Identification of common genetic variants controlling transcript isoform variation in human whole blood

Identification and characterization of whole blood gene expression and splicing quantitative trait loci during early to mid-lactation of dairy cattle

BACKGROUND

Characterization of regulatory variants (e.g., gene expression quantitative trait loci, eQTL; gene splicing QTL, sQTL) is crucial for biologically interpreting molecular mechanisms underlying loci associated with complex traits. However, regulatory variants in dairy cattle, particularly in specific biological contexts (e.g., distinct lactation stages), remain largely unknown. In this study, we explored regulatory variants in whole blood samples collected during early to mid-lactation (22-150 days after calving) of 101 Holstein cows and analyzed them to decipher the regulatory mechanisms underlying complex traits in dairy cattle.

RESULTS

We identified 14,303 genes and 227,705 intron clusters expressed in the white blood cells of 101 cattle. The average heritability of gene expression and intron excision ratio explained by cis-SNPs is 0.28 ± 0.13 and 0.25 ± 0.13, respectively. We identified 23,485 SNP-gene expression pairs and 18,166 SNP-intron cluster pairs in dairy cattle during early to mid-lactation. Compared with the 2,380,457 cis-eQTLs reported to be present in blood in the Cattle Genotype-Tissue Expression atlas (CattleGTEx), only 6,114 cis-eQTLs (P < 0.05) were detected in the present study. By conducting colocalization analysis between cis-e/sQTL and the results of genome-wide association studies (GWAS) from four traits, we identified a cis-e/sQTL (rs109421300) of the DGAT1 gene that might be a key marker in early to mid-lactation for milk yield, fat yield, protein yield, and somatic cell score (PP4 > 0.6). Finally, transcriptome-wide association studies (TWAS) revealed certain genes (e.g., FAM83H and TBC1D17) whose expression in white blood cells was significantly (P < 0.05) associated with complex traits.

CONCLUSIONS

This study investigated the genetic regulation of gene expression and alternative splicing in dairy cows during early to mid-lactation and provided new insights into the regulatory mechanisms underlying complex traits of economic importance.

Comparing feature selection and machine learning approaches for predicting CYP2D6 methylation from genetic variation

Introduction

Pharmacogenetics currently supports clinical decision-making on the basis of a limited number of variants in a few genes and may benefit paediatric prescribing where there is a need for more precise dosing. Integrating genomic information such as methylation into pharmacogenetic models holds the potential to improve their accuracy and consequently prescribing decisions. Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene conventionally associated with the metabolism of commonly used drugs and endogenous substrates. We thus sought to predict epigenetic loci from single nucleotide polymorphisms (SNPs) related to CYP2D6 in children from the GUSTO cohort.

Methods

Buffy coat DNA methylation was quantified using the Illumina Infinium Methylation EPIC beadchip. CpG sites associated with CYP2D6 were used as outcome variables in Linear Regression, Elastic Net and XGBoost models. We compared feature selection of SNPs from GWAS mQTLs, GTEx eQTLs and SNPs within 2 MB of the CYP2D6 gene and the impact of adding demographic data. The samples were split into training (75%) sets and test (25%) sets for validation. In Elastic Net model and XGBoost models, optimal hyperparameter search was done using 10-fold cross validation. Root Mean Square Error and R-squared values were obtained to investigate each models' performance. When GWAS was performed to determine SNPs associated with CpG sites, a total of 15 SNPs were identified where several SNPs appeared to influence multiple CpG sites.

Results

Overall, Elastic Net models of genetic features appeared to perform marginally better than heritability estimates and substantially better than Linear Regression and XGBoost models. The addition of nongenetic features appeared to improve performance for some but not all feature sets and probes. The best feature set and Machine Learning (ML) approach differed substantially between CpG sites and a number of top variables were identified for each model.

Discussion

The development of SNP-based prediction models for CYP2D6 CpG methylation in Singaporean children of varying ethnicities in this study has clinical application. With further validation, they may add to the set of tools available to improve precision medicine and pharmacogenetics-based dosing.

Disentangling the complexity of psoriasis in the post-genome-wide association era

In recent years, genome-wide association studies (GWAS) have been instrumental in unraveling the genetic architecture of complex diseases, including psoriasis. The application of large-scale GWA studies in psoriasis has illustrated several associated loci that participate in the cutaneous inflammation, however explaining a fraction of the disease heritability. With the advent of high-throughput sequencing technologies and functional genomics approaches, the post-GWAS era aims to unravel the functional mechanisms underlying the inter-individual variability in psoriasis patients. In this review, we present the key advances of psoriasis GWAS in under-represented populations, rare, non-coding and structural variants and epistatic phenomena that orchestrate the interplay between different cell types. We further review the gene-gene and gene-environment interactions contributing to the disease predisposition and development of comorbidities through Mendelian randomization studies and pleiotropic effects of psoriasis-associated loci. We finally examine the holistic approaches conducted in psoriasis through system genetics and state-of-the-art transcriptomic analyses, discussing their potential implication in the expanding field of precision medicine and characterization of comorbidities.

Genome- and transcriptome-wide splicing associations with alcohol use disorder

Genetic mechanisms of alternative mRNA splicing have been shown in the brain for a variety of neuropsychiatric traits, but not substance use disorders. Our study utilized RNA-sequencing data on alcohol use disorder (AUD) in four brain regions (n = 56; ages 40-73; 100% 'Caucasian'; PFC, NAc, BLA and CEA) and genome-wide association data on AUD (n = 435,563, ages 22-90; 100% European-American). Polygenic scores of AUD were associated with AUD-related alternative mRNA splicing in the brain. We identified 714 differentially spliced genes between AUD vs controls, which included both putative addiction genes and novel gene targets. We found 6463 splicing quantitative trait loci (sQTLs) that linked to the AUD differentially spliced genes. sQTLs were enriched in loose chromatin genomic regions and downstream gene targets. Additionally, the heritability of AUD was enriched for DNA variants in and around differentially spliced genes associated with AUD. Our study also performed splicing transcriptome-wide association studies (TWASs) of AUD and other drug use traits that unveiled specific genes for follow-up and splicing correlations across SUDs. Finally, we showed that differentially spliced genes between AUD vs control were also associated with primate models of chronic alcohol consumption in similar brain regions. Our study found substantial genetic contributions of alternative mRNA splicing in AUD.

The molecular genetic basis of creativity: a mini review and perspectives

Although creativity is one of the defining features of human species, it is just the beginning of an ambitious attempt for psychologists to understand its genetic basis. With ongoing efforts, great progress has been achieved in molecular genetic studies of creativity. In this mini review, we highlighted recent molecular genetic findings for both domain-general and domain-specific creativity, and provided some perspectives for future studies. It is expected that this work will provide an update on the knowledge regarding the molecular genetic basis of creativity, and contribute to the further development of this field.

