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Interrogating local population structure for fine mapping in genome-wide association studies.

ABSTRACT: Adjustment for population structure is necessary to avoid bias in genetic association studies of susceptibility variants for complex diseases. Population structure may differ from one genomic region to another due to the variability of individual ancestry associated with migration, random genetic drift or natural selection. Current association methods for correcting population stratification usually involve adjustment of global ancestry between study subjects.We suggest interrogating local population structure for fine mapping to more accurately locate true casual genes by better adjusting the confounding effect due to local ancestry. By extensive simulations on genome-wide datasets, we show that adjusting global ancestry may lead to false positives when local population structure is an important confounding factor. In contrast, adjusting local ancestry can effectively prevent false positives due to local population structure and thus can improve fine mapping for disease gene localization. We applied the local and global adjustments to the analysis of datasets from three genome-wide association studies, including European Americans, African Americans and Nigerians. Both European Americans and African Americans demonstrate greater variability in local ancestry than Nigerians. Adjusting local ancestry successfully eliminated the known spurious association between SNPs in the LCT gene and height due to the population structure existed in European Americans.xiaofeng.zhu@case.eduSupplementary data are available at Bioinformatics online.

SUBMITTER: Qin H

PROVIDER: S-EPMC2982153 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature

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Interrogating local population structure for fine mapping in genome-wide association studies.

Qin Huaizhen H Morris Nathan N Kang Sun J SJ Li Mingyao M Tayo Bamidele B Lyon Helen H Hirschhorn Joel J Cooper Richard S RS Zhu Xiaofeng X

Bioinformatics (Oxford, England) 20100930 23

<h4>Motivation</h4>Adjustment for population structure is necessary to avoid bias in genetic association studies of susceptibility variants for complex diseases. Population structure may differ from one genomic region to another due to the variability of individual ancestry associated with migration, random genetic drift or natural selection. Current association methods for correcting population stratification usually involve adjustment of global ancestry between study subjects.<h4>Results</h4>W ...[more]

PMID: 20889494

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Project description:Post-GWAS analysis, in many cases, focuses on fine-mapping targeted genetic regions discovered at GWAS-stage; that is, the aim is to pinpoint potential causal variants and susceptibility genes for complex traits and disease outcomes using next-generation sequencing (NGS) technologies. Large-scale GWAS cohorts are necessary to identify target regions given the typically modest genetic effect sizes. In this context, two-phase sampling design and analysis is a cost-reduction technique that utilizes data collected during phase 1 GWAS to select an informative subsample for phase 2 sequencing. The main goal is to make inference for genetic variants measured via NGS by efficiently combining data from phases 1 and 2. We propose two approaches for selecting a phase 2 design under a budget constraint. The first method identifies sampling fractions that select a phase 2 design yielding an asymptotic variance covariance matrix with certain optimal characteristics, for example, smallest trace, via Lagrange multipliers (LM). The second relies on a genetic algorithm (GA) with a defined fitness function to identify exactly a phase 2 subsample. We perform comprehensive simulation studies to evaluate the empirical properties of the proposed designs for a genetic association study of a quantitative trait. We compare our methods against two ranked designs: residual-dependent sampling and a recently identified optimal design. Our findings demonstrate that the proposed designs, GA in particular, can render competitive power in combined phase 1 and 2 analysis compared with alternative designs while preserving type 1 error control. These results are especially evident under the more practical scenario where design values need to be defined a priori and are subject to misspecification. We illustrate the proposed methods in a study of triglyceride levels in the North Finland Birth Cohort of 1966. R code to reproduce our results is available at github.com/egosv/TwoPhase_postGWAS.

Dataset Information

Interrogating local population structure for fine mapping in genome-wide association studies.

Publications

Interrogating local population structure for fine mapping in genome-wide association studies.

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