Family Relationship Inference Using Knights Landing Platform

Abstract

Using genetic data to infer relatedness has been crucial for genetics studies for decades. In a previously published paper together with the KING software, we demonstrated that the kinship coefficient, a measure of relatedness between a pair of individuals, can be accurately estimated using their genome-wide SNP data, without estimating the allele frequencies at each SNP in the whole dataset. The computational efficiency of this algorithm has been substantially improved in the second generation of KING. Three levels of computational speed-up are implemented in KING 2.0, including: 1) bit-level parallelism; 2) multiple-core parallelism using OpenMP; and 3) a multi-stage procedure to eliminate unrelated or distantly related pairs of individuals. The efficient implementation in KING 2.0 allows instant relationship inference in a matter of seconds in a typical dataset (with 10,000s individuals). To demonstrate the computational performance and scalability of KING 2.0, we use the Knights Landing platform to infer relatedness in a dataset consisting of 303,750 individuals each typed at 168,749 autosome SNPs. The computational time to identify all first-degree relatives by scanning 46 billion pairs of individuals is ∼10 minutes using 256 threads, a noticeable speed-up comparing to the general-purpose CPU. Algorithm improvement in the second generation of KING and the use of the latest computing system such as the Knights Landing platform makes it feasible for researchers to infer relatedness in their genetic datasets in the largest size up-to-date on a single computer.