Phenome-wide association studies across large population cohorts support drug target validation

Whole-exome sequencing identifies protein-coding variants associated with brain iron in 29,828 individuals

Iron plays a fundamental role in multiple brain disorders. However, the genetic underpinnings of brain iron and its implications for these disorders are still lacking. Here, we conduct an exome-wide association analysis of brain iron, measured by quantitative susceptibility mapping technique, across 26 brain regions among 26,789 UK Biobank participants. We find 36 genes linked to brain iron, with 29 not being previously reported, and 16 of them can be replicated in an independent dataset with 3,039 subjects. Many of these genes are involved in iron transport and homeostasis, such as FTH1 and MLX. Several genes, while not previously connected to brain iron, are associated with iron-related brain disorders like Parkinson's (STAB1, KCNA10), Alzheimer's (SHANK1), and depression (GFAP). Mendelian randomization analysis reveals six causal relationships from regional brain iron to brain disorders, such as from the hippocampus to depression and from the substantia nigra to Parkinson's. These insights advance our understanding of the genetic architecture of brain iron and offer potential therapeutic targets for brain disorders.

Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention and intervention

Identifying risk protein targets and their therapeutic drugs is crucial for effective cancer prevention. Here, we conduct integrative and fine-mapping analyses of large genome-wide association studies data for breast, colorectal, lung, ovarian, pancreatic, and prostate cancers, and characterize 710 lead variants independently associated with cancer risk. Through mapping protein quantitative trait loci (pQTL) for these variants using plasma proteomics data from over 75,000 participants, we identify 365 proteins associated with cancer risk. Subsequent colocalization analysis identifies 101 proteins, including 74 not reported in previous studies. We further characterize 36 potential druggable proteins for cancers or other disease indications. Analyzing >3.5 million electronic health records, we uncover five drugs (Haloperidol, Trazodone, Tranexamic Acid, Haloperidol, and Captopril) associated with increased cancer risk and two drugs (Caffeine and Acetazolamide) linked to reduced colorectal cancer risk. This study offers novel insights into therapeutic drugs targeting risk proteins for cancer prevention and intervention.

Refining the impact of genetic evidence on clinical success

The cost of drug discovery and development is driven primarily by failure1, with only about 10% of clinical programmes eventually receiving approval2-4. We previously estimated that human genetic evidence doubles the success rate from clinical development to approval5. In this study we leverage the growth in genetic evidence over the past decade to better understand the characteristics that distinguish clinical success and failure. We estimate the probability of success for drug mechanisms with genetic support is 2.6 times greater than those without. This relative success varies among therapy areas and development phases, and improves with increasing confidence in the causal gene, but is largely unaffected by genetic effect size, minor allele frequency or year of discovery. These results indicate we are far from reaching peak genetic insights to aid the discovery of targets for more effective drugs.

Phenotyping Down syndrome: discovery and predictive modelling with electronic medical records

BACKGROUND

Individuals with Down syndrome (DS) have a heightened risk for various co-occurring health conditions, including congenital heart disease (CHD). In this two-part study, electronic medical records (EMRs) were leveraged to examine co-occurring health conditions among individuals with DS (Study 1) and to investigate health conditions linked to surgical intervention among DS cases with CHD (Study 2).

METHODS

De-identified EMRs were acquired from Vanderbilt University Medical Center and facilitated creating a cohort of N = 2282 DS cases (55% females), along with comparison groups for each study. In Study 1, DS cases were one-by-two sex and age matched with samples of case-controls and of individuals with other intellectual and developmental difficulties (IDDs). The phenome-disease association study (PheDAS) strategy was employed to reveal co-occurring health conditions in DS versus comparison groups, which were then ranked for how often they are discussed in relation to DS using the PubMed database and Novelty Finding Index. In Study 2, a subset of DS individuals with CHD [N = 1098 (48%)] were identified to create longitudinal data for N = 204 cases with surgical intervention (19%) versus 204 case-controls. Data were included in predictive models and assessed which model-based health conditions, when more prevalent, would increase the likelihood of surgical intervention.

RESULTS

In Study 1, relative to case-controls and those with other IDDs, co-occurring health conditions among individuals with DS were confirmed to include heart failure, pulmonary heart disease, atrioventricular block, heart transplant/surgery and primary pulmonary hypertension (circulatory); hypothyroidism (endocrine/metabolic); and speech and language disorder and Alzheimer's disease (neurological/mental). Findings also revealed more versus less prevalent co-occurring health conditions in individuals with DS when comparing with those with other IDDs. Findings with high Novelty Finding Index were abnormal electrocardiogram, non-rheumatic aortic valve disorders and heart failure (circulatory); acid-base balance disorder (endocrine/metabolism); and abnormal blood chemistry (symptoms). In Study 2, the predictive models revealed that among individuals with DS and CHD, presence of health conditions such as congestive heart failure (circulatory), valvular heart disease and cardiac shunt (congenital), and pleural effusion and pulmonary collapse (respiratory) were associated with increased likelihood of surgical intervention.

CONCLUSIONS

Research efforts using EMRs and rigorous statistical methods could shed light on the complexity in health profile among individuals with DS and other IDDs and motivate precision-care development.

The Role of Genetics in Advancing Cardiometabolic Drug Development

PURPOSE OF REVIEW

The objective of this review is to explore the role of genetics in cardiometabolic drug development. The declining costs of sequencing and the availability of large-scale genomic data have deepened our understanding of cardiometabolic diseases, revolutionizing drug discovery and development methodologies. We highlight four key areas in which genetics is empowering drug development for cardiometabolic disease: (1) identifying drug candidates, (2) anticipating drug target failures, (3) silencing and editing genes, and (4) enriching clinical trials.

RECENT FINDINGS

Identifying novel drug targets through genetic discovery studies and the use of genetic variants as indicators of potential drug efficacy and safety have become critical components of cardiometabolic drug discovery. We highlight the successes of genetically-informed therapeutic strategies, such as PCSK9 and ANGPTL3 inhibitors in lipid lowering and the emerging role of polygenic risk scores in improving the efficiency of clinical trials. Additionally, we explore the potential of gene silencing and editing technologies, such as antisense oligonucleotides and small interfering RNA, showcasing their promise in addressing diseases refractory to conventional treatments. In this review, we highlight four use cases that demonstrate the vital role of genetics in cardiometabolic drug development: (1) identifying drug candidates, (2) anticipating drug target failures, (3) silencing and editing genes, and (4) enriching clinical trials. Through these advances, genetics has paved the way to increased efficiency of drug development as well as the discovery of more personalized and effective treatments for cardiometabolic disease.

Adopting Mechanistic Molecular Biology Approaches in Exposome Research for Causal Understanding

Through investigating the combined impact of the environmental exposures experienced by an individual throughout their lifetime, exposome research provides opportunities to understand and mitigate negative health outcomes. While current exposome research is driven by epidemiological studies that identify associations between exposures and effects, new frameworks integrating more substantial population-level metadata, including electronic health and administrative records, will shed further light on characterizing environmental exposure risks. Molecular biology offers methods and concepts to study the biological and health impacts of exposomes in experimental and computational systems. Of particular importance is the growing use of omics readouts in epidemiological and clinical studies. This paper calls for the adoption of mechanistic molecular biology approaches in exposome research as an essential step in understanding the genotype and exposure interactions underlying human phenotypes. A series of recommendations are presented to make the necessary and appropriate steps to move from exposure association to causation, with a huge potential to inform precision medicine and population health. This includes establishing hypothesis-driven laboratory testing within the exposome field, supported by appropriate methods to read across from model systems research to human.

