CoMM-S4: A Collaborative Mixed Model Using Summary-Level eQTL and GWAS Datasets in Transcriptome-Wide Association Studies

Motivation: Genome-wide association studies (GWAS) have achieved remarkable success in identifying SNP-trait associations in the last decade. However, it is challenging to identify the mechanisms that connect the genetic variants with complex traits as the majority of GWAS associations are in non-coding regions. Methods that integrate genomic and transcriptomic data allow us to investigate how genetic variants may affect a trait through their effect on gene expression. These include CoMM and CoMM-S2, likelihood-ratio-based methods that integrate GWAS and eQTL studies to assess expression-trait association. However, their reliance on individual-level eQTL data render them inapplicable when only summary-level eQTL results, such as those from large-scale eQTL analyses, are available.Result: We develop an efficient probabilistic model, CoMM-S4, to explore the expression-trait association using summary-level eQTL and GWAS datasets. Compared with CoMM-S2, which uses individual-level eQTL data, CoMM-S4 requires only summary-level eQTL data. To test expression-trait association, an efficient variational Bayesian EM algorithm and a likelihood ratio test were constructed. We applied CoMM-S4 to both simulated and real data. The simulation results demonstrate that CoMM-S4 can perform as well as CoMM-S2 and S-PrediXcan, and analyses using GWAS summary statistics from Biobank Japan and eQTL summary statistics from eQTLGen and GTEx suggest novel susceptibility loci for cardiovascular diseases and osteoporosis.Availability and implementation: The developed R package is available at https://github.com/gordonliu810822/CoMM.

Transcriptional regulation of photosynthesis under heat stress in poplar

Background Photosynthesis has been recognized as a complicated process that is modulated through the intricate regulating network at transcriptional level. However, its underlying mechanism at molecular level under heat stress remains to be understood. Analysis of the adaptive response and regulatory networks of trees to heat stress will expand our understanding of thermostability in perennial plants. In this study, we used a multi-gene network to investigate the regulatory pathway under heat stress, as constructed by a multifaceted approach of combining time-course RNA-seq, regulatory motif enrichment, and expression-trait association analysis. Results By analyzing changes in the transcriptome under heat stress, we identified 77 key photosynthetic genes, of which 97.4% (75 genes) were down-regulated, and these results conformed to the decreased photosynthesis measured values. According to analysis of regulating motif enrichment, these 77 differentially expressed genes (DEGs) had common vital light-responsive elements involved in photosynthesis. When integrating all the differential expressed genes, 5 co-expressed gene modules (1,548 genes) were identified to be significantly correlated with 4 photosynthesis-related traits. Thus, based on this, a three-layered gene regulatory network (GRN) was established, which had included 77 photosynthetic genes (in the bottom layer), 40 TFs/miRNAs (in the second layer), as well as 20 TFs/miRNAs (in the top layer), using a backward elimination random forest (BWERF) algorithm. Importantly, 6 miRNAs and 4 TFs were found to be key regulators in this regulatory pathway, emphasizing the significant roles of TFs/miRNAs in affecting photosynthetic traits. The results imply a functional role for these key genes in mediating photosynthesis under heat stress, demonstrating the potential of combining time-course transcriptome-based regulatory pathway construction, cis-elements enrichment analysis, and expression-trait association approaches to dissect complex genetic networks. Conclusions The heat-responsive pathway in regulating photosynthesis is a multi-layered complex network which is co-controlled by TFs and miRNAs. Our work not only imply a functional role for these key genes in mediating photosynthesis responding to abiotic stress in poplar, but demonstrate time-course transcriptome-based regulatory network construction will facilitate further the genetic network and key nodes examining in plants.

Integrative transcriptome imputation reveals tissue-specific and shared biological mechanisms mediating susceptibility to complex traits

Transcriptome-wide association studies integrate gene expression data with common risk variation to identify gene-trait associations. By incorporating epigenome data to estimate the functional importance of genetic variation on gene expression, we improve the accuracy of transcriptome prediction and the power to detect significant expression-trait associations. Joint analysis of 14 large-scale transcriptome datasets and 58 traits identify 13,724 significant expression-trait associations that converge to biological processes and relevant phenotypes in human and mouse phenotype databases. We perform drug repurposing analysis and identify known and novel compounds that mimic or reverse trait-specific changes. We identify genes that exhibit agonistic pleiotropy for genetically correlated traits that converge on shared biological pathways and elucidate distinct processes in disease etiopathogenesis. Overall, this comprehensive analysis provides insight into the specificity and convergence of gene expression on susceptibility to complex traits.

The expression tractability of a biological trait

Understanding how gene expression is translated to phenotype is central to modern molecular biology, but the success is contingent on the intrinsic tractability of the specific traits under examination. However, an a priori estimate of trait tractability from the perspective of gene expression is unavailable. Motivated by the concept of entropy in a thermodynamic system, we here propose such an estimate (ST) by gauging the number (N) of different expression states that underlie the same trait abnormality, with large ST corresponding to large N. By analyzing over 200 yeast morphological traits we show that ST is constrained by natural selection, which builds co-regulated gene modules to minimize the total number of possible expression states. We further show that ST is a good measure of the titer of recurrent patterns of an expression-trait relationship, predicting the extent to which the trait could be deterministically understood with gene expression data.