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

An atlas of single-cell eQTLs dissects autoimmune disease genes and identifies novel drug classes for treatment

Most variants identified from genome-wide association studies (GWASs) are non-coding and regulate gene expression. However, many risk loci fail to colocalize with expression quantitative trait loci (eQTLs), potentially due to limited GWAS and eQTL analysis power or cellular heterogeneity. Population-scale single-cell RNA-sequencing (scRNA-seq) datasets are emerging, enabling mapping of eQTLs in different cell types (sc-eQTLs). Compared to eQTL data from bulk tissues (bk-eQTLs), sc-eQTL datasets are smaller. We propose a joint model of bk-eQTLs as a weighted sum of sc-eQTLs (JOBS) from constituent cell types to improve power. Applying JOBS to One1K1K and eQTLGen data, we identify 586% more eQTLs, matching the power of 4× the sample sizes of OneK1K. Integrating sc-eQTLs with GWAS data creates an atlas for 14 immune-mediated disorders, colocalizing 29.9% or 32.2% more loci than using sc-eQTL or bk-eQTL alone. Extending JOBS, we develop a drug-repurposing pipeline and identify novel drugs validated by real-world data.

Probing the limits of cis-acting gene regulation using a model of allelic imbalance quantitative trait loci

Imbalance in gene expression between alleles is a hallmark of cis-acting expression quantitative trait loci (eQTLs) and several methods have been developed to exploit allelic imbalance to support the identification of eQTLs. Allelic imbalance is also of scientific and, potentially, clinical interest as it can erode the degree to which the effects of deleterious variants are buffered in a diploid organism and has been reported to be associated with the penetrance of pathological genomic variants. Here, we develop and apply a statistical model that is designed to evaluate whether the genotype of a locus is associated with the degree of allelic imbalance of a gene and refer to such loci as allelic imbalance quantitative trait loci (aiQTLs). An advantage of our approach is that it does not depend on linkage disequilibrium between the aiQTL and the associated gene and is, therefore, suited to the identification of eQTLs that act in cis over very large distances. We applied our model to data from the GTEx consortium and examined the relationship between the distance of an eQTL from the TSS of the associated gene and the evidence that the eQTL acts in cis. Previous studies have used a distance of 1Mb from the target gene as an indication that an eQTL acts in cis; however, our results suggest that the majority of eQTLs at distances more than 500 kb from the TSS of the target gene are likely to act in trans (and thus to affect both gene copies). The model used here is also well suited to comparing the overall extent of allelic imbalance between samples. We show that in some tissues allelic imbalance is correlated with age; however, this correlation may be due to changes in the abundance of immune cell populations with age, as we found strong correlations between sample-level allelic imbalance and the inferred abundance of multiple immune cell types across whole blood samples.

Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms

BACKGROUND

Understanding the genetic causes underlying variability in chromatin accessibility can shed light on the molecular mechanisms through which genetic variants may affect complex traits. Thousands of ATAC-seq samples have been collected that hold information about chromatin accessibility across diverse cell types and contexts, but most of these are not paired with genetic information and come from distinct projects and laboratories.

RESULTS

We report here joint genotyping, chromatin accessibility peak calling, and discovery of quantitative trait loci which influence chromatin accessibility (caQTLs), demonstrating the capability of performing caQTL analysis on a large scale in a diverse sample set without pre-existing genotype information. Using 10,293 profiling samples representing 1454 unique donor individuals across 653 studies from public databases, we catalog 24,159 caQTLs in total. After joint discovery analysis, we cluster samples based on accessible chromatin profiles to identify context-specific caQTLs. We find that caQTLs are strongly enriched for annotations of gene regulatory elements across diverse cell types and tissues and are often linked with genetic variation associated with changes in expression (eQTLs), indicating that caQTLs can mediate genetic effects on gene expression. We demonstrate sharing of causal variants for chromatin accessibility across human traits, enabling a more complete picture of the genetic mechanisms underlying complex human phenotypes.

CONCLUSIONS

Our work provides a proof of principle for caQTL calling from previously ungenotyped samples and represents one of the largest, most diverse caQTL resources currently available, informing mechanisms of genetic regulation of gene expression and contribution to disease.

Transcriptome-wide association studies at cell state level using single-cell eQTL data

Transcriptome-wide association studies (TWAS) have been widely used to prioritize relevant genes for diseases. Current methods for TWAS test gene-disease associations at bulk tissue or cell-type-specific pseudobulk level, which do not account for the heterogeneity within cell types. We present TWiST, a statistical method for TWAS analysis at cell state resolution using single-cell expression quantitative trait loci (eQTL) data. Our method uses pseudotime to represent cell states and models the effect of gene expression on trait as a continuous pseudotemporal curve. Therefore, it allows flexible hypothesis testing of global, dynamic, and nonlinear effects. Through simulation studies and real data analysis, we demonstrated that TWiST leads to significantly improved power compared to pseudobulk methods that ignores heterogeneity due to cell states. Application to the OneK1K study identified hundreds of genes with dynamic effects on autoimmune diseases along the trajectory of immune cell differentiation. TWiST presents great promise to understand disease genetics using single-cell eQTL studies.

Deciphering the coordinated roles of the host genome, duodenal mucosal genes, and microbiota in regulating complex traits in chickens

BACKGROUND

The complex interactions between host genetics and the gut microbiome are well documented. However, the specific impacts of gene expression patterns and microbial composition on each other remain to be further explored.

RESULTS

Here, we investigated this complex interplay in a sizable population of 705 hens, employing integrative analyses to examine the relationships among the host genome, mucosal gene expression, and gut microbiota. Specific microbial taxa, such as the cecal family Christensenellaceae, which showed a heritability of 0.365, were strongly correlated with host genomic variants. We proposed a novel concept of regulatability ( r b 2 ), which was derived from h2, to quantify the cumulative effects of gene expression on the given phenotypes. The duodenal mucosal transcriptome emerged as a potent influencer of duodenal microbial taxa, with much higher r b 2 values (0.17 ± 0.01, mean ± SE) than h2 values (0.02 ± 0.00). A comparative analysis of chickens and humans revealed similar average microbiability values of genes (0.18 vs. 0.20) and significant differences in average r b 2 values of microbes (0.17 vs. 0.04). Besides, cis ( h cis 2 ) and trans heritability ( h trans 2 ) were estimated to assess the effects of genetic variations inside and outside the cis window of the gene on its expression. Higher h trans 2 values than h cis 2 values and a greater prevalence of trans-regulated genes than cis-regulated genes underscored the significant role of loci outside the cis window in shaping gene expression levels. Furthermore, our exploration of the regulatory effects of duodenal mucosal genes and the microbiota on 18 complex traits enhanced our understanding of the regulatory mechanisms, in which the CHST14 gene and its regulatory relationships with Lactobacillus salivarius jointly facilitated the deposition of abdominal fat by modulating the concentration of bile salt hydrolase, and further triglycerides, total cholesterol, and free fatty acids absorption and metabolism.

