ArticleBiometrics2009
Gene-trait similarity regression for multimarker-based association analysis.
Article in Biometrics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Hi-C informed kernel association test for integrating 3D genome structure into variant-set analysis.Briefings in bioinformatics · 2026Article
- Hi-C informed kernel association test: integrating 3-dimensional genome structure into variant-set association for whole-genome sequencing data.bioRxiv : the preprint server for biology · 2025Article
- An optimal kernel-based multivariate U-statistic to test for associations with multiple phenotypes.Biostatistics (Oxford, England) · 2022Article
- Identifying individual risk rare variants using protein structure guided local tests (POINT).PLoS computational biology · 2019Article
- Testing cross-phenotype effects of rare variants in longitudinal studies of complex traits.Genetic epidemiology · 2018Article
- Reexamining Dis/Similarity-Based Tests for Rare-Variant Association with Case-Control Samples.Genetics · 2018Article
- Methods to evaluate rare variants gene-age interaction for triglycerides.BMC proceedings · 2018Article
- Cis-SNPs Set Testing and PrediXcan Analysis for Gene Expression Data using Linear Mixed Models.Scientific reports · 2017Article
- Powerful Genetic Association Analysis for Common or Rare Variants with High-Dimensional Structured Traits.Genetics · 2017Article
- A generalized association test based on U statistics.Bioinformatics (Oxford, England) · 2017Article
- Testing Allele Transmission of an SNP Set Using a Family-Based Generalized Genetic Random Field Method.Genetic epidemiology · 2016Article
- An Object-Oriented Regression for Building Disease Predictive Models with Multiallelic HLA Genes.Genetic epidemiology · 2016Article
- A New Method for Detecting Associations with Rare Copy-Number Variants.PLoS genetics · 2015Article
- A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction.Genetic epidemiology · 2015Article
- Set-based tests for genetic association in longitudinal studies.Biometrics · 2015Observational
- Assessing gene-environment interactions for common and rare variants with binary traits using gene-trait similarity regression.Genetics · 2015Article
- Using Hamming Distance as Information for SNP-Sets Clustering and Testing in Disease Association Studies.PloS one · 2015Article
- A weighted U-statistic for genetic association analyses of sequencing data.Genetic epidemiology · 2014Article
- A generalized genetic random field method for the genetic association analysis of sequencing data.Genetic epidemiology · 2014Article
- Incorporating gene-environment interaction in testing for association with rare genetic variants.Human heredity · 2014Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
We propose a similarity-based regression method to detect associations between traits and multimarker genotypes. The model regresses similarity in traits for pairs of "unrelated" individuals on their haplotype similarities, and detects the significance by a score test for which the limiting distribution is derived. The proposed method allows for covariates, uses phase-independent similarity measures to bypass the needs to impute phase information, and is applicable to traits of general types (e.g., quantitative and qualitative traits). We also show that the gene-trait similarity regression is closely connected with random effects haplotype analysis, although commonly they are considered as separate modeling tools. This connection unites the classic haplotype sharing methods with the variance-component approaches, which enables direct derivation of analytical properties of the sharing statistics even when the similarity regression model becomes analytically challenging.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.