Benefits and limitations of genome-wide association studies

Optimal variable identification for accurate detection of causal expression Quantitative Trait Loci with applications in heart-related diseases

Gene expression plays a pivotal role in various diseases, contributing significantly to their mechanisms. Most GWAS risk loci are in non-coding regions, potentially affecting disease risk by altering gene expression in specific tissues. This expression is notably tissue-specific, with genetic variants substantially influencing it. However, accurately detecting the expression Quantitative Trait Loci (eQTL) is challenging due to limited heritability in gene expression, extensive linkage disequilibrium (LD), and multiple causal variants. The single variant association approach in eQTL analysis is limited by its susceptibility to capture the combined effects of multiple variants, and a bias towards common variants, underscoring the need for a more robust method to accurately identify causal eQTL variants. To address this, we developed an algorithm, CausalEQTL, which integrates L 0 +L 1 penalized regression with an ensemble approach to localize eQTL, thereby enhancing prediction performance precisely. Our results demonstrate that CausalEQTL outperforms traditional models, including LASSO, Elastic Net, Ridge, in terms of power and overall performance. Furthermore, analysis of heart tissue data from the GTEx project revealed that eQTL sites identified by our algorithm provide deeper insights into heart-related tissue eQTL detection. This advancement in eQTL mapping promises to improve our understanding of the genetic basis of tissue-specific gene expression and its implications in disease. The source code and identified causal eQTLs for CausalEQTL are available on GitHub: https://github.com/zhc-moushang/CausalEQTL.

GETdb: A comprehensive database for genetic and evolutionary features of drug targets

The development of an innovative drug is complex and time-consuming, and the drug target identification is one of the critical steps in drug discovery process. Effective and accurate identification of drug targets can accelerate the drug development process. According to previous research, evolutionary and genetic information of genes has been found to facilitate the identification of approved drug targets. In addition, allosteric proteins have great potential as targets due to their structural diversity. However, this information that could facilitate target identification has not been collated in existing drug target databases. Here, we construct a comprehensive drug target database named Genetic and Evolutionary features of drug Targets database (GETdb, http://zhanglab.hzau.edu.cn/GETdb/page/index.jsp). This database not only integrates and standardizes data from dozens of commonly used drug and target databases, but also innovatively includes the genetic and evolutionary information of targets. Moreover, this database features an effective allosteric protein prediction model. GETdb contains approximately 4000 targets and over 29,000 drugs, and is a user-friendly database for searching, browsing and downloading data to facilitate the development of novel targets.

Long-term PM2.5 exposure associated with severity of angina pectoris and related health status in patients admitted with acute coronary syndrome: Modification effect of genetic susceptibility and disease history

Long-term particulate matter with aerodynamic diameters ≤2.5 μm (PM2.5) exposure has been associated with the occurrence of acute coronary syndrome (ACS). However, the impact of PM2.5 exposure and its components on the severity of angina pectoris and disease-related health status in patients hospitalized for ACS is understudied. To assess the association between long-term exposure to PM2.5 components and the angina pectoris severity in ACS patients, as well as the modification effects of genetic factors and disease history in north China. During 2017-2019, 6729 ACS patients were collected in Shandong Province and Beijing, with their angina pectoris severity evaluated using Seattle Angina Questionnaire (SAQ). The 0-3 years' average concentrations of PM2.5 and its five major components were assigned to each patient's residential address. Linear mixed-effects model, weighted quantile regression, and quantile g-computation were used to estimate the effects of both single and joint associations between PM2.5 components and SAQ scores. The interactive effect was estimated by polygenic risk scores and disease history. For each interquartile range increase in PM2.5, the overall SAQ score changed by -3.71% (95%CI: -4.54% to -2.88%), with score of angina stability more affected than angina frequency and other dimensions of angina pectoris severity. Sulfate and ammonium were major contributors to the effect of PM2.5 exposure. Significant modification effect was only observed for disease history, especially for the dimension of physical limitation. Among a series of pre-existing diseases, patients with a family history of coronary artery disease, previous percutaneous coronary intervention or coronary artery bypass grafting, and stroke were more susceptible to PM2.5 exposure than others. Greater exposure to PM2.5 is associated with more serious angina pectoris and worse disease-related health status in ACS patients. Public health and clinical priority should be given to cutting down key effective components and protecting highly vulnerable individuals.

The Future of Obesity Management through Precision Nutrition: Putting the Individual at the Center

PURPOSE OF REVIEW: The prevalence of obesity continues to rise steadily. While obesity management typically relies on dietary and lifestyle modifications, individual responses to these interventions vary widely. Clinical guidelines for overweight and obesity stress the importance of personalized approaches to care. This review aims to underscore the role of precision nutrition in delivering tailored interventions for obesity management. RECENT FINDINGS: Recent technological strides have expanded our ability to detect obesity-related genetic polymorphisms, with machine learning algorithms proving pivotal in analyzing intricate genomic data. Machine learning algorithms can also predict postprandial glucose, triglyceride, and insulin levels, facilitating customized dietary interventions and ultimately leading to successful weight loss. Additionally, given that adherence to dietary recommendations is one of the key predictors of weight loss success, employing more objective methods for dietary assessment and monitoring can enhance sustained long-term compliance. Biomarkers of food intake hold promise for a more objective dietary assessment. Acknowledging the multifaceted nature of obesity, precision nutrition stands poised to transform obesity management by tailoring dietary interventions to individuals' genetic backgrounds, gut microbiota, metabolic profiles, and behavioral patterns. However, there is insufficient evidence demonstrating the superiority of precision nutrition over traditional dietary recommendations. The integration of precision nutrition into routine clinical practice requires further validation through randomized controlled trials and the accumulation of a larger body of evidence to strengthen its foundation.

Relation Between Obesity and Type 2 Diabetes: Evolutionary Insights, Perspectives and Controversies

PURPOSE OF REVIEW

Since the mid-twentieth century, obesity and its related comorbidities, notably insulin resistance (IR) and type 2 diabetes (T2D), have surged. Nevertheless, their underlying mechanisms remain elusive. Evolutionary medicine (EM) sheds light on these issues by examining how evolutionary processes shape traits and diseases, offering insights for medical practice. This review summarizes the pathogenesis and genetics of obesity-related IR and T2D. Subsequently, delving into their evolutionary connections. Addressing limitations and proposing future research directions aims to enhance our understanding of these conditions, paving the way for improved treatments and prevention strategies.

RECENT FINDINGS

Several evolutionary hypotheses have been proposed to unmask the origin of obesity-related IR and T2D, e.g., the "thrifty genotype" hypothesis suggests that certain "thrifty genes" that helped hunter-gatherer populations efficiently store energy as fat during feast-famine cycles are now maladaptive in our modern obesogenic environment. The "drifty genotype" theory suggests that if thrifty genes were advantageous, they would have spread widely, but proposes genetic drift instead. The "behavioral switch" and "carnivore connection" hypotheses propose insulin resistance as an adaptation for a brain-dependent, low-carbohydrate lifestyle. The thrifty phenotype theory suggests various metabolic outcomes shaped by genes and environment during development. However, the majority of these hypotheses lack experimental validation. Understanding why ancestral advantages now predispose us to diseases may aid in drug development and prevention of disease. EM helps us to understand the evolutionary relation between obesity-related IR and T2D. But still gaps and contradictions persist. Further interdisciplinary research is required to elucidate complete mechanisms.

DiGAS: Differential gene allele spectrum as a descriptor in genetic studies

Diagnosing individuals with complex genetic diseases is a challenging task. Computational methodologies exploit information at the genotype level by taking into account single nucleotide polymorphisms (SNPs) leveraging the results of genome-wide association studies analysis to assign a statistical significance to each SNP. Recent methodologies extend such an approach by aggregating SNP significance at the genetic level to identify genes that are related to the condition under study. However, such methodologies still suffer from the initial SNP analysis limitations. Here, we present DiGAS, a tool for diagnosing genetic conditions by computing significance, by means of SNP information, directly at the complex level of genetic regions. Such an approach is based on a generalized notion of allele spectrum, which evaluates the complete genetic alterations of the SNP set belonging to a genetic region at the population level. The statistical significance of a region is then evaluated through a differential allele spectrum analysis between the conditions of individuals belonging to the population. Tests, performed on well-established datasets regarding Alzheimer's disease, show that DiGAS outperforms the state of the art in distinguishing between sick and healthy subjects.

The weight of weights: Causal impact of overweight on major depressive disorder and its disparate association with suicide attempts in over 450,000 individuals of European ancestry (AM-SRNMA 003)

AIM

Some studies and societal discussions have suggested a potential link between overweight and a higher risk of mental disorders and suicidal ideation. However, the causal relationships between these factors remain unclear. This study aims to assess the bidirectional causal associations between overweight and mental disorders, including suicidal attempts.

