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

Multi-ancestry and multi-trait genome-wide association meta-analyses inform clinical risk prediction for systemic lupus erythematosus

Systemic lupus erythematosus is a heritable autoimmune disease that predominantly affects young women. To improve our understanding of genetic etiology, we conduct multi-ancestry and multi-trait meta-analysis of genome-wide association studies, encompassing 12 systemic lupus erythematosus cohorts from 3 different ancestries and 10 genetically correlated autoimmune diseases, and identify 16 novel loci. We also perform transcriptome-wide association studies, computational drug repurposing analysis, and cell type enrichment analysis. We discover putative drug classes, including a histone deacetylase inhibitor that could be repurposed to treat lupus. We also identify multiple cell types enriched with putative target genes, such as non-classical monocytes and B cells, which may be targeted for future therapeutics. Using this newly assembled result, we further construct polygenic risk score models and demonstrate that integrating polygenic risk score with clinical lab biomarkers improves the diagnostic accuracy of systemic lupus erythematosus using the Vanderbilt BioVU and Michigan Genomics Initiative biobanks.

MicroRNA-eQTLs in the developing human neocortex link miR-4707-3p expression to brain size

Expression quantitative trait loci (eQTL) data have proven important for linking non-coding loci to protein-coding genes. But eQTL studies rarely measure microRNAs (miRNAs), small non-coding RNAs known to play a role in human brain development and neurogenesis. Here, we performed small-RNA sequencing across 212 mid-gestation human neocortical tissue samples, measured 907 expressed miRNAs, discovering 111 of which were novel, and identified 85 local-miRNA-eQTLs. Colocalization of miRNA-eQTLs with GWAS summary statistics yielded one robust colocalization of miR-4707-3p expression with educational attainment and brain size phenotypes, where the miRNA expression increasing allele was associated with decreased brain size. Exogenous expression of miR-4707-3p in primary human neural progenitor cells decreased expression of predicted targets and increased cell proliferation, indicating miR-4707-3p modulates progenitor gene regulation and cell fate decisions. Integrating miRNA-eQTLs with existing GWAS yielded evidence of a miRNA that may influence human brain size and function via modulation of neocortical brain development.

Integrative Post-Genome-Wide Association Study Analyses Relevant to Psychiatric Disorders: Imputing Transcriptome and Proteome Signals

Background

The genome-wide association study (GWAS) is a common tool to identify genetic variants associated with complex traits, including psychiatric disorders (PDs). However, post-GWAS analyses are needed to extend the statistical inference to biologically relevant entities, e.g., genes, proteins, and pathways. To achieve this goal, researchers developed methods that incorporate biologically relevant intermediate molecular phenotypes, such as gene expression and protein abundance, which are posited to mediate the variant-trait association. Transcriptome-wide association study (TWAS) and proteome-wide association study (PWAS) are commonly used methods to test the association between these molecular mediators and the trait.

Summary

In this review, we discuss the most recent developments in TWAS and PWAS. These methods integrate existing "omic" information with the GWAS summary statistics for trait(s) of interest. Specifically, they impute transcript/protein data and test the association between imputed gene expression/protein level with phenotype of interest by using (i) GWAS summary statistics and (ii) reference transcriptomic/proteomic/genomic datasets. TWAS and PWAS are suitable as analysis tools for (i) primary association scan and (ii) fine-mapping to identify potentially causal genes for PDs.

Key Messages

As post-GWAS analyses, TWAS and PWAS have the potential to highlight causal genes for PDs. These prioritized genes could indicate targets for the development of novel drug therapies. For researchers attempting such analyses, we recommend Mendelian randomization tools that use GWAS statistics for both trait and reference datasets, e.g., summary Mendelian randomization (SMR). We base our recommendation on (i) being able to use the same tool for both TWAS and PWAS, (ii) not requiring the pre-computed weights (and thus easier to update for larger reference datasets), and (iii) most larger transcriptome reference datasets are publicly available and easy to transform into a compatible format for SMR analysis.

Long-read transcriptome sequencing reveals allele-specific variants at high resolution

Disturbance in the regulation of transcript structure plays a crucial role in human disease. In a recent study, Glinos et al. characterized allele-specific transcript alterations in long-read RNA sequencing (RNA-seq) data derived from multiple human tissues and provide a high-resolution view of how disease-associated genetic variants affect transcript structure.

Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries

Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and east Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high-confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Crosstissue analyses indicated that over a third of effector genes most probably act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.

A network approach for low dimensional signatures from high throughput data

One of the main objectives of high-throughput genomics studies is to obtain a low-dimensional set of observables-a signature-for sample classification purposes (diagnosis, prognosis, stratification). Biological data, such as gene or protein expression, are commonly characterized by an up/down regulation behavior, for which discriminant-based methods could perform with high accuracy and easy interpretability. To obtain the most out of these methods features selection is even more critical, but it is known to be a NP-hard problem, and thus most feature selection approaches focuses on one feature at the time (k-best, Sequential Feature Selection, recursive feature elimination). We propose DNetPRO, Discriminant Analysis with Network PROcessing, a supervised network-based signature identification method. This method implements a network-based heuristic to generate one or more signatures out of the best performing feature pairs. The algorithm is easily scalable, allowing efficient computing for high number of observables ([Formula: see text]-[Formula: see text]). We show applications on real high-throughput genomic datasets in which our method outperforms existing results, or is compatible with them but with a smaller number of selected features. Moreover, the geometrical simplicity of the resulting class-separation surfaces allows a clearer interpretation of the obtained signatures in comparison to nonlinear classification models.

Genes Associated with Biological Nitrogen Fixation Efficiency Identified Using RNA Sequencing in Red Clover (Trifolium pratense L.)

Commonly studied in the context of legume-rhizobia symbiosis, biological nitrogen fixation (BNF) is a key component of the nitrogen cycle in nature. Despite its potential in plant breeding and many years of research, information is still lacking as to the regulation of hundreds of genes connected with plant-bacteria interaction, nodulation, and nitrogen fixation. Here, we compared root nodule transcriptomes of red clover (Trifolium pratense L.) genotypes with contrasting nitrogen fixation efficiency, and we found 491 differentially expressed genes (DEGs) between plants with high and low BNF efficiency. The annotation of genes expressed in nodules revealed more than 800 genes not yet experimentally confirmed. Among genes mediating nodule development, four nod-ule-specific cysteine-rich (NCR) peptides were confirmed in the nodule transcriptome. Gene duplication analyses revealed that genes originating from tandem and dispersed duplication are significantly over-represented among DEGs. Weighted correlation network analysis (WGCNA) organized expression profiles of the transcripts into 16 modules linked to the analyzed traits, such as nitrogen fixation efficiency or sample-specific modules. Overall, the results obtained broaden our knowledge about transcriptomic landscapes of red clover's root nodules and shift the phenotypic description of BNF efficiency on the level of gene expression in situ.

Illuminating links between cis-regulators and trans-acting variants in the human prefrontal cortex

BACKGROUND

Neuropsychiatric disorders afflict a large portion of the global population and constitute a significant source of disability worldwide. Although Genome-wide Association Studies (GWAS) have identified many disorder-associated variants, the underlying regulatory mechanisms linking them to disorders remain elusive, especially those involving distant genomic elements. Expression quantitative trait loci (eQTLs) constitute a powerful means of providing this missing link. However, most eQTL studies in human brains have focused exclusively on cis-eQTLs, which link variants to nearby genes (i.e., those within 1 Mb of a variant). A complete understanding of disease etiology requires a clearer understanding of trans-regulatory mechanisms, which, in turn, entails a detailed analysis of the relationships between variants and expression changes in distant genes.

METHODS

By leveraging large datasets from the PsychENCODE consortium, we conducted a genome-wide survey of trans-eQTLs in the human dorsolateral prefrontal cortex. We also performed colocalization and mediation analyses to identify mediators in trans-regulation and use trans-eQTLs to link GWAS loci to schizophrenia risk genes.

