Multitrait tests can improve power to detect associations between individual single-nucleotide polymorphisms (SNPs) and several related traits. Here, we develop methods for multi-SNP transcriptome-wide association (TWAS) tests to test the association between predicted gene expression levels and multiple phenotypes. We show that the correlation in TWAS test statistics for multiple phenotypes has the same form as multitrait statistics for the single-SNP setting. Thus, established methods for combining single-SNP test statistics across multiple traits can be extended directly to the TWAS setting. We performed an extensive evaluation across eight multitrait methods in simulations that varied gene-phenotype effect sizes in addition to the underlying covariance structure among the phenotypes. We found that all multitrait TWAS tests have well-calibrated Type I error (except ASSET, which can have a slightly elevated or depressed Type I error rate). Our results show that multitrait TWAS can improve statistical power compared with multiple single-trait TWAS followed by Bonferroni correction. To illustrate our approach to real data, we conducted a multitrait TWAS of four circulating lipid traits from the Global Lipids Genetics Consortium. We found that our multitrait Wald TWAS approach identified 506 genes associated with lipid levels compared with 87 identified through Bonferroni-corrected single-trait TWAS. Overall, we find that our proposed multitrait TWAS framework outperforms single-trait approaches to identify new genetic associations, especially for functionally correlated phenotypes and phenotypes with overlapping genome-wide association studies samples, leading to insights into the genetic architecture of multiple phenotypes.

中文翻译：

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多性状全转录组关联研究 (TWAS) 测试

多性状测试可以提高检测单个单核苷酸多态性 (SNP) 与多个相关性状之间关联的能力。在这里，我们开发了用于多 SNP 转录组范围关联 (TWAS) 测试的方法，以测试预测的基因表达水平与多种表型之间的关联。我们表明，多种表型的 TWAS 测试统计数据中的相关性与单 SNP 设置的多性状统计数据具有相同的形式。因此，可以将已建立的跨多个性状组合单 SNP 测试统计数据的方法直接扩展到 TWAS 设置。我们在模拟中对八种多性状方法进行了广泛的评估，除了表型之间的潜在协方差结构之外，还改变了基因表型效应大小。我们发现所有多特征 TWAS 测试都具有经过良好校准的 I 类错误（ASSET 除外，它的 I 类错误率可能略有升高或降低）。我们的结果表明，与多个单性状 TWAS 之后进行 Bonferroni 校正相比，多性状 TWAS 可以提高统计功效。为了说明我们处理真实数据的方法，我们对全球脂质遗传学联盟的四种循环脂质性状进行了多性状 TWAS。我们发现我们的多性状 Wald TWAS 方法确定了 506 个与脂质水平相关的基因，而通过 Bonferroni 校正的单性状 TWAS 确定了 87 个。总的来说，我们发现我们提出的多性状 TWAS 框架优于单性状方法来识别新的遗传关联，