Genome-wide functional screen of 3'UTR variants uncovers causal variants for human disease and evolution.

3' untranslated region (3'UTR) variants are strongly associated with human traits and diseases, yet few have been causally identified. We developed the massively parallel reporter assay for 3'UTRs (MPRAu) to sensitively assay 12,173 3'UTR variants. We applied MPRAu to six human cell lines, focusing on genetic variants associated with genome-wide association studies (GWAS) and human evolutionary adaptation. MPRAu expands our understanding of 3'UTR function, suggesting that simple sequences predominately explain 3'UTR regulatory activity. We adapt MPRAu to uncover diverse molecular mechanisms at base pair resolution, including an adenylate-uridylate (AU)-rich element of LEPR linked to potential metabolic evolutionary adaptations in East Asians. We nominate hundreds of 3'UTR causal variants with genetically fine-mapped phenotype associations. Using endogenous allelic replacements, we characterize one variant that disrupts a miRNA site regulating the viral defense gene TRIM14 and one that alters PILRB abundance, nominating a causal variant underlying transcriptional changes in age-related macular degeneration.

Mapping Causal Variants with Single-Nucleotide Resolution Reveals Biochemical Drivers of Phenotypic Change.

Understanding the sequence determinants that give rise to diversity among individuals and species is the central challenge of genetics. However, despite ever greater numbers of sequenced genomes, most genome-wide association studies cannot distinguish causal variants from linked passenger mutations spanning many genes. We report that this inherent challenge can be overcome in model organisms. By pushing the advantages of inbred crossing to its practical limit in Saccharomyces cerevisiae, we improved the statistical resolution of linkage analysis to single nucleotides. This "super-resolution" approach allowed us to map 370 causal variants across 26 quantitative traits. Missense, synonymous, and cis-regulatory mutations collectively gave rise to phenotypic diversity, providing mechanistic insight into the basis of evolutionary divergence. Our data also systematically unmasked complex genetic architectures, revealing that multiple closely linked driver mutations frequently act on the same quantitative trait. Single-nucleotide mapping thus complements traditional deletion and overexpression screening paradigms and opens new frontiers in quantitative genetics.

Impact of Genetic Polymorphisms on Human Immune Cell Gene Expression.

While many genetic variants have been associated with risk for human diseases, how these variants affect gene expression in various cell types remains largely unknown. To address this gap, the DICE (database of immune cell expression, expression quantitative trait loci [eQTLs], and epigenomics) project was established. Considering all human immune cell types and conditions studied, we identified cis-eQTLs for a total of 12,254 unique genes, which represent 61% of all protein-coding genes expressed in these cell types. Strikingly, a large fraction (41%) of these genes showed a strong cis-association with genotype only in a single cell type. We also found that biological sex is associated with major differences in immune cell gene expression in a highly cell-specific manner. These datasets will help reveal the effects of disease risk-associated genetic polymorphisms on specific immune cell types, providing mechanistic insights into how they might influence pathogenesis (https://dice-database.org).

Regulatory features aid interpretation of 3'UTR variants.

Our ability to determine the clinical impact of variants in 3' untranslated regions (UTRs) of genes remains poor. We provide a thorough analysis of 3' UTR variants from several datasets. Variants in putative regulatory elements, including RNA-binding protein motifs, eCLIP peaks, and microRNA sites, are up to 16 times more likely than variants not in these elements to have gene expression and phenotype associations. Variants in regulatory motifs result in allele-specific protein binding in cell lines and allele-specific gene expression differences in population studies. In addition, variants in shared regions of alternatively polyadenylated isoforms and those proximal to polyA sites are more likely to affect gene expression and phenotype. Finally, pathogenic 3' UTR variants in ClinVar are up to 20 times more likely than benign variants to fall in a regulatory site. We incorporated these findings into RegVar, a software tool that interprets regulatory elements and annotations for any 3' UTR variant and predicts whether the variant is likely to affect gene expression or phenotype. This tool will help prioritize variants for experimental studies and identify pathogenic variants in individuals.