METHOD

We conducted a Two-Sample Mendelian Randomization study utilizing data from over 450,000 individuals of European ancestry sourced from a comprehensive Genome-Wide Association Study database. We chose single-nucleotide polymorphisms as instrumental variables.

RESULTS

Our analyses consistently supported a unidirectional causal association of overweight with the increased risk of major depressive disorder (MDD, β = 0.152, se = 0.069, P = 0.027), bipolar affective disorders (BD, β = 0.197, se = 0.092, P = 0.033), and attention-deficit / hyperactivity disorder (ADHD, β = 0.308, se = 0.080, P = 1.366 × 10-4). We observed no significant causal relationships for the exposure of overweight to anxiety disorder (AD), manic episode (MA), panic disorder (PD), schizophrenia (SZ), substance use disorder (SUD), autism spectrum disorder (ASD), ever attempted suicide, recent thoughts of suicide or self-harm, and suicide or other intentional self-harm events.

CONCLUSION

This study has provided evidence for the causal relationship between overweight and MDD, BD, ADHD, with no observed relationship between overweight and AD, MA, PD, SZ, SUD, ASD, ever attempted suicide, recent thoughts of suicide or self-harm, and suicide or other intentional self-harm events.

Genome-wide association study on meningioma risk in Japan: a multicenter prospective study

PURPOSE

Although meningiomas are the most common primary intracranial tumors, their genetic etiologies have not been fully elucidated. To date, only two genome-wide association studies (GWASs) have focused on European ancestries, despite ethnic differences in the incidence of meningiomas. The aim of this study was to conduct the first GWAS of Japanese patients with meningiomas to identify the SNPs associated with meningioma susceptibility.

METHODS

In this multicenter prospective case-control study, we studied 401 Japanese patients with meningioma admitted in five institutions in Japan, and 50,876 control participants of Japanese ancestry enrolled in Biobank Japan.

RESULTS

The quality control process yielded 536,319 variants and imputation resulted in 8,224,735 variants on the autosomes and 224,820 variants on the X chromosomes. This GWAS eventually revealed no genetic variants with genome-wide significance (P < 5 × 10 - 8) and observed no significant association in the previously reported risk variants rs11012732 and rs2686876 due to low minor allele frequency in the Japanese population.

CONCLUSION

This is the first GWAS of meningiomas in East Asian populations and is expected to contribute to the development of GWAS research for meningiomas.

The importance of functional genomics studies in precision rheumatology

Rheumatic diseases, those that affect the musculoskeletal system, cause significant morbidity. Among risk factors of these diseases is a significant genetic component. Recent advances in high-throughput omics techniques now allow a comprehensive profiling of patients at a genetic level through genome-wide association studies. Without functional interpretation of variants identified through these studies, clinical insight remains limited. Strategies include statistical fine-mapping that refine the list of variants in loci associated with disease, whilst colocalization techniques attempt to attribute function to variants that overlap a genetically active chromatin annotation. Functional validation using genome editing techniques can be used to further refine genetic signals and identify key pathways in cell types relevant to rheumatic disease biology. Insight gained from the combination of genetic studies and functional validation can be used to improve precision medicine in rheumatic diseases by allowing risk prediction and drug repositioning.

A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes

In-depth multiomic phenotyping provides molecular insights into complex physiological processes and their pathologies. Here, we report on integrating 18 diverse deep molecular phenotyping (omics-) technologies applied to urine, blood, and saliva samples from 391 participants of the multiethnic diabetes Qatar Metabolomics Study of Diabetes (QMDiab). Using 6,304 quantitative molecular traits with 1,221,345 genetic variants, methylation at 470,837 DNA CpG sites, and gene expression of 57,000 transcripts, we determine (1) within-platform partial correlations, (2) between-platform mutual best correlations, and (3) genome-, epigenome-, transcriptome-, and phenome-wide associations. Combined into a molecular network of > 34,000 statistically significant trait-trait links in biofluids, our study portrays "The Molecular Human". We describe the variances explained by each omics in the phenotypes (age, sex, BMI, and diabetes state), platform complementarity, and the inherent correlation structures of multiomics data. Further, we construct multi-molecular network of diabetes subtypes. Finally, we generated an open-access web interface to "The Molecular Human" ( http://comics.metabolomix.com ), providing interactive data exploration and hypotheses generation possibilities.

Adam19 Deficiency Impacts Pulmonary Function: Human GWAS Follow-up in a Mouse Knockout Model

PURPOSE

Over 550 loci have been associated with human pulmonary function in genome-wide association studies (GWAS); however, the causal role of most remains uncertain. Single nucleotide polymorphisms in a disintegrin and metalloprotease domain 19 (ADAM19) are consistently related to pulmonary function in GWAS. Thus, we used a mouse model to investigate the causal link between Adam19 and pulmonary function.

METHODS

We created an Adam19 knockout (KO) mouse model and validated the gene targeting using RNA-Seq and RT-qPCR. Mouse body composition was assessed using dual-energy X-ray absorptiometry. Mouse lung function was measured using flexiVent.

RESULTS

Contrary to prior publications, the KO was not neonatal lethal. KO mice had lower body weight and shorter tibial length than wild-type (WT) mice. Their body composition revealed lower soft weight, fat weight, and bone mineral content. Adam19 KO had decreased baseline respiratory system elastance, minute work of breathing, tissue damping, tissue elastance, and forced expiratory flow at 50% forced vital capacity but higher FEV0.1 and FVC. Adam19 KO had attenuated tissue damping and tissue elastance in response to methacholine following LPS exposure. Adam19 KO also exhibited attenuated neutrophil extravasation into the airway after LPS administration compared to WT. RNA-Seq analysis of KO and WT lungs identified several differentially expressed genes (Cd300lg, Kpna2, and Pttg1) implicated in lung biology and pathogenesis. Gene set enrichment analysis identified negative enrichment for TNF pathways.

CONCLUSION

Our murine findings support a causal role of ADAM19, implicated in human GWAS, in regulating pulmonary function.

Assessing inorganic nanoparticle toxicity through omics approaches

In the last two decades, the development of nanotechnology has resulted in inorganic nanoparticles playing crucial roles in key industries, ranging from healthcare to energy technologies. For instance, gold and silver nanoparticles are widely used in rapid COVID-19 and flu tests, titania and zinc oxide nanoparticles are commonly found in cosmetic products, and superparamagnetic iron oxide nanoparticles have been clinically exploited as contrast agents and anti-anemia medicines. As a result, human exposure to nanomaterials is continuously increasing, raising concerns about their potential adverse health effects. Historically, the study of nanoparticle toxicity has largely relied on macroscopic observations obtained in different in vitro and in vivo models, resulting in readouts such as median lethal dose, biodistribution profile, and/or histopathological assessment. In recent years, omics methodologies, including transcriptomics, epigenomics, proteomics, metabolomics, and lipidomics, are increasingly used to characterize the biological interactions of nanomaterials, providing a better and broader understanding of their impact and mechanisms of toxicity. These approaches have been able to identify important genes and gene products that mediate toxicological effects, as well as endogenous functions and pathways dysregulated by nanoparticles. Omics methods improve our understanding of nanoparticle biology, and unravel mechanistic insights into nanomedicine-based therapies. This review aims to provide a deeper understanding and new perspectives of omics approaches to characterize the toxicity and biological interactions of inorganic nanoparticles, and improve the safety of nanoparticle applications.

Decoding the connection between lncRNA and obesity: Perspective from humans and Drosophila

Background

Obesity is a burgeoning global health problem with an escalating prevalence and severe implications for public health. New evidence indicates that long non-coding RNAs (lncRNAs) may play a pivotal role in regulating adipose tissue function and energy homeostasis across various species. However, the molecular mechanisms underlying obesity remain elusive.

Scope of review

This review discusses obesity and fat metabolism in general, highlighting the emerging importance of lncRNAs in modulating adipogenesis. It describes the regulatory networks, latest tools, techniques, and approaches to enhance our understanding of obesity and its lncRNA-mediated epigenetic regulation in humans and Drosophila.

Major conclusions

This review analyses large datasets of human and Drosophila lncRNAs from published databases and literature with experimental evidence supporting lncRNAs role in fat metabolism. It concludes that lncRNAs play a crucial role in obesity-related metabolism. Cross-species comparisons highlight the relevance of Drosophila findings to human obesity, emphasizing their potential role in adipose tissue biology. Furthermore, it discusses how recent technological advancements and multi-omics data integration enhance our capacity to characterize lncRNAs and their function. Additionally, this review briefly touches upon innovative methodologies like experimental evolution and advanced sequencing technologies for identifying novel genes and lncRNA regulators in Drosophila, which can potentially contribute to obesity research.

