Joint analysis of multiple phenotypes: summary of results and discussions from the Genetic Analysis Workshop 19.

Stefan Konigorski,Arne Schillert

doi:10.1186/s12863-015-0317-6

Abstract

For Genetic Analysis Workshop 19, 2 extensive data sets were provided, including whole genome and whole exome sequence data, gene expression data, and longitudinal blood pressure outcomes, together with nongenetic covariates. These data sets gave researchers the chance to investigate different aspects of more complex relationships within the data, and the contributions in our working group focused on statistical methods for the joint analysis of multiple phenotypes, which is part of the research field of data integration. The analysis of data from different sources poses challenges to researchers but provides the opportunity to model the real-life situation more realistically.Our 4 contributions all used the provided real data to identify genetic predictors for blood pressure. In the contributions, novel multivariate rare variant tests, copula models, structural equation models and a sparse matrix representation variable selection approach were applied. Each of these statistical models can be used to investigate specific hypothesized relationships, which are described together with their biological assumptions.The results showed that all methods are ready for application on a genome-wide scale and can be used or extended to include multiple omics data sets. The results provide potentially interesting genetic targets for future investigation and replication. Furthermore, all contributions demonstrated that the analysis of complex data sets could benefit from modeling correlated phenotypes jointly as well as by adding further bioinformatics information.

Highlights

For Genetic Analysis Workshop (GAW) 19, a large collection of different types of data were provided [1]
The single nucleotide polymorphisms (SNPs) have not been reported in previous genome-wide association studies (GWAS) and could be investigated further in future studies. These results indicate that with the computationally efficient score test, structural equation models (SEM) of complex relationships between multiple phenotypes can be investigated with strum on a genome-wide scale
While there was no indication for inflated type 1 errors under the proposed copula approach, the results indicate that joint models using copula functions can estimate the genetic effects of both common and rare variants more efficiently and with higher power compared to standard univariate regression models as well as the popular multimarker tests of sequence kernel association test (SKAT) and SKAT-O

Summary

Introduction

For Genetic Analysis Workshop (GAW) 19, a large collection of different types of data were provided [1]. Researchers were able to use both systolic (SBP) and diastolic blood pressure (DBP) phenotypes, measured at multiple time points, gene expression measures, and sequencing data, as well as single nucleotide polymorphisms (SNPs) from families and unrelated individuals. This enabled participating researchers to investigate a multitude of complex questions, which often involved combining data from various sources. When searching for a common underlying genetic background, pleiotropic SNVs [12] influencing both blood pressure measurements, or different SNVs in the same gene, which are in high linkage disequilibrium and influencing either blood pressure (BP), can explain some of the dependence between SBP and DBP. With BP being an important clinical and measurable variable with relevance for cardiovascular disease and other diseases, such as stroke or kidney damage, investigating longitudinal BP profiles and Published Articles Journal

Methods

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Discussion