A METHOD FOR ESTABLISHING AN EPIGENETIC CLOCK FOR BIRD SPECIES

Abstract

The invention pertains to a computer-implemented method for establishing an epigenetic clock for a bird species. The method comprises (a.) identifying and determining the methylation levels of specific CpG sites within genomic DNA obtained from a plurality of different biological sample materials derived from the bird species and representing specific points in time within the chronological lifespan of this bird species, (b.) excluding all CpG sites associated with single nucleotide polymorphisms (SNPs) from the CpG sites identified in step (a.), (c.) excluding all CpG sites located on the sex chromosomes (Z and W) from the CpG sites obtained in step (b.), (d.) performing a tissue-specific normalization step for the CpG sites obtained in step (c.), and (e.) correlating the CpG methylation levels of the CpG sites obtained in step (d.).) with chronological age using a penalized regression model.

Description

A METHOD FOR ESTABLISHING AN EPIGENETIC CLOCK FOR BIRD SPECIES FIELD OF INVENTION The present invention describes methods for establishing whole-tissue epigenetic clocks for bird species. The epigenetic clocks thus obtained are particularly robust, generalizable, and provide high specificity, accuracy, and precision. BACKGROUND OF THE INVENTION Bird species, and Galliformes in particular, such as chickens (Gallus gallus), are a significant source of commercially produced meat and eggs. Factors influencing growth, pathogen resistance, and meat quality in chickens are therefore of considerable scientific and economic interest. Extensive genome-wide association studies have been conducted to determine the underlying genetic framework. Epigenetic modifications provide an important complement and extension to genetic variants but have remained relatively unexplored in chickens. Animal methylomes can be highly diverse, ranging from certain insect genomes with sparse methylation patterns and only tens of thousands of methylation marks to mammalian genomes with dense methylation patterns and tens of millions of methylation marks. To date, little is known about DNA methylation patterns across the genomes of non-mammalian vertebrates, and particularly birds. DNA methylation is correlated with aging processes and represents an epigenetic modification with a high specificity of CpG dinucleotides (5'-C-phosphate-G3'), that is, regions of DNA where a cytosine nucleotide is followed by a guanine nucleotide in the linear sequence of bases along its 5' -> 3' direction. The set of genomic methylation modifications constitutes the methylome of a given cell. Low-methylation regions (LMRs) represent a key feature of the dynamic methylome. LMRs are local reductions in the DNA methylation landscape and represent CpG-poor distal regulatory regions that frequently reflect the binding of transcription factors and other DNA-binding proteins. LMRs were originally described in mice (Stadler et al. Nature 480, 490-495 (2011)). The evolutionary conservation of LMRs beyond mammals has remained unexplored. Age-correlated DNA methylation changes have been identified in discrete sets of CpGs in the human genome and used to predict age (Horvath, S. (2013). DNA methylation age in human tissue and cell types. Genome Biology 14:3156). These “epigenetic clocks” can estimate DNA methylation age in tissue-specific or tissue-independent ways and can predict mortality and time of death. Epigenetic age is highly correlated with chronological age but can also respond to environmental factors that accelerate or decelerate aging processes, resulting in substantial deviations from chronological age. Accelerated epigenetic aging (epigenetic age > chronological age) suggests that the underlying tissue ages faster than expected based on chronological age, while a negative value (epigenetic age < chronological age, decelerated aging) suggests that the tissue ages more slowly than expected. Accelerated epigenetic aging is associated with a greater number of age-related conditions and diseases, such as inflammatory processes. Given the conditions that accelerate biological / epigenetic aging, age-correlated performance biomarkers are particularly useful tools for animal husbandry, as they facilitate the monitoring of large groups of animals and provide objective quality assurance. Bird species present a unique challenge for the development of performance biomarkers, as they combine considerable economic importance with a relatively short lifespan. Accordingly, the objective of the present invention was to provide a method for establishing a whole-tissue epigenetic clock for bird species that can be used as a performance biomarker for the respective bird species and that provides robustness and generalizability while also providing high specificity, accuracy, and