Genetic Variants in p53 Pathway Genes Affect Survival of Patients with HBV-Related Hepatocellular Carcinoma

Purpose

P53 is a suppressor gene closely related to carcinogenesis. However, the associations between genetic variants in the p53 signaling pathway and prognosis in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) remain unknown. The current study aims to analyze associations between the single nucleotide polymorphisms (SNPs) in p53 pathway-related genes and survival of patients with HBV-HCC.

Methods

We evaluated the associations between 4698 SNPs in 70 genes of the p53 pathway and overall survival (OS) of 866 patients in additive genetic models by using Cox proportional hazards regression analysis. Stepwise multivariable Cox regression analysis was conducted to determine the independent effects of identified SNPs in single-locus analyses. The expression of quantitative trait loci (eQTL) was also analyzed using data from GTEx and 1000 Genomes Project, and functional prediction of SNPs was performed by using RegulomeDB v2.2, 3DSNP v2.0, HaploReg v4.2 and VannoPortal.

Results

We found that two novel SNPs of CD82 rs7925603 A > G and PMAIP1 rs4396625 A > T, were significantly and independently associated with OS [adjusted hazards ratios (HRs) and 95% confidence intervals (CI) were 1.27 (1.10-1.48) and 0.77 (0.66-0.91), respectively; P = 0.001 and = 0.002, respectively] and that the combined risk genotypes of these SNPs showed a significant association with OS in patients with HBV-HCC (P trend < 0.001). Further eQTL analysis in the GTEx dataset showed that the rs7925603 G allele was associated with lower CD82 mRNA expression levels, while the rs4396625 T allele was associated with higher PMAIP1 mRNA expression levels in whole blood cells.

Conclusion

We identified two observed survival-associated SNPs in CD82 and PMAIP1 in the p53 pathway, which influenced HBV-HCC survival possibly through a mechanism of altering mRNA expression. Large studies are warranted to validate our findings.

High-resolution global diversity copy number variation maps and association with ctyper

Genetic analysis of copy number variations (CNVs), especially in complex regions, is challenging due to reference bias and ambiguous alignment of Next-Generation Sequencing (NGS) reads to repetitive DNA. Consequently, aggregate copy numbers are typically analyzed, overlooking variation between gene copies. Pangenomes contain diverse sequences of gene copies and enable the study of sequence-resolved CNVs. We developed a method, ctyper, to discover sequence-resolved CNVs in NGS data by leveraging CNV genes from pangenomes. From 118 public assemblies, we constructed a database of 3,351 CNV genes, distinguishing each gene copy as a resolved allele. We used phylogenetic trees to organize alleles into highly similar allele-types that revealed events of linked small variants due to stratification, structural variation, conversion, and duplication. Saturation analysis showed that new samples share an average of 97.8% CNV alleles with the database. The ctyper method traces individual gene copies in NGS data to their nearest alleles in the database and identifies allele-specific copy numbers using multivariate linear regression on k-mer counts and phylogenetic clustering. Applying ctyper to 1000 Genomes Project (1kgp) samples showed Hardy-Weinberg Equilibrium on 99.3% of alleles and a 97.6% F1 score on genotypes based on 641 1kgp trios. Leave-one-out analysis on 39 assemblies matched to 1kgp samples showed that 96.5% of variants in query sequences match the genotyped allele. Genotyping 1kgp data revealed 226 population-specific CNVs, including a conversion on SMN2 to SMN1, potentially impacting Spinal Muscular Atrophy diagnosis in Africans. Our results revealed two models of CNV: recent CNVs due to ongoing duplications and polymorphic CNVs from ancient paralogs missing from the reference. To measure the functional impact of CNVs, after merging allele-types, we conducted genome-wide Quantitative Trait Locus analysis on 451 1kgp samples with Geuvadis rRNA-seqs. Using a linear mixed model, our genotyping enables the inference of relative expression levels of paralogs within a gene family. In a global evolutionary context, 150 out of 1,890 paralogs (7.94%) and 546 out of 16,628 orthologs (3.28%) had significantly different expression levels, suggesting divergent expression from original genes. Specific examples include lower expression on the converted SMN and increased expression on translocated AMY2B (GTEx pancreas data). Our method enables large cohort studies on complex CNVs to uncover hidden health impacts and overcome reference bias.

Leveraging Random Effects in Cistrome-Wide Association Studies for Decoding the Genetic Determinants of Prostate Cancer

Cistrome-wide association studies (CWAS) are pivotal for identifying genetic determinants of diseases by correlating genetically regulated cistrome states with phenotypes. Traditional CWAS typically develops a model based on cistrome and genotype data to associate predicted cistrome states with phenotypes. The random effect cistrome-wide association study (RECWAS), reevaluates the necessity of cistrome state prediction in CWAS. RECWAS utilizes either a linear model or marginal effect for initial feature selection, followed by kernel-based feature aggregation for association testing is introduced. Through simulations and analysis of prostate cancer data, a thorough evaluation of CWAS and RECWAS is conducted. The results suggest that RECWAS offers improved power compared to traditional CWAS, identifying additional genomic regions associated with prostate cancer. CWAS identified 102 significant regions, while RECWAS found 50 additional significant regions compared to CWAS, many of which are validated. Validation encompassed a range of biological evidence, including risk signals from the GWAS catalog, susceptibility genes from the DisGeNET database, and enhancer-domain scores. RECWAS consistently demonstrated improved performance over traditional CWAS in identifying genomic regions associated with prostate cancer. These findings demonstrate the benefits of incorporating kernel methods into CWAS and provide new insights for genetic discovery in complex diseases.

Genome-wide association study of therapeutic response to statin drugs in cardiovascular disease

Cardiovascular disease (CVD) is one of the main causes of death in the world. The increased level of blood cholesterol is significantly correlated to CVD incidents. Statins are a group of drugs that decrease the synthesis of cholesterol in the liver by inhibiting the final enzyme of the pathway named HMG-CoA reductase. Several investigations showed that different patients give different responses to the administration of statin drugs according to their genetic background. In this research study, using Genome-Wide Association Studies (GWAS) data analysis methods, such as the SimpleM statistical approach and genomic connection matrix, we tried to discover the novel candidate SNPs that were involved in response to statin drugs. The investigation was carried out using 3,221 cardiovascular patients' data about genotypes and phenotypes of two important parameters including total cholesterol, and LDL level, in response to statin administration. Functional annotation of nearest genes to candidate SNPs was also carried out by using comprehensive databases and tools such as BioMart-Ensembl, UCSC, NCBI, and WebGestalt software. Our results represented eight novel SNPs (rs10820084, rs4803750, rs10989887, rs1966503, rs17502794, rs10785232, rs484071, rs4785621) significantly associated with statin response in different individual cardiovascular patients for the first time. In addition, the groups of genes that are close to the SNPs were also represented and evaluated in detail. Our results illustrated that some of the genes such as BAAT, BCL3, and CMTM6 have a direct functional impact on cholesterol level or LDL biosynthesis which confirmed the effects of neighbor SNPs on the response to statin drugs. Today, finding the loci, genes, and molecular mechanisms involved in the response to drugs is of great importance in pharmacogenomics and personalized medicine.

Innovative approaches to atrial fibrillation prediction: should polygenic scores and machine learning be implemented in clinical practice?

Atrial fibrillation (AF) prediction and screening are of important clinical interest because of the potential to prevent serious adverse events. Devices capable of detecting short episodes of arrhythmia are now widely available. Although it has recently been suggested that some high-risk patients with AF detected on implantable devices may benefit from anticoagulation, long-term management remains challenging in lower-risk patients and in those with AF detected on monitors or wearable devices as the development of clinically meaningful arrhythmia burden in this group remains unknown. Identification and prediction of clinically relevant AF is therefore of unprecedented importance to the cardiologic community. Family history and underlying genetic markers are important risk factors for AF. Recent studies suggest a good predictive ability of polygenic risk scores, with a possible additive value to clinical AF prediction scores. Artificial intelligence, enabled by the exponentially increasing computing power and digital data sets, has gained traction in the past decade and is of increasing interest in AF prediction using a single or multiple lead sinus rhythm electrocardiogram. Integrating these novel approaches could help predict AF substrate severity, thereby potentially improving the effectiveness of AF screening and personalizing the management of patients presenting with conditions such as embolic stroke of undetermined source or subclinical AF. This review presents current evidence surrounding deep learning and polygenic risk scores in the prediction of incident AF and provides a futuristic outlook on possible ways of implementing these modalities into clinical practice, while considering current limitations and required areas of improvement.

pyTWMR: transcriptome-wide Mendelian randomization in python

MOTIVATION

Mendelian randomization (MR) is a widely used approach to estimate causal effect of variation in gene expression on complex traits. Among several MR-based algorithms, transcriptome-wide summary statistics-based Mendelian Randomization approach (TWMR) enables the uses of multiple SNPs as instruments and multiple gene expression traits as exposures to facilitate causal inference in observational studies.

RESULTS

Here we present a Python-based implementation of TWMR and revTWMR. Our implementation offers GPU computational support for faster computations and robust computation mode resilient to highly correlated gene expressions and genetic variants.

