The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
In the citing paper
“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]
Citations
Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Draper, N. R., & Smith, H. (2005) · Technometrics · doi:10.1198/tech.2005.s303 · Cited by 17,993
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Lu, Z. Q. J. (2010) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00646_6.x · Cited by 12,916
Ridge regression: Biased estimation for nonorthogonal problems
Hoerl, A. E., & Kennard, R. W. (1970) · Technometrics · doi:10.1080/00401706.1970.10488634 · Cited by 9,648
Ridge Regression: Biased Estimation for Nonorthogonal Problems
Hoerl, A. E., & Kennard, R. W. (2000) · Technometrics · doi:10.2307/1271436 · Cited by 7,001
A haplotype map of the human genome
The International HapMap Consortium (2005) · Nature · doi:10.1038/nature04226 · Cited by 5,858
Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter
Golub, G. H., Heath, M. T., & Wahba, G. (1979) · Technometrics · doi:10.1080/00401706.1979.10489751 · Cited by 3,940
Genome-wide association study identifies novel breast cancer susceptibility loci
Easton, D. F., Pooley, K. A., Dunning, A. M., Pharoah, P. D. P., Thompson, D., Ballinger, D. G., Struewing, J. P., Morrison, J., Field, H., Luben, R., et al. (2007) · Nature · doi:10.1038/nature05887 · Cited by 2,319
A Statistical View of Some Chemometrics Regression Tools
Frank, L. E., & Friedman, J. H. (1993) · Technometrics · doi:10.1080/00401706.1993.10485033 · Cited by 2,217
Cited by
Sorted by citations
Segura, V., Vilhjálmsson, B. J., Platt, A., Korte, A., Seren, Ü., Long, Q., & Nordborg, M. (2012) · Nature Genetics · doi:10.1038/ng.2314 · Cited by 1,129
Polygenic Modeling with Bayesian Sparse Linear Mixed Models
Zhou, X., Carbonetto, P. S., & Stephens, M. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003264 · Cited by 802
Genome-wide association analysis by lasso penalized logistic regression
Wu, T. T., Chen, Y. F., Hastie, T., Sobel, E., & Lange, K. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp041 · Cited by 785
Identifying Causal Variants at Loci with Multiple Signals of Association
Hormozdiari, F., Kostem, E., Kang, E. Y., Pasaniuc, B., & Eskin, E. (2014) · Genetics · doi:10.1534/genetics.114.167908 · Cited by 465
Statistical analysis strategies for association studies involving rare variants
Bansal, V., Libiger, O., Torkamani, A., & Schork, N. J. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2867 · Cited by 455
Self-citation
Metadata
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
