Avian Epigenetic Clock Calibration Across Tissues and CpG Filters
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Solution Overview
Problem
Existing methods for establishing epigenetic clocks in avian species lack robustness, generalizability, and precision, particularly due to confounding factors such as genetic polymorphisms, sex-specific methylation, and tissue-specific maturation rates, which affect the accuracy of age prediction.
Innovation Solution
A computer-implemented method that excludes CpG sites associated with single nucleotide polymorphisms (SNPs) and sex chromosomes, and normalizes methylation values across different tissues, using whole-genome bisulfite sequencing and penalized regression models to establish a CpG and LMR clock for avian species.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods are used to establish epigenetic clocks in avian species, then age prediction can be performed, but the results lack robustness, generalizability, and precision due to confounding factors
Solution Approach 1:
The patent extracts and removes confounding factors from the analysis by excluding CpG sites associated with SNPs, filtering out sex chromosome-specific methylation patterns, and removing tissue-specific maturation effects. This isolation of relevant age-related methylation changes directly improves both precision and reliability of age prediction
Solution Approach 2:
The patent transforms the raw methylation data by normalizing values across different tissues and adjusting for confounding variables. This parameter transformation converts unreliable raw measurements into standardized, comparable metrics that enhance both accuracy and generalizability across different avian species and tissue types
2Loss of information
If all CpG sites are included in the epigenetic clock model, then comprehensive age-related information is captured, but confounding factors such as SNPs and sex-specific methylation reduce prediction accuracy
Solution Approach 1:
The patent segments the genome into functionally distinct regions: autosomal CpG sites versus sex chromosome CpG sites, and further segments autosomal sites into those associated with SNPs versus those that are not. This segmentation allows selective inclusion of only the most reliable age-related markers while excluding confounding regions
Solution Approach 2:
The patent converts the presence of confounding factors into a benefit by using them as exclusion criteria. By identifying and removing CpG sites associated with SNPs and sex chromosomes, the method transforms potential sources of error into a refined set of high-quality, confounder-free markers that improve prediction accuracy
3Adaptability or versatility
If methylation values from different tissues are used directly without normalization, then tissue-specific characteristics are preserved, but tissue-specific maturation rates confound the age prediction
Solution Approach 1:
The patent applies normalization to equalize the methylation values across different tissues, creating an equipotential reference frame where all tissues are compared on the same scale. This removes the confounding effect of tissue-specific maturation rates while preserving the underlying age-related methylation patterns that are common across tissues
Data Source
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AI summary
The invention pertains to a computer-implemented method of establishing an epigenetic clock for an avian species, the method comprising (a.) identifying and determining the methylation levels of specific CpG sites within the genomic DNA obtained from a plurality of different biological sample materials deriving from the avian species and representing specific time points within the chronological lifespan of this avian 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.