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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
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Large-scale multiple testing in genome-wide association studies via region-specific hidden Markov models
Jian Xiao1, Wensheng Zhu, Jianhua Guo
1Key Laboratory for Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, China. wszhu@nenu.edu.cn.
BMC Bioinformatics
|September 27, 2013
Summary
This study introduces a new method for genome-wide association studies (GWAS) that improves the detection of disease-associated single nucleotide polymorphisms (SNPs). The region-specific pooled local index of significance (RSPLIS) method enhances accuracy, especially for variants with small effect sizes.
Area of Science:
- Genetics
- Bioinformatics
- Statistical genomics
Background:
- Identifying genetic variants for complex diseases in genome-wide association studies (GWAS) is challenging due to single nucleotide polymorphism (SNP) dependence.
- Current methods like LIS and PLIS assume a single hidden Markov model (HMM) for entire chromosomes, which may not fit heterogeneous chromosomal regions.
Purpose of the Study:
- To develop a method that accounts for regional heterogeneity within chromosomes in GWAS.
- To improve the detection of disease-associated SNPs by modeling chromosome regions with distinct HMMs.
Main Methods:
- A data-driven penalized criterion and dynamic programming algorithm were used to identify change points, dividing chromosomes into homogeneous regions.
- The pooled local index of significance (PLIS) was extended to region-specific PLIS (RSPLIS) to analyze dependent tests from multiple chromosomes with distinct regions.
Main Results:
- Simulation results demonstrated improved model selection and superior performance of RSPLIS over PLIS in detecting disease-associated SNPs, particularly with multiple change points.
- Application to the Daly study confirmed that RSPLIS more accurately identified disease-associated SNPs compared to PLIS.
Conclusions:
- Genomic rankings derived from RSPLIS differ from those of PLIS.
- RSPLIS offers more efficient and powerful detection of genetic variants with weak effect sizes in GWAS.
