Dataset Information

Highly accurate sequence imputation enables precise QTL mapping in Brown Swiss cattle.

ABSTRACT: Within the last few years a large amount of genomic information has become available in cattle. Densities of genomic information vary from a few thousand variants up to whole genome sequence information. In order to combine genomic information from different sources and infer genotypes for a common set of variants, genotype imputation is required.In this study we evaluated the accuracy of imputation from high density chips to whole genome sequence data in Brown Swiss cattle. Using four popular imputation programs (Beagle, FImpute, Impute2, Minimac) and various compositions of reference panels, the accuracy of the imputed sequence variant genotypes was high and differences between the programs and scenarios were small. We imputed sequence variant genotypes for more than 1600 Brown Swiss bulls and performed genome-wide association studies for milk fat percentage at two stages of lactation. We found one and three quantitative trait loci for early and late lactation fat content, respectively. Known causal variants that were imputed from the sequenced reference panel were among the most significantly associated variants of the genome-wide association study.Our study demonstrates that whole-genome sequence information can be imputed at high accuracy in cattle populations. Using imputed sequence variant genotypes in genome-wide association studies may facilitate causal variant detection.

SUBMITTER: Frischknecht M

PROVIDER: S-EPMC5747239 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Highly accurate sequence imputation enables precise QTL mapping in Brown Swiss cattle.

Frischknecht Mirjam M Pausch Hubert H Bapst Beat B Signer-Hasler Heidi H Flury Christine C Garrick Dorian D Stricker Christian C Fries Ruedi R Gredler-Grandl Birgit B

BMC genomics 20171229 1

<h4>Background</h4>Within the last few years a large amount of genomic information has become available in cattle. Densities of genomic information vary from a few thousand variants up to whole genome sequence information. In order to combine genomic information from different sources and infer genotypes for a common set of variants, genotype imputation is required.<h4>Results</h4>In this study we evaluated the accuracy of imputation from high density chips to whole genome sequence data in Brown ...[more]

PMID: 29284405

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Project description:BACKGROUND:Calving difficulty or dystocia has a great economic impact in the US dairy industry. Reported risk factors associated with calving difficulty are feto-pelvic disproportion, gestation length and conformation. Different dairy cattle breeds have different incidence of calving difficulty, with Holstein having the highest dystocia rates and Jersey the lowest. Genomic selection becomes important especially for complex traits with low heritability, where the accuracy of conventional selection is lower. However, for complex traits where a large number of genes influence the phenotype, genome-wide association studies showed limitations. Biological networks could overcome some of these limitations and better capture the genetic architecture of complex traits. In this paper, we characterize Holstein, Brown Swiss and Jersey breed-specific dystocia networks and employ them in genomic predictions. RESULTS:Marker association analysis identified single nucleotide polymorphisms explaining the largest average proportion of genetic variance on BTA18 in Holstein, BTA25 in Brown Swiss, and BTA15 in Jersey. Gene networks derived from the genome-wide association included 1272 genes in Holstein, 1454 genes in Brown Swiss, and 1455 genes in Jersey. Furthermore, 256 genes in Holstein network, 275 genes in the Brown Swiss network, and 253 genes in the Jersey network were within previously reported dystocia quantitative trait loci. The across-breed network included 80 genes, with 9 genes being within previously reported dystocia quantitative trait loci. The gene-gene interactions in this network differed in the different breeds. Gene ontology enrichment analysis of genes in the networks showed Regulation of ARF GTPase was very significant (FDR???0.0098) on Holstein. Neuron morphogenesis and differentiation was the term most enriched (FDR???0.0539) on the across-breed network. Genomic prediction models enriched with network-derived relationship matrices did not outperform regular GBLUP models. CONCLUSIONS:Regions identified in the genome were in the proximity of previously described quantitative trait loci that would most likely affect calving difficulty by altering the feto-pelvic proportion. Inclusion of identified networks did not increase prediction accuracy. The approach used in this paper could be extended to any instance with asymmetric distribution of phenotypes, for example, resistance to disease data.

Dataset Information

Highly accurate sequence imputation enables precise QTL mapping in Brown Swiss cattle.

Publications

Highly accurate sequence imputation enables precise QTL mapping in Brown Swiss cattle.

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