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

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

Epilepsies of presumed genetic etiology show enrichment of rare variants that occur in the general population.

Previous studies suggested that severe epilepsies, e.g., developmental and epileptic encephalopathies (DEEs), are mainly caused by ultra-rare de novo genetic variants. For milder disease, rare genetic variants could contribute to the phenotype. To determine the importance of rare variants for different epilepsy types, we analyzed a whole-exome sequencing cohort of 9,170 epilepsy-affected individuals and 8,436 control individuals. Here, we separately analyzed three different groups of epilepsies: severe DEEs, genetic generalized epilepsy (GGE), and non-acquired focal epilepsy (NAFE). We required qualifying rare variants (QRVs) to occur in control individuals with an allele count ≥ 1 and a minor allele frequency ≤ 1:1,000, to be predicted as deleterious (CADD ≥ 20), and to have an odds ratio in individuals with epilepsy ≥ 2. We identified genes enriched with QRVs primarily in NAFE (n = 72), followed by GGE (n = 32) and DEE (n = 21). This suggests that rare variants may play a more important role for causality of NAFE than for DEE. Moreover, we found that genes harboring QRVs, e.g., HSGP2, FLNA, or TNC, encode proteins that are involved in structuring the brain extracellular matrix. The present study confirms an involvement of rare variants for NAFE that occur also in the general population, while in DEE and GGE, the contribution of such variants appears more limited.

The landscape of the methodology in drug repurposing using human genomic data: a systematic review.

The process of drug development is expensive and time-consuming. In contrast, drug repurposing can be introduced to clinical practice more quickly and at a reduced cost. Over the last decade, there has been a significant expansion of large biobanks that link genomic data to electronic health record data, public availability of various databases containing biological and clinical information and rapid development of novel methodologies and algorithms in integrating different sources of data. This review aims to provide a thorough summary of different strategies that utilize genomic data to seek drug-repositioning opportunities. We searched MEDLINE and EMBASE databases to identify eligible studies up until 1 May 2023, with a total of 102 studies finally included after two-step parallel screening. We summarized commonly used strategies for drug repurposing, including Mendelian randomization, multi-omic-based and network-based studies and illustrated each strategy with examples, as well as the data sources implemented. By leveraging existing knowledge and infrastructure to expedite the drug discovery process and reduce costs, drug repurposing potentially identifies new therapeutic uses for approved drugs in a more efficient and targeted manner. However, technical challenges when integrating different types of data and biased or incomplete understanding of drug interactions are important hindrances that cannot be disregarded in the pursuit of identifying novel therapeutic applications. This review offers an overview of drug repurposing methodologies, providing valuable insights and guiding future directions for advancing drug repurposing studies.

Uncovering the impact of noncoding variants in neurodegenerative brain diseases.

Neurodegenerative brain diseases (NBDs) are characterized by cognitive decline and movement impairments caused by neuronal loss in different brain regions. A large fraction of the genetic heritability of NBDs is not explained by the current known mutations. Genome-wide association studies identified novel disease-risk loci, adding to the genetic basis of NBDs. Many of the associated variants reside in noncoding regions with distinct molecular functions. Genetic variation in these regions can alter functions and contribute to disease pathogenesis. Here, we discuss noncoding variants associated with NBDs. Methods for better functional interpretation of noncoding variation will expand our knowledge of the genetic architecture of NBDs and broaden the routes for therapeutic strategies.

RAREsim: A simulation method for very rare genetic variants.

