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Open AccessJournal ArticleDOI

Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels

TLDR
This work assessed the gain in accuracy of GEBV in Jersey cattle as a result of using a combined Holstein and Jersey reference population, with either 39,745 or 624,213 single nucleotide polymorphism (SNP) markers.
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This article is published in Journal of Dairy Science.The article was published on 2012-07-01 and is currently open access. It has received 551 citations till now. The article focuses on the topics: Population.

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Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Bjarni J. Vilhjálmsson, +394 more
TL;DR: LDpred is introduced, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel, and outperforms the approach of pruning followed by thresholding, particularly at large sample sizes.
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Advantages and pitfalls in the application of mixed-model association methods

TL;DR: The advantages and pitfalls of MLMA methods as a function of study design are described and quantify and recommendations for the application of these methods in practical settings are provided.
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Polygenic modeling with bayesian sparse linear mixed models.

TL;DR: This work applies Bayesian sparse linear mixed model (BSLMM) and compares it with other methods for two polygenic modeling applications: estimating the proportion of variance in phenotypes explained (PVE) by available genotypes, and phenotype (or breeding value) prediction, and demonstrates that BSLMM considerably outperforms either of the other two methods.
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Polygenic prediction via Bayesian regression and continuous shrinkage priors.

TL;DR: Polygenic risk scores method PRS-CS is presented, a polygenic prediction method that infers posterior effect sizes of single nucleotide polymorphisms (SNPs) using genome-wide association summary statistics and an external linkage disequilibrium (LD) reference panel.
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Pitfalls of predicting complex traits from SNPs

TL;DR: Some of the limitations and pitfalls of prediction analysis are discussed and how naive implementations can lead to severe bias and misinterpretation of results are shown.
References
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Journal ArticleDOI

Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps

TL;DR: It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.
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Efficient Methods to Compute Genomic Predictions

TL;DR: Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and to estimate thousands of marker effects simultaneously, and a blend of first- and second-order Jacobi iteration using 2 separate relaxation factors converged well for allele frequencies and effects.
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Common SNPs explain a large proportion of the heritability for human height

TL;DR: Evidence is provided that the remaining heritability is due to incomplete linkage disequilibrium between causal variants and genotyped SNPs, exacerbated by causal variants having lower minor allele frequency than the SNPs explored to date.
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A Unified Approach to Genotype Imputation and Haplotype-Phase Inference for Large Data Sets of Trios and Unrelated Individuals

TL;DR: It is demonstrated that substantial gains in imputation accuracy accrue with increasingly large reference panel sizes, particularly when imputing low-frequency variants, and that unphased reference panels can provide highly accurate genotype imputation.
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