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Genome-wide analysis of haplotype interaction for the data from the North American Rheumatoid Arthritis Consortium

Recent genome-wide association studies on several complex diseases have focused on individual single-nucleotide polymorphism (SNP) analysis; however, not many studies have reported interactions among genes perhaps because the gene-gene and gene-environment interaction analysis could be infeasible du...

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Autores principales: Park, Jungsun, Namkung, Junghyun, Jhun, Mina, Park, Taesung
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2795932/
https://www.ncbi.nlm.nih.gov/pubmed/20018025
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author Park, Jungsun
Namkung, Junghyun
Jhun, Mina
Park, Taesung
author_facet Park, Jungsun
Namkung, Junghyun
Jhun, Mina
Park, Taesung
author_sort Park, Jungsun
collection PubMed
description Recent genome-wide association studies on several complex diseases have focused on individual single-nucleotide polymorphism (SNP) analysis; however, not many studies have reported interactions among genes perhaps because the gene-gene and gene-environment interaction analysis could be infeasible due to heavy computing requirements. In this study we propose a new strategy for exploring the interactions among haplotypes. The proposed method consists of two steps. Step 1 tests the single-SNP association of whole genome with multiple testing corrections and finds the haplotype blocks of the significant SNPs. Step 2 performs interaction analysis of haplotypes within blocks. Our proposed method is applied to the rheumatoid arthritis data for Genetic Analysis Workshop 16.
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spelling pubmed-27959322009-12-18 Genome-wide analysis of haplotype interaction for the data from the North American Rheumatoid Arthritis Consortium Park, Jungsun Namkung, Junghyun Jhun, Mina Park, Taesung BMC Proc Proceedings Recent genome-wide association studies on several complex diseases have focused on individual single-nucleotide polymorphism (SNP) analysis; however, not many studies have reported interactions among genes perhaps because the gene-gene and gene-environment interaction analysis could be infeasible due to heavy computing requirements. In this study we propose a new strategy for exploring the interactions among haplotypes. The proposed method consists of two steps. Step 1 tests the single-SNP association of whole genome with multiple testing corrections and finds the haplotype blocks of the significant SNPs. Step 2 performs interaction analysis of haplotypes within blocks. Our proposed method is applied to the rheumatoid arthritis data for Genetic Analysis Workshop 16. BioMed Central 2009-12-15 /pmc/articles/PMC2795932/ /pubmed/20018025 Text en Copyright ©2009 Park et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Park, Jungsun
Namkung, Junghyun
Jhun, Mina
Park, Taesung
Genome-wide analysis of haplotype interaction for the data from the North American Rheumatoid Arthritis Consortium
title Genome-wide analysis of haplotype interaction for the data from the North American Rheumatoid Arthritis Consortium
title_full Genome-wide analysis of haplotype interaction for the data from the North American Rheumatoid Arthritis Consortium
title_fullStr Genome-wide analysis of haplotype interaction for the data from the North American Rheumatoid Arthritis Consortium
title_full_unstemmed Genome-wide analysis of haplotype interaction for the data from the North American Rheumatoid Arthritis Consortium
title_short Genome-wide analysis of haplotype interaction for the data from the North American Rheumatoid Arthritis Consortium
title_sort genome-wide analysis of haplotype interaction for the data from the north american rheumatoid arthritis consortium
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2795932/
https://www.ncbi.nlm.nih.gov/pubmed/20018025
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