Enhanced statistical tests for GWAS in admixed populations: assessment using African Americans from CARe and a Breast Cancer Consortium.
Enhanced statistical tests for GWAS in admixed populations: assessment using African Americans from CARe and a Breast Cancer Consortium.
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DOI：
10.1371/journal.pgen.1001371
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发表时间：
2011-04
期刊：
影响因子：
4.5
通讯作者：
Price AL
中科院分区：
文献类型：
--
作者：
Pasaniuc B;Zaitlen N;Lettre G;Chen GK;Tandon A;Kao WH;Ruczinski I;Fornage M;Siscovick DS;Zhu X;Larkin E;Lange LA;Cupples LA;Yang Q;Akylbekova EL;Musani SK;Divers J;Mychaleckyj J;Li M;Papanicolaou GJ;Millikan RC;Ambrosone CB;John EM;Bernstein L;Zheng W;Hu JJ;Ziegler RG;Nyante SJ;Bandera EV;Ingles SA;Press MF;Chanock SJ;Deming SL;Rodriguez-Gil JL;Palmer CD;Buxbaum S;Ekunwe L;Hirschhorn JN;Henderson BE;Myers S;Haiman CA;Reich D;Patterson N;Wilson JG;Price AL
虽然全基因组关联研究（GWAS）主要研究了欧洲血统的人群，但最近的研究通常涉及更多的人群，包括非洲裔美国人和拉丁美洲人等混合人群。在混合群体中，由于不同祖先的染色体片段不同，连锁不平衡（LD）既存在于祖先群体的精细尺度上，也存在于祖先群体的粗尺度上。混合人群的疾病关联统计以前考虑过SNP关联（LD作图）或外加剂关联（外加剂-LD作图），但不是两者都考虑。在这里，我们介绍了一种新的统计框架，将SNP和混合关联结合在病例对照研究中，以及本地祖先感知的imputation方法。我们通过分析来自CARe项目的6209名非裔美国人在Affymetrix 6.0芯片上进行基因分型的数据，结合模拟和真实表型，以及使用来自5761名非裔美国女性的乳腺癌GWAS数据分析FGFR2位点，说明了这些方法所获得的统计能力。我们发现，在类型化snp中，与病例对照研究中的标准方法相比，我们的方法在发现疾病风险位点方面的统计能力提高了8%。在估算snp时，我们观察到，当我们的本地血统感知估算框架和新的评分统计数据联合使用时，绘制疾病位点的统计能力增加了11%。最后，我们表明，在因果SNP未分型且无法估算的情况下，我们的方法提高了包含因果SNP的区域的统计能力。我们的方法和公开可用的软件广泛适用于混合种群的GWAS。本文提出了一种改进的方法，用于分析混合种群的全基因组关联研究，混合种群是由两个或两个以上遥远的大陆种群在几百年内混合而成的种群（例如，非洲裔美国人或拉丁美洲人）。与对同质人群（如欧洲人）的研究相比，对混合人群的研究提供了捕获额外遗传多样性的希望。在混合群体中，遗传变异之间的相关性既存在于祖先群体的精细尺度上，也存在于不同祖先的染色体片段的粗糙尺度上。混合人群的疾病关联统计以前只考虑其中一种或另一种类型的相关性，而不是两者都考虑。在这项工作中，我们开发了新的统计方法来解释这两种类型的遗传相关性，我们表明，联合方法比单独应用任何一种方法获得更大的统计能力。我们提供了对非裔美国男性和女性进行的主要研究的模拟和真实数据的分析，以显示我们的方法比分析混合人群关联研究的标准方法获得的改进。
While genome-wide association studies (GWAS) have primarily examined populations of European ancestry, more recent studies often involve additional populations, including admixed populations such as African Americans and Latinos. In admixed populations, linkage disequilibrium (LD) exists both at a fine scale in ancestral populations and at a coarse scale (admixture-LD) due to chromosomal segments of distinct ancestry. Disease association statistics in admixed populations have previously considered SNP association (LD mapping) or admixture association (mapping by admixture-LD), but not both. Here, we introduce a new statistical framework for combining SNP and admixture association in case-control studies, as well as methods for local ancestry-aware imputation. We illustrate the gain in statistical power achieved by these methods by analyzing data of 6,209 unrelated African Americans from the CARe project genotyped on the Affymetrix 6.0 chip, in conjunction with both simulated and real phenotypes, as well as by analyzing the FGFR2 locus using breast cancer GWAS data from 5,761 African-American women. We show that, at typed SNPs, our method yields an 8% increase in statistical power for finding disease risk loci compared to the power achieved by standard methods in case-control studies. At imputed SNPs, we observe an 11% increase in statistical power for mapping disease loci when our local ancestry-aware imputation framework and the new scoring statistic are jointly employed. Finally, we show that our method increases statistical power in regions harboring the causal SNP in the case when the causal SNP is untyped and cannot be imputed. Our methods and our publicly available software are broadly applicable to GWAS in admixed populations. This paper presents improved methodologies for the analysis of genome-wide association studies in admixed populations, which are populations that came about by the mixing of two or more distant continental populations over a few hundred years (e.g., African Americans or Latinos). Studies of admixed populations offer the promise of capturing additional genetic diversity compared to studies over homogeneous populations such as Europeans. In admixed populations, correlation between genetic variants exists both at a fine scale in the ancestral populations and at a coarse scale due to chromosomal segments of distinct ancestry. Disease association statistics in admixed populations have previously considered either one or the other type of correlation, but not both. In this work we develop novel statistical methods that account for both types of genetic correlation, and we show that the combined approach attains greater statistical power than that achieved by applying either approach separately. We provide analysis of simulated and real data from major studies performed in African-American men and women to show the improvement obtained by our methods over the standard methods for analyzing association studies in admixed populations.
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