Joint genetic analysis of gene expression data with inferred cellular phenotypes.
TLDR
It is found that the inferred phenotypes are associated with locus genotypes and environmental conditions and can explain genetic associations to genes in trans for the first time.Abstract:
Even within a defined cell type, the expression level of a gene differs in individual samples. The effects of genotype, measured factors such as environmental conditions, and their interactions have been explored in recent studies. Methods have also been developed to identify unmeasured intermediate factors that coherently influence transcript levels of multiple genes. Here, we show how to bring these two approaches together and analyse genetic effects in the context of inferred determinants of gene expression. We use a sparse factor analysis model to infer hidden factors, which we treat as intermediate cellular phenotypes that in turn affect gene expression in a yeast dataset. We find that the inferred phenotypes are associated with locus genotypes and environmental conditions and can explain genetic associations to genes in trans. For the first time, we consider and find interactions between genotype and intermediate phenotypes inferred from gene expression levels, complementing and extending established results.read more
Citations
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References
More filters
Journal ArticleDOI
Statistical significance for genomewide studies
John D. Storey,Robert Tibshirani +1 more
TL;DR: This work proposes an approach to measuring statistical significance in genomewide studies based on the concept of the false discovery rate, which offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted.
Journal ArticleDOI
An introduction to variational methods for graphical models
TL;DR: This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models (Bayesian networks and Markov random fields), and describes a general framework for generating variational transformations based on convex duality.
Journal ArticleDOI
Correlation between Protein and mRNA Abundance in Yeast
TL;DR: The results clearly delineate the technical boundaries of current approaches for quantitative analysis of protein expression and reveal that simple deduction from mRNA transcript analysis is insufficient to predict protein expression levels from quantitative mRNA data.
Journal ArticleDOI
Genome-wide association studies for common diseases and complex traits
TL;DR: Genome-wide association studies will soon become possible, and could open new frontiers in the understanding and treatment of disease, however, the execution and analysis of such studies will require great care.
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