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Region-based analysis in genome-wide association study of Framingham Heart Study blood lipid phenotypes
Due to the high-dimensionality of single-nucleotide polymorphism (SNP) data, region-based methods are an attractive approach to the identification of genetic variation associated with a certain phenotype. A common approach to defining regions is to identify the most significant SNPs from a single-SN...
Autores principales: | , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2795900/ https://www.ncbi.nlm.nih.gov/pubmed/20017993 |
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author | Asimit, Jennifer L Yoo, Yun Joo Waggott, Daryl Sun, Lei Bull, Shelley B |
author_facet | Asimit, Jennifer L Yoo, Yun Joo Waggott, Daryl Sun, Lei Bull, Shelley B |
author_sort | Asimit, Jennifer L |
collection | PubMed |
description | Due to the high-dimensionality of single-nucleotide polymorphism (SNP) data, region-based methods are an attractive approach to the identification of genetic variation associated with a certain phenotype. A common approach to defining regions is to identify the most significant SNPs from a single-SNP association analysis, and then use a gene database to obtain a list of genes proximal to the identified SNPs. Alternatively, regions may be defined statistically, via a scan statistic. After categorizing SNPs as significant or not (based on the single-SNP association p-values), a scan statistic is useful to identify regions that contain more significant SNPs than expected by chance. Important features of this method are that regions are defined statistically, so that there is no dependence on a gene database, and both gene and inter-gene regions can be detected. In the analysis of blood-lipid phenotypes from the Framingham Heart Study (FHS), we compared statistically defined regions with those formed from the top single SNP tests. Although we missed a number of single SNPs, we also identified many additional regions not found as SNP-database regions and avoided issues related to region definition. In addition, analyses of candidate genes for high-density lipoprotein, low-density lipoprotein, and triglyceride levels suggested that associations detected with region-based statistics are also found using the scan statistic approach. |
format | Text |
id | pubmed-2795900 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-27959002009-12-18 Region-based analysis in genome-wide association study of Framingham Heart Study blood lipid phenotypes Asimit, Jennifer L Yoo, Yun Joo Waggott, Daryl Sun, Lei Bull, Shelley B BMC Proc Proceedings Due to the high-dimensionality of single-nucleotide polymorphism (SNP) data, region-based methods are an attractive approach to the identification of genetic variation associated with a certain phenotype. A common approach to defining regions is to identify the most significant SNPs from a single-SNP association analysis, and then use a gene database to obtain a list of genes proximal to the identified SNPs. Alternatively, regions may be defined statistically, via a scan statistic. After categorizing SNPs as significant or not (based on the single-SNP association p-values), a scan statistic is useful to identify regions that contain more significant SNPs than expected by chance. Important features of this method are that regions are defined statistically, so that there is no dependence on a gene database, and both gene and inter-gene regions can be detected. In the analysis of blood-lipid phenotypes from the Framingham Heart Study (FHS), we compared statistically defined regions with those formed from the top single SNP tests. Although we missed a number of single SNPs, we also identified many additional regions not found as SNP-database regions and avoided issues related to region definition. In addition, analyses of candidate genes for high-density lipoprotein, low-density lipoprotein, and triglyceride levels suggested that associations detected with region-based statistics are also found using the scan statistic approach. BioMed Central 2009-12-15 /pmc/articles/PMC2795900/ /pubmed/20017993 Text en Copyright ©2009 Asimit 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 Asimit, Jennifer L Yoo, Yun Joo Waggott, Daryl Sun, Lei Bull, Shelley B Region-based analysis in genome-wide association study of Framingham Heart Study blood lipid phenotypes |
title | Region-based analysis in genome-wide association study of Framingham Heart Study blood lipid phenotypes |
title_full | Region-based analysis in genome-wide association study of Framingham Heart Study blood lipid phenotypes |
title_fullStr | Region-based analysis in genome-wide association study of Framingham Heart Study blood lipid phenotypes |
title_full_unstemmed | Region-based analysis in genome-wide association study of Framingham Heart Study blood lipid phenotypes |
title_short | Region-based analysis in genome-wide association study of Framingham Heart Study blood lipid phenotypes |
title_sort | region-based analysis in genome-wide association study of framingham heart study blood lipid phenotypes |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2795900/ https://www.ncbi.nlm.nih.gov/pubmed/20017993 |
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