ArticleGenetic epidemiology2011
Relationship between genomic distance-based regression and kernel machine regression for multi-marker association testing.
Article in Genetic epidemiology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A gene based combination test using GWAS summary data.BMC bioinformatics · 2023Pooled it
- Similarity-based multimodal regression.Biostatistics (Oxford, England) · 2024Article
- Phylogenetic association analysis with conditional rank correlation.Biometrika · 2024Article
- DeLIVR: a deep learning approach to IV regression for testing nonlinear causal effects in transcriptome-wide association studies.Biostatistics (Oxford, England) · 2024Article
- Associating somatic mutation with clinical outcomes through kernel regression and optimal transport.Biometrics · 2023Article
- MKMR: a multi-kernel machine regression model to predict health outcomes using human microbiome data.Briefings in bioinformatics · 2023Article
- IDEAS: individual level differential expression analysis for single-cell RNA-seq data.Genome biology · 2022Article
- Compositional Data Analysis using Kernels in mass cytometry data.Bioinformatics advances · 2022Article
- A Statistical Method for Association Analysis of Cell Type Compositions.Statistics in biosciences · 2021Article
- A Multi-Marker Test for Analyzing Paired Genetic Data in Transplantation.Frontiers in genetics · 2021Review
- aPCoA: covariate adjusted principal coordinates analysis.Bioinformatics (Oxford, England) · 2020Article
- TS: a powerful truncated test to detect novel disease associated genes using publicly available gWAS summary data.BMC bioinformatics · 2020Article
- A review of kernel methods for genetic association studies.Genetic epidemiology · 2019Review
- An adaptive gene-level association test for pedigree data.BMC genetics · 2018Article
- KERNEL-PENALIZED REGRESSION FOR ANALYSIS OF MICROBIOME DATA.The annals of applied statistics · 2018Article
- Adaptive SNP-Set Association Testing in Generalized Linear Mixed Models with Application to Family Studies.Behavior genetics · 2018Article
- Article
- Article
- On Robust Association Testing for Quantitative Traits and Rare Variants.G3 (Bethesda, Md.) · 2016Article
- An adaptive two-sample test for high-dimensional means.Biometrika · 2016Article
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Abstract
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.
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