Polygenic risk score for in vitro fertilization

Abstract

Provided are methods for determining disease risk associated with an embryo, comprising constructing a genome of an embryo based on (i) one or more genetic variants in the embryo, (ii) a paternal haplotype, (iii) a maternal haplotype, (iv) a transmission probability of the paternal haplotype, and (v) a transmission probability of the maternal haplotype; assigning a polygenic risk score to the embryo based on the constructed genome of the embryo; determining disease risk associated with the embryo based on the polygenic risk score; and determining the transmission of disease-causing genetic variants and / or haplotypes from the paternal genome and / or maternal genome to the embryo. Also provided are methods for determining a range of disease risks for potential children of a mother and a potential sperm donor. Also provided are methods for determining disease risk in an individual.

Description

[0001] Cross-reference to related applications

[0002] This application claims the benefit of U.S. Provisional Application No. 62 / 908,374, filed on September 30, 2019, and U.S. Provisional Application No. 63 / 062,044, filed on August 6, 2020, each of which is incorporated herein by reference in its entirety. Field of the Invention

[0003] Described are methods for determining disease risk. Background of the Invention

[0005] Currently, IVF clinics test for aneuploidies and single gene disorders that are known to run in families. However, 1 in 2 couples have a family history of common diseases that are influenced by a combination of genetic, environmental, and lifestyle risk factors. In addition, currently, sperm donor clinics test for a predisposition to develop a subset of diseases caused by single gene disorders. There is a need in the art to improve the ability to predict inherited disease risk in individuals and in potential future children. SUMMARY OF THE INVENTION

[0007] Provided is a method for determining disease risk associated with an embryo, the method comprising: performing whole genome sequencing on a biological sample obtained from a paternal subject to identify a genome associated with the paternal subject; performing whole genome sequencing on a biological sample obtained from a maternal subject to identify a genome associated with the maternal subject; phasing the genome associated with the paternal subject to identify a paternal haplotype; phasing the genome associated with the maternal subject to identify a maternal haplotype; performing rare genotyping on an embryo to identify one or more genetic variants in the embryo; and performing a genetic analysis based on (i) one or more genetic variants in the embryo, (ii) the paternal haplotype, and (iii) the maternal haplotype. haplotype, (iii) maternal haplotype, (iv) paternal haplotype, and (v) maternal haplotype; construct a genome of the embryo based on the constructed genome of the embryo; assign a polygenic risk score to the embryo; determine a disease risk associated with the embryo based on the polygenic risk score; determine the transmission of genetic variants causing monogenic diseases and / or haplotypes from the paternal genome and / or the maternal genome to the embryo; and determine a combined disease risk associated with the embryo based on the polygenic disease risk and the transmission of genetic variants causing monogenic diseases and / or haplotypes from the paternal genome and / or the maternal genome to the embryo.

[0008] Also provided is a method for outputting a disease risk score associated with an embryo, the method comprising: receiving a first data set comprising paternal genomic data and maternal genomic data; aligning sequence reads with a reference genome and determining genotypes across the genome using the paternal genomic data and the maternal genomic data; receiving a second data set comprising paternal and maternal sparse genomic data; phasing the paternal genomic data and the maternal genomic data to identify paternal haplotypes and maternal haplotypes; receiving a third data set comprising sparse genomic data of the embryo, paternal transmission probabilities, and maternal transmission probabilities; applying an embryo reconstruction algorithm to (i) paternal haplotypes and maternal haplotypes, (ii) the sparse genomic data of the embryo and (iii) the transmission probabilities of each of the paternal haplotypes and maternal haplotypes to determine a constructed genome of the embryo; applying a polygenic model to the constructed genome of the embryo; outputting a disease risk associated with the embryo; determining the transmission of disease-causing genetic variants and / or haplotypes from the paternal genome and / or maternal genome to the embryo; and outputting the presence or absence of disease-causing variants and / or haplotypes in the embryo. Some methods further include outputting a combined disease risk associated with the embryo based on the transmission of polygenic disease risks and single-gene disease causing genetic variants and / or haplotypes from the paternal genome and / or maternal genome to the embryo.

[0009] In some respects, the method further includes using grandfather's system genome data and / or grandmother's system genome data to determine paternal haplotype and / or maternal haplotype. In some respects, the method further includes using population genotype data and / or population allele frequency to determine the disease risk of the embryo. In some respects, the method further includes using family history of the disease and / or other risk factors to predict the disease risk.

[0010] In some aspects, whole genome sequencing is performed using standard, PCR-free, ligated reads (i.e., synthetic long reads), or long read protocols. In some aspects, rare genotyping is performed using microarray technology; next generation sequencing of embryo biopsies; or cell culture media sequencing. In some aspects, phasing is performed using population-based and / or molecular-based methods (e.g., ligated reads). In some aspects, polygenic risk scores are determined by summing effects across loci in a disease model.

[0011] In some aspects, the population genotype data comprises at least about 300,000 unrelated individuals' allele frequencies and individual genotypes in the UK Biobank. In some aspects, the population phenotype data comprises at least about 300,000 unrelated individuals' self-reports and clinical reports (such as ICD-10 codes) phenotypes in the UK Biobank. In some aspects, the population genotype data comprises population family history data, which comprises at least about 300,000 unrelated individuals' self-report data in the UK Biobank and information derived from the relatives of those individuals in the UK Biobank. In some aspects, the disease risk is further determined by the score of the genetic information shared by the affected individual.

[0012] Also provided is a method for determining a disease risk for one or more potential children, the method comprising: performing whole genome sequencing on (i) an intended mother and one or more potential sperm donors or (ii) an intended father and one or more potential egg donors; phasing the genomes of (i) the intended mother and one or more potential sperm donors or (ii) the intended father and one or more potential egg donors; simulating gametes based on recombination rate estimates; combining the simulated gametes to generate genomes of one or more potential children; assigning a polygenic risk score; and determining a distribution of disease probabilities based on the polygenic risk score.

[0013] Also provided is a method for outputting a probability distribution of disease risks for a potential child, the method comprising: receiving a first data set comprising genomic data of an intended mother; receiving one or more data sets comprising genomic data from one or more intended sperm donors; simulating gametes using estimated recombination rates (e.g., derived from the HapMap Consortium); generating genomes of one or more potential children using potential gamete combinations; estimating a polygenic risk score for the genome of each of the one or more potential children; and outputting a distribution of disease probabilities based on the polygenic risk scores.

[0014] Also provided is a method for determining a range of disease risks for a potential child of (i) an intended mother and a potential sperm donor or (ii) an intended father and a potential egg donor, the method comprising: (a) (i) performing whole genome sequencing on the intended mother and one or more potential sperm donors to obtain a maternal genotype and one or more sperm donor genotypes or (ii) performing whole genome sequencing on the intended father and one or more potential egg donors to obtain a paternal genotype and one or more egg donor genotypes; (b) estimating possible genotypes of one or more potential children using (i) the maternal genotype and the potential sperm donor genotype or (ii) the intended father's genotype and the potential egg donor genotype; and (c) estimating a minimum possible polygenic risk score for the potential child using the possible genotypes of the potential child; and (d) estimating a maximum possible polygenic risk score for the potential child using the possible genotypes of the potential child.

[0015] Also provided is a method for outputting a series of disease risks for a potential child of (i) an intended mother and a potential sperm donor or (ii) an intended father and a potential egg donor, the method comprising: (a) receiving a first data set comprising genomic data of the intended mother or genomic data of the intended father; (b) receiving one or more data sets comprising genomic data from one or more intended sperm donors or one or more intended egg donors; (c) deriving possible genotypes for the potential child using the genotypes of (i) the intended mother and the potential sperm donor or (ii) the intended father and the potential egg donor; (d) estimating a minimum polygenic risk score for the potential child by selecting the genotypes (of those derived in (c)) that minimize the score at each site in the model; (e) estimating a maximum polygenic risk score for the potential child by selecting the genotypes (of those derived in (c)) that maximize the score at each site in the model; and (f) outputting a series of disease risks using the minimum and maximum scores calculated in (d) and (e).

[0016] In some aspects, the method uses a dense genotyping array for sperm donors, followed by genotype imputation for loci of interest that were not directly genotyped. In some aspects, the method uses family history of the disease and other relevant risk factors to determine disease risk.

[0017] In some aspects, whole genome sequencing is performed using standard, PCR-free, ligated reads (i.e., synthetic long reads), or long read protocols. In some aspects, phasing is performed using population-based and / or molecular-based methods (e.g., ligated reads). In some aspects, a polygenic risk score is determined by summing the effects across all sites in a disease model.

[0018] In some aspects, the population genotype data comprises at least about 300,000 unrelated individuals in the UK Biobank, allele frequencies and individual genotypes. In some aspects, the population phenotype data comprises at least about 300,000 unrelated individuals in the UK Biobank, self-reported and clinically reported (e.g., ICD-10 codes) phenotypes. In some aspects, the population family history comprises at least about 300,000 unrelated individuals in the UK Biobank, self-reported data, and information derived from the relatives of those individuals in the UK Biobank. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 An exemplary method for predicting and reducing disease risk is described.

[0021] Figure 2 A flow chart is depicted providing an exemplary method for determining a polygenic risk score.

[0022] Figure 3 An exemplary method for determining disease risk in a child is described.

[0023] Figure 4 Depicts illustrative inputs that may be used to determine disease probability.

[0024] Figure 5 Depicted is a flow chart showing an exemplary method for selecting embryos based on disease likelihood.

[0025] Figure 6 Provides a graphical presentation of the risk reduction curves associated with specific diseases.

[0026] Figure 7 A flow chart is depicted providing an exemplary method for selecting a sperm donor.

[0027] Figure 8 A graphical presentation of risk reduction curves generated for multiple donors for some autoimmune conditions is provided.

[0028] Fig. 9 An exemplary disease risk distribution associated with multiple sperm donors is provided.

[0029] Fig.10 A graphical representation of the ROC curve is provided, showing the improvement in predictive ability associated with determining the risk of prostate cancer.

[0030] Fig.11 An exemplary method of predicting disease risk associated with an embryo is illustrated.

[0031] Fig.12 An exemplary disease risk prediction graph associated with HLA typing for rheumatoid arthritis is shown.

[0032] Fig.13 An exemplary scaffold for identifying chromosome length phasing blocks to improve disease risk prediction capabilities is provided.

