Splicing Quantitative Trait Loci Research Articles

Population-level transcriptomes reveal gene expression and splicing underlying cadmium accumulation in barley.

Genetic variation is an important determinant of gene transcription, which in turn contributes to functional and phenotypic diversity. Identification of the genetic variants controlling gene expression and alternative splicing in crops responding to cadmium (Cd), an important issue for food safety and human health, is of great value to improve our understanding of Cd accumulation-related genes. Here we report an in-depth survey of population-level transcriptome variation of barley (Hordeum vulgare) core accessions under Cd exposure. We reveal marked transcriptomic changes in response to Cd exposure, and these are largely independent of tissues. A genome-wide association study (GWAS) revealed 59 498 expression quantitative trait loci (eQTLs) and 23 854 splicing quantitative trait loci (sQTLs), leading to a complex network that covers 66.6% of the expressed genes, including 68 metal transporter genes. On average, 41.0% of sQTLs overlapped with eQTLs across different tissues, indicating that these two dimensions of transcript variation are largely independent. Moreover, we found that 34.5% of GWAS QTLs that underlie 10 Cd accumulation traits in barley are co-localized with eQTLs and sQTLs, which could imply a mechanistic role of different genetic variants affecting gene expression and alternative splicing in these traits. This study highlights the role of distal and proximal genetic effects on gene expression, splicing, and phenotypic plasticity. We anticipate that our results on the genetic control of expression and splicing underlying Cd accumulation provide a bridge to better understand genetic variation and phenotypic diversity to elucidate the mechanisms underlying Cd accumulation in plants.

Transcriptome-wide Mendelian Randomisation Using Expression, Protein and Splicing Quantitative Trait Loci to Identify Novel Drug Targets Associated With Risk of Gliomagenesis

Abstract AIMS To use two-sample Mendelian Randomisation to prioritise novel genes for drug targeting in glioma risk using expression, protein and splicing quantitative trait loci (eQTLs, pQTLs and sQTLs, respectively). METHOD We used genetic variants from GWAS to compare the effects of eQTLs, pQTLs and sQTLs on genetic liability to glioma risk (n=12,496). eQTL data was retrieved from the MetaBrain study of five different brain tissues (n=108 to 2,970). pQTL data was retrieved from the BrainQTL study of dorsolateral prefrontal cortex tissue (n = 330), and sQTL data was retrieved from Genotype-Tissue Expression (GTEx) Project (v8) of 13 brain tissues (n=114 to 209). SNPs which were trans-QTLs (>1 Mb) due to risk of horizontal pleiotropy, and greater than GWAS significance (P > 5 × 10-8) were excluded. Instruments were constructed using independent SNPs (r2 < 0.001). Results underwent relevant sensitivity analysis; colocalisation and Steiger filtering. RESULTS We found 123 MR associations for 28 genes with genetically predicted altered expression, protein or splicing levels affected risk of glioma. These included genes recurrently associated with glioma risk such as TERT, RTEL1, CDKN2A/B and EGFR, and novel putative genes; BTN3A2, GALNT6, GMPPB and FAIM. Following sensitivity analyses, we found that 58 MR associations for 25 genes had robust evidence. CONCLUSION We identified four novel associations not previously found in GWAS; BTN3A2 (6p22.2), FAIM (3q22.3), GALNT6 (12q13.13) and GMPPB (3p21.31) are implicated in glioma risk and are also not located on a known glioma risk locus. Further analysis is required to identify the suitability of these genes for drug targeting in glioma.

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.

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.

ColocQuiaL: a QTL-GWAS colocalization pipeline.

SummaryIdentifying genomic features responsible for genome-wide association study (GWAS) signals has proven to be a difficult challenge; many researchers have turned to colocalization analysis of GWAS signals with expression quantitative trait loci (eQTL) and splicing quantitative trait loci (sQTL) to connect GWAS signals to candidate causal genes. The ColocQuiaL pipeline provides a framework to perform these colocalization analyses at scale across the genome and returns summary files and locus visualization plots to allow for detailed review of the results. As an example, we used ColocQuiaL to perform colocalization between a recent type 2 diabetes GWAS and Genotype-Tissue Expression (GTEx) v8 single-tissue eQTL and sQTL data.Availability and implementationColocQuiaL is primarily written in R and is freely available on GitHub: https://github.com/bvoightlab/ColocQuiaL.

