Comparison of multi-sample variant calling methods for whole genome sequencing

Abstract

Rapid advancement of next-generation sequencing (NGS) technologies has facilitated the search for genetic susceptibility factors that influence disease risk in the field of human genetics. In particular whole genome sequencing (WGS) has been used to obtain the most comprehensive genetic variation of an individual and perform detailed evaluation of all genetic variation. To this end, sophisticated methods to accurately call high-quality variants and genotypes simultaneously on a cohort of individuals from raw sequence data are required. On chromosome 22 of 818 WGS data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), which is the largest WGS related to a single disease, we compared two multi-sample variant calling methods for the detection of single nucleotide variants (SNVs) and short insertions and deletions (indels) in WGS: (1) reduce the analysis-ready reads (BAM) file to a manageable size by keeping only essential information for variant calling ("<i>REDUCE</i>") and (2) call variants individually on each sample and then perform a joint genotyping analysis of the variant files produced for all samples in a cohort ("<i>JOINT</i>"). <i>JOINT</i> identified 515,210 SNVs and 60,042 indels, while <i>REDUCE</i> identified 358,303 SNVs and 52,855 indels. <i>JOINT</i> identified many more SNVs and indels compared to <i>REDUCE</i>. Both methods had concordance rate of 99.60% for SNVs and 99.06% for indels. For SNVs, evaluation with HumanOmni 2.5M genotyping arrays revealed a concordance rate of 99.68% for <i>JOINT</i> and 99.50% for <i>REDUCE</i>. <i>REDUCE</i> needed more computational time and memory compared to <i>JOINT</i>. Our findings indicate that the multi-sample variant calling method using the <i>JOINT</i> process is a promising strategy for the variant detection, which should facilitate our understanding of the underlying pathogenesis of human diseases.