Identification of genetic variants associated with alternative splicing using sQTLseekeR

Alternative Splicing Regulation in Metabolic Disorders

Alternative splicing (AS) is a fundamental mechanism for enhancing transcriptome diversity and regulating gene expression, crucial for various cellular processes and the development of complex traits. This review examines the role of AS in metabolic disorders, including obesity, weight loss, dyslipidemias, and metabolic syndrome. We explore the molecular mechanisms underlying AS regulation, focusing on the interplay between cis-acting elements and trans-acting factors, and the influence of RNA-binding proteins (RBPs). Advances in high-throughput sequencing and bioinformatics have unveiled the extensive landscape of AS events across different tissues and conditions, highlighting the importance of tissue-specific splicing in metabolic regulation. We discuss the impact of genetic variants on AS, with a particular emphasis on splicing quantitative trait loci (sQTLs) and their association with cardiometabolic traits. The review also covers the regulation of spliceosome components by phosphorylation, the role of m6A modification in AS, and the interaction between transcription and splicing. Additionally, we address the clinical relevance of AS, illustrating how splicing misregulation contributes to metabolic diseases and the potential for therapeutic interventions targeting splicing mechanisms. This comprehensive overview underscores the significance of AS in metabolic health and disease, advocating for further research to harness its therapeutic potential.

Long-read RNA sequencing atlas of human microglia isoforms elucidates disease-associated genetic regulation of splicing

Microglia, the innate immune cells of the central nervous system, have been genetically implicated in multiple neurodegenerative diseases. Mapping the genetics of gene expression in human microglia has identified several loci associated with disease-associated genetic variants in microglia-specific regulatory elements. However, identifying genetic effects on splicing is challenging because of the use of short sequencing reads. Here, we present the isoform-centric microglia genomic atlas (isoMiGA), which leverages long-read RNA sequencing to identify 35,879 novel microglia isoforms. We show that these isoforms are involved in stimulation response and brain region specificity. We then quantified the expression of both known and novel isoforms in a multi-ancestry meta-analysis of 555 human microglia short-read RNA sequencing samples from 391 donors, and found associations with genetic risk loci in Alzheimer's and Parkinson's disease. We nominate several loci that may act through complex changes in isoform and splice-site usage.

Identification of candidate causal variants and target genes at 41 breast cancer risk loci through differential allelic expression analysis

Understanding breast cancer genetic risk relies on identifying causal variants and candidate target genes in risk loci identified by genome-wide association studies (GWAS), which remains challenging. Since most loci fall in active gene regulatory regions, we developed a novel approach facilitated by pinpointing the variants with greater regulatory potential in the disease's tissue of origin. Through genome-wide differential allelic expression (DAE) analysis, using microarray data from 64 normal breast tissue samples, we mapped the variants associated with DAE (daeQTLs). Then, we intersected these with GWAS data to reveal candidate risk regulatory variants and analysed their cis-acting regulatory potential. Finally, we validated our approach by extensive functional analysis of the 5q14.1 breast cancer risk locus. We observed widespread gene expression regulation by cis-acting variants in breast tissue, with 65% of coding and noncoding expressed genes displaying DAE (daeGenes). We identified over 54 K daeQTLs for 6761 (26%) daeGenes, including 385 daeGenes harbouring variants previously associated with BC risk. We found 1431 daeQTLs mapped to 93 different loci in strong linkage disequilibrium with risk-associated variants (risk-daeQTLs), suggesting a link between risk-causing variants and cis-regulation. There were 122 risk-daeQTL with stronger cis-acting potential in active regulatory regions with protein binding evidence. These variants mapped to 41 risk loci, of which 29 had no previous report of target genes and were candidates for regulating the expression levels of 65 genes. As validation, we identified and functionally characterised five candidate causal variants at the 5q14.1 risk locus targeting the ATG10 and ATP6AP1L genes, likely acting via modulation of alternative transcription and transcription factor binding. Our study demonstrates the power of DAE analysis and daeQTL mapping to identify causal regulatory variants and target genes at breast cancer risk loci, including those with complex regulatory landscapes. It additionally provides a genome-wide resource of variants associated with DAE for future functional studies.

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.

L-type calcium channels and neuropsychiatric diseases: Insights into genetic risk variant-associated genomic regulation and impact on brain development

Recent human genetic studies have linked a variety of genetic variants in the CACNA1C and CACNA1D genes to neuropsychiatric and neurodevelopmental disorders. This is not surprising given the work from multiple laboratories using cell and animal models that have established that Cav1.2 and Cav1.3 L-type calcium channels (LTCCs), encoded by CACNA1C and CACNA1D, respectively, play a key role in various neuronal processes that are essential for normal brain development, connectivity, and experience-dependent plasticity. Of the multiple genetic aberrations reported, genome-wide association studies (GWASs) have identified multiple single nucleotide polymorphisms (SNPs) in CACNA1C and CACNA1D that are present within introns, in accordance with the growing body of literature establishing that large numbers of SNPs associated with complex diseases, including neuropsychiatric disorders, are present within non-coding regions. How these intronic SNPs affect gene expression has remained a question. Here, we review recent studies that are beginning to shed light on how neuropsychiatric-linked non-coding genetic variants can impact gene expression via regulation at the genomic and chromatin levels. We additionally review recent studies that are uncovering how altered calcium signaling through LTCCs impact some of the neuronal developmental processes, such as neurogenesis, neuron migration, and neuron differentiation. Together, the described changes in genomic regulation and disruptions in neurodevelopment provide possible mechanisms by which genetic variants of LTCC genes contribute to neuropsychiatric and neurodevelopmental disorders.

Long-read RNA-seq atlas of novel microglia isoforms elucidates disease-associated genetic regulation of splicing

Microglia, the innate immune cells of the central nervous system, have been genetically implicated in multiple neurodegenerative diseases. We previously mapped the genetic regulation of gene expression and mRNA splicing in human microglia, identifying several loci where common genetic variants in microglia-specific regulatory elements explain disease risk loci identified by GWAS. However, identifying genetic effects on splicing has been challenging due to the use of short sequencing reads to identify causal isoforms. Here we present the isoform-centric microglia genomic atlas (isoMiGA) which leverages the power of long-read RNA-seq to identify 35,879 novel microglia isoforms. We show that the novel microglia isoforms are involved in stimulation response and brain region specificity. We then quantified the expression of both known and novel isoforms in a multi-ethnic meta-analysis of 555 human microglia short-read RNA-seq samples from 391 donors, the largest to date, and found associations with genetic risk loci in Alzheimer's disease and Parkinson's disease. We nominate several loci that may act through complex changes in isoform and splice site usage.

