SAIGE-GENE+ improves the efficiency and accuracy of set-based rare variant association tests.
SAIGE-GENE+ improves the efficiency and accuracy of set-based rare variant association tests.
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DOI：
10.1038/s41588-022-01178-w
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发表时间：
2022-10
期刊：
影响因子：
30.8
通讯作者：
Lee, Seunggeun
中科院分区：
文献类型：
--
作者：
Zhou, Wei;Bi, Wenjian;Zhao, Zhangchen;Dey, Kushal K.;Jagadeesh, Karthik A.;Karczewski, Konrad J.;Daly, Mark J.;Neale, Benjamin M.;Lee, Seunggeun
包括英国生物银行（UKBB）在内的几个生物银行正在生成大规模测序数据。现有的方法SAIGE-GENE在测试次要等位基因频率（MAF）≤ 1%的变体时表现良好，但是当限制于MAF ≤ 0.1%或0.01%的变体时，在基于方差分量集的测试中观察到膨胀。在这里，我们提出了SAIGE-GENE+，大大提高了I型错误控制和计算效率，以促进大规模数据中的罕见变异测试。我们进一步表明，将多个MAF截止值和功能注释可以提高功率，从而发现新的基因-表型关联。在对30个数量性状和141个二元性状的UKBB全外显子组测序数据的分析中，SAIGE-GENE+确定了551个基因-表型关联。SAIGE-GENE+通过折叠超罕见变异并进行对应于不同次要等位基因频率截止值和注释的多个测试，以改进的1型错误控制和计算效率执行基于集合的罕见变异关联测试。
Several biobanks, including UK Biobank (UKBB), are generating large-scale sequencing data. An existing method, SAIGE-GENE, performs well when testing variants with minor allele frequency (MAF) ≤ 1%, but inflation is observed in variance component set-based tests when restricting to variants with MAF ≤ 0.1% or 0.01%. Here, we propose SAIGE-GENE+ with greatly improved type I error control and computational efficiency to facilitate rare variant tests in large-scale data. We further show that incorporating multiple MAF cutoffs and functional annotations can improve power and thus uncover new gene–phenotype associations. In the analysis of UKBB whole exome sequencing data for 30 quantitative and 141 binary traits, SAIGE-GENE+ identified 551 gene–phenotype associations. SAIGE-GENE+ performs set-based rare variant association tests with improved type 1 error control and computational efficiency by collapsing ultra-rare variants and conducting multiple tests corresponding to different minor allele frequency cutoffs and annotations.
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影响因子：
64.8
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