precision. BRIEF DESCRIPTION OF THE INVENTION The present invention provides a computer-implemented method for establishing an epigenetic clock for a bird species. The method comprises (a.) identifying and determining the methylation levels of specific CpG sites within genomic DNA obtained from a plurality of different biological sample materials derived from bird species and representing specific points in time within the chronological lifespan of these bird species, (b.) excluding all CpG sites associated with single nucleotide polymorphisms (SNPs) from the CpG sites identified in step (a.), (c.) excluding all CpG sites located on the sex chromosomes (Z and W) from the CpG sites obtained in step (b.), (d.) performing a tissue-specific normalization step for the CpG sites obtained in step (c.), and (e.) correlating the CpG methylation levels of the CpG sites obtained in step (d.).) with chronological age using a penalized regression model. In addition, a computer program is provided, loaded into a computer's memory, which implements the method mentioned above. eenRnn / zznz / E / YiAi Finally, the present invention pertains to a tangible, computer-readable medium comprising a computer-readable code that, when executed by a computer, causes the computer to perform the following operations: (a.) receive information corresponding to the methylation levels of specific CpG sites within the genomic DNA of bird species obtained from a plurality of different biological sample materials representing specific points in time within the chronological lifespan of the bird species, (b.) receive information corresponding to all CpG sites associated with single nucleotide polymorphisms (SNPs) and exclude them from the CpG sites of step (a.), (c.) receive information corresponding to all CpG sites of the sex chromosomes (Z and W) and exclude them from the CpG sites of step (b.), (d.) perform a tissue-specific normalization step for the CpG sites of step (c.), and (e.) correlate the CpG methylation levels of the CpG sites of step (d.) with chronological age using a penalized regression model. DETAILED DESCRIPTION OF THE INVENTION The inventors have identified three unknown confounding factors for methylation clocks for bird species: Genetic polymorphisms are known to strongly affect epigenetic association studies. For example, a genetic polymorphism at a CpG site results in a sequence that can no longer be methylated and will therefore be classified as unmethylated. However, the underlying effect does not represent an age-related change but rather a confounding factor. - Sex chromosomes are known to contain sex-specific methylation marks that facilitate compensation for heterogametic sex chromosome dosage. This effect can confound the identification of age-related methylation changes. Different tissues exhibit different stages of maturation at birth and at different rates with age. Therefore, normalizing aging trajectories can substantially improve the performance and robustness of a multi-tissue watch. The inventors have found that the robustness, specificity, accuracy, and precision of epigenetic clocks for bird species can be significantly improved by (i) excluding from the CpG sites of the initially identified clock all CpG sites associated with single nucleotide polymorphisms (SNPs), (ii.) excluding all CpG sites located on the sex chromosomes (Z and W), and (ii.) normalizing the CpG methylation values. Accordingly, the present invention provides a computer-implemented method for establishing an epigenetic clock for bird species. The method comprises (a.) identifying and determining the methylation levels of specific CpG sites within genomic DNA obtained from a plurality of different biological sample materials derived from bird species and representing specific points in time within the chronological lifespan of these bird species, (b.) excluding all CpG sites associated with single nucleotide polymorphisms (SNPs) from the CpG sites identified in step (a.), (c.) excluding all CpG sites located on the sex chromosomes (Z and W) from the CpG sites obtained in step (b.), (d.) performing a tissue-specific normalization step for the CpG sites obtained in step (c.), and (e.) correlate the CpG methylation levels of the CpG sites obtained in step (d.) with chronological age using a penalized regression model. The term “CpG site,” “clock CpG,” or “CpG location,” as used in the context of the present invention, refers to a CpG position that is potentially methylated. Methylation typically occurs in a nucleic acid containing CpG. The CpG-containing nucleic acid may be present in, for example, a CpG island, a CpG doublet, a promoter, an intron, or an exon of a gene, or in an intergenic region. For example, potential methylation sites may encompass the promoter / enhancer regions of the indicated genes. According to step (a) of the method, the CpG sites within the genomic DNA of bird species were identified and the methylation level of those CpG sites was determined. Therefore, step (a) of the method involves a DNA methylation profiling process, preferably bisulfite sequencing. There, cytosine residues in genomic DNA are transformed into uracil, while 5-methylcytosine residues in genomic DNA are not transformed into uracil. Whole-genome bisulfite sequencing is a genome-wide DNA methylation analysis based on the sodium bisulfite conversion of genomic DNA, which is then sequenced on a next-generation sequencing platform. The sequences are then realigned to the reference genome to determine the methylation states of CpG dinucleotides based on discrepancies resulting from the conversion of unmethylated cytosines to uracil. For example, methylation levels can be measured using the commercial Illumina™ platform. To quantify the level of methylation, several established protocols can be used to calculate the beta methylation value, which is equal to the fraction of methylated cytosines at a specific site. The specific CpG located within the genomic DNA of bird species can be distributed across the entire genome (“genome-wide clock”) or can be localized within MRLs (“MRL clock”). Details for establishing the genome-wide clock and the MRL clock, respectively, are provided below. Genomic DNA obtained from a plurality of different sample materials derived from bird species and representing specific points in time within the chronological lifespan of these bird species. As an example, the sample material can be stratified into four tissues (breast, ileum, spleen, and jejunum) and three age groups (3 days, 15 days, and 34 days). Details regarding suitable sample materials will be provided below. Ideally, the sample material covers the entire life cycle of the bird species under investigation. In step (b) of the method, all CpG sites associated with single nucleotide polymorphisms (SNPs) were excluded from the CpG sites identified in step (a). SNPs can be determined using standard procedures known in the state of the art, such as whole-genome sequencing. Alternatively, SNPs in the genomes of selected species are publicly available in databases, such as dbSNP (https: / / www.ncbi.nlm.nih.gov / snp / ). In step (c) of the method, all CpG sites located on the sex chromosomes (Z and W) were excluded from the CpG sites obtained in step (b). Birds exhibit female heterogamy with the sex chromosomes Z and W (https: / / www.ncbi.nlm.nih.gov / pmc / articles / PMC2567362 / ). Chromosome names are usually annotated in a species' genome assembly. As an example for the chicken (Gallus gallus), the chromosomal location of a CpG can be derived from the annotation in version 5.0 of the Gallus gallus genome assembly (https: / / www.ebi.ac.Uk / ena / data / view / GCA_000002315.3). In step (d) of the method, a tissue-specific normalization step is performed for the CpG sites obtained in step (c). Normalization is carried out by calculating, for each CpG, the average methylation value across all samples of the same tissue and subtracting this value from the CpG value (for the LMR clock: calculating, for each LMR, the total average methylation value across all samples of the same tissue and subtracting this value from the LMR value). This normalization is necessary for the different aging pathways of individual tissues. The CpG sites obtained in step (d), i.e., the CpG sites that remain after correction for the confounding factors mentioned above, were finally correlated with chronological age using a penalized regression model (method step (e)). eenAnn / zznz / E / YiAi The plurality of different biological sample materials derived from bird species and representing specific points in time within the chronological lifespan of these bird species may include material selected from the group consisting of body fluids, excreted material, tissue material, and feather material. In one embodiment of the invention, the plurality of different biological sample materials derived from bird species and representing specific points in time within the chronological lifespan of these bird species include only one specific tissue or at most four different tissues. Preferably, the plurality of different biological sample materials derived from bird species and representing specific points in time within the chronological lifespan of these bird species include at least four different tissues and preferably exactly four different tissues. In one embodiment of the present invention, the plurality of different biological sample materials derived from bird species and representing specific points in time within the chronological lifespan of these bird species comprise or consist of selected tissue material from muscle tissue; organ tissue, such as gut tissue; and