AVAILABILITY AND IMPLEMENTATION

pyTWMR is available at github.com/soreshkov/pyTWMR.

Association mapping with a diverse population of Puccinia graminis f. sp. tritici identified avirulence loci interacting with the barley Rpg1 stem rust resistance gene

BACKGROUND

Wheat stem rust, caused by Puccinia graminis f. sp. tritici (Pgt), is an important disease of barley and wheat. A diverse sexual Pgt population from the Pacific Northwest (PNW) region of the US contains a high proportion of individuals with virulence on the barley stem rust resistance (R) gene, Rpg1. However, the evolutionary mechanisms of this virulence on Rpg1 are mysterious considering that Rpg1 had not been deployed in the region and the gene had remained remarkably durable in the Midwestern US and prairie provinces of Canada.

METHODS AND RESULTS

To identify AvrRpg1 effectors, genome wide association studies (GWAS) were performed using 113 Pgt isolates collected from the PNW (n = 89 isolates) and Midwest (n = 24 isolates) regions of the US. Disease phenotype data were generated on two barley lines Morex and the Golden Promise transgenic (H228.2c) that carry the Rpg1 gene. Genotype data was generated by whole genome sequencing (WGS) of 96 isolates (PNW = 89 isolates and Midwest = 7 isolates) and RNA sequencing (RNAseq) data from 17 Midwestern isolates. Utilizing ~1.2 million SNPs generated from WGS and phenotype data (n = 96 isolates) on the transgenic line H228.2c, 53 marker trait associations (MTAs) were identified. Utilizing ~140 K common SNPs generated from combined analysis of WGS and RNAseq data, two significant MTAs were identified using the cv Morex phenotyping data. The 55 MTAs defined two distinct avirulence loci, on supercontig 2.30 and supercontig 2.11 of the Pgt reference genome of Pgt isolate CRL 75-36-700-3. The major avirulence locus designated AvrRpg1A was identified with the GWAS using both barley lines and was delimited to a 35 kb interval on supercontig 2.30 containing four candidate genes (PGTG_10878, PGTG_10884, PGTG_10885, and PGTG_10886). The minor avirulence locus designated AvrRpg1B identified with cv Morex contained a single candidate gene (PGTG_05433). AvrRpg1A haplotype analysis provided strong evidence that a dominant avirulence gene underlies the locus.

CONCLUSIONS

The association analysis identified strong candidate AvrRpg1 genes. Further analysis to validate the AvrRpg1 genes will fill knowledge gaps in our understanding of rust effector biology and the evolution and mechanism/s of Pgt virulence on Rpg1.

Selection for stress tolerance and longevity in Drosophila melanogaster have strong impacts on microbiome profiles

There is experimental evidence that microbiomes have a strong influence on a range of host traits. Understanding the basis and importance of symbiosis between host and associated microorganisms is a rapidly developing research field, and we still lack a mechanistic understanding of ecological and genetic pressures affecting host-microbiome associations. Here Drosophila melanogaster lines from a large-scale artificial selection experiment were used to investigate whether the microbiota differ in lines selected for different stress resistance traits and longevity. Following multiple generations of artificial selection all selection regimes and corresponding controls had their microbiomes assessed. The microbiome was interrogated based on 16S rRNA sequencing. We found that the microbiome of flies from the different selection regimes differed markedly from that of the unselected control regime, and microbial diversity was consistently higher in selected relative to control regimes. Several common Drosophila bacterial species showed differentially abundance in the different selection regimes despite flies being exposed to similar environmental conditions for two generations prior to assessment. Our findings provide strong evidence for symbiosis between host and microbiomes but we cannot reveal whether the interactions are adaptive, nor whether they are caused by genetic or ecological factors.

Genetic Influence of Oily Fish Intake on Age-Related Macular Degeneration Risk: A Two-Sample Mendelian Randomization Analysis

Purpose

Emerging research indicates a link between the intake of fatty fish and age-related macular degeneration (AMD). However, observational studies fall short in establishing a direct causal link between oily fish intake and AMD. We wanted to determine whether causal association lies between oily fish intake and age-related macular degeneration (AMD) risk in human beings.

Methods

This two-sample mendelian randomization (MR) study used the MR method to probe the genetic causality in the relationship between oily fish intake and AMD. The genome-wide association study (GWAS) data for AMD were acquired from a Finnish database, whereas the data on fish oil intake came from the UK Biobank. The analysis used several approaches such as inverse-variance weighted (IVW), MR Egger, weighted median, simple mode, and weighted mode MR. In addition, the Cochran's Q test was used to evaluate heterogeneity in the MR data. The MR-Egger intercept and MR-pleiotropy residual sum and outlier (MR-PRESSO) tests were used to assess the presence of horizontal pleiotropy. A leave-one-out sensitivity analysis was conducted to determine the reliability of the association.

Results

The IVW method revealed that the intake of oily fish is an independent risk factor for AMD (P = 0.034). It also suggested a minimal likelihood of horizontal pleiotropy affecting the causality (P > 0.05), with no substantial heterogeneity detected in the genetic variants (P > 0.05). The leave-one-out analysis confirmed the reliability and stability of this correlation.

Conclusions

This research used a two-sample MR analysis to provide evidence of a genetic causal relationship between the eating of oily fish and AMD. This discovery held potential significance in AMD prevention.

Interaction between caffeine consumption & genetic susceptibility in Parkinson's disease: A systematic review

BACKGROUND

Caffeine is one of the most consumed psychoactive substances globally. Caffeine-gene interactions in Parkinson's disease (PD) has not been systematically examined.

OBJECTIVES

To conduct a systematic review on the interaction between caffeine consumption and genetic susceptibility to PD.

METHODOLOGY

We conducted PubMed and Embase search using terms "Genetic association studies", "Caffeine", "polymorphism" and "Parkinson's disease", from inception till 2023. Of the initial 2391 studies, 21 case-control studies were included. The demographic, genetic and clinical data were extracted and analyzed.

RESULTS

We identified 21 studies which involved a total of 607,074 study subjects and 17 gene loci (SNCA, MAPT, HLA-DRA, NOS1, NOS3, GBA, ApoE, BST1, ESR2, NAT2, SLC2A13, LRRK2, NOS2A, GRIN2A, CYP1A2, ESR1, ADORA2A) have been investigated for the effect of gene-caffeine interaction and PD risk. The genes were identified through PD GWAS or involved in caffeine or related metabolism pathways. Based on the genetic association and interaction studies, only MAPT, SLC2A13, LRRK2, ApoE, NOS2A, GRIN2A, CYP1A2, and ADORA2A have been shown by at least one study to have a positive caffeine-gene interaction influencing the risk of PD.

CONCLUSION

Studies have shown an interaction between caffeine with genetic variants of MAPT, SLC2A13, LRRK2, ApoE, NOS2A, GRIN2A, CYP1A2, and ADORA2A in modulating the risk of PD. Due to the potential limitations of these discovery/pilot studies, further independent replication studies are needed. Better designed genetic association studies in multi-ancestry and admixed cohorts to identify potential shared or unique multivariate gene-environmental interactions, as well as functional studies of gene-caffeine interactions will be useful.

Combining Hyperspectral Techniques and Genome-Wide Association Studies to Predict Peanut Seed Vigor and Explore Associated Genetic Loci

Seed vigor significantly affects peanut breeding and agricultural yield by influencing seed germination and seedling growth and development. Traditional vigor testing methods are inadequate for modern high-throughput assays. Although hyperspectral technology shows potential for monitoring various crop traits, its application in predicting peanut seed vigor is still limited. This study developed and validated a method that combines hyperspectral technology with genome-wide association studies (GWAS) to achieve high-throughput detection of seed vigor and identify related functional genes. Hyperspectral phenotyping data and physiological indices from different peanut seed populations were used as input data to construct models using machine learning regression algorithms to accurately monitor changes in vigor. Model-predicted phenotypic data from 191 peanut varieties were used in GWAS, gene-based association studies, and haplotype analyses to screen for functional genes. Real-time fluorescence quantitative PCR (qPCR) was used to analyze the expression of functional genes in three high-vigor and three low-vigor germplasms. The results indicated that the random forest and support vector machine models provided effective phenotypic data. We identified Arahy.VMLN7L and Arahy.7XWF6F, with Arahy.VMLN7L negatively regulating seed vigor and Arahy.7XWF6F positively regulating it, suggesting distinct regulatory mechanisms. This study confirms that GWAS based on hyperspectral phenotyping reveals genetic relationships in seed vigor levels, offering novel insights and directions for future peanut breeding, accelerating genetic improvements, and boosting agricultural yields. This approach can be extended to monitor and explore germplasms and other key variables in various crops.