RESULTS

We identified ~80,000 candidate trans-eQTLs (at FDR<0.25) that influence the expression of ~10K target genes (i.e., "trans-eGenes"). We found that many variants associated with these candidate trans-eQTLs overlap with known cis-eQTLs. Moreover, for >60% of these variants (by colocalization), the cis-eQTL's target gene acts as a mediator for the trans-eQTL SNP's effect on the trans-eGene, highlighting examples of cis-mediation as essential for trans-regulation. Furthermore, many of these colocalized variants fall into a discernable pattern wherein cis-eQTL's target is a transcription factor or RNA-binding protein, which, in turn, targets the gene associated with the candidate trans-eQTL. Finally, we show that trans-regulatory mechanisms provide valuable insights into psychiatric disorders: beyond what had been possible using only cis-eQTLs, we link an additional 23 GWAS loci and 90 risk genes (using colocalization between candidate trans-eQTLs and schizophrenia GWAS loci).

CONCLUSIONS

We demonstrate that the transcriptional architecture of the human brain is orchestrated by both cis- and trans-regulatory variants and found that trans-eQTLs provide insights into brain-disease biology.

Whole genome DNA and RNA sequencing of whole blood elucidates the genetic architecture of gene expression underlying a wide range of diseases

To create a scientific resource of expression quantitative trail loci (eQTL), we conducted a genome-wide association study (GWAS) using genotypes obtained from whole genome sequencing (WGS) of DNA and gene expression levels from RNA sequencing (RNA-seq) of whole blood in 2622 participants in Framingham Heart Study. We identified 6,778,286 cis-eQTL variant-gene transcript (eGene) pairs at p < 5 × 10^-8 (2,855,111 unique cis-eQTL variants and 15,982 unique eGenes) and 1,469,754 trans-eQTL variant-eGene pairs at p < 1e-12 (526,056 unique trans-eQTL variants and 7233 unique eGenes). In addition, 442,379 cis-eQTL variants were associated with expression of 1518 long non-protein coding RNAs (lncRNAs). Gene Ontology (GO) analyses revealed that the top GO terms for cis-eGenes are enriched for immune functions (FDR < 0.05). The cis-eQTL variants are enriched for SNPs reported to be associated with 815 traits in prior GWAS, including cardiovascular disease risk factors. As proof of concept, we used this eQTL resource in conjunction with genetic variants from public GWAS databases in causal inference testing (e.g., COVID-19 severity). After Bonferroni correction, Mendelian randomization analyses identified putative causal associations of 60 eGenes with systolic blood pressure, 13 genes with coronary artery disease, and seven genes with COVID-19 severity. This study created a comprehensive eQTL resource via BioData Catalyst that will be made available to the scientific community. This will advance understanding of the genetic architecture of gene expression underlying a wide range of diseases.

Pre-infection antiviral innate immunity contributes to sex differences in SARS-CoV-2 infection

Male sex is a major risk factor for SARS-CoV-2 infection severity. To understand the basis for this sex difference, we studied SARS-CoV-2 infection in a young adult cohort of United States Marine recruits. Among 2,641 male and 244 female unvaccinated and seronegative recruits studied longitudinally, SARS-CoV-2 infections occurred in 1,033 males and 137 females. We identified sex differences in symptoms, viral load, blood transcriptome, RNA splicing, and proteomic signatures. Females had higher pre-infection expression of antiviral interferon-stimulated gene (ISG) programs. Causal mediation analysis implicated ISG differences in number of symptoms, levels of ISGs, and differential splicing of CD45 lymphocyte phosphatase during infection. Our results indicate that the antiviral innate immunity set point causally contributes to sex differences in response to SARS-CoV-2 infection. A record of this paper's transparent peer review process is included in the supplemental information.

DNA methyltransferase 3A controls intestinal epithelial barrier function and regeneration in the colon

Genetic variants in the DNA methyltransferase 3 A (DNMT3A) locus have been associated with inflammatory bowel disease (IBD). DNMT3A is part of the epigenetic machinery physiologically involved in DNA methylation. We show that DNMT3A plays a critical role in maintaining intestinal homeostasis and gut barrier function. DNMT3A expression is downregulated in intestinal epithelial cells from IBD patients and upon tumor necrosis factor treatment in murine intestinal organoids. Ablation of DNMT3A in Caco-2 cells results in global DNA hypomethylation, which is linked to impaired regenerative capacity, transepithelial resistance and intercellular junction formation. Genetic deletion of Dnmt3a in intestinal epithelial cells (Dnmt3a^ΔIEC) in mice confirms the phenotype of an altered epithelial ultrastructure with shortened apical-junctional complexes, reduced Goblet cell numbers and increased intestinal permeability in the colon in vivo. Dnmt3a^ΔIEC mice suffer from increased susceptibility to experimental colitis, characterized by reduced epithelial regeneration. These data demonstrate a critical role for DNMT3A in orchestrating intestinal epithelial homeostasis and response to tissue damage and suggest an involvement of impaired epithelial DNMT3A function in the etiology of IBD.

PCA outperforms popular hidden variable inference methods for molecular QTL mapping

BACKGROUND

Estimating and accounting for hidden variables is widely practiced as an important step in molecular quantitative trait locus (molecular QTL, henceforth "QTL") analysis for improving the power of QTL identification. However, few benchmark studies have been performed to evaluate the efficacy of the various methods developed for this purpose.

RESULTS

Here we benchmark popular hidden variable inference methods including surrogate variable analysis (SVA), probabilistic estimation of expression residuals (PEER), and hidden covariates with prior (HCP) against principal component analysis (PCA)-a well-established dimension reduction and factor discovery method-via 362 synthetic and 110 real data sets. We show that PCA not only underlies the statistical methodology behind the popular methods but is also orders of magnitude faster, better-performing, and much easier to interpret and use.

CONCLUSIONS

To help researchers use PCA in their QTL analysis, we provide an R package PCAForQTL along with a detailed guide, both of which are freely available at https://github.com/heatherjzhou/PCAForQTL . We believe that using PCA rather than SVA, PEER, or HCP will substantially improve and simplify hidden variable inference in QTL mapping as well as increase the transparency and reproducibility of QTL research.

Interpretation of the role of germline and somatic non-coding mutations in cancer: expression and chromatin conformation informed analysis

BACKGROUND

There has been extensive scrutiny of cancer driving mutations within the exome (especially amino acid altering mutations) as these are more likely to have a clear impact on protein functions, and thus on cell biology. However, this has come at the neglect of systematic identification of regulatory (non-coding) variants, which have recently been identified as putative somatic drivers and key germline risk factors for cancer development. Comprehensive understanding of non-coding mutations requires understanding their role in the disruption of regulatory elements, which then disrupt key biological functions such as gene expression.

MAIN BODY

We describe how advancements in sequencing technologies have led to the identification of a large number of non-coding mutations with uncharacterized biological significance. We summarize the strategies that have been developed to interpret and prioritize the biological mechanisms impacted by non-coding mutations, focusing on recent annotation of cancer non-coding variants utilizing chromatin states, eQTLs, and chromatin conformation data.

CONCLUSION

We believe that a better understanding of how to apply different regulatory data types into the study of non-coding mutations will enhance the discovery of novel mechanisms driving cancer.

Genome-wide association study of pain sensitivity assessed by questionnaire and the cold pressor test

ABSTRACT

We deployed an online pain sensitivity questionnaire (PSQ) and an at-home version of the cold pressor test (CPT) in a large genotyped cohort. We performed genome-wide association studies on the PSQ score (25,321 participants) and CPT duration (6853). We identified one new genome-wide significant locus associated with the PSQ score, which was located in the TSSC1 (also known as EIPR1 ) gene (rs58194899, OR = 0.950 [0.933-0.967], P -value = 1.9 × 10 -8 ). Although high pain sensitivity measured by both PSQ and CPT was associated with individual history of chronic and acute pains, genetic correlation analyses surprisingly suggested an opposite direction: PSQ score was inversely genetically correlated with neck and shoulder pain ( rg = -0.71), rheumatoid arthritis (-0.68), and osteoarthritis (-0.38), and with known risk factors, such as the length of working week (-0.65), smoking (-0.36), or extreme BMI (-0.23). Gene-based analysis followed by pathway analysis showed that genome-wide association studies results were enriched for genes expressed in the brain and involved in neuronal development and glutamatergic synapse signaling pathways. Finally, we confirmed that females with red hair were more sensitive to pain and found that genetic variation in the MC1R gene was associated with an increase in self-perceived pain sensitivity as assessed by the PSQ.