skin tissue. Preferably, the plurality of different biological sample materials derived from bird species and representing specific points in time within the chronological lifespan of these bird species include or consist of breast tissue, spleen tissue, ileum tissue, and jejunum tissue. The aforementioned tissue set is particularly preferred and represents a biologically diverse and commercially relevant set of tissues. The plurality of different biological sample materials derived from the bird species and representing specific points in time within the chronological life expectancy of these bird species is preferably selected to represent ages fluctuating between day 3 and day 63, in particular between day 4 and day 42 and preferably between day 5 and day 35. For example, the chicken life cycle begins with eggs taken from breeding hens and placed in an incubator. These eggs are then incubated at a constant temperature for 21 days until the chicks hatch, although at this stage, the chicks can be as young as 72 hours old, hence the name "day-old chick." These chicks are separated by sex, and the females are kept for approximately one year to lay eggs. The lifespan of broiler chickens is significantly short, ranging from 21 to 170 days. An average American broiler is slaughtered after 47 days at a slaughter weight of 2.6 kg, although in Europe the average slaughter age is 42 days (at a weight of 2.5 kg). eenRnn / zznz / E / YiAi Establishment of a genome-wide clock (“CpG clock”) As previously stated, specific CpG sites within the genomic DNA of the bird species can be distributed across the entire genome of the bird species (“genome-wide clock”). In this case, the CpGs were preferably restricted to a specific strand coverage of at least 10. Establishment of an LMR Clock In an alternative approach, specific CpG sites within the bird species' genomic DNA are distributed within low methylation regions (LMRs) in the bird species' genome. In this case, method step (a) includes calculating the LMRs individually for different tissues. Suitable LMR computer programs are known in the state of the art, for example MethyISeekR (Burger L, Gaidatzis D, Schubeler D, Stadler MB. Identification of active regulatory regions from DNA methylation data. Nucleic Acids Res 41, e155 (2013)). To establish the LMR clock, specific CpG sites within the bird species' genomic DNA were preferably restricted to a specific chain coverage of at least greater than 5. An LMR clock allows for the conceptual interpretation of selected features, since LMRs represent transcription factor binding sites. This is a significant advantage compared to all-CpG clocks. Furthermore, LMR clocks are more robust to noise, and the features represent averages over regions, thus canceling out noise. In addition to the above, the present invention pertains to a computer program loaded into a computer memory, which implements any of the methods described above. Finally, the present invention relates to a tangible, computer-readable medium comprising a computer-readable code that, when executed by a computer, causes the computer to perform the following operations: (a.) receive information corresponding to the methylation levels of specific CpG sites within the genomic DNA of the bird species obtained from a plurality of different biological sample materials that represent specific points in time within the chronological lifespan of the bird species, (b.) receive information corresponding to all CpG sites associated with single nucleotide polymorphisms (SNPs) and exclude them from the CpG sites of step (a.), (c.) receive information corresponding to all CpG sites of the sex chromosomes (Z and W) and exclude them from the CpG sites of step (b.), (d.) perform a tissue-specific normalization step for the CpG sites of step eenAnn / zznz / E / YiAi (c.) and (e.) correlate the CpG methylation levels of the CpG sites of step (d.) with chronological age using a penalized regression model. The applications of the methods according to the invention are for the exemplary development of new epigenetic clocks as biomarkers (i) that help assess the health status of the bird species (individuals or populations) (ii) that monitor the progress or recurrence of clinical or subclinical disorders or (ii) that study the effects of drugs, food compounds and / or special diets on biological age - and thus the health status of the respective bird species. Examples METHODS Samples The animals were stratified into groups of four tissues (breast, ileum, spleen and jejunum) and three ages (3 d, 15 d, 34 d), in the case of the jejunum 14 d, 16 d and 35 d. For each of these 12 groups, DNA was prepared from three independent animals, resulting in 36 genomic DNA samples. Whole genome bisulfite sequencing Whole-genome bisulfite sequencing libraries were prepared using the