Epigenome-metabolism nexus in the retina: implications for aging and disease

Intimate links between epigenome modifications and metabolites allude to a crucial role of cellular metabolism in transcriptional regulation. Retina, being a highly metabolic tissue, adapts by integrating inputs from genetic, epigenetic, and extracellular signals. Precise global epigenomic signatures guide development and homeostasis of the intricate retinal structure and function. Epigenomic and metabolic realignment are hallmarks of aging and highlight a link of the epigenome-metabolism nexus with aging-associated multifactorial traits affecting the retina, including age-related macular degeneration and glaucoma. Here, we focus on emerging principles of epigenomic and metabolic control of retinal gene regulation, with emphasis on their contribution to human disease. In addition, we discuss potential mitigation strategies involving lifestyle changes that target the epigenome-metabolome relationship for maintaining retinal function.

Novel genetic variants in the NLRP3 inflammasome-related PANX1 and APP genes predict survival of patients with hepatitis B virus-related hepatocellular carcinoma

BACKGROUND

The nod-like receptor protein 3 (NLRP3) is one of the most characterized inflammasomes involved in the pathogenesis of several cancers, including hepatocellular carcinoma (HCC). However, the effects of genetic variants in the NLRP3 inflammasome-related genes on survival of hepatitis B virus (HBV)-related HCC patients are unclear.

METHODS

We performed multivariable Cox proportional hazards regression analysis to evaluate associations between 299 single-nucleotide polymorphisms (SNPs) in 16 NLRP3 inflammasome-related genes and overall survival (OS) of 866 patients with HBV-related HCC. We further performed expression quantitative trait loci (eQTL) analysis using the data from the GTEx project and 1000 Genomes projects, and performed differential expression analysis using the TCGA dataset to explore possible molecular mechanisms underlying the observed associations.

RESULTS

We found that two functional SNPs (PANX1 rs3020013 A > G and APP rs9976425 C > T) were significantly associated with HBV-related HCC OS with the adjusted hazard ratio (HR) of 0.83 [95% confidence interval (CI) = 0.73-0.95, P = 0.008], and 1.26 (95% CI = 1.02-1.55, P = 0.033), respectively. Moreover, the eQTL analysis revealed that the rs3020013 G allele was correlated with decreased mRNA expression levels of PANX1 in both normal liver tissues (P = 0.044) and whole blood (P < 0.001) in the GTEx dataset, and PANX1 mRNA expression levels were significantly higher in HCC samples and associated with a poorer survival of HCC patients. However, we did not observe such correlations for APP rs9976425.

CONCLUSIONS

These results indicated that SNPs in the NLRP3 inflammasome-related genes may serve as potential biomarkers for HBV-related HCC survival, once replicated by additional larger studies.

From genetic associations to genes: methods, applications, and challenges

Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.

Polygenic Indices (a.k.a. Polygenic Scores) in Social Science: A Guide for Interpretation and Evaluation

Polygenic indices (PGI)-the new recommended label for polygenic scores (PGS) in social science-are genetic summary scales often used to represent an individual's liability for a disease, trait, or behavior based on the additive effects of measured genetic variants. Enthusiasm for linking genetic data with social outcomes and the inclusion of premade PGIs in social science datasets have facilitated increased uptake of PGIs in social science research-a trend that will likely continue. Yet, most social scientists lack the expertise to interpret and evaluate PGIs in social science research. Here, we provide a primer on PGIs for social scientists focusing on key concepts, unique statistical genetic considerations, and best practices in calculation, estimation, reporting, and interpretation. We summarize our recommended best practices as a checklist to aid social scientists in evaluating and interpreting studies with PGIs. We conclude by discussing the similarities between PGIs and standard social science scales and unique interpretative considerations.

MAGs-centric crack: how long will, spore-positive Frankia and most Protofrankia, microsymbionts remain recalcitrant to axenic growth?

Nearly 50 years after the ground-breaking isolation of the primary Comptonia peregrina microsymbiont under axenic conditions, efforts to isolate a substantial number of Protofrankia and Frankia strains continue with enduring challenges and complexities. This study aimed to streamline genomic insights through comparative and predictive tools to extract traits crucial for isolating specific Frankia in axenic conditions. Pangenome analysis unveiled significant genetic diversity, suggesting untapped potential for cultivation strategies. Shared metabolic strategies in cellular components, central metabolic pathways, and resource acquisition traits offered promising avenues for cultivation. Ecological trait extraction indicated that most uncultured strains exhibit no apparent barriers to axenic growth. Despite ongoing challenges, potential caveats, and errors that could bias predictive analyses, this study provides a nuanced perspective. It highlights potential breakthroughs and guides refined cultivation strategies for these yet-uncultured strains. We advocate for tailored media formulations enriched with simple carbon sources in aerobic environments, with atmospheric nitrogen optionally sufficient to minimize contamination risks. Temperature adjustments should align with strain preferences-28-29°C for Frankia and 32-35°C for Protofrankia-while maintaining an alkaline pH. Given potential extended incubation periods (predicted doubling times ranging from 3.26 to 9.60 days, possibly up to 21.98 days), patience and rigorous contamination monitoring are crucial for optimizing cultivation conditions.

Genome-Wide Association Study as an Efficacious Approach to Discover Candidate Genes Associated with Body Linear Type Traits in Dairy Cattle

Body shape traits are very important and play a crucial role in the economic development of dairy farming. By improving the accuracy of selection for body size traits, we can enhance economic returns across the dairy industry and on farms, contributing to the future profitability of the dairy sector. Registered body conformation traits are reliable and cost-effective tools for use in national cattle breeding selection programs. These traits are significantly related to the production, longevity, mobility, health, fertility, and environmental adaptation of dairy cows. Therefore, they can be considered indirect indicators of economically important traits in dairy cows. Utilizing efficacious genetic methods, such as genome-wide association studies (GWASs), allows for a deeper understanding of the genetic architecture of complex traits through the identification and application of genetic markers. In the current review, we summarize information on candidate genes and genomic regions associated with body conformation traits in dairy cattle worldwide. The manuscript also reviews the importance of body conformation, the relationship between body conformation traits and other traits, heritability, influencing factors, and the genetics of body conformation traits. The information on candidate genes related to body conformation traits provided in this review may be helpful in selecting potential genetic markers for the genetic improvement of body conformation traits in dairy cattle.

Highly homologous miR-135a and miR-135b converting non-small cell lung cancer from suppression to progression via enhancer switching

microRNAs (miRNAs) are short non-coding RNAs that have been increasingly recognized for their significant roles in the progression of cancer. Distinct miRNAs exhibit diverse functions attributed to variations in their sequences. As a result of possessing highly homologous seed sequences, these miRNAs target overlapping or similar gene sets, thus performing analogous roles. However, different from this sight, our study discovered that miR-135a-5p and miR-135b-5p, despite differing by only one nucleotide, exhibit distinct functional roles. Using non-small cell lung cancer (NSCLC) as a paradigm, our findings unveiled the downregulation of miR-135a-5p and upregulation of miR-135b-5p within NSCLC through TCGA database. Consequently, we further investigated their functional differences in A549 cells. Overexpression of miR-135b-5p enhanced the proliferation and migration capabilities of A549 cells, whereas miR-135a-5p transfection exhibited the opposite effect. We demonstrated that the activation of specific enhancers serves as a crucial mechanism underlying the disparate functions exerted by miR-135a-5p and miR-135b-5p in the context of NSCLC, consequently instigating a shift from inhibition to activation in NSCLC progression. Finally, we validated through animal experiments that miR-135b-5p promoted tumor progression, while miR-135a-5p exerted inhibitory effects on NSCLC development. This study offers a novel perspective for researchers to elucidate functional disparities exhibited by highly homologous miRNAs (miR-135a-5p and miR-135b-5p) in the context of NSCLC, along with the transition from inhibitory to progressive states in NSCLC. This study provides a solid foundation for future investigations into the functional roles of highly homologous miRNAs in pathological situation.

Functional magnetic resonance imaging alternations in suicide attempts individuals and their association with gene expression

BACKGROUND

Functional Magnetic Resonance Imaging (fMRI) has shown brain activity alterations in individuals with a history of attempted suicide (SA) who are diagnosed with depression disorder (DD) or bipolar disorder (BD). However, patterns of spontaneous brain activity and their genetic correlations need further investigation.

METHODS

A voxel-based meta-analysis of 19 studies including 26 datasets, involving 742 patients with a history of SA and 978 controls (both nonsuicidal patients and healthy controls) was conducted. We examined fMRI changes in SA patients and analyzed the association between these changes and gene expression profiles using data from the Allen Human Brain Atlas by partial least squares regression analysis.

