Estimating Effects and Making Predictions from Genome-Wide Marker Data

By Michael E. Goddard, Naomi R. Wray, K. Verbyla, Peter M. Visscher. 2010

161 citations recorded by the source corpus.

In genome-wide association studies (GWAS), hundreds of thousands of genetic markers (SNPs) are tested for association with a trait or phenotype. Reported effects tend to be larger in magnitude than the true effects of these markers, the so-called ``winner's curse.'' We argue that the classical definition of unbiasedness is not useful in this context and propose to use a different definition of unbiasedness that is a property of the estimator we advocate. We suggest an integrated approach to the estimation of the SNP effects and to the prediction of trait values, treating SNP effects as random instead of fixed effects. Statistical methods traditionally used in the prediction of trait values in the genetics of livestock, which predates the availability of SNP data, can be applied to analysis of GWAS, giving better estimates of the SNP effects and predictions of phenotypic and genetic values in individuals.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/0d0d8eed1362f79992b5fa066ce5edb414f824f8

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In Genetics

Citations in the corpus

Genetic evidence of assortative mating in humans (2017, background): “[3] reveals that 7 million couples would be required in order to detect a deviation from expectation in genome-wide sharing.”
Estimating missing heritability for disease from genome-wide association studies. (2011, background): “’’(10,11) We have argued previously that the two most plausible explanations for these observations are that either the effect sizes at individual SNPs are so small that they do not reach genome-wide significance in GWAS or that causal variants are not in sufficient LD with SNPs on the commercial ar”
Power and Predictive Accuracy of Polygenic Risk Scores (2013, background): “Shrinkage estimation leads to reduced mean square error for prediction and has some other advantages [35,36], but in the main applications for polygenic scores to date, namely association testing and AUC, it does not improve over the linear regression estimate.”
Naïve Bayesian Classifier and Genetic Risk Score for Genetic Risk Prediction of a Categorical Trait: Not so Different after all! (2012, background): “As expected based on our mathematical calculations, the AUCs of the genetic risk models based on the NBCs and the GRSs with a genotypic weights are identical (Figure 3, left panel), and the predicted probabilities are almost identical (Figure 3, right panel).”
Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores (2015, methodology): “This prediction is optimal in the sense that it minimizes the prediction error variance.(36) Under the linear model described above, the posterior mean phenotype given GWAS summary statistics and LD is”
FaST linear mixed models for genome-wide association studies (2011, methodology): “, SNP effects) integrated over independent Normal distributions having the same variance [9, 10].”
Genomic Relationship Matrix for Correcting Pedigree Errors in Breeding Populations: Impact on Genetic Parameters and Genomic Selection Accuracy (2014, methodology): “The models are then tested in a validation population to obtain GS predicted BVs (GS-BV) and estimate the accuracy of genomic prediction (Goddard et al., 2009).”