Swift Biosciences Accel-NGS Methyl-Seq DNA Library Kit. Two sequencing libraries were barcoded over the sequencing channel. Sequencing was performed on an Illumina HiSeq X platform using a standard paired-end sequencing protocol with a read length of 105 nucleotides. Reading Mapping The reads were cut and mapped using BSMAP 2.5 (X¡Y, L¡W. 2009. BSMAP: whole genome bisulfite sequence MAPping program. BMC Bioinformatics 10:232. doi:10.1186 / 1471 2105-10-232) with the Gallus gallus genome assembly version 5.0 (https: / / www.ebi.ac.Uk / ena / data / view / GCA_000002315.3) as the reference sequence. Duplicates were removed using the Picard tool (http: / / broadinstitute.github.io / picard). Methylation relationships were determined using a Python script (methratio.py) distributed with the BSMAP package by dividing the number of reads that have a methylated CpG at a certain genomic position by the total number of reads covering that position. Normalization and SNP filtering of methylation data All CpGs listed as SNPs in the dbSNP database (https: / / www.ncbi.nlm.nih.gov / snp / ) for the Gallus gallus genome were filtered out. All CpG and LMR assignments to the Galliform sex chromosomes W and Z were filtered out and removed from the eenAnn / zznz / E / YiAi datasets. For genome-wide clock analysis, the analysis was restricted to CpGs that showed a specific strand coverage of more than 10 in each of the sequenced samples, resulting in a set of 257,913 CpGs. The data were then normalized by calculating the average methylation value for each CpG across all samples from the same tissue and subtracting this value from the methylation value of that CpG. For the LMR clock, the analysis was restricted to CpG within the low methylation region that showed a specific chain coverage of more than 5 in each of the sequenced samples, resulting in a set of 67,651 LMRs.The average methylation values ​​of these LMRs were calculated and normalized by calculating for each LMR the average value over all samples of the same tissue and subtracting this value from the value of this LMR. Establishing a chicken DNA methylation clock A penalized regression model (implemented in the R package glmnet [https: / / cran.r-project.org / web / packages / glmnet / ]) was then applied to regress the chronological age of the animals on the normalized methylation values ​​of the CpG probes. In the case of the LMR clock, a penalized regression model was applied to regress the chronological age of the animals on the normalized average methylation values ​​of the LMRs. RESULTS Clock across the genome The alpha parameter of glmnet was varied between 0 and 1 and chosen as 0.7 (net elastic regression) because this value resulted in a fit that was close to the best fit and a manageable number of CpGs. The lambda value was chosen using cross-validation on the training data as 0.4016. This identified a set of 45 CpGs along with corresponding beta values, which define the weights for those CpGs used in the chicken methylation clock. The mean squared error of 6 times the cross-validation using the values ​​of 0.7 for alpha and 0.4016 for lambda was 11.538. This indicates that a new sample can be predicted with an error of approximately 3.4 days. To apply the clock to a new sample, the methylation ratios of this sample in 45 clock CpGs must be provided, and the predict.cv command of the glmnet package must be run with the trained clock. Figure 1 shows the mean squared error of a clock trained for a given alpha at the lambda value that leads to the minimum error. Figure 2 shows the CpG number for the alpha given at the lambda value that leads to the minimum error. Table 1: Clock CpG (genome-wide methylation, alpha = 0.7, lambda = 0.4016, #CpG: 45). 1: Correction factors for different tissues. The respective value must be subtracted. eenAnn / zznz / E / YiAi ID chromosome position weight ileum 1 Spleen 1 Breast 1 Jejunum 1 1 chr1 26806096 -0.333 0.636 0.475 0.464 0.64 2 chr1 27051068 -1.207 0.363 0.124 0.445 0.235 3 chr1 79412910 -3.879 0.467 0.438 0.573 0.414 4 chr1 193007724 -0.894 0.504 0.181 0.398 0.44 5 chr2 84879641 2.595 0.381 0.665 0.191 0.415 6 chr2 139780944 -0.004 0.32 0.198 0.053 0.182 7 chr3 9654592 -2.179 0.503 0.328 0.698 0.589 8 chr3 23119819 -2.285 0.282 0.251 0.31 0.292 9 chr3 32240754 2.209 0.256 0.244 0.148 0.264 10 chr3 55893779 -3.285 0.528 0.563 0.673 0.564 11 chr3 55933564 -0.301 0.335 0.302 0.649 0.165 12 chr4 20608622 -0.825 0.547 0.512 0.554 0.728 13 chr4 48345505 0.468 0.285 0.435 0.239 0.304 14 chr4 70292571 -0.001 0.254 0.235 0.561 0.332 15 chr5 1942965 3.015 0.268 0.532 0.178 0.322 16 chr5 1942982 2.248 0.334 0.562 0.174 0.397 17 chr5 12844701 -0.238 0.583 0.435 0.711 0.691 18 chr5 16850281 1.412 0.651 0.784 0.654 0.723 19 chr5 17507391 -3.468 0.261 0.197 0.115 0.351 20 chr5 39037892 1.739 0.476 0.506 0.379 0.61 