RESULTS

SA patients demonstrated increased spontaneous brain activity in several brain regions including the bilateral inferior temporal gyrus, hippocampus, fusiform gyrus, and right insula, and decreased activity in areas like the bilateral paracentral lobule and inferior frontal gyrus. Additionally, 5,077 genes were identified, exhibiting expression patterns associated with SA-related fMRI alterations. Functional enrichment analyses demonstrated that these SA-related genes were enriched for biological functions including glutamatergic synapse and mitochondrial structure. Concurrently, specific expression analyses showed that these genes were specifically expressed in the brain tissue, in neurons cells, and during early developmental periods.

CONCLUSION

Our findings suggest a neurobiological basis for fMRI abnormalities in SA patients with DD or BD, potentially guiding future genetic and therapeutic research.

Bidirectional causal associations between aging and major mental disorders: A population-based study using the two-sample mendelian randomization method from the UK biobank (AM-SRNMA 002)

AIMS

While observational studies have suggested associations linking aging and mental disorders, the question of causality has remained unclear. This study aimed to explore the causal relationship between aging level and major mental disorders.

METHODS

We utilized Two-Sample Mendelian randomization (2SMR) with mental disorders data and aging indicators information from an extensive genome-wide association study (GWAS) database. The GWAS database is a comprehensive resource that compiles genetic association data, encompassing a sample size of over 450,000 individuals. We employed five methods for 2SMR and single nucleotide polymorphisms were chosen as instrumental variables.

RESULTS

Our analyses consistently supported a bidirectional causal association between the Frailty Index (FI) and Major Depressive Disorder (MDD). Furthermore, our findings indicated potential influences, such as Attention-Deficit/Hyperactivity Disorder (ADHD) and Bipolar Disorder (BD) affecting GrimAge, and Anxiety Disorder (AD) impacting Left Hand Grip Strength (LHGS). In contrast, we observed no significant correlations for other mental disorders on FI, Telomere Length (TL), GrimAge, Appendicular Lean Mass (ALM), and LHGS. In the reverse direction, FI showed a significant impact on the risk of MDD, AD, and ADHD, while LHGS affected the risk of MDD. Importantly, no significant associations were found between other factors and the risk of MDD, BD, AD, Schizophrenia (SZ), and ADHD.

CONCLUSIONS

This 2SMR analysis has presented evidence for a bidirectional causal relationship between FI and MDD, while the relationship between ADHD, BD, and GrimAge should be more considered. Our study provides genetic evidence supporting a causal link between aging indicators and several mental illnesses.

Loss of MEF2C function by enhancer mutation leads to neuronal mitochondria dysfunction and motor deficits in mice

Genetic changes and epigenetic modifications are associated with neuronal dysfunction in the pathogenesis of neurodegenerative disorders. However, the mechanism behind genetic mutations in the non-coding region of genes that affect epigenetic modifications remains unclear. Here, we identified an ALS-associated SNP located in the intronic region of MEF2C (rs304152), residing in a putative enhancer element, using convolutional neural network. The enhancer mutation of MEF2C reduces own gene expression and consequently impairs mitochondrial function in motor neurons. MEF2C localizes and binds to the mitochondria DNA, and directly modulates mitochondria-encoded gene expression. CRISPR/Cas-9-induced mutation of the MEF2C enhancer decreases expression of mitochondria-encoded genes. Moreover, MEF2C mutant cells show reduction of mitochondrial membrane potential, ATP level but elevation of oxidative stress. MEF2C deficiency in the upper and lower motor neurons of mice impairs mitochondria-encoded genes, and leads to mitochondrial metabolic disruption and progressive motor behavioral deficits. Together, MEF2C dysregulation by the enhancer mutation leads to mitochondrial dysfunction and oxidative stress, which are prevalent features in motor neuronal damage and ALS pathogenesis. This genetic and epigenetic crosstalk mechanism provides insights for advancing our understanding of motor neuron disease and developing effective treatments.

Identification of epistatic SNP combinations in rheumatoid arthritis using LAMPLINK and Japanese cohorts

Genome-wide association studies have enabled the identification of important genetic factors in many trait studies. However, only a fraction of the heritability can be explained by known genetic factors, even in the most common diseases. Genetic loci combinations, or epistatic contributions expressed by combinations of single nucleotide polymorphisms (SNPs), have been argued to be one of the critical factors explaining some of the missing heritability, especially in oligogenic/polygenic diseases. Rheumatoid arthritis (RA) is a complex disease with more than 100 reported SNP associations, as well as various HLA haplotypes and amino acids; however, many associations between RA and inter-chromosomal SNP combinations are unknown. To discover novel associations of epistatic interactions with high odds ratios in RA, we applied the LAMPLINK method, a systematic enumerative procedure for identifying high-order SNP combinations, to a Japanese RA cohort (discovery cohort; 4024 patients with RA and 7731 controls). We validated the identified associations in a different Japanese cohort (validation cohort; 810 RA patients and 6303 controls). In this study, we identified 90 significant genetic associations in the discovery cohort. Among these, 74 (82.2%) associations were replicated in the validation cohort, and eight combinations were inter-chromosomal, all of which comprised rs7765379 or rs35265698 located in the HLA region. These two SNPs exhibited strong correlations with valine at amino acid position 11 in HLA-DRB1 (HLA-DRB1-11-Val). Finally, we discovered that rs9624 showed an association with RA through an epistatic interaction with HLA-DRB1-11-Val. Overall, LAMPLINK showed high reliability for identifying epistatic genetic contributions hidden in complex traits.

Post-GWAS Validation of Target Genes Associated with HbF and HbA2 Levels

Genome-Wide Association Studies (GWASs) have identified a huge number of variants associated with different traits. However, their validation through in vitro and in vivo studies often lags well behind their identification. For variants associated with traits or diseases of biomedical interest, this gap delays the development of possible therapies. This issue also impacts beta-hemoglobinopathies, such as beta-thalassemia and sickle cell disease (SCD). The definitive cures for these diseases are currently bone marrow transplantation and gene therapy. However, limitations regarding their effective use restrict their worldwide application. Great efforts have been made to identify whether modulators of fetal hemoglobin (HbF) and, to a lesser extent, hemoglobin A2 (HbA2) are possible therapeutic targets. Herein, we performed the post-GWAS in vivo validation of two genes, cyclin D3 (CCND3) and nuclear factor I X (NFIX), previously associated with HbF and HbA2 levels. The absence of Ccnd3 expression in vivo significantly increased g (HbF) and d (HbA2) globin gene expression. Our data suggest that CCND3 is a possible therapeutic target in sickle cell disease. We also confirmed the association of Nfix with γ-globin gene expression and present data suggesting a possible role for Nfix in regulating Kruppel-like transcription factor 1 (Klf1), a master regulator of hemoglobin switching. This study contributes to filling the gap between GWAS variant identification and target validation for beta-hemoglobinopathies.

Utility of polygenic risk scores to aid in the diagnosis of rheumatic diseases

Rheumatic diseases (RDs) are characterized by autoimmunity and autoinflammation and are recognized as complex due to the interplay of multiple genetic, environmental, and lifestyle factors in their pathogenesis. The rapid advancement of genome-wide association studies (GWASs) has enabled the identification of numerous single nucleotide polymorphisms (SNPs) associated with RD susceptibility. Based on these SNPs, polygenic risk scores (PRSs) have emerged as promising tools for quantifying genetic risk in this disease group. This chapter reviews the current status of PRSs in assessing the risk of RDs and discusses their potential to improve the accuracy of the diagnosis of these complex diseases through their ability to discriminate among different RDs. PRSs demonstrate a high discriminatory capacity for various RDs and show potential clinical utility. As GWASs continue to evolve, PRSs are expected to enable more precise risk stratification by integrating genetic, environmental, and lifestyle factors, thereby refining individual risk predictions and advancing disease management strategies.

INDELpred: Improving the prediction and interpretation of indel pathogenicity within the clinical genome

Small insertions and deletions (indels) are critical yet challenging genetic variations with significant clinical implications. However, the identification of pathogenic indels from neutral variants in clinical contexts remains an understudied problem. Here, we developed INDELpred, a machine-learning-based predictive model for discerning pathogenic from benign indels. INDELpred was established based on key features, including allele frequency, indel length, function-based features, and gene-based features. A set of comprehensive evaluation analyses demonstrated that INDELpred exhibited superior performance over competing methods in terms of computational efficiency and prediction accuracy. Importantly, INDELpred highlighted the crucial role of function-based features in identifying pathogenic indels, with a clear interpretability of the features in understanding the disease-causing variants. We envisage INDELpred as a desirable tool for the detection of pathogenic indels within large-scale genomic datasets, thereby enhancing the precision of genetic diagnoses in clinical settings.