Splicing QTL analysis focusing on coding sequences reveals mechanisms for disease susceptibility loci

Splicing quantitative trait loci (sQTLs) are one of the major causal mechanisms in genome-wide association study (GWAS) loci, but their role in disease pathogenesis is poorly understood. One reason is the complexity of alternative splicing events producing many unknown isoforms. Here, we propose two approaches, namely integration and selection, for this complexity by focusing on protein-structure of isoforms. First, we integrate isoforms with the same coding sequence (CDS) and identify 369-601 integrated-isoform ratio QTLs (i²-rQTLs), which altered protein-structure, in six immune subsets. Second, we select CDS incomplete isoforms annotated in GENCODE and identify 175-337 isoform-ratio QTL (i-rQTL). By comprehensive long-read capture RNA-sequencing among these incomplete isoforms, we reveal 29 full-length isoforms with unannotated CDSs associated with GWAS traits. Furthermore, we show that disease-causal sQTL genes can be identified by evaluating their trans-eQTL effects. Our approaches highlight the understudied role of protein-altering sQTLs and are broadly applicable to other tissues and diseases.

Splicing QTL (sQTL), genetic variants regulating alternative splicing, can be biologically important, but complex to detect and interpret. Here, the authors identify sQTL by focusing on protein coding sequences, as an alternative to junction-based approaches.

Genetic control of RNA splicing and its distinct role in complex trait variation

Most genetic variants identified from genome-wide association studies (GWAS) in humans are noncoding, indicating their role in gene regulation. Previous studies have shown considerable links of GWAS signals to expression quantitative trait loci (eQTLs) but the links to other genetic regulatory mechanisms, such as splicing QTLs (sQTLs), are underexplored. Here, we introduce an sQTL mapping method, testing for heterogeneity between isoform-eQTLeffects (THISTLE), with improved power over competing methods. Applying THISTLE together with a complementary sQTL mapping strategy to brain transcriptomic (n = 2,865) and genotype data, we identified 12,794 genes with cis-sQTLs at P < 5 × 10⁻⁸, approximately 61% of which were distinct from eQTLs. Integrating the sQTL data into GWAS for 12 brain-related complex traits (including diseases), we identified 244 genes associated with the traits through cis-sQTLs, approximately 61% of which could not be discovered using the corresponding eQTL data. Our study demonstrates the distinct role of most sQTLs in the genetic regulation of transcription and complex trait variation.

A powerful method for splicing quantitative trait loci (sQTL) mapping, THISTLE, is presented and applied to a collection of 2,865 brain samples. Integration with GWAS identifies 244 genes associated via cis-sQTLs, of which 61% were not identified using expression QTLs.

Maintenance of quantitative genetic variance in complex, multi-trait phenotypes: The contribution of rare, large effect variants in two Drosophila species

The interaction of evolutionary processes to determine quantitative genetic variation has implications for contemporary and future phenotypic evolution, as well as for our ability to detect causal genetic variants. While theoretical studies have provided robust predictions to discriminate among competing models, empirical assessment of these has been limited. In particular, theory highlights the importance of pleiotropy in resolving observations of selection and mutation, but empirical investigations have typically been limited to few traits. Here, we applied high-dimensional Bayesian Sparse Factor Genetic modeling to gene expression datasets in 2 species, Drosophila melanogaster and Drosophila serrata, to explore the distributions of genetic variance across high-dimensional phenotypic space. Surprisingly, most of the heritable trait covariation was due to few lines (genotypes) with extreme [>3 interquartile ranges (IQR) from the median] values. Intriguingly, while genotypes extreme for a multivariate factor also tended to have a higher proportion of individual traits that were extreme, we also observed genotypes that were extreme for multivariate factors but not for any individual trait. We observed other consistent differences between heritable multivariate factors with outlier lines vs those factors without extreme values, including differences in gene functions. We use these observations to identify further data required to advance our understanding of the evolutionary dynamics and nature of standing genetic variation for quantitative traits.

Transcriptome variation in human tissues revealed by long-read sequencing

Regulation of transcript structure generates transcript diversity and plays an important role in human disease1-7. The advent of long-read sequencing technologies offers the opportunity to study the role of genetic variation in transcript structure8-16. In this Article, we present a large human long-read RNA-seq dataset using the Oxford Nanopore Technologies platform from 88 samples from Genotype-Tissue Expression (GTEx) tissues and cell lines, complementing the GTEx resource. We identified just over 70,000 novel transcripts for annotated genes, and validated the protein expression of 10% of novel transcripts. We developed a new computational package, LORALS, to analyse the genetic effects of rare and common variants on the transcriptome by allele-specific analysis of long reads. We characterized allele-specific expression and transcript structure events, providing new insights into the specific transcript alterations caused by common and rare genetic variants and highlighting the resolution gained from long-read data. We were able to perturb the transcript structure upon knockdown of PTBP1, an RNA binding protein that mediates splicing, thereby finding genetic regulatory effects that are modified by the cellular environment. Finally, we used this dataset to enhance variant interpretation and study rare variants leading to aberrant splicing patterns.

Mapping of promoter usage QTL using RNA-seq data reveals their contributions to complex traits

Genomic variations are associated with gene expression levels, which are called expression quantitative trait loci (eQTL). Most eQTL may affect the total gene expression levels by regulating transcriptional activities of a specific promoter. However, the direct exploration of genomic loci associated with promoter activities using RNA-seq data has been challenging because eQTL analyses treat the total expression levels estimated by summing those of all isoforms transcribed from distinct promoters. Here we propose a new method for identifying genomic loci associated with promoter activities, called promoter usage quantitative trait loci (puQTL), using conventional RNA-seq data. By leveraging public RNA-seq datasets from the lymphoblastoid cell lines of 438 individuals from the GEUVADIS project, we obtained promoter activity estimates and mapped 2,592 puQTL at the 10% FDR level. The results of puQTL mapping enabled us to interpret the manner in which genomic variations regulate gene expression. We found that 310 puQTL genes (16.1%) were not detected by eQTL analysis, suggesting that our pipeline can identify novel variant-gene associations. Furthermore, we identified genomic loci associated with the activity of "hidden" promoters, which the standard eQTL studies have ignored. We found that most puQTL signals were concordant with at least one genome-wide association study (GWAS) signal, enabling novel interpretations of the molecular mechanisms of complex traits. Our results emphasize the importance of the re-analysis of public RNA-seq datasets to obtain novel insights into gene regulation by genomic variations and their contributions to complex traits.

Aggregative trans-eQTL analysis detects trait-specific target gene sets in whole blood

Large scale genetic association studies have identified many trait-associated variants and understanding the role of these variants in the downstream regulation of gene-expressions can uncover important mediating biological mechanisms. Here we propose ARCHIE, a summary statistic based sparse canonical correlation analysis method to identify sets of gene-expressions trans-regulated by sets of known trait-related genetic variants. Simulation studies show that compared to standard methods, ARCHIE is better suited to identify "core"-like genes through which effects of many other genes may be mediated and can capture disease-specific patterns of genetic associations. By applying ARCHIE to publicly available summary statistics from the eQTLGen consortium, we identify gene sets which have significant evidence of trans-association with groups of known genetic variants across 29 complex traits. Around half (50.7%) of the selected genes do not have any strong trans-associations and are not detected by standard methods. We provide further evidence for causal basis of the target genes through a series of follow-up analyses. These results show ARCHIE is a powerful tool for identifying sets of genes whose trans-regulation may be related to specific complex traits.