Investigating the prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits

As popularised by PrediXcan (and related methods), transcriptome-wide association studies (TWAS), in which gene expression is imputed from single-nucleotide polymorphism (SNP) genotypes and tested for association with a phenotype, are a popular approach for investigating the role of gene expression in complex traits. Like gene expression, DNA methylation is an important biological process and, being under genetic regulation, may be imputable from SNP genotypes. Here, we investigate prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits. We start by examining how well CpG methylation can be predicted from SNP genotypes, comparing three penalised regression approaches and examining whether changing the window size improves prediction accuracy. Although methylation at most CpG sites cannot be accurately predicted from SNP genotypes, for a subset it can be predicted well. We next apply our methylation prediction models (trained using the optimal method and window size) to carry out a methylome-wide association study (MWAS) of primary biliary cholangitis. We intersect the regions identified via MWAS with those identified via TWAS, providing insight into the interplay between CpG methylation, gene expression and disease status. We conclude that MWAS has the potential to improve understanding of biological mechanisms in complex traits.

Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS

Genome-wide association study (GWAS) is the most popular approach to dissecting complex traits in plants, humans, and animals. Numerous methods and tools have been proposed to discover the causal variants for GWAS data analysis. Among them, linear mixed models (LMMs) are widely used statistical methods for regulating confounding factors, including population structure, resulting in increased computational proficiency and statistical power in GWAS studies. Recently more attention has been paid to pleiotropy, multi-trait, gene-gene interaction, gene-environment interaction, and multi-locus methods with the growing availability of large-scale GWAS data and relevant phenotype samples. In this review, we have demonstrated all possible LMMs-based methods available in the literature for GWAS. We briefly discuss the different LMM methods, software packages, and available open-source applications in GWAS. Then, we include the advantages and weaknesses of the LMMs in GWAS. Finally, we discuss the future perspective and conclusion. The present review paper would be helpful to the researchers for selecting appropriate LMM models and methods quickly for GWAS data analysis and would benefit the scientific society.

Bridging the splicing gap in human genetics with long-read RNA sequencing: finding the protein isoform drivers of disease

Aberrant splicing underlies many human diseases, including cancer, cardiovascular diseases and neurological disorders. Genome-wide mapping of splicing quantitative trait loci (sQTLs) has shown that genetic regulation of alternative splicing is widespread. However, identification of the corresponding isoform or protein products associated with disease-associated sQTLs is challenging with short-read RNA-seq, which cannot precisely characterize full-length transcript isoforms. Furthermore, contemporary sQTL interpretation often relies on reference transcript annotations, which are incomplete. Solutions to these issues may be found through integration of newly emerging long-read sequencing technologies. Long-read sequencing offers the capability to sequence full-length mRNA transcripts and, in some cases, to link sQTLs to transcript isoforms containing disease-relevant protein alterations. Here, we provide an overview of sQTL mapping approaches, the use of long-read sequencing to characterize sQTL effects on isoforms, the linkage of RNA isoforms to protein-level functions and comment on future directions in the field. Based on recent progress, long-read RNA sequencing promises to be part of the human disease genetics toolkit to discover and treat protein isoforms causing rare and complex diseases.

Evaluation and characterization of expression quantitative trait analysis methods in the Hybrid Rat Diversity Panel

The Hybrid Rat Diversity Panel (HRDP) is a stable and well-characterized set of more than 90 inbred rat strains that can be leveraged for systems genetics approaches to understanding the genetic and genomic variation associated with complex disease. The HRDP exhibits substantial between-strain diversity while retaining substantial within-strain isogenicity, allowing for the precise mapping of genetic variation associated with complex phenotypes and providing statistical power to identify associated variants. In order to robustly identify associated genetic variants, it is important to account for the population structure induced by inbreeding. To this end, we investigate the performance of four plausible approaches towards modeling quantitative traits in the HRDP and quantify their operating characteristics. In particular, we investigate three approaches based on genome-wide mixed model analysis, and one approach based on ordinary least squares linear regression. Towards facilitating study planning and design, we conduct extensive simulations to investigate the power of genetic association analyses in the HRDP, and characterize the impressive attained power. In simulation of eQTL data in the HRDP, we find that a mixed model approach that leverages leave-one-chromosome-out kinship estimation attains the highest power while controlling type I error.

eQTL Set-Based Association Analysis Identifies Novel Susceptibility Loci for Barrett Esophagus and Esophageal Adenocarcinoma

BACKGROUND

Over 20 susceptibility single-nucleotide polymorphisms (SNP) have been identified for esophageal adenocarcinoma (EAC) and its precursor, Barrett esophagus (BE), explaining a small portion of heritability.

METHODS

Using genetic data from 4,323 BE and 4,116 EAC patients aggregated by international consortia including the Barrett's and Esophageal Adenocarcinoma Consortium (BEACON), we conducted a comprehensive transcriptome-wide association study (TWAS) for BE/EAC, leveraging Genotype Tissue Expression (GTEx) gene-expression data from six tissue types of plausible relevance to EAC etiology: mucosa and muscularis from the esophagus, gastroesophageal (GE) junction, stomach, whole blood, and visceral adipose. Two analytical approaches were taken: standard TWAS using the predicted gene expression from local expression quantitative trait loci (eQTL), and set-based SKAT association using selected eQTLs that predict the gene expression.

RESULTS

Although the standard approach did not identify significant signals, the eQTL set-based approach identified eight novel associations, three of which were validated in independent external data (eQTL SNP sets for EXOC3, ZNF641, and HSP90AA1).

CONCLUSIONS

This study identified novel genetic susceptibility loci for EAC and BE using an eQTL set-based genetic association approach.

IMPACT

This study expanded the pool of genetic susceptibility loci for EAC and BE, suggesting the potential of the eQTL set-based genetic association approach as an alternative method for TWAS analysis.

Molecular Quantitative Trait Locus Mapping in Human Complex Diseases

Mapping quantitative trait loci (QTLs) for molecular traits from chromatin to metabolites (i.e., xQTLs) provides insight into the locations and effect modes of genetic variants that influence these molecular phenotypes and the propagation of functional consequences of each variant. xQTL studies indirectly interrogate the functional landscape of the molecular basis of complex diseases, including the impact of non-coding regulatory variants, the tissue specificity of regulatory elements, and their contribution to disease by integrating with genome-wide association studies (GWAS). We summarize a variety of molecular xQTL studies in human tissues and cells. In addition, using the Alzheimer's Disease Sequencing Project (ADSP) as an example, we describe the ADSP xQTL project, a collaborative effort across the ADSP Functional Genomics Consortium (ADSP-FGC). The project's ultimate goal is a reference map of Alzheimer's-related QTLs using existing datasets from multiple omics layers to help us study the consequences of genetic variants identified in the ADSP. xQTL studies enable the identification of the causal genes and pathways in GWAS loci, which will likely aid in the discovery of novel biomarkers and therapeutic targets for complex diseases. © 2022 Wiley Periodicals LLC.