CONCLUSIONS

Our findings highlighted a novel concept of r b 2 to quantify the phenotypic variance attributed to gene expression and emphasize the superior role of intestinal mucosal gene expressions over host genomic variations in elucidating host‒microbe interactions for complex traits. This understanding could assist in devising strategies to modulate host-microbe interactions, ultimately improving economic traits in chickens.

Kidney multiome-based genetic scorecard reveals convergent coding and regulatory variants

Kidney dysfunction is a major cause of mortality, but its genetic architecture remains elusive. In this study, we conducted a multiancestry genome-wide association study in 2.2 million individuals and identified 1026 (97 previously unknown) independent loci. Ancestry-specific analysis indicated an attenuation of newly identified signals on common variants in European ancestry populations and the power of population diversity for further discoveries. We defined genotype effects on allele-specific gene expression and regulatory circuitries in more than 700 human kidneys and 237,000 cells. We found 1363 coding variants disrupting 782 genes, with 601 genes also targeted by regulatory variants and convergence in 161 genes. Integrating 32 types of genetic information, we present the "Kidney Disease Genetic Scorecard" for prioritizing potentially causal genes, cell types, and druggable targets for kidney disease.

Integrating Whole Genome and Transcriptome Sequencing to Characterize the Genetic Architecture of Isoform Variation and its Implications for Health and Disease

We created a comprehensive whole blood splice variation quantitative trait locus (sQTL) resource by analyzing isoform expression ratio (isoform-to-gene) in Framingham Heart Study (FHS) participants (discovery: n=2,622; validation: n=1,094) with whole genome (WGS) and transcriptome sequencing (RNA-seq) data. External replication was conducted using WGS and RNA-seq from the Jackson Heart Study (JHS, n=1,020). We identified over 3.5 million cis -sQTL-isoform pairs ( p <5e-8), comprising 1,176,624 cis -sQTL variants and 10,883 isoform transcripts from 4,971 sGenes, with significant change in isoform-to-gene ratio due to allelic variation. We validated 61% of these pairs in the FHS validation sample ( p <1e-4). External validation ( p <1e-4) in JHS for the top 10,000 and 100,000 most significant cis -sQTL-isoform pairs was 88% and 69%, respectively, while overall pairs validated at 23%. For 20% of cis -sQTLs in the FHS discovery sample, allelic variation did not significantly correlate with overall gene expression. sQTLs are enriched in splice donor and acceptor sites, as well as in GWAS SNPs, methylation QTLs, and protein QTLs. We detailed several sentinel cis -sQTLs influencing alternative splicing, with potential causal effects on cardiovascular disease risk. Notably, rs12898397 (T>C) affects splicing of ULK3 , lowering levels of the full-length transcript ENST00000440863.7 and increasing levels of the truncated transcript ENST00000569437.5, encoding proteins of different lengths. Mendelian randomization analysis demonstrated that a lower ratio of the full-length isoform is causally associated with lower diastolic blood pressure and reduced lymphocyte percentages. This sQTL resource provides valuable insights into how transcriptomic variation may influence health outcomes.

Revolutionizing Personalized Medicine: Synergy with Multi-Omics Data Generation, Main Hurdles, and Future Perspectives

The field of personalized medicine is undergoing a transformative shift through the integration of multi-omics data, which mainly encompasses genomics, transcriptomics, proteomics, and metabolomics. This synergy allows for a comprehensive understanding of individual health by analyzing genetic, molecular, and biochemical profiles. The generation and integration of multi-omics data enable more precise and tailored therapeutic strategies, improving the efficacy of treatments and reducing adverse effects. However, several challenges hinder the full realization of personalized medicine. Key hurdles include the complexity of data integration across different omics layers, the need for advanced computational tools, and the high cost of comprehensive data generation. Additionally, issues related to data privacy, standardization, and the need for robust validation in diverse populations remain significant obstacles. Looking ahead, the future of personalized medicine promises advancements in technology and methodologies that will address these challenges. Emerging innovations in data analytics, machine learning, and high-throughput sequencing are expected to enhance the integration of multi-omics data, making personalized medicine more accessible and effective. Collaborative efforts among researchers, clinicians, and industry stakeholders are crucial to overcoming these hurdles and fully harnessing the potential of multi-omics for individualized healthcare.

Response splicing QTLs in primary human chondrocytes identifies putative osteoarthritis risk genes

Osteoarthritis affects millions worldwide, yet effective treatments remain elusive due to poorly understood molecular mechanisms. While genome-wide association studies (GWAS) have identified over 100 OA-associated loci, identifying the genes impacted at each locus remains challenging. Several studies have mapped expression quantitative trait loci (eQTL) in chondrocytes and colocalized them with OA GWAS variants to identify putative OA risk genes; however, the degree to which genetic variants influence OA risk via alternative splicing has not been explored. We investigated the role of alternative splicing in OA pathogenesis using RNA-seq data from 101 human chondrocyte samples treated with PBS (control) or fibronectin fragment (FN-f), an OA trigger. We identified 590 differentially spliced genes between conditions, with FN-f inducing splicing events similar to those in primary OA tissue. We used CRISPR/Cas9 to mimic an SNRNP70 splicing event observed in OA and FN-f-treated chondrocytes and found that it induced an OA-like expression pattern. Integration with genotyping data revealed 7,188 splicing quantitative trait loci (sQTL) affecting 3,056 genes. While many sQTLs were shared, we identified 738 and 343 condition-specific sQTLs for control and FN-f, respectively. We identified 15 RNA binding proteins whose binding sites were enriched at sQTL splice junctions and found that expression of those RNA binding proteins correlated with exon inclusion. Colocalization with OA GWAS identified 6 putative risk genes, including a novel candidate, PBRM1. Our study highlights the significant impact of alternative splicing in OA and provides potential therapeutic targets for future research.

Spatial transcriptomics reveals strong association between SFRP4 and extracellular matrix remodeling in prostate cancer

Prostate tumor heterogeneity is a major obstacle when studying the biological mechanisms of molecular markers. Increased gene expression levels of secreted frizzled-related protein 4 (SFRP4) is a biomarker in aggressive prostate cancer. To understand how SFRP4 relates to prostate cancer we performed comprehensive spatial and multiomics analysis of the same prostate cancer tissue samples. The experimental workflow included spatial transcriptomics, bulk transcriptomics, proteomics, DNA methylomics and tissue staining. SFRP4 mRNA was predominantly located in cancer stroma, produced by fibroblasts and smooth muscle cells, and co-expressed with extracellular matrix components. We also confirmed that higher SFRP4 gene expression is associated with cancer aggressiveness. Gene expression of SFRP4 was affected by gene promotor methylation. Surprisingly, the high mRNA levels did not reflect SFRP4 protein levels, which was much lower. This study contributes previously unknown insights of SFRP4 mRNA in the prostate tumor environment that potentially can improve diagnosis and treatment.

