Using whole genome sequence to compare variant callers and breed differences of US sheep

Abstract

As whole genome sequence (WGS) data sets have become abundant and widely available, so has the need for variant detection and scoring. The aim of this study was to compare the accuracy of commonly used variant calling programs, Freebayes and GATK HaplotypeCaller (GATK-HC), and to use U.S. sheep WGS data sets to identify novel breed-associated SNPs. Sequence data from 145 sheep consisting of 14 U.S. breeds were filtered and biallelic single nucleotide polymorphisms (SNPs) were retained for genotyping analyses. Genotypes from both programs were compared to each other and to genotypes from bead arrays. The SNPs from WGS were compared to the bead array data with breed heterozygosity, principal component analysis and identifying breed associated SNPs to analyze genetic diversity. The average sequence read depth was 2.78 reads greater with 6.11% more SNPs being identified in Freebayes compared to GATK-HC. The genotype concordance of the variant callers to bead array data was 96.0% and 95.5% for Freebayes and GATK-HC, respectively. Genotyping with WGS identified 10.5 million SNPs from all 145 sheep. This resulted in an 8% increase in measured heterozygosity and greater breed separation in the principal component analysis compared to the bead array analysis. There were 1,849 SNPs identified in only the Romanov sheep where all 10 rams were homozygous for one allele and the remaining 135 sheep from 13 breeds were homozygous for the opposite allele. Both variant calling programs had greater than 95% concordance of SNPs with bead array data, and either was suitably accurate for ovine WGS data sets. The use of WGS SNPs improved the resolution of PCA analysis and was critical for identifying Romanov breed-associated SNPs. Subsets of such SNPs could be used to estimate germplasm composition in animals without pedigree information.

Performance analysis of conventional and AI-based variant callers using short and long reads

“…In the context of variant caller comparisons, numerous studies have sought to assess various tools, occasionally including comparisons with AI-based tools [ 2 , 27 – 32 ]. However, in this study, unlike previous studies that exclusively relied on Illumina data [ 2 , 27 – 32 ], we adopted a more extensive perspective by incorporating a wide range of sequencing technologies, encompassing both short-read (Illumina) and long-read (PacBio HiFi and ONT). This comprehensive approach offers insights into the performance of variant calling tools across diverse sequencing platforms.…”

“…PacBio HiFi has been the preferred sequencing technology by many sequencing initiative projects such as the Earth BioGenome Project [ 33 , 34 ], the Vertebrate Genome Project [ 35 ], the i5K Initiative [ 36 ], and the Ag100Pest Initiative [ 37 ]. These projects, among others, have opted for PacBio HiFi technology to produce high-quality reads, making PacBio HiFi the gold standard for generating high-confidence long-reads [ 38 – 40 ].…”

Species-specific dynamics may cause deviations from general biogeographical predictions – evidence from a population genomics study of a New Guinean endemic passerine bird family (Melampittidae)

“…For the nuclear phylogenetic tree, we used the previously mapped .bam files excluding individuals with very low mean depth-of-coverage (n = 3, DoC < 4 x ) to call variants for each individual using freebayes (v1.3.1-dirty, [ 57 ]). We used freebayes as variant caller in this step as it is suitable to use on non-model organism genomes and is more efficient when using samples with a low to intermediate depth-of-coverage [ 58 ]. Polymorphic sites were filtered based on their quality score (> 20), allelic balance (≥ 0.2), and minimum and maximum depth-of-coverage (3 x / 100 x ).…”

The TiHoCL panel for canine lymphoma: a feasibility study integrating functional genomics and network biology approaches for comparative oncology targeted NGS panel design

“…To obtain a reliable list of potential variants, we used three different variant callers: Freebayes, GATK Haplotypecaller, and BCFtools mpileup and call, and solely considered variants called by all three variant callers for downstream analysis. The three variant callers were chosen because they are partly based on different approaches and are broadly used in the field (97)(98)(99)(100).…”