ArticleGenetic epidemiology2013
Haplotype kernel association test as a powerful method to identify chromosomal regions harboring uncommon causal variants.
Article in Genetic epidemiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Bayesian LASSO for population stratification correction in rare haplotype association studies.Statistical applications in genetics and molecular biology · 2024Article
- 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
- A permutation method for detecting trend correlations in rare variant association studies.Genetics research · 2019Article
- Bivariate logistic Bayesian LASSO for detecting rare haplotype association with two correlated phenotypes.Genetic epidemiology · 2019Article
- An integrative association method for omics data based on a modified Fisher's method with application to childhood asthma.PLoS genetics · 2019Article
- Detecting rare and common haplotype-environment interaction under uncertainty of gene-environment independence assumption.Biometrics · 2017Article
- Comparison of haplotype-based statistical tests for disease association with rare and common variants.Briefings in bioinformatics · 2016Article
- Beyond Rare-Variant Association Testing: Pinpointing Rare Causal Variants in Case-Control Sequencing Study.Scientific reports · 2016Article
- Association of rare haplotypes onBMC proceedings · 2016Article
- Associating Multivariate Quantitative Phenotypes with Genetic Variants in Family Samples with a Novel Kernel Machine Regression Method.Genetics · 2015Article
- 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 Sequence Kernel Association Test for Dichotomous Traits in Family Samples under a Generalized Linear Mixed Model.Human heredity · 2015Article
- Rare-Variant Kernel Machine Test for Longitudinal Data from Population and Family Samples.Human heredity · 2015Article
- Reproducible simulations of realistic samples for next-generation sequencing studies using Variant Simulation Tools.Genetic epidemiology · 2015Article
- FamLBL: detecting rare haplotype disease association based on common SNPs using case-parent triads.Bioinformatics (Oxford, England) · 2014Article
- Kernel-machine testing coupled with a rank-truncation method for genetic pathway analysis.Genetic epidemiology · 2014Article
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8 authors.
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Abstract
For most complex diseases, the fraction of heritability that can be explained by the variants discovered from genome-wide association studies is minor. Although the so-called "rare variants" (minor allele frequency [MAF] < 1%) have attracted increasing attention, they are unlikely to account for much of the "missing heritability" because very few people may carry these rare variants. The genetic variants that are likely to fill in the "missing heritability" include uncommon causal variants (MAF < 5%), which are generally untyped in association studies using tagging single-nucleotide polymorphisms (SNPs) or commercial SNP arrays. Developing powerful statistical methods can help to identify chromosomal regions harboring uncommon causal variants, while bypassing the genome-wide or exome-wide next-generation sequencing. In this work, we propose a haplotype kernel association test (HKAT) that is equivalent to testing the variance component of random effects for distinct haplotypes. With an appropriate weighting scheme given to haplotypes, we can further enhance the ability of HKAT to detect uncommon causal variants. With scenarios simulated according to the population genetics theory, HKAT is shown to be a powerful method for detecting chromosomal regions harboring uncommon causal variants.
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