Meta-analysis for milk fat and protein percentage using imputed sequence variant genotypes in 94,321 cattle from eight cattle breeds

Berg, I. van den; Xiang, Ruidong; Jenko, Janez; Pausch, Hubert; Boussaha, Mekki; Schrooten, C.; Tribout, Thierry; Gjuvsland, Arne B.; Boichard, Didier; Nordbø, Øyvind; Sanchez, Marie-Pierre; Goddard, Mike E.

doi:10.1186/s12711-020-00556-4

Cited by 45 publications

(39 citation statements)

References 48 publications

(83 reference statements)

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“…Figure 1 shows the Manhattan plot of the variances of local GEBV for fat percentage calculated in 250-kb regions across the genome, as an indicator for the genetic variance contained in the regions [23], which indicates whether the region contains important QTL. We compare our results on fat percentage to a recent meta-analysis of eight cattle breeds by van den Berg et al [24], which included the data from the Holstein and Jersey individuals used here. As expected, QTL signals are dominated by the DGAT1 gene, which is located at the beginning of Bos taurus chromosome (BTA)14 [25].…”

Section: Detecting Qtl Using Bayes Gcmentioning

confidence: 99%

“…[26][27][28][29]). The meta-analysis of van den Berg et al [24] detected 80 significant COJO-SNPs (conditional and joint analysis as implemented in GCTA [30]) for fat percentage. Many of these SNPs are not present in our current data due to differences in variant selection criteria and quality control, when processing the sequence data.…”

Section: Detecting Qtl Using Bayes Gcmentioning

confidence: 99%

“…The highest posterior probabilities of the SNPs within each of the two 250-kb regions that are in the top 20 were at 30.112083 Mb and 31.786449 Mb for the first and second peak, respectively. The corresponding positions of the COJO-SNPs detected by [24] were at 30.106314 and 31.909478 Mb, respectively, where the latter variant corresponds to the F279Y mutation in the GHR gene, which has major effects on milk yield and composition [31].…”

Section: Qtl Mappingmentioning

confidence: 99%

“…The 95% credibility interval was between 30.046906 and 30.177482 Mb for the first top-SNP, and between 31.394136 and 32.121047 for the second top-SNP. Both 95%-credibility intervals contained the corresponding COJO SNPs detected by [24]. Posterior probabilities together with their 95% credibility intervals are shown for the QTL at BTA2, 5, 11, and 14 in see Additional file 3: Figures S9 to S12, respectively.…”

Section: Qtl Mappingmentioning

confidence: 99%

“…All these 95% credibility intervals included their corresponding COJO SNPs. At the beginning of BTA14, there are several causal variants that explain the QTL signals [24], and this hampers the positioning of the QTL due The blue bar denotes the 95% credibility interval for the QTL, and the red dot the position of the COJO SNP detected by [23] to carry-over effects of other QTL. The K232A mutation in the DGAT1 gene [25] seems to have been captured by two high peaks at the beginning of BTA14.…”

Section: Qtl Mappingmentioning

confidence: 99%

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On the use of whole-genome sequence data for across-breed genomic prediction and fine-scale mapping of QTL

2021

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Background Whole-genome sequence (WGS) data are increasingly available on large numbers of individuals in animal and plant breeding and in human genetics through second-generation resequencing technologies, 1000 genomes projects, and large-scale genotype imputation from lower marker densities. Here, we present a computationally fast implementation of a variable selection genomic prediction method, that could handle WGS data on more than 35,000 individuals, test its accuracy for across-breed predictions and assess its quantitative trait locus (QTL) mapping precision. Methods The Monte Carlo Markov chain (MCMC) variable selection model (Bayes GC) fits simultaneously a genomic best linear unbiased prediction (GBLUP) term, i.e. a polygenic effect whose correlations are described by a genomic relationship matrix (G), and a Bayes C term, i.e. a set of single nucleotide polymorphisms (SNPs) with large effects selected by the model. Computational speed is improved by a Metropolis–Hastings sampling that directs computations to the SNPs, which are, a priori, most likely to be included into the model. Speed is also improved by running many relatively short MCMC chains. Memory requirements are reduced by storing the genotype matrix in binary form. The model was tested on a WGS dataset containing Holstein, Jersey and Australian Red cattle. The data contained 4,809,520 genotypes on 35,549 individuals together with their milk, fat and protein yields, and fat and protein percentage traits. Results The prediction accuracies of the Jersey individuals improved by 1.5% when using across-breed GBLUP compared to within-breed predictions. Using WGS instead of 600 k SNP-chip data yielded on average a 3% accuracy improvement for Australian Red cows. QTL were fine-mapped by locating the SNP with the highest posterior probability of being included in the model. Various QTL known from the literature were rediscovered, and a new SNP affecting milk production was discovered on chromosome 20 at 34.501126 Mb. Due to the high mapping precision, it was clear that many of the discovered QTL were the same across the five dairy traits. Conclusions Across-breed Bayes GC genomic prediction improved prediction accuracies compared to GBLUP. The combination of across-breed WGS data and Bayesian genomic prediction proved remarkably effective for the fine-mapping of QTL.

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Section: Detecting Qtl Using Bayes Gcmentioning

confidence: 99%

Section: Detecting Qtl Using Bayes Gcmentioning

confidence: 99%

Section: Qtl Mappingmentioning

confidence: 99%

Section: Qtl Mappingmentioning

confidence: 99%

Section: Qtl Mappingmentioning

confidence: 99%

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On the use of whole-genome sequence data for across-breed genomic prediction and fine-scale mapping of QTL

2021

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Using singleton densities to detect recent selection inBos taurus

et al. 2021

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Many quantitative traits are subject to polygenic selection, where several genomic regions undergo small, simultaneous changes in allele frequency that collectively alter a phenotype. The widespread availability of genome data, along with novel statistical techniques, has made it easier to detect these changes. We apply one such method, the “Singleton Density Score” (SDS), to the Holstein breed of Bos taurus to detect recent selection (arising up to around 740 years ago). We identify several genes as candidates for targets of recent selection, including some relating to cell regulation, catabolic processes, neural‐cell adhesion and immunity. We do not find strong evidence that three traits that are important to humans—milk protein content, milk fat content, and stature—have been subject to directional selection. Simulations demonstrate that because B. taurus recently experienced a population bottleneck, singletons are depleted so the power of SDS methods is reduced. These results inform on which genes underlie recent genetic change in B. taurus , while providing information on how polygenic selection can be best investigated in future studies.

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Review: Genetic mutations affecting bull fertility

Pausch

Mapel

2023

animal

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Meta-analysis for milk fat and protein percentage using imputed sequence variant genotypes in 94,321 cattle from eight cattle breeds

Cited by 45 publications

References 48 publications

On the use of whole-genome sequence data for across-breed genomic prediction and fine-scale mapping of QTL

On the use of whole-genome sequence data for across-breed genomic prediction and fine-scale mapping of QTL

Using singleton densities to detect recent selection inBos taurus

Review: Genetic mutations affecting bull fertility

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