Disease-associated Loci Research Articles

Research progress on single-cell expression quantitative trait loci.

Expression quantitative trait loci (eQTL) represent genetic variants that regulate gene expression levels. eQTL analysis has become a crucial method for identifying the functional roles of disease-associated genetic variants in the post-genome-wide association study (GWAS) era, yielding numerous significant discoveries. Traditional eQTL analysis relies on whole-genome sequencing combined with bulk RNA-seq, which obscures gene expression differences between cells and thus fails to identify cell type- or state-dependent eQTL. This limitation makes it challenging to elucidate the roles of disease-associated genetic variants under specific conditions. In recent years, with the development and widespread application of single-cell RNA sequencing (scRNA-seq) technology, scRNA-seq-based eQTL (sc-eQTL) research has emerged as a focal point. The advantage of this approach lies in its ability to leverage the resolution and granularity of single-cell sequencing to uncover eQTL that are dependent on cell type, cell state, and cellular dynamics. This significantly enhances our ability to analyze genetic variants associated with gene expression. Consequently, it holds substantial significance for advancing our understanding of the formation of complex organs and the mechanisms underlying disease onset, progression, intervention, and treatment. This review comprehensively examines the recent advancements in sc-eQTL studies, focusing on their development, experimental design strategies, modeling approaches, and current challenges. The aim is to offer researchers novel perspectives for identifying disease-associated loci and elucidating gene regulatory mechanisms.

A cell and transcriptome atlas of the human arterial vasculature.

Vascular beds show different propensities for different vascular pathologies, yet mechanisms explaining these fundamental differences remain unknown. We sought to build a transcriptomic, cellular, and spatial atlas of human arterial cells across multiple different arterial segments to understand this phenomenon. We found significant cell type-specific segmental heterogeneity. Determinants of arterial identity are predominantly encoded in fibroblasts and smooth muscle cells, and their differentially expressed genes are particularly enriched for vascular disease-associated loci and genes. Adventitial fibroblast-specific heterogeneity in gene expression coincides with numerous vascular disease risk genes, suggesting a previously unrecognized role for this cell type in disease risk. Adult arterial cells from different segments cluster not by anatomical proximity but by embryonic origin, with differentially regulated genes heavily influenced by developmental master regulators. Non-coding transcriptomes across arterial cells contain extensive variation in lnc-RNAs expressed in cell type- and segment-specific patterns, rivaling heterogeneity in protein coding transcriptomes, and show enrichment for non-coding genetic signals for vascular diseases.

Genetic admixture predictors of fetal alcohol spectrum disorders (FASD) in a South African population

Ancestrally admixed populations are underrepresented in genetic studies of complex diseases, which are still dominated by European-descent populations. This is relevant not only from a representation standpoint but also because of admixed populations’ unique features, including being enriched for rare variants, for which effect sizes are disproportionately larger than common polymorphisms. Furthermore, results from these populations may be generalizable to other populations. The South African Cape Coloured (SACC) population is genetically admixed and has one of the highest prevalences of fetal alcohol spectrum disorders (FASD) worldwide. We profiled its admixture and examined associations between ancestry profiles and FASD outcomes using two longitudinal birth cohorts (N=308 mothers, 280 children) designed to examine effects of prenatal alcohol exposure on development. Participants were genotyped via MEGAex array to capture common and rare variants. Rare variants were overrepresented in our SACC cohorts, with numerous polymorphisms being monomorphic in other reference populations (e.g., ∼30,000 and ∼ 221,000 variants in gnomAD European and Asian populations, respectively). The cohorts showed global African (51 %; Bantu and San); European (26 %; Northern/Western); South Asian (18 %); and East Asian (5 %; largely Southern regions) ancestries. The cohorts exhibited high rates of homozygosity (6 %), with regions of homozygosity harboring more deleterious variants when lying within African local-ancestry genomic segments. Both maternal and child ancestry profiles were associated with higher FASD risk, and maternal and child ancestry-by-prenatal alcohol exposure interaction effects were seen on child cognition. Our findings indicate that the SACC population may be a valuable asset to identify novel disease-associated genetic loci for FASD and other diseases.

European and African-specific plasma protein-QTL and metabolite-QTL analyses identify ancestry-specific T2D effector proteins and metabolites.