Transcriptome-wide association study in UK Biobank Europeans identifies associations with blood cell traits

Previous genome-wide association studies (GWAS) of hematological traits have identified over 10 000 distinct trait-specific risk loci. However, at these loci, the underlying causal mechanisms remain incompletely characterized. To elucidate novel biology and better understand causal mechanisms at known loci, we performed a transcriptome-wide association study (TWAS) of 29 hematological traits in 399 835 UK Biobank (UKB) participants of European ancestry using gene expression prediction models trained from whole blood RNA-seq data in 922 individuals. We discovered 557 gene-trait associations for hematological traits distinct from previously reported GWAS variants in European populations. Among the 557 associations, 301 were available for replication in a cohort of 141 286 participants of European ancestry from the Million Veteran Program. Of these 301 associations, 108 replicated at a strict Bonferroni adjusted threshold ($\alpha$= 0.05/301). Using our TWAS results, we systematically assigned 4261 out of 16 900 previously identified hematological trait GWAS variants to putative target genes. Compared to coloc, our TWAS results show reduced specificity and increased sensitivity in external datasets to assign variants to target genes.

Natural Killer cells demonstrate distinct eQTL and transcriptome-wide disease associations, highlighting their role in autoimmunity

Natural Killer cells are innate lymphocytes with central roles in immunosurveillance and are implicated in autoimmune pathogenesis. The degree to which regulatory variants affect Natural Killer cell gene expression is poorly understood. Here we perform expression quantitative trait locus mapping of negatively selected Natural Killer cells from a population of healthy Europeans (n = 245). We find a significant subset of genes demonstrate expression quantitative trait loci specific to Natural Killer cells and these are highly informative of human disease, in particular autoimmunity. A Natural Killer cell transcriptome-wide association study across five common autoimmune diseases identifies further novel associations at 27 genes. In addition to these cis observations, we find novel master-regulatory regions impacting expression of trans gene networks at regions including 19q13.4, the Killer cell Immunoglobulin-like Receptor region, GNLY, MC1R and UVSSA. Our findings provide new insights into the unique biology of Natural Killer cells, demonstrating markedly different expression quantitative trait loci from other immune cells, with implications for disease mechanisms.

Natural Killer cells are key mediators of anti-tumour immunosurveillance and anti-viral immunity. Here, the authors map regulatory genetic variation in primary Natural Killer cells, providing new insights into their role in human health and disease.

Alternative splicing as a source of phenotypic diversity

A major goal of evolutionary genetics is to understand the genetic processes that give rise to phenotypic diversity in multicellular organisms. Alternative splicing generates multiple transcripts from a single gene, enriching the diversity of proteins and phenotypic traits. It is well established that alternative splicing contributes to key innovations over long evolutionary timescales, such as brain development in bilaterians. However, recent developments in long-read sequencing and the generation of high-quality genome assemblies for diverse organisms has facilitated comparisons of splicing profiles between closely related species, providing insights into how alternative splicing evolves over shorter timescales. Although most splicing variants are probably non-functional, alternative splicing is nonetheless emerging as a dynamic, evolutionarily labile process that can facilitate adaptation and contribute to species divergence.

MicrobiomeGWAS: A Tool for Identifying Host Genetic Variants Associated with Microbiome Composition

The microbiome is the collection of all microbial genes and can be investigated by sequencing highly variable regions of 16S ribosomal RNA (rRNA) genes. Evidence suggests that environmental factors and host genetics may interact to impact human microbiome composition. Identifying host genetic variants associated with human microbiome composition not only provides clues for characterizing microbiome variation but also helps to elucidate biological mechanisms of genetic associations, prioritize genetic variants, and improve genetic risk prediction. Since a microbiota functions as a community, it is best characterized by β diversity; that is, a pairwise distance matrix. We develop a statistical framework and a computationally efficient software package, microbiomeGWAS, for identifying host genetic variants associated with microbiome β diversity with or without interacting with an environmental factor. We show that the score statistics have positive skewness and kurtosis due to the dependent nature of the pairwise data, which makes p-value approximations based on asymptotic distributions unacceptably liberal. By correcting for skewness and kurtosis, we develop accurate p-value approximations, whose accuracy was verified by extensive simulations. We exemplify our methods by analyzing a set of 147 genotyped subjects with 16S rRNA microbiome profiles from non-malignant lung tissues. Correcting for skewness and kurtosis eliminated the dramatic deviation in the quantile–quantile plots. We provided preliminary evidence that six established lung cancer risk SNPs were collectively associated with microbiome composition for both unweighted (p = 0.0032) and weighted (p = 0.011) UniFrac distance matrices. In summary, our methods will facilitate analyzing large-scale genome-wide association studies of the human microbiome.

Large-Scale Multi-Omics Studies Provide New Insights into Blood Pressure Regulation

Recent genome-wide association studies uncovered part of blood pressure’s heritability. However, there is still a vast gap between genetics and biology that needs to be bridged. Here, we followed up blood pressure genome-wide summary statistics of over 750,000 individuals, leveraging comprehensive epigenomic and transcriptomic data from blood with a follow-up in cardiovascular tissues to prioritise likely causal genes and underlying blood pressure mechanisms. We first prioritised genes based on coding consequences, multilayer molecular associations, blood pressure-associated expression levels, and coregulation evidence. Next, we followed up the prioritised genes in multilayer studies of genomics, epigenomics, and transcriptomics, functional enrichment, and their potential suitability as drug targets. Our analyses yielded 1880 likely causal genes for blood pressure, tens of which are targets of the available licensed drugs. We identified 34 novel genes for blood pressure, supported by more than one source of biological evidence. Twenty-eight (82%) of these new genes were successfully replicated by transcriptome-wide association analyses in a large independent cohort (n = ~220,000). We also found a substantial mediating role for epigenetic regulation of the prioritised genes. Our results provide new insights into genetic regulation of blood pressure in terms of likely causal genes and involved biological pathways offering opportunities for future translation into clinical practice.

Genetic dissection of the RNA polymerase II transcription cycle

How DNA sequence affects the dynamics and position of RNA Polymerase II (Pol II) during transcription remains poorly understood. Here, we used naturally occurring genetic variation in F1 hybrid mice to explore how DNA sequence differences affect the genome-wide distribution of Pol II. We measured the position and orientation of Pol II in eight organs collected from heterozygous F1 hybrid mice using ChRO-seq. Our data revealed a strong genetic basis for the precise coordinates of transcription initiation and promoter proximal pause, allowing us to redefine molecular models of core transcriptional processes. Our results implicate DNA sequence, including both known and novel DNA sequence motifs, as key determinants of the position of Pol II initiation and pause. We report evidence that initiation site selection follows a stochastic process similar to Brownian motion along the DNA template. We found widespread differences in the position of transcription termination, which impact the primary structure and stability of mature mRNA. Finally, we report evidence that allelic changes in transcription often affect mRNA and ncRNA expression across broad genomic domains. Collectively, we reveal how DNA sequences shape core transcriptional processes at single nucleotide resolution in mammals.

Bioinformatic Prioritization and Functional Annotation of GWAS-Based Candidate Genes for Primary Open-Angle Glaucoma

Background: Primary open-angle glaucoma (POAG) is the most prevalent glaucoma subtype, but its exact etiology is still unknown. In this study, we aimed to prioritize the most likely ‘causal’ genes and identify functional characteristics and underlying biological pathways of POAG candidate genes. Methods: We used the results of a large POAG genome-wide association analysis study from GERA and UK Biobank cohorts. First, we performed systematic gene-prioritization analyses based on: (i) nearest genes; (ii) nonsynonymous single-nucleotide polymorphisms; (iii) co-regulation analysis; (iv) transcriptome-wide association studies; and (v) epigenomic data. Next, we performed functional enrichment analyses to find overrepresented functional pathways and tissues. Results: We identified 142 prioritized genes, of which 64 were novel for POAG. BICC1, AFAP1, and ABCA1 were the most highly prioritized genes based on four or more lines of evidence. The most significant pathways were related to extracellular matrix turnover, transforming growth factor-β, blood vessel development, and retinoic acid receptor signaling. Ocular tissues such as sclera and trabecular meshwork showed enrichment in prioritized gene expression (>1.5 fold). We found pleiotropy of POAG with intraocular pressure and optic-disc parameters, as well as genetic correlation with hypertension and diabetes-related eye disease. Conclusions: Our findings contribute to a better understanding of the molecular mechanisms underlying glaucoma pathogenesis and have prioritized many novel candidate genes for functional follow-up studies.

