic Acid Hybridization, A Practical Approach (Homes and Higgins, eds.) (IRL press, Washington D.C., 1985). In general, whether hybridization takes place is influenced by the length of the oligonucleotide probe and the polynucleotide sequence under test, the pH, the temperature, the concentration of mono- and divalent cations, the proportion of G and C nucleotides in the hybrid-forming region, the viscosity of the medium and the possible presence of denaturants. Such variables also influence the time required for hybridization. The preferred conditions will therefore depend upon the particular application. Such empirical conditions, however, can be routinely determined without undue experimentation.

[0146] After the hybrid-forming step, the probes are washed to remove any unbound nucleic acid with a hybridization buffer. This washing step leaves only bound target polynucleotides. The probes are then examined to identify which probes have hybridized to a target polynucleotide.

[0147] The hybridization reactions are then detected to determine which of the probes has hybridized to a corresponding target sequence. Depending on the nature of the reporter molecule associated with a target polynucleotide, a signal may be instrumentally detected by irradiating a fluorescent label with light and detecting fluorescence in a fluorimeter; by providing for an enzyme system to produce a dye which could be detected using a spectrophotometer; or detection of a dye particle or a coloured colloidal metallic or non-metallic particle using a reflectometer; in the case of using a radioactive label or chemiluminescent molecule employing a radiation counter or autoradiography. Accordingly, a detection means may be adapted to detect or scan light associated with the label which light may include fluorescent, luminescent, focused beam or laser light. In such a case, a charge couple device (CCD) or a photocell can be used to scan for emission of light from a probe:target polynucleotide hybrid from each location in the micro-array and record the data directly in a digital computer. In some cases, electronic detection of the signal may not be necessary. Forexample, with enzymatically generated color spots associated with nucleic acid array format, visual examination of the array will allow interpretation of the pattern on the array. In the case of a nucleic acid array, the detection means is suitably interfaced with pattern recognition software to convert the pattern of signals from the array into a plain language genetic profile. In certain embodiments, oligonucleotide probes specific for different AMD biomarker polynucleotides are in the form of a nucleic acid array and detection of a signal generated from a reporter molecule on the array is performed using a “chip reader”. A detection system that can be used by a “chip reader” is described for example by Pirrung et al. (U.S. Patent No. 5,143,854). The chip reader will typically also incorporate some signal processing to determine whether the signal at a particular array position or feature is a true positive or maybe a spurious signal. Exemplary chip readers are described for example by Fodor et al. (U.S. Patent No. 5,925,525). Alternatively, when the array is made using a mixture of individually addressable kinds of labeled microbeads, the reaction may be detected using flow cytometry.

[0148] In certain embodiments, the AMD biomarker is a target RNA (e.g., mRNA) or a DNA copy of the target RNA whose level or abundance is measured using at least one nucleic acid probe that hybridizes under at least low, medium, or high stringency conditions to the target RNA or to the DNA copy, wherein the nucleic acid probe comprises at least 15 (e.g., 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, or more) contiguous nucleotides of AMD biomarker polynucleotide. In some embodiments, the measured level or abundance of the target RNA or its DNA copy is normalized to the level or abundance of a reference RNA or a DNA copy of the reference RNA. Suitably, the nucleic acid probe is immobilized on a solid or semisolid support. In illustrative examples of this type, the nucleic acid probe forms part of a spatial array of nucleic acid probes. In some embodiments, the level of nucleic acid probe that is bound to the target RNA or to the DNA copy is measured by hybridization (e.g., using a nucleic acid array). In other embodiments, the level of nucleic acid probe that is bound to the target RNA or to the DNA copy is measured by nucleic acid amplification (e.g., using a polymerase chain reaction (PCR)). In still other embodiments, the level of nucleic acid probe that is bound to the target RNA or to the DNA copy is measured by nuclease protection assay.

[0149] Sequencing technologies such as Sanger sequencing, pyrosequencing, sequencing by ligation, massively parallel sequencing, also called “next-generation sequencing” (NGS), and other high-throughput sequencing approaches with or without sequence amplification of the target can also be used to detect or quantify the presence of AMD nucleic acid biomarker in a sample. Sequence-based methods can provide further information regarding alternative splicing and sequence variation in previously identified genes. Sequencing technologies include a number of steps that are grouped broadly as template preparation, sequencing, detection and data analysis. Current methods for template preparation involve randomly breaking genomic DNA into smaller sizes from which each fragment is immobilized to a support. The immobilization of spatially separated fragment allows thousands to billions of sequencing reaction to be performed simultaneously. A sequencing step may use any of avariety of methods that are commonly known in the art. One specific example of a sequencing step uses the addition of nucleotides to the complementary strand to provide the DNA sequence. The detection steps range from measuring bioluminescent signal of a synthesized fragment to four-colour imaging of single molecule. In some embodiments in which NGS is used to detect or quantify the presence of a AMD nucleic acid biomarker in a sample, the methods are suitably selected from semiconductor sequencing (Ion Torrent; Personal Genome Machine); Helicos True Single Molecule Sequencing (tSMS) (Harris et al. 2008, Science 320: 106-109); 454 sequencing (Roche) (Margulies et al. 2005, Nature, 437, 376-380); SOLiD technology (Applied Biosystems); SOLEXA sequencing (Illumina); single molecule, real-time (SMRT™) technology of Pacific Biosciences; nanopore sequencing (Soni and Meller, 2007. Clin Chem 53: 1996-2001 ); DNA nanoball sequencing; sequencing using technology from Dover Systems (Polonator), and technologies that do not require amplification or otherwise transform native DNA prior to sequencing (e.g., Pacific Biosciences and Helicos), such as nanopore-based strategies (e.g., Oxford Nanopore, Genia Technologies, and Nabsys).2.3 Kits

[0150] All the essential reagents required for detecting and quantifying the AMD biomarkers of the invention may be assembled together in a kit. In some embodiments, the kit comprises a reagent that permits quantification of at least one AMD biomarker (e.g., one or more Group 1 AMD biomarker). In some embodiments the kit comprises: (i) a reagent that allows quantification (e.g., determining the presence, absence, or allele) of at least one AMD biomarker. In some embodiments, the kit further comprises (ii) a reagent that allows quantification (e.g., determining the presence, absence, or allele) of at least one group 2 AMD biomarker.

[0151] In the context of the present invention, “kit” is understood to mean a product containing the different reagents necessary for carrying out the methods of the invention packed so as to allow their transport and storage. Materials suitable for packing the components of the kit include crystal, plastic (polyethylene, polypropylene, polycarbonate and the like), bottles, vials, paper, envelopes and the like. Additionally, the kits of the invention can contain instructions for the simultaneous, sequential, or separate use of the different components contained in the kit. The instructions can be in the form of printed material or in the form of an electronic support capable of storing instructions such that they can be read by a subject, such as electronic storage media (magnetic disks, tapes and the like), optical media (CD-ROM, DVD) and the like. Alternatively, or in addition, the media can contain internet addresses that provide the instructions.

[0152] Reagents that allow quantification of an AMD biomarker include compounds or materials, or sets of compounds or materials, which allow quantification of the AMD biomarker. In specific embodiments, the compounds, materials or sets of compounds or materials permit determining the expression level of a gene (e.g., AMD biomarker gene), including without limitation the extraction of RNA material, the determination of the level of acorresponding RNA, etc., primers for the synthesis of a corresponding cDNA, primers for amplification of DNA, and / or probes capable of specifically hybridizing with the RNAs (or the corresponding cDNAs) encoded by the genes, TaqMan probes, etc.

[0153] The kits may also optionally include appropriate reagents for detection of labels, positive and negative controls, washing solutions, blotting membranes, microtiter plates, dilution buffers and the like. For example, a nucleic acid-based detection kit may include (i) an AMD biomarker polynucleotide (which may be used as a positive control); and (ii) a primer or probe that specifically hybridizes to an AMD biomarker polynucleotide. Also included may be enzymes suitable for amplifying nucleic acids including various polymerases (reverse transcriptase, Taq, SEQUENASE™, DNA ligase etc. depending on the nucleic acid amplification technique employed), deoxynucleotides and buffers to provide the necessary reaction mixture for amplification. Such kits also generally will comprise, in suitable means, distinct containers for each individual reagent and enzyme as well as for each primer or probe. The kit can also feature various devices (e.g., one or more) and reagents (e.g., one or more) for performing one of the assays described herein; and / or printed instructions for using the kit to determine the presence or absence of an AMD biomarker gene.

[0154] The reagents described herein, which may be optionally associated with detectable labels, can be presented in the format of a microfluidics card, a chip or chamber, a microarray or a kit adapted for use with the assays described in the examples or below (e.g., RT-PCR or qPCR techniques described herein).

[0155] The reagents also have utility in compositions for detecting and quantifying the biomarkers of the invention. For example, a reverse transcriptase may be used to reverse transcribe RNA transcripts, including mRNA, in a nucleic acid sample, to produce reverse transcribed transcripts, including reverse transcribed mRNA (also referred to as “cDNA”). In specific embodiments, the reverse transcribed mRNA is whole cell reverse transcribed mRNA (also referred to herein as “whole cell cDNA”).

[0156] The reagents are suitably used to quantify the reverse transcribed transcripts. For example, oligonucleotide primers that hybridize to the reverse transcribed transcript can be used to amplify at least a portion of the reverse transcribed transcript via a suitable nucleic acid amplification technique (e.g., RT-PCR or qPCR techniques described herein). Alternatively, oligonucleotide probes may be used to hybridize to the reverse transcribed transcript for the quantification, using a nucleic acid hybridization analysis technique (e.g., microarray analysis), as described for example above. Thus, in some embodiments, a respective oligonucleotide primer or probe is hybridized to a complementary nucleic acid sequence of a reverse transcribed transcript in the compositions of the invention. The compositions typically comprise labeled reagents for detecting and / or quantifying the reverse transcribed transcripts. Representative reagents of this type include labeled oligonucleotide primers or probes that hybridize to RNA transcripts or reverse transcribed RNA, labeled RNA, labeled reverse transcribed RNA as well as labeled oligonucleotide linkers or tags (e.g., alabeled RNA or DNA linker or tag) for labeling (e.g., end labeling such as 3’ end labeling) RNA or reverse transcribed RNA. The primers, probes, RNA or reverse transcribed RNA (i.e., cDNA) (whether labeled or non-labeled) may be immobilized or free in solution.