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 spliceQTLeffects.

Two different aging paths in human blood revealed by integrated analysis of gene Expression, mutation and alternative splicing

Aging is a complex life process that human organs and tissues steadily and continuously decline. Aging has huge heterogeneity, which shows different aging rates among different individuals and in different tissues of the same individual. Many studies of aging are often contradictory and show little common signature. The integrated analysis of these transcriptome datasets will provide an unbiased global view of the aging process. Here, we integrated 8 transcriptome datasets including 757 samples from healthy human blood to study aging from three aspects of gene expression, mutations, and alternative splicing. Surprisingly, we found that transcriptome changes in blood are relatively independent of the chronological age. Further pseudotime analysis revealed two different aging paths (AgingPath1 and AgingPath2) in human blood. The differentially expressed genes (DEGs) along the two paths showed a limited overlap and are enriched in different biological processes. The mutations of DEGs in AgingPath1 are significantly increased in the aging process, while the opposite trend was observed in AgingPath2. Expression quantitative trait loci (eQTL) and splicing quantitative trait loci (sQTL) analysis identified 304 important mutations that can affect both gene expression and alternative splicing during aging. Finally, by comparison between aging and Alzheimer's disease, we identified 37 common DEGs in AgingPath1, AgingPath2 and Alzheimer's disease. These genes may contribute to the shift from aging state to Alzheimer's disease. In summary, this study revealed the two aging paths and the related genes and mutations, which provides a new insight into aging and aging-related disease.

Aberrant RNA Splicing Is a Primary Link between Genetic Variation and Pancreatic Cancer Risk.

In pancreatic cancer, splicing quantitative trait loci analysis identifies a rs1785932 variant that contributes to decreased risk of disease by influencing ELP2 mRNA splicing and blocking the STAT3 oncogenic pathway.

Integration of genetic colocalizations with physiological and pharmacological perturbations identifies cardiometabolic disease genes

BackgroundIdentification of causal genes for polygenic human diseases has been extremely challenging, and our understanding of how physiological and pharmacological stimuli modulate genetic risk at disease-associated loci is limited. Specifically, insulin resistance (IR), a common feature of cardiometabolic disease, including type 2 diabetes, obesity, and dyslipidemia, lacks well-powered genome-wide association studies (GWAS), and therefore, few associated loci and causal genes have been identified.MethodsHere, we perform and integrate linkage disequilibrium (LD)-adjusted colocalization analyses across nine cardiometabolic traits (fasting insulin, fasting glucose, insulin sensitivity, insulin sensitivity index, type 2 diabetes, triglycerides, high-density lipoprotein, body mass index, and waist-hip ratio) combined with expression and splicing quantitative trait loci (eQTLs and sQTLs) from five metabolically relevant human tissues (subcutaneous and visceral adipose, skeletal muscle, liver, and pancreas). To elucidate the upstream regulators and functional mechanisms for these genes, we integrate their transcriptional responses to 21 relevant physiological and pharmacological perturbations in human adipocytes, hepatocytes, and skeletal muscle cells and map their protein-protein interactions.ResultsWe identify 470 colocalized loci and prioritize 207 loci with a single colocalized gene. Patterns of shared colocalizations across traits and tissues highlight different potential roles for colocalized genes in cardiometabolic disease and distinguish several genes involved in pancreatic β-cell function from others with a more direct role in skeletal muscle, liver, and adipose tissues. At the loci with a single colocalized gene, 42 of these genes were regulated by insulin and 35 by glucose in perturbation experiments, including 17 regulated by both. Other metabolic perturbations regulated the expression of 30 more genes not regulated by glucose or insulin, pointing to other potential upstream regulators of candidate causal genes.ConclusionsOur use of transcriptional responses under metabolic perturbations to contextualize genetic associations from our custom colocalization approach provides a list of likely causal genes and their upstream regulators in the context of IR-associated cardiometabolic risk.

