A systematic heritability analysis of the human whole blood transcriptome

Huan, Tianxiao; Liu, Chunyu; Joehanes, Roby; Chen, Brian H.; Johnson, Andrew D.; Yao, Chen; Courchesne, Paul; O’Donnell, Christopher J.; Munson, Peter J.; Levy, Daniel

doi:10.1007/s00439-014-1524-3

Cited by 28 publications

(32 citation statements)

References 40 publications

(71 reference statements)

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“…Gene (mRNA) expression data were obtained from 2446 FHS Offspring (Exam 8) and 3180 Third Generation (Exam 2) participants (Huan et al ., 2015a). DNA methylation data were obtained from 2377 FHS Offspring cohort participants (Exam 8).…”

Section: Methodsmentioning

confidence: 99%

Age‐associated microRNA expression in human peripheral blood is associated with all‐cause mortality and age‐related traits

et al. 2017

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SummaryRecent studies provide evidence of correlations of DNA methylation and expression of protein‐coding genes with human aging. The relations of microRNA expression with age and age‐related clinical outcomes have not been characterized thoroughly. We explored associations of age with whole‐blood microRNA expression in 5221 adults and identified 127 microRNAs that were differentially expressed by age at P < 3.3 × 10−4 (Bonferroni‐corrected). Most microRNAs were underexpressed in older individuals. Integrative analysis of microRNA and mRNA expression revealed changes in age‐associated mRNA expression possibly driven by age‐associated microRNAs in pathways that involve RNA processing, translation, and immune function. We fitted a linear model to predict ‘microRNA age’ that incorporated expression levels of 80 microRNAs. MicroRNA age correlated modestly with predicted age from DNA methylation (r = 0.3) and mRNA expression (r = 0.2), suggesting that microRNA age may complement mRNA and epigenetic age prediction models. We used the difference between microRNA age and chronological age as a biomarker of accelerated aging (Δage) and found that Δage was associated with all‐cause mortality (hazards ratio 1.1 per year difference, P = 4.2 × 10−5 adjusted for sex and chronological age). Additionally, Δage was associated with coronary heart disease, hypertension, blood pressure, and glucose levels. In conclusion, we constructed a microRNA age prediction model based on whole‐blood microRNA expression profiling. Age‐associated microRNAs and their targets have potential utility to detect accelerated aging and to predict risks for age‐related diseases.

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Section: Methodsmentioning

confidence: 99%

Age‐associated microRNA expression in human peripheral blood is associated with all‐cause mortality and age‐related traits

et al. 2017

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“…However, a parametric prior is assumed for the cis-eQTL effect-sizes by both Elastic-Net and BSLMM, which restricts the capability of PrediXcan and FUSION for handling the underlying complex genetic architecture of transcriptomes. Existing studies 11; 12 have also shown that both PrediXcan 11 and FUSION 12 estimated the average regression R 2 (i.e., the percentage of gene expression variation that can be explained by cis-genotypes) as ~5% for human whole blood transcriptome, while the average genome-wide heritability of gene expression in human whole blood transcriptome is estimated to be more than double that quantity 24; 25 . As shown in the result section of this paper, similar underestimation of regression R 2 by the Elastic-Net model (used by PrediXcan 11 ) was observed in both simulated and real datasets.…”

