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

Significance Inbreeding depression (ID) is the reduction of fitness in offspring of related parents. This phenomenon can be quantified from SNP data through a number of measures of inbreeding. Our study addresses two key questions. How accurate are the different methods to estimate ID? And how and why should investigators choose among the multiple inbreeding measures to detect and quantify ID? Here, we compare the behaviors of ID estimates from three commonly used SNP-based measures of inbreeding and provide both theoretical and empirical arguments to answer these questions. Our work illustrates how to analyze SNP data efficiently to detect and quantify ID, across species and traits. Quantifying the effects of inbreeding is critical to characterizing the genetic architecture of complex traits. This study highlights through theory and simulations the strengths and shortcomings of three SNP-based inbreeding measures commonly used to estimate inbreeding depression (ID). We demonstrate that heterogeneity in linkage disequilibrium (LD) between causal variants and SNPs biases ID estimates, and we develop an approach to correct this bias using LD and minor allele frequency stratified inference (LDMS). We quantified ID in 25 traits measured in ∼140,000 participants of the UK Biobank, using LDMS, and confirmed previously published ID for 4 traits. We find unique evidence of ID for handgrip strength, waist/hip ratio, and visual and auditory acuity (ID between −2.3 and −5.2 phenotypic SDs for complete inbreeding; P<0.001). Our results illustrate that a careful choice of the measure of inbreeding combined with LDMS stratification improves both detection and quantification of ID using SNP data.