[0157] Representative reagents of this type include labeled oligonucleotide primers or probes that hybridize to reverse transcribed and transcripts as well as labeled reverse transcribed transcripts. The label can be any reporter molecule as known in the art, illustrative examples of which are described above and elsewhere herein.

[0158] The present invention also encompasses non-reverse transcribed RNA embodiments in which cDNA is not made and the RNA transcripts are directly the subject of the analysis. Thus, in other embodiments, reagents are suitably used to quantify RNA transcripts directly. For example, oligonucleotide probes can be used to hybridize to transcripts for quantification of AMD biomarkers of the invention, using a nucleic acid hybridization analysis technique (e.g., microarray analysis), as described for example above. Thus, in some embodiments, a respective oligonucleotide probe is hybridized to a complementary nucleic acid sequence of an AMD biomarker transcript in the compositions of the invention. In illustrative examples of this type, the compositions may comprise labeled reagents that hybridize to transcripts for detecting and / or quantifying the transcripts. Representative reagents of this type include labeled oligonucleotide probes that hybridize to transcripts as well as labeled transcripts. The primers or probes may be immobilized or free in solution.2.4 Methods of managing therapy

[0159] The present invention also extends to the management of a disease or condition that is associated with the likelihood of a subject developing AMD. Once a subject is positively identified as having a likelihood of developing AMD, the subject may be administered a therapeutic or non-therapeutic treatment regimen with a means to decreasing of developing AMD.

[0160] Typically, the therapeutic agents will be administered in pharmaceutical compositions together with a pharmaceutically acceptable carrier and in an effective amount to achieve their intended purpose. The dose of active compounds administered to a subject should be sufficient to achieve a beneficial response in the subject over time such as a reduction in the likelihood of the subject developing AMD. The quantity of the pharmaceutically active compounds(s) to be administered may depend on the subject to be treated inclusive of the age, sex, weight and general health condition thereof. In this regard, precise amounts of the active compound(s) for administration will depend on the judgment of the practitioner. Those of skill in the art may readily determine suitable dosages of the therapeutic agents and suitable treatment regimens without undue experimentation.

[0161] The present invention also contemplates the use of the indicator-determining methods, apparatus, compositions and kits disclosed herein in methods of preventing or inhibiting the development of AMD in a subject. These methods (also referred to herein as“treatment methods”) generally comprise: exposing the subject to a treatment regimen for preventing AMD, based on an indicator obtained from an indicator-determining method as disclosed herein. In specific embodiments, the treatment methods comprise: (a) determining a plurality of biomarker values for at least one (e.g., 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) AMD biomarkers of the subject, each biomarker value being indicative of a value measured or derived for a respective AMD biomarker; (b) determining an indicator using a combination of the plurality of biomarker values, the indicator being at least partially indicative of the a likelihood of developing AMD, wherein: (i) at least one (e.g., 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) AMD biomarkers; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to predict a likelihood of the subject developing AMD ; and (c) administering to the subject, on the basis that the indicator indicates likelihood of the subject developing AMD, an effective amount of an agent that prevents or inhibits the development of AMD.

[0162] In advantageous embodiments, the treatment methods comprise: (1 ) determining a plurality of measured biomarker values, each measured biomarker value being a measured value of an individual AMD biomarker of the subject; and (2) applying a function to at least two of the measured biomarker values to determine at least one derived biomarker value, the at least one derived biomarker value being indicative of a value of a corresponding derived AMD biomarker. The function suitably includes at least one of: (a) multiplying two biomarker values; (b) dividing two biomarker values; (c) adding two biomarker values; (d) subtracting two biomarker values; (e) a weighted sum of at least two biomarker values; (f) a log sum of at least two biomarker values; (g) a geometric mean of at least two biomarker values; and (h) a sigmoidal function of at least two biomarker values.

[0163] The present invention also contemplates methods in which the indicatordetermining method of the invention is implemented using one or more processing devices. In some embodiments, these methods comprise: (1 ) determining a pair of biomarker values, the pair of biomarker values consisting of a first biomarker value indicative of a presence or absence of a group 1 AMD biomarker SNP (e.g., rs10797980, rs55191 1 , rs6661798, rs 1 1674246, rs 1871671 , rs7637449, rs 12359135, rs731 1672, and rs9901671 ) and one or more group 2 AMD biomarker SNP (e.g., rs10033900, rs1005819, rs10241492, rs1064583, rs10781 177, rs10781 180, rs10922109, rs 10981380, rs 11072508, rs1 1076175, rs1 1080055, rs1 1 120691 , rs1 1 142636, rs1 12635299, rs1 142, rs1 144, rs1 1569520, rs1 1635145, rs11771419, rs1 1884770, rs121913059, rs1221 1410, rs12213415, rs12457465, rs12576144, rs12901081 , rs12913832, rs12949956, rs13081855, rs13255394, rs1378940, rs1378942, rs1393350, rs141853578, rs147859257, rs148553336, rs1605677, rs163494, rs16841920, rs17105278, rs17356664, rs17421410, rs17480689, rs17576, rs1800588, rs187328863, rs1926564, rs201 1092, rs201 1822, rs2043085, rs2070895, rs2170240, rs2232613, rs2280953, rs2367070, rs2414634, rs247616, rs2575876, rs259842, rs2842343, rs2857107, rs3138142, rs3750846, rs3760775, rs3764261 , rs3775220, rs380743, rs3825991 , rs401 186, rs4151671 ,rs4420638, rs4748976, rs4943289, rs5023028, rs550513, rs570618, rs5754206, rs5754222, rs5754227, rs61760904, rs61818925, rs621736, rs66532523, rs6726589, rs6795735, rs6899205, rs704, rs7182946, rs7266392, rs73036519, rs73045269, rs7405901 , rs7428936, rs760070, rs7624060, rs7625101 , rs7663219, rs77516118, rs7874221 , rs7896471 , rs7939052, rs8017304, rs8051675, rs8056814, rs8135665, rs879180, rs9267576, rs943080, rs9821337, rs9973159, rs485632, rs56123646, rs17826006, rs7439493, rs62358361 , rs116306799, rs7803454, rs79037040, rs11143754, rs334362, rs4570483, rs36212733, rs3138141 , rs61941275, rs9564692, rs1956526, rs2414577, rs17231506, rs72802342, rs12948099, rs1137776, rs12019136, rs11569415, rs429358, rs58847685, rs77280782, rs74406464, rs77968014, rs352920876, rs191281603, rs62247658, rs140647181 , rs55975637, rs114092250, rs116503776, rs144629244, rs 114254831 , rs181705462, rs10781182, rs71507014, rs1626340, rs2740488, rs12357257, rs61941274, rs61985136, rs2842339, rs5817082, rs6565597, rs2230199, rs67538026, rs142450006, and rs201459901 ); (2) determining an indicator indicative of the presence or absence of the group 1 and group 2 SNPs; (3) retrieving previously determined indicator references from a database, the indicator references being determined based on indicators determined from a reference population, one of the groups consisting of individuals know to have or develop AMD; (4) comparing the indicator to the indicator references; (5) using the results of the comparison to determine a probability indicative of the subject developing or not developing AMD; and (6) generating a representation of the probability, the representation being displayed to a user to allow the user to assess the likelihood of a biological subject developing AMD.

[0164] Additionally, methods can be provided for determining an indicator used in assessing a likelihood of a subject developing AMD. These methods suitably include: (1 ) determining a plurality of group 1 AMD biomarker values and group 2 AMD biomarker values, each biomarker value being indicative of a value measured or derived for at least one corresponding AMD biomarker of the subject and being at least partially indicative of a presence or absence of the AMD biomarker in a sample taken from the subject; (2) determining the indicator using a combination of the plurality of biomarker values, wherein : at least two biomarkers have a mutual correlation in respect of the development of AMD that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to the likelihood of the development of AMD, the performance threshold being indicative of an explained variance of at least 0.3.

[0165] In this example the method includes determining an indicator based on the presence or absence of one or more biomarkers in a sample from a subject, to assess whether a subject is likely to develop AMD.

[0166] The biomarker values and biomarkers corresponding to the biomarker values can be of any appropriate form and in particular can relate to any attribute of a subject for which a value can be quantified. This technique is particularly suited to high-throughputtechnologies such as mass spectrometry, sequencing platforms, array and hybridization platforms, or any combination of such technologies. In one embodiment, the biomarker value relates to the presence or absence of an expression product or other measurable molecule. In a preferred embodiment, the biomarker value relates to the presence or absence of one or more SNPs selected from Table 1 . In an even more preferred embodiment, the SNPs are selected from rs9901671 , rs7311672, rs1122947, rs3132550, rs7182946, rs879180, rs11635145, rs12576144, rs3775220, rs12359135, rs10797980, rs11674246, and rs2718666.

[0167] The biomarker values may be determined in any one of a number of ways. In one example, the process of determining the biomarker values can include measuring the biomarker values, for example by performing tests on the biological subject. More typically however, the step of determining the biomarker values include having an electronic processing device receive or otherwise obtain biomarker values that have been previously measured or derived. This could include for example, retrieving the biomarker values from data stored such as a remote database, obtaining biomarker values that have been manually input, using an input device, or the like.

[0168] The indicator is determined using one or a combination of biomarker values, the indicator being at least partially indicative of the likelihood of a subject developing AMD.