Identification of rare-variant associations is crucial to full characterization of the genetic architecture of complex traits and diseases. Essential in this process is the evaluation of novel methods in simulated data that mirror the distribution of rare variants and haplotype structure in real data. Additionally, importing real-variant annotation enables in silico comparison of methods, such as rare-variant association tests and polygenic scoring methods, that focus on putative causal variants. Existing simulation methods are either unable to employ real-variant annotation or severely under- or overestimate the number of singletons and doubletons, thereby reducing the ability to generalize simulation results to real studies. We present RAREsim, a flexible and accurate rare-variant simulation algorithm. Using parameters and haplotypes derived from real sequencing data, RAREsim efficiently simulates the expected variant distribution and enables real-variant annotations. We highlight RAREsim's utility across various genetic regions, sample sizes, ancestries, and variant classes.

NATs at a glance.

Most proteins receive an acetyl group at the N terminus while in their nascency as the result of modification by co-translationally acting N-terminal acetyltransferases (NATs). The N-terminal acetyl group can influence several aspects of protein functionality. From studies of NAT-lacking cells, it is evident that several cellular processes are affected by this modification. More recently, an increasing number of genetic cases have demonstrated that N-terminal acetylation has crucial roles in human physiology and pathology. In this Cell Science at a Glance and the accompanying poster, we provide an overview of the human NAT enzymes and their properties, substrate coverage, cellular roles and connections to human disease.

A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer.

Studies continue to uncover contributing risk factors for breast cancer (BC) development including genetic variants. Advances in machine learning and big data generated from genetic sequencing can now be used for predicting BC pathogenicity. However, it is unclear which tool developed for pathogenicity prediction is most suited for predicting the impact and pathogenicity of variant effects. A significant challenge is to determine the most suitable data source for each tool since different tools can yield different prediction results with different data inputs. To this end, this work reviews genetic variant databases and tools used specifically for the prediction of BC pathogenicity. We provide a description of existing genetic variants databases and, where appropriate, the diseases for which they have been established. Through example, we illustrate how they can be used for prediction of BC pathogenicity and discuss their associated advantages and disadvantages. We conclude that the tools that are specialized by training on multiple diverse datasets from different databases for the same disease have enhanced accuracy and specificity and are thereby more helpful to the clinicians in predicting and diagnosing BC as early as possible.

Trace amine associated receptor 1: predicted effects of single nucleotide variants on structure-function in geographically diverse populations.

Trace Amine Associated Receptor 1 (TAAR1) is a novel pharmaceutical target under investigation for the treatment of several neuropsychiatric conditions. TAAR1 single nucleotide variants (SNV) have been found in patients with schizophrenia and metabolic disorders. However, the frequency of variants in geographically diverse populations and the functional effects of such variants are unknown. In this study, we aimed to characterise the distribution of TAAR1 SNVs in five different WHO regions using the Database of Genotypes and Phenotypes (dbGaP) and conducted a critical computational analysis using available TAAR1 structural data to identify SNVs affecting ligand binding and/or functional regions. Our analysis shows 19 orthosteric, 9 signalling and 16 micro-switch SNVs hypothesised to critically influence the agonist induced TAAR1 activation. These SNVs may non-proportionally influence populations from discrete regions and differentially influence the activity of TAAR1-targeting therapeutics in genetically and geographically diverse populations. Notably, our dataset presented with orthosteric SNVs D1033.32N (found only in the South-East Asian Region and Western Pacific Region) and T1945.42A (found only in South-East Asian Region), and 2 signalling SNVs (V1253.54A/T2526.36A, found in African Region and commonly, respectively), all of which have previously demonstrated to influence ligand induced functions of TAAR1. Furthermore, bioinformatics analysis using SIFT4G, MutationTaster 2, PROVEAN and MutationAssessor predicted all 16 micro-switch SNVs are damaging and may further influence the agonist activation of TAAR1, thereby possibly impacting upon clinical outcomes. Understanding the genetic basis of TAAR1 function and the impact of common mutations within clinical populations is important for the safe and effective utilisation of novel and existing pharmacotherapies.

Causal associations of COVID-19 on neurosurgical diseases risk: a Mendelian randomization study.