[0033] Fig.14 A graphical presentation of the distribution of PRS (mean normalized to 0, standard deviation 1) for rheumatoid arthritis cases and controls is provided.

[0034] Fig.15 Shown are the ORs by deciles for rheumatoid arthritis.

[0035] Figure 16 shows the lifetime risk of various conditions in several embryos, where Fig.16A The risk to the first embryo (called "embryo 2") is shown. Fig. 16B The risk of the second embryo (called "embryo 3") is shown, and Fig. 16C The risk for the third embryo (referred to as "embryo 4") is shown.

[0036] Fig.17A Show lifetime risk and risk ratio in several embryos compared with general population risk; Fig. 17B The lifetime risk of the embryo is shown as a function of the polygenic risk score.

[0037] Fig.18 An illustration of an exemplary parent support method for determining fetal disease risk is provided.

[0038] Fig.19 Illustration of a potential workflow for genome-wide prediction of embryos.

[0039] Fig. 20 A schematic diagram is provided of how the whole chromosome phase of an individual can be obtained by performing whole genome sequencing of the individual, his or her partner, and two or more children and determining which loci are inherited by each child.

[0040] Fig.21 is a block diagram of an example computing device. DETAILED DESCRIPTION OF THE INVENTION

[0042] Unless otherwise defined, technical and scientific terms used herein have the meaning commonly understood by one of ordinary skill in the art to which the invention belongs. Unless otherwise indicated, the materials referred to in the following description and examples are available from commercial sources.

[0043] As used herein, the singular forms "a", "an", and "the" refer to both the singular and the plural, unless explicitly stated to refer to the singular only.

[0044] The term "about" means that the understood numerical value is not limited to the exact numerical value listed herein, and is intended to refer to numerical values ​​substantially surrounding the stated numerical value while not departing from the scope of the present invention. As used herein, "about" will be understood by one of ordinary skill in the art and will vary to some extent depending on the context in which it is used. If the use of the term is unclear to one of ordinary skill in the art based on the context in which it is used, "about" will mean up to plus or minus 10% of the particular term.

[0045] The term "gene" refers to a segment of DNA or RNA that encodes a polypeptide or plays a functional role in an organism. A gene can be a wild-type gene, or a variant or mutation of a wild-type gene. "Gene of interest" refers to a gene or a variant of a gene that may or may not be known to be associated with a specific phenotype or a risk of a specific phenotype.

[0046] "Expression" refers to the process of transcribing a polynucleotide from a DNA template (such as into mRNA or other RNA transcripts) and / or the subsequent translation of the transcribed mRNA into a peptide, polypeptide, or protein. The expression of a gene encompasses not only cellular gene expression, but also the transcription and translation of nucleic acids in cloning systems and in any other context. In the case where a nucleic acid sequence encodes a peptide, polypeptide, or protein, gene expression involves the production of nucleic acids (e.g., DNA or RNA, such as mRNA) and / or peptides, polypeptides, or proteins. Thus, "expression level" can refer to the amount of nucleic acid (e.g., mRNA) or protein in a sample.

[0047] "Haplotype" refers to a group of genes or alleles that are inherited or expected to be inherited together from a single ancestor (such as a father, mother, grandfather, grandmother, etc.). The term "ancestor" refers to a person whose descendants a subject is, or in the case of an embryo, whose descendants a potential subject will be. In preferred aspects, an ancestor refers to a mammalian subject, such as a human subject.

[0048] Disease and method

[0049] Provided are methods for identifying a disease that is wholly or partially genetically caused, or a risk of having or inheriting a disease. Genetic disorders can be caused by mutations in one gene (monogenic disorder), mutations in multiple genes (polygenic disorder), a combination of gene mutations and environmental factors (multifactorial disorder), or chromosomal abnormalities (changes in the number or structure of entire chromosomes (structures that carry genes)). In some aspects, the disease is a polygenic disorder, a multifactorial condition, or a rare monogenic disorder (e.g., one that has not been previously identified in a family).

[0050] Some aspects include determining whether the embryo is a carrier of a genetic disorder. Some aspects include determining whether the embryo will develop into a subject with or likely to have a genetic disorder. Some aspects include determining whether the embryo will develop into a subject with or likely to have one or more phenotypes associated with a genetic disorder.

[0051] Some aspects include selecting embryos based on their genetic makeup. For example, some aspects include selecting embryos that have a low risk of carrying a genetic disorder. Some aspects include selecting embryos that have a low risk of carrying a genetic disorder if they develop into children or adults. Some aspects include implanting the selected embryos into the uterus of a subject. Such methods are described in great detail in, for example, Balaban et al., "Laboratory Procedures for Human In Vitro Fertilization," Semin. Reprod. Med., 32(4): 272-82 (2014), which is incorporated herein by reference in its entirety.

[0052] Some aspects include assessing disease risks associated with embryos formed using one or more sperm donors. Some aspects include selecting sperm donors based on disease risk. Some aspects include fertilizing eggs in vitro with the selected sperm.

[0053] Some aspects include determining an individual's health profile, such as based on the presence or absence of polygenic or rare monogenic variants. Some aspects include determining a distribution of disease probabilities, such as based on a polygenic risk score.

[0054] The diseases that can be screened are not limited. In some aspects, the disease is an autoimmune condition. In some aspects, the disease is associated with a specific HLA type. In some aspects, the disease is cancer. Exemplary conditions include coronary artery disease, atrial fibrillation, type 2 diabetes, breast cancer, age-related macular degeneration, psoriasis, colorectal cancer, deep vein thrombosis, Parkinson's disease, glaucoma, rheumatoid arthritis, celiac disease, vitiligo, ulcerative colitis, Crohn's disease, lupus, chronic lymphocytic leukemia, type 1 diabetes, schizophrenia, multiple sclerosis, familial hypercholesterolemia, hyperthyroidism, hypothyroidism, melanoma, cervical cancer, depression, and migraine. Some exemplary diseases include single gene disorders (e.g., sickle cell disease, cystic fibrosis), chromosome copy number disorders (e.g., Turner syndrome, Down syndrome), repeat expansion disorders (e.g., fragile X syndrome), or more complex polygenic disorders (e.g., type 1 diabetes, schizophrenia, Parkinson's disease, etc.). Other exemplary diseases are described in PHYSICIANS'DESK REFERENCE (PRD Network 71st ed. 2016) and THE MERCK MANUAL OF DIAGNOSIS AND THERAPY (Merck 20th ed. 2018), each of which is incorporated herein by reference in its entirety. By definition, genetically complex diseases have multiple genetic loci that contribute to disease risk. In these cases, polygenic risk scores can be calculated and used to stratify embryos into high-risk and low-risk categories.

[0055] Embryonic genome construction

[0056] Provided is a novel and creative method related to embryo genome construction.In some aspects, construction uses chromosome length parent haplotype and rare genotyping (such as using SNP array or low coverage DNA sequencing) of parent and embryo to realize the full genome prediction in embryo.Such heterozygous method can combine the genetic information from parents and other relatives (if available) (such as grandparents and siblings (i.e. brothers and sisters)) and use molecular method (such as long fragment reading technology, 10X Chromium technology, Minion system) to directly obtain haplotype (such as dense haplotype block) from DNA.Chromosome length haplotype can be used for predicting the genome of the embryo in the setting of in vitro fertilization.The genome sequence of such prediction can be used for predicting disease risk, both by directly measuring the transmission of the variant causing Mendelian disease, and by constructing polygenic risk score to predict disease risk.

[0057] In some aspects, the embryonic genome is constructed using the haplotype from two or more ancestors. In some aspects, the embryonic genome is constructed using paternal haplotype and maternal haplotype. In some aspects, haplotype is grandfather's haplotype. In some aspects, haplotype is grandmother's haplotype. In some aspects, the embryonic genome is constructed using paternal haplotype, maternal haplotype, and grandfather's haplotype and grandmother's haplotype one or both. In some aspects, rare embryonic genotype is obtained by DNA sequencing obtained by cell-free DNA in embryo culture medium, blastocyst fluid or self-embryo trophectoderm cell biopsy.

[0058] Some aspects include determining one or more haplotypes for building embryonic genome.For example, such haplotype can be determined based on the genome sequence of ancestor subject.Some aspects include identifying the genome relevant to ancestor subject.Some aspects include implementing whole genome sequencing to identify the genome of ancestor subject to the biological sample obtained from ancestor subject.Some aspects include using one or more sibling embryos to determine haplotype.Such whole genome sequencing can be implemented using any one of multiple technologies, such as standard, no PCR, connection reading (such as synthetic long reading), or long reading scheme. Exemplary sequencing techniques are disclosed, for example, in Huang et al., "Recent Advances in Experimental Whole Genome Haplotyping Methods," Int'l. J. Mol. Sci., 18 (1944): 1-15 (2017); Goodwin et al., "Coming of age: ten years of next-generation sequencingtechnologies," Nat. Rev. Genet., 17:333-351 (2016); Wang et al., "Efficient and unique cobarcoding of second-generation sequencing reads from long DNAmolecules enabling cost-effective and accurate sequencing, haplotyping, and denovo assembly," Genome Res., 29(5):798-808 (2019); and Chen et al. al., “Ultralow-inputsingle-tube linked-read library method enables short-read second-generationsequencing systems to routinely generate highly accurate and economical long-range sequencing information,” Genome Res., 30(6):898-909(2020), each of which is incorporated herein by reference in its entirety.

[0059] Genome phasing

[0060] Some aspects include phasing or imputing the ancestral genome to identify one or more haplotypes. For example, such phasing can be implemented using population-based and / or molecular-based methods (such as linked read methods). Exemplary phasing techniques are disclosed in, for example, Choi et al., “Comparison of phasing strategies for whole human genomes,” PLoS Genetics, 14(4): e1007308 (2018); Wang et al., “Efficient and unique cobarcoding of second-generation sequencing reads from long DNA molecules enabling cost-effective and accurate sequencing, haplotyping, and denovo assembly,” Genome Res., 29(5): 798-808 (2019); and Chen et al., “Ultralow-input single-tube linked-read library method enables short-read second-generation sequencing systems to routinely generate highly accurate and economical long-range sequencing information,” Genome Res., 30(6): 898-909 (2020), each of which is incorporated herein by reference in its entirety.

