Cheng Riyan, Parker Clarissa C, Abney Mark, Palmer Abraham A
Department of Human Genetics, The University of Chicago, Illinois 60637.
G3 (Bethesda). 2013 Oct 3;3(10):1861-7. doi: 10.1534/g3.113.007948.
Genome-wide association studies of complex traits often are complicated by relatedness among individuals. Ignoring or inappropriately accounting for relatedness often results in inflated type I error rates. Either genotype or pedigree data can be used to estimate relatedness for use in mixed-models when undertaking quantitative trait locus mapping. We performed simulations to investigate methods for controlling type I error and optimizing power considering both full and partial pedigrees and, similarly, both sparse and dense marker coverage; we also examined real data sets. (1) When marker density was low, estimating relatedness by genotype data alone failed to control the type I error rate; (2) this was resolved by combining both genotype and pedigree data. (3) When sufficiently dense marker data were used to estimate relatedness, type I error was well controlled and power increased; however, (4) this was only true when the relatedness was estimated using genotype data that excluded genotypes on the chromosome currently being scanned for a quantitative trait locus.
复杂性状的全基因组关联研究常常因个体间的亲缘关系而变得复杂。忽略或不适当地考虑亲缘关系通常会导致第一类错误率膨胀。在进行数量性状基因座定位时，基因型数据或系谱数据均可用于估计亲缘关系，以用于混合模型。我们进行了模拟，以研究在考虑完整和部分系谱以及同样地，稀疏和密集标记覆盖的情况下，控制第一类错误和优化检验效能的方法；我们还检查了实际数据集。（1）当标记密度较低时，仅通过基因型数据估计亲缘关系无法控制第一类错误率；（2）通过结合基因型和系谱数据解决了这一问题。（3）当使用足够密集的标记数据来估计亲缘关系时，第一类错误得到了很好的控制，检验效能提高；然而，（4）只有在使用排除了当前正在扫描数量性状基因座的染色体上的基因型的基因型数据来估计亲缘关系时才是如此。