Many researchers have explored the potential association between one neurosurgical disease and coronavirus disease 2019 (COVID-19), but few systematically analyzed the association and causality between COVID-19 and various neurosurgical diseases. A Mendelian randomization analysis was conducted to evaluate the causal association between COVID-19 (including critically ill COVID-19, hospitalized COVID-19, and respiratory syndrome coronavirus 2 (SARS-CoV-2) infection) and 30 neurosurgical diseases within European populations. The consequences of inverse variance weighted models suggest that genetic susceptibility of critically ill COVID-19 may increase the risk of cerebral infarction (odds ratio [OR] = 1.02; p-value = 0.006), genetic susceptibility of SARS-CoV-2 infection may increase the risk of stroke (OR = 1.02; p-value = 0.047), and conversely, genetic susceptibility of hospitalized COVID-19 may reduce the risk of pituitary adenoma and craniopharyngioma (OR = 0.90; p-value = 0.032). In addition, evidences revealed potential associations between genetic susceptibility of COVID-19 and spinal stenosis (OR = 1.03; p-value = 0.028), diffuse brain injury (OR = 1.21; p-value = 0.040) and focal brain injury (OR = 1.12; p-value = 0.040). By testing for heterogeneity and pleiotropy, the above causal conclusions are robust. In summary, our analysis shows that COVID-19 has an independent and powerful causal influence on multiple neurosurgical disorders.

The diversity and clinical implications of genetic variants influencing clopidogrel bioactivation and response in the Emirati population.

BACKGROUND: Clopidogrel is a widely prescribed prodrug that requires activation via specific pharmacogenes to exert its anti-platelet function. Genetic variations in the genes encoding its transporter, metabolizing enzymes, and target receptor lead to variability in its activation and platelet inhibition and, consequently, its efficacy. This variability increases the risk of secondary cardiovascular events, and therefore, some variations have been utilized as genetic biomarkers when prescribing clopidogrel. METHODS: Our study examined clopidogrel-related genes (CYP2C19, ABCB1, PON1, and P2Y12R) in a cohort of 298 healthy Emiratis individuals. The study used whole exome sequencing (WES) data to comprehensively analyze pertinent variations of these genes, including their minor allele frequencies, haplotype distribution, and their resulting phenotypes. RESULTS: Our data shows that approximately 37% (n = 119) of the cohort are likely to benefit from the use of alternative anti-platelet drugs due to their classification as intermediate or poor CYP2C19 metabolizers. Additionally, more than 50% of the studied cohort exhibited variants in ABCB1, PON1, and P2YR12 genes, potentially influencing clopidogrel's transport, enzymatic clearance, and receptor performance. CONCLUSIONS: Recognizing these alleles and genotype frequencies may explain the clinical differences in medication response across different ethnicities and predict adverse events. Our findings underscore the need to consider genetic variations in prescribing clopidogrel, with potential implications for implementing personalized anti-platelet therapy among Emiratis based on their genetic profiles.

Current RNA strategies in treating cardiovascular diseases.

Cardiovascular disease (CVD) continues to impose a significant global health burden, necessitating the exploration of innovative treatment strategies. Ribonucleic acid (RNA)-based therapeutics have emerged as a promising avenue to address the complex molecular mechanisms underlying CVD pathogenesis. We present a comprehensive review of the current state of RNA therapeutics in the context of CVD, focusing on the diverse modalities that bring about transient or permanent modifications by targeting the different stages of the molecular biology central dogma. Considering the immense potential of RNA therapeutics, we have identified common gene targets that could serve as potential interventions for prevalent Mendelian CVD caused by single gene mutations, as well as acquired CVDs developed over time due to various factors. These gene targets offer opportunities to develop RNA-based treatments tailored to specific genetic and molecular pathways, presenting a novel and precise approach to address the complex pathogenesis of both types of cardiovascular conditions. Additionally, we discuss the challenges and opportunities associated with delivery strategies to achieve targeted delivery of RNA therapeutics to the cardiovascular system. This review highlights the immense potential of RNA-based interventions as a novel and precise approach to combat CVD, paving the way for future advancements in cardiovascular therapeutics.

