Evaluation of estimated genetic values and their application to genome-wide investigation of systolic blood pressure

BMC Proceedings

Proceedings

Geetha Chittoor¹  Anthony G Comuzzie¹  John Blangero¹  Vincent P Diego¹  V Saroja Voruganti¹  Thomas D Dyer¹  Harald HH Göring¹  Ravindranath Duggirala¹  Ellen E Quillen¹  Rohina Rubicz¹  Laura Almasy¹  Jack W Kent¹  Marcio AA Almeida¹  Juan M Peralta² 

[1] Department of Genetics, Texas Biomedical Research Institute, PO Box 760549, 78245, San Antonio, TX, USA;Department of Genetics, Texas Biomedical Research Institute, PO Box 760549, 78245, San Antonio, TX, USA;Centre for Genetic Origins of Health and Disease, University of Western Australia, 35 Stirling Hightway, 6009, Crawley, Western Australia, Australia;

关键词: Systolic Blood Pressure;    Minor Allele Frequency;    Best Linear Unbiased Prediction;    Relationship Matrice;    Best Linear Unbiased Prediction;   

DOI  :  10.1186/1753-6561-8-S1-S66

来源: Springer

【 摘 要 】

The concept of breeding values, an individual's phenotypic deviation from the population mean as a result of the sum of the average effects of the genes they carry, is of great importance in livestock, aquaculture, and cash crop industries where emphasis is placed on an individual's potential to pass desirable phenotypes on to the next generation. As breeding or genetic values (as referred to here) cannot be measured directly, estimated genetic values (EGVs) are based on an individual's own phenotype, phenotype information from relatives, and, increasingly, genetic data. Because EGVs represent additive genetic variation, calculating EGVs in an extended human pedigree is expected to provide a more refined phenotype for genetic analyses. To test the utility of EGVs in genome-wide association, EGVs were calculated for 847 members of 20 extended Mexican American families based on 100 replicates of simulated systolic blood pressure. Calculations were performed in GAUSS to solve a variation on the standard Best Linear Unbiased Predictor (BLUP) mixed model equation with age, sex, and the first 3 principal components of sample-wide genetic variability as fixed effects and the EGV as a random effect distributed around the relationship matrix. Three methods of calculating kinship were considered: expected kinship from pedigree relationships, empirical kinship from common variants, and empirical kinship from both rare and common variants. Genome-wide association analysis was conducted on simulated phenotypes and EGVs using the additive measured genotype approach in the SOLAR software package. The EGV-based approach showed only minimal improvement in power to detect causative loci.

【 授权许可】

Unknown   
© Quillen et al.; licensee BioMed Central Ltd. 2014. This article is published under license to 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), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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.