Regeneron Genetics Center, Tarrytown, New York, United States of America.
Department of Statistics, The University of Chicago, Chicago, Illinois, United States of America.
PLoS Genet. 2023 Nov 7;19(11):e1011020. doi: 10.1371/journal.pgen.1011020. eCollection 2023 Nov.
In genetic association analysis of complex traits, permutation testing can be a valuable tool for assessing significance when the distribution of the test statistic is unknown or not well-approximated. This commonly arises, e.g, in tests of gene-set, pathway or genome-wide significance, or when the statistic is formed by machine learning or data adaptive methods. Existing applications include eQTL mapping, association testing with rare variants, inclusion of admixed individuals in genetic association analysis, and epistasis detection among many others. For genetic association testing in samples with population structure and/or relatedness, use of naive permutation can lead to inflated type 1 error. To address this in quantitative traits, the MVNpermute method was developed. However, for association mapping of a binary trait, the relationship between the mean and variance makes both naive permutation and the MVNpermute method invalid. We propose BRASS, a permutation method for binary traits, for use in association mapping in structured samples. In addition to modeling structure in the sample, BRASS allows for covariates, ascertainment and simultaneous testing of multiple markers, and it accommodates a wide range of test statistics. In simulation studies, we compare BRASS to other permutation and resampling-based methods in a range of scenarios that include population structure, familial relatedness, ascertainment and phenotype model misspecification. In these settings, we demonstrate the superior control of type 1 error by BRASS compared to the other 6 methods considered. We apply BRASS to assess genome-wide significance for association analyses in domestic dog for elbow dysplasia (ED) and idiopathic epilepsy (IE). For both traits we detect previously identified associations, and in addition, for ED, we detect significant association with a SNP on chromosome 35 that was not detected by previous analyses, demonstrating the potential of the method.
在复杂性状的遗传关联分析中，当检验统计量的分布未知或无法很好地近似时，置换检验可以成为评估显著性的有用工具。这种情况通常出现在基因集、途径或全基因组显著性检验中，或者当统计量是通过机器学习或数据自适应方法形成时。现有的应用包括 eQTL 映射、罕见变异关联检验、在遗传关联分析中纳入混合个体，以及检测多位点互作等。对于具有群体结构和/或亲缘关系的样本中的遗传关联检验，使用简单置换会导致Ⅰ型错误膨胀。为了解决这个问题，开发了 MVNpermute 方法。然而，对于二元性状的关联映射，均值和方差之间的关系使得简单置换和 MVNpermute 方法都无效。我们提出了 BRASS，一种用于二元性状的置换方法，用于在结构样本中进行关联映射。BRASS 除了对样本中的结构进行建模外，还允许协变量、确定和同时测试多个标记，并且它适应广泛的检验统计量。在模拟研究中，我们在包括群体结构、家族亲缘关系、确定和表型模型误设定在内的一系列场景中，将 BRASS 与其他置换和基于重采样的方法进行比较。在这些设置中，我们证明了 BRASS 相对于其他 6 种方法在控制Ⅰ型错误方面的优越性。我们应用 BRASS 来评估家犬肘发育不良（ED）和特发性癫痫（IE）的全基因组关联分析的显著性。对于这两个性状，我们都检测到了先前确定的关联，并且对于 ED，我们还检测到了与 35 号染色体上的 SNP 的显著关联，而这一 SNP 先前的分析并未检测到，这证明了该方法的潜力。