[0169] The indicator can be combined with at least one or more genetic or non- genetic risk factors in any one of a number of ways and this can include for example adding, multiplying, subtracting or dividing to determine a polygenetic risk score (PRS). This step is performed so that multiple risk factors can be combined into a single PRS value, providing a more useful and straightforward mechanism for allowing the PRS to be interpreted and hence used in diagnosing the likelihood a subject will develop AMD. The genetic risk factors may be selected from the group consisting of SNPs from Table 2, ethnicity, sex or family history of AMD. The non-genetic risk factors may be selected from the group consisting of age, alcohol consumption history, smoking history, exercise history, body mass index, diet or other age- related ocular conditions.

[0170] Assuming the method is performed using an electronic processing device, an indication of the PRS is optionally displayed or otherwise provided to the used. In this regard, the PRS could be a graphical or alphanumeric representation of a PRS. Alternatively, the PRS could be the result of a comparison of PRS value to predefined thresholds or ranges, or alternatively could be an indication of the likelihood of developing AMD, derived using the PRS.

[0171] The PRS of the invention was validated using several different cohorts (see, “Prediction of AMD risk - Participant characteristics in main validation cohort”). Overall, these findings provide compelling evidence that the SNPs disclosed herein can function as biomarkers for AMD and may potentially serve as a useful diagnostic for early detection for subjects with AMD. In this regard, it is proposed that the methods, apparatus, compositions and kits disclosed herein that are based on these biomarkers may serve in the point-of-care diagnostics that allowfor rapid and inexpensive screening for AMD, which may result in significant cost savings to the medical systems as subjects can be exposed to appropriate therapeutic agents that are suitable for treating AMD or a condition that is associated with AMD. Suitable AMD treatment include, but are not limited to, treatment with an agent that is an anti-VEGF molecule, such as LUCENTIS™ or MACUGEN®, a complement factor inhibitor, or Visudyne used with Photodynamic Therapy. Additional anti-VEGF molecules are known in the art.

[0172] Thus, specific SNPs are disclosed herein as AMD biomarkers that provide a means for identifying the likelihood of a subject developing AMD. Evaluation of these AMD biomarkers through analysis of their absence or presence in a subject or in a sample taken from a subject provides a indicator which determined a PRS that can be used for assessing the likelihood of AMD in a subject.

[0173] The invention involves detection and analysis of a large number of biomarkers (e.g., SNPs) which can be used to calculate a PRS suitable for identifying individuals at a greater risk of progression to advanced AMD. Detection methods for detecting relevant alleles include a variety of methods well known in the art, e.g., gene amplification technologies. For example, detection can include amplifying the polymorphism or a sequence associated therewith and detecting the resulting amplicon. This can include admixing an amplification primer or amplification primer pair with a nucleic acid template isolated from the organism or biological sample (e.g., comprising the SNP or other polymorphism), where the primer or primer pair is complementary or partially complementary to at least a portion of the target gene, or to a sequence proximal thereto.

[0174] Amplification can be performed by DNA polymerization reaction (such as PCR, RT-PCR) comprising a polymerase and the template nucleic acid to generate the amplicon. The amplicon is detected by an available detection method, e.g., sequencing, hybridizing the amplicon to an array (or affixing the amplicon to an array and hybridizing probes to it), digesting the amplicon with a restriction enzyme (e.g., RFLP), realtime PCR analysis, single nucleotide extension, allele-specific hybridization, or the like. Genotyping can also be performed by other known techniques, such as using primer mass extension and MALDI-TOF mass spectrum (MS) analysis.

[0175] In order that the invention may be readily understood and put into practical effect, particular preferred embodiments will now be described by way of the following nonlimiting examples.EXAMPLESMTAG and meta-analysis

[0176] A multitrait GWAS was performed by applying MTAG to the following datasets: AMD-2016 GWAS, AMD-2013 GWAS, UKB GWAS, and early AMD GWAS (Winkler et al. 2020). The inventors then conducted a meta-analysis to combine the MTAG output with GEAR GWAS and Finngen AMD GWAS (a total of 64,885 AMD cases and 568,740 controls inmeta-analysis, including overlapping samples) (Figure 1 ). 114 independent genome-wide significant variants were identified (Table 2) from the meta-analysis result involving 13 previously unreported loci near or within EDEM3, ZFP57, SMAD3, CASP10, CYP26A1, ME3, PLEKHM3, CDSN, KLHL8, TRPM1, C1R, PDGFB, and HIC1 (Table 3). By looking up the novel loci on Human Protein Atlas, all loci showed protein expression in the retina (mouse or human) (Uhlen et al. 2005).

[0177] Eight of the novel genes (ZFP57, SMAD3, CYP26A1, CDSN, KLHL8, TRPM1, C1R, and HIC1) have been significantly associated with HDL cholesterol levels (Richardson et al. 2020; Sakaue et al. 2021 ) and four genes have been linked with glaucoma (CYP26A1, ME3, HIC1) or intraocular pressure (KLHL8) (Gharahkhani et al. 2021 , 2018; Gao et al. 2018). Interestingly, previous small study reported a shared genetic component underlying glaucoma and AMD (Cuellar-Partida et al. 2016). Therefore, the genetic correlation (rg) was tested between AMD SNPs in this study and recent primary open-angle glaucoma (POAG) GWAS (Gharahkhani et al. 2021 ) and performed co-localisation at the region of around 1 MB of four glaucoma genes. The rg between AMD and POAG is 0.105 (± 0.0418, P = 0.0121 ), and none of the causal variants was shared for the loci implicated in AMD and POAG (Posterior possibility of shared causal variants < 1%).TABLE 2INDEPENDENT SNPS (ANNOTATED)TABLE 3LIST OF NOVEL AMD Loci IDENTIFIEDrs551911, rs6661798 and rs7637449 reached genome-wide significance for their conditioned p-valuesGene-based and pathway-based results

[0178] A gene-based association analysis was performed on the meta-analysis result, to gain insight into the genes influencing AMD risk. AMD variants in the meta-analysis were mapped to 18948 genes, and 182 genes reached significance after Bonferroni correction (0.05 / 18948 = 2.64e-06). There were 4 additional risk loci (WDR6, HLA-E, GPR26, and ITFG1) identified from the gene-based analysis (defining independent as located at least 1 Mb away from the risk loci identified in the per SNP meta-analysis).Expression QTL and TWAS

[0179] 114 independent genome-wide significant variants in retina eQTL data fromEyeGEx were assessed (N = 406) (Ratnapriya et al. 2019). Overall, 15 index variants were significant cis-eQTLs (FDR < 0.05) for 22 genes. Of the 15 variant-gene cis-eQTLs, seven were from the novel AMD variants: rs9901671 (cis-eQTL target gene SRR), rs7182946 (MTMR10), rs3775220 (AFF1), rs12359135 (CYP26C1), rs10797980 (C1orf21), rs11674246 (CASP8), rs11674246 (PPIL3). A TWAS was performed using the meta-analysis summary statistics and the EyeGEx data. Among 16550 genes included in the test, 24 conditionally independent genes were identified from fusion conditional test approach (Gusev et al. 2016) that passed Bonferroni correction (0.05 / 16550 = 3.02e-06) (Table 4). 18 of which overlapped (<1 MB) with the top AMD GWAS loci. Six loci were also identified outside AMD risk loci identified in the meta-analysis (CD46, LINC01140, LMO4, PAIP1P1, SIN3A, and ZNF226) (Table 4).

[0180] The inventors then combined the eQTL data from the retina with the GWAS meta-analysis summary statistics by SMR, to examine the causal genes between the expression level of a gene and AMD loci (Zhu et al. 2016). Seven genes (STAG1-DT, PLA2G12A, STK19, HLA-DQB2, TAP2, PLEKHA1, and RLBP1) reaching the multiple-testing threshold of 5599 genes were identified (PSMR < 0.05 / 5599 = 8.93e-06). By applying the HEIDI test within SMR (Zhu et al. 2016), two genes were identified (STAG1-DTand RLBP1) for which the null hypothesis cannot be rejected. The associations found by the SMR are mediated by pleiotropy (PHEIDI > 0.05), suggesting STAG1-DT and RLBP1 are likely to be the functionally relevant genes underlying GWAS hits for AMD risk.TABLE 4INDEPENDENT SNPsPrediction of AMD risk - Participant characteristics in main validation cohort

[0181] The performance of the PRS in 21 ,220 European ancestry individuals from the CLSA cohort was assessed (Table 5).TABLE SCHARACTERISTICS OF EUROPEAN PARTICIPANTS FROM CLSA

[0182] The new AMD PRS was strongly correlated (P < 2e-16 in logistic regression) with AMD status in the CLSA validation cohort. The odds ratio per SD change for the base PRS component alone was 1 .69 [95% Cl :1 .57-1 .81 ], and the odds per SD change for the PRSmeta component alone was 1 .40 [95% Cl :1 .29-1 .51 ]. The AUC for the new PRS (base plus PRSmeta) was 0.6622 [95% Cl: (0.6408-0.6835)]. To benchmark the performance of the new PRS with traditional AMD risk factors, a null model was established using age, sex, smoking status and top 10 PCs (AUC = 0.77 [95% Cl: (0.7528-0.7872)]). In a model fitting traditional AMD risk factors with the new PRS, the AUC increased to 0.791 [95% Cl: (0.7728-0.8083)] (Figure 3).

[0183] To evaluate the improved accuracy of the new PRS in the detection of AMD cases, the new PRS was compared against PRS constructed using previous GWASs (PRS2020 and PRS2016 based on GWAS summary statistics from Han et al. (2020) and (Fritsche et al. 2016), respectively (the same “base” PRS was included for each). The prediction power of the new PRS (AUC = 0.6622) was significantly better than PRS2oie (AUC = 0.6456, 95% Cl: 0.6241 - 0.6671 , P = 0.0050), and PRS2020 (AUC = 0.6419, 95% Cl: 0.62-0.6637, P = 0.0061 ) (Table 6). The PRScs model and the C + T model was also compared for constructing new PRS, the AUC was (non-significantly) higher when applying PRScs model to construct PRSmeta (0.6622 vs. 0.6593, P = 0.53) (Table 6). Moreover, the CLSA sample was divided into deciles based on the new PRS (PRSmeta) , PRS2020 and PRS2016 (the same “base” PRS was included for each), and the Odds ratios was estimated for AMD status for each decile. An increase in the Odds ratios in higher deciles was observed. Compared with New PRS2020 and New PRS2016, individuals at the tenth decile of New PRS had the highest Odds ratios (Figure 3). Furthermore, the NRI of CLSA cohort was calculated when the PRS was added to non-genetic risk factor models (age + sex +smoking status + top 10 PCs) for the new PRS, PRS2020 and PRS2016. After including PRS in the non-genetic risk prediction model for AMD, the new PRS in both PRScs and C + T models indicated reclassification improvement compared to previously published PRS (Table 6).