Heterogeneous associations between interleukin-6 receptor variants and phenotypes across ancestries and implications for therapy

The Phenome-Wide Association Study (PheWAS) is increasingly used to broadly screen for potential treatment effects, e.g., IL6R variant as a proxy for IL6R antagonists. This approach offers an opportunity to address the limited power in clinical trials to study differential treatment effects across patient subgroups. However, limited methods exist to efficiently test for differences across subgroups in the thousands of multiple comparisons generated as part of a PheWAS. In this study, we developed an approach that maximizes the power to test for heterogeneous genotype-phenotype associations and applied this approach to an IL6R PheWAS among individuals of African (AFR) and European (EUR) ancestries. We identified 29 traits with differences in IL6R variant-phenotype associations, including a lower risk of type 2 diabetes in AFR (OR 0.96) vs EUR (OR 1.0, p-value for heterogeneity = 8.5 × 10-3), and higher white blood cell count (p-value for heterogeneity = 8.5 × 10-131). These data suggest a more salutary effect of IL6R blockade for T2D among individuals of AFR vs EUR ancestry and provide data to inform ongoing clinical trials targeting IL6 for an expanding number of conditions. Moreover, the method to test for heterogeneity of associations can be applied broadly to other large-scale genotype-phenotype screens in diverse populations.

Novel strategies for drug repurposing

Synthetic biology, precision medicine, and nanobiotechnology are the three main emerging areas that drive translational innovation toward commercialization. There are several strategies used in precision medicine and drug repurposing is one of the key approaches as it addresses the challenges in drug discovery (high cost and time). Here, we provide a perspective on various new approaches to drug repurposing for cancer precision medicine. We report here our optimized wound healing methodology that can be used to validate drug sensitivity and drug repurposing. Using HeLa as our benchmark, we demonstrated that the assay can be applied to identify drugs that limit cell proliferation. From a future perspective, this assay can be expanded to ex vivo culturing of solid tumors in 2D culture and leukemia in 3D culture.

RNA interference in the era of nucleic acid therapeutics

Two decades of research on RNA interference (RNAi) have transformed a breakthrough discovery in biology into a robust platform for a new class of medicines that modulate mRNA expression. Here we provide an overview of the trajectory of small-interfering RNA (siRNA) drug development, including the first approval in 2018 of a liver-targeted siRNA interference (RNAi) therapeutic in lipid nanoparticles and subsequent approvals of five more RNAi drugs, which used metabolically stable siRNAs combined with N-acetylgalactosamine ligands for conjugate-based liver delivery. We also consider the remaining challenges in the field, such as delivery to muscle, brain and other extrahepatic organs. Today's RNAi therapeutics exhibit high specificity, potency and durability, and are transitioning from applications in rare diseases to widespread, chronic conditions.

Comorbidity Genes of Alzheimer's Disease and Type 2 Diabetes Associated with Memory and Cognitive Function

Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) are comorbidities that result from the sharing of common genes. The molecular background of comorbidities can provide clues for the development of treatment and management strategies. Here, the common genes involved in the development of the two diseases and in memory and cognitive function are reviewed. Network clustering based on protein-protein interaction network identified tightly connected gene clusters that have an impact on memory and cognition among the comorbidity genes of AD and T2DM. Genes with functional implications were intensively reviewed and relevant evidence summarized. Gene information will be useful in the discovery of biomarkers and the identification of tentative therapeutic targets for AD and T2DM.

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

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

Blood donor biobank as a resource in personalised biomedical genetic research

Health questionnaires and donation criteria result in accumulation of highly selected individuals in a blood donor population. To understand better the usefulness of a blood donor-based biobank in personalised disease-associated genetic studies, and for possible personalised blood donation policies, we evaluated the occurrence and distributions of common and rare disease-associated genetic variants in Finnish Blood Service Biobank. We analysed among 31,880 blood donors the occurrence and geographical distribution of (i) 53 rare Finnish-enriched disease-associated variants, (ii) mutations assumed to influence blood donation: four Bernard-Soulier syndrome and two hemochromatosis mutations, (iii) type I diabetes risk genotype HLA-DQ2/DQ8. In addition, we analysed the level of consanguinity in Blood Service Biobank. 80.3% of blood donors carried at least one (range 0-9 per donor) of the rare variants, many in homozygous form, as well. Donors carrying multiple rare variants were enriched in Eastern Finland. Haemochromatosis mutation HFE C282Y homozygosity was 43.8% higher than expected, whereas mutations leading to Bernard-Soulier thrombocytopenia were rare. The frequency of HLA-DQ2/DQ8 genotype was slightly lower than expected. First-degree consanguinity was higher in Blood Service Biobank than in the general population. We demonstrate that despite donor selection, the Blood Service Biobank is a valuable resource for personalised medical research and for genotype-selected samples from unaffected individuals. The geographical genetic substructure of Finland enables efficient recruitment of donors carrying rare variants. Furthermore, we show that blood donor biobank material can be utilised for personalised blood donation policies.

mixWAS: An efficient distributed algorithm for mixed-outcomes genome-wide association studies

Genome-wide association studies (GWAS) have been instrumental in identifying genetic associations for various diseases and traits. However, uncovering genetic underpinnings among traits beyond univariate phenotype associations remains a challenge. Multi-phenotype associations (MPA), or genetic pleiotropy, offer important insights into shared genes and pathways among traits, enhancing our understanding of genetic architectures of complex diseases. GWAS of biobank-linked electronic health record (EHR) data are increasingly being utilized to identify MPA among various traits and diseases. However, methodologies that can efficiently take advantage of distributed EHR to detect MPA are still lacking. Here, we introduce mixWAS, a novel algorithm that efficiently and losslessly integrates multiple EHRs via summary statistics, allowing the detection of MPA among mixed phenotypes while accounting for heterogeneities across EHRs. Simulations demonstrate that mixWAS outperforms the widely used MPA detection method, Phenome-wide association study (PheWAS), across diverse scenarios. Applying mixWAS to data from seven EHRs in the US, we identified 4,534 MPA among blood lipids, BMI, and circulatory diseases. Validation in an independent EHR data from UK confirmed 97.7% of the associations. mixWAS fundamentally improves the detection of MPA and is available as a free, open-source software.

Subset scanning for multi-trait analysis using GWAS summary statistics

MOTIVATION

Multi-trait analysis has been shown to have greater statistical power than single-trait analysis. Most of the existing multi-trait analysis methods only work with a limited number of traits and usually prioritize high statistical power over identifying relevant traits, which heavily rely on domain knowledge.

RESULTS

To handle diseases and traits with obscure etiology, we developed TraitScan, a powerful and fast algorithm that identifies potential pleiotropic traits from a moderate or large number of traits (e.g. dozens to thousands) and tests the association between one genetic variant and the selected traits. TraitScan can handle either individual-level or summary-level GWAS data. We evaluated TraitScan using extensive simulations and found that it outperformed existing methods in terms of both testing power and trait selection when sparsity was low or modest. We then applied it to search for traits associated with Ewing Sarcoma, a rare bone tumor with peak onset in adolescence, among 754 traits in UK Biobank. Our analysis revealed a few promising traits worthy of further investigation, highlighting the use of TraitScan for more effective multi-trait analysis as biobanks emerge. We also extended TraitScan to search and test association with a polygenic risk score and genetically imputed gene expression.