Initially focused on the European population, multiple genome-wide association studies (GWAS) of complex diseases, such as type-2 diabetes (T2D), have now extended to other populations. However, to date, few ancestry-matched omics datasets have been generated or further integrated with the disease GWAS to nominate the key genes and/or molecular traits underlying the disease risk loci. In this study, we generated and integrated plasma proteomics and metabolomics with array-based genotype datasets of European (EUR) and African (AFR) ancestries to identify ancestry-specific muti-omics quantitative trait loci (QTLs). We further applied these QTLs to ancestry-stratified T2D risk to pinpoint key proteins and metabolites underlying the disease-associated genetic loci. We nominated five proteins and four metabolites in the European group and one protein and one metabolite in the African group to be part of the molecular pathways of T2D risk in an ancestry-stratified manner. Our study demonstrates the integration of genetic and omic studies of different ancestries can be used to identify distinct effector molecular traits underlying the same disease across diverse populations. Specifically, in the AFR proteomic findings on T2D, we prioritized the protein QSOX2; while in the AFR metabolomic findings, we pinpointed the metabolite GlcNAc sulfate conjugate of C21H34O2 steroid. Neither of these findings overlapped with the corresponding EUR results.

Intergenic risk variant rs56258221 skews the fate of naive CD4+ T cells via miR4464-BACH2 interplay in primary sclerosing cholangitis

Primary sclerosing cholangitis (PSC) is an immune-mediated liver disease of unknown pathogenesis, with a high risk to develop cirrhosis and malignancies. Functional dysregulation of Tcells and association with genetic polymorphisms in Tcell-related genes were previously reported for PSC. Here, we genotyped a representative PSC cohort for several disease-associated risk loci and identified rs56258221 (BACH2/MIR4464) to correlate with not only the peripheral blood Tcell immunophenotype but also the functional capacities of naive CD4+ T (CD4+ TN) cells in people with PSC. Mechanistically, rs56258221 leads to an increased expression of miR4464, in turn causing attenuated translation of BACH2, a major gatekeeper of Tcell quiescence. Thereby, the fate of CD4+ TN is skewed toward polarization into pro-inflammatory subsets. Clinically, people with PSC carrying rs56258221 show signs of accelerated disease progression. The data presented here highlight the importance of assigning functional outcomes to disease-associated genetic polymorphisms as potential drivers of diseases.

Fine-mapping causal tissues and genes at disease-associated loci.

Heritable diseases often manifest in a highly tissue-specific manner, with different disease loci mediated by genes in distinct tissues or cell types. We propose Tissue-Gene Fine-Mapping (TGFM), a fine-mapping method that infers the posterior probability (PIP) for each gene-tissue pair to mediate a disease locus by analyzing GWAS summary statistics (and in-sample LD) and leveraging eQTL data from diverse tissues to build cis-predicted expression models; TGFM also assigns PIPs to causal variants that are not mediated by gene expression in assayed genes and tissues. TGFM accounts for both co-regulation across genes and tissues and LD between SNPs (generalizing existing fine-mapping methods), and incorporates genome-wide estimates of each tissue's contribution to disease as tissue-level priors. TGFM was well-calibrated and moderately well-powered in simulations; unlike previous methods, TGFM was able to attain correct calibration by modeling uncertainty in cis-predicted expression models. We applied TGFM to 45 UK Biobank diseases/traits (average N = 316K) using eQTL data from 38 GTEx tissues. TGFM identified an average of 147 PIP > 0.5 causal genetic elements per disease/trait, of which 11% were gene-tissue pairs. Implicated gene-tissue pairs were concentrated in known disease-critical tissues, and causal genes were strongly enriched in disease-relevant gene sets. Causal gene-tissue pairs identified by TGFM recapitulated known biology (e.g., TPO-thyroid for Hypothyroidism), but also included biologically plausible novel findings (e.g., SLC20A2-artery aorta for Diastolic blood pressure). Further application of TGFM to single-cell eQTL data from 9 cell types in peripheral blood mononuclear cells (PBMC), analyzed jointly with GTEx tissues, identified 30 additional causal gene-PBMC cell type pairs at PIP > 0.5-primarily for autoimmune disease and blood cell traits, including the biologically plausible example of CD52 in classical monocyte cells for Monocyte count. In conclusion, TGFM is a robust and powerful method for fine-mapping causal tissues and genes at disease-associated loci.

Shared genetic links between hypothyroidism and psychiatric disorders: evidence from a comprehensive genetic analysis.

