Accurate sequence variant genotyping in cattle using variation-aware genome graphs

Abstract

Background Genotyping of sequence variants typically involves, as a first step, the alignment of sequencing reads to a linear reference genome. Because a linear reference genome represents only a small fraction of all the DNA sequence variation within a species, reference allele bias may occur at highly polymorphic or divergent regions of the genome. Graph-based methods facilitate the comparison of sequencing reads to a variation-aware genome graph, which incorporates a collection of non-redundant DNA sequences that segregate within a species. We compared the accuracy and sensitivity of graph-based sequence variant genotyping using the Graphtyper software to two widely-used methods, i.e., GATK and SAMtools , which rely on linear reference genomes using whole-genome sequencing data from 49 Original Braunvieh cattle. Results We discovered 21,140,196, 20,262,913, and 20,668,459 polymorphic sites using GATK , Graphtyper, and SAMtools , respectively. Comparisons between sequence variant genotypes and microarray-derived genotypes showed that Graphtyper outperformed both GATK and SAMtools in terms of genotype concordance, non-reference sensitivity, and non-reference discrepancy. The sequence variant genotypes that were obtained using Graphtyper had the smallest number of Mendelian inconsistencies between sequence-derived single nucleotide polymorphisms and indels in nine sire-son pairs. Genotype phasing and imputation using the Beagle software improved the quality of the sequence variant genotypes for all the tools evaluated, particularly for animals that were sequenced at low coverage. Following imputation, the concordance between sequence- and microarray-derived genotypes was almost identical for the three methods evaluated, i.e., 99.32, 99.46, and 99.24% for GATK , Graphtyper, and SAMtools, respectively. Variant filtration based on commonly used criteria improved genotype concordance slightly but it also decreased sensitivity. Graphtyper required considerably more computing resources than SAMtools but less than GATK . Conclusions Sequence variant genotyping using Graphtyper is accurate, sensitive and computationally feasible in cattle. Graph-based methods enable sequence variant genotyping from variation-aware reference genomes that may incorporate cohort-specific sequence variants, which is not possible with the current implementation of state-of-the-art methods that rely on linear reference genomes. Electronic supplementary material Th...

Bovine breed-specific augmented reference graphs facilitate accurate sequence read mapping and unbiased variant discovery

“…Graphtyper (Additional file 2: Table S1). In agreement with our previous findings [12], genotype concordance was slightly higher using Graphtyper, than either SAMtools or GATK.…”

“…Considering that most cattle breeds have an effective population size between 50 and 200 [38,39], the vast majority of variants with allele frequency greater than 0.1 can be detected from a few sequenced key ancestor animals [13]. Most of the BSW, FV, OBV and HOL animals of our study are also key ancestor animals [7,12,13,40]. As a matter of fact, key ancestor animals have been sequenced for many cattle breeds [7,41].…”

“…Based on investigations in four genetically distinct cattle breeds, we make three important observations: first, variation-aware references that contain random variants for which the allele frequency and haplotype phase in the target populations is unknown, do not improve read mapping accuracy over linear references. Our previous study also showed that adding many random variants does barely affect sequence variant genotyping from reference graphs [12]. Adding random unphased variants increases the number of alternative alignment paths that are not necessarily biologically plausible, thus increasing mapping ambiguity.…”

“…Because approximately 90% of the considered SNPs were already included in the BSW-specific whole-genome graph, they can be detected and genotyped easily from graph-based alignments [17]. These variants can also be detected and genotyped accurately from linear alignments [12,45].…”

“…Yet, all bovine DNA sequences are aligned to the linear consensus reference sequence of a highlyinbred Hereford cow to facilitate reference-guided variant discovery and genotyping [10,11]. A genome-wide alignment of DNA fragments from a B. taurus individual differs from the Herefordbased reference sequence at between 7 and 8 million single nucleotide polymorphisms (SNPs) and small (<50 bp) insertions and deletions (Indels) [12,13]. The number of differences is higher for DNA samples with greater divergence from the reference [14,15].…”