AVAILABILITY AND IMPLEMENTATION

Our algorithm is implemented in an R package "TraitScan" available at https://github.com/RuiCao34/TraitScan.

Phenome-wide association study and precision medicine of cardiovascular diseases in the post-COVID-19 era

SARS-CoV-2 infection causes injuries of not only the lungs but also the heart and endothelial cells in vasculature of multiple organs, and induces systemic inflammation and immune over-reactions, which makes COVID-19 a disease phenome that simultaneously affects multiple systems. Cardiovascular diseases (CVD) are intrinsic risk and causative factors for severe COVID-19 comorbidities and death. The wide-spread infection and reinfection of SARS-CoV-2 variants and the long-COVID may become a new common threat to human health and propose unprecedented impact on the risk factors, pathophysiology, and pharmacology of many diseases including CVD for a long time. COVID-19 has highlighted the urgent demand for precision medicine which needs new knowledge network to innovate disease taxonomy for more precise diagnosis, therapy, and prevention of disease. A deeper understanding of CVD in the setting of COVID-19 phenome requires a paradigm shift from the current phenotypic study that focuses on the virus or individual symptoms to phenomics of COVID-19 that addresses the inter-connectedness of clinical phenotypes, i.e., clinical phenome. Here, we summarize the CVD manifestations in the full clinical spectrum of COVID-19, and the phenome-wide association study of CVD interrelated to COVID-19. We discuss the underlying biology for CVD in the COVID-19 phenome and the concept of precision medicine with new phenomic taxonomy that addresses the overall pathophysiological responses of the body to the SARS-CoV-2 infection. We also briefly discuss the unique taxonomy of disease as Zheng-hou patterns in traditional Chinese medicine, and their potential implications in precision medicine of CVD in the post-COVID-19 era.

Association of HMGCR inhibition with rheumatoid arthritis: a Mendelian randomization and colocalization study

Objective

The objective of this study was to investigate the association between hydroxymethylglutaryl coenzyme A reductase (HMGCR) inhibition and rheumatoid arthritis (RA) using drug-target Mendelian randomization (MR) and genetic colocalization analyses.

Methods

Two sets of genetic instruments were employed to proxy HMGCR inhibitors: expression quantitative trait loci (eQTLs) of target genes from the eQTLGen Consortium and genetic variants associated with low-density lipoprotein cholesterol (LDL-C) levels with HMGCR locus from open genome-wide association studies (GWAS). Positive control analyses were conducted on type 2 diabetes and coronary heart disease, and multiple sensitivity analyses were performed.

Results

Genetically proxied expression of eQTL was associated with a lower risk of RA (OR=0.996, 95% CI =0.992-0.999, p= 0.032). Similarly, hydroxymethylglutaryl coenzyme A reductase (HMGCR)-mediated low-density lipoprotein cholesterol was negatively associated with risk of RA (OR=0.995, 95% CI =0.991-0.998, p= 0.007) in the inverse variance weighted (IVW) method. Colocalization analysis suggested a 74.6% posterior probability of sharing a causal variant within the SNPs locus (PH4 = 74.6%). A causal relationship also existed between HMGCR-mediated LDL and RA risk factors. The results were also confirmed by multiple sensitivity analyses. The results in positive control were consistent with the previous study.

Conclusion

Our study suggested that HMGCR inhibition was associated with an increased risk of RA while also highlighting an increased risk of current smoking and obesity. These findings contribute to a growing body of evidence regarding the adverse effects of HMGCR inhibition on RA risk, calling for further research on alternative approaches using HMGCR inhibitors in RA management.

Circulating Inflammatory Cytokines and Female Reproductive Diseases: A Mendelian Randomization Analysis

CONTEXT

Extensive studies have provided considerable evidence suggesting the role of inflammation in the development of female reproductive diseases. However, causality has not been established.

OBJECTIVE

To explore whether genetically determined circulating levels of cytokines are causally associated with female reproductive diseases and discover potential novel drug targets for these diseases.

METHODS

Instrumental variables (IVs) for 47 circulating cytokines were obtained from a genome-wide association study (GWAS) meta-analysis of 31 112 European individuals. Protein quantitative trait loci and expression quantitative trait loci close to genes served as our IVs. Summary data of 9 female reproductive diseases were mainly derived from GWAS meta-analysis of the UK biobank and FinnGen. We elevated the association using the Wald ratio or inverse variance-weighted Mendelian randomization (MR) with subsequent assessments for MR assumptions in several sensitivity and colocalization analyses. We consider a false discovery rate <0.05 as statistical significance in MR analyses. Replication studies were conducted for further validation, and phenome-wide association studies were designed to explore potential side effects.

RESULTS

Our results indicated that high levels of macrophage colony-stimulating factor (MCSF), growth-regulated oncogene-alpha (GROα), and soluble intercellular adhesion molecule-1 were associated with increased risks of endometriosis, female infertility, and pre-eclampsia, respectively. High platelet-derived growth factor-BB (PDGF-BB) levels that reduced the risk of ovarian aging were also supported. Replication analysis supported the relationship between GROα and female infertility, and between MCSF and endometriosis.

CONCLUSION

We identified 4 correlated pairs that implied potential protein drug targets. Notably, we preferred highlighting the value of PDGF-BB as a drug target for ovarian aging, and MCSF as a drug target for endometriosis.

Gene-Environment Analyses in a UK Biobank Skin Cancer Cohort Identifies Important SNPs in DNA Repair Genes That May Help Prognosticate Disease Risk

BACKGROUND

Despite well-established relationships between sun exposure and skin cancer pathogenesis/progression, specific gene-environment interactions in at-risk individuals remain poorly-understood.

METHODS

We leveraged a UK Biobank cohort of basal cell carcinoma (BCC, n = 17,221), cutaneous squamous cell carcinoma (cSCC, n = 2,331), melanoma in situ (M-is, n = 1,158), invasive melanoma (M-inv, n = 3,798), and healthy controls (n = 448,164) to quantify the synergistic involvement of genetic and environmental factors influencing disease risk. We surveyed 8,798 SNPs from 190 DNA repair genes, and 11 demographic/behavioral risk factors.

RESULTS

Clinical analysis identified darker skin (RR = 0.01-0.65) and hair (RR = 0.27-0.63) colors as protective factors. Eleven SNPs were significantly associated with BCC, three of which were also associated with M-inv. Gene-environment analysis yielded 201 SNP-environment interactions across 90 genes (FDR-adjusted q < 0.05). SNPs from the FANCA gene showed interactions with at least one clinical factor in all cancer groups, of which three (rs9926296, rs3743860, rs2376883) showed interaction with nearly every factor in BCC and M-inv.

CONCLUSIONS

We identified novel risk factors for keratinocyte carcinomas and melanoma, highlighted the prognostic value of several FANCA alleles among individuals with a history of sunlamp use and childhood sunburns, and demonstrated the importance of combining genetic and clinical data in disease risk stratification.

IMPACT

This study revealed genome-wide associations with important implications for understanding skin cancer risk in the context of the rapidly-evolving field of precision medicine. Major individual factors (including sex, hair and skin color, and sun protection use) were significant mediators for all skin cancers, interacting with >200 SNPs across four skin cancer types.

Mechanisms Behind NAFLD: a System Genetics Perspective

PURPOSE OF REVIEW

To summarize the key factors contributing to the onset and progress of nonalcoholic fatty liver disease (NAFLD) and put them in a system genetics context. We particularly focus on how genetic regulation of hepatic lipids contributes to NAFLD.

