Relationship between genomic distance-based regression and kernel machine regression for multi-marker association testing
Open accessAbstract
To detect genetic association with common and complex diseases, two powerful yet quite different multimarker association tests have been proposed, genomic distance‐based regression (GDBR) (Wessel and Schork [2006] Am J Hum Genet 79:821–833) and kernel machine regression (KMR) (Kwee et al. [2008] Am J Hum Genet 82:386–397; Wu et al. [2010] Am J Hum Genet 86:929–942). GDBR is based on relating a multimarker similarity metric for a group of subjects to variation in their trait values, while KMR is based on nonparametric estimates of the effects of the multiple markers on the trait through a kernel function or kernel matrix. Since the two approaches are both powerful and general, but appear quite different, it is important to know their specific relationships. In this report, we show that, under the condition that there is no other covariate, there is a striking correspondence between the two approaches for a quantitative or a binary trait: if the same positive semi‐definite matrix is used as the centered similarity matrix in GDBR and as the kernel matrix in KMR, the F ‐test statistic in GDBR and the score test statistic in KMR are equal (up to some ignorable constants). The result is based on the connections of both methods to linear or logistic (random‐effects) regression models. Genet. Epidemiol 35: 211‐216, 2011 © 2011 Wiley‐Liss, Inc.
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MDMR and KMM have been shown to be equivalent under certain conditions on the corresponding distance and kernel matrices.
“have been shown to be equivalent under certain conditions on the corresponding distance and kernel matrices (Pan 2011).”
From Similarity-based multimodal regression (2024) · cites this paper as Pan 2011
The Abstract states that genomic distance-based regression (GDBR) and kernel-machine regression (KMR) have corresponding test statistics when there are no other covariates and the same positive semidefinite matrix is used as GDBR’s centered similarity matrix and KMR’s kernel matrix. It does not establish equivalence for MDMR and KMM generally; its result concerns these specific methods and conditions. (LLM, conf=0.82)
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- Authors
- Pan, W.
- Affiliations
- University of Minnesota (US)
- Corresponding author
- Pan, Wei
- ORCID
- Pan, Wei
- Venue
- Genetic Epidemiology
- Publisher
- Wiley
- Published
- 2011-02-09
- Volume
- 35
- Issue
- 4
- Pages
- 211-216
- ISSN
- 0741-0395 (ISSN-L)
- 1098-2272
- PMC
- PMC3134543
- OpenAlex
- W2136173440
- Cited by
- 71
- References
- 31
- Citations by year
- 2026: 2 · 2024: 1 · 2023: 6 · 2022: 4 · 2021: 2 · 2020: 4 · 2019: 5 · 2018: 3 · 2017: 6 · 2016: 8 · 2015: 10 · 2014: 9 · 2013: 5 · 2012: 4
- FWCI
- 3.94
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Genetic Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute (R01HL105397, R01HL065462)
- National Institute of General Medical Sciences (R01GM081535)
- National Institute of Diabetes and Digestive and Kidney Diseases (R21DK089351)
- 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
- genetic association, similarity matrix, kernel matrix, random effects regression, quantitative traits, binary traits, statistical equivalence
- MeSH terms
- Female, Genetic Markers, Humans, Phenotype, Regression Analysis, Software, Genome, Human, Polymorphism, Single Nucleotide, Genomics, Quantitative Trait Loci, Genome-Wide Association Study, Genetic Association Studies
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