Effect direction meta-analysis of GWAS identifies extreme, prevalent and shared pleiotropy in a large mammal

Xiang, Ruidong; Berg, I. van den; MacLeod, Iona M.; Daetwyler, Hans D.; Goddard, Michael E.

doi:10.1038/s42003-020-0823-6

Cited by 32 publications

(35 citation statements)

References 47 publications

(80 reference statements)

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“…This is also consistent with our previous observation, i.e. that the comparison of the directions of variant effects across GWAS results is more powerful than the comparison of p-values across GWAS [12]. As previously stated, different LD structures in different GWAS populations can lead to different selections of top variants using the same p-value threshold.…”

Section: Discussionsupporting

confidence: 92%

“…The GWAS for the Australian dataset were performed across breeds, but separately for bulls (AUSB) and cows (AUSC). The Australian animals and the GWAS model are described in a previous report [12]. Briefly, the AUSB dataset contained 9739 Holstein, 2059 Jersey and 125 Australian Red bulls, and the AUSC dataset consisted of 22,899 Holstein, 6174 Jersey, 424 Australian Red and 2850 crossbred cows.…”

Section: Phenotypes Used For Within-population Gwasmentioning

confidence: 99%

“…To validate QTL, we compared the direction of the Z-score and p-value in the meta-analyses. Our previous study showed that the comparison of the direction of the effect of variants across different GWAS results can be more powerful in detecting consistent signals than the sole comparison of p-values between different GWAS [12].…”

Section: Validation Meta-analysismentioning

confidence: 99%

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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

et al. 2020

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Background: Sequence-based genome-wide association studies (GWAS) provide high statistical power to identify candidate causal mutations when a large number of individuals with both sequence variant genotypes and phenotypes is available. A meta-analysis combines summary statistics from multiple GWAS and increases the power to detect trait-associated variants without requiring access to data at the individual level of the GWAS mapping cohorts. Because linkage disequilibrium between adjacent markers is conserved only over short distances across breeds, a multi-breed meta-analysis can improve mapping precision. Results: To maximise the power to identify quantitative trait loci (QTL), we combined the results of nine within-population GWAS that used imputed sequence variant genotypes of 94,321 cattle from eight breeds, to perform a largescale meta-analysis for fat and protein percentage in cattle. The meta-analysis detected (p ≤ 10 −8) 138 QTL for fat percentage and 176 QTL for protein percentage. This was more than the number of QTL detected in all within-population GWAS together (124 QTL for fat percentage and 104 QTL for protein percentage). Among all the lead variants, 100 QTL for fat percentage and 114 QTL for protein percentage had the same direction of effect in all within-population GWAS. This indicates either persistence of the linkage phase between the causal variant and the lead variant across breeds or that some of the lead variants might indeed be causal or tightly linked with causal variants. The percentage of intergenic variants was substantially lower for significant variants than for non-significant variants, and significant variants had mostly moderate to high minor allele frequencies. Significant variants were also clustered in genes that are known to be relevant for fat and protein percentages in milk. Conclusions: Our study identified a large number of QTL associated with fat and protein percentage in dairy cattle. We demonstrated that large-scale multi-breed meta-analysis reveals more QTL at the nucleotide resolution than within-population GWAS. Significant variants were more often located in genic regions than non-significant variants and a large part of them was located in potentially regulatory regions.

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Section: Discussionsupporting

confidence: 92%

Section: Phenotypes Used For Within-population Gwasmentioning

confidence: 99%

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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

et al. 2020

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“…Further integration of our QTLs with functional annotations of a range of tissues from the on-going Functional Annotation of Animal Genomes (FAANG) project will provide valuable opportunities to understand transcriptional/post-transcriptional regulatory mechanisms underpinning GWAS hits for agronomic traits 20 . Our study will enable exploring the molecular mechanisms underlying the extensive pleiotropic effects identified in livestock 21 . This information will allow the understanding of mechanisms of response to intended selection as well as disentangling unintended and unfavorable correlated responses to this same selection (e.g.…”