RECENT FINDINGS

NAFLD is characterized by excessive accumulation of fat in the liver. This can progress to steatohepatitis (inflammation and hepatocyte injury) and eventually, cirrhosis. The severity of NAFLD is determined by a combination of factors including obesity, insulin resistance, and lipotoxic lipids, along with genetic susceptibility. Numerous studies have been conducted on large human cohorts and mouse panels, to identify key determinants in the genome, transcriptome, proteome, lipidome, microbiome and different environmental conditions contributing to NAFLD. We review common factors contributing to NAFLD and put them in a systems genetics context. In particular, we describe how genetic regulation of liver lipids contributes to NAFLD. The combination of an unhealthy lifestyle and genetic predisposition increases the likelihood of accumulating lipotoxic specie lipids that may be one of the driving forces behind developing severe forms of NAFLD.

pH sensing at the intersection of tissue homeostasis and inflammation

pH is tightly maintained at cellular, tissue, and systemic levels, and altered pH - particularly in the acidic range - is associated with infection, injury, solid tumors, and physiological and pathological inflammation. However, how pH is sensed and regulated and how it influences immune responses remain poorly understood at the tissue level. Applying conceptual frameworks of homeostatic and inflammatory circuitries, we categorize cellular and tissue components engaged in pH regulation, drawing parallels from established cases in physiology. By expressing various intracellular (pHi) and extracellular pH (pHe)-sensing receptors, the immune system may integrate information on tissue and cellular states into the regulation of homeostatic and inflammatory programs. We introduce the novel concept of resistance and adaptation responses to rationalize pH-dependent immunomodulation intertwined with homeostatic equilibrium and inflammatory control. We discuss emerging challenges and opportunities in understanding the immunological roles of pH sensing, which might reveal new strategies to combat inflammation and restore tissue homeostasis.

Therapeutic opportunities for the treatment of NASH with genetically validated targets

The identification of genetic variants associated with fatty liver disease (FLD) from genome-wide association studies started in 2008 when single nucleotide polymorphisms in PNPLA3, the gene encoding patatin-like phospholipase domain-containing 3, were found to be associated with altered hepatic fat content. Since then, several genetic variants associated with protection from, or an increased risk of, FLD have been identified. The identification of these variants has provided insight into the metabolic pathways that cause FLD and enabled the identification of potential therapeutic targets. In this mini-review, we will examine the therapeutic opportunities derived from genetically validated targets in FLD, including oligonucleotide-based therapies targeting PNPLA3 and HSD17B13 that are currently being evaluated in clinical trials for the treatment of NASH (non-alcoholic steatohepatitis).

Mendelian Randomization Using the Druggable Genome Reveals Genetically Supported Drug Targets for Psychiatric Disorders

BACKGROUND AND HYPOTHESIS

Psychiatric disorders impose a huge health and economic burden on modern society. However, there is currently no proven completely effective treatment available, partly owing to the inefficiency of drug target identification and validation. We aim to identify therapeutic targets relevant to psychiatric disorders by conducting Mendelian randomization (MR) analysis.

STUDY DESIGN

We performed genome-wide MR analysis by integrating expression quantitative trait loci (eQTL) of 4479 actionable genes that encode druggable proteins and genetic summary statistics from genome-wide association studies of psychiatric disorders. After conducting colocalization analysis on the brain MR findings, we employed protein quantitative trait loci (pQTL) data as genetic proposed instruments for intersecting the colocalized genes to provide further genetic evidence.

STUDY RESULTS

By performing MR and colocalization analysis with eQTL genetic instruments, we obtained 31 promising drug targets for psychiatric disorders, including 21 significant genes for schizophrenia, 7 for bipolar disorder, 2 for depression, 1 for attention deficit and hyperactivity (ADHD) and none for autism spectrum disorder. Combining MR results using pQTL genetic instruments, we finally proposed 8 drug-targeting genes supported by the strongest MR evidence, including gene ACE, BTN3A3, HAPLN4, MAPK3 and NEK4 for schizophrenia, gene NEK4 and HAPLN4 for bipolar disorder, and gene TIE1 for ADHD.

CONCLUSIONS

Our findings with genetic support were more likely to be to succeed in clinical trials. In addition, our study prioritizes approved drug targets for the development of new therapies and provides critical drug reuse opportunities for psychiatric disorders.

Ancestral diversity in lipoprotein(a) studies helps address evidence gaps

INTRODUCTION

The independent and causal cardiovascular disease risk factor lipoprotein(a) (Lp(a)) is elevated in >1.5 billion individuals worldwide, but studies have prioritised European populations.

METHODS

Here, we examined how ancestrally diverse studies could clarify Lp(a)'s genetic architecture, inform efforts examining application of Lp(a) polygenic risk scores (PRS), enable causal inference and identify unexpected Lp(a) phenotypic effects using data from African (n=25 208), East Asian (n=2895), European (n=362 558), South Asian (n=8192) and Hispanic/Latino (n=8946) populations.

RESULTS

Fourteen genome-wide significant loci with numerous population specific signals of large effect were identified that enabled construction of Lp(a) PRS of moderate (R2=15% in East Asians) to high (R2=50% in Europeans) accuracy. For all populations, PRS showed promise as a 'rule out' for elevated Lp(a) because certainty of assignment to the low-risk threshold was high (88.0%-99.9%) across PRS thresholds (80th-99th percentile). Causal effects of increased Lp(a) with increased glycated haemoglobin were estimated for Europeans (p value =1.4×10-6), although inverse effects in Africans and East Asians suggested the potential for heterogeneous causal effects. Finally, Hispanic/Latinos were the only population in which known associations with coronary atherosclerosis and ischaemic heart disease were identified in external testing of Lp(a) PRS phenotypic effects.

CONCLUSIONS

Our results emphasise the merits of prioritising ancestral diversity when addressing Lp(a) evidence gaps.

Association between polygenic risk for Alzheimer's disease and brain structure in children and adults

BACKGROUND

The correlations between genetic risk for Alzheimer's disease (AD) with comprehensive brain regions at a regional scale are still not well understood. We aim to explore whether these associations vary across different age stages.

METHODS

This study used large existing genome-wide association datasets to calculate polygenic risk score (PRS) for AD in two populations from the UK Biobank (N ~ 23 000) and Adolescent Brain Cognitive Development Study (N ~ 4660) who had multimodal macrostructural and microstructural magnetic resonance imaging (MRI) metrics. We used linear mixed-effect models to assess the strength of the association between AD PRS and multiple MRI metrics of regional brain structures at different stages of life.

RESULTS

Compared to those with lower PRSs, adolescents with higher PRSs had thinner cortex in the caudal anterior cingulate and supramarginal. In the middle-aged and elderly population, AD PRS had correlations with regional structure shrink primarily located in the cingulate, prefrontal cortex, hippocampus, thalamus, amygdala, and striatum, whereas the brain expansion was concentrated near the occipital lobe. Furthermore, both adults and adolescents with higher PRSs exhibited widespread white matter microstructural changes, indicated by decreased fractional anisotropy (FA) or increased mean diffusivity (MD).

CONCLUSIONS

In conclusion, our results suggest genetic loading for AD may influence brain structures in a highly dynamic manner, with dramatically different patterns at different ages. This age-specific change is consistent with the classical pattern of brain impairment observed in AD patients.