Machine Learning of Three-Dimensional Protein Structures to Predict the Functional Impacts of Genome Variation

Research in the human genome sciences generates a substantial amount of genetic data for hundreds of thousands of individuals, which concomitantly increases the number of variants of unknown significance (VUS). Bioinformatic analyses can successfully reveal rare variants and variants with clear associations with disease-related phenotypes. These studies have had a significant impact on how clinical genetic screens are interpreted and how patients are stratified for treatment. There are few, if any, computational methods for variants comparable to biological activity predictions. To address this gap, we developed a machine learning method that uses protein three-dimensional structures from AlphaFold to predict how a variant will influence changes to a gene's downstream biological pathways. We trained state-of-the-art machine learning classifiers to predict which protein regions will most likely impact transcriptional activities of two proto-oncogenes, nuclear factor erythroid 2 (NFE2L2)-related factor 2 (NRF2) and c-Myc. We have identified classifiers that attain accuracies higher than 80%, which have allowed us to identify a set of key protein regions that lead to significant perturbations in c-Myc or NRF2 transcriptional pathway activities.

An integrative genomic toolkit for studying the genetic, evolutionary, and molecular underpinnings of eusociality in insects

While genomic resources for social insects have vastly increased over the past two decades, we are still far from understanding the genetic and molecular basis of eusociality. Here, we briefly review three scientific advancements that, when integrated, can be highly synergistic for advancing our knowledge of the genetics and evolution of eusocial traits. Population genomics provides a natural way to quantify the strength of natural selection on coding and regulatory sequences, highlighting genes that have undergone adaptive evolution during the evolution or maintenance of eusociality. Genome-wide association studies (GWAS) can be used to characterize the complex genetic architecture underlying eusocial traits and identify candidate causal variants. Concurrently, CRISPR/Cas9 enables the precise manipulation of gene function to both validate genotype-phenotype associations and study the molecular biology underlying interesting traits. While each approach has its own advantages and disadvantages, which we discuss herein, we argue that their combination will ultimately help us better understand the genetics and evolution of eusocial behavior. Specifically, by triangulating across these three different approaches, researchers can directly identify and study loci that have a causal association with key phenotypes and have evidence of positive selection over the relevant timescales associated with the evolution and maintenance of eusociality in insects.

The Role of Genetics in Managing Peripheral Arterial Disease

BACKGROUND

Genome wide association studies (GWAS) have allowed for a rapid increase in our understanding of the underlying genetics and biology of many diseases. By capitalizing on common genetic variation between individuals, GWAS can identify DNA variants associated with diseases of interest. A variety of statistical methods can be applied to GWAS results which allows for risk factor identification, stratification, and to identify potential treatments. Peripheral artery disease (PAD) is a common vascular disease that has been shown to have a strong genetic component. This article provides a review of the modern literature and our current understanding of the role of genetics in PAD.

METHODS

All available GWAS studies on PAD were reviewed. A literature search involving these studies was conducted and relevant articles applying the available GWAS data were summarized to provide a comprehensive review of our current understanding of the genetic component in PAD.

RESULTS

The largest available GWAS on PAD has identified 19 genome wide significant loci, with factor V Leiden and genes responsible for circulating lipoproteins being implicated in the development of PAD. Mendelian randomization (MR) studies have identified risk factors and causal associations with smoking, diabetes, and obesity and many other traits; protein-based MR has also identified circulating lipid and clotting factor levels associated with the incidence of PAD. Polygenic risk scores may allow for improved prediction of disease incidence and allow for early identification of at-risk patients but more work needs to be done to validate this approach.

CONCLUSIONS

Genetic epidemiology has allowed for an increased understanding of PAD in the past decade. Genome-wide association studies have led to improved detection of genetic contributions to PAD, and further genetic analyses have validated risk factors and may provide options for improved screening in at-risk populations. Ongoing biobank studies of chronic limb threatening ischemia patients and the increasing ancestral diversity in biobank enrollment will allow for even further exploration into the pathogenesis and progression of PAD.

Epistatic interaction between ERAP2 and HLA modulates HIV-1 adaptation and disease outcome in an Australian population

A strong genetic predictor of outcome following untreated HIV-1 infection is the carriage of specific alleles of human leukocyte antigens (HLAs) that present viral epitopes to T cells. Residual variation in outcome measures may be attributed, in part, to viral adaptation to HLA-restricted T cell responses. Variants of the endoplasmic reticulum aminopeptidases (ERAPs) influence the repertoire of T cell epitopes presented by HLA alleles as they trim pathogen-derived peptide precursors to optimal lengths for antigen presentation, along with other functions unrelated to antigen presentation. We investigated whether ERAP variants influence HLA-associated HIV-1 adaptation with demonstrable effects on overall HIV-1 disease outcome. Utilizing host and viral data of 249 West Australian individuals with HIV-1 subtype B infection, we identified a novel association between two linked ERAP2 single nucleotide polymorphisms (SNPs; rs2248374 and rs2549782) with plasma HIV RNA concentration (viral load) (P adjusted = 0.0024 for both SNPs). Greater HLA-associated HIV-1 adaptation in the HIV-1 Gag gene correlated significantly with higher viral load, lower CD4+ T cell count and proportion; P = 0.0103, P = 0.0061, P = 0.0061, respectively). When considered together, there was a significant interaction between the two ERAP2 SNPs and HLA-associated HIV-1 adaptation on viral load (P = 0.0111). In a comprehensive multivariate model, addition of ERAP2 haplotypes and HLA associated adaptation as an interaction term to known HLA and CCR5 determinants and demographic factors, increased the explanatory variance of population viral load from 17.67% to 45.1% in this dataset. These effects were not replicated in publicly available datasets with comparably sized cohorts, suggesting that any true global epistasis may be dependent on specific HLA-ERAP allelic combinations. Our data raises the possibility that ERAP2 variants may shape peptide repertoires presented to HLA class I-restricted T cells to modulate the degree of viral adaptation within individuals, in turn contributing to disease variability at the population level. Analyses of other populations and experimental studies, ideally with locally derived ERAP genotyping and HLA-specific viral adaptations are needed to elucidate this further.

CAUSAL ROLES OF SERUM URIC ACID LEVELS AND GOUT IN SEPSIS: A MENDELIAN RANDOMIZATION STUDY

ABSTRACT

Objective: Several epidemiological studies have identified a potential link between serum uric acid (UA), gout, and sepsis. The primary objective of this study is to delve deeper into this connection, investigating the causal effect of UA and gout on sepsis by applying Mendelian randomization (MR). Methods: The causal relationship was analyzed using data from Genome-Wide Association Study (GWAS). Inverse variance weighting (IVW) was used as the main analysis method. Three complementary methods were used for our MR analysis, which included the MR-Egger regression method, the weighted median method, the simple median method. Horizontal pleiotropy was identified by MR-Egger intercept test. Cochran's Q statistics were employed to assess the existence of instrument heterogeneity. The leave-one-out method was used as a sensitivity analysis. Results: The IVW results indicated that there was a positive causal relationship between UA and sepsis (critical care) (odds ratio [OR] = 0.24, 95% confidence interval [CI]: 0.04 to 0.43, P = 0.018, F = 4,291.20). There was no significant association between UA and sepsis (28-day death in critical care) (OR = 0.10, 95% CI = -0.29 to 0.50, P = 0.604). There was no significant association between gout and sepsis (critical care) (OR = 0.85, 95% CI = -4.87 to 6.57, P = 0.771), and sepsis (28-day death in critical care) (OR = -6.30, 95% CI = -17.41 to 4.81, P = 0.267). Horizontal pleiotropy was absent in this study. The results were robust under all sensitivity analyses. Conclusion: The study revealed that elevated UA levels were causally linked with sepsis (critical care). No causal relationship had been found between UA and sepsis (28-day death in critical care), as well as between gout and sepsis.

Genetic Causality between Type 1 Diabetes and Arrhythmia Identified by a Two-sample Mendelian Randomization Study

BACKGROUND

Clinical studies have shown that cardiovascular diseases in patients with type 1 diabetes (T1D) are often atypical or asymptomatic. The link between T1D and arrhythmia remains unclear. To infer causality between T1D and arrhythmia at the genetic level, we conducted a Mendelian randomization study through the genetic tools of T1D.

METHODS

In this study, we used genetic variables and summary statistics from genome-wide association studies of T1D and arrhythmia. Single nucleotide polymorphisms were selected based on the assumptions of instrumental variables. The inverse variance-weighted method was used as the primary analysis to summarize the causal effects between exposure and outcome. The weighted median and weighted mode methods were used as secondary methods. We tested for horizontal pleiotropy using the MR-Egger method and detected heterogeneity using the Q-test. A leave-one-out sensitivity analysis was performed. Scatter plots, forest plots, and funnel plots were used to visualize the results of the MR analysis.

RESULTS

In this study, we selected 28 T1D-related SNPs as instrumental variables. The IVW [odds ratio (OR) = 0.98, 95 % confidence interval (CI) = 0.97-1.00, P = 0.008], weighted median (OR = 0.98, 95 % CI = 0.96 - 0.99, P = 0.009), and weighted mode (OR = 0.98, 95 % CI = 0.96-0.99, P = 0.018) analysis methods suggested a causal effect of T1D on arrhythmia. The MR-Egger method indicated no horizontal pleiotropy (P = 0.649), and the Q-test showed no heterogeneity (IVW, P = 0.653).