[0061] In some aspects, phasing uses self-ligated read sequencing, long fragment reads, fosmid pool-based phasing, continuity-preserving transposon sequencing, whole genome sequencing, Hi-C methods, dilution-based sequencing, targeted sequencing (including HLA typing), or microarray-generated data.

[0062] Some aspects include using independently obtained rare phased genotypes to provide scaffolds to guide phase separation. Computer software such as HapCUT, SHAPEIT, MaCH, BEAGLE or EAGLE can be used for phasing the genotype of ancestors. In some cases, computer programs use reference groups such as 1000Genomes or Haplotype Reference Consortium to phase genotypes. In some cases, phase separation accuracy can be improved by adding genotype data from relatives such as grandparents, siblings, or children.

[0063] Predicting embryonic genome sequence

[0064] Some aspects include using the rare genotyping combination of phased parental genome and embryo to predict the genome of the embryo, which can allow the presence / disappearance of the clinically relevant variant identified in the parents and in the embryo to be determined. This can be extended to include the risk / susceptibility allele identified in the parents and HLA types. In some aspects, rare genotyping is obtained using next generation sequencing. Rare genotyping is described in great detail in Kumar et al., "Whole genome prediction for preimplantation genetic diagnosis," Genome Med., 7(1): Article 35, pages 1-8 (2015); Srebniak et al., "Genomic SNP array as a gold standard for prenatal diagnosis of foetal ultrasound abnormalities," Molceular Cytogenet., 5: Article 14, pages 1-4 (2012); and Bejjani et al., "Clinical Utility of Contemporary Molecular Cytogenetics," Annu. Rev. Genomics Hum. Genet., 9: 71-86 (2008), each of which is incorporated herein by reference in its entirety.

[0065] Rare genotyping can be implemented to the part of embryo extraction.Therefore, some aspects include extracting or obtaining one or more cells (such as via biopsy) from embryo.Some aspects include extracting or obtaining nucleic acid (such as DNA) from embryo or from one or more cells from embryo.Some aspects include extracting embryo material from embryo culture medium.

[0066] Some aspects use rare embryo genotypes as scaffolds to phase the ancestral subject genome. Some aspects use information from one or more grandparent subjects (e.g., grandfather and / or grandmother subjects) to phase the parental genome. Some aspects use information from large reference panels (e.g., data based on populations) to phase the parental genome.

[0067] In some aspects, the embryo is reconstructed using a biological sample obtained from one or more ancestral subjects. Exemplary biological samples include one or more tissues selected from brain, heart, lung, kidney, liver, muscle, bone, stomach, intestine, esophagus, and skin tissue; and / or one or more biological fluids selected from urine, blood, plasma, serum, saliva, semen, sputum, cerebrospinal fluid, mucus, sweat, vitreous humor, and milk. Some aspects include obtaining a biological sample from a subject.

[0068] Some aspects include determining the probability of transmission of one or more ancestral haplotypes. In some aspects, the transmission of variants from one or more maternal heterozygous sites may involve sequencing the maternal genome, sequencing or genotyping one or more biopsies from embryos, assembling or phasing maternal DNA samples into haplotype blocks, using information from multiple embryos (e.g., parental support technology) to construct chromosome length haplotypes for the parents, and using statistical methods like HMMs to predict the inheritance or transmission of these haplotype blocks. In some aspects, HMMs can also predict transitions between haplotype blocks or correct errors in maternal phasing.

[0069] Methods for predicting the transmission of variants from one or more paternal heterozygous sites can involve sequencing the paternal genome, sequencing or genotyping one or more biopsies from an embryo, assembling or phasing the paternal DNA sample into haplotype blocks, using information from multiple embryos to improve the continuity of haplotype blocks with chromosome length, and using statistical methods such as HMMs to predict the inheritance or transmission of these haplotype blocks. In some aspects, HMMs can also predict transitions between haplotype blocks or correct errors in maternal phasing.

[0070] The situation that both the mother and the father are heterozygous can be predicted in the above manner. In the case where both parents are homozygous for the same allele or different alleles, the embryo genotype is easily predicted.

[0071] In some aspects, the transmission probability is determined using the methods described in U.S. Application Serial Nos. 11 / 603,406; 12 / 076,348; or 13 / 110,685; or PCT Application Nos. PCT / US09 / 52730 or PCT / US10 / 050824, each of which is incorporated herein by reference in its entirety. In some aspects, regions with a transmission probability of 95% or higher are used to construct embryonic genomes.

[0072] In some respects, the embryonic genome is constructed using one or more genes or genetic variants in the embryo. In some respects, one or more genes or genetic variants are identified using rare genotyping of the embryo. In some respects, rare genotyping is implemented using microarray technology.

[0073] In some aspects, the embryonic genome is constructed using (i) one or more genetic variants in the embryo, (ii) one or more ancestral haplotypes (e.g., paternal haplotypes and maternal haplotypes), and (iii) the transmission probability of one or more haplotypes (e.g., paternal haplotypes and maternal haplotypes). In some aspects, rare genotyping is implemented using next generation sequencing.

[0074] Some aspects include embryonic genome predictions that use 1) whole genome sequences of two grandparents on each side of the family, 2) phased whole genome sequences from each parent, 3) rare genotypes of the parents measured by array, and 4) rare genotypes of the embryo. Without being bound by theory, it is believed that for a well-studied CEPH family, 99.8% prediction accuracy can be achieved across 96.9% of the embryonic genome using such methods.

[0075] Some aspects include phasing the parental genome using 1) WGS of a single grandparent, 2) rare parental genotypes measured by array, and 3) a haplotype-resolved reference panel. Some aspects include phasing the parental genome using 1) rare parental genotypes measured by array, and 2) a haplotype-resolved reference panel (e.g., 1000 Genomes). Some aspects include phasing the parental genome using only a haplotype-resolved reference panel (e.g., 1000 Genomes).

[0076] Risk determination

[0077] Also provided is a method for determining the disease risk associated with an embryo (e.g., a genome based on the construction of an embryo). Some aspects include determining whether the genetic variant causing the disease from the ancestral genome has been delivered to the embryo. Some aspects include determining whether a haplotype (e.g., related to the genetic variant causing the disease) has been delivered to the embryo. Some aspects include determining the presence or absence of a genetic variant causing a disease or improving disease susceptibility, including, but not limited to, single nucleotide variants (SNVs), small insertions / deletions, and copy number variants (CNVs). Some aspects include determining the presence or absence of a disease-related HLA type in an embryo.

[0078] In some aspects, one or more diseases (e.g., a group of diseases) that can be ranked based on age of onset and disease severity can be used to determine the phenotypic risk in the embryo. In some aspects, disease ranking can be combined with polygenic risk prediction to rank embryos according to potential disease risk.

[0079] Some aspects include determining that the embryo has 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, or more disease risk. Some aspects include determining that the embryo has 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, 5%, 1%, or less disease risk. Some aspects include selecting embryos based on disease risk (e.g., selecting embryos with relatively low disease risk) and / or based on the presence or absence of specific gene variants (e.g., SNV, haplotype, insertion / deletion, and / or CNV).

[0080] In some aspects, the disease risk associated with the embryo is determined using a polygenic risk score. In some aspects, the polygenic risk score (also referred to as a "PRS") is determined by summing the effects across sites in the disease model. In some aspects, the polygenic risk score is determined using population data. For example, population data may involve allele frequencies, individual genotypes, self-reported phenotypes, clinically reported phenotypes (e.g., ICD-10 codes), and / or family history (e.g., derived from related individuals in one or more population databases) information. Such population data can be obtained from any of a number of databases, including the United Kingdom (UK) Biobank (which has information on approximately 300,000 unrelated individuals); various genotype-phenotype data sets as part of the Genotype and Phenotype Database (dbGaP) maintained by the (US) National Center for Biotechnology Information (NCBI); European Genome-Phenome Archives; OMIM; GWASdb; PheGenl; Genetic Association Database (GAD); and PhenomicDB.

[0081] In some aspects, disease risk is determined based on a polygenic risk score cutoff. For example, such a cutoff may include the top about 1% of a PRS distribution, the top about 2% of a PRS distribution, the top about 3% of a PRS distribution, the top about 4% of a PRS distribution, or the top 4% of a PRS distribution. Preferably, the cutoff is based on the top 3% of a PRS distribution. The polygenic risk score cutoff may also be determined based on an absolute risk increase, such as about 5%, about 10%, or about 15%. Preferably, the polygenic risk score cutoff is determined based on an absolute risk increase of 10%.

[0082] Some aspects include using the predicted embryo genome to estimate phenotypic risk. In some aspects, the risk estimate uses 1) the predicted genome of the embryo, 2) the genotypes of the parents at the loci of interest that were not predicted in the embryo (i.e., variants included in the polygenic risk score), and 3) the allele frequencies in a reference cohort (e.g., UKBB) at the loci of interest that were not predicted in the embryo (e.g., variants included in the polygenic risk score).

[0083] Some aspects include determining risk based on the probability of transmission of one or more genetic variants (e.g., based on ancestral haplotypes). Some aspects include determining the combined risk associated with the embryo based on the probability of transmission of polygenic disease risk and one or more genetic variants (e.g., genetic variants causing monogenic diseases and / or haplotypes from paternal genome and / or maternal genome to the embryo).

[0084] A non-limiting exemplary system for predicting and reducing disease risk is described in Figure 1 A non-limiting exemplary polygenic risk score workflow is shown in Figure 2 Displayed in.

[0085] Donor selection

[0086] Also provided are methods for selecting sperm and / or egg donors. The estimated value of the risk of a subject passing a disease to his or her offspring can be calculated by simulating the genome of a virtual child and calculating the disease risk of each child. Some aspects include determining the disease risk of the intended mother and one or more potential sperm donors. Some aspects include determining the disease risk of the intended father and one or more potential egg donors.

[0087] Some aspects include simulating gametes from potential mothers and fathers using phased parental genomes and simulated haplotype recombination sites (e.g., as determined using the HapMap database). Some aspects take into account the respective recombination rates during meiosis that produced these gametes. In some aspects, these simulated gametes are combined with each other to produce numerous combination possibilities to approximate the range of potential child genomes. Such child genome arrays can be converted into disease probability arrays to predict the distribution of disease risks across each child. See Figure 3 .

