Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals

Abstract

Understanding the consequences of regulatory variation in the human genome remains a major challenge, with important implications for understanding gene regulation and interpreting the many disease-risk variants that fall outside of protein-coding regions. Here, we provide a direct window into the regulatory consequences of genetic variation by sequencing RNA from 922 genotyped individuals. We present a comprehensive description of the distribution of regulatory variation-by the specific expression phenotypes altered, the properties of affected genes, and the genomic characteristics of regulatory variants. We detect variants influencing expression of over ten thousand genes, and through the enhanced resolution offered by RNAsequencing, for the first time we identify thousands of variants associated with specific phenotypes including splicing and allelic expression. Evaluating the effects of both long-range intra-chromosomal and trans (cross-chromosomal) regulation, we observe modularity in the regulatory network, with three-dimensional chromosomal configuration playing a particular role in regulatory modules within each chromosome. We also observe a significant depletion of regulatory variants affecting central and critical genes, along with a trend of reduced effect sizes as variant frequency increases, providing evidence that purifying selection and buffering have limited the deleterious impact of regulatory variation on the cell. Further, generalizing beyond observed variants, we have analyzed the genomic properties of variants associated with expression and splicing and developed a Bayesian model to predict regulatory consequences of genetic variants, applicable to the interpretation of individual genomes and disease studies. Together, these results represent a critical step toward characterizing the complete landscape of human regulatory variation.

Genetic effects on gene expression across human tissues

“…Despite the scope of these data, we remain underpowered to detect trans -eQTLs. Larger cohorts of individuals with a smaller number of tissues have yielded hundreds of trans -eGenes 4,6,8,9 , and we similarly expect trans -eQTL discoveries to increase with additional samples in the final phase of GTEx. Furthermore, some genetic effects may manifest only within a specific cell type, rather than an entire heterogeneous tissue.…”

“…A large-scale, multi-tissue resource of ASE estimates complements eQTL mapping by providing access to individual-specific effects, which assists in the interpretation of rare variants, somatic mutations and patterns of imprinting 8,13,14 . We measured ASE at more than 135 million sites across tissues and donors, with a median of over 10,000 genes quantified per donor (Supplementary Information 11 and Supplementary Fig.…”

“…Integration of genomic annotations such as chromatin state has been demonstrated to improve power for eQTL discovery 8,21–23 . For 26 GTEx tissues matched with cell-type specific annotations from the Roadmap Epigenomics project, we applied a Bayesian hierarchical model for eQTL discovery by incorporating variant-level genomic annotations 24 that provided a substantial boost to discovery power.…”

EIF3G is associated with narcolepsy across ethnicities

“…The details of the cis-eQTL analysis are described in Battle et al 12 Briefly, expression data was first normalized to remove the effect of technical factors, population structure and 'hidden' factors (which have been shown to reduce the statistical power to identify cis-eQTLs 14 ). Such normalization approaches can significantly increase power for detecting cis-eQTLs; however, in doing so, such approaches also remove broader expression patterns that may be driven by trans-regulation (as a trans-acting factor may affect the expression levels of multiple target genes) or reflect coregulation.…”

“…The complete procedure for whole blood processing, RNA sequencing and genotyping is described in Battle et al 12 Briefly, whole blood was collected in PAXGene tubes for RNA and in acid-citrate-dextrose tubes for DNA. Tubes were stored at − 80°C.…”