The resources generated by the GTEx consortium offer unprecedented opportunities to advance our understanding of the biology of human diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genome-wide association study-discovered loci. Across a broad set of complex traits and diseases, we demonstrate widespread dose-dependent effects of RNA expression and splicing. We develop a data-driven framework to benchmark methods that prioritize causal genes and find no single approach outperforms the combination of multiple approaches. Using colocalization and association approaches that take into account the observed allelic heterogeneity of gene expression, we propose potential target genes for 47% (2519 out of 5385) of the GWAS loci examined.

Transcriptome data can facilitate the interpretation of the effects of rare genetic variants. Here, we introduce ANEVA (analysis of expression variation) to quantify genetic variation in gene dosage from allelic expression (AE) data in a population. Application of ANEVA to the Genotype-Tissues Expression (GTEx) data showed that this variance estimate is robust and correlated with selective constraint in a gene. Using these variance estimates in a dosage outlier test (ANEVA-DOT) applied to AE data from 70 Mendelian muscular disease patients showed accuracy in detecting genes with pathogenic variants in previously resolved cases and led to one confirmed and several potential new diagnoses. Using our reference estimates from GTEx data, ANEVA-DOT can be incorporated in rare disease diagnostic pipelines to use RNA-sequencing data more effectively.

The resources generated by the GTEx consortium oer unprecedented opportunities to advance our understanding of the biology of human traits and diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genetic loci discovered by genome-wide association studies (GWAS). Across a broad set of complex traits and diseases, we nd widespread dosedependent eects of RNA expression and splicing, with higher impact on molecular phenotypes translating into higher impact downstream. Using colocalization and association approaches that take into account the observed allelic heterogeneity, we propose potential target genes for 47% (2,519 out of 5,385) of the GWAS loci examined. Our results demonstrate the translational relevance of the GTEx resources and highlight the need to increase their resolution and breadth to further our understanding of the genotypephenotype link. Harmonized GWAS and QTL datasetsThe nal GTEx data release (v8) includes 54 primary human tissues, 49 of which included at least 65 samples and were used for cis-QTL mapping ( Fig. 1) (9). This phase increases the number of available tissues relative to previous GTEx publications (v6p; 44 tissues) (8) and doubles the sample size from 7,051 RNA-Seq samples from 449 individuals to 15,253 samples from 838 individuals, now all with whole genome sequencing data as opposed to genotype imputation in v6p. Furthermore, the v8 core data resources now include splicing QTLs (9), allowing parallel analysis of both expression and splicing variation underlying complex traits. Using these resources, we investigated the contribution of expression and splicing QTLs in cis (eQTL and sQTL, respectively) to complex trait variance and etiology.We retained 87 GWAS datasets representing 74 distinct complex traits for further analyses (table S1 and g. S1) after stringent quality control (g. S2; (21)) and data harmonization(g. S3, g. S4). 6We found a signicantly higher correlation in mediating eect between primary and secondary eQTLs for a given gene compared to a null distribution obtained by sampling GWAS eect sizes from a bivariate normal distribution to account for the small observed LD between primary and secondary eQTLs ( Fig. 2D-E) while keeping the observed eQTL eect sizes (p < 1 × 10 −30 ).Interestingly, the correlation between primary and secondary eQTLs for non-colocalized genes (rcp < 0.01), which were used as controls (9, 21), was signicantly higher than this more accurate null, indicating that even eQTLs with very low colocalization probability include many genes that are likely causal. Given this concordance between multiple independent eQTLs, it is clear that with widespread allelic heterogeneity detected with currently available sample sizes, methods that assume single causal variants are highly limited. The approaches described here enable insights into how multiple regulatory effects converge to mediate the same trait association. 7 * alphabetic order

AlkB is a DNA/RNA repair enzyme that removes base alkylations such as N1-methyladenosine (m1A) or N3-methylcytosine (m3C) from DNA and RNA. The AlkB enzyme has been used as a critical tool to facilitate tRNA sequencing and identification of mRNA modifications. As a tool, AlkB mutants with better reactivity and new functionalities are highly desired; however, previous identification of such AlkB mutants was based on the classical approach of targeted mutagenesis. Here, we introduce a high-throughput screening method to evaluate libraries of AlkB variants for demethylation activity on RNA and DNA substrates. This method is based on a fluorogenic RNA aptamer with an internal modified RNA/DNA residue which can block reverse transcription or introduce mutations leading to loss of fluorescence inherent in the cDNA product. Demethylation by an AlkB variant eliminates the blockage or mutation thereby restores the fluorescence signals. We applied our screening method to sites D135 and R210 in the Escherichia coli AlkB protein and identified a variant with improved activity beyond a previously known hyperactive mutant toward N1-methylguanosine (m1G) in RNA. We also applied our method to O6-methylguanosine (O6mG) modified DNA substrates and identified candidate AlkB variants with demethylating activity. Our study provides a high-throughput screening method for in vitro evolution of any demethylase enzyme.

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