A fast non-parametric test of association for multiple traits

The increasing availability of multidimensional phenotypic data in large cohorts of genotyped individuals requires efficient methods to identify genetic effects on multiple traits. Permutational multivariate analysis of variance (PERMANOVA) offers a powerful non-parametric approach. However, it relies on permutations to assess significance, which hinders the analysis of large datasets. Here, we derive the limiting null distribution of the PERMANOVA test statistic, providing a framework for the fast computation of asymptotic p values. Our asymptotic test presents controlled type I error and high power, often outperforming parametric approaches. We illustrate its applicability in the context of QTL mapping and GWAS.

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.

Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer

Somatic mutations within non-coding regions and even exons may have unidentified regulatory consequences that are often overlooked in analysis workflows. Here we present RegTools ( www.regtools.org ), a computationally efficient, free, and open-source software package designed to integrate somatic variants from genomic data with splice junctions from bulk or single cell transcriptomic data to identify variants that may cause aberrant splicing. We apply RegTools to over 9000 tumor samples with both tumor DNA and RNA sequence data. RegTools discovers 235,778 events where a splice-associated variant significantly increases the splicing of a particular junction, across 158,200 unique variants and 131,212 unique junctions. To characterize these somatic variants and their associated splice isoforms, we annotate them with the Variant Effect Predictor, SpliceAI, and Genotype-Tissue Expression junction counts and compare our results to other tools that integrate genomic and transcriptomic data. While many events are corroborated by the aforementioned tools, the flexibility of RegTools also allows us to identify splice-associated variants in known cancer drivers, such as TP53, CDKN2A, and B2M, and other genes.

Łabaj P, Mangul S. RNA-seq data science: From raw data to effective interpretation

RNA sequencing (RNA-seq) has become an exemplary technology in modern biology and clinical science. Its immense popularity is due in large part to the continuous efforts of the bioinformatics community to develop accurate and scalable computational tools to analyze the enormous amounts of transcriptomic data that it produces. RNA-seq analysis enables genes and their corresponding transcripts to be probed for a variety of purposes, such as detecting novel exons or whole transcripts, assessing expression of genes and alternative transcripts, and studying alternative splicing structure. It can be a challenge, however, to obtain meaningful biological signals from raw RNA-seq data because of the enormous scale of the data as well as the inherent limitations of different sequencing technologies, such as amplification bias or biases of library preparation. The need to overcome these technical challenges has pushed the rapid development of novel computational tools, which have evolved and diversified in accordance with technological advancements, leading to the current myriad of RNA-seq tools. These tools, combined with the diverse computational skill sets of biomedical researchers, help to unlock the full potential of RNA-seq. The purpose of this review is to explain basic concepts in the computational analysis of RNA-seq data and define discipline-specific jargon.

Identification of disease-related aberrantly spliced transcripts in myeloma and strategies to target these alterations by RNA-based therapeutics

Novel drug discoveries have shifted the treatment paradigms of most hematological malignancies, including multiple myeloma (MM). However, this plasma cell malignancy remains incurable, and novel therapies are therefore urgently needed. Whole-genome transcriptome analyses in a large cohort of MM patients demonstrated that alterations in pre-mRNA splicing (AS) are frequent in MM. This manuscript describes approaches to identify disease-specific alterations in MM and proposes RNA-based therapeutic strategies to eradicate such alterations. As a "proof of concept", we examined the causes of aberrant HMMR (Hyaluronan-mediated motility receptor) splicing in MM. We identified clusters of single nucleotide variations (SNVs) in the HMMR transcript where the altered splicing took place. Using bioinformatics tools, we predicted SNVs and splicing factors that potentially contribute to aberrant HMMR splicing. Based on bioinformatic analyses and validation studies, we provided the rationale for RNA-based therapeutic strategies to selectively inhibit altered HMMR splicing in MM. Since splicing is a hallmark of many cancers, strategies described herein for target identification and the design of RNA-based therapeutics that inhibit gene splicing can be applied not only to other genes in MM but also more broadly to other hematological malignancies and solid tumors as well.

A powerful global test for spliceQTL effects

Statistical methods to test for effects of single nucleotide polymorphisms (SNPs) on exon inclusion exist but often rely on testing of associations between multiple exon-SNP pairs, with sometimes subsequent summarization of results at the gene level. Such approaches require heavy multiple testing corrections and detect mostly events with large effect sizes. We propose here a test to find spliceQTL (splicing quantitative trait loci) effects that takes all exons and all SNPs into account simultaneously. For any chosen gene, this score-based test looks for an association between the set of exon expressions and the set of SNPs, via a random-effects model framework. It is efficient to compute and can be used if the number of SNPs is larger than the number of samples. In addition, the test is powerful in detecting effects that are relatively small for individual exon-SNP pairs but are observed for many pairs. Furthermore, test results are more often replicated across datasets than pairwise testing results. This makes our test more robust to exon-SNP pair-specific effects, which do not extend to multiple pairs within the same gene. We conclude that the test we propose here offers more power and better replicability in the search for spliceQTL effects.

Identification of Candidate mRNA Isoforms for Prostate Cancer-Risk SNPs Utilizing Iso-eQTL and sQTL Methods

Single nucleotide polymorphisms (SNPs) impacting the alternative splicing (AS) process (sQTLs) or isoform expression (iso-eQTL) are implicated as important cancer regulatory elements. To find the sQTL and iso-eQTL, we retrieved prostate cancer (PrCa) tissue RNA-seq and genotype data originating from 385 PrCa European patients from The Cancer Genome Atlas. We conducted RNA-seq analysis with isoform-based and splice event-based approaches. The MatrixEQTL was used to identify PrCa-associated sQTLs and iso-eQTLs. The overlap between sQTL and iso-eQTL with GWAS loci and those that are differentially expressed between cancer and normal tissue were identified. The cis-acting associations (FDR < 0.05) for PrCa-risk SNPs identified 42, 123, and 90 PrCa-associated cassette exons, intron retention, and mRNA isoforms belonging to 25, 95, and 83 genes, respectively; while assessment of trans-acting association (FDR < 0.05) yielded 59, 65, and 196 PrCa-associated cassette exons, intron retention and mRNA isoforms belonging to 35, 55, and 181 genes, respectively. The results suggest that functional PrCa-associated SNPs can play a role in PrCa genesis by making an important contribution to the dysregulation of AS and, consequently, impacting the expression of the mRNA isoforms.