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

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

A transcriptome-wide association study to detect novel genes for volumetric bone mineral density

Transcriptome-wide association studies (TWAS) systematically investigate the association of genetically predicted gene expression with disease risk, providing an effective approach to identify novel susceptibility genes. Osteoporosis is the most common metabolic bone disease, associated with reduced bone mineral density (BMD) and increased risk of osteoporotic fractures, whereas genetic factors explain approximately 70% of the variance in phenotypes associated with bone. BMD is commonly assessed using dual-energy X-ray absorptiometry (DXA) to obtain measurements (g/cm2) of areal BMD. However, quantitative computed tomography (QCT) measured 3D volumetric BMD (vBMD) (g/cm3) has important advantages compared with DXA since it can evaluate cortical and trabecular microstructural features of bone quality, which can be used to directly predict fracture risk. Here, we performed the first TWAS for volumetric BMD (vBMD) by integrating genome-wide association studies (GWAS) data from two independent cohorts, namely the Framingham Heart Study (FHS, n = 3298) and the Osteoporotic Fractures in Men (MrOS, n = 4641), with tissue-specific gene expression data from the Genotype-Tissue Expression (GTEx) project. We first used stratified linkage disequilibrium (LD) score regression approach to identify 12 vBMD-relevant tissues, for which vBMD heritability is enriched in tissue-specific genes of the given tissue. Focusing on these tissues, we subsequently leveraged GTEx expression reference panels to predict tissue-specific gene expression levels based on the genotype data from FHS and MrOS. The associations between predicted gene expression levels and vBMD variation were then tested by MultiXcan, an innovative TWAS method that integrates information available across multiple tissues. We identified 70 significant genes associated with vBMD, including some previously identified osteoporosis-related genes such as LYRM2 and NME8, as well as some novel loci such as DNAAF2 and SPAG16. Our findings provide novel insights into the pathophysiological mechanisms of osteoporosis and highlight several novel vBMD-associated genes that warrant further investigation.

Integrating genome-wide association and transcriptome prediction model identifies novel target genes for osteoporosis

In this study, we integrated large-scale GWAS summary data and used the predicted transcriptome-wide association study method to discover novel genes associated with osteoporosis. We identified 204 candidate genes, which provide novel clues for understanding the genetic mechanism of osteoporosis and indicate potential therapeutic targets.

INTRODUCTION

Osteoporosis is a highly polygenetic disease characterized by low bone mass and deterioration of the bone microarchitecture. Our objective was to discover novel candidate genes associated with osteoporosis.

METHODS

To identify potential causal genes of the associated loci, we investigated trait-gene expression associations using the transcriptome-wide association study (TWAS) method. This method directly imputes gene expression effects from genome-wide association study (GWAS) data using a statistical prediction model trained on GTEx reference transcriptome data. We then performed a colocalization analysis to evaluate the posterior probability of biological patterns: associations characterized by a single causal variant or multiple distinct causal variants. Finally, a functional enrichment analysis of gene sets was performed using the VarElect and CluePedia tools, which assess the causal relationships between genes and a disease and search for potential gene's functional pathways. The osteoporosis-associated genes were further confirmed based on the differentially expressed genes profiled from mRNA expression data of bone tissue.

RESULTS

Our analysis identified 204 candidate genes, including 154 genes that have been previously associated with osteoporosis, 50 genes that have not been previously discovered. A biological function analysis found that 20 of the candidate genes were directly associated with osteoporosis. Further analysis of multiple gene expression profiles showed that 15 genes were differentially expressed in patients with osteoporosis. Among these, SLC11A2, MAP2K5, NFATC4, and HSP90B1 were enriched in four pathways, namely, mineral absorption pathway, MAPK signaling pathway, Wnt signaling pathway, and PI3K-Akt signaling pathway, which indicates a causal relationship with the occurrence of osteoporosis.

CONCLUSIONS

We demonstrated that transcriptome fine-mapping identifies more osteoporosis-related genes and provides key insight into the development of novel targeted therapeutics for the treatment of osteoporosis.

Integrating Transcriptome-Wide Association Study and mRNA Expression Profiling Identifies Novel Genes Associated With Osteonecrosis of the Femoral Head

Objective

This study aims to identify novel candidate genes associated with osteonecrosis of the femoral head (ONFH).

Methods

A transcriptome-wide association study (TWAS) was performed by integrating the genome-wide association study dataset of osteonecrosis (ON) in the UK Biobank with pre-computed mRNA expression reference weights of muscle skeleton (MS) and blood. The ON-associated genes identified by TWAS were further subjected to gene ontology (GO) analysis by the DAVID tool. Finally, a trans-omics comparative analysis of TWAS and genome-wide mRNA expression profiling was conducted to identify the common genes and the GO terms shared by both DNA-level TWAS and mRNA-level expression profile for ONFH.

Results

TWAS totally identified 564 genes that were with P_TWAS value <0.05 for MS and blood, such as CBX1 (P_TWAS = 0.0001 for MS), SRPK2 (P_TWAS = 0.0002 for blood), and MYO5A (P_TWAS = 0.0005 for blood). After comparing the genes detected by TWAS with the differentially expressed genes identified by mRNA expression profiling, we detected 59 overlapped genes, such as STEAP4 [P_TWAS = 0.0270, FC (fold change)_mRNA = 7.03], RABEP1 (P_TWAS = 0.010, FC_mRNA = 2.22), and MORC3 (P_TWAS = 0.0053, FC_mRNA = 2.92). The GO analysis of TWAS-identified genes discovered 53 GO terms for ON. Further comparing the GO results of TWAS and mRNA expression profiling identified four overlapped GO terms, including cysteine-type endopeptidase activity (P_TWAS = 0.0006, P_mRNA = 0.0227), extracellular space (P_TWAS = 0.0342, P_mRNA = 0.0012), protein binding (P_TWAS = 0.0112, P_mRNA = 0.0106), and ATP binding (P_TWAS = 0.0464, P_mRNA = 0.0033).

Conclusion

Several ONFH-associated genes and GO terms were identified by integrating TWAS and mRNA expression profiling. It provides novel clues to reveal the pathogenesis of ONFH.

Uncovering genetic mechanisms of hypertension through multi-omic analysis of the kidney

The kidney is an organ of key relevance to blood pressure (BP) regulation, hypertension and antihypertensive treatment. However, genetically mediated renal mechanisms underlying susceptibility to hypertension remain poorly understood. We integrated genotype, gene expression, alternative splicing and DNA methylation profiles of up to 430 human kidneys to characterize the effects of BP index variants from genome-wide association studies (GWASs) on renal transcriptome and epigenome. We uncovered kidney targets for 479 (58.3%) BP-GWAS variants and paired 49 BP-GWAS kidney genes with 210 licensed drugs. Our colocalization and Mendelian randomization analyses identified 179 unique kidney genes with evidence of putatively causal effects on BP. Through Mendelian randomization, we also uncovered effects of BP on renal outcomes commonly affecting patients with hypertension. Collectively, our studies identified genetic variants, kidney genes, molecular mechanisms and biological pathways of key relevance to the genetic regulation of BP and inherited susceptibility to hypertension.

