Detection and quantification of inbreeding depression for complex traits from SNP data.

Area of Science:

• Genetics
• Quantitative Genetics
• Bioinformatics

Background:

• Inbreeding depression (ID) significantly impacts complex traits, necessitating accurate quantification.
• Current single-nucleotide polymorphism (SNP)-based methods for estimating ID have limitations.
• Understanding the genetic architecture of traits requires precise measurement of inbreeding effects.

Purpose of the Study:

• To evaluate the strengths and weaknesses of common SNP-based inbreeding measures.
• To develop a novel method for correcting biases in inbreeding depression estimation.
• To quantify inbreeding depression across multiple complex traits using the improved method.

Main Methods:

• Theoretical analysis and simulations to assess SNP-based inbreeding measures.
• Development of a bias-correction approach using linkage disequilibrium (LD) and minor allele frequency stratified inference (LDMS).
• Application of LDMS to quantify inbreeding depression for 25 traits in UK Biobank participants.

Main Results:

• Heterogeneity in linkage disequilibrium (LD) between causal variants and SNPs introduces bias in ID estimates.
• The LDMS method effectively corrects for LD-related biases in inbreeding depression quantification.
• Significant evidence of inbreeding depression was found for handgrip strength, waist/hip ratio, and visual/auditory acuity.

Conclusions:

• The choice of inbreeding measure and the application of LDMS stratification are crucial for accurate ID detection and quantification.
• LDMS provides a more reliable approach to studying the genetic architecture of complex traits influenced by inbreeding.
• This study advances the understanding of inbreeding depression's impact on human phenotypic variation.