Genome-wide QTL and eQTL analyses using Mendel. Issue 7 (October 2016)

• Record Type: Journal Article
• Authors: Zhou, Hua; Zhou, Jin; Hu, Tao; Sobel, Eric; Lange, Kenneth
• Is Part Of: BMC proceedings. Volume 10:Issue 7(2016)
• Page Start: 239
• Page End: 244
• Publication Date: 2016-10
• Subjects: Medicine -- Congresses; Biology -- Congresses; 610.5
• DOI: 10.1186/s12919-016-0037-6
• Languages: English
• ISSNs: 1753-6561

Abstract

Abstract 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 athttp://www.genetics.ucla.edu/software .