A resource for integrated genomic analysis of the human liver

In this study, we generated whole-transcriptome RNA-Seq from n = 192 genotyped liver samples and used these data with existing data from the GTEx Project (RNA-Seq) and previous liver eQTL (microarray) studies to create an enhanced transcriptomic sequence resource in the human liver. Analyses of genotype-expression associations show pronounced enrichment of associations with genes of drug response. The associations are primarily consistent across the two RNA-Seq datasets, with some modest variation, indicating the importance of obtaining multiple datasets to produce a robust resource. We further used an empirical Bayesian model to compare eQTL patterns in liver and an additional 20 GTEx tissues, finding that MHC genes, and especially class II genes, are enriched for liver-specific eQTL patterns. To illustrate the utility of the resource to augment GWAS analysis with small sample sizes, we developed a novel meta-analysis technique to combine several liver eQTL data sources. We also illustrate its application using a transcriptome-enhanced re-analysis of a study of neutropenia in pancreatic cancer patients. The associations of genotype with liver expression, including splice variation and its genetic associations, are made available in a searchable genome browser.

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.

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.

Deciphering associations between three RNA splicing-related genetic variants and lung cancer risk

Limited efforts have been made in assessing the effect of genome-wide profiling of RNA splicing-related variation on lung cancer risk. In the present study, we first identified RNA splicing-related genetic variants linked to lung cancer in a genome-wide profiling analysis and then conducted a two-stage (discovery and replication) association study in populations of European ancestry. Discovery and validation were conducted sequentially with a total of 29,266 cases and 56,450 controls from both the Transdisciplinary Research in Cancer of the Lung and the International Lung Cancer Consortium as well as the OncoArray database. For those variants identified as significant in the two datasets, we further performed stratified analyses by smoking status and histological type and investigated their effects on gene expression and potential regulatory mechanisms. We identified three genetic variants significantly associated with lung cancer risk: rs329118 in JADE2 (P = 8.80E-09), rs2285521 in GGA2 (P = 4.43E-08), and rs198459 in MYRF (P = 1.60E-06). The combined effects of all three SNPs were more evident in lung squamous cell carcinomas (P = 1.81E-08, P = 6.21E-08, and P = 7.93E-04, respectively) than in lung adenocarcinomas and in ever smokers (P = 9.80E-05, P = 2.70E-04, and P = 2.90E-05, respectively) than in never smokers. Gene expression quantitative trait analysis suggested a role for the SNPs in regulating transcriptional expression of the corresponding target genes. In conclusion, we report that three RNA splicing-related genetic variants contribute to lung cancer susceptibility in European populations. However, additional validation is needed, and specific splicing mechanisms of the target genes underlying the observed associations also warrants further exploration.

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.

Placental Gene Transcript Proportions are Altered in the Presence of In Utero Arsenic and Cadmium Exposures, Genetic Variants, and Birth Weight Differences

Background: In utero arsenic and cadmium exposures are linked with reduced birth weight as well as alterations in placental molecular features. However, studies thus far have focused on summarizing transcriptional activity at the gene level and do not capture transcript specification, an important resource during fetal development to enable adaptive responses to the rapidly changing in utero physiological conditions. In this study, we conducted a genome-wide analysis of the placental transcriptome to evaluate the role of differential transcript usage (DTU) as a potential marker of in utero arsenic and cadmium exposure and fetal growth restriction. Methods: Transcriptome-wide RNA sequencing was performed in placenta samples from the Rhode Island Child Health Study (RICHS, n = 199). Arsenic and cadmium levels were measured in maternal toenails using ICP-MS. Differential transcript usage (DTU) contrasting small (SGA) and appropriate (AGA) for gestational age infants as well as above vs. below median exposure to arsenic and cadmium were assessed using the DRIMSeq R package. Genetic variants that influence transcript usage were determined using the sQTLseeker R package. Results: We identified 82 genes demonstrating DTU in association with SGA status at an FDR <0.05. Among these, one gene, ORMDL1, also demonstrated DTU in association with arsenic exposure, and fifteen genes (CSNK1E, GBA, LAMTOR4, MORF4L1, PIGO, PSG1, PSG3, PTMA, RBMS1, SLC38A2, SMAD4, SPCS2, TUBA1B, UBE2A, YIPF5) demonstrated DTU in association with cadmium exposure. In addition to cadmium exposure and SGA status, proportions of the LAMTOR4 transcript ENST00000474141.5 also differed by genetic variants (rs10231604, rs12878, and rs3736591), suggesting a pathway by which an in utero exposure and genetic variants converge to impact fetal growth through perturbations of placental processes. Discussion: We report the first genome-wide characterization of placental transcript usage and associations with intrauterine metal exposure and fetal growth restriction. These results highlight the utility of interrogating the transcriptome at finer-scale transcript-level resolution to identify novel placental biomarkers of exposure-induced outcomes.

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.

Subcutaneous adipose tissue splice quantitative trait loci reveal differences in isoform usage associated with cardiometabolic traits

Alternate splicing events can create isoforms that alter gene function, and genetic variants associated with alternate gene isoforms may reveal molecular mechanisms of disease. We used subcutaneous adipose tissue of 426 Finnish men from the METSIM study and identified splice junction quantitative trait loci (sQTLs) for 6,077 splice junctions (FDR < 1%). In the same individuals, we detected expression QTLs (eQTLs) for 59,443 exons and 15,397 genes (FDR < 1%). We identified 595 genes with an sQTL and exon eQTL but no gene eQTL, which could indicate potential isoform differences. Of the significant sQTL signals, 2,114 (39.8%) included at least one proxy variant (linkage disequilibrium r2 > 0.8) located within an intron spanned by the splice junction. We identified 203 sQTLs that colocalized with 141 genome-wide association study (GWAS) signals for cardiometabolic traits, including 25 signals for lipid traits, 24 signals for body mass index (BMI), and 12 signals for waist-hip ratio adjusted for BMI. Among all 141 GWAS signals colocalized with an sQTL, we detected 26 that also colocalized with an exon eQTL for an exon skipped by the sQTL splice junction. At a GWAS signal for high-density lipoprotein cholesterol colocalized with an NR1H3 sQTL splice junction, we show that the alternative splice product encodes an NR1H3 transcription factor that lacks a DNA binding domain and fails to activate transcription. Together, these results detect splicing events and candidate mechanisms that may contribute to gene function at GWAS loci.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

SplicingFactory-splicing diversity analysis for transcriptome data

MOTIVATION

Alternative splicing contributes to the diversity of RNA found in biological samples. Current tools investigating patterns of alternative splicing check for coordinated changes in the expression or relative ratio of RNA isoforms where specific isoforms are up- or down-regulated in a condition. However, the molecular process of splicing is stochastic and changes in RNA isoform diversity for a gene might arise between samples or conditions. A specific condition can be dominated by a single isoform, while multiple isoforms with similar expression levels can be present in a different condition. These changes might be the result of mutations, drug treatments or differences in the cellular or tissue environment. Here, we present a tool for the characterization and analysis of RNA isoform diversity using isoform level expression measurements.

