The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the version 8 data, examining 15,201 RNA-sequencing samples from 49 tissues of 838 postmortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue specificity of genetic effects and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.

The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues, and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the v8 data, based on 17,382 RNA-sequencing samples from 54 tissues of 948 post-mortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue-specificity of genetic effects, and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.

Genome-wide association studies (GWAS) of neurological diseases have identified thousands of variants associated with disease phenotypes. However, the majority of these variants do not alter coding sequences, making it difficult to assign their function. Here, we present a multi-omic epigenetic atlas of the adult human brain through profiling of single-cell chromatin accessibility landscapes and three-dimensional (3D) chromatin interactions of diverse adult brain regions across a cohort of cognitively healthy individuals. We developed a machine-learning classifier to integrate this multi-omic framework and predict dozens of functional single-nucleotide polymorphisms (SNPs) for Alzheimer’s disease (AD) and Parkinson’s disease (PD), nominating target genes and cell types for previously orphaned GWAS loci. Moreover, we dissected the complex inverted haplotype of the MAPT (encoding tau) PD risk locus, identifying putative ectopic regulatory interactions in neurons that may mediate this disease association. This work expands our understanding of inherited variation and provides a roadmap for the epigenomic dissection of causal regulatory variation in disease.

Engineering and study of protein function by directed evolution has been limited by the requirement to introduce DNA libraries of defined size or to use global mutagenesis. Here, we develop a strategy to repurpose the somatic hypermutation machinery used in antibody affinity maturation to efficiently perform protein engineering in situ. Using catalytically inactive Cas9 (dCas9) to recruit variants of the deaminase AID (CRISPR-X), we can specifically mutagenize endogenous targets with limited off-target damage. This generates diverse libraries of localized point mutations, in contrast to insertions and deletions created by active Cas9, and can be used to mutagenize multiple genomic locations simultaneously. With this technology, we mutagenize GFP and select for spectrum-shifted variants, including EGFP. In addition, we mutate the target of the cancer therapeutic bortezomib, PSMB5, and identify known and novel mutations that confer resistance to treatment. Finally, we utilize a hyperactive AID variant with dramatically increased activity to mutagenize endogenous loci both upstream and downstream of transcriptional start sites. These experiments illustrate a powerful new approach to create highly complex libraries of genetic variants in native context, which can be broadly applied to investigate and improve protein function.

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