关于基因多效性检验中互斥假设检验的总结与探索
Summary and Exploration of Exclusive Hypothesis Test of Pleiotropy
DOI: 10.12677/AAM.2023.124159, PDF,   
作者: 范宇轩, 李兰玉*：青岛大学数学与统计学院，山东 青岛
关键词: 互斥检验；基因多效性；汇总统计量；Exclusive Hypothesis Test； Pleiotropy； Summary Statistics
摘要: 基因多效性是指单个基因能够影响多个性状的表达水平。能够有效检测基因多效性的存在并了解其产生的原因具有重要的统计学以及生物学意义。复杂原假设条件下的互斥检验方法十分适用于该类问题且具有较好的说服力。不失一般性的情况下，本文主要针对于两个性状的汇总统计量进行研究。我们将新构建的方法与IUT (intersection-union test)、PLACO (pleiotropic analysis under composite null hypothesis)、DACT (divide-aggregate composite-null test)、MAIUP (mixture-adjusted inter-section-union test for pleiotropy test)几种方法进行了对比。模拟结果显示，新方法与MAIUP方法相比在能够在某些特定的情形下能够更好的控制一类错误同时保证检验功效。最后将上述方法应用于冠状动脉疾病以及扁桃体切除的数据来进行实证分析。
Abstract: Pleiotropy means single gene can influence multiple traits. Detection and understanding of pleiot-ropy are statistically and biologically important. Mutually exclusive test under composite null hy-pothesis is a suitable and convincing approach. Without loss of generality, we mainly focus on ana-lyzing summary statistics of two traits in this study. We compared our methods with IUT, PLACO, DACT, and MAIUP through simulations. Simulations show that the type I error can be controlled better by proposed method compared with MAIUP under specific situations with maintaining the statistical power simultaneously.
文章引用：范宇轩, 李兰玉. 关于基因多效性检验中互斥假设检验的总结与探索[J]. 应用数学进展, 2023, 12(4): 1531-1548. https://doi.org/10.12677/AAM.2023.124159

