通过回归统一遗传关联测试:前瞻性和回顾性,参数化和非参数化,以及基于基因型和等位基因的测试

IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY Canadian Journal of Statistics-Revue Canadienne De Statistique Pub Date : 2022-09-23 DOI:10.1002/cjs.11729
Lin Zhang, Lei Sun
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引用次数: 1

摘要

遗传关联分析评估遗传标记与复杂遗传性状之间的关系,是全基因组关联研究的基础。已开发的许多关联检测通常可分为前瞻性与回顾性、参数化与非参数化、基因型与等位基因型。虽然方法分类是有用的,但是对于从业者来说,决定为他们的数据使用“最佳”测试可能是令人困惑和具有挑战性的。我们超越了一些流行的关联测试之间已知的差异,并提供了新的结果,显示了基于人群和基于家庭的研究设计的测试之间的分析联系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Unifying genetic association tests via regression: Prospective and retrospective, parametric and nonparametric, and genotype- and allele-based tests

Genetic association analysis, which evaluates relationships between genetic markers and complex, heritable traits, is the basis of genome-wide association studies. The many association tests that have been developed can generally be classified as prospective versus retrospective, parametric versus nonparametric, and genotype- versus allele-based. While method classifications are useful, it can be confusing and challenging for practitioners to decide on the “optimal” test to use for their data. We go beyond known differences between some popular association tests and provide new results that show analytical connections between tests, for both population- and family-based study designs.

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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
62
审稿时长
>12 weeks
期刊介绍: The Canadian Journal of Statistics is the official journal of the Statistical Society of Canada. It has a reputation internationally as an excellent journal. The editorial board is comprised of statistical scientists with applied, computational, methodological, theoretical and probabilistic interests. Their role is to ensure that the journal continues to provide an international forum for the discipline of Statistics. The journal seeks papers making broad points of interest to many readers, whereas papers making important points of more specific interest are better placed in more specialized journals. The levels of innovation and impact are key in the evaluation of submitted manuscripts.
期刊最新文献
Issue Information Issue Information Issue Information Censored autoregressive regression models with Student-t innovations Acknowledgement of referees' services remerciements aux membres des jurys
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