Integrating 3D genomic and epigenomic data to enhance target gene discovery and drug repurposing in transcriptome-wide association studies

Transcriptome-wide association studies (TWAS) are popular approaches to test for association between imputed gene expression levels and traits of interest. Here, we propose an integrative method PUMICE (Prediction Using Models Informed by Chromatin conformations and Epigenomics) to integrate 3D genomic and epigenomic data with expression quantitative trait loci (eQTL) to more accurately predict gene expressions. PUMICE helps define and prioritize regions that harbor cis-regulatory variants, which outperforms competing methods. We further describe an extension to our method PUMICE +, which jointly combines TWAS results from single- and multi-tissue models. Across 79 traits, PUMICE + identifies 22% more independent novel genes and increases median chi-square statistics values at known loci by 35% compared to the second-best method, as well as achieves the narrowest credible interval size. Lastly, we perform computational drug repurposing and confirm that PUMICE + outperforms other TWAS methods.

Allelic imbalance of chromatin accessibility in cancer identifies candidate causal risk variants and their mechanisms

While many germline cancer risk variants have been identified through genome-wide association studies (GWAS), the mechanisms by which these variants operate remain largely unknown. Here we used 406 cancer ATAC-Seq samples across 23 cancer types to identify 7,262 germline allele-specific accessibility QTLs (as-aQTLs). Cancer as-aQTLs had stronger enrichment for cancer risk heritability (up to 145 fold) than any other functional annotation across seven cancer GWAS. Most cancer as-aQTLs directly altered transcription factor (TF) motifs and exhibited differential TF binding and gene expression in functional screens. To connect as-aQTLs to putative risk mechanisms, we introduced the regulome-wide associations study (RWAS). RWAS identified genetically associated accessible peaks at >70% of known breast and prostate loci and discovered new risk loci in all examined cancer types. Integrating as-aQTL discovery, motif analysis and RWAS identified candidate causal regulatory elements and their probable upstream regulators. Our work establishes cancer as-aQTLs and RWAS analysis as powerful tools to study the genetic architecture of cancer risk.

Whole Genome DNA and RNA Sequencing of Whole Blood Elucidates the Genetic Architecture of Gene Expression Underlying a Wide Range of Diseases

To create a scientific resource of expression quantitative trail loci (eQTL), we conducted a genome-wide association study (GWAS) using genotypes obtained from whole genome sequencing (WGS) of DNA and gene expression levels from RNA sequencing (RNA-seq) of whole blood in 2622 participants in Framingham Heart Study. We identified 6,778,286 cis -eQTL variant-gene transcript (eGene) pairs at p  < 5x10 - 8 (2,855,111 unique cis -eQTL variants and 15,982 unique eGenes) and 1,469,754 trans -eQTL variant-eGene pairs at p  < 1e-12 (526,056 unique trans -eQTL variants and 7,233 unique eGenes). In addition, 442,379 cis -eQTL variants were associated with expression of 1518 long non-protein coding RNAs (lncRNAs). Gene Ontology (GO) analyses revealed that the top GO terms for cis- eGenes are enriched for immune functions (FDR < 0.05). The cis -eQTL variants are enriched for SNPs reported to be associated with 815 traits in prior GWAS, including cardiovascular disease risk factors. As proof of concept, we used this eQTL resource in conjunction with genetic variants from public GWAS databases in causal inference testing (e.g., COVID-19 severity). After Bonferroni correction, Mendelian randomization analyses identified putative causal associations of 60 eGenes with systolic blood pressure, 13 genes with coronary artery disease, and seven genes with COVID-19 severity. This study created a comprehensive eQTL resource via BioData Catalyst that will be made available to the scientific community. This will advance understanding of the genetic architecture of gene expression underlying a wide range of diseases.

Novel role of prostate cancer risk variant rs7247241 on PPP1R14A isoform transition through allelic TF binding and CpG methylation

Although previous studies identified numerous single nucleotide polymorphisms (SNPs) and their target genes predisposed to prostate cancer (PrCa) risks, SNP-related splicing associations are rarely reported. In this study, we applied distance-based sQTL analysis (sQTLseekeR) using RNA-seq and SNP genotype data from benign prostate tissue (n = 467) and identified significant associations in 3344 SNP-transcript pairs (P ≤ 0.05) at PrCa risk loci. We characterized a common SNP (rs7247241) and its target gene (PPP1R14A) located in chr19q13, an sQTL with risk allele T associated with upregulation of long isoform (P = 9.99E-7). We confirmed the associations in both TCGA (P = 2.42E-24) and GTEX prostate cohorts (P = 9.08E-78). To functionally characterize this SNP, we performed chromatin immunoprecipitation qPCR and confirmed stronger CTCF and PLAGL2 binding in rs7247241 C than T allele. We found that CTCF binding enrichment was negatively associated with methylation level at the SNP site in human cell lines (r = -0.58). Bisulfite sequencing showed consistent association of rs7247241-T allele with nearby sequence CpG hypermethylation in prostate cell lines and tissues. Moreover, the methylation level at CpG sites nearest to the CTCF binding and first exon splice-in (ψ) of PPP1R14A was significantly associated with aggressive phenotype in the TCGA PrCa cohort. Meanwhile, the long isoform of the gene also promoted cell proliferation. Taken together, with the most updated gene annotations, we reported a set of sQTL associated with multiple traits related to human prostate diseases and revealed a unique role of PrCa risk SNP rs7247241 on PPP1R14A isoform transition.

Reconstruct high-resolution 3D genome structures for diverse cell-types using FLAMINGO

High-resolution reconstruction of spatial chromosome organizations from chromatin contact maps is highly demanded, but is hindered by extensive pairwise constraints, substantial missing data, and limited resolution and cell-type availabilities. Here, we present FLAMINGO, a computational method that addresses these challenges by compressing inter-dependent Hi-C interactions to delineate the underlying low-rank structures in 3D space, based on the low-rank matrix completion technique. FLAMINGO successfully generates 5 kb- and 1 kb-resolution spatial conformations for all chromosomes in the human genome across multiple cell-types, the largest resources to date. Compared to other methods using various experimental metrics, FLAMINGO consistently demonstrates superior accuracy in recapitulating observed structures with raises in scalability by orders of magnitude. The reconstructed 3D structures efficiently facilitate discoveries of higher-order multi-way interactions, imply biological interpretations of long-range QTLs, reveal geometrical properties of chromatin, and provide high-resolution references to understand structural variabilities. Importantly, FLAMINGO achieves robust predictions against high rates of missing data and significantly boosts 3D structure resolutions. Moreover, FLAMINGO shows vigorous cross cell-type structure predictions that capture cell-type specific spatial configurations via integration of 1D epigenomic signals. FLAMINGO can be widely applied to large-scale chromatin contact maps and expand high-resolution spatial genome conformations for diverse cell-types.