[0184] The PRS model was calculated using nine novel loci and observed a significant correlation (Binomial test P-value = 0.018) between PRS and AMD status in CLSA. The result suggested the effect of novel loci could be replicated in the CLSA dataset.TABLE 6COMPARISON OF PREDICTIVE POWER BETWEEN NEW PRS AND PREVIOUSLY PUBLISHED PRSPRS performance in CLSA cohort

[0185] To explore the performance of new PRS on AMD patients’ stratification, 20% of individuals in CLSA with high PRS and 20% with low PRS in CLSA cohort were selected (n of each = 4244). For the entire CLSA cohort, the prevalence of AMD was approximately 3.5%, the prevalence in “high-PRS individuals” was approximately 7%, and the prevalence in “low-PRS individuals” was approximately 2%. The cumulative incidence was also calculated to assess the rising AMD risk with age in each PRS group. For people over 80, the cumulative incidence in the whole CLSA cohort was 14.3% (± 0.7%), and it increased to 28.3% (± 2%) in “high-PRS individuals” compared to 8.0% (± 1 %) in “low-PRS individuals” and 1 1.2% (± 0.8%) in “mid-PRS individuals” (Figure 4).PRS performance in individuals carrying 4 CFH / ARMS risk alleles

[0186] The ability of new PRS to optimise risk stratification and prediction in 363 “4 CFH / ARMS2 risk allele carriers” was assessed (defined as individuals with 4 of the high-risk alleles at the peak CFH and the peak ARMS2 SNPs). The top 20% of “4 CFH / ARMS2 risk allele carriers”, the bottom 20% of “4 CFH / ARMS2 risk allele carriers" (n of each = 73), and the 60% of the rest of the “4 CFH / ARMS2 risk allele carriers” (n = 217) were selected. The prevalence in the top 20% of “4 CFH / ARMS2 risk allele carriers” was approximately 1 in 5 (approx. 20.5%), while for the bottom 20% the prevalence was approximately 1 in 37 (approx. 3.0%). For people over 80, the cumulative incidence was 60.9% (± 14%) in the top 20% of PRS individuals compared to 0% (one of the 73 individuals under the age of 80 had AMD) in the bottom 20% PRS individuals (Figure 6).PRS performance in individuals carrying 3+ CFH / ARMS risk alleles

[0187] Moreover, the ability of new PRS to optimise risk stratification and prediction in 3350 individuals with at least 3 high-risk alleles at the peak CFH and the peak ARMS2 SNPs were assessed. The top 20% of “3 / 4 CFH / ARMS2 risk allele carriers”, the bottom 20% of “3 / 4 CFH / ARMS2 risk allele carriers” (n of each = 670) and 60% of the rest were selected (n = 2010). The prevalence in the top 20% of “high-risk individuals” was approximately 1 in 7 (approx. 13.9%), while for the bottom 20% the prevalence was approximately 1 in 35 (approx. 2.8%). For people over 80, cumulative incidence reached up to 49.5% (± 5%) in the top 20% “high-risk individuals compared to 10.3% (± 3%) in the bottom 20% and 24.2% (± 3%) in the middle 60% (Figure 7). The result suggests that among people carrying the long-established CFH / ARMS2 risk alleles, the new PRS stratifies individuals into well-separated risk groups (Figure 7).Polygenic Risk Score (PRS) for different ancestry

[0188] The inventors then validated the new PRS in some of the major nonEuropean ancestral groups. An ancestry analyses was conducted to investigate the contribution of AMD risk loci in South Asian (152 cases and 4855 controls) and African (86 cases, 4065 controls) ancestral groups in UKB. The AUC for South Asian was 0.583 [95% Cl: (0.5348- 0.6312)]. The new PRS showed a significant correlation with AMD in the logistic model in the South Asian population (P = 0.000327). A reduction of predictive accuracy in the UKB African group was observed, with the AUC at 0.5289 [95% Cl: (0.4676-0.5901 )], with a non-significant p-value (P = 0.36). Similarly, the new PRS in East Asian (116 cases, 4006 controls) and Latino (101 cases, 3892 controls) ancestral groups from GERA were validated. The AUC for the East Asian sample was 0.6003 [95% Cl: (0.5459-0.6548)], and for Latino, the AUC was 0.5676 [95% Cl: (0.5089-0.6263)]. The PRS revealed significant correlations (P<0.05) with AMD status in both East and Latino samples (Table 7).TABLE 7VALIDATION OF NEW PRS IN NON-EUROPEAN ANCESTRAL GROUPSDiscussion

[0189] The inventors conducted a large-scale MTAG and meta-analysis of GWAS for AMD, including 64,885 AMD cases and 568,740 controls from six studies (including overlapped samples). 114 genome-wide significant independent variants were identified,including nine novel loci which were completely distinct from the known loci, with seven of these acting as cis-eQTLs in retinal tissue. A new PRS was constructed using the large GWAS discovery set and showed that PRS performance was improved (relative to that using previous GWAS SNPs) in an independent cohort (CLSA).

[0190] Several novel loci identified in meta-analysis were associated with risk factors of AMD. For instance, eight novel AMR risk loci (ZFP57, SMAD3, CYP26A1, CDSN, KLHL8, TRPM1, C1R, and HIC1) have been previously associated with HDL levels (Richardson et al. 2020), which is consistent with the previous mendelian randomization study that HDL level played a causal role on AMD risk (Burgess and Davey Smith 2017). The present meta-analysis also identified a novel AMD locus CASP10, which has been associated with triglyceride levels (Liu et al. 2017) and potentially plays a role in the reduction of AMD risk (Colijn et al. 2019). Besides, three novel loci EDEM3, PLEKHM3 and PDGFB have been associated with another AMD risk factor-BMI or body weight (Zhang et al. 2016). Altogether, these genes identified here could potentially contribute to the etiology of AMD and help improve understanding of the AMD biological process. Particular interesting were the loci expressed in two retina-related diseases — AMD and glaucoma. These genes potentially function on retina lesions. CYP26A1, KLHL8, and HIC1 have been reported as significant loci in primary open-angle glaucoma (POAG) GWAS (Gharahkhani et al. 2021 ; Gharahkhani et al. 2018). No shared causal variants were identified between glaucoma and AMD around the region of four glaucoma loci from the colocalisation result, suggesting these four loci were not shared causal loci. However, given the genetic correlation between AMD and POAG (Cuellar-Partida et al. 2016), larger studies of both diseases are likely to discover additional common loci. Characterization of common loci between AMD and glaucoma may expand the knowledge of both diseases' genetic pathophysiology. The TWAS and SMR tests enabled the evaluation of numerous loci for future functional follow-up studies, such as CD46 and RLBP1, which had been previously reported in AMD or AMD-related diseases (Lyzogubov et al. 2016; Bagheri et al. 2020). Here, the inventors discovered their transcripts were linked with age-related macular degeneration (AMD).

[0191] CFH and ARMS2 have been identified as top AMD loci in most AMD GWAS (Klein et al. 2005; Cipriani et al. 2012). Therefore, variants around CFH anti ARMS2 loci are likely to have extremely small p-values. Standard numeric data type in R or python (8-byte double precision) does not support such small numbers, resulting in the executing error in many R or Python-based packages. For instance, the top CFH variant in (Fritsche et al. 2016) has the p-value of 9.6e-618 (unadjusted) with an odds ratio of 0.38 (unadjusted), while in the MTAG result from Han et al. (2020) the top CFH variant has the p-value of 3.9E-421 with an odds ratio of 0.48. The approximation error in small p-values is inconsequential for the purpose of identifying new hits from GWAS since CFH and ARMRS2 are already well-known AMD loci. However, the numerical issue with extreme p-values may affect the accuracy of PRS due to most of the popular PRS models leverage effect sizes or p-values in their algorithm (e.g., PRScs, LDpred2, Lassosum). To avoid this issue when predicting AMD risk, a base PRS wascomputed using 9 top variants from AMD consortium GWAS (Fritsche et al. 2016). PRS computational methods were applied to the meta-analysis variants excluding CFH and ARMS2 regions (e.g., using Plink and PRScs). In the CLSA European cohort, the new PRS improved prediction relative to PRS using SNPs identified in previous studies ((Fritsche et al. 2016) and 2020 (Han et al. 2020)). The PRS2020 SNPs were generated through MTAG since the top AMD risk loci could be removed due to approximation errors. A PRS was computed using non- CFH / ARMS2 SNPs in the 2020 GWAS and combined it with the base PRS. The new PRS 2020 showed a significant improvement in AUC (AUC = 0.653, P = 0.039) than the original PRS previously reported in Han et al. 2020. This implies the removal of SNPs with extreme p-values will influence the power of prediction.

[0192] By PRScs method to all variants within 5MB of CFH / ARMS2 region, the AUC is higher than the Plink method (P = 0.54). Next, Plink and PRScs model were compared for constructing the new PRS (base PRS + PRSmeta). The PRSmeta calculated by PRScs performed marginally better than Plink (Table 6). Although the missing predictive power was restored through merging two PRS, the ideal design is expected to restore the top AMD loci during the meta-analysis, which requires larger-scale GWAS. Fitting multiple operators in GLM model may lead to potential overfitting issues. Apart from that, constructing PRS through the combination model usually meets trouble in standardisation because two PRS might be computed under different scales of effect sizes, which may cause trouble in communication.

