ArticleAmerican journal of human genetics2007
Association mapping via regularized regression analysis of single-nucleotide-polymorphism haplotypes in variable-sized sliding windows.
Article in American journal of human genetics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- Exploring the genetic basis of essential hypertension: association with AGT gene variants in an Iranian population.BMC genomics · 2025Article
- Effect of genomic regions harboring putative lethal haplotypes on reproductive performance in closed experimental selection lines of Nellore cattle.Scientific reports · 2025Article
- Unraveling genomic regions with transmission ratio distortion harboring putative lethal alleles and their biological implications in Nellore cattle from experimental selection lines.Journal of animal science · 2025Article
- Article
- Soft windowing application to improve analysis of high-throughput phenotyping data.Bioinformatics (Oxford, England) · 2020Article
- Sliding window haplotype approaches overcome single SNP analysis limitations in identifying genes for meat tenderness in Nelore cattle.BMC genetics · 2019Article
- Article
- Comparison of haplotype-based statistical tests for disease association with rare and common variants.Briefings in bioinformatics · 2016Article
- Efficient Genome-Wide Sequencing and Low-Coverage Pedigree Analysis from Noninvasively Collected Samples.Genetics · 2016Article
- Exploiting Linkage Disequilibrium for Ultrahigh-Dimensional Genome-Wide Data with an Integrated Statistical Approach.Genetics · 2016Article
- Causal Genetic Inference Using Haplotypes as Instrumental Variables.Genetic epidemiology · 2016Article
- Genes and environment in neonatal intraventricular hemorrhage.Seminars in perinatology · 2015Review
- 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
- Genetic association of key Th1/Th2 pathway candidate genes, IRF2, IL6, IFNGR2, STAT4 and IL4RA, with atopic asthma in the Indian population.Journal of human genetics · 2015Article
- A penalized likelihood approach for investigating gene-drug interactions in pharmacogenetic studies.Biometrics · 2015Article
- Exploiting genome structure in association analysis.Journal of computational biology : a journal of computational molecular cell biology · 2014Article
- Rule-based induction method for haplotype comparison and identification of candidate disease loci.Genome medicine · 2012Article
- Localization of association signal from risk and protective variants in sequencing studies.Frontiers in genetics · 2012Article
- Evaluation of a LASSO regression approach on the unrelated samples of Genetic Analysis Workshop 17.BMC proceedings · 2011Article
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3 authors.
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Abstract
Large-scale haplotype association analysis, especially at the whole-genome level, is still a very challenging task without an optimal solution. In this study, we propose a new approach for haplotype association analysis that is based on a variable-sized sliding-window framework and employs regularized regression analysis to tackle the problem of multiple degrees of freedom in the haplotype test. Our method can handle a large number of haplotypes in association analyses more efficiently and effectively than do currently available approaches. We implement a procedure in which the maximum size of a sliding window is determined by local haplotype diversity and sample size, an attractive feature for large-scale haplotype analyses, such as a whole-genome scan, in which linkage disequilibrium patterns are expected to vary widely. We compare the performance of our method with that of three other methods--a test based on a single-nucleotide polymorphism, a cladistic analysis of haplotypes, and variable-length Markov chains--with use of both simulated and experimental data. By analyzing data sets simulated under different disease models, we demonstrate that our method consistently outperforms the other three methods, especially when the region under study has high haplotype diversity. Built on the regression analysis framework, our method can incorporate other risk-factor information into haplotype-based association analysis, which is becoming an increasingly necessary step for studying common disorders to which both genetic and environmental risk factors contribute.
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