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Highly accurate sequence imputation enables precise QTL mapping in Brown Swiss cattle
Mirjam Frischknecht1,2, Hubert Pausch3,4,5, Beat Bapst6
1Qualitas AG, Chamerstrasse 56a, 6300, Zug, Switzerland. Mirjam.frischknecht@qualitasag.ch.
BMC Genomics
|December 30, 2017
Summary
Genomic imputation accurately infers whole-genome sequence data in Brown Swiss cattle. This method enhances genome-wide association studies, aiding in the detection of causal variants for traits like milk fat percentage.
Area of Science:
- Animal Genomics
- Bioinformatics
- Quantitative Genetics
Background:
- Genomic information in cattle has rapidly expanded, ranging from dense marker panels to whole-genome sequences.
- Genotype imputation is essential for integrating diverse genomic datasets and inferring genotypes for a common set of variants.
Purpose of the Study:
- To evaluate the accuracy of imputing whole-genome sequence data from high-density SNP chip data in Brown Swiss cattle.
- To assess the performance of popular imputation programs and reference panel compositions.
Main Methods:
- Imputation of sequence variant genotypes using Beagle, FImpute, Impute2, and Minimac.
- Utilized various reference panel compositions for imputation.
- Performed genome-wide association studies (GWAS) for milk fat percentage using imputed sequence variant genotypes.
Main Results:
- High accuracy was achieved when imputing sequence variant genotypes from high-density chips to whole-genome sequence data.
- Differences in accuracy among imputation programs and reference panel scenarios were minimal.
- Identified significant quantitative trait loci (QTL) for milk fat percentage in early and late lactation.
Conclusions:
- Whole-genome sequence information can be imputed with high accuracy in cattle populations.
- Imputed sequence variant genotypes are valuable for enhancing genome-wide association studies and facilitating causal variant discovery.
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