[0088] Risk estimates as described herein (e.g., in the Embryo Genome Construction section and / or the Examples section) can be used in the context of family planning in embryo selection and / or sperm donor selection during an IVF cycle. In some embodiments, potential parents receive a report containing individual risk estimates for multiple phenotypes across all available embryos or a series of risk values ​​for each potential sperm donor. In some aspects, sperm donors are ranked based on disease risk for a condition or a group of conditions. In some aspects, donors are selected using a python script disclosed in U.S. Provisional Application No. 63 / 062,044 filed on August 6, 2020, or a modified version thereof.

[0089] Some aspects include selecting an embryo based on a risk score. Some aspects include selecting an egg donor based on a risk score. Some aspects include selecting a sperm donor based on a risk score.

[0090] Implementation system

[0091] The methods described herein can be implemented on a variety of systems. For example, in some aspects, a system (e.g., for genomic embryo construction, donor selection, risk determination, and / or implementation of health reporting) includes one or more processors coupled to a memory. The method can be implemented using code and data stored and executed on one or more electronic devices. Such electronic devices can store and communicate (internally and / or via a network with other electronic devices) code and data using computer-readable media, such as non-transitory computer-readable storage media (e.g., magnetic disks; optical disks; random access memory; read-only memory; flash memory devices; phase change memory) and temporary computer-readable transmission media (e.g., electrical, optical, acoustic or other forms of propagation signals, such as carrier waves, infrared signals, digital signals).

[0092] The memory can be loaded with computer instructions to train the model as needed (e.g., for identifying disease risk). In some aspects, the system is implemented on a computer, such as a personal computer, a portable computer, a workstation, a computer terminal, a network computer, a supercomputer, a massively parallel computing platform, a television, a mainframe, a server farm, a widely distributed loosely networked collection of computers, or any other data processing system or user device.

[0093] The method may be implemented by processing logic including hardware (e.g., circuitry, dedicated logic, etc.), firmware, software (e.g., embodied on a non-transitory computer-readable medium), or a combination of both. The operations described may be performed in any sequential order or in parallel.

[0094] Typically, the processor can receive instructions and data from a read-only memory or a random access memory, or both. Computers typically contain a processor that can perform actions in accordance with instructions and one or more storage devices for storing instructions and data. Typically, a computer will also include or be operatively coupled to receive or transfer data from or to one or more mass storage devices (such as magnetic disks, magneto-optical disks, optical disks, or solid-state drives) for storing data, or both. However, a computer need not have such devices. In addition, a computer can be embedded in other devices, such as a smartphone, a mobile audio or media player, a game console, a global positioning system (GPS) receiver, or a portable storage device (such as a universal serial bus (USB) flash drive), to name a few. Apparatus suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and storage devices, including, by way of example, semiconductor storage devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks, such as internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM optical disks. The processor and memory may be supplemented by or incorporated into special purpose logic circuits.

[0095] A system of one or more computers may be configured to perform a specific operation or action by installing software, firmware, hardware, or a combination thereof on the system that causes the system to perform the action when operated. One or more computer programs may be configured to perform a specific operation or action by including instructions that, when executed by a data processing device, cause the device to perform the action.

[0096] An exemplary implementation system is Fig.21 Such systems may be used to implement one or more operations described herein. The computing device may be connected to other computing devices in a LAN, an intranet, an extranet, and / or the Internet. The computing device may operate in the capacity of a server machine in a client-server network environment or in the capacity of a client machine in a peer-to-peer network environment.

[0097] The following examples are provided to illustrate the invention, but it should be understood that the invention is not limited to the specific conditions or details of these examples. Example

[0098] Example 1: Parental genomic phasing for parental recurrence risk assessment and disease prediction in embryos for preimplantation genetic testing - Use of predicted embryonic genomic sequences in in vitro fertilization (IVF)

[0099] Embryo coverage and accuracy were calculated using three different protocols. According to the first protocol, embryonic genome predictions were made using 1) whole genome sequences (WGS) of two grandparents on each side of the family, 2) phased WGS from each parent, 3) rare genotypes of the parents measured by array, and 4) rare genotypes of the embryo ( Figure 4 For one well-studied CEPH family, the protocol achieved 99.8% prediction accuracy across 96.9% of the embryonic genome. (Also covered is a protocol using 1) WGS of a single grandparent, 2) sparse parental genotypes measured by array, and 3) a haplotype-resolved reference panel.)

[0100] According to the second approach, embryo predictions use 1) the sparse parental genotypes measured by array and 2) a haplotype-resolved reference panel (e.g., 1000Genomes).

[0101] According to the third approach, embryo predictions are made using only a haplotype-resolved reference panel (e.g., 1000Genomes).

[0102] Results from all three schemes are shown below in Table 1. The PRS showed results for approximately 1.4 million loci that were important in disease risk prediction.

[0103] Table 1: Embryo coverage and accuracy achieved with various phasing strategies

[0104]

[0105] Example 2: Using the predicted embryonic genome to estimate phenotypic risk

[0106] The probability of using a possible genotype (AA, AB, BB) given a parental genotype (M, D) at an unpredicted site in the embryo genome (see Equation 1 below). In cases where parental genotypes are not available, the use of a cohort affects the allele frequency (AF EA )(Equation 2).

[0107] Equation 1: β*P(AA|M,D)+β*P(AB|M,D)+β*P(BB|M,D)

[0108] Equation 2: 2*β*AF EA

[0109] Predicted embryos fell within the risk score percentile of the true score in 27 of the 30 models (90%).

[0110] Another approach involves using 1) the predicted genome of the embryo, and 2) the allele frequencies in a reference cohort (e.g., UKBB) at the loci of interest (i.e., variants included in the polygenic risk score) that were not predicted in the embryo. Allele frequencies are used as described in equation 2 above. Using this approach, the predicted embryo falls within the risk score percentile of 23 (77%) of the 30 models. When the parental genotypes are included, the 30 predicted scores all fall within 5% of the true score.

[0111] Example 3: Estimation and Improvement of Phenotypic Risk Estimates Using Polygenic Risk Models

[0112] Statistical framework

[0113] The robust model used for disease simulation and empirical analysis is the threshold liability model. Diseases are assumed to have a genetic component g~N(0, h 2 )(where h 2 is the narrow sense heritability) and the error component∈~N(0,1-h 2 ). The assumed responsibility l is given by:

[0114] l=g+∈~N(0,1)

[0115] is called potential liability, and it is assumed that the sample is at risk on the potential liability scale. The threshold T is estimated based on the disease prevalence p, so that This is calculated from the distribution of a standard normal random variable. Without being bound by theory, it is believed that everyone affected by the disease has l>T.

[0116] Modeling families involves modeling genetic liability, which is modeled as the sum of three components: two genetic components – the portion measured by the PRS, the “unmeasured” portion of the residual genetic risk alone, and the irreducible non-genetic error. The potential genetic risk g from above can be decomposed into

[0117] g=g R +g U

[0118] Defined as

[0119] g U =gg R

[0120] This last component is uncorrelated among family members. On the other hand, if the variance explained by the PRS on the responsibility scale is σ 2 , and g R,i and g R,j is the PRS component of the responsibility of the two first-degree relatives, then the covariance is given by:

[0121]

[0122] If g U,i and g U,j is the remaining unmeasured component of the responsibility of the two first-degree relatives, and h 2 is the heritability of the trait, then the covariance is given by:

[0123]

[0124] If g i is the child of g1 and g2, then

[0125]

[0126] For two first-degree relatives i and j with the following responsibilities,

[0127] l i =g R，i +g U，i +∈ i

[0128] l j =g R，j +g U，j +∈ i

[0129] We can see

[0130]

[0131] Because the error terms are uncorrelated.

[0132] IVF Embryo Selection Simulation

[0133] IVF simulations were performed to answer the following question: Given a set of n embryos and a clinical phenotype of interest, how much less likely is the embryo with the minimum polygenic risk score to develop the disease during its lifetime than a randomly selected embryo? In other words, what is the relative risk reduction of the selection?

[0134] To answer this question, we used a two-step procedure to generate parameters for parents and subsequently their offspring. This procedure, or a variation of it, was used in simulations testing the effectiveness of donor selection and IVF embryo selection.

[0135] The following inputs are used in the embryo selection model: 2 , the variance explained by the polygenic risk score on the liability scale; h 2 , the additive heritability of the traits on the liability scale; p, the lifetime prevalence of the traits.

[0136] The output from this simulation is the risk reduction across different numbers of available embryos, which allows prospective couples undergoing IVF to find out which diseases can be meaningfully screened for.

[0137] Regulations

[0138] Step 1. For each parent, generate a distribution N(0,σ ) if drawn from the general population or some other distribution (such as a variation of the mean or a truncated normal distribution) to represent elevated risk from family history. 2 ) R The distribution is N(0,h 2 -σ 2 ) or the remaining unmeasured genetic risk from the other conditions above g U .

[0139] Step 2. By calculating l1,…,l n To simulate n children:

[0140] Calculate the mean PRS of both parents:

[0141]

[0142] Calculate the mean residual genetic risk of the middle parent:

[0143]

[0144] For each child, the distribution is calculated as N(0,1-h 2 )’s independent error∈ i .

[0145] For each child, calculate the independent PRS recombinant

[0146]

[0147] For each child, calculate the independent unmeasured / residual risk from the recombination

[0148]

[0149] Calculate the responsibility of child i by summing

[0150] l i =M R +M U +R P，i +R U，i +∈ i

[0151] Step 3. To determine the risk reduction, simulate millions of families over a range of n = 3, 4, ..., 10. For each family, look at the responsibility l for the embryo with the smallest PRS min Whether it exceeds the threshold value t=Φ -1 (1-p), where Φ is the cumulative distribution function of the standard normal distribution.

[0152] Statistical Description

[0153] As a supplement, it can be proved that R P,i and R U,i To show that the covariances between siblings and between children and parents are correct, note that

[0154]

[0155] because the last two terms are 0. The same calculation applies to unmeasured genetic risk, i.e.

[0156]

[0157] So for g i =g R,i +g U,i ,

[0158] A similar set of calculations shows that the parent-child covariance also satisfies the correct equation.

