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  "Title": "A Combined Association Test for Genes using Summary Statistics",
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  "Date": "2018-01-14",
  "Author": "Minghui Wang, Yiyuan Liu, Shizhong Han",
  "Maintainer": "Minghui Wang <m.h.wang@live.com>",
  "Description": "Genome-wide association studies (GWAS) have been widely\nused for identifying common variants associated with complex\ndiseases. Due to the small effect sizes of common variants, the\npower to detect individual risk variants is generally low.\nComplementary to SNP-level analysis, a variety of gene-based\nassociation tests have been proposed. However, the power of\nexisting gene-based tests is often dependent on the underlying\ngenetic models, and it is not known a priori which test is\noptimal.  Here we proposed COMBined Association Test (COMBAT)\nto incorporate strengths from multiple existing gene-based\ntests, including VEGAS, GATES and simpleM. Compared to\nindividual tests, COMBAT shows higher overall performance and\nrobustness across a wide range of genetic models. The algorithm\nbehind this method is described in Wang et al (2017)\n<doi:10.1534/genetics.117.300257>.",
  "License": "GPL-2",
  "Repository": "https://mw201608.r-universe.dev",
  "Date/Publication": "2021-10-08 03:31:08 UTC",
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      "title": "A Combined Gene-based Association Test",
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