A comparative analysis of family-based and population-based association tests using whole genome sequence data

Persistent Link:
http://hdl.handle.net/10150/610090
Title:
A comparative analysis of family-based and population-based association tests using whole genome sequence data
Author:
Zhou, Jin; Yip, Wai-Ki; Cho, Michael; Qiao, Dandi; McDonald, Merry-Lynn; Laird, Nan
Affiliation:
Biostatistics Department, Harvard School of Public Health, Boston, MA 02115 USA; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Division of Epidemiology and Biostatistics, College of Public Health, University of Arizona, Tucson, AZ 85724, USA
Issue Date:
2014
Publisher:
BioMed Central
Citation:
Zhou et al. BMC Proceedings 2014, 8(Suppl 1):S33 http://www.biomedcentral.com/1753-6561/8/S1/S33
Journal:
BMC Proceedings
Rights:
© 2014 Zhou et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0)
Collection Information:
This item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at repository@u.library.arizona.edu.
Abstract:
The revolution in next-generation sequencing has made obtaining both common and rare high-quality sequence variants across the entire genome feasible. Because researchers are now faced with the analytical challenges of handling a massive amount of genetic variant information from sequencing studies, numerous methods have been developed to assess the impact of both common and rare variants on disease traits. In this report, whole genome sequencing data from Genetic Analysis Workshop 18 was used to compare the power of several methods, considering both family-based and population-based designs, to detect association with variants in the MAP4 gene region and on chromosome 3 with blood pressure. To prioritize variants across the genome for testing, variants were first functionally assessed using prediction algorithms and expression quantitative trait loci (eQTLs) data. Four set-based tests in the family-based association tests (FBAT) framework--FBAT-v, FBAT-lmm, FBAT-m, and FBAT-l--were used to analyze 20 pedigrees, and 2 variance component tests, sequence kernel association test (SKAT) and genome-wide complex trait analysis (GCTA), were used with 142 unrelated individuals in the sample. Both set-based and variance-component-based tests had high power and an adequate type I error rate. Of the various FBATs, FBAT-l demonstrated superior performance, indicating the potential for it to be used in rare-variant analysis. The updated FBAT package is available at: http://www.hsph.harvard.edu/fbat/ webcite.
EISSN:
1753-6561
DOI:
10.1186/1753-6561-8-S1-S33
Version:
Final published version
Additional Links:
http://www.biomedcentral.com/1753-6561/8/S1/S33

Full metadata record

DC FieldValue Language
dc.contributor.authorZhou, Jinen
dc.contributor.authorYip, Wai-Kien
dc.contributor.authorCho, Michaelen
dc.contributor.authorQiao, Dandien
dc.contributor.authorMcDonald, Merry-Lynnen
dc.contributor.authorLaird, Nanen
dc.date.accessioned2016-05-20T08:58:20Z-
dc.date.available2016-05-20T08:58:20Z-
dc.date.issued2014en
dc.identifier.citationZhou et al. BMC Proceedings 2014, 8(Suppl 1):S33 http://www.biomedcentral.com/1753-6561/8/S1/S33en
dc.identifier.doi10.1186/1753-6561-8-S1-S33en
dc.identifier.urihttp://hdl.handle.net/10150/610090-
dc.description.abstractThe revolution in next-generation sequencing has made obtaining both common and rare high-quality sequence variants across the entire genome feasible. Because researchers are now faced with the analytical challenges of handling a massive amount of genetic variant information from sequencing studies, numerous methods have been developed to assess the impact of both common and rare variants on disease traits. In this report, whole genome sequencing data from Genetic Analysis Workshop 18 was used to compare the power of several methods, considering both family-based and population-based designs, to detect association with variants in the MAP4 gene region and on chromosome 3 with blood pressure. To prioritize variants across the genome for testing, variants were first functionally assessed using prediction algorithms and expression quantitative trait loci (eQTLs) data. Four set-based tests in the family-based association tests (FBAT) framework--FBAT-v, FBAT-lmm, FBAT-m, and FBAT-l--were used to analyze 20 pedigrees, and 2 variance component tests, sequence kernel association test (SKAT) and genome-wide complex trait analysis (GCTA), were used with 142 unrelated individuals in the sample. Both set-based and variance-component-based tests had high power and an adequate type I error rate. Of the various FBATs, FBAT-l demonstrated superior performance, indicating the potential for it to be used in rare-variant analysis. The updated FBAT package is available at: http://www.hsph.harvard.edu/fbat/ webcite.en
dc.language.isoenen
dc.publisherBioMed Centralen
dc.relation.urlhttp://www.biomedcentral.com/1753-6561/8/S1/S33en
dc.rights© 2014 Zhou et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0)en
dc.titleA comparative analysis of family-based and population-based association tests using whole genome sequence dataen
dc.typeArticleen
dc.identifier.eissn1753-6561en
dc.contributor.departmentBiostatistics Department, Harvard School of Public Health, Boston, MA 02115 USAen
dc.contributor.departmentChanning Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USAen
dc.contributor.departmentDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USAen
dc.contributor.departmentDivision of Epidemiology and Biostatistics, College of Public Health, University of Arizona, Tucson, AZ 85724, USAen
dc.identifier.journalBMC Proceedingsen
dc.description.collectioninformationThis item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
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