Profiling genetically driven alternative splicing across the Indonesian archipelago

One of the regulatory mechanisms influencing the functional capacity of genes is alternative splicing (AS). Previous studies exploring the splicing landscape of human tissues have shown that AS has contributed to human biology, especially in disease progression and the immune response. Nonetheless, this phenomenon remains poorly characterized across human populations, and it is unclear how genetic and environmental variation contribute to AS. Here, we examine a set of 115 Indonesian samples from three traditional island populations spanning the genetic ancestry cline that characterizes Island Southeast Asia. We conduct a global AS analysis between islands to ascertain the degree of functionally significant AS events and their consequences. Using an event-based statistical model, we detected over 1,500 significant differential AS events across all comparisons. Additionally, we identify over 6,000 genetic variants associated with changes in splicing (splicing quantitative trait loci [sQTLs]), some of which are driven by Papuan-like genetic ancestry, and only show partial overlap with other publicly available sQTL datasets derived from other populations. Computational predictions of RNA binding activity reveal that a fraction of these sQTLs directly modulate the binding propensity of proteins involved in the splicing regulation of immune genes. Overall, these results contribute toward elucidating the role of genetic variation in shaping gene regulation in one of the most diverse regions in the world.

Fine-mapping and molecular characterisation of primary sclerosing cholangitis genetic risk loci

Genome-wide association studies of primary sclerosing cholangitis have identified 23 susceptibility loci. The majority of these loci reside in non-coding regions of the genome and are thought to exert their effect by perturbing the regulation of nearby genes. Here, we aim to identify these genes to improve the biological understanding of primary sclerosing cholangitis, and nominate potential drug targets. We first build an eQTL map for six primary sclerosing cholangitis-relevant T-cell subsets obtained from the peripheral blood of primary sclerosing cholangitis and ulcerative colitis patients. These maps identify 10,459 unique eGenes, 87% of which are shared across all six primary sclerosing cholangitis T-cell types. We then search for colocalisations between primary sclerosing cholangitis loci and eQTLs and undertake Bayesian fine-mapping to identify disease-causing variants. In this work, colocalisation analyses nominate likely primary sclerosing cholangitis effector genes and biological mechanisms at five non-coding (UBASH3A, PRKD2, ETS2 and AP003774.1/CCDC88B) and one coding (SH2B3) primary sclerosing cholangitis loci. Through fine-mapping we identify likely causal variants for a third of all primary sclerosing cholangitis-associated loci, including two to single variant resolution.

Genome-wide study identifies novel genes associated with bone toxicities in children with acute lymphoblastic leukaemia

Bone toxicities are common among paediatric patients treated for acute lymphoblastic leukaemia (ALL) with potentially major negative impact on patients' quality of life. To identify the underlying genetic contributors, we conducted a genome-wide association study (GWAS) and a transcriptome-wide association study (TWAS) in 260 patients of European-descent from the DFCI 05-001 ALL trial, with validation in 101 patients of European-descent from the DFCI 11-001 ALL trial. We identified a significant association between rs844882 on chromosome 20 and bone toxicities in the DFCI 05-001 trial (p = 1.7 × 10-8). In DFCI 11-001 trial, we observed a consistent trend of this variant with fracture. The variant was an eQTL for two nearby genes, CD93 and THBD. In TWAS, genetically predicted ACAD9 expression was associated with an increased risk of bone toxicities, which was confirmed by meta-analysis of the two cohorts (meta-p = 2.4 × 10-6). In addition, a polygenic risk score of heel quantitative ultrasound speed of sound was associated with fracture risk in both cohorts (meta-p = 2.3 × 10-3). Our findings highlight the genetic influence on treatment-related bone toxicities in this patient population. The genes we identified in our study provide new biological insights into the development of bone adverse events related to ALL treatment.

Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms

Background

Understanding the genetic causes for variability in chromatin accessibility can shed light on the molecular mechanisms through which genetic variants may affect complex traits. Thousands of ATAC-seq samples have been collected that hold information about chromatin accessibility across diverse cell types and contexts, but most of these are not paired with genetic information and come from diverse distinct projects and laboratories.

Results

We report here joint genotyping, chromatin accessibility peak calling, and discovery of quantitative trait loci which influence chromatin accessibility (caQTLs), demonstrating the capability of performing caQTL analysis on a large scale in a diverse sample set without pre-existing genotype information. Using 10,293 profiling samples representing 1,454 unique donor individuals across 653 studies from public databases, we catalog 23,381 caQTLs in total. After joint discovery analysis, we cluster samples based on accessible chromatin profiles to identify context-specific caQTLs. We find that caQTLs are strongly enriched for annotations of gene regulatory elements across diverse cell types and tissues and are often strongly linked with genetic variation associated with changes in expression (eQTLs), indicating that caQTLs can mediate genetic effects on gene expression. We demonstrate sharing of causal variants for chromatin accessibility and diverse complex human traits, enabling a more complete picture of the genetic mechanisms underlying complex human phenotypes.

Conclusions

Our work provides a proof of principle for caQTL calling from previously ungenotyped samples, and represents one of the largest, most diverse caQTL resources currently available, informing mechanisms of genetic regulation of gene expression and contribution to disease.

Significance of Fibrillin-1, Filamin A, MMP2 and SOX9 in Mitral Valve Pathology

Genetic factors play a significant role in the pathogenesis of mitral valve diseases, including mitral valve prolapse (MVP) and mitral valve regurgitation. Genes like Fibrillin-1 (FBN1), Filamin A (FLNA), matrix metalloproteinase 2 (MMP2), and SRY-box transcription factor 9 (SOX9) are known to influence mitral valve pathology but knowledge of the exact mechanism is far from clear. Data regarding serum parameters, transesophageal echocardiography, and genetic and histopathologic parameters were investigated in 54 patients who underwent cardiovascular surgery for mitral valve regurgitation. The possible association between Fibrillin-1, Filamin A, MMP2, and SOX9 gene expressions was checked in relationship with the parameters of systemic inflammatory response. The mRNA expression levels (RQ-relative quantification) were categorized into three distinct groups: low (RQ < 1), medium/normal (RQ = 1-2), and high (RQ > 2). Severe fibrosis of the mitral valve was reflected by high expression of FBN1 and low expression of MMP2 (p < 0.05). The myxoid degeneration level was associated with the mRNA expression level for FBN1 and a low lymphocyte-monocyte ratio was associated with an increased mRNA expression of FBN1 (p < 0.05). A high number of monocytes was associated with high values of FBN1 whereas the increase in the number of lymphocytes was associated with high levels of MMP2. In addition, we observed that the risk of severe hyalinization was enhanced by a low mRNA expression of FLNA and/or SOX9. In conclusion, a lower FLNA mRNA expression can reflect the aging process that is highlighted in mitral valve pathology as a higher risk for hyalinization, especially in males, that might be prevented by upregulation of the SOX9 gene. FBN1 and MMP2 influence the inflammation-related fibrotic degeneration of the mitral valve. Understanding the genetic base of mitral valve pathology can provide insights into disease mechanisms, risk stratification, and potential therapeutic targets.