[0159] This procedure can be found in Figure 5 An example of a risk reduction curve with inputs is shown in Figure 6 The variance explained by the polygenic risk score is shown in Table 2 below, where “h2_lee” is the variance.

[0160] Table 2: Variance explained by polygenic risk scores for various conditions

[0161] Phenotype <![CDATA[h 2 _lee]]> Prevalence Disease Type Heritability AMD 0.017064 0.0655 other 0.50 Breast cancer 0.026747 0.1240 cancer 0.31 Prostate cancer 0.051717 0.1160 cancer 0.58 CLL 0.045575 0.0057 cancer 0.60 psoriasis 0.079081 0.0400 Autoimmunity 0.75 Rheumatoid arthritis 0.017422 0.0140 Autoimmunity 0.60 Celiac disease 0.246643 0.0100 Autoimmunity 0.80 Crohn's disease 0.021475 0.0050 Autoimmunity 0.80 Type 1 diabetes 0.098359 0.0050 Autoimmunity 0.72 Type 2 diabetes 0.022617 0.2570 other 0.50 Atrial fibrillation 0.014569 0.2720 other 0.67 Bipolar disorder 0.030115 0.0250 Mental illness 0.55 Schizophrenia 0.035857 0.0050 Mental illness 0.80 Vitiligo 0.062567 0.0200 Autoimmunity 0.50 Inflammatory bowel disease 0.022788 0.0200 Autoimmunity 0.50

[0162] There is a simulated donor family

[0163] To identify a donor with lower risk, the following is performed: (1) calculate the polygenic risk score for the expected mother, (2) calculate the polygenic risk score across N donors, and (3) select the donor with the lowest polygenic risk score. The procedure is essentially the same as above, with two changes: First, multiple donors (n = 10, 20, 30, ..., 100) are simulated and the polygenic risk score is minimized over the polygenic risk score of the donors instead of minimizing recombination. A flowchart of the method is shown in Figure 7 Displayed in.

[0164] Use the following input: 2 ,The variance explained by PRS on the responsibility scale; h 2 , the additive heritability of the trait on the liability scale; p, the lifetime prevalence of the trait. The output from this simulation is the risk reduction across different numbers of available donors to be minimized, which allows clients using sperm or egg donors to find out which diseases can be meaningfully screened for. With the same example input as above, risk reduction curves are generated for different numbers of donors for some autoimmune conditions, which are Figure 8 Displayed in.

[0165] Additional embryo selection after donor selection

[0166] Another application of donor selection involves first selecting a donor and then selecting embryos with a lower disease risk. More specifically, a subject (e.g., a female subject) who is interested in using donor sperm for her children is provided with disease risk information. First, using her genetic test results and family history, multiple gametes are simulated and combined with a simulated sperm sample to obtain the risk of known genetic causes of heart disease. This is her "personalized risk" of having a child with the condition and is a refinement of the "baseline risk." Second, using the genetic information from the various donors and information about which variants are phased with each other, a range of disease probabilities are calculated assuming gametes from individual donors. Finally, assuming that the donor was selected, multiple embryos (E1, E2, E3) fall within the disease risk distribution. See Fig. 9 .

[0167] These methods can be used in the context of family planning during sperm donor selection. Potential parents can indicate phenotypes that they are particularly interested in, and risk scores for those phenotypes can be generated for each donor. Those scores are used to predict disease risk for potential children of each sperm donor. Parents can be given a report containing these risk values, allowing them to select donor options that will reduce the risk of the phenotype of interest.

[0168] Family history

[0169] Family history can be incorporated to predict disease risk. There are a number of diseases for which parents and siblings self-report disease status in UK Biobank: diabetes, heart disease, Alzheimer's disease, Parkinson's disease, breast cancer, and a few others. In addition, there are over 10,000 sibling pairs and a large number of half-sibling or other second-degree relative pairs. A model was built with a binary variable for family history, meaning that: (i) for a set of diseases for which there was a self-reported family history in UK Biobank, either a sibling or parent had the disease; or (ii) for any other disease, all samples had a first-degree relative in UK Biobank. Given this definition of the 'family_history_presence' dummy, for each condition on the appropriate cohort, a logistic regression was run using the following formula: log(P / (1-P)) = β_1*PRS+β_2*sex_male+β_3*family_history_presence.

[0170] In summary, the inputs include: data from a biobank containing self-reported family history of disease, and pairs of first-degree relatives with medical records. The outputs include: a model from logistic regression that incorporates PRS and family history to improve the accuracy of our predictions. The model is used to prioritize which patients are at higher risk of developing disease during their lifetime. An exemplary output is listed in Table 3 below, where β_1 (PRS), β_2 (sex dummy), and β_3 (family history dummy) are estimated for multiple conditions.

[0171] Table 3: Data from the logistic regression model incorporating the PRS

[0172]

[0173] When the family history dummy was added to the logistic regression, the improvement in prediction was quantified by the ROC curve for prostate cancer, as shown in Fig.10 Displayed in.

[0174] Increased model complexity

[0175] The model becomes more complex by including second and third degree relatives, more complex pedigrees, and / or related phenotypes. The above shows how to model first degree relatives. To allow for the inclusion of second degree family history, two additional family members can also be modeled for each parent. If P1 is a relative with R 1,i If the parent is one, then we can generate the second-level family members by the following assumptions:

[0176]

[0177] where σ 2 Is PRS or unmeasured genetic risk g UThe variance components of the potential responsibility scale.

[0178] Another layer of complexity can be added to the simulation: thresholds based on age and sex. If the prevalence of the disease varies depending on these variables, the thresholds can be adjusted to determine whether a sample from a family has the disease. For example, suppose that for type 2 diabetes, the prevalence is 20% in men over 80 years old, while the prevalence is 4% in women aged 55 years old. By substituting the empirical lifetime risk of the disease in the model above, the lifetime prevalence can be replaced by the lifetime risk. The thresholds for such a sample would be 1-Φ(0.20) and 1-Φ(0.04), respectively, where Φ is the cumulative distribution function of the standard normal random variable. When one imposes a condition on a family pedigree, they are imposing a condition on a set of samples.

[0179] s i =g R，i +g U，i +∈ i >T i

[0180] Exceeding their age- and sex-specific thresholds T i .

[0181] Given a family tree Ped with information about the disease history, such as a father and grandfather with the disease, and three siblings without the disease, we can calculate

[0182] E(g U |ped)

[0183] The goal is to verify theoretical predictions about the quantity:

[0184] P(g R +g U +∈>T|g U = x)

[0185] This allows calculation of odds ratios.

[0186] HLA phenotype

[0187] Risk determination may involve phenotypes with a strong HLA component and associated HLA alleles that are not well marked by SNVs. However, this approach can be applied to any condition with a known disease association with an HLA allele with a significant effect size and involving other loci. Examples of complex phenotypes with HLA involvement include (but are not limited to) psoriasis, multiple sclerosis, type 1 diabetes, inflammatory bowel disease, Crohn's disease, ulcerative colitis, vitiligo, celiac disease, and systemic lupus erythematosus.

[0188] These methods can be applied in a variety of situations, including but not limited to individual disease risk prediction, risk reduction in both embryo selection and sperm donor selection scenarios, and prescribing guidelines for certain drugs where multiple genetic factors (including HLA type) influence the likelihood of response or adverse drug reaction.

[0189] HLA typing results are obtained from DNA-based methods, such as Sanger sequencing-based typing or derived from whole genome sequencing (WGS). First, a polygenic risk score is determined, for example using a genome-wide association study (GWAS) effect size. An example is to add the product of the effect size and the effect allele dosage to all associated variants that are not in the MHC region. Second, the associated HLA alleles are combined or merged based on the HLA typing results (not the tag SNPs) using one of the following methods.

[0190] Combine PRS and HLA OR: Calculate the polygenic risk score for all individuals in the validation cohort to obtain metadata (e.g., mean, standard deviation, etc.). Obtain the odds ratio (OR) for HLA alleles with established associations with the phenotype of interest. Combine the OR derived from the individual's PRS compared to the validation cohort and HLA typing as follows:

[0191] OR=OR HLA *OR PRS *OR 入口统计学

[0192] The hazard ratio (RR) was calculated using the OR derived above and the prevalence of the disease in the validation cohort. This was then used to estimate the lifetime risk of the disease.

[0193] Incorporate HLA directly into the PRS: Incorporate HLA effect alleles directly into the polygenic risk score by adding the product of the effect size and the dose of each effect allele to the base PRS. This will be called the PRS HLA+ . PRS was calculated for all individuals in the validation cohort HLA+ , and obtain metadata (such as mean, standard deviation, etc.). HLA+ The RR was calculated from the model-derived OR and the disease prevalence in the validation cohort, which was then used to estimate the lifetime risk of disease.

[0194] Example 4: A method for ranking disease risk profiles for embryo and sperm donor selection

[0195] Provided are exemplary methods for ranking disease risk profiles, such as Fig.11 First, a weight w is calculated for each disease in a set of d diseases. d , that is, age of onset w a and disease severitys The sum of the weights of birth-onset diseases (e.g., celiac disease) a Greater than the w of diseases that do not usually develop until adulthood (such as coronary artery disease) a Similarly, more serious diseases (like breast cancer) s Greater than w for diseases with milder phenotypes (like vitiligo) s .

[0196] Next, family history and polygenic risk scores are combined to generate a predicted risk for each condition of interest for each embryo.

[0197] Finally, the disease ranking and risk prediction are combined to generate a single score S for each embryo using the following equation: T , where RR is the relative risk derived from the combination of the polygenic risk score and family history for a given disease:

[0198]

[0199] Assume that the onset of w in adulthood, childhood, or at birth s are 0.5, 1, or 2, respectively. Similarly, assuming that w a The weights are 0.5, 1, or 2, respectively, with the ability to select intermediate values ​​for diseases with variable phenotypes. Table 4 below lists the weights for a small set of conditions based on these values:

[0200] Table 4: Weights of various conditions

[0201] disease Age of onset <![CDATA[w a ]]> Severity <![CDATA[w s ]]> <![CDATA[w d ]]> Breast cancer adult 0.5 Moderate-severe 1.5 2 Celiac disease born 2 Moderate 1 3 psoriasis childhood 1 Mild-Moderate 0.75 1.75

[0202] Assuming three embryos have the following RRs for each of the above conditions, calculate the overall score for each embryo and rank accordingly. For embryo 1, the score is calculated as follows:

[0203] S T =(2*2.4)+(3*1.4)+(1.75*2.7)=24.85

[0204] The disease risk for each of the three embryos is listed in Table 5 .