[0193] The inventors included two rare variants (MAF < 0.01 ) in CFI and C3 in their base PRS. Including these only made a small difference to the OR per SD for the base PRS (OR / SD = 1 .67 before addition compared to OR / SD = 1 .69). Although given their large effect sizes (ORs of 5.12 and 3.22), these alleles lead to large changes in risk for the small number of individuals who carry them. Including additional rare variants in the future will help optimize risk prediction although at the population level the AUC change will be relatively small due to the variants being very rare. Future studies which seek to use rare variants are likely to require a sequencing-based approach (de Breuk et al., 2021 ), as opposed to the array and imputationbased approach used here.

[0194] The inventors also developed an overall prediction model for CLSA AMD patients. The CLSA AMD cases have a mean age of 72, the genetic effects were likely to be attenuated by additional factors related to ageing (Winkler et al. 2018). Therefore, even though adding PRS helped to raise the power of prediction, age accounted for most of the predictive power in our overall prediction model. One possible future direction would be replicating the PRS in younger populations.

[0195] One general conclusion from years of investigative efforts, is that much of the ability to predict AMD comes from the CFH and ARMS2 loci (Cooke Bailey et al. 2016). However, within the group of people carrying multiple CFH and ARMS2 risk alleles AMD risk can vary substantially. The inventors explored the predictive performance of their PRS in CLSA participants with three or four CFH / ARMS2 risk alleles. It was found that loci other than CFHand ARMS2, can offer important risk stratification. For people over 80, the prevalence and cumulative incidence of AMD demonstrated large differences between high PRS and low PRS individuals.

[0196] The meta-analysis approach of the present invention combined GWAS summary statistics from several cohorts; the input datasets included overlapping samples. Overlapping samples were adjusted using a multi-trait meta-analysis approach (MTAG) which is known to effectively account for sample overlap in GWAS. There was no sample overlap between the meta-analysis data and CLSA data used for testing the PRS models. However, the CLSA AMD phenotypes were based on the self-reported data, it was expected to achieve higher AUC and stratification performance when applied to data with more accurate phenotype information.

[0197] The predictive performance of the new PRS in non-European populations in UKB and GERA was also explored. The new PRS has the ability to predict AMD in South Asian samples from UKB, East Asian and Latino samples from GERA. The new PRS was not significantly associated with AMD risk in African samples, although due to the small sample size the confidence intervals overlap both the null (AUC = 0.5) and the AUC values seen in the other ancestral groups. A large cross ancestry GWAS is needed for to establish a more powerful prediction model which works effectively across a wider range of ancestral groups.

[0198] In summary, nine novel AMD loci were identified from multi-trait GWAS and meta-analysis, and developed a new AMD PRS. The PRS improved previous work by outlining a pragmatic approach to improve performance. More specifically, the number of SNPs in the present model was increased, employing the most accurate weights for the prediction and ensured the proper weights were used for some of the model's key SNPs (e.g. CFH where the astronomical p-value results in some prediction software packages dropping some of the key SNPs).Materials & MethodsStudy design

[0199] A meta-analysis of GWAS from six AMD cohorts was performed: International AMD Genomics Consortium (IAMDGC) AMD-2016 GWAS (16,144 advanced AMD cases and 17,832 controls) (Fritsche et al., 2016), IAMDGC AMD-2013 GWAS (17,181 cases and 60,074 controls) (Fritsche et al., 2013), UK Biobank AMD GWAS (8864 cases and 140,685 controls), Finngen R6 AMD GWAS (4645 cases and 243,951 controls), Genetic Epidemiology Research on Aging study (4017 AMD cases and 14,984 controls) (Kvale et al., 2015), and an early-AMD study (11 sources including 14,034 cases and 91 ,214 controls) (Winkler et al., 2020). The MTAG was applied to account for the sample overlap among IAMDGC, UK Biobank, and early-AMD GWAS. A PRS based on the meta-analysis results was then developed and the prediction power in independent datasets tested.Discovery dataset 1: UK Biobank

[0200] The UK Biobank (UKB) is a large-scale United Kingdom biomedical database containing in-depth genetic and phenotypic data from approximately half a million participants between the age of 40 to 69 years old at recruitment (2006 - 2010).

[0201] In UKB, -488,000 participants were genotyped on Axiom arrays. The genotype data underwent quality control and imputation procedures as previously described (Bycroft et al. 2018). Approximately 96 million variations were imputed utilising resources of the Haplotype Reference Consortium (HRC) and UK10K haplotype and 487,409 individuals were retained after genotyping quality control. To validate the ancestral background from UKB selfreport ethnicity (data field 21000), the k-means clustering method was used and clustered the top 20 principal components (PCs) into 20 clusters. The PCA clusters were compared with the self-report ethnicity. For the discovery GWAS, UKB individuals with consistent self-reported ethnicity and genetic similarity for “European” ancestry were selected (mainly white British, N = 438,240). Other ancestries were used in the validation cohort (below). 8864 AMD cases were identified using the following criteria: International Classification of Diseases (ICD) 9 and ICD10 diagnosis codes, self-reported “macular degenerations” in “Eye problems / disorders” (Data-Field 6148), and self-reported “macular degeneration” in non-cancer illness (Data-Field 20002). Controls were 140,685 people without diabetes related eye disease, glaucoma, injury or trauma resulting in loss of vision, cataract, macular degeneration or other serious eye conditions (Data- Field 6148) (UKB phenotype Sep 2021 update).Discovery dataset 2 and 3: International AMD Genomics Consortium 2013 and 2016

[0202] The International AMD Genomics Consortium (IAMDGC) AMD-2013 GWAS contained 17,181 cases and 60,074 controls (Fritsche et al., 2013), with many (but not all) of the samples re-genotyped as part of the AMD-2016 GWAS (16,144 cases and 17,832 controls) (Fritsche et al., 2016). A subset of samples in the 2016 GWAS was not in the 2013 study; hence, the largest sample size can be obtained by including all samples in one or both studies.Discovery dataset 4: Genetic Epidemiology Research on Acting (GERA)

[0203] The GERA cohort is a sub study of the longitudinal cohort enrolled in the Kaiser Permanente Research Program on Genes, Environment and Health. Detailed information on the study can be found in the database of Genotypes and Phenotypes (dbGaP, accession Number: phs000674.v1 ,p1 ) (Kvale et al., 2015; Banda et al., 2015). GWAS data from 4017 cases and 14,984 controls of European ancestry were taken from a previous study (Han et al., 2020).Discovery dataset 5: FinnGen study AMD data

[0204] The FinnGen project (https: / / www.finngen.fi / en) was launched in 2017 and over six years plans to collect biological samples from 500,000 biobank individuals in Finland (~ 10% of the population). Finngen conducted a GWAS of AMD (dry and / or wet), with the GWAS summary statistics from the public release of FinnGen data freeze 6 for 4645 AMD cases and 243,951 controls.Discovery dataset 6: Early AMD GWAS

[0205] The early-AMD summary statistics published in 2020 (Winkler et al. 2020) was used. The meta-analysis contained 11 sources of data: GHS-1 , GHS-2, LIFE, NICOLA, KORA, AugUR, publicly available studies from dbGaP (ARIC, CHS, WHI; accession numbers: phs000090.v5.p1 , phs000287.v6.p1 , phs000746.v2.p3), an early AMD GWAS from IAMDGC is based on 24,527 individuals from 26 sources and UKB data (total of 14,034 early AMD cases, 91 ,214 controls). The meta-analysis was conducted by the inverse-variance weighted fixed- effect method.Validation cohort 1: The Canadian Longitudinal Study on Acting (CLSA)

[0206] CLSA is a Canadian nationwide, longitudinal study of 51 ,338 participants aged 45 to 85 at initial enrolment. CLSA data from the "Comprehensive cohort" was used, which consists of information on -30,000 individuals collected via in-person questionnaire, clinical / physical tests, and biological samples (e.g. for genetic data) in seven Canadian provinces. In this study, 777 AMD cases were available from a total sample set of 29,884 CLSA participants from the “Baseline Comprehensive Dataset version 4.0” and “Follow-up 1 Comprehensive Dataset version 1 .0”. Cases were defined as those who self-reported AMD at both baseline and at follow-up; participants without self-reported AMD at both baseline and follow-up were regarded as controls. As some participants had discordant findings at baseline and at follow-up, to retain individuals with the most reliable self report phenotypes, only people whose AMD status was consistent between the baseline and follow-up datasets were retained. Furthermore, Individuals with a recorded of age at diagnosis for AMD, where their AMD status was “No”, were excluded from the study.

[0207] This study used the CLSA genotype data released in March 2021 . The Affymetrix protocol (Axiom 2.0 Assay Automated workflow on Affymetrix NIMBUS) was followed during sample preparation. Samples were hybridised to UK Biobank arrays (Thermo Fisher Catalog #902502). Axiom Array plates were prepared on the Affymetrix GeneTitan MultiChannel (MC) Instrument. The quality control after genotyping was documented on the CLSA document (available at https: / / www.clsa-elcv.ca / researchers / data-support-documentation). A total of 26,622 CLSA participants and 653,729 variants passed quality control after genotype calling and input into the imputation process (Forgetta et al. 2022). Approximately 308 million variants were imputed using the TOPMed reference panel (Taliun et al. 2021 ) using the University of Michigan Imputation Service (Das et al. 2016).

