Translating GWAS Findings to Novel Therapeutic Targets for Coronary Artery Disease

Using Genomics to Identify Novel Therapeutic Targets for Aortic Disease

Aortic disease, including dissection, aneurysm, and rupture, carries significant morbidity and mortality and is a notable cause of sudden cardiac death. Much of our knowledge regarding the genetic basis of aortic disease has relied on the study of individuals with Mendelian aortopathies and, until recently, the genetic determinants of population-level variance in aortic phenotypes remained unclear. However, the application of machine learning methodologies to large imaging datasets has enabled researchers to rapidly define aortic traits and mine dozens of novel genetic associations for phenotypes such as aortic diameter and distensibility. In this review, we highlight the emerging potential of genomics for identifying causal genes and candidate drug targets for aortic disease. We describe how deep learning technologies have accelerated the pace of genetic discovery in this field. We then provide a blueprint for translating genetic associations to biological insights, reviewing techniques for locus and cell type prioritization, high-throughput functional screening, and disease modeling using cellular and animal models of aortic disease.

multi-GPA-Tree: Statistical approach for pleiotropy informed and functional annotation tree guided prioritization of GWAS results

Genome-wide association studies (GWAS) have successfully identified over two hundred thousand genotype-trait associations. Yet some challenges remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), most with small or moderate effect sizes, making them difficult to detect. Second, many complex traits share a common genetic basis due to 'pleiotropy' and and though few methods consider it, leveraging pleiotropy can improve statistical power to detect genotype-trait associations with weaker effect sizes. Third, currently available statistical methods are limited in explaining the functional mechanisms through which genetic variants are associated with specific or multiple traits. We propose multi-GPA-Tree to address these challenges. The multi-GPA-Tree approach can identify risk SNPs associated with single as well as multiple traits while also identifying the combinations of functional annotations that can explain the mechanisms through which risk-associated SNPs are linked with the traits. First, we implemented simulation studies to evaluate the proposed multi-GPA-Tree method and compared its performance with existing statistical approaches. The results indicate that multi-GPA-Tree outperforms existing statistical approaches in detecting risk-associated SNPs for multiple traits. Second, we applied multi-GPA-Tree to a systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), and to a Crohn's disease (CD) and ulcertive colitis (UC) GWAS, and functional annotation data including GenoSkyline and GenoSkylinePlus. Our results demonstrate that multi-GPA-Tree can be a powerful tool that improves association mapping while facilitating understanding of the underlying genetic architecture of complex traits and potential mechanisms linking risk-associated SNPs with complex traits.

Genome-wide association studies: utility and limitations for research in physiology

Physiological systems are subject to interindividual variation encoded by genetics. Genome-wide association studies (GWAS) operate by surveying thousands of genetic variants from a substantial number of individuals and assessing their association to a trait of interest, be it a physiological variable, a molecular phenotype (e.g. gene expression), or even a disease or condition. Through a myriad of methods, GWAS downstream analyses then explore the functional consequences of each variant and attempt to ascertain a causal relationship to the phenotype of interest, as well as to delve into its links to other traits. This type of investigation allows mechanistic insights into physiological functions, pathological disturbances and shared biological processes between traits (i.e. pleiotropy). An exciting example is the discovery of a new thyroid hormone transporter (SLC17A4) and hormone metabolising enzyme (AADAT) from a GWAS on free thyroxine levels. Therefore, GWAS have substantially contributed with insights into physiology and have been shown to be useful in unveiling the genetic control underlying complex traits and pathological conditions; they will continue to do so with global collaborations and advances in genotyping technology. Finally, the increasing number of trans-ancestry GWAS and initiatives to include ancestry diversity in genomics will boost the power for discoveries, making them also applicable to non-European populations.

Advances and Challenges for GWAS Analysis in Cardiac Diseases: A Focus on Coronary Artery Disease (CAD)

The achievement of genome-wide association studies (GWAS) has rapidly progressed our understanding of the etiology of coronary artery disease (CAD). It unlocks new strategies to strengthen the stalling of CAD drug development. In this review, we highlighted the recent drawbacks, mainly pointing out those involved in identifying causal genes and interpreting the connections between disease pathology and risk variants. We also benchmark the novel insights into the biological mechanism behind the disease primarily based on outcomes of GWAS. Furthermore, we also shed light on the successful discovery of novel treatment targets by introducing various layers of "omics" data and applying systems genetics strategies. Lastly, we discuss in-depth the significance of precision medicine that is helpful to improve through GWAS analysis in cardiovascular research.