Associations of CXCL12 polymorphisms with clinicopathological features in breast cancer: a case-control study

BackgroundPrevious studies suggested that CXCL12 was involved in the development, metastasis, and invasion of breast cancer, and genetic variants were associated with the diagnosis and prognosis of patients with breast cancer. The present study was aimed to assess the relationships between CXCL12 polymorphisms (rs1801157, rs2297630, and rs2839693) and susceptibility and clinicopathological features of breast cancer.MethodsA case-control study was conducted in 434 breast cancer patients and 450 health controls. Student t-test and chi-square test were used to analyze the differences of age distribution and genotype frequencies between the two groups. Correlations between polymorphisms and clinical parameters were also assessed by chi-square test. The potential effects of the three polymorphisms on CXCL12 were investigated by the public database.ResultsA statistical association was found between CXCL12 rs1801157 polymorphism and breast cancer risk, possibility of metastasis, and estrogen receptor status. Patients with rs2839693 C/T or C/T-T/T genotypes were more likely to be progesterone receptor-negative. However, no associations of rs2297630 polymorphism with breast cancer risk or any clinicopathological characteristics were observed. In addition, rs2297630 affected the splicing quantitative trait loci of CXCL12 in the subcutaneous fat, rs2839693 polymorphism affected the splicing quantitative trait loci of CXCL12 in the human breast mammary tissues.ConclusionsThose results indicated that CXCL12 polymorphisms might be potential diagnostic indicators, and more investigation is needed in the future.

Functional variants in cytochrome b5 type A (CYB5A) are enriched in Southwest American Indian individuals and associate with obesity.

ObjectiveThis study aimed to identify genetic variants enriched in Southwest American Indian (SWAI) individuals that associate with BMI.MethodsWhole genome sequencing data (n = 296) were used to identify potentially functional variants that are common in SWAI individuals (minor allele frequency ≥10%) but rare in other ethnic groups (minor allele frequency < 0.1%). Enriched variants were tested for association with BMI in 5,870 SWAI individuals. One variant was studied using a luciferase reporter, and haplotypes that included this variant were analyzed for association with various measures of obesity (n = 917‐5,870), 24‐hour energy expenditure (24‐h EE; n = 419), and skeletal muscle biopsy expression data (n = 207).ResultsA 5′ untranslated region variant in cytochrome b5 type A (CYB5A), rs548402150, met the enrichment criteria and associated with increased BMI (β = 2%, p = 0.004). Functionally, rs548402150 decreased luciferase expression by 30% (p = 0.003) and correlated with decreased skeletal muscle CYB5A expression (β = −0.5 SD, p = 0.0008). Combining rs548402150 with two splicing quantitative trait loci in CYB5A identified a haplotype carried almost exclusively in SWAI individuals that associated with increased BMI (β = 3%, p = 0.0003) and decreased CYB5A expression, whereas the most common haplotype in all ethnic groups associated with lower BMI and percentage of body fatness, increased 24‐h EE, and increased CYB5A expression.ConclusionsFurther studies on the effects of CYB5A on 24‐h EE and BMI may provide insights into obesity‐related physiology.

Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies.

Microglia have emerged as important players in brain aging and pathology. To understand how genetic risk for neurological and psychiatric disorders is related to microglial function, large transcriptome studies are essential. Here, we describe the transcriptome analysis of 255 primary human microglia samples isolated at autopsy from multiple brain regions of 100 human subjects. We performed systematic analyses to investigate various aspects of microglial heterogeneities, including brain region and aging. We mapped expression and splicing quantitative trait loci and showed that many neurological disease susceptibility loci are mediated through gene expression or splicing in microglia. Fine-mapping of these loci nominated candidate causal variants that are within microglia-specific enhancers, finding associations with microglia expression of USP6NL for Alzheimer’s disease and P2RY12 for Parkinson’s disease. We have built the most comprehensive catalog to date of genetic effects on the microglia transcriptome and propose candidate functional variants in neurological and psychiatric disorders.