Adverse effect of PNPLA3 p.I148M genetic variant on kidney function in middle-aged individuals with metabolic dysfunction

BACKGROUND

The PNPLA3 p.I148M variant is the main genetic determinant of nonalcoholic fatty liver disease, and PNPLA3 silencing is being evaluated to treat this liver condition. Data suggest that the p.I148M variant predisposes to kidney damage, but the relative contribution to kidney function, compared to overall genetic susceptibility, is not defined.

AIMS

We aimed to assess the effect of PNPLA3 p.I148M on the estimated glomerular filtration rate (eGFR) in individuals with metabolic dysfunction.

METHODS

We included 1144 middle-aged individuals from the Liver-Bible-2022 cohort. Glomerular filtration rate (eGFR) was estimated using the Chronic Kidney Disease Epidemiology Collaboration equation. The effect of PNPLA3 p.I148M on eGFR_CKD-EPI levels was tested under additive genetic models adjusted for clinical predictors, ethnicity and a polygenic risk score of chronic kidney disease (PRS-CKD). In a subset of 144 individuals, we examined the effect of PNPLA3 p.I148M on eGFR_CKD-EPI over a median follow-up of 17 months.

RESULTS

The p.I148M variant was associated with lower eGFR_CKD-EPI levels (-1.24 mL/min/1.73 m² per allele, 95% CI: -2.32 to -0.17; p = 0.023), independent of age, sex, height, waist circumference, systolic blood pressure, LDL-cholesterol, transaminases, fasting insulin, albuminuria, lipid-lowering drugs, ethnicity and PRS-CKD score. In the prospective evaluation, the p.I148M variant was independently associated with faster eGFR_CKD-EPI decline (ΔeGFR_CKD-EPI -3.57 mL/min/1.73 m² per allele, 95% CI: -6.94 to -0.21; p = 0.037).

CONCLUSIONS

We found a detrimental impact of the PNPLA3 p.I148M variant on eGFR_CKD-EPI levels in middle-aged individuals with metabolic dysfunction. This association was independent of established risk factors, ethnicity and genetic predisposition to CKD. PNPLA3 p.I148M silencing may protect against kidney damage progression in carriers.

Mendelian randomization and clinical trial evidence supports TYK2 inhibition as a therapeutic target for autoimmune diseases

BACKGROUND

To explore the associations of genetically proxied TYK2 inhibition with a wide range of disease outcomes and biomarkers to identify therapeutic repurposing opportunities, adverse effects, and biomarkers of efficacy.

METHODS

The loss-of-function missense variant rs34536443 in TYK2 gene was used as a genetic instrument to proxy the effect of TYK2 inhibition. A phenome-wide Mendelian randomization (MR) study was conducted to explore the associations of genetically-proxied TYK2 inhibition with 1473 disease outcomes in UK Biobank (N = 339,197). Identified associations were examined for replication in FinnGen (N = 260,405). We further performed tissue-specific gene expression MR, colocalization analyses, and MR with 247 blood biomarkers. A systematic review of randomized controlled trials (RCTs) on TYK2 inhibitor was performed to complement the genetic evidence.

FINDINGS

PheWAS-MR found that genetically-proxied TYK2 inhibition was associated with lower risk of a wide range of autoimmune diseases. The associations with hypothyroidism and psoriasis were confirmed in MR analysis of tissue-specific TYK2 gene expression and the associations with systemic lupus erythematosus, psoriasis, and rheumatoid arthritis were observed in colocalization analysis. There were nominal associations of genetically-proxied TYK2 inhibition with increased risk of prostate and breast cancer but not in tissue-specific expression MR or colocalization analyses. Thirty-seven blood biomarkers were associated with the TYK2 loss-of-function mutation. Evidence from RCTs confirmed the effectiveness of TYK2 inhibitors on plaque psoriasis and reported several adverse effects.

INTERPRETATION

This study supports TYK2 inhibitor as a potential treatment for psoriasis and several other autoimmune diseases. Increased pharmacovigilance is warranted in relation to the potential adverse effects.

FUNDING

None.

Using human genetics to improve safety assessment of therapeutics

Human genetics research has discovered thousands of proteins associated with complex and rare diseases. Genome-wide association studies (GWAS) and studies of Mendelian disease have resulted in an increased understanding of the role of gene function and regulation in human conditions. Although the application of human genetics has been explored primarily as a method to identify potential drug targets and support their relevance to disease in humans, there is increasing interest in using genetic data to identify potential safety liabilities of modulating a given target. Human genetic variants can be used as a model to anticipate the effect of lifelong modulation of therapeutic targets and identify the potential risk for on-target adverse events. This approach is particularly useful for non-clinical safety evaluation of novel therapeutics that lack pharmacologically relevant animal models and can contribute to the intrinsic safety profile of a drug target. This Review illustrates applications of human genetics to safety studies during drug discovery and development, including assessing the potential for on- and off-target associated adverse events, carcinogenicity risk assessment, and guiding translational safety study designs and monitoring strategies. A summary of available human genetic resources and recommended best practices is provided. The challenges and future perspectives of translating human genetic information to identify risks for potential drug effects in preclinical and clinical development are discussed.

Harnessing the Power of Electronic Health Records and Genomics for Drug Discovery

A long-standing recognition that information from human genetics studies has the potential to accelerate drug discovery has led to decades of research on how to leverage genetic and phenotypic information for drug discovery. Established simple and advanced statistical methods that allow the simultaneous analysis of genotype and clinical phenotype data by genome- and phenome-wide analyses, colocalization analyses with quantitative trait loci data from transcriptomics and proteomics data sets from different tissues, and Mendelian randomization are essential tools for drug development in the postgenomic era. Numerous studies have demonstrated how genomic data provide opportunities for the identification of new drug targets, the repurposing of drugs, and drug safety analyses. With an increase in the number of biobanks that enable linking in-depth omics data with rich repositories of phenotypic traits via electronic health records, more powerful ways for the evaluation and validation of drug targets will continue to expand across different disciplines of clinical research.

Genetically proxied therapeutic prolyl-hydroxylase inhibition and cardiovascular risk

Prolyl hydroxylase (PHD) inhibitors are in clinical development for anaemia in chronic kidney disease. Epidemiological studies have reported conflicting results regarding safety of long-term therapeutic haemoglobin (Hgb) rises through PHD inhibition on risk of cardiovascular disease. Genetic variation in genes encoding PHDs can be used as partial proxies to investigate the potential effects of long-term Hgb rises. We used Mendelian randomization to investigate the effect of long-term Hgb level rises through genetically proxied PHD inhibition on coronary artery disease (CAD: 60 801 cases; 123 504 controls), myocardial infarction (MI: 42 561 cases; 123 504 controls) or stroke (40 585 cases; 406 111 controls). To further characterize long-term effects of Hgb level rises, we performed a phenome-wide association study (PheWAS) in up to 451 099 UK Biobank individuals. Genetically proxied therapeutic PHD inhibition, equivalent to a 1.00 g/dl increase in Hgb levels, was not associated (at P < 0.05) with increased odds of CAD; odd ratio (OR) [95% confidence intervals (CI)] = 1.06 (0.84, 1.35), MI [OR (95% CI) = 1.02 (0.79, 1.33)] or stroke [OR (95% CI) = 0.91 (0.66, 1.24)]. PheWAS revealed associations with blood related phenotypes consistent with EGLN's role, relevant kidney- and liver-related biomarkers like estimated glomerular filtration rate and microalbuminuria, and non-alcoholic fatty liver disease (Bonferroni-adjusted P < 5.42E-05) but these were not clinically meaningful. These findings suggest that long-term alterations in Hgb through PHD inhibition are unlikely to substantially increase cardiovascular disease risk; using large disease genome-wide association study data, we could exclude ORs of 1.35 for cardiovascular risk with a 1.00 g/dl increase in Hgb.

