A robust and efficient statistical method for genetic association studies using case and control samples from multiple cohorts.
A robust and efficient statistical method for genetic association studies using case and control samples from multiple cohorts.
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使用来自多个队列的病例和对照样本进行遗传关联研究的稳健而有效的统计方法
DOI：
10.1186/1471-2164-14-88
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发表时间：
2013-02-08
期刊：
影响因子：
4.4
通讯作者：
Luo Z
中科院分区：
文献类型：
--
作者：
Wang M;Wang L;Jiang N;Jia T;Luo Z
背景全基因组关联研究（GWAS）的理论基础是对任何多态性标记与假定的疾病位点之间的连锁不平衡（LD）进行统计推断。大多数广泛用于此类分析的方法容易受到几个关键人口因素的影响，并且在检测真正关联方面的统计能力很差，而且假阳性率很高。在这里，我们提出了一种基于似然性的统计方法，该方法适当地考虑了病例对照样本在研究人群中基因型分布的非随机性，并赋予了在存在不同混杂因素（如人群结构，样本的非随机性等）的情况下测试遗传关联的灵活性。重新分析最近发表的帕金森病（PD）病例对照样本。真实的数据分析和计算机模拟表明，新方法不仅显著提高了关联检测的统计能力，而且对非随机抽样和遗传结构带来的困难具有鲁棒性。特别是，新方法在25个大小< 1 Mb的染色体区域内检测到44个显著的SNP，但这些区域中的两个区域中只有6个SNP先前通过基于趋势检验的方法检测到。它发现了两个SNPs位于1.18 Mb和0.18 Mb的PD候选人，FGF 20和PARK 8，而不调用假阳性risk.Conclusions我们开发了一种新的似然为基础的方法，它提供了足够的估计LD和其他人口模型参数，通过使用病例和对照样本，易于整合来自多个遗传差异群体的这些样本，从而提供统计学上稳健和强大的分析的GWAS。通过对真实的数据集的仿真研究和分析，证明了该方法较GWAS中最常用的非参数趋势检验方法有显著的改进。
BackgroundThe theoretical basis of genome-wide association studies (GWAS) is statistical inference of linkage disequilibrium (LD) between any polymorphic marker and a putative disease locus. Most methods widely implemented for such analyses are vulnerable to several key demographic factors and deliver a poor statistical power for detecting genuine associations and also a high false positive rate. Here, we present a likelihood-based statistical approach that accounts properly for non-random nature of case–control samples in regard of genotypic distribution at the loci in populations under study and confers flexibility to test for genetic association in presence of different confounding factors such as population structure, non-randomness of samples etc.ResultsWe implemented this novel method together with several popular methods in the literature of GWAS, to re-analyze recently published Parkinson’s disease (PD) case–control samples. The real data analysis and computer simulation show that the new method confers not only significantly improved statistical power for detecting the associations but also robustness to the difficulties stemmed from non-randomly sampling and genetic structures when compared to its rivals. In particular, the new method detected 44 significant SNPs within 25 chromosomal regions of size < 1 Mb but only 6 SNPs in two of these regions were previously detected by the trend test based methods. It discovered two SNPs located 1.18 Mb and 0.18 Mb from the PD candidates,FGF20andPARK8, without invoking false positive risk.ConclusionsWe developed a novel likelihood-based method which provides adequate estimation of LD and other population model parameters by using case and control samples, the ease in integration of these samples from multiple genetically divergent populations and thus confers statistically robust and powerful analyses of GWAS. On basis of simulation studies and analysis of real datasets, we demonstrated significant improvement of the new method over the non-parametric trend test, which is the most popularly implemented in the literature of GWAS.
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影响因子：
2.1
作者：
Wang, Tao;Jacob, Howard;Ghosh, Soumitra;Wang, Xujing;Zeng, Zhao-Bang
通讯作者：
Zeng, Zhao-Bang
影响因子：
4.4
作者：
DEVLIN, B;RISCH, N
通讯作者：
RISCH, N
影响因子：
30.8
作者：
Yu, JM;Pressoir, G;Buckler, ES
通讯作者：
Buckler, ES
DOI：
10.1214/09-sts297
发表时间：
2009-11-01
期刊：
Statistical science : a review journal of the Institute of Mathematical Statistics
影响因子：
--
作者：
Chatterjee N;Chen YH;Luo S;Carroll RJ
通讯作者：
Carroll RJ
影响因子：
5.3
作者：
Mizuta, Ikuko;Tsunoda, Tatsuhiko;Toda, Tatsushi
通讯作者：
Toda, Tatsushi