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

Ellen E. Quillen, V. Saroja Voruganti, Geetha Chittoor, Rohina Rubicz, Juan M. Peralta, Marcio A.A. Almeida, Jack W. Kent, Vincent P. Diego, Thomas D. Dyer, Anthony G. Comuzzie, Harald H.H. Göring, Ravindranath Duggirala, Laura Almasy, John Blangero

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article numberS66
JournalBMC Proceedings
Volume8
DOIs
StatePublished - Jun 17 2014
Externally publishedYes

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

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