Cell-type-specific expression quantitative trait loci associated with Alzheimer disease in blood and brain tissue

Because regulation of gene expression is heritable and context-dependent, we investigated AD-related gene expression patterns in cell types in blood and brain. Cis-expression quantitative trait locus (eQTL) mapping was performed genome-wide in blood from 5257 Framingham Heart Study (FHS) participants and in brain donated by 475 Religious Orders Study/Memory & Aging Project (ROSMAP) participants. The association of gene expression with genotypes for all cis SNPs within 1 Mb of genes was evaluated using linear regression models for unrelated subjects and linear-mixed models for related subjects. Cell-type-specific eQTL (ct-eQTL) models included an interaction term for the expression of "proxy" genes that discriminate particular cell type. Ct-eQTL analysis identified 11,649 and 2533 additional significant gene-SNP eQTL pairs in brain and blood, respectively, that were not detected in generic eQTL analysis. Of note, 386 unique target eGenes of significant eQTLs shared between blood and brain were enriched in apoptosis and Wnt signaling pathways. Five of these shared genes are established AD loci. The potential importance and relevance to AD of significant results in myeloid cell types is supported by the observation that a large portion of GWS ct-eQTLs map within 1 Mb of established AD loci and 58% (23/40) of the most significant eGenes in these eQTLs have previously been implicated in AD. This study identified cell-type-specific expression patterns for established and potentially novel AD genes, found additional evidence for the role of myeloid cells in AD risk, and discovered potential novel blood and brain AD biomarkers that highlight the importance of cell-type-specific analysis.

Genetic Effects on Transcriptome Profiles in Colon Epithelium Provide Functional Insights for Genetic Risk Loci

BACKGROUND & AIMS

The association of genetic variation with tissue-specific gene expression and alternative splicing guides functional characterization of complex trait-associated loci and may suggest novel genes implicated in disease. Here, our aims were as follows: (1) to generate reference profiles of colon mucosa gene expression and alternative splicing and compare them across colon subsites (ascending, transverse, and descending), (2) to identify expression and splicing quantitative trait loci (QTLs), (3) to find traits for which identified QTLs contribute to single-nucleotide polymorphism (SNP)-based heritability, (4) to propose candidate effector genes, and (5) to provide a web-based visualization resource.

METHODS

We collected colonic mucosal biopsy specimens from 485 healthy adults and performed bulk RNA sequencing. We performed genome-wide SNP genotyping from blood leukocytes. Statistical approaches and bioinformatics software were used for QTL identification and downstream analyses.

RESULTS

We provided a complete quantification of gene expression and alternative splicing across colon subsites and described their differences. We identified thousands of expression and splicing QTLs and defined their enrichment at genome-wide regulatory regions. We found that part of the SNP-based heritability of diseases affecting colon tissue, such as colorectal cancer and inflammatory bowel disease, but also of diseases affecting other tissues, such as psychiatric conditions, can be explained by the identified QTLs. We provided candidate effector genes for multiple phenotypes. Finally, we provided the Colon Transcriptome Explorer web application.

CONCLUSIONS

We provide a large characterization of gene expression and splicing across colon subsites. Our findings provide greater etiologic insight into complex traits and diseases influenced by transcriptomic changes in colon tissue.

Genome-wide fine-mapping identifies pleiotropic and functional variants that predict many traits across global cattle populations

The difficulty in finding causative mutations has hampered their use in genomic prediction. Here, we present a methodology to fine-map potentially causal variants genome-wide by integrating the functional, evolutionary and pleiotropic information of variants using GWAS, variant clustering and Bayesian mixture models. Our analysis of 17 million sequence variants in 44,000+ Australian dairy cattle for 34 traits suggests, on average, one pleiotropic QTL existing in each 50 kb chromosome-segment. We selected a set of 80k variants representing potentially causal variants within each chromosome segment to develop a bovine XT-50K genotyping array. The custom array contains many pleiotropic variants with biological functions, including splicing QTLs and variants at conserved sites across 100 vertebrate species. This biology-informed custom array outperformed the standard array in predicting genetic value of multiple traits across populations in independent datasets of 90,000+ dairy cattle from the USA, Australia and New Zealand.

A fusion method based on alignment software with SNP and Indel detection methods

BACKGROUND

With the advent of the second generation sequencing technology, the discovery of sequence alignment and sequence variation is a long-standing challenge.

RESULTS

A method based on general alignment software, SNP and Indel software tools was proposed in this paper. By comparing the advantages of traditional alignment software, we can produce the best alignment. SNP and Indel detection tools fusion research found that different depth of sequencing effect is different. When the sequence depth reaches a certain value, select one of the software for testing.

CONCLUSIONS

Finally, the experimental verification shows that SNP and Indel have certain advantages in the comparison of the effects of the fusion method.

Gene Expression and RNA Splicing Imputation Identifies Novel Candidate Genes Associated with Osteoporosis

CONTEXT

Though genome-wide association studies (GWASs) have identified hundreds of genetic variants associated with osteoporosis related traits, such as bone mineral density (BMD) and fracture, it remains a challenge to interpret their biological functions and underlying biological mechanisms.

OBJECTIVE

Integrate diverse expression quantitative trait loci and splicing quantitative trait loci data with several powerful GWAS datasets to identify novel candidate genes associated with osteoporosis.

DESIGN, SETTING, AND PARTICIPANTS

Here, we conducted a transcriptome-wide association study (TWAS) for total body BMD (TB-BMD) (n = 66 628 for discovery and 7697 for validation) and fracture (53 184 fracture cases and 373 611 controls for discovery and 37 857 cases and 227 116 controls for validation), respectively. We also conducted multi-SNP-based summarized mendelian randomization analysis to further validate our findings.

RESULTS

In total, we detected 88 genes significantly associated with TB-BMD or fracture through expression or ribonucleic acid splicing. Summarized mendelian randomization analysis revealed that 78 of the significant genes may have potential causal effects on TB-BMD or fracture in at least 1 specific tissue. Among them, 64 genes have been reported in previous GWASs or TWASs for osteoporosis, such as ING3, CPED1, and WNT16, as well as 14 novel genes, such as DBF4B, GRN, TMUB2, and UNC93B1.

CONCLUSIONS

Overall, our findings provide novel insights into the pathogenesis mechanisms of osteoporosis and highlight the power of a TWAS to identify and prioritize potential causal genes.

Cell type-specific genetic regulation of gene expression across human tissues

The Genotype-Tissue Expression (GTEx) project has identified expression and splicing quantitative trait loci in cis (QTLs) for the majority of genes across a wide range of human tissues. However, the functional characterization of these QTLs has been limited by the heterogeneous cellular composition of GTEx tissue samples. We mapped interactions between computational estimates of cell type abundance and genotype to identify cell type-interaction QTLs for seven cell types and show that cell type-interaction expression QTLs (eQTLs) provide finer resolution to tissue specificity than bulk tissue cis-eQTLs. Analyses of genetic associations with 87 complex traits show a contribution from cell type-interaction QTLs and enables the discovery of hundreds of previously unidentified colocalized loci that are masked in bulk tissue.

