More powerful genetic association testing via a new statistical framework for integrative genomics
Open accessAbstract
Summary: Integrative genomics offers a promising approach to more powerful genetic association studies. The hope is that combining outcome and genotype data with other types of genomic information can lead to more powerful SNP detection. We present a new association test based on a statistical model that explicitly assumes that genetic variations affect the outcome through perturbing gene expression levels. It is shown analytically that the proposed approach can have more power to detect SNPs that are associated with the outcome through transcriptional regulation, compared to tests using the outcome and genotype data alone, and simulations show that our method is relatively robust to misspecification. We also provide a strategy for applying our approach to high‐dimensional genomic data. We use this strategy to identify a potentially new association between a SNP and a yeast cell's response to the natural product tomatidine, which standard association analysis did not detect.
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Zhao et al. (2014) considered the same omic datasets as Huang et al. (2014) but focused on testing the indirect effect of SNPs on disease outcome that is regulated by gene expression.
“Zhao et al. (2014) considered the same omic datasets that Huang et al. (2014) have dealt with, i.e., SNP, gene expression, and disease data collected on the same set of subjects. However, Zhao et al. (2014) focused on testing the IE of the SNPs on the disease outcome that is regulated by gene expression.”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Zhao et al. (2014)
Even when a direct effect is present and model (8) mis-specifies true disease risk, Zhao et al.'s method is still more powerful than the standard method when the magnitude of the direct effect is lower than that of the indirect effect.
“Even in the presence of a DE so that model (8) mis-specifies the true disease risk, Zhao et al. (2014) showed, both analytically and numerically, that their method is still more powerful than the standard method when the magnitude of DE is lower than the magnitude of IE.”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Zhao et al. (2014)
When a particular gene set or pathway is of interest and the number of genes in E does not exceed the number of subjects, Zhao et al. proposed standard estimating equation theory for inference.
“When a particular set of genes or a pathway is of interest such that the number of genes in E does not exceed the number of subjects, Zhao et al. (2014) proposed to use the standard estimating equation theory for inference.”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Zhao et al. (2014)
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- Authors
- Zhao, S. D., Cai, T. T., & Li, H.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- University of Pennsylvania (US)
- Corresponding author
- Zhao, Sihai D.
- Venue
- Biometrics
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-06-26
- Volume
- 70
- Issue
- 4
- Pages
- 881-890
- ISSN
- 0006-341X (ISSN-L)
- 1541-0420
- PMC
- PMC4425276
- OpenAlex
- W1912093789
- Cited by
- 31
- References
- 37
- Citations by year
- 2026: 1 · 2024: 1 · 2023: 4 · 2021: 2 · 2020: 3 · 2019: 8 · 2018: 1 · 2017: 2 · 2016: 5 · 2015: 3 · 2013: 1
- FWCI
- 2.16
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Biometrics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Cancer Institute (CA127334)
- National Institute of General Medical Sciences (GM097525)
- National Science Foundation (DMS-1208982)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- genetic association testing, integrative genomics, single nucleotide polymorphisms, gene expression, transcriptional regulation, statistical power, model misspecification, high-dimensional genomic data, tomatidine
- MeSH terms
- Algorithms, Base Sequence, Computer Simulation, DNA Mutational Analysis, Models, Genetic, Molecular Sequence Data, Models, Statistical, Sequence Analysis, DNA, Systems Integration, Polymorphism, Single Nucleotide, Genetic Association Studies
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