Survey of the Heritability and Sparse Architecture of Gene Expression Traits across Human Tissues.
Understanding the genetic architecture of gene expression traits is key to elucidating the underlying mechanisms of complex traits. Here, for the first time, we perform a systematic survey of the heritability and the distribution of effect sizes across all representative tissues in the human body. We find that local h2 can be relatively well characterized with 59% of expressed genes showing significant h2 (FDR < 0.1) in the DGN whole blood cohort. However, current sample sizes (n ≤ 922) do not allow us to compute distal h2. Bayesian Sparse Linear Mixed Model (BSLMM) analysis provides strong evidence that the genetic contribution to local expression traits is dominated by a handful of genetic variants rather than by the collective contribution of a large number of variants each of modest size. In other words, the local architecture of gene expression traits is sparse rather than polygenic across all 40 tissues (from DGN and GTEx) examined. This result is confirmed by the sparsity of optimal performing gene expression predictors via elastic net modeling. To further explore the tissue context specificity, we decompose the expression traits into cross-tissue and tissue-specific components using a novel Orthogonal Tissue Decomposition (OTD) approach. Through a series of simulations we show that the cross-tissue and tissue-specific components are identifiable via OTD. Heritability and sparsity estimates of these derived expression phenotypes show similar characteristics to the original traits. Consistent properties relative to prior GTEx multi-tissue analysis results suggest that these traits reflect the expected biology. Finally, we apply this knowledge to develop prediction models of gene expression traits for all tissues. The prediction models, heritability, and prediction performance R2 for original and decomposed expression phenotypes are made publicly available (https://github.com/hakyimlab/PrediXcan).
Department of Biology, Loyola University Chicago, Chicago, Illinois, United States of America.|Department of Computer Science, Loyola University Chicago, Chicago, Illinois, United States of America.
Heather E
HE
Wheeler
Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America.
Kaanan P
KP
Shah
Department of Computer Science, Loyola University Chicago, Chicago, Illinois, United States of America.
Jonathon
J
Brenner
Center for Research Informatics, University of Chicago, Chicago, Illinois, United States of America.
Tzintzuni
T
Garcia
Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America.
Keston
K
Aquino-Michaels
Division of Genetic Medicine, Vanderbilt University, Nashville, Tennessee, United States of America.
Nancy J
NJ
Cox
Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America.
Dan L
DL
Nicolae
Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America.
Hae Kyung
http://orcid.org/0000-0003-0333-5685
HK
Im
D001499:Bayes Theorem; D005786:Gene Expression Regulation / Q000235:genetics*; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D020412:Multifactorial Inheritance / Q000235:genetics; D009928:Organ Specificity / Q000235:genetics*; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D019655:Quantitative Trait, Heritable; D018401:Sample Size