Whole Genome DNA and RNA Sequencing of Whole Blood Elucidates the Genetic Architecture of Gene Expression Underlying a Wide Range of Diseases

To create a scientific resource of expression quantitative trail loci (eQTL), we conducted a genome-wide association study (GWAS) using genotypes obtained from whole genome sequencing (WGS) of DNA and gene expression levels from RNA sequencing (RNA-seq) of whole blood in 2622 participants in Framingham Heart Study. We identified 6,778,286 cis -eQTL variant-gene transcript (eGene) pairs at p <5×10 -8 (2,855,111 unique cis -eQTL variants and 15,982 unique eGenes) and 1,469,754 trans -eQTL variant-eGene pairs at p <1e-12 (526,056 unique trans -eQTL variants and 7,233 unique eGenes). In addition, 442,379 cis -eQTL variants were associated with expression of 1518 long non-protein coding RNAs (lncRNAs). Gene Ontology (GO) analyses revealed that the top GO terms for cis- eGenes are enriched for immune functions (FDR <0.05). The cis -eQTL variants are enriched for SNPs reported to be associated with 815 traits in prior GWAS, including cardiovascular disease risk factors. As proof of concept, we used this eQTL resource in conjunction with genetic variants from public GWAS databases in causal inference testing (e.g., COVID-19 severity). After Bonferroni correction, Mendelian randomization analyses identified putative causal associations of 60 eGenes with systolic blood pressure, 13 genes with coronary artery disease, and seven genes with COVID-19 severity. This study created a comprehensive eQTL resource via BioData Catalyst that will be made available to the scientific community. This will advance understanding of the genetic architecture of gene expression underlying a wide range of diseases.

Functional Characterization of Genetic Variant Effects on Expression

Thousands of common genetic variants in the human population have been associated with disease risk and phenotypic variation by genome-wide association studies (GWAS). However, the majority of GWAS variants fall into noncoding regions of the genome, complicating our understanding of their regulatory functions, and few molecular mechanisms of GWAS variant effects have been clearly elucidated. Here, we set out to review genetic variant effects, focusing on expression quantitative trait loci (eQTLs), including their utility in interpreting GWAS variant mechanisms. We discuss the interrelated challenges and opportunities for eQTL analysis, covering determining causal variants, elucidating molecular mechanisms of action, and understanding context variability. Addressing these questions can enable better functional characterization of disease-associated loci and provide insights into fundamental biological questions of the noncoding genetic regulatory code and its control of gene expression. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

A general framework for predicting the transcriptomic consequences of non-coding variation and small molecules

Genome wide association studies (GWASs) for complex traits have implicated thousands of genetic loci. Most GWAS-nominated variants lie in noncoding regions, complicating the systematic translation of these findings into functional understanding. Here, we leverage convolutional neural networks to assist in this challenge. Our computational framework, peaBrain, models the transcriptional machinery of a tissue as a two-stage process: first, predicting the mean tissue specific abundance of all genes and second, incorporating the transcriptomic consequences of genotype variation to predict individual abundance on a subject-by-subject basis. We demonstrate that peaBrain accounts for the majority (>50%) of variance observed in mean transcript abundance across most tissues and outperforms regularized linear models in predicting the consequences of individual genotype variation. We highlight the validity of the peaBrain model by calculating non-coding impact scores that correlate with nucleotide evolutionary constraint that are also predictive of disease-associated variation and allele-specific transcription factor binding. We further show how these tissue-specific peaBrain scores can be leveraged to pinpoint functional tissues underlying complex traits, outperforming methods that depend on colocalization of eQTL and GWAS signals. We subsequently: (a) derive continuous dense embeddings of genes for downstream applications; (b) highlight the utility of the model in predicting transcriptomic impact of small molecules and shRNA (on par with in vitro experimental replication of external test sets); (c) explore how peaBrain can be used to model difficult-to-study processes (such as neural induction); and (d) identify putatively functional eQTLs that are missed by high-throughput experimental approaches.

Aberrant RNA splicing is a primary link between genetic variation and pancreatic cancer risk

Understanding the genetic variation underlying transcript splicing is essential for fully dissecting the molecular mechanisms of common diseases. The available evidence from splicing quantitative trait locus (sQTL) studies using pancreatic ductal adenocarcinoma (PDAC) tissues have been limited to small sample sizes. Here we present a genome-wide sQTL analysis to identify single nucleotide polymorphisms (SNPs) that control mRNA splicing in 176 PDAC samples from TCGA. From this analysis, 16,175 sQTLs were found to be significantly enriched in RNA binding protein (RBP) binding sites and chromatin regulatory elements and overlapped with known loci from PDAC genome-wide association studies (GWAS). sQTLs and expression QTLs (eQTL) showed mostly non-overlapping patterns, suggesting sQTLs provide additional insights into the etiology of disease. Target genes affected by sQTLs were closely related to cancer signaling pathways, high mutational burden, immune infiltration, and pharmaceutical targets, which will be helpful for clinical applications. Integration of a large-scale population consisting of 2,782 PDAC patients and 7,983 healthy controls identified an sQTL variant rs1785932-T allele that promotes alternative splicing of ELP2 exon 6 and leads to a lower level of the ELP2 full-length isoform (ELP2_V1) and a higher level of a truncated ELP2 isoform (ELP2_V2), resulting in decreased risk of PDAC (OR=0.83, 95%CI=0.77-0.89, P=1.16×10-6). The ELP2_V2 isoform functioned as a potential tumor suppressor gene, inhibiting PDAC cell proliferation by exhibiting stronger binding affinity to JAK1/STAT3 than ELP2_V1 and subsequently blocking the pathological activation of the p-STAT3 pathway. Collectively, these findings provide an informative sQTL resource and insights into the regulatory mechanisms linking splicing variants to PDAC risk.

UACA locus is associated with breast cancer chemoresistance and survival

Few germline genetic variants have been robustly linked with breast cancer outcomes. We conducted trans-ethnic meta genome-wide association study (GWAS) of overall survival (OS) in 3973 breast cancer patients from the Pathways Study, one of the largest prospective breast cancer survivor cohorts. A locus spanning the UACA gene, a key regulator of tumor suppressor Par-4, was associated with OS in patients taking Par-4 dependent chemotherapies, including anthracyclines and anti-HER2 therapy, at a genome-wide significance level (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P = 1.27 \times 10^{ - 9}$$\end{document}P=1.27×10−9). This association was confirmed in meta-analysis across four independent prospective breast cancer cohorts (combined hazard ratio = 1.84, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P = 1.28 \times 10^{ - 11}$$\end{document}P=1.28×10−11). Transcriptome-wide association study revealed higher UACA gene expression was significantly associated with worse OS (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P = 4.68 \times 10^{ - 7}$$\end{document}P=4.68×10−7). Our study identified the UACA locus as a genetic predictor of patient outcome following treatment with anthracyclines and/or anti-HER2 therapy, which may have clinical utility in formulating appropriate treatment strategies for breast cancer patients based on their genetic makeup.

Genetic associations at regulatory phenotypes improve fine-mapping of causal variants for 12 immune-mediated diseases

The resolution of causal genetic variants informs understanding of disease biology. We used regulatory quantitative trait loci (QTLs) from the BLUEPRINT, GTEx and eQTLGen projects to fine-map putative causal variants for 12 immune-mediated diseases. We identify 340 unique loci that colocalize with high posterior probability (≥98%) with regulatory QTLs and apply Bayesian frameworks to fine-map associations at each locus. We show that fine-mapping credible sets derived from regulatory QTLs are smaller compared to disease summary statistics. Further, they are enriched for more functionally interpretable candidate causal variants and for putatively causal insertion/deletion (INDEL) polymorphisms. Finally, we use massively parallel reporter assays to evaluate candidate causal variants at the ITGA4 locus associated with inflammatory bowel disease. Overall, our findings suggest that fine-mapping applied to disease-colocalizing regulatory QTLs can enhance the discovery of putative causal disease variants and enhance insights into the underlying causal genes and molecular mechanisms.