参考文献

[1] 徐大胜. 遗传与作物育种[M]. 成都: 四川大学出版社, 2011: 48.
[2] Chung, D., Yang, C., Li, C., Gelernter, J. and Zhao, H. (2014) GPA: A Statistical Approach to Prioritizing GWAS Results by Integrating Pleiotropy and Annotation. PLOS Genetics, 10, e1004787. [Google Scholar] [CrossRef] [PubMed]
[3] Hackinger, S. and Zeggini, E. (2017) Statistical Methods to De-tect Pleiotropy in Human Complex Traits. Open Biology, 7, Article ID: 170125. [Google Scholar] [CrossRef] [PubMed]
[4] Maier, R.M., Zhu, Z., Lee, S.H., et al. (2018) Improving Genetic Predic-tion by Leveraging Genetic Correlations among Human Diseases and Traits. Nature Communications, 9, Article No. 989. [Google Scholar] [CrossRef] [PubMed]
[5] Solovieff, N., Cotsapas, C., Lee, P.H., Purcell, S.M. and Smoller, J.W. (2013) Pleiotropy in Complex Traits: Challenges and Strategies. Nature Reviews Genetics, 14, 483-495. [Google Scholar] [CrossRef] [PubMed]
[6] Ferreira, M.A. and Purcell, S.M. (2009) A Multivariate Test of Association. Bioinformatics, 25, 132-133. [Google Scholar] [CrossRef] [PubMed]
[7] Schaid, D.J., Tong, X., Larrabee, B., et al. (2016) Statistical Methods for Testing Genetic Pleiotropy. Genetics, 204, 483-497. [Google Scholar] [CrossRef] [PubMed]
[8] Wang, Y., Wu, P., Tong, X. and Sun, J. (2021) A Weighted Method for the Exclusive Hypothesis Test with Application to Typhoon Data. Canadian Journal of Statistics, 49, 1258-1272. [Google Scholar] [CrossRef] 
[9] Ray, D. and Chatterjee, N. (2020) A Powerful Method for Plei-otropic Analysis under Composite Null Hypothesis Identifies Novel Shared Loci between Type 2 Diabetes and Prostate Cancer. PLOS Genetics, 16, e1009218. [Google Scholar] [CrossRef] [PubMed]
[10] Liu, Z., Shen, J., Barfield, R., et al. (2022) Large-Scale Hy-pothesis Testing for Causal Mediation Effects with Applications in Genome-wide Epigenetic Studies. Journal of the American Statistical Association, 117, 67-81. [Google Scholar] [CrossRef] [PubMed]
[11] Wang, T., Lu, H. and Zeng, P. (2021) Identifying Pleiotropic Genes for Complex Phenotypes with Summary Statistics From a Perspective of Composite Null Hypothesis Testing. Briefings in Bioinformatics, 23, Article ID: Bbab389. [Google Scholar] [CrossRef] [PubMed]
[12] Broadaway, K.A., Cutler, D.J., Duncan, R., Moore, J.L., Ware, E.B., Jhun, M.A., et al. (2016) A Statistical Approach for Testing Cross-Phenotype Effects of Rare Variants. The American Journal of Human Genetics, 98, 525-540. [Google Scholar] [CrossRef] [PubMed]
[13] Barfield, R., Shen, J., Just, A.C., et al. (2017) Testing for the Indi-rect Effect under the Null for Genome-Wide Mediation Analyses. Genetic Epidemiology, 41, 824-833. [Google Scholar] [CrossRef] [PubMed]
[14] Craig, C.C. (1936) On the Frequency Function of xy. Annals of Mathemat-ical Statistics, 7, 1-15. [Google Scholar] [CrossRef] 
[15] Huang, Y-T. (2019) Genome-Wide Analyses of Sparse Mediation Effects under Composite Null Hypotheses. Annals of Applied Statistics, 13, 60-84. [Google Scholar] [CrossRef] 
[16] Efron, B., Tibshirani, R., Storey, J.D. and Tusher, V. (2001) Empirical Bayes Analysis of a Microarray Experiment. Journal of the American Statistical Association, 96, 1151-1160. [Google Scholar] [CrossRef] 
[17] Jin, J. and Cai, T.T. (2007) Estimating the Null and the Propor-tion of Nonnull Effects in Large-Scale Multiple Comparisons. Journal of the American Statistical Association, 102, 495-506. [Google Scholar] [CrossRef] 
[18] Dai, J.Y., Stanford, J.L. and LeBlanc, M. (2022) A Multiple-Testing Procedure for High-Dimensional Mediation Hypotheses. Journal of the American Statistical Associa-tion, 117, 198-213. [Google Scholar] [CrossRef] [PubMed]
[19] Storey, J.D. and Tibshirani, R. (2003) Statistical Significance for Genomewide Studies. Proceedings of the National Academy of Sciences of the United States of America, 100, 9440-9445. [Google Scholar] [CrossRef] [PubMed]
[20] Cotsapas, C., Voight, B.F., Rossin, E., et al. (2011) Pervasive Sharing of Genetic Effects in Autoimmune Disease. PLOS Genetics, 7, e1002254. [Google Scholar] [CrossRef] [PubMed]
[21] Amare, A.T., Vaez, A., Hsu, Y.-H., et al. (2020) Bivariate Ge-nome-Wide Association Analyses of the Broad Depression Phenotype Combined with Major Depressive Disorder, Bi-polar Disorder or Schizophrenia Reveal Eight Novel Genetic Loci for Depression. Molecular Psychiatry, 25, 1420-1429. [Google Scholar] [CrossRef] [PubMed]
[22] Zhu, Z., Zhu, X., Liu, C.-L., et al. (2019) Shared Genetics of Asthma and Mental Health Disorders: A Large-Scale Genome-Wide Cross-Trait Analysis. European Respiratory Jour-nal, 54, Article ID: 1901507. [Google Scholar] [CrossRef] [PubMed]
[23] Andreassen, O.A., Zuber, V., Thompson, W.K., et al. (2014) Shared Common Variants in Prostate Cancer and Blood Lipids. International Journal of Epidemiology, 43, 1205-1214. [Google Scholar] [CrossRef] [PubMed]
[24] Pickrell, J., Berisa, T., Liu, J.Z., et al. (2016) Detection and Interpre-tation of Shared Genetic Influences on 42 Human Traits. Nature Genetics, 48, 709-717. [Google Scholar] [CrossRef] [PubMed]

为你推荐