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单倍型多样性能否预测疾病易感性关联定位的效能?

Does haplotype diversity predict power for association mapping of disease susceptibility?

作者信息

Zhang Weihua, Collins Andrew, Morton Newton E

机构信息

Human Genetics Division, University of Southampton, Southampton General Hospital, SO16 6YD Southampton, UK.

出版信息

Hum Genet. 2004 Jul;115(2):157-64. doi: 10.1007/s00439-004-1122-x. Epub 2004 Jun 4.

Abstract

Many recent studies have established that haplotype diversity in a small region may not be greatly diminished when the number of markers is reduced to a smaller set of "haplotype-tagging" single-nucleotide polymorphisms (SNPs) that identify the most common haplotypes. These studies are motivated by the assumption that retention of haplotype diversity assures retention of power for mapping disease susceptibility by allelic association. Using two bodies of real data, three proposed measures of diversity, and regression-based methods for association mapping, we found no scenario for which this assumption was tenable. We compared the chi-square for composite likelihood and the maximum chi-square for single SNPs in diplotypes, excluding the marker designated as causal. All haplotype-tagging methods conserve haplotype diversity by selecting common SNPs. When the causal marker has a range of allele frequencies as in real data, chi-square decreases faster than under random selection as the haplotype-tagging set diminishes. Selecting SNPs by maximizing haplotype diversity is inefficient when their frequency is much different from the unknown frequency of the causal variant. Loss of power is minimized when the difference between minor allele frequencies of the causal SNP and a closely associated marker SNP is small, which is unlikely in ignorance of the frequency of the causal SNP unless dense markers are used. Therefore retention of haplotype diversity in simulations that do not mirror genomic allele frequencies has no relevance to power for association mapping. TagSNPs that are assigned to bins instead of haplotype blocks also lose power compared with random SNPs. This evidence favours a multi-stage design in which both models and density change adaptively.

摘要

许多近期研究表明,当标记数量减少为一组较小的“单倍型标签”单核苷酸多态性(SNP)以识别最常见单倍型时,小区域内的单倍型多样性可能不会大幅降低。这些研究的动机是假设保留单倍型多样性可确保通过等位基因关联来绘制疾病易感性图谱的能力得以保留。使用两组真实数据、三种提议的多样性度量方法以及基于回归的关联映射方法,我们发现没有哪种情况能使该假设成立。我们比较了复合似然的卡方值和双倍型中单个SNP的最大卡方值,不包括被指定为因果关系的标记。所有单倍型标签方法都通过选择常见SNP来保留单倍型多样性。当因果标记具有如真实数据中那样的一系列等位基因频率时,随着单倍型标签集的减少,卡方值下降得比随机选择时更快。当SNP的频率与因果变异的未知频率差异很大时,通过最大化单倍型多样性来选择SNP是低效的。当因果SNP与紧密关联的标记SNP的次要等位基因频率差异很小时,能力损失最小化,而在不知道因果SNP频率的情况下这是不太可能的,除非使用密集标记。因此,在不反映基因组等位基因频率的模拟中保留单倍型多样性与关联映射的能力无关。与随机SNP相比,被分配到箱而非单倍型块的标签SNP也会失去能力。这一证据支持一种多阶段设计,其中模型和密度都能自适应变化。

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