Pathogenic impact of transcript isoform switching in 1,209 cancer samples covering 27 cancer types using an isoform-specific interaction network

Under normal conditions, cells of almost all tissue types express the same predominant canonical transcript isoform at each gene locus. In cancer, however, splicing regulation is often disturbed, leading to cancer-specific switches in the most dominant transcripts (MDT). To address the pathogenic impact of these switches, we have analyzed isoform-specific protein-protein interaction disruptions in 1,209 cancer samples covering 27 different cancer types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project of the International Cancer Genomics Consortium (ICGC). Our study revealed large variations in the number of cancer-specific MDT (cMDT) with the highest frequency in cancers of female reproductive organs. Interestingly, in contrast to the mutational load, cancers arising from the same primary tissue had a similar number of cMDT. Some cMDT were found in 100% of all samples in a cancer type, making them candidates for diagnostic biomarkers. cMDT tend to be located at densely populated network regions where they disrupted protein interactions in the proximity of pathogenic cancer genes. A gene ontology enrichment analysis showed that these disruptions occurred mostly in protein translation and RNA splicing pathways. Interestingly, samples with mutations in the spliceosomal complex tend to have higher number of cMDT, while other transcript expressions correlated with mutations in non-coding splice-site and promoter regions of their genes. This work demonstrates for the first time the large extent of cancer-specific alterations in alternative splicing for 27 different cancer types. It highlights distinct and common patterns of cMDT and suggests novel pathogenic transcripts and markers that induce large network disruptions in cancers.

The Role of Noncoding Variants in Heritable Disease

The genetic basis of disease has largely focused on coding regions. However, it has become clear that a large proportion of the noncoding genome is functional and harbors genetic variants that contribute to disease etiology. Here, we review recent examples of inherited noncoding alterations that are responsible for Mendelian disorders or act to influence complex traits. We explore both rare and common genetic variants and discuss the wide range of mechanisms by which they affect gene regulation to promote disease. We also debate the challenges and progress associated with identifying and interpreting the functional and clinical significance of genetic variation in the context of the noncoding regulatory landscape.

Splicing mutations in inherited retinal diseases

Mutations which induce aberrant transcript splicing represent a distinct class of disease-causing genetic variants in retinal disease genes. Such mutations may either weaken or erase regular splice sites or create novel splice sites which alter exon recognition. While mutations affecting the canonical GU-AG dinucleotides at the splice donor and splice acceptor site are highly predictive to cause a splicing defect, other variants in the vicinity of the canonical splice sites or those affecting additional cis-acting regulatory sequences within exons or introns are much more difficult to assess or even to recognize and require additional experimental validation. Splicing mutations are unique in that the actual outcome for the transcript (e.g. exon skipping, pseudoexon inclusion, intron retention) and the encoded protein can be quite different depending on the individual mutation. In this article, we present an overview on the current knowledge about and impact of splicing mutations in inherited retinal diseases. We introduce the most common sub-classes of splicing mutations including examples from our own work and others and discuss current strategies for the identification and validation of splicing mutations, as well as therapeutic approaches, open questions, and future perspectives in this field of research.

CancerSplicingQTL: a database for genome-wide identification of splicing QTLs in human cancer

Alternative splicing (AS) is a widespread process that increases structural transcript variation and proteome diversity. Aberrant splicing patterns are frequently observed in cancer initiation, progress, prognosis and therapy. Increasing evidence has demonstrated that AS events could undergo modulation by genetic variants. The identification of splicing quantitative trait loci (sQTLs), genetic variants that affect AS events, might represent an important step toward fully understanding the contribution of genetic variants in disease development. However, no database has yet been developed to systematically analyze sQTLs across multiple cancer types. Using genotype data from The Cancer Genome Atlas and corresponding AS values calculated by TCGASpliceSeq, we developed a computational pipeline to identify sQTLs from 9 026 tumor samples in 33 cancer types. We totally identified 4 599 598 sQTLs across all cancer types. We further performed survival analyses and identified 17 072 sQTLs associated with patient overall survival times. Furthermore, using genome-wide association study (GWAS) catalog data, we identified 1 180 132 sQTLs overlapping with known GWAS linkage disequilibrium regions. Finally, we constructed a user-friendly database, CancerSplicingQTL (http://www.cancersplicingqtl-hust.com/) for users to conveniently browse, search and download data of interest. This database provides an informative sQTL resource for further characterizing the potential functional roles of SNPs that control transcript isoforms in human cancer.

CoMM: a collaborative mixed model to dissecting genetic contributions to complex traits by leveraging regulatory information

MOTIVATION

Genome-wide association studies (GWASs) have been successful in identifying many genetic variants associated with complex traits. However, the mechanistic links between these variants and complex traits remain elusive. A scientific hypothesis is that genetic variants influence complex traits at the organismal level via affecting cellular traits, such as regulating gene expression and altering protein abundance. Although earlier works have already presented some scientific insights about this hypothesis and their findings are very promising, statistical methods that effectively harness multilayered data (e.g. genetic variants, cellular traits and organismal traits) on a large scale for functional and mechanistic exploration are highly demanding.

RESULTS

In this study, we propose a collaborative mixed model (CoMM) to investigate the mechanistic role of associated variants in complex traits. The key idea is built upon the emerging scientific evidence that genetic effects at the cellular level are much stronger than those at the organismal level. Briefly, CoMM combines two models: the first model relating gene expression with genotype and the second model relating phenotype with predicted gene expression using the first model. The two models are fitted jointly in CoMM, such that the uncertainty in predicting gene expression has been fully accounted. To demonstrate the advantages of CoMM over existing methods, we conducted extensive simulation studies, and also applied CoMM to analyze 25 traits in NFBC1966 and Genetic Epidemiology Research on Aging (GERA) studies by integrating transcriptome information from the Genetic European in Health and Disease (GEUVADIS) Project. The results indicate that by leveraging regulatory information, CoMM can effectively improve the power of prioritizing risk variants. Regarding the computational efficiency, CoMM can complete the analysis of NFBC1966 dataset and GERA datasets in 2 and 18 min, respectively.

AVAILABILITY AND IMPLEMENTATION

The developed R package is available at https://github.com/gordonliu810822/CoMM.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genetics' Piece of the PI: Inferring the Origin of Complex Traits and Diseases from Proteome-Wide Protein-Protein Interaction Dynamics

How do common and rare genetic polymorphisms contribute to quantitative traits or disease risk and progression? Multiple human traits have been extensively characterized at the genomic level, revealing their complex genetic architecture. However, it is difficult to resolve the mechanisms by which specific variants contribute to a phenotype. Recently, analyses of variant effects on molecular traits have uncovered intermediate mechanisms that link sequence variation to phenotypic changes. Yet, these methods only capture a fraction of genetic contributions to phenotype. Here, in reviewing the field, it is proposed that complex traits can be understood by characterizing the dynamics of biochemical networks within living cells, and that the effects of genetic variation can be captured on these networks by using protein-protein interaction (PPI) methodologies. This synergy between PPI methodologies and the genetics of complex traits opens new avenues to investigate the molecular etiology of human diseases and to facilitate their prevention or treatment.