[0205] Table 5: Disease risk profile of three embryos

[0206] disease RR Embryo 1 RR Embryo 2 RR Embryo 3 Breast cancer 2.4 1.1 0.7 Celiac disease 1.4 1.6 1.4 psoriasis 2.7 7.3 2.7 <![CDATA[S T ]]> 13.7 19.8 10.3 Ranking 2 3 1

[0207] The same procedure was applied to sperm donor selection, where each donor received a ranking across all diseases of interest. In both the embryo and donor selection contexts, scores were calculated for subsets of diseases (e.g., conditions for which the intended parents had a family history) or across all diseases for which the polygenic model was implemented.

[0208] Alternatively, this method can be used without summing all conditions of interest to prioritize the results of a single embryo / individual. Each condition will receive a score, and the condition with the highest score will be prioritized. Using embryo 1 above as an example, the scores and rankings listed in Table 6 were generated.

[0209] Table 6: Embryo scores and rankings

[0210] disease RR Embryo 1 <![CDATA[Disease score (RR*w d )]]> Disease ranking Breast cancer 2.4 4.8 1 Celiac disease 1.4 4.2 3 psoriasis 2.7 4.7 2

[0211] Example 5: Prediction of transmission of disease susceptibility variants to embryos

[0212] A copy of a colorectal cancer susceptibility variant (APC c.3920T>a) (and / or insertion, deletion, and / or copy number variant) was found in the father's WGS. This allele does not exist in the mother. This variant is not directly measured in the rare genotyping of the embryo. The whole chromosome haplotype of the parents is obtained by any single or combined method described above. The reconstruction of the embryo genome determines that the haplotype block containing the risk allele is passed from the father to one of the embryos. The risk allele is annotated as "present" in the embryo.

[0213] Example 6: Polygenic risk of common diseases using embryo prediction

[0214] Breast cancer has a common genetic component. The genetic risk score uses 69 variants to assess breast cancer risk. Of these variants, only 13% (9 / 69) were directly genotyped in the embryo. The percentile of the genetic risk score for the embryo based on these variants was 84.6%. After embryo reconstruction, 98.6% (68 / 69) of the embryos had their genotypes imputed / inferred, and the new percentile of the genetic risk score for the embryo was 77.7%. After the embryos were born, the DNA of the children was genotyped, and the PRS percentile was 76.2%. This shows that the genetic risk score from whole genome embryo reconstruction has higher accuracy and less uncertainty due to the information of the additional variants.

[0215] Example 7: Prediction of transmission of disease-associated HLA types to embryos

[0216] The mother suffers from rheumatoid arthritis (RA). HLA typing results (from WGS, PCR+Sanger sequencing or any other appropriate method) reveal that she carries one copy of the HLA-DRB1*01:02 allele associated with an increased risk for this condition. The father is homozygous for HLA-DRB1*04:02 (an allele not known to be associated with an increased risk for RA). Based on complete phasing of chromosome 6 in each parent and reconstruction of the embryonic genome, it is determined that the mother's haplotype 2 (HM2) and the father's haplotype 2 (HF2) are transmitted to the embryo. The RA risk allele is carried on the mother's haplotype 1 (HM1), so the embryo is predicted not to carry the risk allele. See e.g. Fig.12 .

[0217] Example 8: Providing Families with a Spectrum of Disease Risk in Their Children

[0218] Two parents have expressed to their doctor that they are interested in the risk of multiple genetic diseases in their future children. The method described above is used to calculate the mean and recombinant values ​​based on the genomes of the two parents to predict the range of disease risks for their children, thereby providing guidance for future IVF treatment. Fig. 9 .

[0219] Similarly, in the case of sperm donation, polygenic risk score distributions based on WGS of the mother and potential sperm donors can be simulated by recombination (see Fig. 9 ).

[0220] Example 9: Incorporation of Family History (FHx) to Improve Risk Estimates

[0221] Based on family history of the disease, the risk of developing psoriasis was estimated to be 10-30%. Using the polygenic model alone in embryos where one parent had psoriasis showed only a small difference in risk across embryos. Inclusion of family history provided much better separation between embryo 1 and embryos 2 and 3, which clearly had other risk factors besides FHx, as shown in Table 7.

[0222] Table 7: Embryo risk scores incorporating family history

[0223]

[0224] Similarly, family history can be incorporated to improve risk estimates for predicting the transmission of disease-associated HLA types.

[0225] Example 10: Incorporating HLA typing into psoriasis disease risk estimation

[0226] The presence or absence of two HLA types associated with the risk of developing psoriasis across embryos has a significant impact on overall disease risk. This example can be extended to the context of sperm donor selection or personal genome reporting, as shown in Table 8.

[0227] Table 8: Lifetime risk of psoriasis in multiple embryos

[0228] HLA-C*06:02 HLA-C*12:03 <![CDATA[OR prs ]]> RR Lifetime risk Embryo 1 Missing 1 copy 0.67 0.83 3.3％ Embryo 2 1 copy 1 copy 0.75 2.91 11.6％ Embryo 3 1 copy Missing 0.88 2.49 10.0％

[0229] Family history can be incorporated to further improve risk estimates in predicting the transmission of disease-associated HLA types. This technique can be extended to predicting blood type from the embryonic genome, including the Rh status of the resulting fetus.

[0230] Example 11: Improving trait prediction accuracy

[0231] When the genotype of the variant in the polygenic model is unknown in the embryo, the parental genotype can be used to improve the trait prediction accuracy. Use the possible genotype probability in view of the parental genotype at the site, rather than the genotype of the population allele frequency (AF) or interpolation. Using the probability in the following table 9, the dosage of each possible genotype is added to the risk score. In practice, this improves the prediction accuracy measured by the prediction percentile of the polygenic risk, as shown in the following table 10, which shows the improvement of the prediction of the polygenic model of Crohn's disease, wherein 4 variants are not predicted in the embryo. The true polygenic risk score percentile ("true value") is determined using direct genotyping from WGS.

[0232] Table 9: Probability of embryo genotype based on parental genotype

[0233] Mother Father P(AA|M,D) P(AT|M,D) P(TT|M,D) AT TT 0 0.25 0.75

[0234] Table 10: Percentiles of polygenic risk scores

[0235] True Value Group AF dose 73.9％ 62.5％ 71.2％

[0236] Example 12: Haplotype Disease Risk

[0237] Some disease risks are based on phased haplotypes rather than individual variants. Embryo reconstruction generates phased haplotypes for more accurate prediction of trait risk. Table 11 below lists haplotypes in the gene APOE and their associated risk for Alzheimer's disease (Corder et al., 1994).

[0238] Table 11: Haplotypes in APOE and associated risk of Alzheimer's disease

[0239] Haplotype rs429358 allele rs7412 allele Risk of Alzheimer's disease ε2 T T Protect ε3 T C neutral ε4 C C risk

[0240] The two variants are 138 bp apart in the APOE gene. Neither rs429358 nor rs7412 were measured in the rare measurements in the embryos. This precludes estimating Alzheimer's disease risk in the embryos. However, the embryo reconstruction method uses the genotypes of the parents to predict a fully phased embryonic genome, which can be used to infer that the embryo is ε3 / ε3. This result was later validated by whole genome sequencing of the born children.

[0241] Table 12: Risk of Alzheimer's disease in reconstructed embryos

[0242] APOE haplotype Risk of Alzheimer's disease Mother ε3 / ε3 neutral Father ε3 / ε3 neutral Reconstructing embryos ε3 / ε3 neutral Embryos without reconstruction Unavailable Unavailable

[0243] Therefore, embryo reconstruction enables APOE haplotype and Alzheimer's disease risk prediction, and disease status in general, based on haplotypes.

[0244] Example 13: Rare genotype scaffolds

[0245] Use of rare genotypes as scaffolds for whole genome phasing (see e.g. Fig.13 ) improves performance compared to the reference panel alone, as measured by the switch error rate (SER). Applying this technique to the well-studied sample NA12878, we see a decrease in the overall SER from 0.6% when using the 1000Genomes reference panel alone to 0.54% when using a set of ~140k high-confidence phased genotypes as scaffolds in combination with the reference panel. This difference is largely due to a reduction in long switch errors. For example, on chromosome 1, there was a >60% reduction in the raw number of long switch errors (169 vs. 60). Overall, the combined approach (scaffold + reference panel) resulted in a reduction in the long switch error rate from 0.12% to 0.04%. This is important in embryo reconstruction, as long switch errors can lead to incorrectly predicted blocks being passed on.

[0246] Example 14: Polygenic risk score

[0247] Large-scale genome-wide association studies (GWAS) have identified genetic variants associated with a wide variety of diseases. These associations have paved the way for functional studies of disease biology, drug target discovery, and improved disease risk prediction. Although individual common genetic variants may have little predictive value, combining these variants into a genetic risk score can explain a greater proportion of the genetic risk for disease. These multi-locus genetic risk scores, also called polygenic risk scores (PRS), are most commonly calculated as a weighted sum of disease-associated genotypes.

[0248]

[0249] PRS indis the polygenic risk score for a given individual and disease with n associated variants, w i is the weight of the ith variant, usually taken from the GWAS effect size, and G i is the individual's genotype for the risk allele of the ith variant. PRSs have recently been investigated for their potential to predict the risk of a variety of diseases, including cardiovascular disease, breast cancer, and type 2 diabetes. These approaches have demonstrated the ability to stratify individuals according to their risk for these diseases.

[0250] Described is a method to validate and implement polygenic models and to visualize risk estimates in consumer reports.

[0251] Choosing a polygenic risk model

[0252] Priority was given to previously published polygenic models for each condition of interest that had been tested on at least 1000 individuals from a broad population. This excluded small studies with limited statistical power and studies tested on isolated populations that may not translate to other populations. Models using data from individuals in the UKBB study set were also excluded. Models that reported an area under the curve (AUC) greater than 0.65 and / or an odds ratio (OR) greater than 2 for individuals in the top quantile to the bottom quantile were selected (see below for more information). A range of traits and published models and their evaluation statistics are shown in Table 13.

