Sequence Analysis Using Logic Regression
Abstract
Logic Regression is a new adaptive regression methodology that attempts to construct predictors as Boolean combinations of (binary) covariates. In this paper we use this algorithm to deal with single‐nucleotide polymorphism (SNP) sequence data. The predictors that are found are interpretable as risk factors of the disease. Significance of these risk factors is assessed using techniques like cross‐validation, permutation tests, and independent test sets. These model selection techniques remain valid when data is dependent, as is the case for the family data used here. In our analysis of the Genetic Analysis Workshop 12 data we identify the exact locations of mutations on gene 1 and gene 6 and a number of mutations on gene 2 that are associated with the affected status, without selecting any false positives. © 2001 Wiley‐Liss, Inc.
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Logic regression, first introduced for sequence data analysis, does not require variants to be collapsed beforehand.
“Logic regression, which is similar in ways to the method proposed by Han and Pan,40 was initially proposed for analyzing sequence data and does not assume that variants have been collapsed a priori.”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [67]
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Detecting gene–gene interactions that underlie human diseases
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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 · see citing works
Iterative random forests to discover predictive and stable high-order interactions
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- Authors
- Kooperberg, C., Ruczinski, I., LeBlanc, M. L., & Hsu, L.
- Affiliations
- Fred Hutch Cancer Center (US)
- Venue
- Genetic Epidemiology
- Publisher
- Wiley
- Published
- 2001-01
- Volume
- 21
- Issue
- S1
- Pages
- S626-31
- ISSN
- 0741-0395 (ISSN-L)
- 1098-2272
- OpenAlex
- W2114740540
- Cited by
- 138
- References
- 5
- Citations by year
- 2024: 4 · 2023: 5 · 2022: 1 · 2021: 1 · 2020: 3 · 2019: 3 · 2018: 5 · 2017: 2 · 2015: 1 · 2014: 6 · 2013: 4 · 2012: 8
- FWCI
- 2.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Genetic Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Cancer Institute (CA74841)
- 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 › Molecular Biology › Gene expression and cancer classification
- Keywords
- logic regression, single nucleotide polymorphisms, genetic association, disease risk factors, model selection, permutation tests, family data, genetic mutations
- MeSH terms
- Algorithms, Chromosome Mapping, DNA Mutational Analysis, Female, Humans, Male, Models, Genetic, Logistic Models, Quantitative Trait, Heritable, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide
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