Fast genome-wide pedigree quantitative trait loci analysis using MENDEL

Persistent Link:
http://hdl.handle.net/10150/610091
Title:
Fast genome-wide pedigree quantitative trait loci analysis using MENDEL
Author:
Zhou, Hua; Zhou, Jin; Sobel, Eric; Lange, Kenneth
Affiliation:
Department of Statistics, North Carolina State University, Raleigh, NC27695 USA; Division of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, Tucson, AZ85721-0066, USA; Department of Human Genetics, University of California, Los Angeles, CA90095, USA; Department of Biomathematics, University of California, Los Angeles, CA90095, USA; Department of Statistics, University of California, Los Angeles, CA90095, USA
Issue Date:
2014
Publisher:
BioMed Central
Citation:
Zhou et al. BMC Proceedings 2014, 8(Suppl 1):S93 http://www.biomedcentral.com/1753-6561/8/S1/S93
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 linkage era left a rich legacy of pedigree samples that can be used for modern genome-wide association sequencing (GWAS) or next-generation sequencing (NGS) studies. Family designs are naturally equipped to detect rare variants, control for population stratification, and facilitate the study of parent-of-origin effects. Unfortunately, pedigree likelihoods are notoriously hard to compute, and current software for association mapping in pedigrees is prohibitively slow in processing dense marker maps. In a recent release of the comprehensive genetic analysis software MENDEL, we implemented an ultra-fast score test for association mapping with pedigree-based GWAS or NGS study data. Our implementation (a) works for random sample data, pedigree data, or a mix of both; (b) allows for covariate adjustment, including correction for population stratification; (c) accommodates both univariate and multivariate quantitative traits; and (d) allows missing values in multivariate traits. In this paper, we assess the capabilities of MENDEL on the Genetic Analysis Workshop 18 sequencing data. For instance, when jointly testing the 4 longitudinally measured diastolic blood pressure traits, it takes MENDEL less than 51 minutes on a standard laptop computer to read, quality check, and analyze a data set with 959 individuals and 8.3 million single-nucleotide polymorphisms (SNPs). Our analysis reveals association of one SNP in the q32.2 region of chromosome 1. MENDEL is freely available on http://www.genetics.ucla.edu/software webcite.
EISSN:
1753-6561
DOI:
10.1186/1753-6561-8-S1-S93
Version:
Final published version
Additional Links:
http://www.biomedcentral.com/1753-6561/8/S1/S93

Full metadata record

DC FieldValue Language
dc.contributor.authorZhou, Huaen
dc.contributor.authorZhou, Jinen
dc.contributor.authorSobel, Ericen
dc.contributor.authorLange, Kennethen
dc.date.accessioned2016-05-20T08:58:21Z-
dc.date.available2016-05-20T08:58:21Z-
dc.date.issued2014en
dc.identifier.citationZhou et al. BMC Proceedings 2014, 8(Suppl 1):S93 http://www.biomedcentral.com/1753-6561/8/S1/S93en
dc.identifier.doi10.1186/1753-6561-8-S1-S93en
dc.identifier.urihttp://hdl.handle.net/10150/610091-
dc.description.abstractThe linkage era left a rich legacy of pedigree samples that can be used for modern genome-wide association sequencing (GWAS) or next-generation sequencing (NGS) studies. Family designs are naturally equipped to detect rare variants, control for population stratification, and facilitate the study of parent-of-origin effects. Unfortunately, pedigree likelihoods are notoriously hard to compute, and current software for association mapping in pedigrees is prohibitively slow in processing dense marker maps. In a recent release of the comprehensive genetic analysis software MENDEL, we implemented an ultra-fast score test for association mapping with pedigree-based GWAS or NGS study data. Our implementation (a) works for random sample data, pedigree data, or a mix of bothen
dc.description.abstract(b) allows for covariate adjustment, including correction for population stratificationen
dc.description.abstract(c) accommodates both univariate and multivariate quantitative traitsen
dc.description.abstractand (d) allows missing values in multivariate traits. In this paper, we assess the capabilities of MENDEL on the Genetic Analysis Workshop 18 sequencing data. For instance, when jointly testing the 4 longitudinally measured diastolic blood pressure traits, it takes MENDEL less than 51 minutes on a standard laptop computer to read, quality check, and analyze a data set with 959 individuals and 8.3 million single-nucleotide polymorphisms (SNPs). Our analysis reveals association of one SNP in the q32.2 region of chromosome 1. MENDEL is freely available on http://www.genetics.ucla.edu/software webcite.en
dc.language.isoenen
dc.publisherBioMed Centralen
dc.relation.urlhttp://www.biomedcentral.com/1753-6561/8/S1/S93en
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.titleFast genome-wide pedigree quantitative trait loci analysis using MENDELen
dc.typeArticleen
dc.identifier.eissn1753-6561en
dc.contributor.departmentDepartment of Statistics, North Carolina State University, Raleigh, NC27695 USAen
dc.contributor.departmentDivision of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, Tucson, AZ85721-0066, USAen
dc.contributor.departmentDepartment of Human Genetics, University of California, Los Angeles, CA90095, USAen
dc.contributor.departmentDepartment of Biomathematics, University of California, Los Angeles, CA90095, USAen
dc.contributor.departmentDepartment of Statistics, University of California, Los Angeles, CA90095, 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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