Genetics and Genomics of Pulmonary Fibrosis: Charting the Molecular Landscape and Shaping Precision Medicine.

Recent genetic and genomic advancements have elucidated the complex etiology of idiopathic pulmonary fibrosis (IPF) and other progressive fibrotic interstitial lung diseases (ILDs), emphasizing the contribution of heritable factors. This state-of-the-art review synthesizes evidence on significant genetic contributors to pulmonary fibrosis (PF), including rare genetic variants and common single nucleotide polymorphisms (SNPs). The MUC5B promoter variant is unusual, a common SNP that markedly elevates the risk of early and established PF. We address the utility of genetic variation in enhancing understanding of disease pathogenesis, clinical phenotypes, improving disease definitions, and informing prognosis and treatment response. Critical research gaps are highlighted, particularly the underrepresentation of non-European ancestries in PF genetic studies and the exploration of PF phenotypes beyond usual interstitial pneumonia (UIP)/IPF. We discuss the role of telomere length, often critically short in PF, and its link to progression and mortality, underscoring the genetic complexity involving telomere biology genes (TERT, TERC) and others like SFTPC and MUC5B. Additionally, we address the potential of gene-by-environment interactions to modulate disease manifestation, advocating for precision medicine in PF. Insights from gene expression profiling studies and multi-omic analyses highlight the promise for understanding disease pathogenesis and offer new approaches to clinical care, therapeutic drug development, and biomarker discovery. Finally, we discuss the ethical, legal, and social implications of genomic research and therapies in PF, stressing the need for sound practices and informed clinical genetic discussions. Looking forward, we advocate for comprehensive genetic testing panels and polygenic risk scores to improve the management of PF and related ILDs across diverse populations.

Fast and accurate variant identification tool for sequencing-based studies.

BACKGROUND: Accurate identification of genetic variants, such as point mutations and insertions/deletions (indels), is crucial for various genetic studies into epidemic tracking, population genetics, and disease diagnosis. Genetic studies into microbiomes often require processing numerous sequencing datasets, necessitating variant identifiers with high speed, accuracy, and robustness. RESULTS: We present QuickVariants, a bioinformatics tool that effectively summarizes variant information from read alignments and identifies variants. When tested on diverse bacterial sequencing data, QuickVariants demonstrates a ninefold higher median speed than bcftools, a widely used variant identifier, with higher accuracy in identifying both point mutations and indels. This accuracy extends to variant identification in virus samples, including SARS-CoV-2, particularly with significantly fewer false negative indels than bcftools. The high accuracy of QuickVariants is further demonstrated by its detection of a greater number of Omicron-specific indels (5 versus 0) and point mutations (61 versus 48-54) than bcftools in sewage metagenomes predominated by Omicron variants. Much of the reduced accuracy of bcftools was attributable to its misinterpretation of indels, often producing false negative indels and false positive point mutations at the same locations. CONCLUSIONS: We introduce QuickVariants, a fast, accurate, and robust bioinformatics tool designed for identifying genetic variants for microbial studies. QuickVariants is available at https://github.com/caozhichongchong/QuickVariants .

Genetic variants in hypoxia-inducible factor pathway are associated with colorectal cancer risk and immune infiltration.