RNA Sequencing in Disease Diagnosis

RNA sequencing (RNA-seq) enables the accurate measurement of multiple transcriptomic phenotypes for modeling the impacts of disease variants. Advances in technologies, experimental protocols, and analysis strategies are rapidly expanding the application of RNA-seq to identify disease biomarkers, tissue- and cell-type-specific impacts, and the spatial localization of disease-associated mechanisms. Ongoing international efforts to construct biobank-scale transcriptomic repositories with matched genomic data across diverse population groups are further increasing the utility of RNA-seq approaches by providing large-scale normative reference resources. The availability of these resources, combined with improved computational analysis pipelines, has enabled the detection of aberrant transcriptomic phenotypes underlying rare diseases. Further expansion of these resources, across both somatic and developmental tissues, is expected to soon provide unprecedented insights to resolve disease origin, mechanism of action, and causal gene contributions, suggesting the continued high utility of RNA-seq in disease diagnosis.

Critical reasoning on the co-expression module QTL in the dorsolateral prefrontal cortex

Expression quantitative trait locus (eQTL) analysis is a popular method of gaining insight into the function of regulatory variation. While cis-eQTL resources have been instrumental in linking genome-wide association study variants to gene function, complex trait heritability may be additionally mediated by other forms of gene regulation. Toward this end, novel eQTL methods leverage gene co-expression (module-QTL) to investigate joint regulation of gene modules by single genetic variants. Here we broadly define a "module-QTL" as the association of a genetic variant with a summary measure of gene co-expression. This approach aims to reduce the multiple testing burden of a trans-eQTL search through the consolidation of gene-based testing and provide biological context to eQTLs shared between genes. In this article we provide an in-depth examination of the co-expression module eQTL (module-QTL) through literature review, theoretical investigation, and real-data application of the module-QTL to three large prefrontal cortex genotype-RNA sequencing datasets. We find module-QTLs in our study that are disease associated and reproducible are not additionally informative beyond cis- or trans-eQTLs for module genes. Through comparison to prior studies, we highlight promises and limitations of the module-QTL across study designs and provide recommendations for further investigation of the module-QTL framework.

Pan-transcriptome reveals a large accessory genome contribution to gene expression variation in yeast

Gene expression is an essential step in the translation of genotypes into phenotypes. However, little is known about the transcriptome architecture and the underlying genetic effects at the species level. Here we generated and analyzed the pan-transcriptome of ~1,000 yeast natural isolates across 4,977 core and 1,468 accessory genes. We found that the accessory genome is an underappreciated driver of transcriptome divergence. Global gene expression patterns combined with population structure showed that variation in heritable expression mainly lies within subpopulation-specific signatures, for which accessory genes are overrepresented. Genome-wide association analyses consistently highlighted that accessory genes are associated with proportionally more variants with larger effect sizes, illustrating the critical role of the accessory genome on the transcriptional landscape within and between populations.

Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes

Transcriptome-wide association study (TWAS) is a popular approach to dissect the functional consequence of disease associated non-coding variants. Most existing TWAS use bulk tissues and may not have the resolution to reveal cell-type specific target genes. Single-cell expression quantitative trait loci (sc-eQTL) datasets are emerging. The largest bulk- and sc-eQTL datasets are most conveniently available as summary statistics, but have not been broadly utilized in TWAS. Here, we present a new method EXPRESSO (EXpression PREdiction with Summary Statistics Only), to analyze sc-eQTL summary statistics, which also integrates 3D genomic data and epigenomic annotation to prioritize causal variants. EXPRESSO substantially improves existing methods. We apply EXPRESSO to analyze multi-ancestry GWAS datasets for 14 autoimmune diseases. EXPRESSO uniquely identifies 958 novel gene x trait associations, which is 26% more than the second-best method. Among them, 492 are unique to cell type level analysis and missed by TWAS using whole blood. We also develop a cell type aware drug repurposing pipeline, which leverages EXPRESSO results to identify drug compounds that can reverse disease gene expressions in relevant cell types. Our results point to multiple drugs with therapeutic potentials, including metformin for type 1 diabetes, and vitamin K for ulcerative colitis.

Shaping immunity: The influence of natural selection on population immune diversity

Humans exhibit considerable variability in their immune responses to the same immune challenges. Such variation is widespread and affects individual and population-level susceptibility to infectious diseases and immune disorders. Although the factors influencing immune response diversity are partially understood, what mechanisms lead to the wide range of immune traits in healthy individuals remain largely unexplained. Here, we discuss the role that natural selection has played in driving phenotypic differences in immune responses across populations and present-day susceptibility to immune-related disorders. Further, we touch on future directions in the field of immunogenomics, highlighting the value of expanding this work to human populations globally, the utility of modeling the immune response as a dynamic process, and the importance of considering the potential polygenic nature of natural selection. Identifying loci acted upon by evolution may further pinpoint variants critically involved in disease etiology, and designing studies to capture these effects will enrich our understanding of the genetic contributions to immunity and immune dysregulation.

Investigating the role of common cis-regulatory variants in modifying penetrance of putatively damaging, inherited variants in severe neurodevelopmental disorders

Recent work has revealed an important role for rare, incompletely penetrant inherited coding variants in neurodevelopmental disorders (NDDs). Additionally, we have previously shown that common variants contribute to risk for rare NDDs. Here, we investigate whether common variants exert their effects by modifying gene expression, using multi-cis-expression quantitative trait loci (cis-eQTL) prediction models. We first performed a transcriptome-wide association study for NDDs using 6987 probands from the Deciphering Developmental Disorders (DDD) study and 9720 controls, and found one gene, RAB2A, that passed multiple testing correction (p = 6.7 × 10-7). We then investigated whether cis-eQTLs modify the penetrance of putatively damaging, rare coding variants inherited by NDD probands from their unaffected parents in a set of 1700 trios. We found no evidence that unaffected parents transmitting putatively damaging coding variants had higher genetically-predicted expression of the variant-harboring gene than their child. In probands carrying putatively damaging variants in constrained genes, the genetically-predicted expression of these genes in blood was lower than in controls (p = 2.7 × 10-3). However, results for proband-control comparisons were inconsistent across different sets of genes, variant filters and tissues. We find limited evidence that common cis-eQTLs modify penetrance of rare coding variants in a large cohort of NDD probands.