Genomic variants-driven drug repurposing for tuberculosis by utilizing the established bioinformatic-based approach

A major challenge in translating genomic variants of Tuberculosis (TB) into clinical implementation is to integrate the disease-associated variants and facilitate drug discovery through the concept of genomic-driven drug repurposing. Here, we utilized two established genomic databases, namely a Genome-Wide Association Study (GWAS) and a Phenome-Wide Association Study (PheWAS) to identify the genomic variants associated with TB disease and further utilize them for drug-targeted genes. We evaluated 3.425 genomic variants associated with TB disease which overlapped with 200 TB-associated genes. To prioritize the biological TB risk genes, we devised an in-silico pipeline and leveraged an established bioinformatics method based on six functional annotations (missense mutation, cis-eQTL, biological process, cellular component, molecular function, and KEGG molecular pathway analysis). Interestingly, based on the six functional annotations that we applied, we discovered that 14 biological TB risk genes are strongly linked to the deregulation of the biological TB risk genes. Hence, we demonstrated that 12 drug target genes overlapped with 40 drugs for other indications and further suggested that the drugs may be repurposed for the treatment of TB. We highlighted that CD44, CCR5, CXCR4, and C3 are highly promising proposed TB targets since they are connected to SELP and HLA-B, which are biological TB risk genes with high systemic scores on functional annotations. In sum, the current study shed light on the genomic variants involved in TB pathogenesis as the biological TB risk genes and provided empirical evidence that the genomics of TB may contribute to drug discovery.

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The feasibility of utilizing genomic variants to facilitate drug repurposing for Tuberculosis.

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Genomic information can be effectively used for drug discovery and treatment through genomic-based therapies.

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Findings from our research support the possibility of drug repurposing for Tuberculosis based on genomic variations.

Pain perception genes, asthma, and oral health: A reverse genetics study

Pain is an experience of a subjective nature, interpreted in a personal way and according to an extensive palette of factors unique to each individual. Orofacial pain can be acute or chronic and it is usually the main reason for the patient to seek dental care. Pain perception varies widely among individuals. This variability is considered a mosaic of factors, which include biopsychosocial factors and genetic factors. Understanding these differences can be extremely beneficial for pain management in a personalized and more efficient way. We performed association studies to investigate phenotypes associated with genetic markers in pain-related genes in two groups of patients who received more or less anesthesia during dental treatment. The study group was comprised of 1289 individuals participating in the Dental Registry and DNA Repository Project (DRDR) of the University of Pittsburgh, with 900 participants in the group that received the most anesthesia and 389 constituting the comparison group that received less anesthesia. We tested 58 phenotypes and genotypic data of seven SNPs in genes that are associated with pain perception, pain modulation and response to drugs used in pain treatment: COMT (rs4818 and rs6269), GCH1 (rs3783641), DRD2 (rs6276), OPRM1 (rs1799971), SCN9A (rs6746030) and SCN10A (rs6795970). The analysis revealed a protective effect of rs1799971 on asthma in the total sample. rs3783641 was associated with salivary secretion disorders in females who received more anesthesia. rs1799971 was also associated with periodontitis in Whites who received less anesthesia. rs4818 was associated with disease and other tongue conditions in the group composed of Blacks who received less anesthesia. In conclusion, our study implicated variants in pain-related genes in asthma and oral phenotypes.

Transcriptomics-based network medicine approach identifies metformin as a repurposable drug for atrial fibrillation

Effective drugs for atrial fibrillation (AF) are lacking, resulting in significant morbidity and mortality. This study demonstrates that network proximity analysis of differentially expressed genes from atrial tissue to drug targets can help prioritize repurposed drugs for AF. Using enrichment analysis of drug-gene signatures and functional testing in human inducible pluripotent stem cell (iPSC)-derived atrial-like cardiomyocytes, we identify metformin as a top repurposed drug candidate for AF. Using the active compactor, a new design analysis of large-scale longitudinal electronic health record (EHR) data, we determine that metformin use is significantly associated with a reduced risk of AF (odds ratio = 0.48, 95%, confidence interval [CI] 0.36-0.64, p < 0.001) compared with standard treatments for diabetes. This study utilizes network medicine methodologies to identify repurposed drugs for AF treatment and identifies metformin as a candidate drug.

The Role of Systems Biology in Deciphering Asthma Heterogeneity

Asthma is one of the most common and lifelong and chronic inflammatory diseases characterized by inflammation, bronchial hyperresponsiveness, and airway obstruction episodes. It is a heterogeneous disease of varying and overlapping phenotypes with many confounding factors playing a role in disease susceptibility and management. Such multifactorial disorders will benefit from using systems biology as a strategy to elucidate molecular insights from complex, quantitative, massive clinical, and biological data that will help to understand the underlying disease mechanism, early detection, and treatment planning. Systems biology is an approach that uses the comprehensive understanding of living systems through bioinformatics, mathematical, and computational techniques to model diverse high-throughput molecular, cellular, and the physiologic profiling of healthy and diseased populations to define biological processes. The use of systems biology has helped understand and enrich our knowledge of asthma heterogeneity and molecular basis; however, such methods have their limitations. The translational benefits of these studies are few, and it is recommended to reanalyze the different studies and omics in conjugation with one another which may help understand the reasons for this variation and help overcome the limitations of understanding the heterogeneity in asthma pathology. In this review, we aim to show the different factors that play a role in asthma heterogeneity and how systems biology may aid in understanding and deciphering the molecular basis of asthma.

Trends in the Contribution of Genetic Susceptibility Loci to Hyperuricemia and Gout and Associated Novel Mechanisms

Hyperuricemia and gout are complex diseases mediated by genetic, epigenetic, and environmental exposure interactions. The incidence and medical burden of gout, an inflammatory arthritis caused by hyperuricemia, increase every year, significantly increasing the disease burden. Genetic factors play an essential role in the development of hyperuricemia and gout. Currently, the search on disease-associated genetic variants through large-scale genome-wide scans has primarily improved our understanding of this disease. However, most genome-wide association studies (GWASs) still focus on the basic level, whereas the biological mechanisms underlying the association between genetic variants and the disease are still far from well understood. Therefore, we summarized the latest hyperuricemia- and gout-associated genetic loci identified in the Global Biobank Meta-analysis Initiative (GBMI) and elucidated the comprehensive potential molecular mechanisms underlying the effects of these gene variants in hyperuricemia and gout based on genetic perspectives, in terms of mechanisms affecting uric acid excretion and reabsorption, lipid metabolism, glucose metabolism, and nod-like receptor pyrin domain 3 (NLRP3) inflammasome and inflammatory pathways. Finally, we summarized the potential effect of genetic variants on disease prognosis and drug efficacy. In conclusion, we expect that this summary will increase our understanding of the pathogenesis of hyperuricemia and gout, provide a theoretical basis for the innovative development of new clinical treatment options, and enhance the capabilities of precision medicine for hyperuricemia and gout treatment.

