Calling known variants and identifying new variants while rapidly aligning sequence data

Abstract

Whole-genome sequencing studies can identify causative mutations for subsequent use in genomic evaluations. Speed and accuracy of sequence alignment can be improved by accounting for known variant locations during alignment instead of calling the variants after alignment as in previous programs. The new programs Findmap and Findvar were compared with alignment using Burrows-Wheeler alignment (BWA) or SNAP and variant identification using Genome Analysis ToolKit (GATK) or SAMtools. Findmap stores the reference map and any known variant locations while aligning reads and counting reference and alternate alleles for each DNA source. Findmap also outputs potential new single nucleotide variant, insertion, and deletion alleles. Findvar separates likely true variants from read errors and outputs genotype probabilities. Strategies were tested using cattle, human, and a completely random reference map and simulated or actual data. Most tests simulated 10 bulls, each with 10× simulated sequence reads containing 39 million variants from the 1000 Bull Genomes Project. With 10 processors, clock times for processing 100× data were 105 h for BWA, 25 h for GATK, and 11 h for SAMtools but only about 4 h for SNAP, 3 h for Findmap, and 1 h for Findvar. Alignment programs required about the same total memory; BWA used 46 GB (4.6 GB/processor), whereas >10 processors can share the same memory in SNAP and Findmap, which used 40 and 46 GB, respectively. Findmap correctly mapped 92.9% of reads (compared with 92.6% from SNAP and 90.5% from BWA) and had high accuracy of calling alleles for known variants. For new variants, Findvar found 99.8% of single nucleotide variants, 79% of insertions, and 67% of deletions; GATK found 99.4, 95, and 90%, respectively; and SAMtools found 99.8, 12, and 16%, respectively. False positives (as percentages of true variants) were 11% of single nucleotide variants, 0.4% of insertions, and 0.3% of deletions from Findvar; 12, 8.4, and 2.9%, respectively, from GATK; and 37, 1.3, and 0.4%, respectively, from SAMtools. Advantages of Findmap and Findvar are fast processing, precise alignment, more useful data summaries, more compact output, and fewer steps. Calling known variants during alignment allows more efficient and accurate sequence-based genotyping.

Development and comparison of RNA-sequencing pipelines for more accurate SNP identification: practical example of functional SNP detection associated with feed efficiency in Nellore beef cattle

“…The SNPs that are uniquely detected by Approach i) may represent SNPs that are present in a small subset of animals and hence are not representative of a specific RFI group. For SNPs with a low non-reference allele frequency, merging reads from multiple samples could lead to dilution of reads supporting the variant and consequently be called as homozygous for reference [32]. Alternatively, the Phred quality score of a SNP may be inflated when detected in a large number of samples and lead to some SNPs being uniquely detected by Approach i) (non-merged), which could have been removed by the quality filters in the merging methods suggesting possible false positives [33].…”

Detection of potential functional variants based on systems-biology: the case of feed efficiency in beef cattle

“…To select a variant calling tool, one needs to have a good combination of processing time, precision, and sensitivity (call quality) of the genotyping. When comparing GATK with other tools (i.e., Findvar, SAMtools, and Graphtyper) that display similar functions and considering the processing time, the GATK is at a disadvantage [ 33 , 34 ]. However, when comparing the number of polymorphic sites found by the tools (homozygous and heterozygous), the GATK is of great advantage [ 34 , 35 ].…”

The search for genetic functional variants of feed efficiency in Nelore cattle

“…To select a variant calling tool, one needs to have a good combination of processing time, precision and sensitivity (call quality) of the genotyping. When analyzing GATK with other tools (Findvar, SAMtools, Graphtyper) that have the same functions and considering the processing time, GATK is at a disadvantage compared to the other tools [48,49]. However, when comparing the number of polymorphic sites found by the tools (homozygous and heterozygous), GATK is of great advantage [49,50].…”

“…When analyzing GATK with other tools (Findvar, SAMtools, Graphtyper) that have the same functions and considering the processing time, GATK is at a disadvantage compared to the other tools [48,49]. However, when comparing the number of polymorphic sites found by the tools (homozygous and heterozygous), GATK is of great advantage [49,50]. Regarding the call for false positives, the tool with the lowest percentages was Findvar, followed by GATK and later by SAMtools [49].…”

“…However, when comparing the number of polymorphic sites found by the tools (homozygous and heterozygous), GATK is of great advantage [49,50]. Regarding the call for false positives, the tool with the lowest percentages was Findvar, followed by GATK and later by SAMtools [49]. Although GATK has a long processing time, it can be compensated by the number of polymorphic sites and the median filtering of false positives, making it a balanced tool for the identification of PFVs.…”