Epidemiologic studies have suggested co-morbidity between hypothyroidism and psychiatric disorders. However, the shared genetic etiology and causal relationship between them remain currently unclear. We assessed the genetic correlations between hypothyroidism and psychiatric disorders [anxiety disorders (ANX), schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BIP)] using summary association statistics from genome-wide association studies (GWAS). Two disease-associated pleiotropic risk loci and genes were identified, and pathway enrichment, tissue enrichment, and other analyses were performed to determine their specific functions. Furthermore, we explored the causal relationship between them through Mendelian randomization (MR) analysis. We found significant genetic correlations between hypothyroidism with ANX, SCZ, and MDD, both in the Linkage disequilibrium score regression (LDSC) approach and the high-definition likelihood (HDL) approach. Meanwhile, the strongest correlation was observed between hypothyroidism and MDD (LDSC: rg=0.264, P=7.35×10-12; HDL: rg=0.304, P=4.14×10-17). We also determined a significant genetic correlation between MDD with free thyroxine (FT4) and thyroid-stimulating hormone (TSH) levels. A total of 30 pleiotropic risk loci were identified between hypothyroidism and psychiatric disorders, of which the 15q14 locus was identified in both ANX and SCZ (P values are 6.59×10-11 and 2.10×10-12, respectively) and the 6p22.1 locus was identified in both MDD and SCZ (P values are 1.05×10-8 and 5.75×10-14, respectively). Sixteen pleiotropic risk loci were identified between MDD and indicators of thyroid function, of which, four loci associated with MDD (1p32.3, 6p22.1, 10q21.1, 11q13.4) were identified in both FT4 normal level and Hypothyroidism. Further, 79 pleiotropic genes were identified using Magma gene analysis (P<0.05/18776=2.66×10-6). Tissue-specific enrichment analysis revealed that these genes were highly enriched into six brain-related tissues. The pathway analysis mainly involved nucleosome assembly and lipoprotein particles. Finally, our two-sample MR analysis showed a significant causal effect of MDD on the increased risk of hypothyroidism, and BIP may reduce TSH normal levels. Our findings not only provided evidence of a shared genetic etiology between hypothyroidism and psychiatric disorders, but also provided insights into the causal relationships and biological mechanisms that underlie their relationship. These findings contribute to a better understanding of the pleiotropy between hypothyroidism and psychiatric disorders, while having important implications for intervention and treatment goals for these disorders.

Abstract 1102: Post Gwas Functional Genomics Analysis To Identify Non-coding Single Nucleotide Polymorphisms Controlling The Risk Of Spontaneous Coronary Artery Dissection

Background: Spontaneous Coronary Artery Dissection (SCAD) is a non-atherosclerotic coronary artery disease and the leading cause of acute myocardial infarction in young females. SCAD results from the combination of genetic susceptibility and acquired triggering factors including inflammation and sex hormones. Three loci have been revealed by two large-scale genome-wide association studies (GWAS) in SCAD. However, GWAS only reveals the statistical associations between the genetic loci and diseases, and the biological mechanisms remain unknown. Methods: GWAS-reported tag single nucleotide polymorphisms (SNPs) and the linked haplotype SNPs were collected. Synthesized oligonucleotides representing the SNP alleles were subjected to electrophoretic mobility shift assays (EMSA) with nuclear proteins extracted from vascular endothelial cells (ECs), smooth muscle cells (SMCs) and fibroblasts. Hi-C analysis was performed to predict the potential target gene(s) based on chromatin interactions. The functionality of the SNP-associated target genes was examined in cultured vascular cells by RNA silencing. Results: The risk allele of tag SNPs in three genetic loci (chr1q21.2, 6p24.11 and 12q13.1) were associated with 1.5 to 1.8-fold higher risk of SCAD. Intronic, non-tag SNP rs7549396 (G/A) in chr1q21.2 locus was identified as the functional SNP in EMSA, given its consistent allele-imbalanced gel shifting pattern in ECs, SMCs and fibroblasts. Hi-C analysis in human aortic tissue identified a 490kb long-range chromatin interaction between SNP rs7549396 and the promoter of Annexin A9 (ANXA9), an extracellular matrix (ECM) and an inflammation-related protein potentially associated with the pathogenesis of SCAD. The knockdown of ANXA9 in vascular cells showed significant changes in inflammatory makers (IL-1β, vCAM-1 and CCL-2) and ECM degradation enzyme (MMP-1). Conclusions: Leveraging our post-GWAS functional genomics approach, we identified functional non-coding SNPs in disease-associated genetic locus and predicted a novel potential pathogenic gene ANXA9 for SCAD. Further studies on the functions of this SNP and its associated target gene ANXA9 will provide genetics and mechanistic explanation for this mysterious disease.