Section: Introductionmentioning

confidence: 99%

TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits

Nagpal

Meng

Epstein

et al. 2018

Preprint

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27The transcriptome-wide association studies (TWAS) that test for association between the study 28 trait and the imputed gene expression levels from cis-acting expression quantitative trait loci (cis-29 eQTL) genotypes have successfully enhanced the discovery of genetic risk loci for complex traits. 30By using the gene expression imputation models fitted from reference datasets that have both 31 genetic and transcriptomic data, TWAS facilitates gene-based tests with GWAS data while 32 accounting for the reference transcriptomic data. The existing TWAS tools like PrediXcan and 33 FUSION use parametric imputation models that have limitations for modeling the complex genetic 34 architecture of transcriptomic data. Therefore, to improve on this, we propose to use a Bayesian 35 method that assumes a data-driven nonparametric prior to impute gene expression. The 36nonparametric Bayesian method is flexible and general because it includes both of the parametric 37 imputation models used by PrediXcan and FUSION as special cases. Our simulation studies 38 2 showed that the nonparametric Bayesian model improved both imputation " for transcriptomic 39 data and the TWAS power over PrediXcan. In real applications, our nonparametric Bayesian 40 method fitted transcriptomic imputation models for 57.6% more genes over PrediXcan, thus 41 improving the power of follow-up TWAS. Hence, the nonparametric Bayesian model is preferred 42 for modeling the complex genetic architecture of transcriptomes and is expected to enhance 43 transcriptome-integrated genetic association studies. We implement our Bayesian approach in a 44 convenient software tool "TIGAR" (Transcriptome-Integrated Genetic Association Resource), 45 which imputes transcriptomic data and performs subsequent TWAS using individual-level or 46 summary-level GWAS data. 47 48 Introduction 49Genome-wide association studies (GWAS) have successfully identified thousands of 50 genetic risk loci for complex traits. However, the majority of these loci are located within noncoding 51 regions whose molecular mechanisms remain unknown 1-3 . Recent studies have shown that these 52 associated regions were enriched for regulatory elements such as enhancers (H3K27ac marks) 4; 53 5 and expression of quantitative trait loci (eQTL) 6; 7 , suggesting that the genetically regulated gene 54 expression might play a key role in the biological mechanisms of complex traits. Multiple studies 55 have recently generated rich transcriptomic datasets for diverse tissues of the human body, e.g., 56the Genotype-Tissue Expression (GTEx) project for 44 human tissues 6 , Genetic European 57Variation in Health and Disease (GEUVADIS) for lymphoblastoid cell lines 8 , Depression Genes 58 and Networks (DGN) for whole-blood samples 9 , and the North American Brain Expression 59 Consortium (NABEC) for cortex tissues 10 . Previous studies [11][12][13][14][15][16] have also shown that integrating 60 transcriptomic data in GWAS can help identify novel functional loci. 61The majority of GWAS projects do not possess tra...

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“…Other studies have reported smokingrelated DNA methylation patterns (12)(13)(14)(15)(16)(17)(18). Gene expression is under strong genetic and epigenetic control (19,20). Transcriptomic analyses may expand our understanding of molecular mechanisms affected by smoking.…”

Section: Introductionmentioning

confidence: 99%

A Whole-Blood Transcriptome Meta-Analysis Identifies Gene Expression Signatures of Cigarette Smoking

et al. 2016

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Cigarette smoking is a leading modifiable cause of death worldwide. We hypothesized that cigarette smoking induces extensive transcriptomic changes that lead to target-organ damage and smoking-related diseases. We performed a metaanalysis of transcriptome-wide gene expression using whole blood-derived RNA from 10,233 participants of European ancestry in six cohorts (including 1421 current and 3955 former smokers) to identify associations between smoking and altered gene expression levels. At a false discovery rate (FDR) <0.1, we identified 1270 differentially expressed genes in current vs. never smokers, and 39 genes in former vs. never smokers. Expression levels of 12 genes remained elevated up to 30 years after smoking cessation, suggesting that the molecular consequence of smoking may persist for decades. Gene ontology analysis revealed enrichment of smoking-related genes for activation of platelets and lymphocytes, immune response, and apoptosis. Many of the top smoking-related differentially expressed genes, including LRRN3 and GPR15, have DNA methylation loci in promoter regions that were recently reported to be hypomethylated among smokers. By linking differential gene expression with smoking-related disease phenotypes, we demonstrated that stroke and pulmonary function show enrichment for smoking-related gene expression signatures. Mediation analysis revealed the expression of several genes (e.g. ALAS2) to be putative mediators of the associations between smoking and inflammatory biomarkers (IL6 and C-reactive protein levels). Our transcriptomic study provides potential insights into the effects of cigarette smoking on gene expression in whole blood and their relations to smoking-related diseases. The results of such analyses may highlight attractive targets for treating or preventing smoking-related health effects.

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A systematic heritability analysis of the human whole blood transcriptome

Cited by 28 publications

References 40 publications

Age‐associated microRNA expression in human peripheral blood is associated with all‐cause mortality and age‐related traits

Age‐associated microRNA expression in human peripheral blood is associated with all‐cause mortality and age‐related traits

TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits

A Whole-Blood Transcriptome Meta-Analysis Identifies Gene Expression Signatures of Cigarette Smoking

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