Joint ancestry and association testing in admixed individuals.
Joint ancestry and association testing in admixed individuals.
复制标题
DOI：
10.1371/journal.pcbi.1002325
复制
发表时间：
2011-12
影响因子：
4.3
通讯作者：
Rotimi CN
中科院分区：
文献类型：
--
作者：
Shriner D;Adeyemo A;Rotimi CN
对于混合个体的样本，可以通过混合映射来测试祖先效应，并通过关联映射来测试基因型效应。在这里，我们描述了一种称为 BMIX 的联合测试，它结合了单个标记的混合和关联统计数据。我们首先使用当地血统进行高密度混合映射。然后，我们使用分层回归进行关联作图，其中对于每个标记基因型，根据当地血统进行分层。在这两个阶段中，我们都使用广义线性模型，其优点是联合测试可以与具有适当链接函数的任何表型分布一起使用。为了定义混合映射和关联映射的替代密度，我们描述了一种基于自相关的方法，以凭经验估计混合映射和关联映射的测试负担。然后，我们描述了一种联合测试，该测试使用混合映射的后验概率作为关联映射的先验概率，利用混合映射相对于关联映射减少的测试负担。通过模拟，我们表明 BMIX 可能比 MIX 分数强大几个数量级，而 MIX 分数是目前最强大的频率派联合测试。我们通过对霍华德大学家庭研究中 922 名无血缘关系、非糖尿病、混血非裔美国人的空腹血糖进行分析来说明权力的增益。我们通过混合作图检测到 1q24 和 6q26 处的基因座具有全基因组显着性；两个基因座均通过连锁分析独立报告。使用关联数据，我们将 1q24 信号解析为两个区域。 FAM78B 基因上游的一个区域包含三个转录因子 PPARG 结合位点和两个 HNF1A 结合位点，这两个位点先前都与 2 型糖尿病的病理学有关。两个基因座都显示出祖先效应的事实可能为非洲血统个体空腹血糖的遗传结构提供新的见解。迄今为止进行的大多数全基因组关联研究都集中在具有欧洲血统的个体上。混血非裔美国人患许多常见、复杂疾病的风险往往更高。混合个体的疾病或性状图谱可以受益于祖先和基因型效应的联合分析。我们开发了一种联合测试，它比单独进行的祖先效应混合作图或基因型效应关联作图更强大。我们的联合测试充分利用了混合映射相对于关联映射减少的测试负担。该测试基于广义线性模型，可以使用标准统计软件进行。我们通过在不相关的非裔美国人样本中检测两个空腹血糖基因座来说明联合测试的功效增强，而传统关联分析没有检测到这两个基因座具有显着性。
For samples of admixed individuals, it is possible to test for both ancestry effects via admixture mapping and genotype effects via association mapping. Here, we describe a joint test called BMIX that combines admixture and association statistics at single markers. We first perform high-density admixture mapping using local ancestry. We then perform association mapping using stratified regression, wherein for each marker genotypes are stratified by local ancestry. In both stages, we use generalized linear models, providing the advantage that the joint test can be used with any phenotype distribution with an appropriate link function. To define the alternative densities for admixture mapping and association mapping, we describe a method based on autocorrelation to empirically estimate the testing burdens of admixture mapping and association mapping. We then describe a joint test that uses the posterior probabilities from admixture mapping as prior probabilities for association mapping, capitalizing on the reduced testing burden of admixture mapping relative to association mapping. By simulation, we show that BMIX is potentially orders-of-magnitude more powerful than the MIX score, which is currently the most powerful frequentist joint test. We illustrate the gain in power through analysis of fasting plasma glucose among 922 unrelated, non-diabetic, admixed African Americans from the Howard University Family Study. We detected loci at 1q24 and 6q26 as genome-wide significant via admixture mapping; both loci have been independently reported from linkage analysis. Using the association data, we resolved the 1q24 signal into two regions. One region, upstream of the gene FAM78B, contains three binding sites for the transcription factor PPARG and two binding sites for HNF1A, both previously implicated in the pathology of type 2 diabetes. The fact that both loci showed ancestry effects may provide novel insight into the genetic architecture of fasting plasma glucose in individuals of African ancestry. Most genome-wide association studies performed to date have focused on individuals with European ancestry. Admixed African Americans tend to have disproportionately higher risk for many common, complex diseases. Disease or trait mapping in admixed individuals can benefit from joint analysis of ancestry and genotype effects. We developed a joint test that is more powerful than either admixture mapping of ancestry effects or association mapping of genotype effects performed separately. Our joint test fully capitalizes on the reduced testing burden of admixture mapping relative to association mapping. The test is based on generalized linear models and can be performed using standard statistical software. We illustrate the increased power of the joint test by detecting two loci for fasting plasma glucose in a sample of unrelated African American individuals, neither of which loci was detected as significant by traditional association analysis.
登录
查看更多内容
影响因子：
3.7
作者：
Kang, Guolian;Gao, Guimin;Allison, David B.
通讯作者：
Allison, David B.
影响因子：
3.8
作者：
Shriner, D.
通讯作者：
Shriner, D.
影响因子：
4.5
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
通讯作者：
Price AL
影响因子：
7.7
作者：
Prokopenko I;Zeggini E;Hanson RL;Mitchell BD;Rayner NW;Akan P;Baier L;Das SK;Elliott KS;Fu M;Frayling TM;Groves CJ;Gwilliam R;Scott LJ;Voight BF;Hattersley AT;Hu C;Morris AD;Ng M;Palmer CN;Tello-Ruiz M;Vaxillaire M;Wang CR;Stein L;Chan J;Jia W;Froguel P;Elbein SC;Deloukas P;Bogardus C;Shuldiner AR;McCarthy MI;International Type 2 Diabetes 1q Consortium
通讯作者：
International Type 2 Diabetes 1q Consortium
影响因子：
9.8
作者：
Sankararaman, Sriram;Sridhar, Srinath;Halperin, Eran
通讯作者：
Halperin, Eran