RESULTS

We developed an R package called SplicingFactory, to calculate various RNA isoform diversity metrics, and compare them across conditions. Using the package, we tested the effect of RNA-seq quantification tools, quantification uncertainty, gene expression levels and isoform numbers on the isoform diversity calculation. We analyzed a set of CD34+ hematopoietic stem cells and myelodysplastic syndrome samples and found a set of genes whose isoform diversity change is associated with SF3B1 mutations.

AVAILABILITY AND IMPLEMENTATION

The SplicingFactory package is freely available under the GPL-3.0 license from Bioconductor for the Windows, MacOS and Linux operating systems (https://www.bioconductor.org/packages/release/bioc/html/SplicingFactory.html).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Brain-trait-associated variants impact cell-type-specific gene regulation during neurogenesis

Interpretation of the function of non-coding risk loci for neuropsychiatric disorders and brain-relevant traits via gene expression and alternative splicing quantitative trait locus (e/sQTL) analyses is generally performed in bulk post-mortem adult tissue. However, genetic risk loci are enriched in regulatory elements active during neocortical differentiation, and regulatory effects of risk variants may be masked by heterogeneity in bulk tissue. Here, we map e/sQTLs, and allele-specific expression in cultured cells representing two major developmental stages, primary human neural progenitors (n = 85) and their sorted neuronal progeny (n = 74), identifying numerous loci not detected in either bulk developing cortical wall or adult cortex. Using colocalization and genetic imputation via transcriptome-wide association, we uncover cell-type-specific regulatory mechanisms underlying risk for brain-relevant traits that are active during neocortical differentiation. Specifically, we identified a progenitor-specific eQTL for CENPW co-localized with common variant associations for cortical surface area and educational attainment.

Single-cell alternative polyadenylation analysis delineates GABAergic neuron types

Background

Alternative polyadenylation (APA) is emerging as an important mechanism in the post-transcriptional regulation of gene expression across eukaryotic species. Recent studies have shown that APA plays key roles in biological processes, such as cell proliferation and differentiation. Single-cell RNA-seq technologies are widely used in gene expression heterogeneity studies; however, systematic studies of APA at the single-cell level are still lacking.

Results

Here, we described a novel computational framework, SAPAS, that utilizes 3′-tag-based scRNA-seq data to identify novel poly(A) sites and quantify APA at the single-cell level. Applying SAPAS to the scRNA-seq data of phenotype characterized GABAergic interneurons, we identified cell type-specific APA events for different GABAergic neuron types. Genes with cell type-specific APA events are enriched for synaptic architecture and communications. In further, we observed a strong enrichment of heritability for several psychiatric disorders and brain traits in altered 3′ UTRs and coding sequences of cell type-specific APA events. Finally, by exploring the modalities of APA, we discovered that the bimodal APA pattern of Pak3 could classify chandelier cells into different subpopulations that are from different laminar positions.

Conclusions

We established a method to characterize APA at the single-cell level. When applied to a scRNA-seq dataset of GABAergic interneurons, the single-cell APA analysis not only identified cell type-specific APA events but also revealed that the modality of APA could classify cell subpopulations. Thus, SAPAS will expand our understanding of cellular heterogeneity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-021-01076-3.

Deep transcriptome sequencing of subgenual anterior cingulate cortex reveals cross-diagnostic and diagnosis-specific RNA expression changes in major psychiatric disorders

Despite strong evidence of heritability and growing discovery of genetic markers for major mental illness, little is known about how gene expression in the brain differs across psychiatric diagnoses, or how known genetic risk factors shape these differences. Here we investigate expressed genes and gene transcripts in postmortem subgenual anterior cingulate cortex (sgACC), a key component of limbic circuits linked to mental illness. RNA obtained postmortem from 200 donors diagnosed with bipolar disorder, schizophrenia, major depression, or no psychiatric disorder was deeply sequenced to quantify expression of over 85,000 gene transcripts, many of which were rare. Case-control comparisons detected modest expression differences that were correlated across disorders. Case-case comparisons revealed greater expression differences, with some transcripts showing opposing patterns of expression between diagnostic groups, relative to controls. The ~250 rare transcripts that were differentially-expressed in one or more disorder groups were enriched for genes involved in synapse formation, cell junctions, and heterotrimeric G-protein complexes. Common genetic variants were associated with transcript expression (eQTL) or relative abundance of alternatively spliced transcripts (sQTL). Common genetic variants previously associated with disease risk were especially enriched for sQTLs, which together accounted for disproportionate fractions of diagnosis-specific heritability. Genetic risk factors that shape the brain transcriptome may contribute to diagnostic differences between broad classes of mental illness.

Quantifying splice-site usage: a simple yet powerful approach to analyze splicing

RNA splicing, and variations in this process referred to as alternative splicing, are critical aspects of gene regulation in eukaryotes. From environmental responses in plants to being a primary link between genetic variation and disease in humans, splicing differences confer extensive phenotypic changes across diverse organisms (1–3). Regulation of splicing occurs through differential selection of splice sites in a splicing reaction, which results in variation in the abundance of isoforms and/or splicing events. However, genomic determinants that influence splice-site selection remain largely unknown. While traditional approaches for analyzing splicing rely on quantifying variant transcripts (i.e. isoforms) or splicing events (i.e. intron retention, exon skipping etc.) (4), recent approaches focus on analyzing complex/mutually exclusive splicing patterns (5–8). However, none of these approaches explicitly measure individual splice-site usage, which can provide valuable information about splice-site choice and its regulation. Here, we present a simple approach to quantify the empirical usage of individual splice sites reflecting their strength, which determines their selection in a splicing reaction. Splice-site strength/usage, as a quantitative phenotype, allows us to directly link genetic variation with usage of individual splice-sites. We demonstrate the power of this approach in defining the genomic determinants of splice-site choice through GWAS. Our pilot analysis with more than a thousand splice sites hints that sequence divergence in cis rather than trans is associated with variations in splicing among accessions of Arabidopsis thaliana. This approach allows deciphering principles of splicing and has broad implications from agriculture to medicine.