Organization of gene programs revealed by unsupervised analysis of diverse gene-trait associations

Genome wide association studies provide statistical measures of gene–trait associations that reveal how genetic variation influences phenotypes. This study develops an unsupervised dimensionality reduction method called UnTANGLeD (Unsupervised Trait Analysis of Networks from Gene Level Data) which organizes 16,849 genes into discrete gene programs by measuring the statistical association between genetic variants and 1,393 diverse complex traits. UnTANGLeD reveals 173 gene clusters enriched for protein–protein interactions and highly distinct biological processes governing development, signalling, disease, and homeostasis. We identify diverse gene networks with robust interactions but not associated with known biological processes. Analysis of independent disease traits shows that UnTANGLeD gene clusters are conserved across all complex traits, providing a simple and powerful framework to predict novel gene candidates and programs influencing orthogonal disease phenotypes. Collectively, this study demonstrates that gene programs co-ordinately orchestrating cell functions can be identified without reliance on prior knowledge, providing a method for use in functional annotation, hypothesis generation, machine learning and prediction algorithms, and the interpretation of diverse genomic data.

Manipulating Microbiota to Treat Atopic Dermatitis: Functions and Therapies

Atopic dermatitis (AD) is a globally prevalent skin inflammation with a particular impact on children. Current therapies for AD are challenged by the limited armamentarium and the high heterogeneity of the disease. A novel promising therapeutic target for AD is the microbiota. Numerous studies have highlighted the involvement of the skin and gut microbiota in the pathogenesis of AD. The resident microbiota at these two epithelial tissues can modulate skin barrier functions and host immune responses, thus regulating AD progression. For example, the pathogenic roles of Staphylococcus aureus in the skin are well-established, making this bacterium an attractive target for AD treatment. Targeting the gut microbiota is another therapeutic strategy for AD. Multiple oral supplements with prebiotics, probiotics, postbiotics, and synbiotics have demonstrated promising efficacy in both AD prevention and treatment. In this review, we summarize the association of microbiota dysbiosis in both the skin and gut with AD, and the current knowledge of the functions of commensal microbiota in AD pathogenesis. Furthermore, we discuss the existing therapies in manipulating both the skin and gut commensal microbiota to prevent or treat AD. We also propose potential novel therapies based on the cutting-edge progress in this area.

StarGazer: A Hybrid Intelligence Platform for Drug Target Prioritization and Digital Drug Repositioning Using Streamlit

Target prioritization is essential for drug discovery and repositioning. Applying computational methods to analyze and process multi-omics data to find new drug targets is a practical approach for achieving this. Despite an increasing number of methods for generating datasets such as genomics, phenomics, and proteomics, attempts to integrate and mine such datasets remain limited in scope. Developing hybrid intelligence solutions that combine human intelligence in the scientific domain and disease biology with the ability to mine multiple databases simultaneously may help augment drug target discovery and identify novel drug-indication associations. We believe that integrating different data sources using a singular numerical scoring system in a hybrid intelligent framework could help to bridge these different omics layers and facilitate rapid drug target prioritization for studies in drug discovery, development or repositioning. Herein, we describe our prototype of the StarGazer pipeline which combines multi-source, multi-omics data with a novel target prioritization scoring system in an interactive Python-based Streamlit dashboard. StarGazer displays target prioritization scores for genes associated with 1844 phenotypic traits, and is available via https://github.com/AstraZeneca/StarGazer.

Are phenome-wide association studies feasible in a developing country?

Phenome-wide association studies (PheWASs), a powerful approach that examines phenotypes associated with a genetic marker, have been used extensively in highly developed countries. Although there may be a clear need for PheWAS in a developing country such as the Philippines, limitations related to resources and practicality would make conducting them a challenge.

Metabolic dysfunction-associated fatty liver disease: a year in review

PURPOSE OF REVIEW

In 2020, a novel comprehensive redefinition of fatty liver disease was proposed by an international panel of experts. This review aims to explore current evidence regarding the impact of this new definition on the current understanding of the epidemiology, pathogenesis, diagnosis, and clinical trials for fatty liver disease.

RECENT FINDINGS

The effectiveness of metabolic dysfunction-associated fatty liver disease (MAFLD) was compared to the existing criteria for nonalcoholic fatty liver disease (NAFLD). Recent data robustly suggest the superior utility of MAFLD in identifying patients at high risk for metabolic dysfunction, the hepatic and extra-hepatic complications, as well as those who would benefit from genetic testing, including patients with concomitant liver diseases. This change in name and criteria also appears to have improved disease awareness among patients and physicians.

SUMMARY

The transformation in name and definition from NAFLD to MAFLD represents an important milestone, which indicates significant tangible progress towards a more inclusive, equitable, and patient-centred approach to addressing the profound challenges of this disease. Growing evidence has illustrated the broader and specific contexts that have tremendous potential for positively influencing the diagnosis and treatment. In addition, the momentum accompanying this name change has included widespread public attention to the unique burden of this previously underappreciated disease.

Translational Analysis of Moderate to Severe Asthma GWAS Signals Into Candidate Causal Genes and Their Functional, Tissue-Dependent and Disease-Related Associations

Asthma affects more than 300 million people globally and is both under diagnosed and under treated. The most recent and largest genome-wide association study investigating moderate to severe asthma to date was carried out in 2019 and identified 25 independent signals. However, as new and in-depth downstream databases become available, the translational analysis of these signals into target genes and pathways is timely. In this study, unique (U-BIOPRED) and publicly available datasets (HaploReg, Open Target Genetics and GTEx) were investigated for the 25 GWAS signals to identify 37 candidate causal genes. Additional traits associated with these signals were identified through PheWAS using the UK Biobank resource, with asthma and eosinophilic traits amongst the strongest associated. Gene expression omnibus dataset examination identified 13 candidate genes with altered expression profiles in the airways and blood of asthmatic subjects, including MUC5AC and STAT6. Gene expression analysis through publicly available datasets highlighted lung tissue cell specific expression, with both MUC5AC and SLC22A4 genes showing enriched expression in ciliated cells. Gene enrichment pathway and interaction analysis highlighted the dominance of the HLA-DQA1/A2/B1/B2 gene cluster across many immunological diseases including asthma, type I diabetes, and rheumatoid arthritis. Interaction and prediction analyses found IL33 and IL18R1 to be key co-localization partners for other genes, predicted that CD274 forms co-expression relationships with 13 other genes, including the HLA-DQA1/A2/B1/B2 gene cluster and that MUC5AC and IL37 are co-expressed. Drug interaction analysis revealed that 11 of the candidate genes have an interaction with available therapeutics. This study provides significant insight into these GWAS signals in the context of cell expression, function, and disease relationship with the view of informing future research and drug development efforts for moderate-severe asthma.

Metabolic dysfunction-associated fatty liver disease: advances in genetic and epigenetic implications

PURPOSE OF REVIEW

Fatty liver associated with metabolic dysfunction, also known under the acronym NAFLD (nonalcoholic fatty liver disease) is the leading global cause of chronic liver disease. In this review, we address the state of research on genetics and epigenetics of NAFLD with focus on key discoveries and conceptual advances over the past 2 years.

RECENT FINDINGS

The analysis of NAFLD-associated genetic variant effects on the whole-transcriptome, including quantitative trait loci (QTL) associated with gene expression (eQTL) or splicing (sQTL) may explain pleiotropic effects. Functional experiments on NAFLD-epigenetics, including profiling of liver chromatin accessibility quantitative trait loci (caQTL) show co-localization with numerous genome-wide association study signals linked to metabolic and cardiovascular traits. Novel studies provide insights into the modulation of the hepatic transcriptome and epigenome by tissue microbiotas. Genetic variation of components of the liver cellular respirasome may result in broad cellular and metabolic effects. Mitochondrial noncoding RNAs may regulate liver inflammation and fibrogenesis. RNA modifications as N6-methyladenosine may explain sex-specific differences in liver gene transcription linked to lipid traits.