HDAC9 is implicated in atherosclerotic aortic calcification and affects vascular smooth muscle cell phenotype

Aortic calcification is an important independent predictor of future cardiovascular events. We performed a genome-wide association meta-analysis to determine single nucleotide polymorphisms (SNPs) associated with the extent of abdominal (AAC, n = 9,417) or descending thoracic (TAC, n = 8,422) aortic calcification. Two genetic loci, HDAC9 and RAP1GAP, were associated with AAC at a genome-wide level (P < 5.0 × 10⁻⁸). No SNPs were associated with TAC at the genome-wide threshold. Increased expression of HDAC9 in human aortic smooth muscle cells (HASMCs) promoted calcification and reduced contractility, while inhibition of HDAC9 in HASMCs inhibited calcification and enhanced cell contractility. In matrix Gla protein (MGP)-deficient mice, a model of human vascular calcification, mice lacking HDAC9 had a 40% reduction in aortic calcification and improved survival. This translational genomic study identifies the first genetic risk locus associated with calcification of the abdominal aorta and describes a novel role for HDAC9 in the development of vascular calcification.

Genome-wide analyses identify variants near HDAC9 associated with abdominal aortic calcification and other cardiovascular phenotypes. Functional work shows that HDAC9 promotes an osteogenic vascular smooth muscle cell phenotype, enhancing calcification and reducing contractility.

Association of the Rheumatoid Arthritis Severity Variant rs26232 with the Invasive Activity of Synovial Fibroblasts

rs26232, located in intron one of C5orf30, is associated with the susceptibility to and severity of rheumatoid arthritis (RA). Here, we investigate the relationship between this variant and the biological activities of rheumatoid arthritis synovial fibroblasts (RASFs). RASFs were isolated from the knee joints of 33 RA patients. The rs26232 genotype was determined and cellular migration, invasion, and apoptosis were compared using in vitro techniques. The production of adhesion molecules, chemokines, and proteases was measured by ELISA or flow cytometry. Cohort genotypes were CC n = 16; CT n = 14; TT n = 3. In comparison with the RASFs of the CT genotype, the CC genotype showed a 1.48-fold greater invasiveness in vitro (p = 0.02), 1.6-fold higher expression intracellular adhesion molecule (ICAM)-1 (p = 0.001), and 5-fold IFN-γ inducible protein-10 (IP-10) (p = 0.01). There was no association of the rs26232 genotype with the expression levels of either total C5orf30 mRNA or any of the three transcript variants. The rs26232 C allele, which has previously been associated with both the risk and severity of RA, is associated with greater invasive activity of RASFs in vitro, and with higher expression of ICAM-1 and IP-10. In resting RASFs, rs26232 is not a quantitative trait locus for C5orf30 mRNA, indicating a more complex mechanism underlying the genotype‒phenotype relationship.

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.

Imputed gene associations identify replicable trans-acting genes enriched in transcription pathways and complex traits

Regulation of gene expression is an important mechanism through which genetic variation can affect complex traits. A substantial portion of gene expression variation can be explained by both local (cis) and distal (trans) genetic variation. Much progress has been made in uncovering cis-acting expression quantitative trait loci (cis-eQTL), but trans-eQTL have been more difficult to identify and replicate. Here we take advantage of our ability to predict the cis component of gene expression coupled with gene mapping methods such as PrediXcan to identify high confidence candidate trans-acting genes and their targets. That is, we correlate the cis component of gene expression with observed expression of genes in different chromosomes. Leveraging the shared cis-acting regulation across tissues, we combine the evidence of association across all available Genotype-Tissue Expression Project tissues and find 2,356 trans-acting/target gene pairs with high mappability scores. Reassuringly, trans-acting genes are enriched in transcription and nucleic acid binding pathways and target genes are enriched in known transcription factor binding sites. Interestingly, trans-acting genes are more significantly associated with selected complex traits and diseases than target or background genes, consistent with percolating trans effects. Our scripts and summary statistics are publicly available for future studies of trans-acting gene regulation.

Identification of Novel T1D Risk Loci and Their Association With Age and Islet Function at Diagnosis in Autoantibody-Positive T1D Individuals: Based on a Two-Stage Genome-Wide Association Study

OBJECTIVE

Type 1 diabetes (T1D) is a highly heritable disease with much lower incidence but more adult-onset cases in the Chinese population. Although genome-wide association studies (GWAS) have identified >60 T1D loci in Caucasians, less is known in Asians.

RESEARCH DESIGN AND METHODS

We performed the first two-stage GWAS of T1D using 2,596 autoantibody-positive T1D case subjects and 5,082 control subjects in a Chinese Han population and evaluated the associations between the identified T1D risk loci and age and fasting C-peptide levels at T1D diagnosis.

RESULTS

We observed a high genetic correlation between children/adolescents and adult T1D case subjects (r _g = 0.87), as well as subgroups of autoantibody status (r _g ≥ 0.90). We identified four T1D risk loci reaching genome-wide significance in the Chinese Han population, including two novel loci, rs4320356 near BTN3A1 (odds ratio [OR] 1.26, P = 2.70 × 10^-8) and rs3802604 in GATA3 (OR 1.24, P = 2.06 × 10^-8), and two previously reported loci, rs1770 in MHC (OR 4.28, P = 2.25 × 10^-232) and rs705699 in SUOX (OR 1.46, P = 7.48 × 10^-20). Further fine mapping in the MHC region revealed five independent variants, including another novel locus, HLA-C position 275 (omnibus P = 9.78 × 10^-12), specific to the Chinese population. Based on the identified eight variants, we achieved an area under the curve value of 0.86 (95% CI 0.85-0.88). By building a genetic risk score (GRS) with these variants, we observed that the higher GRS were associated with an earlier age of T1D diagnosis (P = 9.08 × 10^-11) and lower fasting C-peptide levels (P = 7.19 × 10^-3) in individuals newly diagnosed with T1D.

CONCLUSIONS

Our results extend current knowledge on genetic contributions to T1D risk. Further investigations in different populations are needed for genetic heterogeneity and subsequent precision medicine.

Analysis of genetically driven alternative splicing identifies FBXO38 as a novel COPD susceptibility gene

While many disease-associated single nucleotide polymorphisms (SNPs) are associated with gene expression (expression quantitative trait loci, eQTLs), a large proportion of complex disease genome-wide association study (GWAS) variants are of unknown function. Some of these SNPs may contribute to disease by regulating gene splicing. Here, we investigate whether SNPs that are associated with alternative splicing (splice QTL or sQTL) can identify novel functions for existing GWAS variants or suggest new associated variants in chronic obstructive pulmonary disease (COPD). RNA sequencing was performed on whole blood from 376 subjects from the COPDGene Study. Using linear models, we identified 561,060 unique sQTL SNPs associated with 30,333 splice sites corresponding to 6,419 unique genes. Similarly, 708,928 unique eQTL SNPs involving 15,913 genes were detected at 10% FDR. While there is overlap between sQTLs and eQTLs, 55.3% of sQTLs are not eQTLs. Co-localization analysis revealed that 7 out of 21 loci associated with COPD (p<1x10-6) in a published GWAS have at least one shared causal variant between the GWAS and sQTL studies. Among the genes identified to have splice sites associated with top GWAS SNPs was FBXO38, in which a novel exon was discovered to be protective against COPD. Importantly, the sQTL in this locus was validated by qPCR in both blood and lung tissue, demonstrating that splice variants relevant to lung tissue can be identified in blood. Other identified genes included CDK11A and SULT1A2. Overall, these data indicate that analysis of alternative splicing can provide novel insights into disease mechanisms. In particular, we demonstrated that SNPs in a known COPD GWAS locus on chromosome 5q32 influence alternative splicing in the gene FBXO38.