Subset-based method for cross-tissue transcriptome-wide association studies improves power and interpretability

Integrating results from genome-wide association studies (GWASs) and studies of molecular phenotypes such as gene expressions can improve our understanding of the biological functions of trait-associated variants and can help prioritize candidate genes for downstream analysis. Using reference expression quantitative trait locus (eQTL) studies, several methods have been proposed to identify gene-trait associations, primarily based on gene expression imputation. To increase the statistical power by leveraging substantial eQTL sharing across tissues, meta-analysis methods aggregating such gene-based test results across multiple tissues or contexts have been developed as well. However, most existing meta-analysis methods have limited power to identify associations when the gene has weaker associations in only a few tissues and cannot identify the subset of tissues in which the gene is "activated." For this, we developed a cross-tissue subset-based transcriptome-wide association study (CSTWAS) meta-analysis method that improves power under such scenarios and can extract the set of potentially associated tissues. To improve applicability, CSTWAS uses only GWAS summary statistics and pre-computed correlation matrices to identify a subset of tissues that have the maximal evidence of gene-trait association. Through numerical simulations, we found that CSTWAS can maintain a well-calibrated type-I error rate, improves power especially when there is a small number of associated tissues for a gene-trait association, and identifies an accurate associated tissue set. By analyzing GWAS summary statistics of three complex traits and diseases, we demonstrate that CSTWAS could identify biological meaningful signals while providing an interpretation of disease etiology by extracting a set of potentially associated tissues.

Trans-eQTL mapping in gene sets identifies network effects of genetic variants

Nearly all trait-associated variants identified in genome-wide association studies (GWASs) are noncoding. The cis regulatory effects of these variants have been extensively characterized, but how they affect gene regulation in trans has been the subject of fewer studies because of the difficulty in detecting trans-expression quantitative loci (eQTLs). We developed trans-PCO for detecting trans effects of genetic variants on gene networks. Our simulations demonstrate that trans-PCO substantially outperforms existing trans-eQTL mapping methods. We applied trans-PCO to two gene expression datasets from whole blood, DGN (N = 913) and eQTLGen (N = 31,684), and identified 14,985 high-quality trans-eSNP-module pairs associated with 197 co-expression gene modules and biological processes. We performed colocalization analyses between GWAS loci of 46 complex traits and the trans-eQTLs. We demonstrated that the identified trans effects can help us understand how trait-associated variants affect gene regulatory networks and biological pathways.

Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast

Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.

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.

The impact of genetically controlled splicing on exon inclusion and protein structure

Common variants affecting mRNA splicing are typically identified though splicing quantitative trait locus (sQTL) mapping and have been shown to be enriched for GWAS signals by a similar degree to eQTLs. However, the specific splicing changes induced by these variants have been difficult to characterize, making it more complicated to analyze the effect size and direction of sQTLs, and to determine downstream splicing effects on protein structure. In this study, we catalogue sQTLs using exon percent spliced in (PSI) scores as a quantitative phenotype. PSI is an interpretable metric for identifying exon skipping events and has some advantages over other methods for quantifying splicing from short read RNA sequencing. In our set of sQTL variants, we find evidence of selective effects based on splicing effect size and effect direction, as well as exon symmetry. Additionally, we utilize AlphaFold2 to predict changes in protein structure associated with sQTLs overlapping GWAS traits, highlighting a potential new use-case for this technology for interpreting genetic effects on traits and disorders.

Global characterization of RNA editing in genetic regulation of multiple ovarian cancer subtypes

RNA editing plays an extensive role in the initiation and progression of cancer. However, the overall profile and molecular functions of RNA editing in different ovarian cancer subtypes have not been fully characterized and elucidated. Here, we conducted a study on RNA editing in four cohorts of ovarian cancer subtypes through large-scale parallel reporting and bioinformatics analysis. Our findings revealed that RNA editing patterns exhibit subtype-specific characteristics within cancer subtypes. The expression pattern of ADAR and the number of differential editing sites varied under different conditions. CCOC and EOC exhibited significant editing deficiency, whereas HGSC and MOC displayed significant editing excess. The sites within the turquoise module of the coedited network also revealed their correlation with ovarian cancer. In addition, we identified an average of over 40,000 cis-edQTLs in the four subtypes. Finally, we explored the association between RNA editing and drug response, uncovering several potentially effective editing-drug pairs (EDP) and suggesting the conceivable utility of RNA editing sites as therapeutic targets for cancer treatment. Overall, our comprehensive study has identified and characterized RNA editing events in various subtypes of ovarian cancer, providing a new perspective for ovarian cancer research and facilitating the development of medical interventions and treatments.

SURGE: uncovering context-specific genetic-regulation of gene expression from single-cell RNA sequencing using latent-factor models

Genetic regulation of gene expression is a complex process, with genetic effects known to vary across cellular contexts such as cell types and environmental conditions. We developed SURGE, a method for unsupervised discovery of context-specific expression quantitative trait loci (eQTLs) from single-cell transcriptomic data. This allows discovery of the contexts or cell types modulating genetic regulation without prior knowledge. Applied to peripheral blood single-cell eQTL data, SURGE contexts capture continuous representations of distinct cell types and groupings of biologically related cell types. We demonstrate the disease-relevance of SURGE context-specific eQTLs using colocalization analysis and stratified LD-score regression.

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.

Non-additive genetic components contribute significantly to population-wide gene expression variation

Gene expression variation, an essential step between genotype and phenotype, is collectively controlled by local (cis) and distant (trans) regulatory changes. Nevertheless, how these regulatory elements differentially influence gene expression variation remains unclear. Here, we bridge this gap by analyzing the transcriptomes of a large diallel panel consisting of 323 unique hybrids originating from genetically divergent Saccharomyces cerevisiae isolates. Our analysis across 5,087 transcript abundance traits showed that non-additive components account for 36% of the gene expression variance on average. By comparing allele-specific read counts in parent-hybrid trios, we found that trans-regulatory changes underlie the majority of gene expression variation in the population. Remarkably, most cis-regulatory variations are also exaggerated or attenuated by additional trans effects. Overall, we showed that the transcriptome is globally buffered at the genetic level mainly due to trans-regulatory variation in the population.

An X Chromosome Transcriptome Wide Association Study Implicates ARMCX6 in Alzheimer's Disease

Background

The X chromosome is often omitted in disease association studies despite containing thousands of genes that may provide insight into well-known sex differences in the risk of Alzheimer's disease (AD).

Objective

To model the expression of X chromosome genes and evaluate their impact on AD risk in a sex-stratified manner.

Methods

Using elastic net, we evaluated multiple modeling strategies in a set of 175 whole blood samples and 126 brain cortex samples, with whole genome sequencing and RNA-seq data. SNPs (MAF > 0.05) within the cis-regulatory window were used to train tissue-specific models of each gene. We apply the best models in both tissues to sex-stratified summary statistics from a meta-analysis of Alzheimer's Disease Genetics Consortium (ADGC) studies to identify AD-related genes on the X chromosome.

Results

Across different model parameters, sample sex, and tissue types, we modeled the expression of 217 genes (95 genes in blood and 135 genes in brain cortex). The average model R2 was 0.12 (range from 0.03 to 0.34). We also compared sex-stratified and sex-combined models on the X chromosome. We further investigated genes that escaped X chromosome inactivation (XCI) to determine if their genetic regulation patterns were distinct. We found ten genes associated with AD at p < 0.05, with only ARMCX6 in female brain cortex (p = 0.008) nearing the significance threshold after adjusting for multiple testing (α = 0.002).