A large-scale transcriptome-wide association study (TWAS) of 10 blood cell phenotypes reveals complexities of TWAS fine-mapping

Hematological measures are important intermediate clinical phenotypes for many acute and chronic diseases and are highly heritable. Although genome-wide association studies (GWAS) have identified thousands of loci containing trait-associated variants, the causal genes underlying these associations are often uncertain. To better understand the underlying genetic regulatory mechanisms, we performed a transcriptome-wide association study (TWAS) to systematically investigate the association between genetically predicted gene expression and hematological measures in 54,542 Europeans from the Genetic Epidemiology Research on Aging cohort. We found 239 significant gene-trait associations with hematological measures; we replicated 71 associations at p < 0.05 in a TWAS meta-analysis consisting of up to 35,900 Europeans from the Women's Health Initiative, Atherosclerosis Risk in Communities Study, and BioMe Biobank. Additionally, we attempted to refine this list of candidate genes by performing conditional analyses, adjusting for individual variants previously associated with hematological measures, and performed further fine-mapping of TWAS loci. To facilitate interpretation of our findings, we designed an R Shiny application to interactively visualize our TWAS results by integrating them with additional genetic data sources (GWAS, TWAS from multiple reference panels, conditional analyses, known GWAS variants, etc.). Our results and application highlight frequently overlooked TWAS challenges and illustrate the complexity of TWAS fine-mapping.

Meta-imputation of transcriptome from genotypes across multiple datasets by leveraging publicly available summary-level data

Transcriptome wide association studies (TWAS) can be used as a powerful method to identify and interpret the underlying biological mechanisms behind GWAS by mapping gene expression levels with phenotypes. In TWAS, gene expression is often imputed from individual-level genotypes of regulatory variants identified from external resources, such as Genotype-Tissue Expression (GTEx) Project. In this setting, a straightforward approach to impute expression levels of a specific tissue is to use the model trained from the same tissue type. When multiple tissues are available for the same subjects, it has been demonstrated that training imputation models from multiple tissue types improves the accuracy because of shared eQTLs between the tissues and increase in effective sample size. However, existing joint-tissue methods require access of genotype and expression data across all tissues. Moreover, they cannot leverage the abundance of various expression datasets across various tissues for non-overlapping individuals. Here, we explore the optimal way to combine imputed levels across training models from multiple tissues and datasets in a flexible manner using summary-level data. Our proposed method (SWAM) combines arbitrary number of transcriptome imputation models to linearly optimize the imputation accuracy given a target tissue. By integrating models across tissues and/or individuals, SWAM can improve the accuracy of transcriptome imputation or to improve power to TWAS while only requiring individual-level data from a single reference cohort. To evaluate the accuracy of SWAM, we combined 49 tissue-specific gene expression imputation models from the GTEx Project as well as from a large eQTL study of Depression Susceptibility Genes and Networks (DGN) Project and tested imputation accuracy in GEUVADIS lymphoblastoid cell lines samples. We also extend our meta-imputation method to meta-TWAS to leverage multiple tissues in TWAS analysis with summary-level statistics. Our results capitalize on the importance of integrating multiple tissues to unravel regulatory impacts of genetic variants on complex traits.

The gene expression levels within a cell are affected by various factors, including DNA variation, cell type, cellular microenvironment, disease status, and other environmental factors surrounding the individual. The genetic component of gene expression is known to explain a substantial fraction of transcriptional variation among individuals and can be imputed from genotypes in a tissue-specific manner, by training from population-scale transcriptomic profiles designed to identify expression quantitative loci (eQTLs). Imputing gene expression levels is shown to help understand the genetic basis of human disease through Transcriptome-wide association analysis (TWAS) and Mendelian Randomization (MR). However, it has been unclear how to integrate multiple imputation models trained from individual datasets to maximize their accuracy without having to access individual genotypes and expression levels that are often protected for privacy concerns. We developed SWAM (Smartly Weighted Averaging across Multiple datasets), a meta-imputation framework which can accurately impute gene expression levels from genotypes by integrating multiple imputation models without requiring individual-level data. Our method examines the similarity or differences between resources and borrowing information most relevant to the tissue of interest. We demonstrate that SWAM outperforms existing single-tissue and multi-tissue imputation models and continue to increase accuracy when integrating additional imputation models.

Role of ARHGEF3 as a GEF and mTORC2 Regulator

Guanine nucleotide exchange factors (GEFs) activate GTPases by stimulating the release of guanosine diphosphate to permit the binding of guanosine triphosphate. ARHGEF3 or XPLN (exchange factor found in platelets, leukemic, and neuronal tissues) is a selective guanine nucleotide exchange factor for Rho GTPases (RhoGEFs) that activates RhoA and RhoB but not RhoC, RhoG, Rac1, or Cdc42. ARHGEF3 contains the diffuse B-cell lymphoma homology and pleckstrin homology domains but lacks similarity with other known functional domains. ARHGEF3 also binds the mammalian target of rapamycin complex 2 (mTORC2) and subsequently inhibits mTORC2 and Akt. In vivo investigation has also indicated the communication between ARHGEF3 and autophagy-related muscle pathologies. Moreover, studies on genetic variation in ARHGEF3 and genome-wide association studies have predicted exciting novel roles of ARHGEF3 in controlling bone mineral density, platelet formation and differentiation, and Hirschsprung disease. In conclusion, we hypothesized that additional biochemical and functional studies are required to elucidate the detailed mechanism of ARHGEF3-related pathologies and therapeutics.

Splice variants denote differences between a cancer stem cell side population of EWSR1‑ERG‑based Ewing sarcoma cells, its main population and EWSR1‑FLI‑based cells

Ewing sarcoma is a challenging cancer entity, which, besides the characteristic presence of a fusion gene, is driven by multiple alternative splicing events. So far, splice variants in Ewing sarcoma cells were mainly analyzed for EWSR1‑FLI1. The present study provided a comprehensive alternative splicing study on CADO‑ES1, an Ewing model cell line for an EWSR1‑ERG fusion gene. Based on a well‑-characterized RNA‑sequencing dataset with extensive control mechanisms across all levels of analysis, the differential spliced genes in Ewing cancer stem cells were ATP13A3 and EPB41, while the main population was defined by ACADVL, NOP58 and TSPAN3. All alternatively spliced genes were further characterized by their Gene Ontology (GO) terms and by their membership in known protein complexes. These results confirm and extend previous studies towards a systematic whole‑transcriptome analysis. A highlight is the striking segregation of GO terms associated with five basic splice events. This mechanistic insight, together with a coherent integration of all observations with prior knowledge, indicates that EWSR1‑ERG is truly a close twin to EWSR1‑FLI1, but still exhibits certain individuality. Thus, the present study provided a measure of variability in Ewing sarcoma, whose understanding is essential both for clinical procedures and basic mechanistic insight.

Genetic Regulation of Transcription in the Endometrium in Health and Disease

The endometrium is a complex and dynamic tissue essential for fertility and implicated in many reproductive disorders. The tissue consists of glandular epithelium and vascularised stroma and is unique because it is constantly shed and regrown with each menstrual cycle, generating up to 10 mm of new mucosa. Consequently, there are marked changes in cell composition and gene expression across the menstrual cycle. Recent evidence shows expression of many genes is influenced by genetic variation between individuals. We and others have reported evidence for genetic effects on hundreds of genes in endometrium. The genetic factors influencing endometrial gene expression are highly correlated with the genetic effects on expression in other reproductive (e.g., in uterus and ovary) and digestive tissues (e.g., salivary gland and stomach), supporting a shared genetic regulation of gene expression in biologically similar tissues. There is also increasing evidence for cell specific genetic effects for some genes. Sample size for studies in endometrium are modest and results from the larger studies of gene expression in blood report genetic effects for a much higher proportion of genes than currently reported for endometrium. There is also emerging evidence for the importance of genetic variation on RNA splicing. Gene mapping studies for common disease, including diseases associated with endometrium, show most variation maps to intergenic regulatory regions. It is likely that genetic risk factors for disease function through modifying the program of cell specific gene expression. The emerging evidence from our gene mapping studies coupled with tissue specific studies, and the GTEx, eQTLGen and EpiMap projects, show we need to expand our understanding of the complex regulation of gene expression. These data also help to link disease genetic risk factors to specific target genes. Combining our data on genetic regulation of gene expression in endometrium, and cell types within the endometrium with gene mapping data for endometriosis and related diseases is beginning to uncover the specific genes and pathways responsible for increased risk of these diseases.

