Comparing heritability estimators under alternative structures of linkage disequilibrium

Area of Science:

• Quantitative genetics
• Statistical genomics
• Population genetics

Background:

• Single nucleotide polymorphism (SNP) heritability quantifies trait variance explained by genome-wide SNPs.
• Current estimation methods often assume fixed or random SNP effects.
• These methods rely on assumptions about individual relatedness and marker linkage disequilibrium (LD).

Purpose of the Study:

• To investigate how linkage disequilibrium structure and familial relatedness affect SNP heritability estimation.
• To determine the robustness of different heritability estimation methods against model misspecifications.

Main Methods:

• Comparison of heritability estimation methods under varying LD structures.
• Analysis of the impact of different degrees of familial relatedness on estimates.
• Investigation of fixed versus random SNP effects models.

Main Results:

• Heritability estimation accuracy is highly sensitive to the specific patterns of linkage disequilibrium and the degree of familial relatedness.
• The performance of various estimation techniques varies significantly based on these genetic architecture components.
• Equivalence was demonstrated between two method-of-moments estimators using fixed and random SNP effects.

Conclusions:

• Understanding the interplay between LD and relatedness is crucial for accurate SNP heritability estimation.
• No single method is universally robust; method choice should consider the specific genetic context.
• The study clarifies theoretical underpinnings of common heritability estimation approaches.