Priors, population sizes, and power in genome-wide hypothesis tests.
Priors, population sizes, and power in genome-wide hypothesis tests.
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DOI：
10.1186/s12859-023-05261-9
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发表时间：
2023-04-26
影响因子：
3
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中科院分区：
文献类型：
--
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全基因组测试，包括种系遗传变异的全基因组关联研究（GWAS）、癌症体细胞突变的驱动测试和RNAseq数据的转录组全关联测试，具有很高的多重测试负担。这种负担可以通过招募更大的队列来克服，或者通过使用先前的生物学知识来支持某些假设而不是其他假设来减轻。在这里，我们比较了这两种方法在提高假设检验能力方面的能力。我们对队列规模的进展进行了定量估计，并对神谕硬先验的力量进行了理论分析：选择假设子集进行测试的先验，具有神谕保证所有真阳性都在测试子集内。这一理论表明，对于GWAS，将检测限制在100-1000个基因的强大先验比典型的每年20-40%的队列规模增长提供的力量要小。此外，即使从测试集中排除一小部分真阳性的非神谕先验也比根本不使用先验表现得更差。我们的结果为简单、无偏的单变量假设检验在GWAS中持续占据主导地位提供了理论解释：如果一个统计问题可以通过更大的队列规模来回答，那么它应该通过更大的队列规模来回答，而不是通过更复杂的涉及先验的偏置方法。我们认为先验更适合于生物学的非统计方面，如通路结构和因果关系，这些还不容易被标准假设检验捕获。
Genome-wide tests, including genome-wide association studies (GWAS) of germ-line genetic variants, driver tests of cancer somatic mutations, and transcriptome-wide association tests of RNAseq data, carry a high multiple testing burden. This burden can be overcome by enrolling larger cohorts or alleviated by using prior biological knowledge to favor some hypotheses over others. Here we compare these two methods in terms of their abilities to boost the power of hypothesis testing. We provide a quantitative estimate for progress in cohort sizes and present a theoretical analysis of the power of oracular hard priors: priors that select a subset of hypotheses for testing, with an oracular guarantee that all true positives are within the tested subset. This theory demonstrates that for GWAS, strong priors that limit testing to 100–1000 genes provide less power than typical annual 20–40% increases in cohort sizes. Furthermore, non-oracular priors that exclude even a small fraction of true positives from the tested set can perform worse than not using a prior at all. Our results provide a theoretical explanation for the continued dominance of simple, unbiased univariate hypothesis tests for GWAS: if a statistical question can be answered by larger cohort sizes, it should be answered by larger cohort sizes rather than by more complicated biased methods involving priors. We suggest that priors are better suited for non-statistical aspects of biology, such as pathway structure and causality, that are not yet easily captured by standard hypothesis tests.
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基因发现和多基因预测，从基因组全基因组协会的教育程度研究中，有110万个人。
影响因子：
30.8
作者：
Lee JJ;Wedow R;Okbay A;Kong E;Maghzian O;Zacher M;Nguyen-Viet TA;Bowers P;Sidorenko J;Karlsson Linnér R;Fontana MA;Kundu T;Lee C;Li H;Li R;Royer R;Timshel PN;Walters RK;Willoughby EA;Yengo L;23andMe Research Team;COGENT (Cognitive Genomics Consortium);Social Science Genetic Association Consortium;Alver M;Bao Y;Clark DW;Day FR;Furlotte NA;Joshi PK;Kemper KE;Kleinman A;Langenberg C;Mägi R;Trampush JW;Verma SS;Wu Y;Lam M;Zhao JH;Zheng Z;Boardman JD;Campbell H;Freese J;Harris KM;Hayward C;Herd P;Kumari M;Lencz T;Luan J;Malhotra AK;Metspalu A;Milani L;Ong KK;Perry JRB;Porteous DJ;Ritchie MD;Smart MC;Smith BH;Tung JY;Wareham NJ;Wilson JF;Beauchamp JP;Conley DC;Esko T;Lehrer SF;Magnusson PKE;Oskarsson S;Pers TH;Robinson MR;Thom K;Watson C;Chabris CF;Meyer MN;Laibson DI;Yang J;Johannesson M;Koellinger PD;Turley P;Visscher PM;Benjamin DJ;Cesarini D
通讯作者：
Cesarini D
影响因子：
30.8
作者：
Williams MJ;Werner B;Barnes CP;Graham TA;Sottoriva A
通讯作者：
Sottoriva A
影响因子：
64.5
作者：
Boyle EA;Li YI;Pritchard JK
通讯作者：
Pritchard JK
影响因子：
14.9
作者：
Buniello, Annalisa;MacArthur, Jacqueline A. L.;Parkinson, Helen
通讯作者：
Parkinson, Helen
DOI：
10.1007/978-1-62703-447-0_25
发表时间：
2013-01-01
期刊：
Methods in molecular biology (Clifton, N.J.)
影响因子：
--
作者：
Petersen, Ashley;Spratt, Justin;Tintle, Nathan L
通讯作者：
Tintle, Nathan L