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.

Genome-wide association study reveals novel loci for adult type 1 diabetes in a 5-year nested case-control study.

BACKGROUNDType 1 diabetes (T1D) is a severe and prevalent metabolic disease. Due to its high heredity, an increasing number of genome-wide association studies have been performed, most of which were from hospital-based case-control studies with a relatively small sample size. The association of single nucleotide polymorphisms (SNPs) and T1D has been less studied and is less understood in natural cohorts.AIMTo investigate the significant variants of T1D, which could be potential biomarkers for T1D prediction or even therapy.METHODSA genome-wide association study (GWAS) of adult T1D was performed in a nested case-control study (785 cases vs 804 controls) from a larger 5-year cohort study in Suzhou, China. Potential harmful or protective SNPs were evaluated for T1D. Subsequent expression and splicing quantitative trait loci (eQTL and sQTL) analyses were carried out to identify target genes modulated by these SNPs.RESULTSA harmful SNP for T1D, rs3117017 [odds ratio (OR) = 3.202, 95% confidence interval (CI): 2.296-4.466, P = 9.33 × 10-4] and three protective SNPs rs55846421 (0.113, 0.081-0.156, 1.76 × 10-9), rs75836320 (0.283, 0.205-0.392, 1.07 × 10-4), rs362071 (0.568, 0.495-0.651, 1.66 × 10-4) were identified. Twenty-two genes were further identified as potential candidates for T1D onset. CONCLUSIONWe identified a potential genetic basis of T1D, both protective and harmful, using a GWAS in a larger nested case-control study of a Chinese population.

Genome-wide association and colocalization analyses identify target genes for brain arteriolosclerosis.

Brain arteriolosclerosis (B-ASC) is a small-vessel cerebrovascular pathologic change involving sclerotic thickening of arterioles in the brain. B-ASC pathology is common in aged autopsy cohorts, is associated with worse cognitive functioning, and is also associated with other neuropathologies. The genetics of dementia and other pathologic changes have been studied extensively, but to date no in-depth study has been conducted on genetic risk of autopsy-proven B-ASC. We performed the first genome-wide association study (GWAS) focusing on B-ASC using multiple cohorts. We then followed up on identified risk variants with functional analysis to investigate potential biological mechanisms. Individual GWAS and mega-analyses were conducted using data from participants in the National Alzheimer's Coordinating Center (NACC) (N = 3318) and the Religious Orders Study and Memory and Aging Project (ROSMAP) (N=1190). In total, 4,577,283 variants (MAF > 5%) were present in both datasets and passed quality control. Variants identified in GWAS were then checked for quantitative trait loci (QTL) associations in the Genotype Tissue Expression (GTEx) project. Colocalization analysis was then performed on identified QTL in brain and vascular tissues. Variants most highly associated with B-ASC were also examined for association with neuroimaging correlates of B-ASC in the Alzheimer's Disease Neuroimaging Initiative. One intergenic locus on Chromosome 6 was significantly associated with B-ASC in NACC (P = 1.4e-08) and colocalized with ELOVL4 gene expression in the brain. Other loci in both cohorts achieved less stringent thresholds of association and colocalized with multiple QTL in GTEx. One intergenic locus on Chromosome 10 near SORCS3 was suggestively associated with B-ASC in NACC and was replicated in ROSMAP. Genetic loci associated with B-ASC pathology were identified using multiple cohorts. Most loci associated with B-ASC in one cohort were not associated in the other. Multiple identified risk loci colocalized with gene expression or splicing QTL, providing evidence for potential biological mechanisms through which variants may influence disease risk. Due to the relatively small sample sizes and heterogeneity between the cohorts used, additional studies are merited to better characterize genetic risk of B-ASC.

Alternative RNA splicing in stem cells and cancer stem cells: Importance of transcript-based expression analysis.