CONCLUSIONS

Our MR analysis revealed a causal association between T1D and the development of arrhythmia, indicating that patients with T1D had a higher risk of arrhythmia.

Mendelian randomization study of lipid metabolism characteristics and migraine risk

BACKGROUND

The association between serum lipids and migraine is controversial. However, randomized controlled trials have suggested that statins may be efficacious for the prevention of migraine. In this study, we aim to investigate the relationship between lipids metabolism and migraine risk.

METHODS

Single-nucleotide polymorphisms (SNPs), relating to the serum lipid traits and the effect of lipid-lowering drugs that target APOB, CETP, HMGCR, NPC1L1, and PCSK9, were extracted from genome-wide association studies (GWAS) summary data. The GWAS summary data were obtained from the Global Lipids Genetic Consortium (GLGC), the UK Biobank, and the FinnGen study, respectively. Mendelian randomization (MR) analysis was performed to evaluate the association between serum lipid traits and lipid-lowering drugs with migraine risk.

RESULTS

Regarding serum lipids, it was found that SNPs related to high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), non-high-density lipoprotein cholesterol (non-HDL-C), total cholesterol (TC), or triglycerides (TG) levels were not associated with migraine, migraine with aura (MA) or migraine without aura (MO). In addition, genotypes of HMGCR related to higher LDL-C levels were associated with increased risk of migraine (OR = 1.46, p = 0.035) and MA (OR = 2.03, p = 0.008); However, genotypes of PCSK9 related to higher LDL-C levels were associated with decreased risk of migraine (OR = 0.75, p = 0.001) and MA (OR = 0.69, p = 0.004); And genotypes of APOB related to higher LDL-C levels were associated with decreased risk of MO (OR = 0.62, p = 0.000).

CONCLUSIONS

There is a relationship between lipid metabolism characteristics and migraine risk.

SIGNIFICANCE

Based on the genome-wide association summary data, single-nucleotide polymorphisms (SNPs) related to high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), non-high-density lipoprotein cholesterol (non-HDL-C), total cholesterol (TC), or triglycerides (TG) level were not associated with risk of migraine, migraine with aura (MA) or migraine without aura (MO). However, genotypes of HMGCR related to higher LDL-C levels have shown an increased risk on migraine and MA. And genotypes of APOB or PCSK9 related to higher LDL-C levels have shown a decreased risk on MO, or migraine and MA, respectively. These results suggested that there may be a relationship between lipid metabolism characteristics and the risk for migraine development.

Discovering mechanisms of human genetic variation and controlling cell states at scale

Population-scale sequencing efforts have catalogued substantial genetic variation in humans such that variant discovery dramatically outpaces interpretation. We discuss how single-cell sequencing is poised to reveal genetic mechanisms at a rate that may soon approach that of variant discovery. The functional genomics toolkit is sufficiently modular to systematically profile almost any type of variation within increasingly diverse contexts and with molecularly comprehensive and unbiased readouts. As a result, we can construct deep phenotypic atlases of variant effects that span the entire regulatory cascade. The same conceptual approach to interpreting genetic variation should be applied to engineering therapeutic cell states. In this way, variant mechanism discovery and cell state engineering will become reciprocating and iterative processes towards genomic medicine.

Drug Approval for the Treatment of Geographic Atrophy: How We Got Here and Where We Need to Go

PURPOSE

To discuss the clinical trial results leading to the US Food and Drug Administration (FDA) approval of anti-complement therapies for geographic atrophy (GA), perspectives on functional data from the GA clinical trials, and how lessons from the FDA approval may guide future directions for basic and clinical research in AMD.

DESIGN

Selected literature review with analysis and perspective METHODS: We performed a targeted review of publicly available data from the clinical trials of pegcetacoplan and avacincaptad for the treatment of GA, as well as scientific literature on the natural history of GA and the genetics and basic science of complement in AMD.

RESULTS

The approval of pegcetacoplan and avacincaptad was based on an anatomic endpoint of a reduction in the rate of GA expansion over time. However, functional data from 2 phase 3 clinical trials for each drug demonstrated no visual benefit to patients in the treatment groups. Review of the genetics of AMD and the basic science of the role for complement in AMD provides only modest support for targeting complement as treatment for GA expansion, and alternative molecular targets for GA treatment are therefore discussed. Reasons for the disconnect between anatomic and functional outcomes in the clinical trials of anti-complement therapies are discussed, providing insight to guide the configuration of future clinical studies for GA.

CONCLUSION

Although avacincaptad and pegcetacoplan are our first FDA-approved treatments for GA, results from the clinical trials failed to show any functional improvement after 1 and 2 years, respectively, calling into question whether the drugs represent a "clinically relevant outcome." To improve the chances of more impactful therapies in the future, we provide basic-science rationale for pursuing non-complement targets; emphasize the importance of ongoing clinical research that more closely pins anatomic features of GA to functional outcomes; and provide suggestions for clinical endpoints for future clinical trials on GA.

The role and molecular mechanisms of the early growth response 3 gene in schizophrenia

Schizophrenia is a chronic, debilitating mental illness caused by both genetic and environmental factors. Genetic factors play a major role in schizophrenia development. Early growth response 3 (EGR3) is a member of the EGR family, which is associated with schizophrenia. Accumulating studies have investigated the relationship between EGR3 and schizophrenia. However, the role of EGR3 in schizophrenia pathogenesis remains unclear. In the present review, we focus on the progress of research related to the role of EGR3 in schizophrenia, including association studies between EGR3 and schizophrenia, abnormal gene expressional analysis of EGR3 in schizophrenia, biological function studies of EGR3 in schizophrenia, the molecular regulatory mechanism of EGR3 and schizophrenia susceptibility candidate genes, and possible role of EGR3 in the immune system function in schizophrenia. In summary, EGR3 is a schizophrenia risk candidate factor and has comprehensive regulatory roles in schizophrenia pathogenesis. Further studies investigating the molecular mechanisms of EGR3 in schizophrenia are warranted for understanding the pathophysiology of this disorder as well as the development of new therapeutic strategies for the treatment and control of this disorder.

Boosting genome-wide association power and genomic prediction accuracy for date palm fruit traits with advanced statistics

The date palm is economically vital in the Middle East and North Africa, providing essential fibres, vitamins, and carbohydrates. Understanding the genetic architecture of its traits remains complex due to the tree's perennial nature and long generation times. This study aims to address these complexities by employing advanced genome-wide association (GWAS) and genomic prediction models using previously published data involving fruit acid content, sugar content, dimension, and colour traits. The multivariate GWAS model identified seven QTL, including five novel associations, that shed light on the genetic control of these traits. Furthermore, the research evaluates different genomic prediction models that considered genotype by environment and genotype by trait interactions. While colour- traits demonstrate strong predictive power, other traits display moderate accuracies across different models and scenarios aligned with the expectations when using small reference populations. When designing the cross-validation to predict new individuals, the accuracy of the best multi-trait model was significantly higher than all single-trait models for dimension traits, but not for the remaining traits, which showed similar performances. However, the cross-validation strategy that masked random phenotypic records (i.e., mimicking the unbalanced phenotypic records) showed significantly higher accuracy for all traits except acid contents. The findings underscore the importance of understanding genetic architecture for informed breeding strategies. The research emphasises the need for larger population sizes and multivariate models to enhance gene tagging power and predictive accuracy to advance date palm breeding programs. These findings support more targeted breeding in date palm, improving productivity and resilience to various environments.

Investigation of missense mutation-related type 1 diabetes mellitus through integrating genomic databases and bioinformatic approach

Though genes are already known to be responsible for type 1 diabetes mellitus (T1DM), the knowledge of missense mutation of that disease gene has still to be under covered. A genomic database and a bioinformatics-based approach are integrated in the present study in order to address this issue. Initially, nine variants associated with T1DM were retrieved from the GWAS catalogue. Different genomic algorithms such as PolyPhen2.0, SNPs and GTEx analyser programs were used to study the structural and functional effects of these mutations. Subsequently, SNPnexus was also employed to understand the effect of these mutations on the function of the expressed protein. Nine missense variants of T1DM were identified using the GWAS catalogue database. Among these nine SNPs, three were predicted to be related to the progression of T1DM disease by affecting the protein level. TYK2 gene variants with SNP rs34536443 were thought to have a probably damaging effect. Meanwhile, both COL4A3 and IFIH1 genes with SNPs rs55703767 and rs35667974, respectively, might alter protein function through a possibly damaging prediction. Among the variants of the three genes, the TYK2 gene with SNP rs34536443 had the strongest contribution in affecting the development of T1DM, with a score of 0.999. We sincerely hope that the results could be of immense importance in understanding the genetic basis of T1DM.