Characterization of the genetic determinants of context-specific DNA methylation in primary monocytes

To better understand inter-individual variation in sensitivity of DNA methylation (DNAm) to immune activity, we characterized effects of inflammatory stimuli on primary monocyte DNAm (n= 190). We find that monocyte DNAm is site-dependently sensitive to lipopolysaccharide (LPS), with LPS-induced demethylation occurring following hydroxymethylation. We identify 7,359 high-confidence immune-modulated CpGs (imCpGs) that differ in genomic localization and transcription factor usage according to whether they represent a gain or loss in DNAm. Demethylated imCpGs are profoundly enriched for enhancers and colocalize to genes enriched for disease associations, especially cancer. DNAm is age associated, and we find that 24-h LPS exposure triggers approximately 6months of gain in epigenetic age, directly linking epigenetic aging with innate immune activity. By integrating LPS-induced changes in DNAm with genetic variation, we identify 234 imCpGs under local genetic control. Exploring shared causal loci between LPS-induced DNAm responses and human disease traits highlights examples of disease-associated loci that modulate imCpG formation.

TOPMed imputed genomics enhances genomic atlas of the human proteome in brain, cerebrospinal fluid, and plasma

Comprehensive expression quantitative trait loci studies have been instrumental for understanding tissue-specific gene regulation and pinpointing functional genes for disease-associated loci in a tissue-specific manner. Compared to gene expressions, proteins more directly affect various biological processes, often dysregulated in disease, and are important drug targets. We previously performed and identified tissue-specific protein quantitative trait loci in brain, cerebrospinal fluid, and plasma. We now enhance this work by analyzing more proteins (1,300 versus 1,079) and an almost twofold increase in high quality imputed genetic variants (8.4 million versus 4.4 million) by using TOPMed reference panel. We identified 38 genomic regions associated with 43 proteins in brain, 150 regions associated with 247 proteins in cerebrospinal fluid, and 95 regions associated with 145 proteins in plasma. Compared to our previous study, this study newly identified 12 loci in brain, 30 loci in cerebrospinal fluid, and 22 loci in plasma. Our improved genomic atlas uncovers the genetic control of protein regulation across multiple tissues. These resources are accessible through the Online Neurodegenerative Trait Integrative Multi-Omics Explorer for use by the scientific community.

Cell-type-specific and disease-associated expression quantitative trait loci in the human lung

Common genetic variants confer substantial risk for chronic lung diseases, including pulmonary fibrosis. Defining the genetic control of gene expression in a cell-type-specific and context-dependent manner is critical for understanding the mechanisms through which genetic variation influences complex traits and disease pathobiology. To this end, we performed single-cell RNA sequencing of lung tissue from 66 individuals with pulmonary fibrosis and 48 unaffected donors. Using a pseudobulk approach, we mapped expression quantitative trait loci (eQTLs) across 38 cell types, observing both shared and cell-type-specific regulatory effects. Furthermore, we identified disease interaction eQTLs and demonstrated that this class of associations is more likely to be cell-type-specific and linked to cellular dysregulation in pulmonary fibrosis. Finally, we connected lung disease risk variants to their regulatory targets in disease-relevant cell types. These results indicate that cellular context determines the impact of genetic variation on gene expression and implicates context-specific eQTLs as key regulators of lung homeostasis and disease.

The relationship between 11 different polygenic longevity scores, parental lifespan, and disease diagnosis in the UK Biobank

Large-scale genome-wide association studies (GWAS) strongly suggest that most traits and diseases have a polygenic component. This observation has motivated the development of disease-specific “polygenic scores (PGS)” that are weighted sums of the effects of disease-associated variants identified from GWAS that correlate with an individual’s likelihood of expressing a specific phenotype. Although most GWAS have been pursued on disease traits, leading to the creation of refined “Polygenic Risk Scores” (PRS) that quantify risk to diseases, many GWAS have also been pursued on extreme human longevity, general fitness, health span, and other health-positive traits. These GWAS have discovered many genetic variants seemingly protective from disease and are often different from disease-associated variants (i.e., they are not just alternative alleles at disease-associated loci) and suggest that many health-positive traits also have a polygenic basis. This observation has led to an interest in “polygenic longevity scores (PLS)” that quantify the “risk” or genetic predisposition of an individual towards health. We derived 11 different PLS from 4 different available GWAS on lifespan and then investigated the properties of these PLS using data from the UK Biobank (UKB). Tests of association between the PLS and population structure, parental lifespan, and several cancerous and non-cancerous diseases, including death from COVID-19, were performed. Based on the results of our analyses, we argue that PLS are made up of variants not only robustly associated with parental lifespan, but that also contribute to the genetic architecture of disease susceptibility, morbidity, and mortality.

