Estimating Effects and Making Predictions from Genome-Wide Marker Data
Estimating Effects and Making Predictions from Genome-Wide Marker Data
复制标题
DOI：
10.1214/09-sts306
复制
发表时间：
2009-11-01
影响因子：
5.7
通讯作者：
Visscher, Peter M.
中科院分区：
文献类型：
--
作者：
Goddard, Michael E.;Wray, Naomi R.;Visscher, Peter M.
在全基因组关联研究中，数十万个遗传标记(SNPs)被测试与某一性状或表型的关联。报道的影响往往比这些标记物的真实影响更大，也就是所谓的“赢家诅咒”。我们认为经典的无偏性定义在这种情况下没有用处，并建议使用不同的无偏性定义，这是我们倡导的估计量的一个性质。我们提出了一种估计SNP效应和预测性状值的综合方法，将SNP效应视为随机效应，而不是固定效应。在SNP数据可用之前，传统上用于预测家畜遗传学中的性状值的统计方法可以应用于GWAS的分析，更好地估计SNP效应以及对个体的表型和遗传值的预测。
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.