A transcription-centric model of SNP-age interaction

Complex age-associated phenotypes are caused, in part, by an interaction between an individual's genotype and age. The mechanisms governing such interactions are however not entirely understood. Here, we provide a novel transcriptional mechanism-based framework-SNiPage, to investigate such interactions, whereby a transcription factor (TF) whose expression changes with age (age-associated TF), binds to a polymorphic regulatory element in an allele-dependent fashion, rendering the target gene's expression dependent on both, the age and the genotype. Applying SNiPage to GTEx, we detected ~637 significant TF-SNP-Gene triplets on average across 25 tissues, where the TF binds to a regulatory SNP in the gene's promoter or putative enhancer and potentially regulates its expression in an age- and allele-dependent fashion. The detected SNPs are enriched for epigenomic marks indicative of regulatory activity, exhibit allele-specific chromatin accessibility, and spatial proximity to their putative gene targets. Furthermore, the TF-SNP interaction-dependent target genes have established links to aging and to age-associated diseases. In six hypertension-implicated tissues, detected interactions significantly inform hypertension state of an individual. Lastly, the age-interacting SNPs exhibit a greater proximity to the reported phenotype/diseases-associated SNPs than eSNPs identified in an interaction-independent fashion. Overall, we present a novel mechanism-based model, and a novel framework SNiPage, to identify functionally relevant SNP-age interactions in transcriptional control and illustrate their potential utility in understanding complex age-associated phenotypes.

Identification and analysis of splicing quantitative trait loci across multiple tissues in the human genome

Alternative splicing (AS) is a fundamental step in eukaryotic mRNA biogenesis. Here, we develop an efficient and reproducible pipeline for the discovery of genetic variants that affect AS (splicing QTLs, sQTLs). We use it to analyze the GTEx dataset, generating a comprehensive catalog of sQTLs in the human genome. Downstream analysis of this catalog provides insight into the mechanisms underlying splicing regulation. We report that a core set of sQTLs is shared across multiple tissues. sQTLs often target the global splicing pattern of genes, rather than individual splicing events. Many also affect the expression of the same or other genes, uncovering regulatory loci that act through different mechanisms. sQTLs tend to be located in post-transcriptionally spliced introns, which would function as hotspots for splicing regulation. While many variants affect splicing patterns by altering the sequence of splice sites, many more modify the binding sites of RNA-binding proteins. Genetic variants affecting splicing can have a stronger phenotypic impact than those affecting gene expression.

A splicing factor switch controls hematopoietic lineage specification of pluripotent stem cells

Alternative splicing (AS) leads to transcriptome diversity in eukaryotic cells and is one of the key regulators driving cellular differentiation. Although AS is of crucial importance for normal hematopoiesis and hematopoietic malignancies, its role in early hematopoietic development is still largely unknown. Here, by using high-throughput transcriptomic analyses, we show that pervasive and dynamic AS takes place during hematopoietic development of human pluripotent stem cells (hPSCs). We identify a splicing factor switch that occurs during the differentiation of mesodermal cells to endothelial progenitor cells (EPCs). Perturbation of this switch selectively impairs the emergence of EPCs and hemogenic endothelial progenitor cells (HEPs). Mechanistically, an EPC-induced alternative spliced isoform of NUMB dictates EPC specification by controlling NOTCH signaling. Furthermore, we demonstrate that the splicing factor SRSF2 regulates splicing of the EPC-induced NUMB isoform, and the SRSF2-NUMB-NOTCH splicing axis regulates EPC generation. The identification of this splicing factor switch provides a new molecular mechanism to control cell fate and lineage specification.

Variation in genes implicated in B-cell development and antibody production affects susceptibility to pemphigus

Pemphigus foliaceus (PF) is an autoimmune blistering skin disease characterized by the presence of pathogenic autoantibodies against desmoglein 1, a component of intercellular desmosome junctions. PF occurs sporadically across the globe and is endemic in some Brazilian regions. Because PF is a B-cell-mediated disease, we aimed to study the impact of variants within genes encoding molecules involved in the different steps of B-cell development and antibody production on the susceptibility of endemic PF. We analysed 3,336 single nucleotide polymorphisms (SNPs) from 167 candidate genes genotyped with Illumina microarray in a cohort of 227 PF patients and 193 controls. After quality control and exclusion of non-informative and redundant SNPs, 607 variants in 149 genes remained in the logistic regression analysis, in which sex and ancestry were included as covariates. Our results revealed 10 SNPs within or nearby 11 genes that were associated with susceptibility to endemic PF (OR >1.56; p < 0.005): rs6657275*G (TGFB2); rs1818545*A (RAG1/RAG2/IFTAP);rs10781530*A (PAXX), rs10870140*G and rs10781522*A (TRAF2); rs535068*A (TNFRSF1B); rs324011*A (STAT6);rs6432018*C (YWHAQ); rs17149161*C (YWHAG); and rs2070729*C (IRF1). Interestingly, these SNPs have been previously associated with differential gene expression, mostly in peripheral blood, in publicly available databases. For the first time, we show that polymorphisms in genes involved in B-cell development and antibody production confer differential susceptibility to endemic PF, and therefore are candidates for possible functional studies to understand immunoglobulin gene rearrangement and its impact on diseases.

Detecting Allele-Specific Alternative Splicing from Population-Scale RNA-Seq Data

RNA sequencing (RNA-seq) is a powerful technology for studying human transcriptome variation. We introduce PAIRADISE (Paired Replicate Analysis of Allelic Differential Splicing Events), a method for detecting allele-specific alternative splicing (ASAS) from RNA-seq data. Unlike conventional approaches that detect ASAS events one sample at a time, PAIRADISE aggregates ASAS signals across multiple individuals in a population. By treating the two alleles of an individual as paired, and multiple individuals sharing a heterozygous SNP as replicates, we formulate ASAS detection using PAIRADISE as a statistical problem for identifying differential alternative splicing from RNA-seq data with paired replicates. PAIRADISE outperforms alternative statistical models in simulation studies. Applying PAIRADISE to replicate RNA-seq data of a single individual and to population-scale RNA-seq data across many individuals, we detect ASAS events associated with genome-wide association study (GWAS) signals of complex traits or diseases. Additionally, PAIRADISE ASAS analysis detects the effects of rare variants on alternative splicing. PAIRADISE provides a useful computational tool for elucidating the genetic variation and phenotypic association of alternative splicing in populations.

Genetic colocalization atlas points to common regulatory sites and genes for hematopoietic traits and hematopoietic contributions to disease phenotypes

BACKGROUND

Genetic associations link hematopoietic traits and disease end-points, but most causal variants and genes underlying these relationships are unknown. Here, we used genetic colocalization to nominate loci and genes related to shared genetic signal for hematopoietic, cardiovascular, autoimmune, neuropsychiatric, and cancer phenotypes.

