Bayesian LASSO for population stratification correction in rare haplotype association studies.

IF 0.9 4区 数学 Q3 Mathematics
Zilu Liu, Asuman Seda Turkmen, Shili Lin
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引用次数: 0

Abstract

Population stratification (PS) is one major source of confounding in both single nucleotide polymorphism (SNP) and haplotype association studies. To address PS, principal component regression (PCR) and linear mixed model (LMM) are the current standards for SNP associations, which are also commonly borrowed for haplotype studies. However, the underfitting and overfitting problems introduced by PCR and LMM, respectively, have yet to be addressed. Furthermore, there have been only a few theoretical approaches proposed to address PS specifically for haplotypes. In this paper, we propose a new method under the Bayesian LASSO framework, QBLstrat, to account for PS in identifying rare and common haplotypes associated with a continuous trait of interest. QBLstrat utilizes a large number of principal components (PCs) with appropriate priors to sufficiently correct for PS, while shrinking the estimates of unassociated haplotypes and PCs. We compare the performance of QBLstrat with the Bayesian counterparts of PCR and LMM and a current method, haplo.stats. Extensive simulation studies and real data analyses show that QBLstrat is superior in controlling false positives while maintaining competitive power for identifying true positives under PS.

贝叶斯 LASSO 用于稀有单倍型关联研究中的人群分层校正。
人群分层(PS)是单核苷酸多态性(SNP)和单体型关联研究中混杂因素的一个主要来源。为解决单核苷酸多态性问题,主成分回归(PCR)和线性混合模型(LMM)是目前 SNP 关联研究的标准,也是单体型研究常用的方法。然而,PCR 和 LMM 分别带来的欠拟合和过拟合问题仍有待解决。此外,专门针对单倍型的 PS 理论方法也寥寥无几。在本文中,我们在贝叶斯 LASSO 框架下提出了一种新方法 QBLstrat,用于在识别与感兴趣的连续性状相关的稀有和常见单倍型时考虑 PS。QBLstrat 利用大量具有适当先验的主成分(PC)来充分校正多态性,同时缩小无关联单倍型和 PC 的估计值。我们将 QBLstrat 的性能与 PCR 和 LMM 的贝叶斯对应方法以及当前的一种方法 haplo.stats 进行了比较。广泛的模拟研究和实际数据分析表明,QBLstrat 在控制假阳性方面更胜一筹,同时在 PS 下识别真阳性方面也保持了竞争力。
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来源期刊
CiteScore
1.20
自引率
11.10%
发文量
8
审稿时长
6-12 weeks
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
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