21 chr5 54227250 -1.625 0.225 0.358 0.361 0.28 22 chr5 58662889 5.718 0.46 0.621 0.364 0.503 23 chr6 5240214 -0.287 0.262 0.317 0.196 0.213 24 chr6 7819244 4.26 0.209 0.511 0.234 0.188 25 chr6 12024016 -2.447 0.662 0.24 0.575 0.515 26 chr6 12065954 1.12 0.286 0.388 0.249 0.325 27 chr7 9815074 -5.1 0.726 0.46 0.738 0.655 28 chr7 11137846 -0.002 0.367 0.286 0.587 0.326 29 chr7 14040077 -1.945 0.431 0.309 0.357 0.366 30 chr7 21995171 -2.653 0.192 0.057 0.244 0.137 31 chr7 30586853 0.837 0.335 0.391 0.176 0.501 32 chr8 3444574 1.024 0.255 0.654 0.388 0.256 33 chr8 8196471 0.618 0.56 0.802 0.691 0.565. cenAnn / zznz / E / YiAi 34 chr8 18912606 -1.112 0.442 0.333 0.599 0.542 35 chr8 27250408 -0.755 0.473 0.413 0.394 0.735 36 chr10 20035839 -0.002 0.251 0.14 0.142 0.234 37 chr11 7627454 0.396 0.593 0.601 0.222 0.672 38 chr14 9143159 -3.085 0.519 0.34 0.564 0.355 39 chr14 9143204 -2.843 0.678 0.401 0.615 0.388 40 chr15 201524 6.892 0.596 0.634 0.3 0.559 41 chr15 8945553 -13.223 0.766 0.724 0.87 0.542 42 chr17 1673086 -0.441 0.616 0.305 0.472 0.669 43 chr19 7327224 5.149 0.657 0.492 0.266 0.648 44 chr23 172291 -0.279 0.646 0.538 0.562 0.479 45 chr23 5568087 -1.692 0.277 0.183 0.18 0.255 Y-intercept of the linear model equation found by glmnet: 17.365 eenRnn / zznz / E / YiAi LMR Watch Example 1: The alpha parameter of glmnet was varied between 0 and 1 and chosen as 0.84 (net elastic regression) because this value resulted in a fit close to the best fit and a manageable number of MRLs. The lambda value was chosen using cross-validation on the training data as 0.3194. This identified a set of 39 MRLs along with their corresponding beta values, which define the weights for those MRLs used in the chicken methylation clock. The mean squared error of 6 times the cross-validation using the values ​​of 0.84 for alpha and 0.3194 for lambda was 13.4831. This indicates that a new sample can be predicted with an error of approximately 3.7 days. To apply the clock to a new sample, the methylation ratios of this sample at the 39 clock MRLs must be provided, and the `predict.cv` command of the glmnet package must be run with the trained clock. Figure 3 shows the mean squared error of a clock trained for a given alpha at the lambda value that leads to the minimum error. Figure 4 shows the number of LMRs for a given alpha at the lambda value that leads to the minimum error. Table 2: Clock CpG (MRL methylation, alpha = 0.84, lambda = 0.3194, #MRL: 39). 1: Correction factors for different tissues. The respective value must be subtracted. ID chrom Start end weight ileum 1 Spleen 1 Breast 1 Jejunum 1 1 chr1 44395372 44398932 -11.474 0.085 0.119 0.087 0.111 2 chr1 83295508 83295820 3.676 0.277 0.463 0.204 0.305 3 chr1 194750612 194750882 1.159 0.09 0.199 0.071 0.101 4 chr2 8123576 8124320 3.335 0.179 0.168 0.113 0.279 5 chr2 31316252 31316368 11.63 0.129 0.087 0.08 0.111 6 chr2 35582600 35584144 12.066 0.305 0.357 0.341 0.317 7 chr2 42878428 42879088 -1.381 0.479 0.245 0.336 0.44 8 chr2 63925292 63925632 7.773 0.086 0.321 0.117 0.115 9 chr2 81161918 81161974 3.276 0.234 0.491 0.269 0.241 10 chr2 91174539 91175128 -28.595 0.235 0.262 0.181 0.238 11 chr2 103673926 103674122 -1.539 0.215 0.104 0.191 0.174 12 chr3 77360372 77360404 1.67 0.152 0.263 0.1 0.199 13 chr5 839710 840094 5.314 0.231 0.328 0.145 0.233 14 chr5 1942054 1942842 1.067 0.325 0.414 0.23 0.349 15 chr5 28482294 28482418 0.767 0.113 0.304 0.09 0.264 16 chr5 39059306 39059368 3.441 0.025 0.068 0.028 0.058 17 chr6 8416238 8416588 21.541 0.13 0.2 0.09 0.16 18 chr7 5169488 5169670 2.308 0.232 0.23 0.244 0.213 19 chr7 17839660 17839728 -5.446 0.685 0.445 0.579 0.617 20 chr9 23812488 23812678 4.227 0.155 0.382 0.185 0.151 21 chr11 675297 675546 -1.501 0.316 0.329 0.59 0.346 22 chr12 1688020 1688132 0.37 0.163 0.359 0.166 0.213 23 chr12 6875861 6876152 -0.25 0.301 0.084 0.212 0.277 24 chr12 10983288 10984278 -0.007 0.258 0.294 0.303 0.225 25 chr12 16248174 16248357 -1.758 0.598 0.583 0.819 0.317 26 chr13 13146982 13147888 -17.978 0.167 0.113 0.13 0.179 27 chr13 16017638 16017826 -0.017 0.155 0.224 0.199 0.14 28 chr13 16716158 16716440 -0.034 0.153 0.273 0.147 0.18 29 chr14 4137808 4137912 -0.166 0.259 0.137 0.22 0.215 30 chr15 8945392 8945554 -8.922 0.493 0.464 0.727 0.324 31 chr17 2483692 2483848 8.025 0.142 0.286 0.097 0.204 32 chr17 3822992 3823290 2.947 0.207 0.512 0.206 0.228 33 chr17 10211804 10212170 -3.233 0.099 0.087 0.189 0.08 34 chr20 2469403 2470309 -4.959 0.173 0.273 0.253 0.262 35 chr20 10704150 10704244 -2.422 0.216 0.137 0.169 0.195 36 chr20 11718629 11718916 3.151 0.149 0.379 0.23 0.201. ρπρηρηι / ζηζ / Ε / γίΛι 37 chr23 2763708 2763780 2.721 0.331 0.61 0.428 0.366 38 chr23 5159782 5159918 -2.9 0.283 0.171 0.309 0.228 39 chr28 2874382 2874447 0.005 0.369 0.328 0.322 0.327 Y-intercept of the linear model equation found by glmnet: 17.411 eenAnn / zznz / E / YiAi Example 2: The alpha value was varied in a range between 0 and 1 and was chosen as 0.9 (net elastic regression). This identified a set of 32 MRLs along with corresponding beta values, which define the weights of those MRLs used in the chicken methylation clock (Table 3). Table 3. Clock LMR (alpha = 0.9, lambda = 0.3147). ID chromosome Start end weight ileum spleen breast jejunum 1 chr1 3310966 3311076 5.106 0.089 0.117 0.048 0.108 2 chr1 13486724 13487721 -1.078 0.421 0.180 0.224 0.424 3 chr1 77403928 77404268 5.291 0.106 0.160 0.040 0.183 4 chr1 131728204 131729184 -6.235 0.407 0.363 0.318 0.197 5 chr1 135369614 135369882 -1.194 0.436 0.184 0.403 0.419 6 chr1 165806748 165806816 -0.009 0.477 0.527 0.844 0.542 7 chr2 31315302 31315823 0.961 0.148 0.099 0.104 0.200 8 chr2 31316250 31316368 15.824 0.129 0.087 0.059 0.111 9 chr2 91174537 91175128 -26,554 0.235 0.262 0.188 0.238 10 chr4 1489570 1490794 -8.003 0.176 0.149 0.158 0.214 11 chr4 8453114 8454528 3.325 0.159 0.524 0.316 0.211 12 chr4 31342294 31342536 0.228 0.638 0.574 0.638 0.640 13 chr5 839708 840094 2.227 0.231 0.328 0.153 0.233 14 chr5 1942052 1942842 2.613 0.325 0.414 0.204 0.349 15 chr5 39059304 39059368 0.307 0.025 0.068 0.024 0.058 16 chr5 52951604 52951808 2.676 0.070 0.148 0.024 0.091 17 chr6 8416236 8416588 12.930 0.130 0.200 0.099 0.160 18 chr8 13056204 13056776 4.557 0.142 0.269 0.122 0.150. 19 chr9 23812486 23812678 6.756 0.155 0.382 0.179 0.151 20 chr11 675295 675546 -3.678 0.316 0.329 0.638 0.346 21 chr12 9433040 9433568 9.905 0.406 0.351 0.132 0.409 22 chr12 16248172 16248357 -0.539 0.598 0.583 0.815 0.317 23 chr13 13146980 13147888 -10.892 0.167 0.113 0.135 0.179 24 CHR13 16716156 16716440 -0.540 0.153 0.273 0.166 0.180 25 CHR14 4137806 4137912 -6.589 0.259 0.137 0.232 0.215 26 CHR15 8945390 8945554 -3.262 0.493 0.464 0.741 0.324 27 CHR18 2358384 2359684 -2.706 0.448 0.368 0.364 0.472 28 CHR19 9052179 9052244 -9.309 0.601 0.295 0.258 0.523 29 chr20 11718627 11718916 20.167 0.149 0.379 0.193 0.201 30 chr23 5568088 5568140 -2.259 0.402 0.290 0.436 0.439 31 chr25 1101298 1101396 -0.093 0.493 0.267 0.204 0.416 32 chr26 4608324 4608370 2.441 0.163 0.416 0.228 0.203 Ordenada al origen de la modelo linear encontrado porglmnet: 17.345 rrnRnn / zznz / E / YiAi Correction factors are indicated for different tissues. For correction, the corresponding value must be subtracted. Figure 5 shows the mean squared error of a clock trained for a given alpha at the lambda value that leads to the minimum error. Figure 6 shows the number of LMRs for a given alpha at the lambda value that leads to the minimum error. Justification for normalization of methylation data as clock input The average methylation values ​​for these LMRs were calculated and normalized by calculating, for each LMR, the average value across all samples from the same tissue and subtracting this value from the LMR value (in the case of the CpG clock, calculating, for each CpG, the average value across all samples from the same tissue and subtracting this value from the CpG value), see above. The rationale for this approach is illustrated in Figure 7, which shows the first two principal components of a component analysis (PCA) of the LMR methylation data. PC2 (variance explained: 22.8%) shows a strong positive correlation with subject age (r = 0.466), while PC1 (variance explained: 45.6%) shows no correlation with subject age (r = -0.005). This leads to the interpretation that PC2 reflects subject age, with older age corresponding to a higher sample value on PC2.Consequently, the PC2 values ​​for the different samples represent an age-related ranking of the samples. However, even the oldest breast tissue samples still showed a lower value than the youngest spleen tissue samples, although the ranking within the sample set for a specific tissue is fairly accurate. This indicates a tissue-specific “skew” for PC2 ranking that reflects age, likely caused by the different maturation stages of the various tissues at certain points in the early life of the chicken. Since this skew likely affects the training of the methylation clock algorithm, a corresponding correction was introduced. Predicting age in breast tissue from a completely independent validation dataset: To validate the LMR clock, whole-genome bisulfite sequencing was performed on six breast samples from two age groups (14 and 28 days) in a completely independent animal trial. The age prediction showed a root mean square error of 2.7 days and 3.8 days, respectively, which is consistent with the prediction error obtained after cross-validation. The results are shown in Figure 8. Analysis of jejunal samples showed a pronounced and highly consistent acceleration of aging, particularly on days 14 and 16 (Figure 9). A control group was injected with the non-inflammatory agent GpC and did not respond at all.