[0253] Table 13: Published disease models

[0254]

[0255]

[0256] When published models were not available, scores were constructed using SNPs from the GWAS catalog that met a genome-wide significance p-value threshold (p<5e-8) as previously described (PMID: 30309464).

[0257] Defining each phenotype in UK Biobank

[0258] Each model was validated and standardized using data from the UK Biobank cohort. This resource includes genetic and disease information for 500,000 individuals. Analysis below uses only unrelated individuals. As shown in Table 14, a combination of ICD-9 and ICD-10 codes, self-reported diseases, and procedure codes for defining each phenotype of interest was used.

[0259] Table 14: UKBB phenotype definitions for each trait evaluated

[0260]

[0261]

[0262] A subset of diseases is shown in Table 15 below.

[0263] Table 15: Frequency of disease subsets in UK Biobank

[0264] disease frequency disease frequency Celiac disease 0.62％ Atrial fibrillation 4.29％ Coronary artery disease 6.64％ Breast cancer 3.66％

[0265] Individuals were stratified according to their polygenic risk score (PGS) and the prevalence of the disease in this group was investigated.

[0266] Evaluate the model using the UKBB dataset

[0267] The polygenic risk score was calculated as a weighted sum of disease-associated genotypes. The score was calculated for each individual in the UKBB, and multiple metrics were used to evaluate the performance of the model.

[0268] Distribution of PRS across cases and controls

[0269] The dataset was decomposed into cases and controls for each trait, and distributions of scores were generated separately for cases and controls. Visual inspection of these distributions gave a rough idea of ​​how well each model could distinguish cases from controls. For example, Fig.14 The distribution of PRS for rheumatoid arthritis cases and controls is shown (mean scaled to 0, standard deviation 1).

[0270] Receiver Operating Curve (ROC)

[0271] ROC and area under the curve (AUC) were calculated by plotting the sensitivity and specificity of the model at different risk thresholds.

[0272] Stratification into PRS deciles

[0273] Individuals in UK Biobank were stratified into groups with different risk profiles for disease. Individuals at highest risk (top decile of PRS) were compared to those at median risk (those with a PRS in the middle 40-60 percentiles of the distribution). Disease prevalence was plotted for each disease across deciles, and the ratio of high risk to median risk was calculated across diseases. Fig.15 Shown are the ORs for rheumatoid arthritis by deciles.

[0274] Regression analysis including age and sex

[0275] Logistic regression was applied to each model after calculating the PRS across all unrelated individuals in the UK Biobank dataset.

[0276] β PGSis the regression coefficient of the PRS, corresponding to the odds ratio when the PRS is standardized to mean 0 and standard deviation 1. Age and sex were included where available and applicable.

[0277] LOR|GS=β0+β PRS PRS+β 年龄 Mean(age)

[0278] The odds ratios were then used to determine the threshold for high risk for the intermediate outcome for reporting purposes.

[0279] OR / SD by disease (central z-transformed means)

[0280] Based on the logistic model presented above, the effect sizes were calculated after standardizing the PRS variables (mean 0, standard deviation 1) to obtain the OR / SD of the PRS. This process helps achieve two goals. First, the risk stratification ability of PRSs can be directly compared across diseases. PRSs for different diseases vary in the number of SNPs and their respective effect sizes, and therefore vary on very different scales. Without standardization, their corresponding effect sizes will not be directly comparable. By standardizing all PRSs, models can be directly ranked based on their OR / SD, resulting in a ranking that reflects their ability to stratify populations based on disease risk. Second, it allows for statistically accurate application of UKBB effect estimates to the U.S. population. Effect sizes were estimated using UKBB and then converted to odds ratios. When estimating relative risks from these odds ratios (see below), the U.S. population disease prevalence was used to accurately capture the relative risk for individuals in the U.S. with a given PRS. Standardization of the UKBB PRS (using the UKBB mean and standard deviation) allows the use of PRSs for U.S. individuals in the model (after adjustment with the U.S. PRS mean and standard deviation) to be used in the model. Due to random assortment in genetics, similar means and standard deviations for PRS across populations can be expected, at least for individuals of European ancestry. The results from this analysis are shown in Table 16.

[0281] Table 16: Model validation statistics

[0282]

[0283]

[0284] Disease-to-age PRS stratification

[0285] After stratifying individuals into risk groups, the UKBB data is used to estimate the percentage of people within these different groups who are diagnosed with the disease. This information is visualized across the different strata, including high risk (top 5% of individuals by PRS) and average risk (across the population) groups. It is shown as the predicted percentage of diagnosis for a group of individuals with similar genetic risk to our given individual of interest, assuming the individual of interest has a PRS at the 75th percentile.

[0286] These figures help illustrate the utility of the PRS in stratifying individuals based on disease risk. Seeing the clear distinction in the proportion of groups diagnosed within different PRS strata confirms the model's ability to distinguish individuals based on their risk.

[0287] Calculate adjusted lifetime risk for an individual

[0288] We can start with the average lifetime risk for the sex of Americans. Next, we assess the risk markers in the genome and calculate a polygenic score based on the markers. This information is converted into an "odds ratio" using data from the UKBB described above. Finally, we use a formula that includes this odds ratio and the average lifetime risk to estimate the lifetime risk of an individual with this variation:

[0289]

[0290] Adjusted lifetime risk = c0*RR

[0291] Where p0 is the prevalence of the condition in the UKBB, c0 is the average lifetime risk of the condition in the United States, and OR is the odds ratio calculated above. The result is an estimate of the individual's own lifetime risk compared to the population average. For some conditions, the average lifetime risk is not available. In these cases, indicate whether the genetics analyzed indicate an increased risk.

[0292] Defining the threshold for “high risk”

[0293] In some cases, a threshold for high genetic risk was set based on known risk factors. For example, the relative risk for developing type 1 diabetes in an individual with an affected first-degree relative is 6.6. Therefore, the high risk threshold for the PRS for type 1 diabetes was set to correspond to this relative risk. For phenotypes where this was not available or the model could not achieve a threshold, we assigned individuals with a 2-fold increased relative risk or a 10% increased absolute risk as high risk. The assessment measures for the subset of phenotypes where lifestyle or clinical factors informed the high risk threshold are shown in Table 17.

[0294] Table 17: Evaluation of the model in a subset of unrelated UKBB individuals

[0295]

[0296]

[0297] Example 15: Multifactorial Condition (Polygenic Risk Score)

[0298] Genomic DNA obtained from the submitted samples was sequenced using Illumina or BGI technology. Reads were aligned to the reference sequence (hg19) and sequence changes were identified. For some genes, only specific changes were analyzed. Deletions and duplications were not checked unless otherwise stated above. In some cases, independent verification of HLA types may have been performed by external laboratories. Selected variants were annotated and interpreted in accordance with ACMG (American College of Medical Genetics) guidelines. Only pathogenic or likely pathogenic variants were reported. Embryo and parent genotyping and subsequent "parent support" analysis were performed. The embryonic genome was reconstructed using the embryonic genotype and the parental whole genome sequence using a genome reconstruction algorithm. Only variants observed in the parental genome that were predicted to have an effect on the embryo were checked in the reconstructed embryonic genome. For a subset of conditions, polygenic risk scores were calculated. Models for each condition were evaluated on the UK Biobank population. Some polygenic risk scores can be refined using HLA types. The lifetime risk of an individual is calculated by adjusting the baseline risk (in the US population) according to its demographic information and polygenic risk score. Models that resulted in a 10% lifetime risk difference or a 1.9-fold increase in lifetime risk from the first to the last decile were included in the report. Some conditions (e.g., bipolar disorder) were retained in the experimental section at the investigators' discretion based on available evidence for model and genomic reconstruction performance. The lifetime risk of each condition for a given embryo was Fig.16A -C is listed.

[0299] Using psoriasis as a specific example, Fig.17A -B shows the risk scores associated with susceptibility to psoriasis in three exemplary embryos.

[0300] Example 16: Whole-genome prediction of embryos using haplotype-resolved genome sequences

[0301] The haplotype-resolved genome sequencing is combined with a set of rare genotypes from a single or few cell embryo biopsy of the embryo to predict the whole genome sequence of the embryo. Specifically, the stLFR technology is used for haplotype-resolved genome sequencing of the father. Performance is evaluated at rare heterozygous positions (defined as allele frequencies of 1% or less). The inheritance of 230,117 sites is predicted in embryos with 89.5% accuracy.

[0302] The material used in this study was obtained retrospectively from participants who had previously undergone a successful round of IVF with preimplantation genetic diagnosis (Table 16). Trophectoderm biopsies from a total of 10 embryos (day 5) were genotyped across a panel of 300,000 common SNPs using an accelerated 24-hour microarray protocol for each biopsy. In addition, each parent and all four grandparents were genotyped across the same panel.

[0303] Table 16: Tissue samples used for proof of concept

[0304]

[0305] Genomic DNA was extracted from whole blood or saliva samples. Newborn and maternal DNA was processed using 30X WGS on the BGI platform. Paternal samples were processed using stLFR. DNA was extracted, amplified, and genotyped along with parents and grandparents from a trophectoderm biopsy from one of 10 day 5 embryos using a rapid microarray protocol across all samples using the Illumina CytoSNP-12 chip. Sibling embryo and parental SNP array measurements were combined using the "Parental Support" (PS) method ( Fig.18 , 19), as described in detail in Kumar et al., 2015. The whole genome sequence of the embryo is predicted by combining the PS embryo genotype with the parental haplotype blocks (see Fig.18 ).

[0306] Example 17: Construction of whole chromosome haplotypes from haplotype blocks and parental information

[0307] To construct chromosome length haplotypes in the IVF setting, haplotype-resolved genome sequencing of both parents is combined with information from sparse genotypes of sibling embryos. As part of the "Parent Support" (PS) approach, maximum likelihood estimates (MLEs) of heterozygous SNVs in each parent are created by combining recombination frequencies from the HapMap database with SNP array measurements from the parents and SNP array measurements from sibling embryos. This sparse, chromosome length haplotype is not sufficient to predict the genome of the embryo, but can be combined with dense haplotypes obtained molecularly from parental samples (e.g. using long fragment read technology, 10x Genomics, CPT-seq, Pacific Biosciences, Hi-C) to predict the inherited genome sequence.