Alternative ribonucleic acid (RNA) splicing can lead to the assembly of different protein isoforms with distinctive functions. The outcome of alternative splicing (AS) can result in a complete loss of function or the acquisition of new functions. There is a gap in knowledge of abnormal RNA splice variants promoting cancer stem cells (CSCs), and their prospective contribution in cancer progression. AS directly regulates the self-renewal features of stem cells (SCs) and stem-like cancer cells. Notably, octamer-binding transcription factor 4A spliced variant of octamer-binding transcription factor 4 contributes to maintaining stemness properties in both SCs and CSCs. The epithelial to mesenchymal transition pathway regulates the AS events in CSCs to maintain stemness. The alternative spliced variants of CSCs markers, including cluster of differentiation 44, aldehyde dehydrogenase, and doublecortin-like kinase, α6β1 integrin, have pivotal roles in increasing self-renewal properties and maintaining the pluripotency of CSCs. Various splicing analysis tools are considered in this study. LeafCutter software can be considered as the best tool for differential splicing analysis and identification of the type of splicing events. Additionally, LeafCutter can be used for efficient mapping splicing quantitative trait loci. Altogether, the accumulating evidence re-enforces the fact that gene and protein expression need to be investigated in parallel with alternative splice variants.

P-052: Identification of novel genetic loci for risk of multiple myeloma by functional annotation

<h3>Background</h3> Genetic factors have proven to have an impact on multiple myeloma (MM) susceptibility, with genome-wide association studies (GWAS) discovering 23 loci associated with MM risk. However, much of the heritability of MM remains unexplained. The stringent significance threshold used in GWAS (p<5×10^-8) accounts for the numerous statistical tests being performed but is prone to the risk of false negatives. One strategy for reducing the number of tests is to consider only SNPs with heightened prior probabilities of association, according to meaningful biological criteria. The best candidate SNPs identified with this approach can then be tested in additional MM cases and controls from independent populations. We aimed at surveying the effect of single nucleotide polymorphisms (SNPs), predicted to have a functional role, on MM risk. <h3>Methods</h3> The association study consisted of two GWAS as discovery datasets, namely the InterLymph consortium and the German GWAS and a replication dataset, namely the the International Multiple Myeloma rESEarch (IMMEnSE) consortium, for an overall total of 5442 MM cases and 6174 controls. SNPs were first ranked according to p-value and concordance of the association between the two discovery datasets, then ranked by functional annotation, using bioinformatic tools and databases. We considered the following classes of functional SNPs: missense, synonymous and non-sense SNPs, expression quantitative trait loci (eQTLs), splicing quantitative trait loci (sQTLs),), SNPs in SNPs affecting function of long non-coding RNAs (lncRNA), and SNPs modifying transcription factor binding sites. We prioritized the resulting SNPs for replication in IMMEnSE by p-values for association in InterLymph and German GWAS and by evidence for a functional role. <h3>Results</h3> In the two discovery datasets, 136 SNPs fit the criteria of association with MM risk with p<10^-4 and did not map close to known MM risk loci. After pruning for linkage disequilibrium, four SNPs (rs12038685, rs2664188, rs12652920, rs29794) were chosen for replication in IMMEnSE. Among these, rs2664188 showed to be significantly associated also in the replication dataset (OR=1.30, 95% CI=1.16-1.46, p=0.001). The final meta-analysis including the three datasets, using a random-effect model, confirmed the association (OR=1.18, 95% CI=1.07-1.30, p=0.0007). <h3>Conclusion</h3> The G-allele of rs2664188, that showed a consistent association with increased risk of developing MM in all phases of our analysis is an eQTL, according to GTEx., The G-allele is associated with increased expression of the N4BP2 gene in whole blood. N4BP2 encodes a protein which binds to B-cell leukemia/lymphoma 3 (BCL-3), a well-known proto-oncogene, known to play a role in cell proliferation and apoptosis inhibition in myeloma cell lines.