Conclusions

We optimized the expression prediction of X chromosome genes, applied these models to sex-stratified AD GWAS summary statistics, and identified one putative AD risk gene, ARMCX6.

Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings

Deep learning methods have recently become the state of the art in a variety of regulatory genomic tasks1-6, including the prediction of gene expression from genomic DNA. As such, these methods promise to serve as important tools in interpreting the full spectrum of genetic variation observed in personal genomes. Previous evaluation strategies have assessed their predictions of gene expression across genomic regions; however, systematic benchmarking is lacking to assess their predictions across individuals, which would directly evaluate their utility as personal DNA interpreters. We used paired whole genome sequencing and gene expression from 839 individuals in the ROSMAP study7 to evaluate the ability of current methods to predict gene expression variation across individuals at varied loci. Our approach identifies a limitation of current methods to correctly predict the direction of variant effects. We show that this limitation stems from insufficiently learned sequence motif grammar and suggest new model training strategies to improve performance.

Evaluating 17 methods incorporating biological function with GWAS summary statistics to accelerate discovery demonstrates a tradeoff between high sensitivity and high positive predictive value

Where sufficiently large genome-wide association study (GWAS) samples are not currently available or feasible, methods that leverage increasing knowledge of the biological function of variants may illuminate discoveries without increasing sample size. We comprehensively evaluated 17 functional weighting methods for identifying novel associations. We assessed the performance of these methods using published results from multiple GWAS waves across each of five complex traits. Although no method achieved both high sensitivity and positive predictive value (PPV) for any trait, a subset of methods utilizing pleiotropy and expression quantitative trait loci nominated variants with high PPV (>75%) for multiple traits. Application of functionally weighting methods to enhance GWAS power for locus discovery is unlikely to circumvent the need for larger sample sizes in truly underpowered GWAS, but these results suggest that applying functional weighting to GWAS can accurately nominate additional novel loci from available samples for follow-up studies.

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 (GWAS), 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 nonhuman primate models.

Transcriptomics and chromatin accessibility in multiple African population samples

Mapping the functional human genome and impact of genetic variants is often limited to European-descendent population samples. To aid in overcoming this limitation, we measured gene expression using RNA sequencing in lymphoblastoid cell lines (LCLs) from 599 individuals from six African populations to identify novel transcripts including those not represented in the hg38 reference genome. We used whole genomes from the 1000 Genomes Project and 164 Maasai individuals to identify 8,881 expression and 6,949 splicing quantitative trait loci (eQTLs/sQTLs), and 2,611 structural variants associated with gene expression (SV-eQTLs). We further profiled chromatin accessibility using ATAC-Seq in a subset of 100 representative individuals, to identity chromatin accessibility quantitative trait loci (caQTLs) and allele-specific chromatin accessibility, and provide predictions for the functional effect of 78.9 million variants on chromatin accessibility. Using this map of eQTLs and caQTLs we fine-mapped GWAS signals for a range of complex diseases. Combined, this work expands global functional genomic data to identify novel transcripts, functional elements and variants, understand population genetic history of molecular quantitative trait loci, and further resolve the genetic basis of multiple human traits and disease.

A targeted sequencing extension for transcript genotyping in single-cell transcriptomics

As no existing methods within the single-cell RNA sequencing repertoire combine genotyping of specific genomic loci with high throughput, we evaluated a straightforward, targeted sequencing approach as an extension to high-throughput droplet-based single-cell RNA sequencing. Overlaying standard gene expression data with transcript level genotype information provides a strategy to study the impact of genetic variants. Here, we describe this targeted sequencing extension, explain how to process the data and evaluate how technical parameters such as amount of input cDNA, number of amplification rounds, and sequencing depth influence the number of transcripts detected. Finally, we demonstrate how targeted sequencing can be used in two contexts: (1) simultaneous investigation of the presence of a somatic variant and its potential impact on the transcriptome of affected cells and (2) evaluation of allele-specific expression of a germline variant in ad hoc cell subsets. Through these and other comparable applications, our targeted sequencing extension has the potential to improve our understanding of functional effects caused by genetic variation.

Coexistence of Multiple Functional Variants and Genes Underlies Genetic Risk Locus 11p11.2 of Alzheimer's Disease

BACKGROUND

Genome-wide association studies have identified dozens of genetic risk loci for Alzheimer's disease (AD), yet the underlying causal variants and biological mechanisms remain elusive, especially for loci with complex linkage disequilibrium and regulation.

METHODS

To fully untangle the causal signal at a single locus, we performed a functional genomic study of 11p11.2 (the CELF1/SPI1 locus). Genome-wide association study signals at 11p11.2 were integrated with datasets of histone modification, open chromatin, and transcription factor binding to distill potentially functional variants (fVars). Their allelic regulatory activities were confirmed by allele imbalance, reporter assays, and base editing. Expressional quantitative trait loci and chromatin interaction data were incorporated to assign target genes to fVars. The relevance of these genes to AD was assessed by convergent functional genomics using bulk brain and single-cell transcriptomic, epigenomic, and proteomic datasets of patients with AD and control individuals, followed by cellular assays.

RESULTS

We found that 24 potential fVars, rather than a single variant, were responsible for the risk of 11p11.2. These fVars modulated transcription factor binding and regulated multiple genes by long-range chromatin interactions. Besides SPI1, convergent evidence indicated that 6 target genes (MTCH2, ACP2, NDUFS3, PSMC3, C1QTNF4, and MADD) of fVars were likely to be involved in AD development. Disruption of each gene led to cellular amyloid-β and phosphorylated tau changes, supporting the existence of multiple likely causal genes at 11p11.2.

CONCLUSIONS

Multiple variants and genes at 11p11.2 may contribute to AD risk. This finding provides new insights into the mechanistic and therapeutic challenges of AD.

Systematic differences in discovery of genetic effects on gene expression and complex traits

Most signals in genome-wide association studies (GWAS) of complex traits implicate noncoding genetic variants with putative gene regulatory effects. However, currently identified regulatory variants, notably expression quantitative trait loci (eQTLs), explain only a small fraction of GWAS signals. Here, we show that GWAS and cis-eQTL hits are systematically different: eQTLs cluster strongly near transcription start sites, whereas GWAS hits do not. Genes near GWAS hits are enriched in key functional annotations, are under strong selective constraint and have complex regulatory landscapes across different tissue/cell types, whereas genes near eQTLs are depleted of most functional annotations, show relaxed constraint, and have simpler regulatory landscapes. We describe a model to understand these observations, including how natural selection on complex traits hinders discovery of functionally relevant eQTLs. Our results imply that GWAS and eQTL studies are systematically biased toward different types of variant, and support the use of complementary functional approaches alongside the next generation of eQTL studies.

