ArticleGenetic epidemiology2009
Generalized linear modeling with regularization for detecting common disease rare haplotype association.
Article in Genetic epidemiology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multivariate meta-analysis of the association of G-protein beta 3 gene (GNB3) haplotypes with cardiovascular phenotypes.Molecular biology reports · 2014Pooled it
- Bayesian LASSO for population stratification correction in rare haplotype association studies.Statistical applications in genetics and molecular biology · 2024Article
- Haplotype based testing for a better understanding of the selective architecture.BMC bioinformatics · 2023Article
- Bivariate quantitative Bayesian LASSO for detecting association of rare haplotypes with two correlated continuous phenotypes.Frontiers in genetics · 2023Article
- Comparison of haplotype-based tests for detecting gene-environment interactions with rare variants.Briefings in bioinformatics · 2020Review
- Bivariate logistic Bayesian LASSO for detecting rare haplotype association with two correlated phenotypes.Genetic epidemiology · 2019Article
- A Family-Based Rare Haplotype Association Method for Quantitative Traits.Human heredity · 2018Article
- Comparison of haplotype-based statistical tests for disease association with rare and common variants.Briefings in bioinformatics · 2016Article
- Kullback-Leibler divergence for detection of rare haplotype common disease association.European journal of human genetics : EJHG · 2015Article
- Detecting associations of rare variants with common diseases: collapsing or haplotyping?Briefings in bioinformatics · 2015Article
- A penalized likelihood approach for investigating gene-drug interactions in pharmacogenetic studies.Biometrics · 2015Article
- Test of rare variant association based on affected sib-pairs.European journal of human genetics : EJHG · 2015Article
- Pathway-guided identification of gene-gene interactions.Annals of human genetics · 2014Article
- FamLBL: detecting rare haplotype disease association based on common SNPs using case-parent triads.Bioinformatics (Oxford, England) · 2014Article
- A rare variant association test based on combinations of single-variant tests.Genetic epidemiology · 2014Article
- A novel test for testing the optimally weighted combination of rare and common variants based on data of parents and affected children.Genetic epidemiology · 2014Article
- Penalized regression approaches to testing for quantitative trait-rare variant association.Frontiers in genetics · 2014Article
- Detecting rare haplotype-environment interaction with logistic Bayesian LASSO.Genetic epidemiology · 2014Article
- Evaluation of logistic Bayesian LASSO for identifying association with rare haplotypes.BMC proceedings · 2014Article
- Combined genotype and haplotype tests for region-based association studies.BMC genomics · 2013Article
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2 authors.
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Abstract
Whole genome association studies (WGAS) have surged in popularity in recent years as technological advances have made large-scale genotyping more feasible and as new exciting results offer tremendous hope and optimism. The logic of WGAS rests upon the common disease/common variant (CD/CV) hypothesis. Detection of association under the common disease/rare variant (CD/RV) scenario is much harder, and the current practices of WGAS may be under-power without large enough sample sizes. In this article, we propose a generalized linear model with regularization (rGLM) approach for detecting disease-haplotype association using unphased single nucleotide polymorphisms data that is applicable to both CD/CV and CD/RV scenarios. We borrow a dimension-reduction method from the data mining and statistical learning literature, but use it for the purpose of weeding out haplotypes that are not associated with the disease so that the associated haplotypes, especially those that are rare, can stand out and be accounted for more precisely. By using high-dimensional data analysis techniques, which are frequently employed in microarray analyses, interacting effects among haplotypes in different blocks can be investigated without much concern about the sample size being overwhelmed by the number of haplotype combinations. Our simulation study demonstrates the gain in power for detecting associations with moderate sample sizes. For detecting association under CD/RV, regression type methods such as that implemented in hapassoc may fail to provide coefficient estimates for rare associated haplotypes, resulting in a loss of power compared to rGLM. Furthermore, our results indicate that rGLM can uncover the associated variants much more frequently than can hapassoc.
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