Ethnic, gender and other sociodemographic biases in genome-wide association studies for the most burdensome non-communicable diseases: 2005-2022

INTRODUCTION

Since 2005, disease-related human genetic diversity has been intensively characterized using genome-wide association studies (GWAS). Understanding how and by whom this work was performed may yield valuable insights into the generalizability of GWAS discoveries to global populations and how high-impact genetics research can be equitably sustained in the future.

MATERIALS AND METHODS

We mined the NHGRI-EBI GWAS Catalog (2005-2022) for the most burdensome non-communicable causes of death worldwide. We then compared (i) the geographic, ethnic and socioeconomic characteristics of study populations; (ii) the geographic and socioeconomic characteristics of the regions within which researchers were located and (iii) the extent to which male and female investigators undertook and led the research.

RESULTS

The research institutions leading the work are often US-based (37%), while the origin of samples is more diverse, with the Nordic countries having contributed as much data to GWAS as the United States (~17% of data). The majority of first (60%), senior (75%) and all (66%) authors are male; although proportions vary by disease and leadership level, male co-authors are the ubiquitous majority. The vast majority (91%) of complex trait GWAS has been performed in European ancestry populations, with cohorts and scientists predominantly located in medium-to-high socioeconomically ranked countries; apart from East Asians (~5%), other ethnicities rarely feature in published GWAS. See: https://hugofitipaldi.shinyapps.io/gwas_results/ to browse all results.

CONCLUSION

Most GWAS cohorts are of European ancestry residing outside the United States, with a smaller yet meaningful proportion of East Asian ancestry. Papers describing GWAS research are predominantly authored by male scientists based in medium-to-high income countries.

Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models

Background

Numerous approaches have been proposed for the detection of epistatic interactions within GWAS datasets in order to better understand the drivers of disease and genetics.

Methods

A selection of state-of-the-art approaches were assessed. These included the statistical tests, fast-epistasis, BOOST, logistic regression and wtest; swarm intelligence methods, namely AntEpiSeeker, epiACO and CINOEDV; and data mining approaches, including MDR, GSS, SNPRuler and MPI3SNP. Data were simulated to provide randomly generated models with no individual main effects at different heritabilities (pure epistasis) as well as models based on penetrance tables with some main effects (impure epistasis). Detection of both two and three locus interactions were assessed across a total of 1,560 simulated datasets. The different methods were also applied to a section of the UK biobank cohort for Atrial Fibrillation.

Results

For pure, two locus interactions, PLINK’s implementation of BOOST recovered the highest number of correct interactions, with 53.9% and significantly better performing than the other methods (p = 4.52e − 36). For impure two locus interactions, MDR exhibited the best performance, recovering 62.2% of the most significant impure epistatic interactions (p = 6.31e − 90 for all but one test). The assessment of three locus interaction prediction revealed that wtest recovered the highest number (17.2%) of pure epistatic interactions(p = 8.49e − 14). wtest also recovered the highest number of three locus impure epistatic interactions (p = 6.76e − 48) while AntEpiSeeker ranked as the most significant the highest number of such interactions (40.5%). Finally, when applied to a real dataset for Atrial Fibrillation, most notably finding an interaction between SYNE2 and DTNB.

Reproducibility in the UK biobank of genome-wide significant signals discovered in earlier genome-wide association studies

With the establishment of large biobanks, discovery of single nucleotide variants (SNVs, also known as single nucleotide polymorphisms (SNVs)) associated with various phenotypes has accelerated. An open question is whether genome-wide significant SNVs identified in earlier genome-wide association studies (GWAS) are replicated in later GWAS conducted in biobanks. To address this, we examined a publicly available GWAS database and identified two, independent GWAS on the same phenotype (an earlier, “discovery” GWAS and a later, “replication” GWAS done in the UK biobank). The analysis evaluated 136,318,924 SNVs (of which 6289 reached P < 5e−8 in the discovery GWAS) from 4,397,962 participants across nine phenotypes. The overall replication rate was 85.0%; although lower for binary than quantitative phenotypes (58.1% versus 94.8% respectively). There was a 18.0% decrease in SNV effect size for binary phenotypes, but a 12.0% increase for quantitative phenotypes. Using the discovery SNV effect size, phenotype trait (binary or quantitative), and discovery P value, we built and validated a model that predicted SNV replication with area under the Receiver Operator Curve = 0.90. While non-replication may reflect lack of power rather than genuine false-positives, these results provide insights about which discovered associations are likely to be replicated across subsequent GWAS.