An X Chromosome Transcriptome Wide Association Study Implicates ARMCX6 in Alzheimer's Disease

Background

The X chromosome is often omitted in disease association studies despite containing thousands of genes which may provide insight into well-known sex differences in the risk of Alzheimer's Disease.

Objective

To model the expression of X chromosome genes and evaluate their impact on Alzheimer's Disease risk in a sex-stratified manner.

Methods

Using elastic net, we evaluated multiple modeling strategies in a set of 175 whole blood samples and 126 brain cortex samples, with whole genome sequencing and RNA-seq data. SNPs (MAF>0.05) within the cis-regulatory window were used to train tissue-specific models of each gene. We apply the best models in both tissues to sex-stratified summary statistics from a meta-analysis of Alzheimer's disease Genetics Consortium (ADGC) studies to identify AD-related genes on the X chromosome.

Results

Across different model parameters, sample sex, and tissue types, we modeled the expression of 217 genes (95 genes in blood and 135 genes in brain cortex). The average model R2 was 0.12 (range from 0.03 to 0.34). We also compared sex-stratified and sex-combined models on the X chromosome. We further investigated genes that escaped X chromosome inactivation (XCI) to determine if their genetic regulation patterns were distinct. We found ten genes associated with AD at p 0.05, with only ARMCX6 in female brain cortex (p = 0.008) nearing the significance threshold after adjusting for multiple testing (α = 0.002).

Conclusions

We optimized the expression prediction of X chromosome genes, applied these models to sex-stratified AD GWAS summary statistics, and identified one putative AD risk gene, ARMCX6.

Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast

Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.

Non-additive genetic components contribute significantly to population-wide gene expression variation

Gene expression variation, an essential step between genomic variation and phenotypic landscape, is collectively controlled by local (cis) and distant (trans) regulatory changes. Nevertheless, how these regulatory elements differentially influence the heritability of expression traits remains unclear. Here, we bridge this gap by analyzing the transcriptomes of a large diallel panel consisting of 323 unique hybrids originated from genetically divergent yeast isolates. We estimated the broad- and narrow-sense heritability across 5,087 transcript abundance traits and showed that non-additive components account for 36% of the phenotypic variance on average. By comparing allelic expression ratios in the hybrid and the corresponding parental pair, we identified regulatory changes in 25% of all cases, with a majority acting in trans. We further showed that trans-regulation could underlie coordinated expression variation across highly connected genes, resulting in significantly higher non-additive variance and most likely in some of the missing heritability of gene expression traits.

Hidden Genetic Regulation of Human Complex Traits via Brain Isoforms

Alternative splicing exists in most multi-exonic genes, and exploring these complex alternative splicing events and their resultant isoform expressions is essential. However, it has become conventional that RNA sequencing results have often been summarized into gene-level expression counts mainly due to the multiple ambiguous mapping of reads at highly similar regions. Transcript-level quantification and interpretation are often overlooked, and biological interpretations are often deduced based on combined transcript information at the gene level. Here, for the most variable tissue of alternative splicing, the brain, we estimate isoform expressions in 1,191 samples collected by the Genotype-Tissue Expression (GTEx) Consortium using a powerful method that we previously developed. We perform genome-wide association scans on the isoform ratios per gene and identify isoform-ratio quantitative trait loci (irQTL), which could not be detected by studying gene-level expressions alone. By analyzing the genetic architecture of the irQTL, we show that isoform ratios regulate educational attainment via multiple tissues including the frontal cortex (BA9), cortex, cervical spinal cord, and hippocampus. These tissues are also associated with different neuro-related traits, including Alzheimer's or dementia, mood swings, sleep duration, alcohol intake, intelligence, anxiety or depression, etc. Mendelian randomization (MR) analysis revealed 1,139 pairs of isoforms and neuro-related traits with plausible causal relationships, showing much stronger causal effects than on general diseases measured in the UK Biobank (UKB). Our results highlight essential transcript-level biomarkers in the human brain for neuro-related complex traits and diseases, which could be missed by merely investigating overall gene expressions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-023-00100-6.

Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq

Sequencing the human genome empowers translational medicine, facilitating transcriptome-wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short-read RNA sequencing (RNA-seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA-seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA-seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA-seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA-seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long-read or single-cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high-density array data-to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs.

Narcolepsy risk loci outline role of T cell autoimmunity and infectious triggers in narcolepsy

Narcolepsy type 1 (NT1) is caused by a loss of hypocretin/orexin transmission. Risk factors include pandemic 2009 H1N1 influenza A infection and immunization with Pandemrix®. Here, we dissect disease mechanisms and interactions with environmental triggers in a multi-ethnic sample of 6,073 cases and 84,856 controls. We fine-mapped GWAS signals within HLA (DQ0602, DQB1*03:01 and DPB1*04:02) and discovered seven novel associations (CD207, NAB1, IKZF4-ERBB3, CTSC, DENND1B, SIRPG, PRF1). Significant signals at TRA and DQB1*06:02 loci were found in 245 vaccination-related cases, who also shared polygenic risk. T cell receptor associations in NT1 modulated TRAJ*24, TRAJ*28 and TRBV*4-2 chain-usage. Partitioned heritability and immune cell enrichment analyses found genetic signals to be driven by dendritic and helper T cells. Lastly comorbidity analysis using data from FinnGen, suggests shared effects between NT1 and other autoimmune diseases. NT1 genetic variants shape autoimmunity and response to environmental triggers, including influenza A infection and immunization with Pandemrix®.

A scoping review on deep learning for next-generation RNA-Seq. data analysis

In the last decade, transcriptome research adopting next-generation sequencing (NGS) technologies has gathered incredible momentum amongst functional genomics scientists, particularly amongst clinical/biomedical research groups. The progressive enfoldment/adoption of NGS technologies has incited an abundance of next-generation transcriptomic data harbouring an opulence of new knowledge in public databases. Nevertheless, knowledge discovery from these next-generation RNA-Seq. data analysis necessitates extensive bioinformatics know-how besides elaborate data analysis software packages consistent with the type and context of data analysis. Several reliability and reproducibility concerns continue to impede RNA-Seq. data analysis. Characteristic challenges comprise of data quality, hardware and networking provisions, selection and prioritisation of data analysis tools, and yet significantly implementing of robust machine learning algorithms for maximised exploitation of these experimental transcriptomic data. Over the years, numerous machine learning algorithms have been implemented for improved transcriptomic data analysis executing predominantly shallow learning approaches. More recently, deep learning algorithms are becoming more mainstream, and enactment for next-generation RNA-Seq. data analysis could be revolutionary in the coming years in the biomedical domain. In this scoping review, we attempt to determine the existing literature's size and potential nature in deep learning and NGS RNA-Seq. data analysis. An analysis of the contemporary topics of next-generation RNA-Seq. data analysis based on deep learning algorithms is critically reviewed, emphasising open-source resources.

