Correlation between relatives given complete genotypes: from identity by descent to identity by function.

Area of Science:

• Quantitative genetics
• Population genetics
• Genomics

Background:

• Classical quantitative genetics uses Identity by Descent (IBD) to link phenotypes to pedigrees.
• Current methods for genotype-based analysis lack a probabilistic generative model for genotypic and phenotypic similarity.

Purpose of the Study:

• Introduce a novel generative model for diploid allele effects based on the infinite allele mutation process.
• Define and utilize the concept of Identity by Function (IBF) to model phenotypic covariance.
• Establish a probabilistic framework connecting genotypic and phenotypic similarity.

Main Methods:

• Developed a generative model for diploid allele effects using the infinite allele mutation process.
• Introduced Identity by Function (IBF) to express phenotypic covariance in terms of functional identity states.
• Applied the IBF framework to gene-scale functional regions as quantitative trait loci (QTLs).

Main Results:

• IBF parameters define genetic architecture without reference to specific alleles or populations.
• Demonstrated applications in phenotype and effect prediction, association studies, and heritability estimation via simulation.
• Decomposed heritability into Explained and Unexplained components within the IBF framework, addressing the Missing Heritability problem.

Conclusions:

• Identity by Function (IBF) provides a probabilistic generative model linking genotype to phenotype.
• The IBF framework offers a flexible approach to understanding genetic architecture and heritability.
• IBF has potential applications in various genetic analyses, including addressing the Missing Heritability problem.