Robust Inference of Identity by Descent from Exome-Sequencing Data

Identifying and characterizing genomic regions that are shared identical by descent (IBD) among individuals can yield insight into population history, facilitate the identification of adaptively evolving loci, and be an important tool in disease gene mapping. Although increasingly large collections of exome sequences have been generated, it is challenging to detect IBD segments in exomes, precluding many potentially informative downstream analyses. Here, we describe an approach, ExIBD, to robustly detect IBD segments in exome-sequencing data, rigorously evaluate its performance, and apply this method to high-coverage exomes from 6,515 European and African Americans. Furthermore, we show how IBD networks, constructed from patterns of pairwise IBD between individuals, and principles from graph theory provide insight into recent population history and reveal cryptic population structure in European Americans. Our results enable IBD analyses to be performed on exome data, which will expand the scope of inferences that can be made from existing massively large exome-sequencing datasets.