Genome-wide association analysis by lasso penalized logistic regression
Open accessAbstract
Motivation: In ordinary regression, imposition of a lasso penalty makes continuous model selection straightforward. Lasso penalized regression is particularly advantageous when the number of predictors far exceeds the number of observations. Method: The present article evaluates the performance of lasso penalized logistic regression in case–control disease gene mapping with a large number of SNPs (single nucleotide polymorphisms) predictors. The strength of the lasso penalty can be tuned to select a predetermined number of the most relevant SNPs and other predictors. For a given value of the tuning constant, the penalized likelihood is quickly maximized by cyclic coordinate ascent. Once the most potent marginal predictors are identified, their two-way and higher order interactions can also be examined by lasso penalized logistic regression. Results: This strategy is tested on both simulated and real data. Our findings on coeliac disease replicate the previous SNP results and shed light on possible interactions among the SNPs. Availability: The software discussed is available in Mendel 9.0 at the UCLA Human Genetics web site. Contact: klange@ucla.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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The elastic net algorithm was used because penalization improves prediction accuracy and maintains clinical interpretability compared with less transparent machine learning algorithms.
“We used the elastic net algorithm, given substantial evidence that penalization improves prediction accuracy,25–27 alongside its well-established accuracy and robustness28,29 and ability to maintain clinical interpretability compared with less transparent machine learning algorithms (eg, random forests, neural networks).”
From A pilot study using frequent inpatient assessments of suicidal thinking to predict short-term postdischarge suicidal behavior (2021) · cites this paper as [25]
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References 35 · Cited by 785
- Authors
- Wu, T. T., Chen, Y. F., Hastie, T., Sobel, E., & Lange, K.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Lange, Kenneth
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-01-28
- Volume
- 25
- Issue
- 6
- Pages
- 714-721
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC2732298
- OpenAlex
- W2122189635
- Cited by
- 785
- References
- 35
- Citations by year
- 2026: 9 · 2025: 23 · 2024: 31 · 2023: 32 · 2022: 43 · 2021: 48 · 2020: 52 · 2019: 61 · 2018: 67 · 2017: 50 · 2016: 49 · 2015: 42 · 2014: 64 · 2013: 66 · 2012: 60
- FWCI
- 28.86
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Public Health Service (MH59490, GM53275)
- National Institute of Mental Health (MH59490)
- National Institute of General Medical Sciences (R01GM053275, GM 53275)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- genome-wide association study, single nucleotide polymorphisms, gene mapping, interaction analysis, celiac disease, high-dimensional regression
- MeSH terms
- Software, Logistic Models, Computational Biology, Internet, Polymorphism, Single Nucleotide, Genome-Wide Association Study
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