Biology subjects

Fries, R.

Publications and source records attributed to Fries, R..

3 recordsLinked to original sources

bioRxiv2017

Meta-Analysis Of Sequence-Based Association Studies Across Three Cattle Breeds Reveals 25 QTL For Fat And Protein Percentages In Milk At Nucleotide Resolution

BackgroundGenotyping and whole-genome sequencing data have been collected in many cattle breeds. The compilation of large reference panels facilitates imputing sequence variant genotypes for animals that have been genotyped using dense genotyping arrays. Association studies with imputed sequence variant genotypes allow characterization of quantitative trait loci (QTL) at nucleotide resolution particularly when individuals from several breeds are included in the mapping populations.\n\nResultsWe imputed genotypes for more than 28 million sequence variants in 17,229 animals of the Braunvieh (BV), Fleckvieh (FV) and Holstein (HOL) cattle breeds in order to generate large mapping populations that are required to identify sequence variants underlying milk production traits. Within-breed association tests between imputed sequence variant genotypes and fat and protein percentages in milk uncovered between six and thirteen QTL (P<1e-8) per breed. Eight of the detected QTL were significant in more than one breed. We combined the association studies across three breeds using meta-analysis and identified 25 QTL including six that were not significant in the within-breed association studies. Closer inspection of the QTL revealed that two well-known causal missense mutations in the ABCG2 (p.Y581S, rs43702337, P=4.3e-34) and GHR (p.F279Y, rs385640152, P=1.6e-74) genes were the top variants at two QTL on chromosomes 6 and 20. Another true causal missense mutation in the DGAT1 gene (p.A232K, rs109326954, P=8.4e-1436) was the second top variant at a QTL on chromosome 14 but its allelic substitution effects were not consistent across three breeds analyzed. It turned out that the conflicting allelic substitution effects resulted from flaws in the imputed genotypes due to the use of a multi-breed reference population for genotype imputation.\n\nConclusionsMany QTL for milk production traits segregate across breeds. Metaanalysis of association studies across breeds has greater power to detect such QTL than within-breed association studies. True causal mutations can be readily detected among the most significantly associated variants at QTL when the accuracy of imputation is high. However, true causal mutations may show conflicting allelic substitution effects across breeds when the imputed sequence variant genotypes contain flaws. Validating the effect of known causal variants is highly recommended in order to assess the ability to detect true causal mutations in association studies with imputed sequence variant genotypes.

genomics↗

bioRxiv2017

Development of a high-density, 2M SNP genotyping array and 670k SNP imputation array for the domestic horse

BackgroundTo date, genome-scale analyses in the domestic horse have been limited by suboptimal single nucleotide polymorphism (SNP) density and uneven genomic coverage of the current SNP genotyping arrays. The recent availability of whole genome sequences has created the opportunity to develop a next generation, high-density equine SNP array.\n\nResultsUsing whole genome sequence from 153 individuals representing 24 distinct breeds collated by the equine genomics community, we cataloged over 23 million de novo discovered genetic variants. Leveraging genotype data from individuals with both whole genome sequence, and genotypes from lower-density, legacy SNP arrays, a subset of [~]5 million high-quality, high-density array candidate SNPs were selected based on breed representation and uniform spacing across the genome. Considering probe design recommendations from a commercial vendor (Affymetrix, now Thermo Fisher Scientific) a set of [~]2 million SNPs were selected for a next-generation high-density SNP chip (MNEc2M). Genotype data were generated using the MNEc2M array from a cohort of 332 horses from 20 breeds and a lower-density array, consisting of [~]670 thousand SNPs (MNEc670k), was designed for genotype imputation.\n\nConclusionsHere, we document the steps taken to design both the MNEc2M and MNEc670k arrays, report genomic and technical properties of these genotyping platforms, and demonstrate the imputation capabilities of these tools for the domestic horse.

genomics↗

bioRxiv2016

Evaluation of the accuracy of imputed sequence variants and their utility for causal variant detection in cattle

BackgroundThe availability of dense genotypes and whole-genome sequence variants from various sources offers the opportunity to compile large data sets consisting of tens of thousands of animals with genotypes for millions of polymorphic sites that may enhance the power of genomic analyses. The imputation of missing genotypes ensures that all individuals have genotypes for a shared set of variants.\n\nResultsWe evaluated the accuracy of imputation from dense genotypes to whole-genome sequence variants in 249 Fleckvieh and 450 Holstein cattle using Minimac and FImpute. The sequence variants of a subset of the animals were reduced to the variants that were included in the Illumina BovineHD genotyping array and subsequently inferred in silico using either within- or multi-breed reference populations. The accuracy of imputation varied considerably across chromosomes and dropped at regions where the bovine genome contains segmental duplications. Depending on the imputation strategy, the correlation between imputed and true genotypes ranged from 0.898 to 0.952. The accuracy of imputation was higher with Minimac than FImpute particularly for rare alleles. Considering a multi-breed reference population increased the accuracy of imputation, particularly when FImpute was used to infer genotypes. When the sequence variants were imputed using Minimac, the true genotypes were more correlated to predicted allele dosages than best-guess genotypes. The computing costs to impute 23,256,743 sequence variants in 6958 animals were 10-fold higher with Minimac than FImpute. Association studies with imputed sequence variants revealed seven quantitative trait loci (QTL) for milk fat percentage. Two known causal mutations in the DGAT1 and GHR genes were the most significantly associated variants at two QTL on chromosomes 14 and 20 when Minimac was used to infer genotypes.\n\nConclusionsThe population-based imputation of millions of sequence variants in large cohorts provides accurate genotypes and is computationally feasible. Using a reference population that includes individuals from many breeds increases the accuracy of imputation particularly at low-frequency variants. Considering allele dosages rather than best-guess genotypes as explanatory variables is advantageous for association studies with imputed sequence variants.

genomics↗