Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/10/2018
Publication Date: 4/1/2019
Citation: Van Raden, P.M., Bickhart, D.M., O'Connell, J.R. 2019. Calling known variants and identifying new variants while rapidly aligning sequence data. Journal of Dairy Science. 102(4):3216–3229. https://doi.org/10.3168/jds.2018-15172.
Interpretive Summary: Studies to identify genetic differences from DNA sequence data often require much computation. Most previous programs to align short reads to the reference map and to find simple variants such as single base substitutions, small insertions, and deletions use no previous information about variant locations. New program Findmap uses the reference map and a list of already known variants during alignment, and program Findvar separates new variants from probable DNA read errors. Advantages are faster processing, more precise alignment, more compact output, and fewer steps. Accounting for known mutations during alignment allows more efficient and accurate sequence-based genotyping.
Technical Abstract: Whole-genome sequencing studies can identify causative mutations for subsequent use in genomic evaluations. Speed and accuracy of sequence data processing can be improved by calling known variant alleles during alignment instead of the separate alignment and calling steps in previous programs. Alignment using Burrows–Wheeler Alignment (BWA) or SNAP and variant identification using Genome Analysis ToolKit (GATK) or SAMtools were compared to new programs Findmap and Findvar. Findmap stores both the reference map and known alternate alleles in a hash table to simultaneously align reads and count reference and alternate alleles for each DNA source. Potential new single nucleotide variant, insertion, and deletion alleles are output for summary by variant identification program Findvar. 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 to process 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%; 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% from GATK; and 37, 1.3, and 0.4% from SAMtools, respectively. Advantages of Findmap and Findvar are fast processing, precise alignment, more useful data summaries, more compact output, and fewer steps. Calling known and identifying new variants during alignment allows more efficient and accurate sequence-based genotyping.