Omics-squared: human genomic, transcriptomic and phenotypic data for genetic analysis workshop 19

Authors

John Blangero, The University of Texas Rio Grande Valley
Tanya M. Teslovich
Xueling Sim
Marcio Almeida, The University of Texas Rio Grande Valley
Goo Jun
Thomas D. Dyer, The University of Texas Rio Grande Valley
Matthew P. Johnson, The University of Texas Rio Grande Valley
Juan M. Peralta, The University of Texas Rio Grande Valley
Alisa K. Manning
Andrew R. Wood
Christian Fuchsberger
Vidya S. Farook, The University of Texas Rio Grande Valley
Rector Arya, The University of Texas Rio Grande Valley
David C. Glahn, Olin Neuropsychiatry Research Center
Joanne E. Curran, The University of Texas Rio Grande ValleyFollow
Christopher Jenkinson, The University of Texas Rio Grande Valley
Harald H. H. Goring, The University of Texas Rio Grande Valley
Ravindranath Duggirala, The University of Texas Rio Grande Valley
Laura Almasy, The University of Texas Rio Grande Valley

Document Type

Conference Proceeding

Publication Date

8-2014

Abstract

Background

The Genetic Analysis Workshops (GAW) are a forum for development, testing, and comparison of statistical genetic methods and software. Each contribution to the workshop includes an application to a specified data set. Here we describe the data distributed for GAW19, which focused on analysis of human genomic and transcriptomic data.

Methods

GAW19 data were donated by the T2D-GENES Consortium and the San Antonio Family Heart Study and included whole genome and exome sequences for odd-numbered autosomes, measures of gene expression, systolic and diastolic blood pressures, and related covariates in two Mexican American samples. These two samples were a collection of 20 large families with whole genome sequence and transcriptomic data and a set of 1943 unrelated individuals with exome sequence. For each sample, simulated phenotypes were constructed based on the real sequence data. ‘Functional’ genes and variants for the simulations were chosen based on observed correlations between gene expression and blood pressure. The simulations focused primarily on additive genetic models but also included a genotype-by-medication interaction. A total of 245 genes were designated as ‘functional’ in the simulations with a few genes of large effect and most genes explaining < 1 % of the trait variation. An additional phenotype, Q1, was simulated to be correlated among related individuals, based on theoretical or empirical kinship matrices, but was not associated with any sequence variants. Two hundred replicates of the phenotypes were simulated. The GAW19 data are an expansion of the data used at GAW18, which included the family-based whole genome sequence, blood pressure, and simulated phenotypes, but not the gene expression data or the set of 1943 unrelated individuals with exome sequence.

Comments

Copyright The Authors.

Recommended Citation

Blangero, J., Teslovich, T. M., Sim, X., Almeida, M. A., Jun, G., Dyer, T. D., Johnson, M., Peralta, J. M., Manning, A., Wood, A. R., Fuchsberger, C., Kent, J. W., Jr, Aguilar, D. A., Below, J. E., Farook, V. S., Arya, R., Fowler, S., Blackwell, T. W., Puppala, S., Kumar, S., … T2D-GENES Consortium (2016). Omics-squared: human genomic, transcriptomic and phenotypic data for genetic analysis workshop 19. BMC proceedings, 10(Suppl 7), 71–77. https://doi.org/10.1186/s12919-016-0008-y

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Publication Title

BMC Proceedings

DOI

10.1186/s12919-016-0008-y

Academic Level

faculty

Mentor/PI Department

Office of Human Genetics