Medical Genetics Commons^™

Benchmarking Relatedness Inference Methods With Genome-Wide Data From Thousands Of Relatives, Monica D. Ramstetter, Thomas D. Dyer, Donna M. Lehman, Joanne E. Curran, Ravindranath Duggirala, John Blangero, Jason G. Mezey, Amy L. Williams

School of Medicine Publications and Presentations

Inferring relatedness from genomic data is an essential component of genetic association studies, population genetics, forensics, and genealogy. While numerous methods exist for inferring relatedness, thorough evaluation of these approaches in real data has been lacking. Here, we report an assessment of 12 state-of-the-art pairwise relatedness inference methods using a data set with 2485 individuals contained in several large pedigrees that span up to six generations. We find that all methods have high accuracy (92–99%) when detecting first- and second-degree relationships, but their accuracy dwindles to76% of relative pairs. Overall, the most accurate methods are Estimation of Recent Shared Ancestry …