A compendium of uniformly processed human gene expression and splicing quantitative trait loci

Many gene expression quantitative trait locus (eQTL) studies have published their summary statistics, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and co-localization. However, technical differences between these datasets are a barrier to their widespread use. Consequently, target genes for most genome-wide association study (GWAS) signals have still not been identified. In the present study, we present the eQTL Catalogue (https://www.ebi.ac.uk/eqtl), a resource of quality-controlled, uniformly re-computed gene expression and splicing QTLs from 21 studies. We find that, for matching cell types and tissues, the eQTL effect sizes are highly reproducible between studies. Although most QTLs were shared between most bulk tissues, we identified a greater diversity of cell-type-specific QTLs from purified cell types, a subset of which also manifested as new disease co-localizations. Our summary statistics are freely available to enable the systematic interpretation of human GWAS associations across many cell types and tissues.

GWAS-Top Polymorphisms Associated With Late-Onset Alzheimer Disease in Brazil: Pointing Out Possible New Culprits Among Non-Coding RNAs.

Several genome-wide association studies (GWAS) have been carried out with late-onset Alzheimer’s disease (LOAD), mainly in European and Asian populations. Different polymorphisms were associated, but several of them without a functional explanation. GWAS are fundamental for identifying loci associated with diseases, although they often do not point to causal polymorphisms. In this sense, functional investigations are a fundamental tool for discovering causality, although the failure of this validation does not necessarily indicate a non-causality. Furthermore, the allele frequency of associated genetic variants may vary widely between populations, requiring replication of these associations in other ethnicities. In this sense, our study sought to replicate in 150 AD patients and 114 elderly controls from the South Brazilian population 18 single-nucleotide polymorphisms (SNPs) associated with AD in European GWAS, with further functional investigation using bioinformatic tools for the associated SNPs. Of the 18 SNPs investigated, only four were associated in our population: rs769449 (APOE), rs10838725 (CELF1), rs6733839, and rs744373 (BIN1–CYP27C1). We identified 54 variants in linkage disequilibrium (LD) with the associated SNPs, most of which act as expression or splicing quantitative trait loci (eQTLs/sQTLs) in genes previously associated with AD or with a possible functional role in the disease, such as CELF1, MADD, MYBPC3, NR1H3, NUP160, SPI1, and TOMM40. Interestingly, eight of these variants are located within long non-coding RNA (lncRNA) genes that have not been previously investigated regarding AD. Some of these polymorphisms can result in changes in these lncRNAs’ secondary structures, leading to either loss or gain of microRNA (miRNA)-binding sites, deregulating downstream pathways. Our pioneering work not only replicated LOAD association with polymorphisms not yet associated in the Brazilian population but also identified six possible lncRNAs that may interfere in LOAD development. The results lead us to emphasize the importance of functional exploration of associations found in large-scale association studies in different populations to base personalized and inclusive medicine in the future.

The impact of cell type and context-dependent regulatory variants on human immune traits

BackgroundThe vast majority of trait-associated variants identified using genome-wide association studies (GWAS) are noncoding, and therefore assumed to impact gene regulation. However, the majority of trait-associated loci are unexplained by regulatory quantitative trait loci (QTLs).ResultsWe perform a comprehensive characterization of the putative mechanisms by which GWAS loci impact human immune traits. By harmonizing four major immune QTL studies, we identify 26,271 expression QTLs (eQTLs) and 23,121 splicing QTLs (sQTLs) spanning 18 immune cell types. Our colocalization analyses between QTLs and trait-associated loci from 72 GWAS reveals that genetic effects on RNA expression and splicing in immune cells colocalize with 40.4% of GWAS loci for immune-related traits, in many cases increasing the fraction of colocalized loci by two fold compared to previous studies. Notably, we find that the largest contributors of this increase are splicing QTLs, which colocalize on average with 14% of all GWAS loci that do not colocalize with eQTLs. By contrast, we find that cell type-specific eQTLs, and eQTLs with small effect sizes contribute very few new colocalizations. To investigate the 60% of GWAS loci that remain unexplained, we collect H3K27ac CUT&Tag data from rheumatoid arthritis and healthy controls, and find large-scale differences between immune cells from the different disease contexts, including at regions overlapping unexplained GWAS loci.ConclusionAltogether, our work supports RNA splicing as an important mediator of genetic effects on immune traits, and suggests that we must expand our study of regulatory processes in disease contexts to improve functional interpretation of as yet unexplained GWAS loci.