Claims

CLAIMS 1. A computer-implemented method for establishing an epigenetic clock for a bird species, the method comprising (a.) identifying and determining the methylation levels of specific CpG sites within genomic DNA obtained from a plurality of different biological sample materials derived from the bird species and representing specific points in time within the chronological lifespan of this bird species, (b.) excluding all CpG sites associated with single nucleotide polymorphisms from the CpG sites identified in step (a.), (c.) excluding all CpG sites located on sex chromosomes from the CpG sites obtained in step (b.), (d.) performing a tissue-specific normalization step for the CpG sites obtained in step (c.), and (e.) correlating the CpG methylation levels of the CpG sites obtained in step (d.) with chronological age using a penalized regression model.

2. The method according to claim 1, wherein the plurality of different biological sample material derived from the bird species and representing specific points in time within the chronological lifespan of this bird species includes material selected from the group consisting of body fluids, excreted material, tissue material, and feather material.

3. The method according to claim 1, wherein the plurality of different biological sample materials derived from the bird species and representing specific points in time within the chronological lifespan of this bird species includes at least four different tissues.

4. The method according to any of the preceding claims, wherein the plurality of different biological sample materials derived from the bird species and representing specific points in time within the chronological lifespan of this bird species comprises or consists of tissue material selected from muscle tissue, gut tissue, organ tissue, and skin tissue.

5. The method according to any of the preceding claims, wherein the plurality of different biological sample materials derived from the bird species and representing specific points in time within the chronological lifespan of this bird species includes breast tissue, spleen tissue, ileum tissue, and jejunum tissue.

6. The method according to any of the preceding claims, wherein the plurality of different biological sample materials derived from the bird species and representing specific points in time within the chronological lifespan of this bird species is selected to represent ages fluctuating between 3 days and 63 days.

7. The method according to any of the preceding claims, wherein step (a) involves a whole genome bisulfite sequencing process.

8. The method according to any of the preceding claims, wherein the specific CpG sites within the genomic DNA of the bird species are distributed over the entire genome of the bird species and are restricted to a specific chain coverage of at least 10.

9. The method according to claim 8, wherein the tissue-specific normalization step is performed by calculating for each CpG the average value over all samples of the same tissue and subtracting this value from the value of this CpG.

10. The method according to any of claims 1 to 7, wherein the specific CpG sites within the genomic DNA of the bird species are distributed within the low methylation regions (LMR) in the genome of the bird species.

11. The method according to claim 9, wherein the specific CpG sites within the genomic DNA of the bird species were restricted to a specific chain coverage of at least greater than 5.

12. The method according to claim 10 or according to claim 11, wherein the tissue-specific normalization step is performed by calculating for each MRL the average value over all samples of the same tissue and subtracting this value from the value of this MRL.

13. A computer program loaded into a computer memory, implementing the method of any of claims 1 to 9.

14. A tangible, computer-readable medium comprising a computer-readable code that, when executed by a computer, causes the computer to perform the operations comprising: (a.) receiving information corresponding to the methylation levels of specific CpG sites within the genomic DNA of the bird species obtained from a plurality of different biological sample materials representing specific points in time within the chronological lifespan of the bird species, (b.) receiving information corresponding to all CpG sites associated with single nucleotide polymorphisms and excluding them from the CpG sites of step (a.), (c.) receiving information corresponding to all CpG sites of the sex chromosomes and excluding them from the CpG sites of step (b.), (d.) performing a tissue-specific normalization step for the CpG sites of step (c.) and eenAnn / zznz / E / YiAi (e.) correlate the CpG methylation levels of the CpG sites from step (d.) with chronological age using a penalized regression model.