Integrative analysis of transcriptome-wide association study data and mRNA expression profiles identified candidate genes and pathways associated with atrial fibrillation

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia characterized by extensive structural, contractile and electrophysiological remodeling. The genetic basis of AF remained elusive until now. Transcriptome-wide association study (TWAS) was conducted by FUSION tool using gene expression weights of 7 tissues combined with a large-scale genome-wide association study (GWAS) dataset of AF, totally involving 8180 AF cases and 28,612 controls. Significant genes identified by TWAS were then subjected to gene ontology (GO) and pathway enrichment analysis. The genome-wide mRNA gene expression profiling of AF was compared with the results of TWAS to detect common genes shared by TWAS and mRNA expression profiling of AF. TWAS detected a group of candidate genes with P_TWAS values < 0.05 across the seven tissues for AF, such as CMAH (P_TWAS = 3.15 × 10^-25 for whole blood), INCENP (P_TWAS = 1.77 × 10^-22 for artery aorta), CMAHP (P_TWAS = 4.57 × 10^-20 for artery aorta). Pathway enrichment analysis identified multiple candidate pathways, such as protein K48-linked ubiquitination (P value = 0.0124), positive regulation of leukocyte chemotaxis (P value = 0.0046) and fatty acid degradation (P value = 0.0295). Further comparing the GO results of TWAS and mRNA expression profiling, 2 common GO terms were identified, including actin binding (P_TWAS = 0.0446, P_mRNA = 7.00 × 10^-4) and extracellular matrix (P_TWAS = 0.0037, P_mRNA = 3.00 × 10^-6). We detected multiple novel candidate genes, GO terms and pathways for AF, providing novel clues for understanding the genetic mechanism of AF.

Aberrant expression of alternative splicing variants in multiple sclerosis - A systematic review

OBJECTIVE

Alternative splicing is an important form of RNA processing that affects nearly all human genes. The differential expression of specific transcript and protein isoforms holds the potential of novel biomarkers for complex diseases. In this systematic review, we compiled the existing literature on aberrant alternative splicing events in multiple sclerosis (MS).

METHODS

A systematic literature search in the PubMed database was carried out and supplemented by screening the reference lists of the identified articles. We selected only MS-related original research studies which compared the levels of different isoforms of human protein-coding genes. A narrative synthesis of the research findings was conducted. Additionally, we performed a case-control analysis using high-density transcriptome microarray data to reevaluate the genes that were examined in the reviewed studies.

RESULTS

A total of 160 records were screened. Of those, 36 studies from the last two decades were included. Most commonly, peripheral blood samples were analyzed (32 studies), and PCR-based techniques were usually employed (27 studies) for measuring the expression of selected genes. Two studies used an exploratory genome-wide approach. Overall, 27 alternatively spliced genes were investigated. Nine of these genes appeared in at least two studies (CD40, CFLAR, FOXP3, IFNAR2, IL7R, MOG, PTPRC, SP140 and TNFRSF1A). The microarray data analysis confirmed differential alternative pre-mRNA splicing for 19 genes.

CONCLUSIONS

An altered RNA processing of genes mediating immune signaling pathways has been repeatedly implicated in MS. The analysis of individual exon-level expression patterns is stimulated by the advancement of transcriptome profiling technologies. In particular, the examination of genes encoded in MS-associated genetic regions may provide important insights into the pathogenesis of the disease and help to identify new biomarkers.

Splice-site variant in ACSL5: a marker promoting opposing effect on cell viability and protein expression

Long-chain Acyl-CoA synthetases (ACSLs) activate fatty acids (FAs) by thioesterification with Coenzyme A (CoA), generating FA-CoAs. These products are essential for lipid metabolism and carcinogenesis. In previous study, we identified an intronic variant rs2256368:A>G, whose G allele promotes exon 20 skipping in up to 43% of ACSL5 transcripts but its functional relevance is unclear. Here, we compared the expression of splice (Spl) and nonsplice (NSpl) ACSL5 variants and the effect on cell viability under culture conditions that force cells to metabolize fatty acids. We found that lymphoblastoid cell lines from 1000 Genomes Project, bearing Spl genotypes, showed a reduced expression of total ACSL5 protein due to an inefficient translation of the Spl RNA. These cells impaired growth in cultures with phorbol myristate acetate-ionomycin (PMA-Io) or medium deprived of glucose, while production of reactive oxygen species increased in PMA-Io. Specific ACSL5-isoform transfection in HEK239T (kidney), U87 (astroglioma), and HOG (oligodendrocyte) cells showed the Spl protein to be the causal factor of cell-growth inhibition, despite its reduced protein expression. Our findings indicate that the variant rs2256368:A>G can predict a growth inhibitory activity, caused by the Spl isoform of ACSL5 protein, opposed to the activity of the NSpl. Deep understanding of its functioning might have application in metabolic diseases and cancer.

S-CAP extends pathogenicity prediction to genetic variants that affect RNA splicing

Exome analysis of patients with a likely monogenic disease does not identify a causal variant in over half of cases. Splice-disrupting mutations make up the second largest class of known disease-causing mutations. Each individual (singleton) exome harbors over 500 rare variants of unknown significance (VUS) in the splicing region. The existing relevant pathogenicity prediction tools tackle all non-coding variants as one amorphic class and/or are not calibrated for the high sensitivity required for clinical use. Here we calibrate seven such tools and devise a novel tool called Splicing Clinically Applicable Pathogenicity prediction (S-CAP) that is over twice as powerful as all previous tools, removing 41% of patient VUS at 95% sensitivity. We show that S-CAP does this by using its own features and not via meta-prediction over previous tools, and that splicing pathogenicity prediction is distinct from predicting molecular splicing changes. S-CAP is an important step on the path to deriving non-coding causal diagnoses.

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.

A Multiplexed Assay for Exon Recognition Reveals that an Unappreciated Fraction of Rare Genetic Variants Cause Large-Effect Splicing Disruptions

Mutations that lead to splicing defects can have severe consequences on gene function and cause disease. Here, we explore how human genetic variation affects exon recognition by developing a multiplexed functional assay of splicing using Sort-seq (MFASS). We assayed 27,733 variants in the Exome Aggregation Consortium (ExAC) within or adjacent to 2,198 human exons in the MFASS minigene reporter and found that 3.8% (1,050) of variants, most of which are extremely rare, led to large-effect splice-disrupting variants (SDVs). Importantly, we find that 83% of SDVs are located outside of canonical splice sites, are distributed evenly across distinct exonic and intronic regions, and are difficult to predict a priori. Our results indicate extant, rare genetic variants can have large functional effects on splicing at appreciable rates, even outside the context of disease, and MFASS enables their empirical assessment at scale.

