Detecting associations of rare variants with common diseases: collapsing or haplotyping?
Detecting associations of rare variants with common diseases: collapsing or haplotyping?
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DOI：
10.1093/bib/bbu050
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发表时间：
2015-01
影响因子：
9.5
通讯作者：
M. Wang;Shili Lin
中科院分区：
文献类型：
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作者：
M. Wang;Shili Lin
近年来，已经提出了无数新的统计方法来检测罕见的单核苷酸变异（SNV）与常见疾病的关联。这些方法通常可以被分类为基于“折叠”或“单倍型分型”。前者是占主导地位的类，由迄今为止提出的大多数罕见的变体关联方法组成。然而，最近的研究表明，基于单倍型的方法可能提供优势，甚至在某些情况下比折叠方法更强大。在这篇文章中，我们回顾和比较崩溃与单倍型为基础的方法/软件的功率和I型错误。对于折叠方法，我们考虑三种方法：组合多元和折叠，序列核关联测试和基于家族的关联测试（FBAT）：前两种是基于人口的，是最流行的;最后一种测试是基于家族的，从流行的FBAT修改，以适应罕见的SNV。对于基于单倍型的方法，我们包括用于人口数据的Logistic贝叶斯套索（LBL）和用于家庭（三人组）数据的基于家庭的LBL（famLBL）。选择这两种方法，因为它们可以用于测试特定罕见和常见单倍型的关联。我们的研究结果表明，单倍型方法可以更强大的折叠方法，如果有相互作用的SNV导致更大的单倍型效应。即使仅对常见SNV进行基因分型，单体型方法仍然可以检测标记罕见因果SNV的特定罕见单体型。正如预期的那样，基于家庭的方法是强大的，而人口为基础的方法是敏感的，人口子结构。然而，基于群体的单倍型方法似乎具有比其崩溃对应物更小的I型错误膨胀。
In recent years, a myriad of new statistical methods have been proposed for detecting associations of rare single-nucleotide variants (SNVs) with common diseases. These methods can be generally classified as 'collapsing' or 'haplotyping' based. The former is the predominant class, composed of most of the rare variant association methods proposed to date. However, recent works have suggested that haplotyping-based methods may offer advantages and can even be more powerful than collapsing methods in certain situations. In this article, we review and compare collapsing- versus haplotyping-based methods/software in terms of both power and type I error. For collapsing methods, we consider three approaches: Combined Multivariate and Collapsing, Sequence Kernel Association Test and Family-Based Association Test (FBAT): the first two are population based and are among the most popular; the last test is family based, a modification from the popular FBAT to accommodate rare SNVs. For haplotyping-based methods, we include Logistic Bayesian Lasso (LBL) for population data and family-based LBL (famLBL) for family (trio) data. These two methods are selected, as they can be used to test association for specific rare and common haplotypes. Our results show that haplotype methods can be more powerful than collapsing methods if there are interacting SNVs leading to larger haplotype effects. Even if only common SNVs are genotyped, haplotype methods can still detect specific rare haplotypes that tag rare causal SNVs. As expected, family-based methods are robust, whereas population-based methods are susceptible, to population substructure. However, the population-based haplotype approach appears to have smaller inflation of type I error than its collapsing counterparts.