Genome-wide QTL and eQTL analyses using Mendel

Pedigree genome-wide association studies (GWAS) (Option 29) in the current version of the Mendel software is an optimized subroutine for performing large-scale genome-wide quantitative trait locus (QTL) analysis. This analysis (a) works for random sample data, pedigree data, or a mix of both; (b) is...

Full description

Bibliographic Details
Main Authors:	Zhou, Hua, Zhou, Jin, Hu, Tao, Sobel, Eric M., Lange, Kenneth
Format:	Online
Language:	English
Published:	BioMed Central 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133530/

id	pubmed-5133530
recordtype	oai_dc
spelling	pubmed-51335302016-12-15 Genome-wide QTL and eQTL analyses using Mendel Zhou, Hua Zhou, Jin Hu, Tao Sobel, Eric M. Lange, Kenneth Proceedings Pedigree genome-wide association studies (GWAS) (Option 29) in the current version of the Mendel software is an optimized subroutine for performing large-scale genome-wide quantitative trait locus (QTL) analysis. This analysis (a) works for random sample data, pedigree data, or a mix of both; (b) is highly efficient in both run time and memory requirement; (c) accommodates both univariate and multivariate traits; (d) works for autosomal and x-linked loci; (e) correctly deals with missing data in traits, covariates, and genotypes; (f) allows for covariate adjustment and constraints among parameters; (g) uses either theoretical or single nucleotide polymorphism (SNP)–based empirical kinship matrix for additive polygenic effects; (h) allows extra variance components such as dominant polygenic effects and household effects; (i) detects and reports outlier individuals and pedigrees; and (j) allows for robust estimation via the t-distribution. This paper assesses these capabilities on the genetics analysis workshop 19 (GAW19) sequencing data. We analyzed simulated and real phenotypes for both family and random sample data sets. For instance, when jointly testing the 8 longitudinally measured systolic blood pressure and diastolic blood pressure traits, it takes Mendel 78 min on a standard laptop computer to read, quality check, and analyze a data set with 849 individuals and 8.3 million SNPs. Genome-wide expression QTL analysis of 20,643 expression traits on 641 individuals with 8.3 million SNPs takes 30 h using 20 parallel runs on a cluster. Mendel is freely available at http://www.genetics.ucla.edu/software. BioMed Central 2016-10-18 /pmc/articles/PMC5133530/ /pubmed/27980643 http://dx.doi.org/10.1186/s12919-016-0037-6 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Zhou, Hua Zhou, Jin Hu, Tao Sobel, Eric M. Lange, Kenneth
spellingShingle	Zhou, Hua Zhou, Jin Hu, Tao Sobel, Eric M. Lange, Kenneth Genome-wide QTL and eQTL analyses using Mendel
author_facet	Zhou, Hua Zhou, Jin Hu, Tao Sobel, Eric M. Lange, Kenneth
author_sort	Zhou, Hua
title	Genome-wide QTL and eQTL analyses using Mendel
title_short	Genome-wide QTL and eQTL analyses using Mendel
title_full	Genome-wide QTL and eQTL analyses using Mendel
title_fullStr	Genome-wide QTL and eQTL analyses using Mendel
title_full_unstemmed	Genome-wide QTL and eQTL analyses using Mendel
title_sort	genome-wide qtl and eqtl analyses using mendel
description	Pedigree genome-wide association studies (GWAS) (Option 29) in the current version of the Mendel software is an optimized subroutine for performing large-scale genome-wide quantitative trait locus (QTL) analysis. This analysis (a) works for random sample data, pedigree data, or a mix of both; (b) is highly efficient in both run time and memory requirement; (c) accommodates both univariate and multivariate traits; (d) works for autosomal and x-linked loci; (e) correctly deals with missing data in traits, covariates, and genotypes; (f) allows for covariate adjustment and constraints among parameters; (g) uses either theoretical or single nucleotide polymorphism (SNP)–based empirical kinship matrix for additive polygenic effects; (h) allows extra variance components such as dominant polygenic effects and household effects; (i) detects and reports outlier individuals and pedigrees; and (j) allows for robust estimation via the t-distribution. This paper assesses these capabilities on the genetics analysis workshop 19 (GAW19) sequencing data. We analyzed simulated and real phenotypes for both family and random sample data sets. For instance, when jointly testing the 8 longitudinally measured systolic blood pressure and diastolic blood pressure traits, it takes Mendel 78 min on a standard laptop computer to read, quality check, and analyze a data set with 849 individuals and 8.3 million SNPs. Genome-wide expression QTL analysis of 20,643 expression traits on 641 individuals with 8.3 million SNPs takes 30 h using 20 parallel runs on a cluster. Mendel is freely available at http://www.genetics.ucla.edu/software.
publisher	BioMed Central
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133530/
_version_	1613752551174307840

Genome-wide QTL and eQTL analyses using Mendel

Similar Items