Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of diseas

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease

Enhancing GTEx by bridging the gaps between genotype, gene expression, and disease

Academi

Environmental signal propagation in sedimentary systems across timescales

Earth-surface processes operate across erosionally dominated landscapes and deliver sediment to depositional systems that can be preserved over a range of timescales. The geomorphic and stratigraphic products of this source-to-sink sediment transfer record signals of external environmental forcings, as well as internal, or autogenic, dynamics of the sedimentary system. Here, we evaluate environmental signal propagation across sediment-routing systems with emphasis on sediment supply, Qs, as the carrier of up-system forcings. We review experimental, numerical, and natural examples of source-to-sink sediment routing and signal propagation during three timescales: (1) Historic, which includes measurement and monitoring of events and processes of landscape change and deposition during decades to centuries; (2) Centuries to several millions of years, referred to as intermediate timescale; and (3) Deep time. We discuss issues related to autogenic dynamics of sediment transport, transient storage, and release that can introduce noise, lags, and/or completely mask signals of external environmental forcings. We provide a set of conceptual and practical tools for evaluating sediment supply within a source-to-sink context, which can inform interpretations of signals from the sedimentary record. These tools include stratigraphic and sediment-routing system characterization, sediment budget determination, geochronology, detrital mineral analysis (e.g., thermochronology), comparative analog approaches, and modeling techniques to measure, calculate, or estimate the magnitude and frequency of external forcings compared to the characteristic response time of the sediment-routing systems

Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease

Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index.[Display omitted]•29% of lncRNA genes with eQTLs show tissue-specific genetic regulation•Co-expression networks and single-cell data provide annotations for 94% of lncRNAs•Rare variants near lncRNA expression outliers impact complex traits, like BMI•We identify 800 lncRNA-trait relationships not explained by protein-coding genesA systematic analysis of NIH Genotype Tissue Expression (GTEx) project data provides insights into lncRNA expression patterns and functions, explores the impact of genetic variation on lncRNAs, and connects lncRNAs to complex traits and human disease