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).
Title
Survey of the Heritability and Sparse Architecture of Gene Expression Traits across Human Tissues
Content Type
Article
Published in
PLOS Genetics
Funding Information
National Institutes of Health, R01MH107666
National Institutes of Health, K12 CA139160
National Institutes of Health, T32 MH020065
National Institutes of Health, R01 MH101820
National Institutes of Health, P30 DK20595
National Institutes of Health, P60 DK20595
National Institutes of Health, P50 DA037844
National Institutes of Health, P50 MH094267
Loyola University Chicago
National Institutes of Health, Common Fund of the Office of the Director
NCI
NHGRI
NHLBI
NIDA
NIMH
NINDS
National Disease Research Interchange, 10XS170
Roswell Park Cancer Institute, 10XS171
Science Care, Inc., X10S172
HHSN268201000029C
Van Andel Research Institute, 10ST1035
Leidos Biomedical Research, Inc., HHSN261200800001E
University of Miami, DA006227
Statistical Methods, development grant, MH090941
Statistical Methods, development grant, MH101814
Statistical Methods, development grant, MH090951
Statistical Methods, development grant, MH090937
Statistical Methods, development grant, MH101825
Statistical Methods, development grant, MH101820
Statistical Methods, development grant, MH090936
Statistical Methods, development grant, MH101819
Statistical Methods, development grant, MH090948
Statistical Methods, development grant, MH101782
Statistical Methods, development grant, MH101810
Statistical Methods, development grant, MH101822
National Institute of Mental Health, 5RC2MH089916
National Institute of Mental Health, 3R01MH090941
National Heart, Lung, and Blood Institute, N01-HC-25195
National Heart, Lung, and Blood Institute, HHSN268201500001I
NHLBI, N02-HL- 64278
NHLBI, Intramural funds from Andrew D. Johnson and Christopher J. O’Donnell
NHLBI, Division of Intramural Research
NHLBI, Center for Population Studies
Gordon and Betty Moore Foundation
National Science Foundation
University of Chicago
Publication Date
2016-11-11
Language
English
Record Created
2023-07-25