Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark.
The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK.
Sci Rep. 2017 May 25;7(1):2413. doi: 10.1038/s41598-017-02281-3.
The genomic best linear unbiased prediction (GBLUP) model has proven to be useful for prediction of complex traits as well as estimation of population genetic parameters. Improved inference and prediction accuracy of GBLUP may be achieved by identifying genomic regions enriched for causal genetic variants. We aimed at searching for patterns in GBLUP-derived single-marker statistics, by including them in genetic marker set tests, that could reveal associations between a set of genetic markers (genomic feature) and a complex trait. GBLUP-derived set tests proved to be powerful for detecting genomic features, here defined by gene ontology (GO) terms, enriched for causal variants affecting a quantitative trait in a population with low degree of relatedness. Different set test approaches were compared using simulated data illustrating the impact of trait- and genomic feature-specific factors on detection power. We extended the most powerful single trait set test, covariance association test (CVAT), to a multiple trait setting. The multiple trait CVAT (MT-CVAT) identified functionally relevant GO categories associated with the quantitative trait, chill coma recovery time, in the unrelated, sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel.
基因组最佳线性无偏预测（GBLUP）模型已被证明可用于预测复杂性状和估计群体遗传参数。通过鉴定因果遗传变异富集的基因组区域，可以提高 GBLUP 的推断和预测准确性。我们旨在通过将单标记统计数据纳入遗传标记集测试中，来搜索 GBLUP 衍生的单标记统计数据中的模式，这些模式可以揭示一组遗传标记（基因组特征）与复杂性状之间的关联。GBLUP 衍生的集合测试被证明可用于检测基因组特征，这里通过基因本体（GO）术语来定义，这些特征富集了影响低亲缘关系群体中数量性状的因果变异。使用模拟数据比较了不同的集合测试方法，这些数据说明了性状和基因组特征特定因素对检测能力的影响。我们将最强大的单性状集合测试，协方差关联测试（CVAT）扩展到多性状设置。多性状 CVAT（MT-CVAT）鉴定了与数量性状，即黑腹果蝇遗传参考面板无关的测序近交系的冷昏迷恢复时间相关的功能相关 GO 类别。