[0208] Using the genetic data, k-means cluster method was applied and clustered the top 20 principal components (PCs) into 20 clusters. The PCA clusters were compared with the self-report ethnicity. There were 24,521 CLSA individuals with consistent self-reported ethnicity and who clustered as “European” using the genetic data (recoded as “White” in CLSA data, N = 24,521 ). Following merging of genetic and phenotypic data, 733 AMD cases and 20,487 controls were retained for analysis. Other ancestral groups in the analysis were omitted,due to the small size of the non-European ancestral groups (N < 600). Information on age, sex and smoking status was retrieved for use as covariates.Validation cohort 2: Non-Europeans from UK BioBank

[0209] Following the same process as indicated above for UK Biobank, nonEuropeans were selected for use as a validation cohort. UK Biobank participants with consistent self-reported ethnicity and inferred genetic ancestral groups were selected. The two largest nonEuropean ancestry groups in UK Biobank were selected; these were African (mostly African, Caribbean, n = 8736) and South Asian (mostly Indian, Pakistani, and Bangladeshi, n = 11 ,767). People who corresponded to UKB AMD case definitions were extracted. There were 86 cases and 4065 controls of African ancestry and 152 cases and 4855 controls of South Asian ancestry that were utilised for PRS validation. Asians (mostly Chinese sample, n = 2543) in UK Biobank were not evaluated, as there were too few AMD cases.Validation cohort 3: Genetic Epidemiology Research on Acting (GERA)

[0210] To capture the cross-ancestry effects of PRS, the new PRS in non-European ancestry individuals from the GERA study were validated. GERA included 3826 individuals who self-reported African American ancestry, 5188 individuals who self-reported East Asian ancestry and 7154 individuals who self-reported Latino ancestry; each major group was genotyped on a different ancestry targeted array.

[0211] The Macular degeneration information from ICD-9 diagnosis codes (362.5, 362.50, 362.51 , 362.52 and 362.57) was obtained from the Electronic Health Records (EHRs) of the participants.

[0212] Pre-imputation genotype quality control was conducted using Plink (version 1 .90b6.21 ), by excluding markers with missing rate < 0.05, minor allele frequency > 0.01 , P value of Hardy-Weinberg equilibrium test < 1 x 10-6, and samples with missing genotypes rate < 0.03. The HRC imputation preparation was also performed and checking following the Haplotype Reference Consortium (HRC) web source (HRC-1000G-check-bim.pl from https: / / www.well. ox. ac.uk / ~wrayner / tools / ). The genotype data was imputation by eagle (version: 2.4.1 ) (Loh et al. 2016) and Minimac4 (20190807 release) (Das et al. 2016) using 1000 Genomes Phase 3 reference panel as the imputation reference. The ancestry information was inferred using continental ancestry (estimated from the top 20 PCs using reference populations from the 1000 Genomes Project Phase 3). The individuals with consistent self-report and genetically derived ancestry were included in the analysis.

[0213] Individuals with genetic data and AMD status in their electronic medical records were taken forward for analysis. The two largest non-Hispanic white groups in GERA; East Asians (116 cases, 4006 controls) and Latinos (101 cases, 3892 controls) were selected. Information on birth year, gender and smoking status were also included in the analysis for establishing prediction models.Statistical analysis - GWAS meta analysis

[0214] A GWAS for UKB AMD samples was conducted using the software Regenie (version 2.2.4) (Mbatchou et al. 2021 ), adjusting for sex, age and top 10 principal components (PCs). The GWAS summary statistics from all discovery cohorts were then combined using either standard meta-analysis or MTAG (Figure 1 ). The AMD consortium GWAS (2013 and 2016), the Early-AMD GWAS (Winkler et al. 2020) and the UKB AMD GWAS using MTAG were then combined. MTAG uses bivariate linkage disequilibrium (LD) score regression to account for sample overlap between input GWAS (Turley et al. 2018), with the approach producing effect estimates for each of the input traits. MTAG also estimates the genetic correlation between the input datasets which is appropriate for this study because the phenotype definitions for AMD vary across the cohorts; essentially if the correlation is less than 1 then this is modelled in the MTAG-based meta-analysis. The MTAG output (with IAMDGC AMD-2016 GWAS used as the trait of interest from MTAG) with the summary statistics from the other two studies which had non-overlapping samples (GERA and Finngen) by meta-analysis using the inverse variance fixed-effect scheme (METAL software: 5th May 2020) (Wilier et al. 2010) were then combined. Any variants with imputation quality score (INFO) < 0.3 and MAF < 0.01 were removed. A LD- clumping procedure was then performed to identify statistically independent AMD variants (P threshold = 5x10-8, r2 threshold = 0.01 , window of 1 megabase (MB)). The LD reference included 4990 randomly selected people from White British ancestry in UKB.Statistical analysis - Post- GWAS analysis

[0215] A co-localisation to test for shared loci between AMD and European GWAS from published primary-open angle glaucoma (POAG) study (Gharahkhani et al. 2021 ) was performed, using the package coloc (version 5.1 .0) (Giambartolomei et al. 2014). The genetic correlation was estimated by LD Score Regression (LDSC) (version 1 .0.1 ) (Bulik-Sullivan et al. 2015; Bulik-Sullivan et al. 2015). The Open target genetics platform (https: / / genetics.opentargets.org / ) was used to identify nearby genes for each independent AMD variant. Aa gene-based analysis via mBAT-combo was performed, a variance estimation approach implemented in the GCTA (version: 1 .4.1 ) (Li et al. 2022). The mBAT-combo approach gains power by integrating multivariate set-Based Association Test (mBAT) and fast set-based test (fastBAT) statistics through a Cauchy combination method (Li et al. 2022). The tissue-specific protein expression of newly identified loci via Human Protein Atlas was identified (HPA, https: / / www.proteinatlas.org / ). The AMD lead variants were compared with retina eQTL data of 453 samples from the Eye Genotype Expression (EyeGEx) database (Ratnapriya et al. 2019) to identify expression Quantitative Trait Loci (eQTL). A Transcriptome-Wide Association Study (TWAS) and the following Joint / conditional tests (default boundary of 100000) were conducted using the software Fusion (Release 1st Feb 2022) with the LD reference from 1000 Genomes (Gusev et al. 2016) to map differentially expressed genes that could increase disease risk. A summary data-based Mendelian randomisation (SMR) was conducted, andheterogeneity in the dependent instruments (HEIDI) (Version 1 .3.0) (Zhu et al. 2016) tests using the summary statistics from the meta-analysis and EyeGEx eQTL data (Table 8).Statistical analysis - Polygenic risk score analysis

[0216] The output from the meta-analysis to construct a PRS was used and then tested it in the validation cohorts. To construct a PRS, due to an issue with handling a small number of SNPs with large effects and extreme p-values, two independent PRSs (“base” and “non-base”) were constructed and then combined to obtain an overall PRS. Initially, a "base" model was utilised, comprising nine genome-wide significant SNPs (see, Table 9) with either very large effect sizes or where the previous consortium GWAS had identified rare variants of interest (rs141853578 in CFI, SNP rs147859257 in C3; such rare variants did not appear in the GWAS, which focused solely on common variants). SNPs with very large effect sizes in a small number of genes (CFH, ARMS2) were explicitly included in the "base" model because their extreme p-values caused them to be filtered out from the meta-analysis. The “base” model was constructed using a clumping and thresholding (C + T) model (in Plink version 1 .90b6.21 ). To avoid double-counting SNPs around CFH and ARMS2, five megabases around each gene were removed (2.5 MB on each side) prior to computing a “non-base” PRS. Note that rs141853578 and rs147859257 are rare SNPs therefore SNPs around C3 and CFI were reserved in “non- base” PRS.

[0217] The “non-base” PRS (PRSmeta) was constructed using a model which included variants from the new meta-analysis, using PRScs (version 4th Jun 2021 ) with a default global shrinkage prior of 1 .05e-04 and an LD reference panel from UKB (Ge et al. 2019). PRScs estimates the posterior effect sizes from the continuous shrinkage (CS) prior of all SNPs in the GWAS summary statistics (Ge et al. 2019).

[0218] To compare the performance of PRSmeta against the previous AMD PRS, PRS2016 was derived using the previously published 45 genome-wide significant SNPs with their locus-wide conditioned odds ratios and p-values from the 2016 paper (Fritsche et al. 2016). Similarly, PRS2020 was derived using previously published 69 top AMD risk SNPs from the 2020 paper (Han et al. 2020). PRS2oie and PRS2O2owere developed using the C + T method. To compare the performance of a PRS derived using different methods for PRS construction, a PRSmeta-piink was also developed by applying a C + T model (in Plink version 1 ,90b6.21 ) to the meta-analysis summary statistics. The PRSmeta-piink was constructed using independent genomewide significant SNPs from the meta-analysis result; similar to PRSmeta, SNPs within 5MB of CFH and ARMS2 were excluded (2.5 MB on each side). PRSmeta-piink was then combined with the base model. Moreover, the net reclassification improvement (NRI) was calculated when the different PRSs were combined with non-genetic risk factors models (including age at visit, sex, smoking status and top 10 PCs) for AMD using the predictABEL (Version: 1 .24) package (Kundu et al. 201 1 ), to evaluate the improvement in reclassification. In all PRS models, a term for the “base” model and a separate term for the non-base SNPs were estimated.TABLE 8INDEPENDENT TWAS GENESTABLE 9SNPs AND THEIR WEIGHTS IN BASE PRS MODEL

[0219] For the novel loci identified by the meta-analysis, it is anticipated that the individual SNPs effects will be small and hence the CLSA cohort will not replicate each of the novel SNPs simply due to lack of power. However, the power to detect the combined effect of several SNPs will be larger. To evaluate whether the combined effect of only the novel variants from the meta-analysis result can be replicated in the CLSA cohort, a PRS of novel loci using the C + T model in Plink (version 1 .90b6.21 ) was computed.

[0220] The PRS in individuals of European ancestry from CLSA was validated. Additionally, the new PRS in non-European ancestral groups was also validated, including African and South Asian samples from UKB, and East Asian and Latino samples from Kaiser. The base PRS in each ancestry group was derived using the C + T model in Plink (version 1 .90b6.21 ) and PRSmeta was derived using PRScs. The posterior effect sizes in PRScs were calculated using corresponding ancestry reference samples from UKB.