[0308] This information is obtained using several data streams. To generate dense haplotype blocks, shotgun sequencing with 34x and 30x median coverage was first performed on the mother and father, respectively. Next, 94.2% of the 1.94 million heterozygous SNVs in the mother and 92.4% of the 1.89 million heterozygous SNVs in the father were directly phased as long haplotype blocks by sequencing a haploid subset of genomic DNA obtained by in vitro dilution pool amplification. The "dense haplotype blocks" obtained by these molecules are combined with rare, but chromosome-length haplotypes to construct chromosome-length haplotype-resolved genomic sequences of the parents. This sequence information is then used to predict the inherited genomic sequence of the embryo, but can also be used to predict the potential offspring of both parents (for example, by simulating the potential eggs and sperm that will produce future children).

[0309] A potential workflow for genome-wide prediction of embryos is Fig.19 At the initial visit, the patient gives blood, which is used to generate a whole genome sequence for each parent and used to predict possible conditions that the couple is at risk for. After counseling, the parents undergo IVF, and the embryos are genotyped using conventional IVF PGD technology, combining this information with the parents' whole genome sequence information (haplotype resolved) to predict the embryo's inherited genome and assess disease risk.

[0310] The chromosome length parental haplotypes were constructed using sibling embryos and parental genotypes. The phase of the parents was determined using statistical methods (e.g., maximum likelihood estimation) based on the noise information obtained from each sibling embryo and a database of meiotic recombination frequencies.

[0311] Whole chromosome haplotype construction

[0312] Whole chromosome haplotypes are constructed by sequencing the genomes of the individual's relatives (including but not limited to parents, grandparents or children). If the individual has two or more children from the same person, the whole chromosome haplotype of the individual can be obtained by performing whole genome sequencing on the individual, his or her partner and two or more children and determining the loci inherited by each child. Fig. 20 ). This will provide haplotype information based on the whole chromosome without modifying the DNA sequencing process. This can be important, for example, in a situation where a couple already has two children and wishes to have another child, and does not have any grandparent DNA samples.

[0313] Chromosomal haplotypes from individual sperm

[0314] The method of Example 17 was performed using whole chromosome haplotypes obtained by sequencing DNA obtained from individual sperm.

[0315] Example 18: Using embryonic genomic predictions to calculate polygenic risk scores for complex genetic diseases

[0316] Genome-wide association studies can model polygenic risk scores for conditions such as type 1 diabetes, schizophrenia, Crohn's disease, celiac disease, Alzheimer's disease, and others. These approaches involve taking a list of genome-wide significant SNPs with observed odds ratios for disease-associated SNPs and calculating a "risk score" for each individual based on the set of SNPs seen in the individual. This approach was used to calculate polygenic risk scores for siblings to mimic the polygenic risk scores seen when comparing sibling embryos in an IVF cycle. Genome sequences from a publicly available family were used, with 12 siblings, two parents, and four grandparents. Each genomic variant file (VCF file) was converted to a PLINK file and the plink –score command was used on the variant table to calculate a polygenic risk score for each individual in the family. Polygenic risk scores were calculated for each sibling and both parents. Polygenic risk scores were also calculated for each individual in the 1000Genomes cohort (approximately 2500 individuals) and for a subset of Caucasian individuals (approximately 200-300 individuals). The polygenic risk score of each family member was compared to that of a group of population-matched (European) individuals to determine whether the individual was at high or low risk.

[0317] A polygenic risk score for celiac disease has been developed in a Caucasian population incorporating multiple SNPs (Abraham et al., 2014; PMC PMC3923679). The model has high sensitivity for celiac disease, and the negative predictive value of the approach can be calculated at a certain PRS threshold. Assuming a family history of celiac disease, we estimated that the negative predictive value at a specific PRS (less than -1) was 99.4%. After calculating the PRS for each individual, two individuals had a PRS less than this threshold. In the IVF setting, we estimated that these two embryos could be selected for implantation with an approximately 10-fold reduction in disease risk.

[0318] A polygenic risk score for Alzheimer's disease has been previously established and found to be associated with early onset of Alzheimer's disease (Desikan et al., 2017; PMC5360219; Table 2). The parental PRS is shown as a dark blue dashed line. The PRS for each embryo is shown as a gray dashed line. After calculating the PRS for each individual, the individual with the lowest polygenic risk score was predicted to have a reduced risk of Alzheimer's disease compared to the embryo with the highest polygenic risk score (median age of onset 87 years instead of 80 years).

[0319] Table 17: Single nucleotide polymorphisms used to construct a polygenic risk score for Alzheimer's disease

[0320]

[0321]

[0322] Example 19: Correlation calculation

[0323] Use the embryo genotype to calculate the correlation index of the individual with the adverse genetic trait. For example, consider that the maternal grandparent has schizophrenia. Step 1: After inferring the embryo genome from Examples 1 and 2, calculate the correlation between each embryo and the genome of the affected individual. Step 2: Select the embryo with the lowest correlation with the affected individual.

[0324] Example 20: Predicting disease risk via IBD (Identity by Descent) using computational genetic correlations

[0325] An extension of Example 3 in which IBD is used instead of the genetic relevance of the affected individual in the disease prediction. Since various sibling embryos will have different IBDs than the affected family relatives, this information can be used in addition to the PRS score to further refine the probability of the embryo's disease risk. The following example assumes that the risk of disease is evenly spread throughout the genome of the affected individual, so the risk is linear with the degree of IBD in the affected individual.

[0326] log(P / (1-P))=β_1*PRS+β_2*sex_male+β_3*family history_presence+β_4*IBD_affected individuals.

[0327] Example 21: Regions of shared genomic information

[0328] Identify regions of shared genetic information between two individuals and select embryos that do not contain homozygous regions that increase the odds of a Mendelian condition. In consanguineous couples or couples with a shared genetic background, there is a chance that the offspring will be homozygous for disease-causing regions. Since genes with known disease associations are spread heterogeneously throughout the genome, disease can be minimized by avoiding homozygous regions within known disease-causing regions of the genome. Step 1: Identify regions of shared genetic information between the two parents. Step 2: Calculate the fraction of homozygous regions in each embryo. Step 3: Select embryos with the lowest homozygous regions overall or across known disease-causing regions.

Claims

1. A method for determining a combined disease risk associated with an embryo, the method comprising: (a) performing whole genome sequencing on a biological sample obtained from the paternal subject to identify a genome associated with the paternal subject; (b) performing whole genome sequencing on a biological sample obtained from the maternal subject to identify a genome associated with the maternal subject; (c) phasing the genome associated with the paternal subject to identify the paternal haplotype; (d) phasing the genome associated with the maternal subject to identify the maternal haplotype; (e) performing rare genotyping on the embryo to identify one or more genetic variants in the embryo; (f) constructing a genome of the embryo based on (i) one or more genetic variants in the embryo, (ii) a paternal haplotype, (iii) a maternal haplotype, (iv) a probability of transmission of the paternal haplotype, and (v) a probability of transmission of the maternal haplotype; (g) assigning a polygenic risk score to the embryo based on the constructed genome of the embryo, wherein the polygenic risk score is determined by summing the effects across multiple loci in the disease model; (h) determine the transmission of genetic variants and / or paternal haplotypes and / or maternal haplotypes causing single gene diseases to the embryo; and (i) determining a combined disease risk associated with the embryo based on (g) a polygenic risk score and (h) transmission of genetic variants and / or paternal haplotypes and / or maternal haplotypes causing the single gene disease to the embryo.

2. A method for outputting a combined disease risk associated with an embryo, the method comprising: (a) receiving a first data set comprising paternal genomic data and maternal genomic data; (b) aligning sequence reads to a reference genome and determining genotypes across the genome using paternal genome data and maternal genome data; (c) receiving a second data set comprising paternal and maternal sparse genomic data; (d) phasing the paternal genomic data and the maternal genomic data based on the paternal and maternal sparse genomic data to identify the paternal haplotype and the maternal haplotype; (e) receiving a third data set comprising sparse genomic data of the embryo, a paternal transmission probability, and a maternal transmission probability; (f) applying an embryo reconstruction algorithm to (i) the paternal haplotype and the maternal haplotype, (ii) the sparse genomic data of the embryo, and (iii) the transmission probability of each of the paternal haplotype and the maternal haplotype to determine a constructed genome of the embryo; (g) applying the polygenic model to the constructed genome of the embryo to assign a polygenic risk score; (h) determine the disease-causing genetic variants and / or the transmission from the paternal haplotype and / or the maternal haplotype to the embryo; and (i) outputting a combined disease risk associated with the embryo based on (g) the polygenic risk score and (h) the transmission of genetic variants and / or paternal haplotypes and / or maternal haplotypes causing the single gene disease to the embryo.

3. The method of claim 1 or 2, wherein the method further comprises using the grandfather's genomic data and / or the grandmother's genomic data to determine the paternal haplotype and / or the maternal haplotype.

4. The method of claim 1 or 2, wherein the method further uses population genotype data and / or population phenotype data to determine the disease risk of the embryo.

5. The method of claim 1 or 2, wherein the method further uses family history of the disease to predict disease risk.

6. The method of claim 1, wherein whole genome sequencing is performed using a standard protocol, a PCR-free protocol, a ligated read protocol, or a long read protocol.

7. The method of claim 6, wherein the ligated read scheme is a synthetic long read scheme.

8. The method of claim 1, wherein rare genotyping is performed using microarray technology, next generation sequencing of embryo biopsies, or cell culture medium sequencing.

9. The method of claim 1 or 2, wherein the phase separation is performed using a population-based and / or a molecular-based approach.

10. The method of claim 9, wherein the molecular-based method is ligation reading.

11. The method of claim 4, wherein the population genotype data comprises allele frequencies and individual genotypes of at least 300,000 unrelated individuals in UK Biobank.

12. The method of claim 4, wherein the population phenotype data comprises both self-reported phenotypes and clinically reported phenotypes of at least 300,000 unrelated individuals in UK Biobank.

13. The method of claim 12, wherein the clinically reported phenotype is an ICD-10 code.

14. The method of claim 4, wherein the population genotype data comprises population family history data comprising self-reported data of at least 300,000 unrelated individuals in UK Biobank and information derived from relatives of said unrelated individuals in UK Biobank.

15. The method of claim 14, wherein disease risk is further determined by the fraction of genetic information shared by affected individuals.

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