Section: Discussionmentioning

confidence: 99%

A comprehensive catalogue of regulatory variants in the cattle transcriptome

Liu

Gao

Canela-Xandri

et al. 2020

Preprint

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Characterization of genetic regulatory variants acting on the transcriptome of livestock is essential for interpreting the molecular mechanisms underlying traits of economic value and for increasing the rate of genetic gain through artificial selection. Here, we build a cattle Genotype-Tissue Expression atlas (cGTEx, http://cgtex.roslin.ed.ac.uk/) for the research community based on 11,642 RNA sequences from publicly available datasets representing over 100 cattle tissues. We describe the landscape of the transcriptome across tissues and report hundreds of thousands of cis- and trans- genetic associations with gene expression and alternative splicing for 24 major tissues. We evaluate the specificity/similarity of these genetic regulatory effects across tissues, and functionally annotate them using a combination of multi-omics data. Finally, we link gene expression in different tissues to 43 economically important traits using a large transcriptome-wide association study (TWAS) to provide novel biological insights into the molecular regulatory mechanisms underpinning agronomic traits in cattle.

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“…This reduced the number of SNP from 633,375 to 316,396 (pruned HDnGBS), making genomic prediction analysis more computationally efficient. We tested the accuracy of the full panel versus the pruned panel in several analyses and found no significant difference between the full and the reduced marker sets, so we presented only the GP with pruned HDnGBS genotypes in this paper; and 2 https://datagene.com.au/ (3) customized set of 46,516 SNP (XT_50k) which were selected from whole genome sequence according to multiple criteria to be closer to or potentially the causal mutations for 34 economically important traits in dairy cattle (Xiang et al, 2019(Xiang et al, , 2020.…”

Section: Genotypesmentioning

confidence: 99%

Improving Genomic Prediction of Crossbred and Purebred Dairy Cattle

et al. 2020

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This study assessed the accuracy and bias of genomic prediction (GP) in purebred Holstein (H) and Jersey (J) as well as crossbred (H and J) validation cows using different reference sets and prediction strategies. The reference sets were made up of different combinations of 36,695 H and J purebreds and crossbreds. Additionally, the effect of using different sets of marker genotypes on GP was studied (conventional panel: 50k, custom panel enriched with, or close to, causal mutations: XT_50k, and conventional high-density with a limited custom set: pruned HDnGBS). We also compared the use of genomic best linear unbiased prediction (GBLUP) and Bayesian (emBayesR) models, and the traits tested were milk, fat, and protein yields. On average, by including crossbred cows in the reference population, the prediction accuracies increased by 0.01–0.08 and were less biased (regression coefficient closer to 1 by 0.02–0.16), and the benefit was greater for crossbreds compared to purebreds. The accuracy of prediction increased by 0.02 using XT_50k compared to 50k genotypes without affecting the bias. Although using pruned HDnGBS instead of 50k also increased the prediction accuracy by about 0.02, it increased the bias for purebred predictions in emBayesR models. Generally, emBayesR outperformed GBLUP for prediction accuracy when using 50k or pruned HDnGBS genotypes, but the benefits diminished with XT_50k genotypes. Crossbred predictions derived from a joint pure H and J reference were similar in accuracy to crossbred predictions derived from the two separate purebred reference sets and combined proportional to breed composition. However, the latter approach was less biased by 0.13. Most interestingly, using an equalized breed reference instead of an H-dominated reference, on average, reduced the bias of prediction by 0.16–0.19 and increased the accuracy by 0.04 for crossbred and J cows, with a little change in the H accuracy. In conclusion, we observed improved genomic predictions for both crossbreds and purebreds by equalizing breed contributions in a mixed breed reference that included crossbred cows. Furthermore, we demonstrate, that compared to the conventional 50k or high-density panels, our customized set of 50k sequence markers improved or matched the prediction accuracy and reduced bias with both GBLUP and Bayesian models.

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Effect direction meta-analysis of GWAS identifies extreme, prevalent and shared pleiotropy in a large mammal

Cited by 32 publications

References 47 publications

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

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

A comprehensive catalogue of regulatory variants in the cattle transcriptome

Improving Genomic Prediction of Crossbred and Purebred Dairy Cattle

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