Genetic regulatory effects in response to a high-cholesterol, high-fat diet in baboons

Steady-state expression quantitative trait loci (eQTLs) explain only a fraction of disease-associated loci identified through genome-wide association studies (GWASs), while eQTLs involved in gene-by-environment (GxE) interactions have rarely been characterized in humans due to experimental challenges. Using a baboon model, we found hundreds of eQTLs that emerge in adipose, liver, and muscle after prolonged exposure to high dietary fat and cholesterol. Diet-responsive eQTLs exhibit genomic localization and genic features that are distinct from steady-state eQTLs. Furthermore, the human orthologs associated with diet-responsive eQTLs are enriched for GWAS genes associated with human metabolic traits, suggesting that context-responsive eQTLs with more complex regulatory effects are likely to explain GWAS hits that do not seem to overlap with standard eQTLs. Our results highlight the complexity of genetic regulatory effects and the potential of eQTLs with disease-relevant GxE interactions in enhancing the understanding of GWAS signals for human complex disease using non-human primate models.

Shared genomic segments analysis identifies MHC class I and class III molecules as genetic risk factors for juvenile idiopathic arthritis

Juvenile idiopathic arthritis (JIA) is a complex rheumatic disease encompassing several clinically defined subtypes of varying severity. The etiology of JIA remains largely unknown, but genome-wide association studies (GWAS) have identified up to 22 genes associated with JIA susceptibility, including a well-established association with HLA-DRB1. Continued investigation of heritable risk factors has been hindered by disease heterogeneity and low disease prevalence. In this study, we utilized shared genomic segments (SGS) analysis on whole-genome sequencing of 40 cases from 12 multi-generational pedigrees significantly enriched for JIA. Subsets of cases are connected by a common ancestor in large extended pedigrees, increasing the power to identify disease-associated loci. SGS analysis identifies genomic segments shared among disease cases that are likely identical by descent and anchored by a disease locus. This approach revealed statistically significant signals for major histocompatibility complex (MHC) class I and class III alleles, particularly HLA-A*02:01, which was observed at a high frequency among cases. Furthermore, we identified an additional risk locus at 12q23.2-23.3, containing genes primarily expressed by naïve B cells, natural killer cells, and monocytes. The recognition of additional risk beyond HLA-DRB1 provides a new perspective on immune cell dynamics in JIA. These findings contribute to our understanding of JIA and may guide future research and therapeutic strategies.

A cost-effective sequencing method for genetic studies combining high-depth whole exome and low-depth whole genome

Whole genome sequencing (WGS) at high-depth (30X) allows the accurate discovery of variants in the coding and non-coding DNA regions and helps elucidate the genetic underpinnings of human health and diseases. Yet, due to the prohibitive cost of high-depth WGS, most large-scale genetic association studies use genotyping arrays or high-depth whole exome sequencing (WES). Here we propose a cost-effective method which we call “Whole Exome Genome Sequencing” (WEGS), that combines low-depth WGS and high-depth WES with up to 8 samples pooled and sequenced simultaneously (multiplexed). We experimentally assess the performance of WEGS with four different depth of coverage and sample multiplexing configurations. We show that the optimal WEGS configurations are 1.7–2.0 times cheaper than standard WES (no-plexing), 1.8–2.1 times cheaper than high-depth WGS, reach similar recall and precision rates in detecting coding variants as WES, and capture more population-specific variants in the rest of the genome that are difficult to recover when using genotype imputation methods. We apply WEGS to 862 patients with peripheral artery disease and show that it directly assesses more known disease-associated variants than a typical genotyping array and thousands of non-imputable variants per disease-associated locus.

ZNF142 mutation causes sex-dependent neurologic disorder

BackgroundSex-specific predilection in neurological diseases caused by mutations in autosomal genes is a phenomenon whose molecular basis is poorly understood. We studied females of consanguineous Bedouin kindred presenting with severe...