[0221] Due to the different datasets using different genome builds, positions of variants were updated to Human Genome Reference Consortium Human Build 38 via package HftOverPlink (https: / / github.com / sritchie73 / liftOverPlink) for analysis. Logistic regression was used to test for association between each PRS and the AMD phenotype in each validation cohort using the stats package in R (version 4.0.2) (Team, n.d.). Each PRS was normalised by calculating (score - score mean) I standard deviation. To assess model goodness of fit, Nagelkerke R-square was calculated using the R package fmsb (Version: 0.7.3) (Nakazawa, n.d.). The linear predictor calculated in logistic regression was used to calculate the area under the curve (AUC), AUCs were compared through DeLong's test, as implemented in the package pROC (Robin et al. 2011 ).

[0222] The new PRS stratifies risk in individuals who carry all four of the risk alleles at the long-established loci at CFH and ARMS2 (rs10922109 and rs3750846, respectively) was also assessed. To identify those likely to carry all 4 risk alleles, Plink (version 1 .90b6.21 ) was used to construct a PRS using (only) the 2 top CFH and ARMS2 variants (rs3750846, effect size = 1 .075, and rs10922109, effect size = -0.67) for CLSA cohort. Given perfect imputation, people with 4 risk AMD loci would have PRS scores of 2.15 (2 * 1 .075, two risk loci and zero protective loci); to allow for a small amount of uncertainty in imputation, only individuals with scores greater than 0.9 * 2.15 = 1 .94 were selected.

[0223] People who were likely to have at least three high-risk alleles at the peak CFH and the peak ARMS2 SNPs were also selected. In other words, people with the PRS score of 1 .48 (2 * 1 .075-0.67 two risk loci and one protective locus), 1 .075 (1 .075 * 1 , one risk loci and two protective loci) or 2.15 (2 * 1 .075). In practice, to allow for a small amount of uncertainty in imputation, individuals with scores greater than 0.9 * 1 .075 = 0.97 were selected. The prevalence and cumulative incidence were calculated among CFH / ARMS2 carriers. To further assess the stratification performance of new PRS, the cumulative incidence was calculated through package cmprsk (version 2.2-11 ) for CFH / ARMS2 carriers (Table 10).

[0224] All data analyses were performed based on R environment (version: 4.0.2), via the R package dplyr (https: / / github.com / tidyverse / dplyr) and data. table (https: / / github.com / Rdatatable / data.table). Visualisations of most of the analyses were produced with the ggplot2 package (Wickham 2009).TABLE 10PRS MODELS

[0225] The disclosure of every patent, patent application, and publication cited herein is hereby incorporated herein by reference in its entirety.

[0226] The citation of any reference herein should not be construed as an admission that such reference is available as “prior art” to the instant application. Furthermore, the citation of any reference in this specification is not, and should not be taken as, an acknowledgement or any form of suggestion that the reference forms part of the common general knowledge in Australia.

[0227] Throughout the specification the aim has been to describe the preferred embodiments of the invention without limiting the invention to any one embodiment or specific collection of features. Those of skill in the art will therefore appreciate that, in light of the instant disclosure, various modifications and changes can be made in the particular embodiments exemplified without departing from the scope of the present invention. All such modifications and changes are intended to be included within the scope of the appended claims.REFERENCESBagheri, Saghar, Madhulatha Pantrangi, Simrat K. Sodhi, Sayeh Bagheri, Patrick Oellers, and Hendrik P. N. Scholl. 2020. A novel large homozygous deletion in the cellular retinaldehyde-binding protein gene (rlbpl ) in a patient with retinitis punctata albescens. Retinal Cases & Brief Reports 14 (1 ): 85-89.Banda, Yambazi, Mark N. Kvale, Thomas J. Hoffmann, Stephanie E. Hesselson, Dilrini Ranatunga, Hua Tang, Chiara Sabatti, et al. 2015. 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Claims

What Is Claimed Is:1 . A method for determining an indicator used in assessing a likelihood of a subject developing age-related macular degeneration (AMD), the method comprising, consisting, or consisting essentially of: a. determining a biomarker value that is measured or derived for at least one group 1 AMD biomarker (e.g., 1 , 2, 3, 4, 5, 6, 7, 8, or 9 AMD biomarkers) in a sample from the subject, wherein the at least one group 1 AMD biomarker is a single nucleotide polymorphism (SNP) selected from rs10797980, rs551911 , rs6661798, rs11674246, rs1871671 , rs7637449, rs12359135, rs7311672, and rs9901671 ; and b. determining the indicator using the biomarker value(s), wherein the indicator is at least partially indicative of the likelihood of the subject developing AMD.

2. The method of claim 1 , wherein the method further comprises a step of determining a biomarker value that is measured or derived for at least one group 2 AMD biomarker, wherein the group 2 AMD biomarker is selected from the list comprising rs10033900, rs1005819, rs10241492, rs1064583, rs10781177, rs10781180, rs10922109, rs10981380, rs11072508, rs11076175, rs 11080055, rs 11120691 , rs11142636, rs112635299, rs1142, rs1144, rs11569520, rs11635145, rs11771419, rs 11884770, rs121913059, rs12211410, rs12213415, rs12457465, rs12576144, rs12901081 , rs12913832, rs12949956, rs13081855, rs13255394, rs1378940, rs1378942, rs1393350, rs141853578, rs147859257, rs148553336, rs1605677, rs163494, rs16841920, rs17105278, rs17356664, rs17421410, rs17480689, rs17576, rs1800588, rs187328863, rs1926564, rs2011092, rs2011822, rs2043085, rs2070895, rs2170240, rs2232613, rs2280953, rs2367070, rs2414634, rs247616, rs2575876, rs259842, rs2842343, rs2857107, rs3138142, rs3750846, rs3760775, rs3764261 , rs3775220, rs380743, rs3825991 , rs401186, rs4151671 , rs4420638, rs4748976, rs4943289, rs5023028, rs550513, rs570618, rs5754206, rs5754222, rs5754227, rs61760904, rs61818925, rs621736, rs66532523, rs6726589, rs6795735, rs6899205, rs704, rs7182946, rs7266392, rs73036519, rs73045269, rs7405901 , rs7428936, rs760070, rs7624060, rs7625101 , rs7663219, rs77516118, rs7874221 , rs7896471 , rs7939052, rs8017304, rs8051675, rs8056814, rs8135665, rs879180, rs9267576, rs943080, rs9821337, rs9973159, rs485632, rs56123646, rs17826006, rs7439493, rs62358361 , rs116306799, rs7803454, rs79037040, rs11143754, rs334362, rs4570483, rs36212733, rs3138141 , rs61941275, rs9564692, rs1956526, rs2414577, rs17231506, rs72802342, rs12948099, rs1137776, rs12019136, rs11569415, rs429358, rs58847685, rs77280782, rs74406464, rs77968014, rs352920876, rs191281603, rs62247658, rs140647181 , rs55975637, rs114092250, rs116503776, rs144629244, rs114254831 , rs181705462, rs10781182,rs71507014, rs1626340, rs2740488, rs12357257, rs61941274, rs61985136, rs2842339, rs5817082, rs6565597, rs2230199, rs67538026, rs142450006, and rs201459901 ..

3. The method of claim 1 or claim 2, wherein the method is performed at least in part using an electronic processing device.

4. The method of any one of claim 1 to claim 3, wherein the method includes, in at least one processing device, generating a representation of the indicator.

5. The method of claim 4, wherein the representation includes an alphanumeric indication of the indicator.

6. The method for determining a polygenic risk score (PRS), wherein the PRS is based on the indicator of claims 1 to 5 and at least one genetic risk factor and / or non-genetic risk factors.

7. The method of claim 6, wherein one or more genetic risk factors may be selected from the group consisting of SNPs from Table 2, ethnicity, sex or family history of AMD.

8. The method of claim 6, wherein one or more non-genetic risk factors may be selected from the group consisting of age, alcohol consumption history, smoking history, exercise history, body mass index, diet, or other age-related ocular conditions.

9. The method of claim 1 to 8, wherein the method includes: a. comparing the PRS to a PRS reference; and b. determining a likelihood in accordance with results of the comparison.

10. The method of claim 9, wherein the PRS reference is based on at least one of: a. an PRS threshold range; b. an PRS threshold; and c. an PRS distribution.11 . The method of claim 9 or claim 10, wherein the PRS reference is derived from the indicator of claims 1 to 4 and at least one or more genetic risk factors and / or non- genetic risk factors determined for a number of individuals in a reference population.

12. The method of claim 11 , wherein the reference population consists of individuals diagnosed as having AMD or lacking AMD.

13. The method of claim 11 or claim 12, wherein the reference population includes: a. a plurality of individuals of different sexes; b. a plurality of individuals of different ethnicities; c. a plurality of healthy individuals; d. a plurality of individuals diagnosed with AMD;e. a plurality of individuals showing clinical signs of AMD; f. first and second groups of individuals, each group of individuals diagnosed with AMD; and g. first and second groups of individuals, the first group of individuals diagnosed with AMD and the second group of individuals lacking AMD.

14. The method according to any one of the claims 9 to 13, wherein the PRS reference is retrieved from a database.

15. The method according to any one of the claims 9 to 14, wherein the likelihood of a subject developing AMD is based on a probability generated using the results of a comparison.

16. The method of claims 6 to 15, wherein a PRS above a predetermined threshold indicates the subject has a likelihood of developing AMD.

17. The method of claims 6 to 15, wherein a PRS below a predetermined threshold indicates the subject is less likely to develop AMD.

18. The method of any one of the preceding claims, wherein the method includes: a. obtaining a sample taken from the subject, the sample including polynucleotide expression products; and b. quantifying at least some of the polynucleotide expression products within the sample to determine the presence or absence of one or more SNP.

19. The method according to claim 18, wherein the method includes: a. quantifying polynucleotide expression products by: i. amplifying at least some polynucleotide expression products in the sample; and ii. determining an amplification amount representing a degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products; and b. determining the indicator by determining a difference between the amplification amounts.