Repurposing Drugs to Treat Heart and Brain Illness

Drug development is a complicated, slow and expensive process with high failure rates. One strategy to mitigate these factors is to recycle existing drugs with viable safety profiles and have gained Food and Drug Administration approval following extensive clinical trials. Cardiovascular and neurodegenerative diseases are difficult to treat, and there exist few effective therapeutics, necessitating the development of new, more efficacious drugs. Recent scientific studies have led to a mechanistic understanding of heart and brain disease progression, which has led researchers to assess myriad drugs for their potential as pharmacological treatments for these ailments. The focus of this review is to survey strategies for the selection of drug repurposing candidates and provide representative case studies where drug repurposing strategies were used to discover therapeutics for cardiovascular and neurodegenerative diseases, with a focus on anti-inflammatory processes where new drug alternatives are needed.

Perspectives on Drug Repurposing

Complex common diseases are a significant burden for our societies and demand not only preventive measures but also more effective, safer, and more affordable treatments. The whole process of the current model of drug discovery and development implies a high investment by the pharmaceutical industry, which ultimately impact in high drug prices. In this sense, drug repurposing would help meet the needs of patients to access useful and novel treatments. Unlike the traditional approach, drug repurposing enters both the preclinical evaluation and clinical trials of the compound of interest faster, budgeting research and development costs, and limiting potential biosafety risks. The participation of government, society, and private investors is needed to secure the funds for experimental design and clinical development of repurposing candidates to have affordable, effective, and safe repurposed drugs. Moreover, extensive advertising of repurposing as a concept in the health community, could reduce prescribing bias when enough clinical evidence exists, which will support the employment of cheaper and more accessible repurposed compounds for common conditions.

Age and sex specific effects of APOE genotypes on ischemic heart disease and its risk factors in the UK Biobank

APOE genotypes are associated with ischemic heart disease (IHD), several other cardiovascular diseases and dementia. Previous studies have not comprehensively considered all genotypes, especially ε2ε2, nor associations by age and sex, although IHD incidence differs by sex. In the UK Biobank, including 391,992 white British participants, we compared effects of APOE genotypes on IHD and its risk factors. Compared to the ε3ε3 genotype, ε2ε2 was not clearly associated with IHD but was associated with lower plasma apolipoprotein B (apoB). The ε2ε3 genotype conferred lower IHD risk, systolic blood pressure (SBP), pulse pressure and plasma apoB than ε3ε3. ε3ε4 and ε4ε4 conferred higher IHD risk, higher pulse pressure and plasma apoB, but lower glycated haemoglobin (HbA1c) than ε3ε3. The associations by age and sex were fairly similar, except ε2ε2 compared to ε3ε3 was marginally positively associated with IHD in the younger age group and nominally inversely associated with SBP in men. ε3ε4 compared to ε3ε3 was nominally positively associated with SBP in women. APOE genotypes affect IHD risk increasingly from ε2ε3, ε3ε3, ε3ε4 to ε4ε4, with similar patterns for pulse pressure and plasma apoB, but not for diabetes. Associations with blood pressure differed by sex. Greater understanding of products of APOE and their effects might generate targets of intervention.

