Alternative polyadenylation (APA) plays an essential role in brain development; however, current transcriptome-wide association studies (TWAS) largely overlook APA in nominating susceptibility genes. Here, we performed a 3′ untranslated region (3′UTR) APA TWAS (3′aTWAS) for 11 brain disorders by combining their genome-wide association studies data with 17,300 RNA-seq samples across 2,937 individuals. We identified 354 3′aTWAS-significant genes, including known APA-linked risk genes, such as SNCA in Parkinson’s disease. Among these 354 genes, ~57% are not significant in traditional expression- and splicing-TWAS studies, since APA may regulate the translation, localization and protein-protein interaction of the target genes independent of mRNA level expression or splicing. Furthermore, we discovered ATXN3 as a 3′aTWAS-significant gene for amyotrophic lateral sclerosis, and its modulation substantially impacted pathological hallmarks of amyotrophic lateral sclerosis in vitro. Together, 3′aTWAS is a powerful strategy to nominate important APA-linked brain disorder susceptibility genes, most of which are largely overlooked by conventional expression and splicing analyses.

The mammalian DNA methylome is formed by two antagonizing processes, methylation by DNA methyltransferases (DNMT) and demethylation by ten-eleven translocation (TET) dioxygenases. Although the dynamics of either methylation or demethylation have been intensively studied in the past decade, the direct effects of their interaction on gene expression remain elusive. Here, we quantify the concurrence of DNA methylation and demethylation by the percentage of unmethylated CpGs within a partially methylated read from bisulfite sequencing. After verifying ‘methylation concurrence’ by its strong association with the co-localization of DNMT and TET enzymes, we observe that methylation concurrence is strongly correlated with gene expression. Notably, elevated methylation concurrence in tumors is associated with the repression of 40~60% of tumor suppressor genes, which cannot be explained by promoter hypermethylation alone. Furthermore, methylation concurrence can be used to stratify large undermethylated regions with negligible differences in average methylation into two subgroups with distinct chromatin accessibility and gene regulation patterns. Together, methylation concurrence represents a unique methylation metric important for transcription regulation and is distinct from conventional metrics, such as average methylation and methylation variation.

Genome-wide association studies (GWAS) have identified thousands of non-coding single-nucleotide polymorphisms (SNPs) associated with human traits and diseases. However, functional interpretation of these SNPs remains a significant challenge. Our recent study established the concept of 3′ untranslated region (3′UTR) alternative polyadenylation (APA) quantitative trait loci (3′aQTLs), which can be used to interpret ∼16.1% of GWAS SNPs and are distinct from gene expression QTLs and splicing QTLs. Despite the growing interest in 3′aQTLs, there is no comprehensive database for users to search and visualize them across human normal tissues. In the 3′aQTL-atlas (https://wlcb.oit.uci.edu/3aQTLatlas), we provide a comprehensive list of 3′aQTLs containing ∼1.49 million SNPs associated with APA of target genes, based on 15,201 RNA-seq samples across 49 human Genotype-Tissue Expression (GTEx v8) tissues isolated from 838 individuals. The 3′aQTL-atlas provides a ∼2-fold increase in sample size compared with our published study. It also includes 3′aQTL searches by Gene/SNP across tissues, a 3′aQTL genome browser, 3′aQTL boxplots, and GWAS-3′aQTL colocalization event visualization. The 3′aQTL-atlas aims to establish APA as an emerging molecular phenotype to explain a large fraction of GWAS risk SNPs, leading to significant novel insights into the genetic basis of APA and APA-linked susceptibility genes in human traits and diseases.

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