METHODS

Our aim was to identify colocalization sites for human traits among established genome-wide significant loci. Using genome-wide association study (GWAS) summary statistics, we determined loci where multiple traits colocalized at a false discovery rate < 5%. We then identified quantitative trait loci among colocalization sites to highlight related genes. In addition, we used Mendelian randomization analysis to further investigate certain trait relationships genome-wide.

RESULTS

Our findings recapitulated developmental hematopoietic lineage relationships, identified loci that linked traits with causal genetic relationships, and revealed novel trait associations. Out of 2706 loci with genome-wide significant signal for at least 1 blood trait, we identified 1779 unique sites (66%) with shared genetic signal for 2+ hematologic traits. We could assign some sites to specific developmental cell types during hematopoiesis based on affected traits, including those likely to impact hematopoietic progenitor cells and/or megakaryocyte-erythroid progenitor cells. Through an expanded analysis of 70 human traits, we defined 2+ colocalizing traits at 2123 loci from an analysis of 9852 sites (22%) containing genome-wide significant signal for at least 1 GWAS trait. In addition to variants and genes underlying shared genetic signal between blood traits and disease phenotypes that had been previously related through Mendelian randomization studies, we defined loci and related genes underlying shared signal between eosinophil percentage and eczema. We also identified colocalizing signals in a number of clinically relevant coding mutations, including sites linking PTPN22 with Crohn's disease, NIPA with coronary artery disease and platelet trait variation, and the hemochromatosis gene HFE with altered lipid levels. Finally, we anticipate potential off-target effects on blood traits related novel therapeutic targets, including TRAIL.

CONCLUSIONS

Our findings provide a road map for gene validation experiments and novel therapeutics related to hematopoietic development, and offer a rationale for pleiotropic interactions between hematopoietic loci and disease end-points.

From GWAS to Function: Using Functional Genomics to Identify the Mechanisms Underlying Complex Diseases

Genome-wide association studies (GWAS) have successfully mapped thousands of loci associated with complex traits. These associations could reveal the molecular mechanisms altered in common complex diseases and result in the identification of novel drug targets. However, GWAS have also left a number of outstanding questions. In particular, the majority of disease-associated loci lie in non-coding regions of the genome and, even though they are thought to play a role in gene expression regulation, it is unclear which genes they regulate and in which cell types or physiological contexts this regulation occurs. This has hindered the translation of GWAS findings into clinical interventions. In this review we summarize how these challenges have been addressed over the last decade, with a particular focus on the integration of GWAS results with functional genomics datasets. Firstly, we investigate how the tissues and cell types involved in diseases can be identified using methods that test for enrichment of GWAS variants in genomic annotations. Secondly, we explore how to find the genes regulated by GWAS loci using methods that test for colocalization of GWAS signals with molecular phenotypes such as quantitative trait loci (QTLs). Finally, we highlight potential future research avenues such as integrating GWAS results with single-cell sequencing read-outs, designing functionally informed polygenic risk scores (PRS), and validating disease associated genes using genetic engineering. These tools will be crucial to identify new drug targets for common complex diseases.

Enteric infection induces Lark-mediated intron retention at the 5' end of Drosophila genes

BACKGROUND

RNA splicing is a key post-transcriptional mechanism that generates protein diversity and contributes to the fine-tuning of gene expression, which may facilitate adaptation to environmental challenges. Here, we employ a systems approach to study alternative splicing changes upon enteric infection in females from classical Drosophila melanogaster strains as well as 38 inbred lines.

RESULTS

We find that infection leads to extensive differences in isoform ratios, which results in a more diverse transcriptome with longer 5' untranslated regions (5'UTRs). We establish a role for genetic variation in mediating inter-individual splicing differences, with local splicing quantitative trait loci (local-sQTLs) being preferentially located at the 5' end of transcripts and directly upstream of splice donor sites. Moreover, local-sQTLs are more numerous in the infected state, indicating that acute stress unmasks a substantial number of silent genetic variants. We observe a general increase in intron retention concentrated at the 5' end of transcripts across multiple strains, whose prevalence scales with the degree of pathogen virulence. The length, GC content, and RNA polymerase II occupancy of these introns with increased retention suggest that they have exon-like characteristics. We further uncover that retained intron sequences are enriched for the Lark/RBM4 RNA binding motif. Interestingly, we find that lark is induced by infection in wild-type flies, its overexpression and knockdown alter survival, and tissue-specific overexpression mimics infection-induced intron retention.

CONCLUSION

Our collective findings point to pervasive and consistent RNA splicing changes, partly mediated by Lark/RBM4, as being an important aspect of the gut response to infection.

Genetic regulation of gene expression and splicing during a 10-year period of human aging

BACKGROUND

Molecular and cellular changes are intrinsic to aging and age-related diseases. Prior cross-sectional studies have investigated the combined effects of age and genetics on gene expression and alternative splicing; however, there has been no long-term, longitudinal characterization of these molecular changes, especially in older age.

RESULTS

We perform RNA sequencing in whole blood from the same individuals at ages 70 and 80 to quantify how gene expression, alternative splicing, and their genetic regulation are altered during this 10-year period of advanced aging at a population and individual level. We observe that individuals are more similar to their own expression profiles later in life than profiles of other individuals their own age. We identify 1291 and 294 genes differentially expressed and alternatively spliced with age, as well as 529 genes with outlying individual trajectories. Further, we observe a strong correlation of genetic effects on expression and splicing between the two ages, with a small subset of tested genes showing a reduction in genetic associations with expression and splicing in older age.

CONCLUSIONS

These findings demonstrate that, although the transcriptome and its genetic regulation is mostly stable late in life, a small subset of genes is dynamic and is characterized by a reduction in genetic regulation, most likely due to increasing environmental variance with age.