Haplotype-aware modeling of cis-regulatory effects highlights the gaps remaining in eQTL data

Expression Quantitative Trait Loci (eQTLs) are critical to understanding the mechanisms underlying disease-associated genomic loci. Nearly all protein-coding genes in the human genome have been associated with one or more eQTLs. Here we introduce a multi-variant generalization of allelic Fold Change (aFC), aFC-n, to enable quantification of the cis-regulatory effects in multi-eQTL genes under the assumption that all eQTLs are known and conditionally independent. Applying aFC-n to 458,465 eQTLs in the Genotype-Tissue Expression (GTEx) project data, we demonstrate significant improvements in accuracy over the original model in estimating the eQTL effect sizes and in predicting genetically regulated gene expression over the current tools. We characterize some of the empirical properties of the eQTL data and use this framework to assess the current state of eQTL data in terms of characterizing cis-regulatory landscape in individual genomes. Notably, we show that 77.4% of the genes with an allelic imbalance in a sample show 0.5 log2 fold or more of residual imbalance after accounting for the eQTL data underlining the remaining gap in characterizing regulatory landscape in individual genomes. We further contrast this gap across tissue types, and ancestry backgrounds to identify its correlates and guide future studies.

Multitissue H3K27ac profiling of GTEx samples links epigenomic variation to disease

Genetic variants associated with complex traits are primarily noncoding, and their effects on gene-regulatory activity remain largely uncharacterized. To address this, we profile epigenomic variation of histone mark H3K27ac across 387 brain, heart, muscle and lung samples from Genotype-Tissue Expression (GTEx). We annotate 282 k active regulatory elements (AREs) with tissue-specific activity patterns. We identify 2,436 sex-biased AREs and 5,397 genetically influenced AREs associated with 130 k genetic variants (haQTLs) across tissues. We integrate genetic and epigenomic variation to provide mechanistic insights for disease-associated loci from 55 genome-wide association studies (GWAS), by revealing candidate tissues of action, driver SNPs and impacted AREs. Lastly, we build ARE–gene linking scores based on genetics (gLink scores) and demonstrate their unique ability to prioritize SNP–ARE–gene circuits. Overall, our epigenomic datasets, computational integration and mechanistic predictions provide valuable resources and important insights for understanding the molecular basis of human diseases/traits such as schizophrenia.

IHH enhancer variant within neighboring NHEJ1 intron causes microphthalmia anophthalmia and coloboma

Genomic sequences residing within introns of few genes have been shown to act as enhancers affecting expression of neighboring genes. We studied an autosomal recessive phenotypic continuum of microphthalmia, anophthalmia and ocular coloboma, with no apparent coding-region disease-causing mutation. Homozygosity mapping of several affected Jewish Iranian families, combined with whole genome sequence analysis, identified a 0.5 Mb disease-associated chromosome 2q35 locus (maximal LOD score 6.8) harboring an intronic founder variant in NHEJ1, not predicted to affect NHEJ1. The human NHEJ1 intronic variant lies within a known specifically limb-development enhancer of a neighboring gene, Indian hedgehog (Ihh), known to be involved in eye development in mice and chickens. Through mouse and chicken molecular development studies, we demonstrated that this variant is within an Ihh enhancer that drives gene expression in the developing eye and that the identified variant affects this eye-specific enhancer activity. We thus delineate an Ihh enhancer active in mammalian eye development whose variant causes human microphthalmia, anophthalmia and ocular coloboma. The findings highlight disease causation by an intronic variant affecting the expression of a neighboring gene, delineating molecular pathways of eye development.

Allele-specific RNA N 6-methyladenosine modifications reveal functional genetic variants in human tissues.

An intricate network of cis- and trans-elements acts on RNA N 6-methyladenosine (m6A), which in turn may affect gene expression and, ultimately, human health. A complete understanding of this network requires new approaches to accurately measure the subtle m6A differences arising from genetic variants, many of which have been associated with common diseases. To address this gap, we developed a method to accurately and sensitively detect transcriptome-wide allele-specific m6A (ASm6A) from MeRIP-seq data and applied it to uncover 12,056 high-confidence ASm6A modifications from 25 human tissues. We also identified 1184 putative functional variants for ASm6A regulation, a subset of which we experimentally validated. Importantly, we found that many of these ASm6A-associated genetic variants were enriched for common disease-associated and complex trait-associated risk loci, and verified that two disease risk variants can change m6A modification status. Together, this work provides a tool to detangle the dynamic network of RNA modifications at the allelic level and highlights the interplay of m6A and genetics in human health and disease.