POIROT: a powerful test for parent-of-origin effects in unrelated samples leveraging multiple phenotypes.
POIROT: a powerful test for parent-of-origin effects in unrelated samples leveraging multiple phenotypes.
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DOI：
10.1093/bioinformatics/btad199
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发表时间：
2023-04-03
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对鉴定表现出亲本来源效应（POE）的遗传变体存在广泛的兴趣，其中等位基因对表型表达的影响取决于其亲本来源。POE可以由不同的现象产生，包括基因组印记，并已被记录为许多复杂的性状。传统的检测需要家庭数据来确定传递等位基因的父母来源。由于大多数全基因组关联研究（GWAS）对无关个体进行采样（其中等位基因亲本来源未知），因此在此类数据集中研究POE需要复杂的统计方法，这些方法利用我们预期在POE存在时观察到的遗传模式。我们提出了一种方法，以提高大规模GWAS样本中POE变体的发现，该方法利用了此类研究中经常收集的多个相关性状之间的潜在多效性。我们的方法比较了杂合子的表型协方差矩阵的基础上，一个强大的综合测试纯合子。我们将我们的方法称为使用多个数量性状的鲁棒综合测试（POIROT）的起源推断的亲本。通过模拟研究，我们比较了POIROT的竞争单变量方差为基础的方法，认为每个表型的单独分析。我们观察到，与单变量方法相比，POIROT校准良好，检测POE的能力有所提高。POIROT对表型的非正态性具有稳健性，并可调整人群分层和其他混杂因素。最后，我们将POIROT应用于来自英国生物银行的GWAS数据，使用BMI和两种胆固醇表型。我们确定了338个全基因组的重要位点进行后续调查。该方法的代码可在https://github.com/staylorhead/POIROT-POE上获得。
There is widespread interest in identifying genetic variants that exhibit parent-of-origin effects (POEs) wherein the effect of an allele on phenotype expression depends on its parental origin. POEs can arise from different phenomena including genomic imprinting and have been documented for many complex traits. Traditional tests for POEs require family data to determine parental origins of transmitted alleles. As most genome-wide association studies (GWAS) sample unrelated individuals (where allelic parental origin is unknown), the study of POEs in such datasets requires sophisticated statistical methods that exploit genetic patterns we anticipate observing when POEs exist. We propose a method to improve discovery of POE variants in large-scale GWAS samples that leverages potential pleiotropy among multiple correlated traits often collected in such studies. Our method compares the phenotypic covariance matrix of heterozygotes to homozygotes based on a Robust Omnibus Test. We refer to our method as the Parent of Origin Inference using Robust Omnibus Test (POIROT) of multiple quantitative traits. Through simulation studies, we compared POIROT to a competing univariate variance-based method which considers separate analysis of each phenotype. We observed POIROT to be well-calibrated with improved power to detect POEs compared to univariate methods. POIROT is robust to non-normality of phenotypes and can adjust for population stratification and other confounders. Finally, we applied POIROT to GWAS data from the UK Biobank using BMI and two cholesterol phenotypes. We identified 338 genome-wide significant loci for follow-up investigation. The code for this method is available at https://github.com/staylorhead/POIROT-POE.
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