Genome-Wide Identification of Splicing Quantitative Trait Loci (sQTLs) in Diverse Ecotypes of Arabidopsis thaliana

Alternative splicing (AS) of pre-mRNAs contributes to transcriptome diversity and enables plants to generate different protein isoforms from a single gene and/or fine-tune gene expression during different development stages and environmental changes. Although AS is pervasive, the genetic basis for differential isoform usage in plants is still emerging. In this study, we performed genome-wide analysis in 666 geographically distributed diverse ecotypes of Arabidopsis thaliana to identify genomic regions [splicing quantitative trait loci (sQTLs)] that may regulate differential AS. These ecotypes belong to different microclimatic conditions and are part of the relict and non-relict populations. Although sQTLs were spread across the genome, we observed enrichment for trans-sQTL (trans-sQTLs hotspots) on chromosome one. Furthermore, we identified several sQTL (911) that co-localized with trait-linked single nucleotide polymorphisms (SNP) identified in the Arabidopsis genome-wide association studies (AraGWAS). Many sQTLs were enriched among circadian clock, flowering, and stress-responsive genes, suggesting a role for differential isoform usage in regulating these important processes in diverse ecotypes of Arabidopsis. In conclusion, the current study provides a deep insight into SNPs affecting isoform ratios/genes and facilitates a better mechanistic understanding of trait-associated SNPs in GWAS studies. To the best of our knowledge, this is the first report of sQTL analysis in a large set of Arabidopsis ecotypes and can be used as a reference to perform sQTL analysis in the Brassicaceae family. Since whole genome and transcriptome datasets are available for these diverse ecotypes, it could serve as a powerful resource for the biological interpretation of trait-associated loci, splice isoform ratios, and their phenotypic consequences to help produce more resilient and high yield crop varieties.

DNA Variant in the RPGRIP1L Gene Influences Alternative Splicing

The retinitis pigmentosa GTPase regulator interacting protein 1-like (RPGRIP1L) gene encodes a ciliary protein that is critical for processes related to brain development, including development of left-right asymmetry, sonic hedgehog signaling, and neural tube formation. RPGRIP1L is a risk factor for retinal degeneration, and rare, deleterious variants in the RPGRIP1L gene cause Joubert syndrome and Meckel syndrome, both autosomal recessive disorders. These syndromes are characterized by dysfunctional primary cilia that result in abnormal development - and even lethality in the case of Meckel syndrome. Genetic studies have also implicated RPGRIP1L in psychiatric disorders by suggestive findings from genome-wide association studies and findings from rare-variant exome analyses for bipolar disorder and de novo mutations in autism. In this study we identify a common variant in RPGRIP1L, rs7203525, that influences alternative splicing, increasing the inclusion of exon 20 of RPGRIP1L. We detected this alternative splicing association in human postmortem brain tissue samples and, using a minigene assay combined with in vitro mutagenesis, confirmed that the alternative splicing is attributable to the alleles of this variant. The predominate RPGRIP1L isoform expressed in adult brains does not contain exon 20; thus, a shift to include this exon may impact brain function.

Genetic association of molecular traits: A help to identify causative variants in complex diseases

In the past 15 years, major progresses have been made in the understanding of the genetic basis of regulation of gene expression. These new insights have revolutionized our approach to resolve the genetic variation underlying complex diseases. Gene transcript levels were the first expression phenotypes that were studied. They are heritable and therefore amenable to genome-wide association studies. The genetic variants that modulate them are called expression quantitative trait loci. Their study has been extended to other molecular quantitative trait loci (molQTLs) that regulate gene expression at the various levels, from chromatin state to cellular responses. Altogether, these studies have generated a wealth of basic information on the genome-wide patterns of gene expression and their inter-individual variation. Most importantly, molQTLs have become an invaluable asset in the genetic study of complex diseases. Although the identification of the disease-causing variants on the basis of their overlap with molQTLs requires caution, molQTLs can help to prioritize the relevant candidate gene(s) in the disease-associated regions and bring a functional interpretation of the associated variants, therefore, bridging the gap between genotypes and clinical phenotypes.

PennDiff: detecting differential alternative splicing and transcription by RNA sequencing

Motivation

Alternative splicing and alternative transcription are a major mechanism for generating transcriptome diversity. Differential alternative splicing and transcription (DAST), which describe different usage of transcript isoforms across different conditions, can complement differential expression in characterizing gene regulation. However, the analysis of DAST is challenging because only a small fraction of RNA-seq reads is informative for isoforms. Several methods have been developed to detect exon-based and gene-based DAST, but they suffer from power loss for genes with many isoforms.

Results

We present PennDiff, a novel statistical method that makes use of information on gene structures and pre-estimated isoform relative abundances, to detect DAST from RNA-seq data. PennDiff has several advantages. First, grouping exons avoids multiple testing for 'exons' originated from the same isoform(s). Second, it utilizes all available reads in exon-inclusion level estimation, which is different from methods that only use junction reads. Third, collapsing isoforms sharing the same alternative exons reduces the impact of isoform expression estimation uncertainty. PennDiff is able to detect DAST at both exon and gene levels, thus offering more flexibility than existing methods. Simulations and analysis of a real RNA-seq dataset indicate that PennDiff has well-controlled type I error rate, and is more powerful than existing methods including DEXSeq, rMATS, Cuffdiff, IUTA and SplicingCompass. As the popularity of RNA-seq continues to grow, we expect PennDiff to be useful for diverse transcriptomics studies.

Availability and implementation

PennDiff source code and user guide is freely available for download at https://github.com/tigerhu15/PennDiff.

Supplementary information

Supplementary data are available at Bioinformatics online.

The Length of the Expressed 3' UTR Is an Intermediate Molecular Phenotype Linking Genetic Variants to Complex Diseases

In the last decades, genome-wide association studies (GWAS) have uncovered tens of thousands of associations between common genetic variants and complex diseases. However, these statistical associations can rarely be interpreted functionally and mechanistically. As the majority of the disease-associated variants are located far from coding sequences, even the relevant gene is often unclear. A way to gain insight into the relevant mechanisms is to study the genetic determinants of intermediate molecular phenotypes, such as gene expression and transcript structure. We propose a computational strategy to discover genetic variants affecting the relative expression of alternative 3′ untranslated region (UTR) isoforms, generated through alternative polyadenylation, a widespread posttranscriptional regulatory mechanism known to have relevant functional consequences. When applied to a large dataset in which whole genome and RNA sequencing data are available for 373 European individuals, 2,530 genes with alternative polyadenylation quantitative trait loci (apaQTL) were identified. We analyze and discuss possible mechanisms of action of these variants, and we show that they are significantly enriched in GWAS hits, in particular those concerning immune-related and neurological disorders. Our results point to an important role for genetically determined alternative polyadenylation in affecting predisposition to complex diseases, and suggest new ways to extract functional information from GWAS data.

Cloudy with a Chance of Insights: Context Dependent Gene Regulation and Implications for Evolutionary Studies

Research in various fields of evolutionary biology has shown that divergence in gene expression is a key driver for phenotypic evolution. An exceptional contribution of cis-regulatory divergence has been found to contribute to morphological diversification. In the light of these findings, the analysis of genome-wide expression data has become one of the central tools to link genotype and phenotype information on a more mechanistic level. However, in many studies, especially if general conclusions are drawn from such data, a key feature of gene regulation is often neglected. With our article, we want to raise awareness that gene regulation and thus gene expression is highly context dependent. Genes show tissue- and stage-specific expression. We argue that the regulatory context must be considered in comparative expression studies.

