FiMAP: A fast identity-by-descent mapping test for biobank-scale cohorts

Although genome-wide association studies (GWAS) have identified tens of thousands of genetic loci, the genetic architecture is still not fully understood for many complex traits. Most GWAS and sequencing association studies have focused on single nucleotide polymorphisms or copy number variations, including common and rare genetic variants. However, phased haplotype information is often ignored in GWAS or variant set tests for rare variants. Here we leverage the identity-by-descent (IBD) segments inferred from a random projection-based IBD detection algorithm in the mapping of genetic associations with complex traits, to develop a computationally efficient statistical test for IBD mapping in biobank-scale cohorts. We used sparse linear algebra and random matrix algorithms to speed up the computation, and a genome-wide IBD mapping scan of more than 400,000 samples finished within a few hours. Simulation studies showed that our new method had well-controlled type I error rates under the null hypothesis of no genetic association in large biobank-scale cohorts, and outperformed traditional GWAS single-variant tests when the causal variants were untyped and rare, or in the presence of haplotype effects. We also applied our method to IBD mapping of six anthropometric traits using the UK Biobank data and identified a total of 3,442 associations, 2,131 (62%) of which remained significant after conditioning on suggestive tag variants in the ± 3 centimorgan flanking regions from GWAS.Author summary: Identity-by-descent (IBD) segments shared from a common ancestor can be used in association mapping to identify genomic regions associated with complex traits such as height, weight and body fat distribution. In this work, we developed FiMAP, an efficient IBD mapping test that can be applied to hundreds of thousands of individuals from biobank-scale cohorts, by leveraging IBD segments from recent efficient IBD detection software programs. Built upon the classical variance component model which has its deep root in linkage analysis, FiMAP utilizes sparse linear algebra and random matrix algorithms to speed up the computation in large samples. We demonstrated accuracy, type I error control and statistical power of the FiMAP algorithm, and illustrated how IBD mapping using FiMAP could identify associations complementary to genome-wide association study findings. With the availability of an abundance of genetic data from large biobank-scale cohorts, FiMAP provides a unique angle to leverage such rich resources and better understand the genetic architecture of complex traits.