An integrative variant analysis pipeline for accurate genotype/haplotype inference in population NGS data

Abstract

Next-generation sequencing is a powerful approach for discovering genetic variation. Sensitive variant calling and haplotype inference from population sequencing data remain challenging. We describe methods for high-quality discovery, genotyping, and phasing of SNPs for low-coverage (approximately 53) sequencing of populations, implemented in a pipeline called SNPTools. Our pipeline contains several innovations that specifically address challenges caused by lowcoverage population sequencing: (1) effective base depth (EBD), a nonparametric statistic that enables more accurate statistical modeling of sequencing data; (2) variance ratio scoring, a variance-based statistic that discovers polymorphic loci with high sensitivity and specificity; and (3) BAM-specific binomial mixture modeling (BBMM), a clustering algorithm that generates robust genotype likelihoods from heterogeneous sequencing data. Last, we develop an imputation engine that refines raw genotype likelihoods to produce high-quality phased genotypes/haplotypes. Designed for large population studies, SNPTools' input/output

An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data

“…For example, the Genome Analysis Toolkit (GATK) (DePristo et al 2011), SAMtools (Li 2011), and SNPTools (Wang et al 2013) are used for variant discovery and genotyping from small to moderate numbers of sequenced samples. However, as the number of sequenced genomes grows, analysis becomes increasingly challenging, requiring complex data processing steps, division of sequence data into many small regions, management and scheduling of analysis jobs, and often, prohibitive demands on computing resources.…”

The population genomics of rhesus macaques (Macaca mulatta) based on whole-genome sequences

“…All animals were captive born in research colonies, with the exception of three wild-born Chinese rhesus macaques (Supplemental Table S1). SNVs were identified using both GATK (DePristo et al 2011) and SNPTools (Wang et al 2013). The intersection of the two variant call sets identified 43.7 million SNVs, 31.9 million among the 124 Indian-origin rhesus macaques (IRh) and 30.1 million variants in the nine Chinese-origin animals (CRh).…”

“…Single-nucleotide variants (SNVs) were identified by mapping the quality-filtered sequence reads to the rheMac2 rhesus macaque whole-genome assembly (Rhesus Macaque Genome Sequencing and Analysis Consortium et al 2007) using BWA (Li and Durbin 2009). SNVs were then called using SNPTools (Wang et al 2013). This process identified slightly more than 53.7 million SNVs.…”

124: Genomic variants associated with resistance to high fat diet induced obesity in a primate model

“…Our initial SNP calls were based on infinite odds ratios (i.e., present in all 4 dams of one group, and absent in all of the other) as estimated by ssahaSNP, which only identifies homozygous alternative allele SNPs. In order to further assure high quality and statistically robust SNP identification in an initial small discovery cohort of 8 animals, three tools were used to call SNPs (Atlas-SNP2 35 , SNPTools 36 , and ssahaSNP 37 ) and the only SNPs called by all three tools were selected as candidates for further analysis. Variant Effect Predictor (VEP) 38 based on rhesus gene models was used to identify functional consequences of the SNPS and SNPs were lifted over to the human genome in order to perform SIFT 39 and PROVEAN 40 predictions of the effect of amino acid differences.…”

“…To assure high quality SNP identification, we used three tools to call SNPs (Atlas-SNP2 35 , SNPTools 36 , and ssahaSNP 37 ) and only selected the 1534 SNPs called by all three for further analysis (Supplemental Fig. 2 and Supplemental Tables S1 and S2).…”