mBAT-combo: A more powerful test to detect gene-trait associations from GWAS data.
mBAT-combo: A more powerful test to detect gene-trait associations from GWAS data.
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mBAT-combo：一种更强大的测试，用于从 GWAS 数据中检测基因-性状关联。
DOI：
10.1016/j.ajhg.2022.12.006
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发表时间：
2023
影响因子：
9.8
通讯作者：
Zeng,Jian
中科院分区：
文献类型：
--
作者：
Li,Ang;Liu,Shouye;Bakshi,Andrew;Jiang,Longda;Chen,Wenhan;Zheng,Zhili;Sullivan,PatrickF;Visscher,PeterM;Wray,NaomiR;Yang,Jian;Zeng,Jian
基于基因的关联测试将多个 SNP 性状关联聚合到由基因边界定义的集合中，并广泛用于 GWAS 后分析。基于基因的测试的常见方法是通过计算 χ2 统计量的总和来组合 SNP 关联。然而，该策略忽略了 SNP 效应的方向，这可能会导致具有掩蔽效应的 SNP 功效丧失，例如，当两个 SNP 效应与连锁不平衡 (LD) 相关性的乘积为负时。在这里，我们介绍“mBAT-combo”，这是一种基于集合的测试，比其他方法更能在掩蔽效应的背景下检测多 SNP 关联。我们通过模拟和实际数据的应用来验证该方法。我们发现，在英国生物库的 35 个血液和尿液生物标志物性状中，有 34 个性状在总共 4,273 个基因-性状对中显示出掩蔽效应的证据，表明掩蔽效应在复杂性状中很常见。我们进一步验证了我们的方法在不同 GWAS 样本量的身高、体重指数和精神分裂症方面的改进能力，结果表明，平均 95.7% 的仅通过 mBAT-combo 检测到的较小样本量的基因可以通过单 SNP 方法识别，样本量增加了 1.7 倍。仅在 mBAT 组合中对精神分裂症有意义的 11 个基因通过功能知情的精细作图或整合基因表达数据的孟德尔随机化得到证实。 mBAT-combo 的框架可应用于任何一组 SNP，以细化隐藏在具有复杂 LD 结构的基因组区域中的性状关联信号。
Gene-based association tests aggregate multiple SNP-trait associations into sets defined by gene boundaries and are widely used in post-GWAS analysis. A common approach for gene-based tests is to combine SNPs associations by computing the sum of χ2statistics. However, this strategy ignores the directions of SNP effects, which could result in a loss of power for SNPs with masking effects, e.g., when the product of two SNP effects and the linkage disequilibrium (LD) correlation is negative. Here, we introduce "mBAT-combo," a set-based test that is better powered than other methods to detect multi-SNP associations in the context of masking effects. We validate the method through simulations and applications to real data. We find that of 35 blood and urine biomarker traits in the UK Biobank, 34 traits show evidence for masking effects in a total of 4,273 gene-trait pairs, indicating that masking effects is common in complex traits. We further validate the improved power of our method in height, body mass index, and schizophrenia with different GWAS sample sizes and show that on average 95.7% of the genes detected only by mBAT-combo with smaller sample sizes can be identified by the single-SNP approach with a 1.7-fold increase in sample sizes. Eleven genes significant only in mBAT-combo for schizophrenia are confirmed by functionally informed fine-mapping or Mendelian randomization integrating gene expression data. The framework of mBAT-combo can be applied to any set of SNPs to refine trait-association signals hidden in genomic regions with complex LD structures.