HMGCLL1 is a predictive biomarker for deep molecular response to imatinib therapy in chronic myeloid leukemia

Achieving a deep molecular response (DMR) to tyrosine kinase inhibitor (TKI) therapy for chronic myeloid leukemia (CML) remains challenging and at present, there is no biomarker to predict DMR in this setting. Herein, we report that an HMGCLL1 genetic variant located in 6p12.1 can be used as a predictive genetic biomarker for intrinsic sensitivity to imatinib (IM) therapy. We measured DMR rate according to HMGCLL1 variant in a discovery set of CML patients (n = 201) and successfully replicated it in a validation set (n = 270). We also investigated the functional relevance of HMGCLL1 blockade with respect to response to TKI therapy and showed that small interfering RNA mediated blockade of HMGCLL1 isoform 3 results in significant decrease in viability of BCR-ABL1-positive cells including K562, CML-T1 or BaF3 cell lines with or without ABL1 kinase domain mutations such as T315I mutation. Decreased cell viability was also demonstrated in murine CML stem cells and human hematopoietic progenitor cells. RNA sequencing showed that blockade of HMGCLL1 was associated with G0/G1 arrest and the cell cycle. In summary, the HMGCLL1 gene polymorphism is a novel genetic biomarker for intrinsic sensitivity to IM therapy in CML patients that predicts DMR in this setting.

Genetic Architecture of Adaptive Immune System Identifies Key Immune Regulators

The immune system is highly diverse, but characterization of its genetic architecture has lagged behind the vast progress made by genome-wide association studies (GWASs) of emergent diseases. Our GWAS for 54 functionally relevant phenotypes of the adaptive immune system in 489 healthy individuals identifies eight genome-wide significant associations explaining 6%-20% of variance. Coding and splicing variants in PTPRC and COMMD10 are involved in memory T cell differentiation. Genetic variation controlling disease-relevant T helper cell subsets includes RICTOR and STON2 associated with Th2 and Th17, respectively, and the interferon-lambda locus controlling regulatory T cell proliferation. Early and memory B cell differentiation stages are associated with variation in LARP1B and SP4. Finally, the latrophilin family member ADGRL2 correlates with baseline pro-inflammatory interleukin-6 levels. Suggestive associations reveal mechanisms of autoimmune disease associations, in particular related to pro-inflammatory cytokine production. Pinpointing these key human immune regulators offers attractive therapeutic perspectives.

Integrating genetic, transcriptional, and biological information provides insights into obesity

Objective:

Indices of body fat distribution are heritable, but few genetic signals have been reported from genome-wide association studies (GWAS) of computed tomography (CT) imaging measurements of body fat distribution. We aimed to identify genes associated with adiposity traits and the key drivers that are central to adipose regulatory networks.

Subjects:

We analyzed gene transcript expression data in blood from participants in the Framingham Heart Study, a large community-based cohort (n up to 4,303), as well as implemented an integrative analysis of these data and existing biological information.

Results:

Our association analyses identified unique and common gene expression signatures across several adiposity traits, including body mass index, waist-hip ratio, waist circumference, and CT-measured indices, including volume and quality of visceral and subcutaneous adipose tissues. We identified six enriched KEGG pathways and two co-expression modules for further exploration of adipose regulatory networks. The integrative analysis revealed four gene sets (Apoptosis, p53 signaling pathway, Proteasome, Ubiquitin mediated proteolysis) and two co-expression modules with significant genetic variants and 94 key drivers/genes whose local networks were enriched with adiposity-associated genes, suggesting that these enriched pathways or modules have genetic effects on adiposity. Most identified key driver genes are involved in essential biological processes such as controlling cell cycle, DNA repair and degradation of regulatory proteins and are cancer related.

Conclusion:

Our integrative analysis of genetic, transcriptional and biological information provides a list of compelling candidates for further follow-up functional studies to uncover the biological mechanisms underlying obesity. These candidates highlight the value of examining CT-derived and central adiposity traits.

Comprehensive Analysis of Alternative Splicing Across Tumors from 8,705 Patients

Our comprehensive analysis of alternative splicing across 32 The Cancer Genome Atlas cancer types from 8,705 patients detects alternative splicing events and tumor variants by reanalyzing RNA and whole-exome sequencing data. Tumors have up to 30% more alternative splicing events than normal samples. Association analysis of somatic variants with alternative splicing events confirmed known trans associations with variants in SF3B1 and U2AF1 and identified additional trans-acting variants (e.g., TADA1, PPP2R1A). Many tumors have thousands of alternative splicing events not detectable in normal samples; on average, we identified ≈930 exon-exon junctions ("neojunctions") in tumors not typically found in GTEx normals. From Clinical Proteomic Tumor Analysis Consortium data available for breast and ovarian tumor samples, we confirmed ≈1.7 neojunction- and ≈0.6 single nucleotide variant-derived peptides per tumor sample that are also predicted major histocompatibility complex-I binders ("putative neoantigens").

Cancer-Specific Splicing Changes and the Potential for Splicing-Derived Neoantigens

In this issue of Cancer Cell, Kahles et al. perform a comprehensive analysis of RNA splicing across cancer types and identify novel correlations between genetic alterations and splicing in cancer. In addition, they identify that tumor-specific splicing has the potential to generate a large new class of tumor-specific neoantigens.

Genetic architecture of gene expression traits across diverse populations

For many complex traits, gene regulation is likely to play a crucial mechanistic role. How the genetic architectures of complex traits vary between populations and subsequent effects on genetic prediction are not well understood, in part due to the historical paucity of GWAS in populations of non-European ancestry. We used data from the MESA (Multi-Ethnic Study of Atherosclerosis) cohort to characterize the genetic architecture of gene expression within and between diverse populations. Genotype and monocyte gene expression were available in individuals with African American (AFA, n = 233), Hispanic (HIS, n = 352), and European (CAU, n = 578) ancestry. We performed expression quantitative trait loci (eQTL) mapping in each population and show genetic correlation of gene expression depends on shared ancestry proportions. Using elastic net modeling with cross validation to optimize genotypic predictors of gene expression in each population, we show the genetic architecture of gene expression for most predictable genes is sparse. We found the best predicted gene in each population, TACSTD2 in AFA and CHURC1 in CAU and HIS, had similar prediction performance across populations with R2 > 0.8 in each population. However, we identified a subset of genes that are well-predicted in one population, but poorly predicted in another. We show these differences in predictive performance are due to allele frequency differences between populations. Using genotype weights trained in MESA to predict gene expression in independent populations showed that a training set with ancestry similar to the test set is better at predicting gene expression in test populations, demonstrating an urgent need for diverse population sampling in genomics. Our predictive models and performance statistics in diverse cohorts are made publicly available for use in transcriptome mapping methods at https://github.com/WheelerLab/DivPop.