Hypoxia-inducible factor (HIF) pathway genes influence tumorigenesis and immune status. However, the associations between genetic variants in hypoxia-related genes and colorectal cancer risk and the immune status of hypoxia-associated genes in colorectal cancer have not been systematically characterized. The associations between genetic variants and colorectal cancer risk were evaluated in Chinese, Japanese and European populations using logistic regression analysis. The relationships between target genes and tumour immune infiltration were predicted by Tumour Immune Estimation Resource (TIMER). We found that rs34533650 in EPAS1 was associated with colorectal cancer risk (OR = 1.43, 95% CI = 1.20-1.70, P(FDR) = 8.35 × 10-4 ), and this finding was validated in two independent populations (Japanese: OR = 1.07, 95% CI = 1.01-1.15, p = 3.38 × 10-2 ; European: OR = 1.11, 95% CI = 1.03-1.19, p = 6.04 × 10-3 ). EPAS1-associated genes were enriched in immune-related pathways. In addition, we found that EPAS1 copy number variation (CNV) was associated with the degree of infiltration of immune cells and observed correlations between EPAS1 expression and immune cell infiltration levels in colorectal cancer. These results highlight that genetic variants of hypoxia-related genes play roles in colorectal cancer risk and provide new insight that EPAS1 might be a promising predictor of colorectal cancer susceptibility and immune status.

Adenylate cyclase 3: a potential genetic link between obesity and major depressive disorder.

Obesity and major depressive disorder (MDD) are both significant health issues that have been increasing in prevalence and are associated with multiple comorbidities. Obesity and MDD have been shown to be bidirectionally associated, and they are both influenced by genetics and environmental factors. However, the molecular mechanisms that link these two diseases are not yet fully understood. It is possible that these diseases are connected through the actions of the cAMP/protein kinase A (PKA) pathway. Within this pathway, adenylate cyclase 3 (Adcy3) has emerged as a key player in both obesity and MDD. Numerous genetic variants in Adcy3 have been identified in humans in association with obesity. Rodent knockout studies have also validated the importance of this gene for energy homeostasis. Furthermore, Adcy3 has been identified as a top candidate gene and even a potential blood biomarker for MDD. Adcy3 and the cAMP/PKA pathway may therefore serve as an important genetic and functional link between these two diseases. In this mini-review, we discuss the role of both Adcy3 and the cAMP/PKA pathway, including specific genetic mutations, in both diseases. Understanding the role that Adcy3 mutations play in obesity and MDD could open the door for precision medicine approaches and treatments for both diseases that target this gene.

CRISPR-Cas13d screens identify KILR, a breast cancer risk-associated lncRNA that regulates DNA replication and repair.

BACKGROUND: Long noncoding RNAs (lncRNAs) have surpassed the number of protein-coding genes, yet the majority have no known function. We previously discovered 844 lncRNAs that were genetically linked to breast cancer through genome-wide association studies (GWAS). Here, we show that a subset of these lncRNAs alter breast cancer risk by modulating cell proliferation, and provide evidence that a reduced expression on one lncRNA increases breast cancer risk through aberrant DNA replication and repair. METHODS: We performed pooled CRISPR-Cas13d-based knockdown screens in breast cells to identify which of the 844 breast cancer-associated lncRNAs alter cell proliferation. We selected one of the lncRNAs that increased cell proliferation, KILR, for follow-up functional studies. KILR pull-down followed by mass spectrometry was used to identify binding proteins. Knockdown and overexpression studies were performed to assess the mechanism by which KILR regulates proliferation. RESULTS: We show that KILR functions as a tumor suppressor, safeguarding breast cells against uncontrolled proliferation. The half-life of KILR is significantly reduced by the risk haplotype, revealing an alternative mechanism by which variants alter cancer risk. Mechanistically, KILR sequesters RPA1, a subunit of the RPA complex required for DNA replication and repair. Reduced KILR expression promotes breast cancer cell proliferation by increasing the available pool of RPA1 and speed of DNA replication. Conversely, KILR overexpression promotes apoptosis in breast cancer cells, but not normal breast cells. CONCLUSIONS: Our results confirm lncRNAs as mediators of breast cancer risk, emphasize the need to annotate noncoding transcripts in relevant cell types when investigating GWAS variants and provide a scalable platform for mapping phenotypes associated with lncRNAs.