Accommodating Linkage Disequilibrium in Genetic-Association Analyses via Ridge Regression
Open accessAbstract
Large-scale genetic-association studies that take advantage of an extremely dense set of genetic markers have begun to produce very compelling statistical associations between multiple makers exhibiting strong linkage disequilibrium (LD) in a single genomic region and a phenotype of interest. However, the ultimate biological or "functional" significance of these multiple associations has been difficult to discern. In fact, the LD relationships between not only the markers found to be associated with the phenotype but also potential functionally or causally relevant genetic variations that reside near those markers have been exploited in such studies. Unfortunately, LD, especially strong LD, between variations at neighboring loci can make it difficult to distinguish the functionally relevant variations from nonfunctional variations. Although there are (rare) situations in which it is impossible to determine the independent phenotypic effects of variations in LD, there are strategies for accommodating LD between variations at different loci, and they can be used to tease out their independent effects on a phenotype. These strategies make it possible to differentiate potentially causative from noncausative variations. We describe one such approach involving ridge regression. We showcase the method by using both simulated and real data. Our results suggest that ridge regression and related techniques have the potential to distinguish causative from noncausative variations in association studies.
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Newer regression techniques using regularization and shrinkage can overcome collinearity and overfitting problems.
“Newer regression techniques that make use of regularization and shrinkage parameters to control for collinearity and overfitting can be used to overcome these problems.”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [74]
Ridge regression and LASSO have been considered in genetic association analysis contexts, and other methods have been proposed.
“Two such techniques, ridge regression74 and the LASSO75,76 have been considered in genetic association analysis contexts, and other methods have also been proposed as well.77–80, 81”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [74]
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- Authors
- Malo, N., Libiger, O., & Schork, N. J.
- Affiliations
- Scripps Research Institute (US)
- Corresponding author
- Schork, Nicholas J.
- ORCID
- Schork, Nicholas J.
- Venue
- The American Journal of Human Genetics
- Publisher
- Elsevier BV
- Published
- 2008-02
- Volume
- 82
- Issue
- 2
- Pages
- 375-385
- ISSN
- 0002-9297 (ISSN-L)
- PMC
- PMC2427310
- OpenAlex
- W1976296849
- Cited by
- 133
- References
- 38
- Citations by year
- 2025: 1 · 2024: 7 · 2023: 3 · 2022: 5 · 2021: 6 · 2020: 10 · 2019: 6 · 2018: 7 · 2017: 4 · 2016: 4 · 2015: 7 · 2014: 7 · 2013: 10 · 2012: 8
- FWCI
- 9.12
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- The American Journal of Human Genetics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Public Health Service (5 R01 HLMH065571-02)
- National Institute on Aging (U19AG023122)
- National Heart, Lung, and Blood Institute (HL074730-02, U01HL064777, R01HL070137, HL070137-01, R01HL074730)
- 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 › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- linkage disequilibrium, genetic association studies, ridge regression, causal variants, high-dimensional regression
- MeSH terms
- Computer Simulation, Data Interpretation, Statistical, Models, Genetic, Phenotype, Regression Analysis, Genetic Variation, Linkage Disequilibrium
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