Cis-regulated alternative splicing divergence and its potential contribution to environmental responses in Arabidopsis

Alternative splicing (AS) plays key roles in plant development and the responses of plants to environmental changes. However, the mechanisms underlying AS divergence (differential expression of transcript isoforms resulting from AS) in plant accessions and its contribution to responses to environmental stimuli remain unclear. In this study, we investigated genome-wide variation of AS in Arabidopsis thaliana accessions Col-0, Bur-0, C24, Kro-0 and Ler-1, as well as their F₁ hybrids, and characterized the regulatory mechanisms for AS divergence by RNA sequencing. We found that most of the divergent AS events in Arabidopsis accessions were cis-regulated by sequence variation, including those in core splice site and splicing motifs. Many genes that differed in AS between Col-0 and Bur-0 were involved in stimulus responses. Further genome-wide association analyses of 22 environmental variables showed that single nucleotide polymorphisms influencing known splice site strength were also associated with environmental stress responses. These results demonstrate that cis-variation in genomic sequences among Arabidopsis accessions was the dominant contributor to AS divergence, and it may contribute to differences in environmental responses among Arabidopsis accessions.

Understanding human DNA variants affecting pre-mRNA splicing in the NGS era

Pre-mRNA splicing, an essential step in eukaryotic gene expression, relies on recognition of short sequences on the primary transcript intron ends and takes place along transcription by RNA polymerase II. Exonic and intronic auxiliary elements may modify the strength of exon definition and intron recognition. Splicing DNA variants (SV) have been associated with human genetic diseases at canonical intron sites, as well as exonic substitutions putatively classified as nonsense, missense or synonymous variants. Their effects on mRNA may be modulated by cryptic splice sites associated to the SV allele, comprehending exon skipping or shortening, and partial or complete intron retention. As splicing mRNA outputs result from combinatorial effects of both intrinsic and extrinsic factors, in vitro functional assays supported by computational analyses are recommended to assist SV pathogenicity assessment for human Mendelian inheritance diseases. The increasing use of next-generating sequencing (NGS) targeting full genomic gene sequence has raised awareness of the relevance of deep intronic SV in genetic diseases and inclusion of pseudo-exons into mRNA. Finally, we take advantage of recent advances in sequencing and computational technologies to analyze alternative splicing in cancer. We explore the Catalog of Somatic Mutations in Cancer (COSMIC) to describe the proportion of splice-site mutations in cis and trans regulatory elements. Genomic data from large cohorts of different cancer types are increasingly available, in addition to repositories of normal and somatic genetic variations. These are likely to bring new insights to understanding the genetic control of alternative splicing by mapping splicing quantitative trait loci in tumors.

Genetic and mechanistic basis for APOBEC3H alternative splicing, retrovirus restriction, and counteraction by HIV-1 protease

Human APOBEC3H (A3H) is a single-stranded DNA cytosine deaminase that inhibits HIV-1. Seven haplotypes (I–VII) and four splice variants (SV154/182/183/200) with differing antiviral activities and geographic distributions have been described, but the genetic and mechanistic basis for variant expression and function remains unclear. Using a combined bioinformatic/experimental analysis, we find that SV200 expression is specific to haplotype II, which is primarily found in sub-Saharan Africa. The underlying genetic mechanism for differential mRNA splicing is an ancient intronic deletion [del(ctc)] within A3H haplotype II sequence. We show that SV200 is at least fourfold more HIV-1 restrictive than other A3H splice variants. To counteract this elevated antiviral activity, HIV-1 protease cleaves SV200 into a shorter, less restrictive isoform. Our analyses indicate that, in addition to Vif-mediated degradation, HIV-1 may use protease as a  counter-defense mechanism against A3H in >80% of sub-Saharan African populations.

Human APOBEC3H has several haplotypes and splice variants with distinct anti-HIV-1 activities, but the genetics underlying the expression of these variants are unclear. Here, the authors identify an intronic deletion in A3H haplotype II resulting in production of the most active splice variant, which is counteracted by HIV-1 protease.

Splicing heterogeneity: separating signal from noise

Single-cell analyses have revealed a tremendous variety among cells in the abundance and chemical composition of RNA. Much of this heterogeneity is due to alternative splicing by the spliceosome. Little is known about how many of the resulting isoforms are biologically functional or just provide noise with little to no impact. The dynamic nature of the spliceosome provides numerous opportunities for regulation but is also the source of stochastic fluctuations. We discuss possible origins of splicing stochasticity, the experimental approaches for studying heterogeneity in isoforms, and the potential biological significance of noisy splicing in development and disease.

Genome-Wide Association Analyses Reveal the Importance of Alternative Splicing in Diversifying Gene Function and Regulating Phenotypic Variation in Maize

Alternative splicing (AS) enhances transcriptome diversity and plays important roles in regulating plant processes. Although widespread natural variation in AS has been observed in plants, how AS is regulated and contribute to phenotypic variation is poorly understood. Here, we report a population-level transcriptome assembly and genome-wide association study to identify splicing quantitative trait loci (sQTLs) in developing maize (Zea mays) kernels from 368 inbred lines. We detected 19,554 unique sQTLs for 6570 genes. Most sQTLs showed small isoform usage changes without involving major isoform switching between genotypes. The sQTL-affected isoforms tend to display distinct protein functions. We demonstrate that nonsense-mediated mRNA decay, microRNA-mediated regulation, and small interfering peptide-mediated peptide interference are frequently involved in sQTL regulation. The natural variation in AS and overall mRNA level appears to be independently regulated with different cis-sequences preferentially used. We identified 214 putative trans-acting splicing regulators, among which ZmGRP1, encoding an hnRNP-like glycine-rich RNA binding protein, regulates the largest trans-cluster. Knockout of ZmGRP1 by CRISPR/Cas9 altered splicing of numerous downstream genes. We found that 739 sQTLs colocalized with previous marker-trait associations, most of which occurred without changes in overall mRNA level. Our findings uncover the importance of AS in diversifying gene function and regulating phenotypic variation.

Gene Regulatory Network Perturbation by Genetic and Epigenetic Variation

Gene regulatory networks underlie biological function and cellular physiology. Alternative splicing (AS) is a fundamental step in gene regulatory networks and plays a key role in development and disease. In addition to the identification of aberrant AS events, an increasing number of studies are focusing on molecular determinants of AS, including genetic and epigenetic regulators. We review here recent efforts to identify various deregulated AS events as well as their molecular determinants that alter biological functions, and discuss clinical features of AS and their druggable potential.