20. The method according to claim 19, wherein the amplification amount is at least one of: a. a cycle time; b. a number of cycles; c. a cycle threshold; d. an amplification time; ande. relative to an amplification amount of another amplified product.21 . The method of claim 18 to claim 20, where in the sample is derived from saliva or blood.

22. The method of claim 21 , wherein polymerase chain reaction (PCR) is used for amplification.

23. The method of claim 22, wherein the PCR contains primers that are specific for the SNPs set forth in Table 1 .

24. The method of claim 23, wherein the primers for amplification are located on a chip.

25. The method of any one of the preceding claims, wherein the method further comprises administering an agent suitable for treating AMD in a subject who has been identified as having the likelihood of developing AMD.

26. A method of treating or preventing AMD in a subject, comprising: a. analysing the sample obtained from a subject to determine an indicator used in assessing a likelihood of the subject developing AMD based on the presence or absence of one or more SNP from Table 1 ; and b. administering an agent suitable for treating AMD in a subject who has been identified as likely developing AMD.

27. A method of treating or preventing AMD in a subject, comprising: c. analysing the sample obtained from a subject to determine an indicator used in assessing a likelihood of the subject developing AMD based on the presence or absence of one or more SNP from Table 1 ; and d. prescribing a treatment regimen to the subject who has been identified as likely developing AMD.

28. The method of claim 27, wherein the treatment regimen comprises reducing or stopping activities known to be risk factors for developing AMD (e.g., smoking, obesity, etc).

29. The method of claim 27 or claim 28, wherein the treatment regimen comprises increasing activities known to be associated with a reduced risk of developing AMD (e.g., exercise).

30. The method of any one of claims 26-29, wherein the method further comprises a step of determining a biomarker value that is measured or derived for at least one group 2 AMD biomarker, wherein the group 2 AMD biomarker is selected from the list comprising rs10033900, rs1005819, rs10241492, rs1064583, rs10781177, rs10781180, rs10922109, rs10981380, rs11072508, rs 11076175, rs 11080055, rs11120691 , rs11142636, rs112635299, rs1142, rs1144, rs11569520, rs11635145, rs11771419, rs11884770, rs121913059, rs12211410, rs12213415, rs12457465, rs12576144, rs12901081 , rs12913832, rs12949956, rs13081855, rs13255394, rs1378940, rs1378942, rs1393350, rs141853578, rs147859257, rs148553336, rs1605677,rs163494, rs16841920, rs17105278, rs17356664, rs17421410, rs17480689, rs17576, rs1800588, rs187328863, rs1926564, rs2011092, rs2011822, rs2043085, rs2070895, rs2170240, rs2232613, rs2280953, rs2367070, rs2414634, rs247616, rs2575876, rs259842, rs2842343, rs2857107, rs3138142, rs3750846, rs3760775, rs3764261 , rs3775220, rs380743, rs3825991 , rs401186, rs4151671 , rs4420638, rs4748976, rs4943289, rs5023028, rs550513, rs570618, rs5754206, rs5754222, rs5754227, rs61760904, rs61818925, rs621736, rs66532523, rs6726589, rs6795735, rs6899205, rs704, rs7182946, rs7266392, rs73036519, rs73045269, rs7405901 , rs7428936, rs760070, rs7624060, rs7625101 , rs7663219, rs77516118, rs7874221 , rs7896471 , rs7939052, rs8017304, rs8051675, rs8056814, rs8135665, rs879180, rs9267576, rs943080, rs9821337, rs9973159, rs485632, rs56123646, rs17826006, rs7439493, rs62358361 , rs116306799, rs7803454, rs79037040, rs11143754, rs334362, rs4570483, rs36212733, rs3138141 , rs61941275, rs9564692, rs1956526, rs2414577, rs17231506, rs72802342, rs12948099, rs1137776, rs12019136, rs11569415, rs429358, rs58847685, rs77280782, rs74406464, rs77968014, rs352920876, rs191281603, rs62247658, rs140647181 , rs55975637, rs114092250, rs116503776, rs144629244, rs114254831 , rs181705462, rs10781182, rs71507014, rs1626340, rs2740488, rs12357257, rs61941274, rs61985136, rs2842339, rs5817082, rs6565597, rs2230199, rs67538026, rs142450006, and rs201459901 .31 . The method of any one of claims 26 to 30, wherein the method is performed at least in part using an electronic processing device.

32. The method of any one of claim 26 to claim 31 , wherein the method includes, in at least one processing device, generating a representation of the indicator.

33. The method of claim 32, wherein the representation includes an alphanumeric indication of the indicator.

34. The method for determining a polygenic risk score (PRS), wherein the PRS is based on the indicator of claims 26 to 33 and at least one genetic risk factor and / or non-genetic risk factors.

35. The method of claim 34, wherein one or more genetic risk factors may be selected from the group consisting of SNPs from Table 2, ethnicity, sex or family history of AMD.

36. The method of claim 35, wherein one or more non-genetic risk factors may be selected from the group consisting of age, alcohol consumption history, smoking history, exercise history, body mass index, diet, or other age-related ocular conditions.

37. The method of claim 26 to 36, wherein the method includes: a. comparing the PRS to a PRS reference; and b. determining a likelihood in accordance with results of the comparison.

38. The method of claim 37, wherein the PRS reference is based on at least one of:a. an PRS threshold range; b. an PRS threshold; and c. an PRS distribution.

39. The method of claim 37 or claim 38, wherein the PRS reference is derived from the indicator of claims 26 to 33 and at least one or more genetic risk factors and / or non- genetic risk factors determined for a number of individuals in a reference population.

40. The method of claim 39, wherein the reference population consists of individuals diagnosed as having AMD or lacking AMD.41 . The method of claim 39 or claim 40, wherein the reference population includes: a. a plurality of individuals of different sexes; b. a plurality of individuals of different ethnicities; c. a plurality of healthy individuals; d. a plurality of individuals diagnosed with AMD; e. a plurality of individuals showing clinical signs of AMD; f. first and second groups of individuals, each group of individuals diagnosed with AMD; and g. first and second groups of individuals, the first group of individuals diagnosed with AMD and the second group of individuals lacking AMD.

42. The method according to any one of the claims 37 to 42, wherein the PRS reference is retrieved from a database.

43. The method according to any one of the claims 37 to 43, wherein the likelihood of a subject developing AMD is based on a probability generated using the results of a comparison.

44. The method of claims 34 to 43, wherein a PRS above a predetermined threshold indicates the subject has a likelihood of developing AMD.

45. The method of claims 34 to 43, wherein a PRS below a predetermined threshold indicates the subject is less likely to develop AMD.

46. A kit comprising reagents suitable for determining the presence, absence or allele, of at least one group 1 AMD biomarker (e.g., 1 , 2, 3, 4, 5, 6, 7, 8, or 9 AMD biomarkers) in a sample from the subject, wherein the at least one group 1 AMD biomarker is a single nucleotide polymorphism (SNP) selected from rs10797980, rs551911 , rs6661798, rs11674246, rs1871671 , rs7637449, rs12359135, rs7311672, and rs9901671 .

47. The kit according to claim 46, wherein the reagents suitable for determining the presence, absence, or allele in the sequence of the SNP are oligonucleotide probes andwherein said probes make up at least 1 % of the total content of the oligonucleotide probes in the kit.

48. The kit according to claim 46 or claim 47, further comprising a reagent suitable for determining the present, absence or allele, of at least one group 2 AMD biomarker (e.g., 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, or any integer in between), wherein the group 2 AMD biomarker is selected from the list comprising: rs10033900, rs1005819, rs10241492, rs1064583, rs10781 177, rs10781 180, rs10922109, rs10981380, rs 11072508, rs 11076175, rs1 1080055, rs1 1 120691 , rs1 1 142636, rs1 12635299, rs1 142, rs1 144, rs1 1569520, rs1 1635145, rs11771419, rs1 1884770, rs121913059, rs1221 1410, rs12213415, rs12457465, rs12576144, rs12901081 , rs12913832, rs12949956, rs13081855, rs13255394, rs1378940, rs1378942, rs1393350, rs141853578, rs147859257, rs148553336, rs1605677, rs163494, rs16841920, rs17105278, rs17356664, rs17421410, rs17480689, rs17576, rs1800588, rs187328863, rs1926564, rs201 1092, rs201 1822, rs2043085, rs2070895, rs2170240, rs2232613, rs2280953, rs2367070, rs2414634, rs24761 6, rs2575876, rs259842, rs2842343, rs2857107, rs3138142, rs3750846, rs3760775, rs3764261 , rs3775220, rs380743, rs3825991 , rs401 186, rs4151671 , rs4420638, rs4748976, rs4943289, rs5023028, rs550513, rs570618, rs5754206, rs5754222, rs5754227, rs61760904, rs61818925, rs621736, rs66532523, rs6726589, rs6795735, rs6899205, rs704, rs7182946, rs7266392, rs73036519, rs73045269, rs7405901 , rs7428936, rs760070, rs7624060, rs7625101 , rs7663219, rs775161 18, rs7874221 , rs7896471 , rs7939052, rs8017304, rs8051675, rs8056814, rs8135665, rs879180, rs9267576, rs943080, rs9821337, rs9973159, rs485632, rs56123646, rs17826006, rs7439493, rs62358361 , rs1 16306799, rs7803454, rs79037040, rs1 1 143754, rs334362, rs4570483, rs36212733, rs3138141 , rs61941275, rs9564692, rs1956526, rs2414577, rs17231506, rs72802342, rs12948099, rs1 137776, rs12019136, rs1 1569415, rs429358, rs58847685, rs77280782, rs74406464, rs77968014, rs352920876, rs191281603, rs62247658, rs140647181 , rs55975637, rs1 14092250, rs1 16503776, rs144629244, rs1 14254831 , rs181705462, rs10781 182, rs71507014, rs1626340, rs2740488, rs12357257, rs61941274, rs61985136, rs2842339, rs5817082, rs6565597, rs2230199, rs67538026, rs142450006, and rs201459901 .

49. Use of a kit of any one of claims 46 to 48 for determining the likelihood of a subject developing AMD.

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