Mapping anorexia nervosa genes to clinical phenotypes

BACKGROUND

Anorexia nervosa (AN) is a psychiatric disorder with complex etiology, with a significant portion of disease risk imparted by genetics. Traditional genome-wide association studies (GWAS) produce principal evidence for the association of genetic variants with disease. Transcriptomic imputation (TI) allows for the translation of those variants into regulatory mechanisms, which can then be used to assess the functional outcome of genetically regulated gene expression (GReX) in a broader setting through the use of phenome-wide association studies (pheWASs) in large and diverse clinical biobank populations with electronic health record phenotypes.

METHODS

Here, we applied TI using S-PrediXcan to translate the most recent PGC-ED AN GWAS findings into AN-GReX. For significant genes, we imputed AN-GReX in the Mount Sinai BioMe™ Biobank and performed pheWASs on over 2000 outcomes to test the clinical consequences of aberrant expression of these genes. We performed a secondary analysis to assess the impact of body mass index (BMI) and sex on AN-GReX clinical associations.

RESULTS

Our S-PrediXcan analysis identified 53 genes associated with AN, including what is, to our knowledge, the first-genetic association of AN with the major histocompatibility complex. AN-GReX was associated with autoimmune, metabolic, and gastrointestinal diagnoses in our biobank cohort, as well as measures of cholesterol, medications, substance use, and pain. Additionally, our analyses showed moderation of AN-GReX associations with measures of cholesterol and substance use by BMI, and moderation of AN-GReX associations with celiac disease by sex.

CONCLUSIONS

Our BMI-stratified results provide potential avenues of functional mechanism for AN-genes to investigate further.

Weak gene-gene interaction facilitates the evolution of gene expression plasticity

BACKGROUND

Individual organisms may exhibit phenotypic plasticity when they acclimate to different conditions. Such plastic responses may facilitate or constrain the adaptation of their descendant populations to new environments, complicating their evolutionary trajectories beyond the genetic blueprint. Intriguingly, phenotypic plasticity itself can evolve in terms of its direction and magnitude during adaptation. However, we know little about what determines the evolution of phenotypic plasticity, including gene expression plasticity. Recent laboratory-based studies suggest dominance of reversing gene expression plasticity-plastic responses that move the levels of gene expression away from the new optima. Nevertheless, evidence from natural populations is still limited.

RESULTS

Here, we studied gene expression plasticity and its evolution in the montane and lowland populations of an elevationally widespread songbird-the Rufous-capped Babbler (Cyanoderma ruficeps)-with reciprocal transplant experiments and transcriptomic analyses; we set common gardens at altitudes close to these populations' native ranges. We confirmed the prevalence of reversing plasticity in genes associated with altitudinal adaptation. Interestingly, we found a positive relationship between magnitude and degree of evolution in gene expression plasticity, which was pertinent to not only adaptation-associated genes but also the whole transcriptomes from multiple tissues. Furthermore, we revealed that genes with weaker expressional interactions with other genes tended to exhibit stronger plasticity and higher degree of plasticity evolution, which explains the positive magnitude-evolution relationship.

CONCLUSIONS

Our experimental evidence demonstrates that species may initiate their adaptation to new habitats with genes exhibiting strong expression plasticity. We also highlight the role of expression interdependence among genes in regulating the magnitude and evolution of expression plasticity. This study illuminates how the evolution of phenotypic plasticity in gene expression facilitates the adaptation of species to challenging environments in nature.

Genome-wide identification of tandem repeats associated with splicing variation across 49 tissues in humans

Tandem repeats (TRs) are one of the largest sources of polymorphism, and their length is associated with gene regulation. Although previous studies reported several tandem repeats regulating gene splicing in cis (spl-TRs), no large-scale study has been conducted. In this study, we established a genome-wide catalog of 9537 spl-TRs with a total of 58,290 significant TR-splicing associations across 49 tissues (false discovery rate 5%) by using Genotype-Tissue expression (GTex) Project data. Regression models explaining splicing variation by using spl-TRs and other flanking variants suggest that at least some of the spl-TRs directly modulate splicing. In our catalog, two spl-TRs are known loci for repeat expansion diseases, spinocerebellar ataxia 6 (SCA6) and 12 (SCA12). Splicing alterations by these spl-TRs were compatible with those observed in SCA6 and SCA12. Thus, our comprehensive spl-TR catalog may help elucidate the pathomechanism of genetic diseases.

Detection of COVID-19-related biomarkers by electrochemical biosensors and potential for diagnosis, prognosis, and prediction of the course of the disease in the context of personalized medicine

As a more efficient and effective way to address disease diagnosis and intervention, cutting-edge technologies, devices, therapeutic approaches, and practices have emerged within the personalized medicine concept depending on the particular patient's biology and the molecular basis of the disease. Personalized medicine is expected to play a pivotal role in assessing disease risk or predicting response to treatment, understanding a person's health status, and, therefore, health care decision-making. This work discusses electrochemical biosensors for monitoring multiparametric biomarkers at different molecular levels and their potential to elucidate the health status of an individual in a personalized manner. In particular, and as an illustration, we discuss several aspects of the infection produced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a current health care concern worldwide. This includes SARS-CoV-2 structure, mechanism of infection, biomarkers, and electrochemical biosensors most commonly explored for diagnostics, prognostics, and potentially assessing the risk of complications in patients in the context of personalized medicine. Finally, some concluding remarks and perspectives hint at the use of electrochemical biosensors in the frame of other cutting-edge converging/emerging technologies toward the inauguration of a new paradigm of personalized medicine.

The genetic and evolutionary basis of gene expression variation in East Africans

BACKGROUND

Mapping of quantitative trait loci (QTL) associated with molecular phenotypes is a powerful approach for identifying the genes and molecular mechanisms underlying human traits and diseases, though most studies have focused on individuals of European descent. While important progress has been made to study a greater diversity of human populations, many groups remain unstudied, particularly among indigenous populations within Africa. To better understand the genetics of gene regulation in East Africans, we perform expression and splicing QTL mapping in whole blood from a cohort of 162 diverse Africans from Ethiopia and Tanzania. We assess replication of these QTLs in cohorts of predominantly European ancestry and identify candidate genes under selection in human populations.

RESULTS

We find the gene regulatory architecture of African and non-African populations is broadly shared, though there is a considerable amount of variation at individual loci across populations. Comparing our analyses to an equivalently sized cohort of European Americans, we find that QTL mapping in Africans improves the detection of expression QTLs and fine-mapping of causal variation. Integrating our QTL scans with signatures of natural selection, we find several genes related to immunity and metabolism that are highly differentiated between Africans and non-Africans, as well as a gene associated with pigmentation.

CONCLUSION

Extending QTL mapping studies beyond European ancestry, particularly to diverse indigenous populations, is vital for a complete understanding of the genetic architecture of human traits and can reveal novel functional variation underlying human traits and disease.