Integrating Multimorbidity into a Whole-Body Understanding of Disease Using Spatial Genomics

Multimorbidity is characterized by multidimensional complexity emerging from interactions between multiple diseases across levels of biological (including genetic) and environmental determinants and the complex array of interactions between and within cells, tissues and organ systems. Advances in spatial genomic research have led to an unprecedented expansion in our ability to link alterations in genome folding with changes that are associated with human disease. Studying disease-associated genetic variants in the context of the spatial genome has enabled the discovery of transcriptional regulatory programmes that potentially link dysregulated genes to disease development. However, the approaches that have been used have typically been applied to uncover pathological molecular mechanisms occurring in a specific disease-relevant tissue. These forms of reductionist, targeted investigations are not appropriate for the molecular dissection of multimorbidity that typically involves contributions from multiple tissues. In this perspective, we emphasize the importance of a whole-body understanding of multimorbidity and discuss how spatial genomics, when integrated with additional omic datasets, could provide novel insights into the molecular underpinnings of multimorbidity.

Single-cell sequencing reveals lineage-specific dynamic genetic regulation of gene expression during human cardiomyocyte differentiation

Dynamic and temporally specific gene regulatory changes may underlie unexplained genetic associations with complex disease. During a dynamic process such as cellular differentiation, the overall cell type composition of a tissue (or an in vitro culture) and the gene regulatory profile of each cell can both experience significant changes over time. To identify these dynamic effects in high resolution, we collected single-cell RNA-sequencing data over a differentiation time course from induced pluripotent stem cells to cardiomyocytes, sampled at 7 unique time points in 19 human cell lines. We employed a flexible approach to map dynamic eQTLs whose effects vary significantly over the course of bifurcating differentiation trajectories, including many whose effects are specific to one of these two lineages. Our study design allowed us to distinguish true dynamic eQTLs affecting a specific cell lineage from expression changes driven by potentially non-genetic differences between cell lines such as cell composition. Additionally, we used the cell type profiles learned from single-cell data to deconvolve and re-analyze data from matched bulk RNA-seq samples. Using this approach, we were able to identify a large number of novel dynamic eQTLs in single cell data while also attributing dynamic effects in bulk to a particular lineage. Overall, we found that using single cell data to uncover dynamic eQTLs can provide new insight into the gene regulatory changes that occur among heterogeneous cell types during cardiomyocyte differentiation.

Investigation of the genetic effect of 56 tobacco-smoking susceptibility genes on DNA methylation and RNA expression in human brain

Although various susceptibility genes have been revealed to influence tobacco smoking, the underlying regulatory mechanisms between genetic variants and smoking are poorly understood. In this study, we investigated cis-expression quantitative trait loci (cis-eQTLs) and methylation quantitative trait loci (mQTLs) for 56 candidate smoking-linked genes using the BrainCloud cohort samples. An eQTL was revealed to significantly affect EGLN2 expression in the European sample and two mQTLs were respectively detected in CpG sites in NRXN1 and CYP2A7. Interestingly, we found for the first time that the minor allele of the single nucleotide polymorphism (SNP) rs3745277 located in CYP2A7P1 (downstream of CYP2B6) significantly decreased methylation at the CpG site for CYP2A7 (cg25427638; P = 5.31 × 10^-7), reduced expression of CYP2B6 (P = 0.03), and lowered the percentage of smokers (8.8% vs. 42.3%; Odds Ratio (OR) = 0.14, 95% Confidence Interval (CI): 0.02-0.62; P = 4.47 × 10^-3) in a dominant way for the same cohort sample. Taken together, our findings resulted from analyzing genetic variation, DNA methylation, mRNA expression, and smoking status together using the same participants revealed a regulatory mechanism linking mQTLs to the smoking phenotype. Moreover, we demonstrated the presence of different regulatory effects of low-frequency and common variants on mRNA expression and DNA methylation.

Genetically-predicted prefrontal DRD4 gene expression modulates differentiated brain responses to food cues in adolescent girls and boys

The dopamine receptor 4 (DRD4) in the prefrontal cortex (PFC) acts to modulate behaviours including cognitive control and motivation, and has been implicated in behavioral inhibition and responsivity to food cues. Adolescence is a sensitive period for the development of habitual eating behaviors and obesity risk, with potential mediation by development of the PFC. We previously found that genetic variations influencing DRD4 function or expression were associated with measures of laboratory and real-world eating behavior in girls and boys. Here we investigated brain responses to high energy–density (ED) and low-ED food cues using an fMRI task conducted in the satiated state. We used the gene-based association method PrediXcan to estimate tissue-specific DRD4 gene expression in prefrontal brain areas from individual genotypes. Among girls, those with lower vs. higher predicted prefrontal DRD4 expression showed lesser activation to high-ED and low-ED vs. non-food cues in a distributed network of regions implicated in attention and sensorimotor processing including middle frontal gyrus, and lesser activation to low-ED vs non-food cues in key regions implicated in valuation including orbitofrontal cortex and ventromedial PFC. In contrast, males with lower vs. higher predicted prefrontal DRD4 expression showed minimal differences in food cue response, namely relatively greater activation to high-ED and low-ED vs. non-food cues in the inferior parietal lobule. Our data suggest sex-specific effects of prefrontal DRD4 on brain food responsiveness in adolescence, with modulation of distributed regions relevant to cognitive control and motivation observable in female adolescents.

Genetically regulated expression in late-onset Alzheimer's disease implicates risk genes within known and novel loci

Late-onset Alzheimer disease (LOAD) is highly polygenic, with a heritability estimated between 40 and 80%, yet risk variants identified in genome-wide studies explain only ~8% of phenotypic variance. Due to its increased power and interpretability, genetically regulated expression (GReX) analysis is an emerging approach to investigate the genetic mechanisms of complex diseases. Here, we conducted GReX analysis within and across 51 tissues on 39 LOAD GWAS data sets comprising 58,713 cases and controls from the Alzheimer's Disease Genetics Consortium (ADGC) and the International Genomics of Alzheimer's Project (IGAP). Meta-analysis across studies identified 216 unique significant genes, including 72 with no previously reported LOAD GWAS associations. Cross-brain-tissue and cross-GTEx models revealed eight additional genes significantly associated with LOAD. Conditional analysis of previously reported loci using established LOAD-risk variants identified eight genes reaching genome-wide significance independent of known signals. Moreover, the proportion of SNP-based heritability is highly enriched in genes identified by GReX analysis. In summary, GReX-based meta-analysis in LOAD identifies 216 genes (including 72 novel genes), illuminating the role of gene regulatory models in LOAD.

Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants

The majority of genetic variants detected in genome wide association studies (GWAS) exert their effects on phenotypes through gene regulation. Motivated by this observation, we propose a multi-omic integration method that models the cascading effects of genetic variants from epigenome to transcriptome and eventually to the phenome in identifying target genes influenced by risk alleles. This cascading epigenomic analysis for GWAS, which we refer to as CEWAS, comprises two types of models: one for linking cis genetic effects to epigenomic variation and another for linking cis epigenomic variation to gene expression. Applying these models in cascade to GWAS summary statistics generates gene level statistics that reflect genetically-driven epigenomic effects. We show on sixteen brain-related GWAS that CEWAS provides higher gene detection rate than related methods, and finds disease relevant genes and gene sets that point toward less explored biological processes. CEWAS thus presents a novel means for exploring the regulatory landscape of GWAS variants in uncovering disease mechanisms.

The majority of genetic variants detected in genome wide association studies (GWAS) exert their effects on phenotypes through gene regulation. Motivated by this observation, we propose a multi-omic integration method that models the cascading effects of genetic variants from epigenome to transcriptome and eventually to the phenome in identifying target genes influenced by risk alleles. This cascading epigenomic analysis for GWAS, which we refer to as CEWAS, combines the effect of genetic variants on DNA methylation as well as gene expression. We show on sixteen brain-related GWAS that CEWAS provides higher gene detection rate than related methods, and finds disease relevant genes and gene sets that point toward less explored biological processes.