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Comparison of variant calling methods for whole genome sequencing data in dairy cattle

Tuesday, August 19, 2014
Posters (The Westin Bayshore)
Christine F Baes , Qualitas AG, Zug, Switzerland
Marlies A Dolezal , University of Veterinary Medicine Vienna, Vienna, Austria
E. Fritz-Waters , Iowa State University, Ames, IA
James E. Koltes , Iowa State University, Ames, IA
Beat Bapst , Qualitas AG, Zug, Switzerland
Christine Flury , Bern University of Applied Sciences, School of Agricultural, Forest and Food Sciences, Zollikofen, Switzerland
Heidi Signer-Hasler , Bern University of Applied Sciences, School of Agriculture, Forest and Food Sciences, Zoll, Switzerland
Christian Stricker , agn-genetics, Davos, Switzerland
Rohan L Fernando , Iowa State University, Ames, IA
Fritz Schmitz-Hsu , swissgenetics, Zollikofen, Switzerland
Dorian J. Garrick , Iowa State University, Ames, IA
Birgit Gredler , Qualitas AG, Zug, Switzerland
Abstract Text:

Accurate identification of SNPs from next-generation sequencing data is crucial for high-quality downstream analysis. Whole genome sequence data of 65 key ancestors of genotyped Swiss dairy populations were available for investigation (24 billion reads, 96.8% mapped to UMD31, 12x coverage). Four publically available variant calling programmes were assessed and different levels of pre-calling handling for each method were tested and compared. SNP concordance was examined with Illuminas BovineHD Genotyping BeadChip. Depending on variant calling software used, between 16,894,054 and 22,048,382 SNP were identified (multi-sample calling). A total of 14,644,310 SNP were identified by all four variant callers (multi-sample calling). InDel counts ranged from 1,997,791 to 2,857,754; 1,708,649 InDels were identified by all four variant callers. A minimum of pre-calling data handling resulted in the highest non-reference sensitivity and the lowest non-reference discrepancy rate.

Keywords:

dairy cattle

variant calling

next generation sequencing