Using functional genomics to advance the understanding of psoriatic arthritis

Psoriatic arthritis (PsA) is a complex disease where susceptibility is determined by genetic and environmental risk factors. Clinically, PsA involves inflammation of the joints and the skin, and, if left untreated, results in irreversible joint damage. There is currently no cure and the few treatments available to alleviate symptoms do not work in all patients. Over the past decade, genome-wide association studies (GWAS) have uncovered a large number of disease-associated loci but translating these findings into functional mechanisms and novel targets for therapeutic use is not straightforward. Most variants have been predicted to affect primarily long-range regulatory regions such as enhancers. There is now compelling evidence to support the use of chromatin conformation analysis methods to discover novel genes that can be affected by disease-associated variants. Here, we will review the studies published in the field that have given us a novel understanding of gene regulation in the context of functional genomics and how this relates to the study of PsA and its underlying disease mechanism.

The Integrated Landscape of Biological Candidate Causal Genes in Coronary Artery Disease

Background

Genome-wide association studies (GWASs) have identified more than 150 genetic loci that demonstrate robust association with coronary artery disease (CAD). In contrast to the success of GWAS, the translation from statistical signals to biological mechanism and exploration of causal genes for drug development remain difficult, owing to the complexity of gene regulatory and linkage disequilibrium patterns. We aim to prioritize the plausible causal genes for CAD at a genome-wide level.

Methods

We integrated the latest GWAS summary statistics with other omics data from different layers and utilized eight different computational methods to predict CAD potential causal genes. The prioritized candidate genes were further characterized by pathway enrichment analysis, tissue-specific expression analysis, and pathway crosstalk analysis.

Results

Our analysis identified 55 high-confidence causal genes for CAD, among which 15 genes (LPL, COL4A2, PLG, CDKN2B, COL4A1, FES, FLT1, FN1, IL6R, LPA, PCSK9, PSRC1, SMAD3, SWAP70, and VAMP8) ranked the highest priority because of consistent evidence from different data-driven approaches. GO analysis showed that these plausible causal genes were enriched in lipid metabolic and extracellular regions. Tissue-specific enrichment analysis revealed that these genes were significantly overexpressed in adipose and liver tissues. Further, KEGG and crosstalk analysis also revealed several key pathways involved in the pathogenesis of CAD.

Conclusion

Our study delineated the landscape of CAD potential causal genes and highlighted several biological processes involved in CAD pathogenesis. Further studies and experimental validations of these genes may shed light on mechanistic insights into CAD development and provide potential drug targets for future therapeutics.

Targeted lipid nanoparticles for RNA therapeutics and immunomodulation in leukocytes

Abnormalities in leukocytes' function are associated with many immune related disorders, such as cancer, autoimmunity and susceptibility to infectious diseases. Recent developments in Genome-wide-association-studies give rise to new opportunities for novel therapeutics. RNA-based modalities, that allow a selective genetic manipulation in vivo, are powerful tools for personalized medicine, enabling downregulation or expression of relevant proteins. Yet, RNA-based therapeutics requires a delivery modality to facilitate the stability, uptake and intracellular release of the RNA molecules. The use of lipid nanoparticles as a drug delivery approach improves the payloads' stability, pharmacokinetics, bio-distribution and therapeutic benefit while reducing side effects. Moreover, a wide variety of targeting moieties allow a precise and modular manipulation of gene expression, together with the ability to identify and selectively affect disease-relevant leukocytes-subsets. Altogether, RNA-based therapeutics, targeting leukocytes subsets, is believed to be one of the most promising therapeutic concepts of the near future, addressing pressing issues in cancer and inflammation heterogeneity.

Network Modeling Approaches and Applications to Unravelling Non-Alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) is a progressive condition of the liver encompassing a range of pathologies including steatosis, non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma. Research into this disease is imperative due to its rapid growth in prevalence, economic burden, and current lack of FDA approved therapies. NAFLD involves a highly complex etiology that calls for multi-tissue multi-omics network approaches to uncover the pathogenic genes and processes, diagnostic biomarkers, and potential therapeutic strategies. In this review, we first present a basic overview of disease pathogenesis, risk factors, and remaining knowledge gaps, followed by discussions of the need and concepts of multi-tissue multi-omics approaches, various network methodologies and application examples in NAFLD research. We highlight the findings that have been uncovered thus far including novel biomarkers, genes, and biological pathways involved in different stages of NAFLD, molecular connections between NAFLD and its comorbidities, mechanisms underpinning sex differences, and druggable targets. Lastly, we outline the future directions of implementing network approaches to further improve our understanding of NAFLD in order to guide diagnosis and therapeutics.