SUMMARY

The latest developments in the field of NAFLD-genomics can be leveraged for identifying novel disease mechanisms and therapeutic targets that may prevent the morbidity and mortality associated with disease progression.

VIDEO ABSTRACT

http://links.lww.com/COL/A23.

Pharmacogenomics: A Step forward Precision Medicine in Childhood Asthma

Personalized medicine, an approach to care in which individual characteristics are used for targeting interventions and maximizing health outcomes, is rapidly becoming a reality for many diseases. Childhood asthma is a heterogeneous disease and many children have uncontrolled symptoms. Therefore, an individualized approach is needed for improving asthma outcomes in children. The rapidly evolving fields of genomics and pharmacogenomics may provide a way to achieve asthma control and reduce future risks in children with asthma. In particular, pharmacogenomics can provide tools for identifying novel molecular mechanisms and biomarkers to guide treatment. Emergent high-throughput technologies, along with patient pheno-endotypization, will increase our knowledge of several molecular mechanisms involved in asthma pathophysiology and contribute to selecting and stratifying appropriate treatment for each patient.

Whole Exome Sequencing in Healthy Individuals of Extreme Constitution Types Reveals Differential Disease Risk: A Novel Approach towards Predictive Medicine

Precision medicine aims to move from traditional reactive medicine to a system where risk groups can be identified before the disease occurs. However, phenotypic heterogeneity amongst the diseased and healthy poses a major challenge for identification markers for risk stratification and early actionable interventions. In Ayurveda, individuals are phenotypically stratified into seven constitution types based on multisystem phenotypes termed “Prakriti”. It enables the prediction of health and disease trajectories and the selection of health interventions. We hypothesize that exome sequencing in healthy individuals of phenotypically homogeneous Prakriti types might enable the identification of functional variations associated with the constitution types. Exomes of 144 healthy Prakriti stratified individuals and controls from two genetically homogeneous cohorts (north and western India) revealed differential risk for diseases/traits like metabolic disorders, liver diseases, and body and hematological measurements amongst healthy individuals. These SNPs differ significantly from the Indo-European background control as well. Amongst these we highlight novel SNPs rs304447 (IFIT5) and rs941590 (SERPINA10) that could explain differential trajectories for immune response, bleeding or thrombosis. Our method demonstrates the requirement of a relatively smaller sample size for a well powered study. This study highlights the potential of integrating a unique phenotyping approach for the identification of predictive markers and the at-risk population amongst the healthy.

Enhancer promoter interactome and Mendelian randomization identify network of druggable vascular genes in coronary artery disease

Coronary artery disease (CAD) is a multifactorial disorder, which is partly heritable. Herein, we implemented a mapping of CAD-associated candidate genes by using genome-wide enhancer-promoter conformation (H3K27ac-HiChIP) and expression quantitative trait loci (eQTL). Enhancer-promoter anchor loops from human coronary artery smooth muscle cells (HCASMC) explained 22% of the heritability for CAD. 3D enhancer-promoter genome mapping of CAD-genes in HCASMC was enriched in vascular eQTL genes. By using colocalization and Mendelian randomization analyses, we identified 58 causal candidate vascular genes including some druggable targets (MAP3K11, CAMK1D, PDGFD, IPO9 and CETP). A network analysis of causal candidate genes was enriched in TGF beta and MAPK pathways. The pharmacologic inhibition of causal candidate gene MAP3K11 in vascular SMC reduced the expression of athero-relevant genes and lowered cell migration, a cardinal process in CAD. Genes connected to enhancers are enriched in vascular eQTL and druggable genes causally associated with CAD.

Covariation of proximal finger and toe phalanges in Homo sapiens: A novel approach to assess covariation of serially corresponding structures

OBJECTIVES

As hands and feet are serially repeated corresponding structures in tetrapods, the morphology of fingers and toes is expected to covary due to a shared developmental origin. The present study focuses on the covariation of the shape of proximal finger and toe phalanges of adult Homo sapiens to determine whether covariation is different in the first ray relative to the others, as its morphology is also different.

MATERIAL AND METHODS

Proximal phalanges of 76 individuals of unknown sex (Muséum national d'Histoire naturelle, Paris, and the Natural History Museum, London) were digitized using a surface scanner. Landmarks were positioned on 3D surface models of the phalanges. Generalized Procrustes analysis and two-block partial least squares (PLS) analyses were conducted. A novel landmark-based geometric morphometric approach focusing on covariation is based on a PCoA of the angles between PLS axes in morphospace. The results can be statistically evaluated.

RESULTS

The difference in PCo scores between the first and the other rays indicates that the integration between the thumb and the big toe is different from that between the lateral rays of the hand and foot.

DISCUSSION

We speculate that the results are possibly the evolutionary consequence of differential selection pressure on the big toe relative to the other toes related to the rise of bipedalism, which is proposed to have emerged very early in the hominin clade. In contrast, thumb morphology and its precision grip never ceased undergoing changes, suggesting less acute selection pressures related to the evolution of the precision grip.

Integrating gene expression and clinical data to identify drug repurposing candidates for hyperlipidemia and hypertension

Discovering novel uses for existing drugs, through drug repurposing, can reduce the time, costs, and risk of failure associated with new drug development. However, prioritizing drug repurposing candidates for downstream studies remains challenging. Here, we present a high-throughput approach to identify and validate drug repurposing candidates. This approach integrates human gene expression, drug perturbation, and clinical data from publicly available resources. We apply this approach to find drug repurposing candidates for two diseases, hyperlipidemia and hypertension. We screen >21,000 compounds and replicate ten approved drugs. We also identify 25 (seven for hyperlipidemia, eighteen for hypertension) drugs approved for other indications with therapeutic effects on clinically relevant biomarkers. For five of these drugs, the therapeutic effects are replicated in the All of Us Research Program database. We anticipate our approach will enable researchers to integrate multiple publicly available datasets to identify high priority drug repurposing opportunities for human diseases.

Mendelian randomization analyses reveal novel drug targets for anorexia nervosa

Among all psychiatric disorders, anorexia nervosa (AN) has the highest mortality rate. However, there is still no pharmacological therapy for AN. The human plasma proteome may be a great cornerstone for the development of new drugs against AN. Here we performed a Mendelian randomization (MR) analysis to identify causal risk proteins for AN. Exposure data were extracted from a large genome-wide association study (GWAS) of 2994 plasma proteins in 3301 subjects of European descent, while outcome data were obtained from another GWAS of AN (16,992 cases and 55,525 controls of European descent). MR analyses were performed using the inverse-variance weighted (IVW) method and other sensitivity analysis methods. Using single nucleotide polymorphisms as instruments, this study suggested that high TXNDC12 levels were associated with a higher risk of AN (IVW Odd's ratio [OR]: 1.12; 95% confidence interval [CI]: 1.08-1.16; P = 2.35 × 10^-10), while another protein ADH1B showed the opposite effect (IVW OR: 0.89; 95% CI: 0.85-0.93; P = 2.99 × 10^-7). The causal associations were robust in multivariable models, genome-wide significant models, and with additional MR methods. No pleiotropy was observed. Our findings suggest that TXNDC12 was associated with a high risk of AN, while AHD1B was associated with a low risk of AN. They might both have implications in AN by regulating the brain dopamine reward system. In combination with existing knowledge on AN, these proteins may be novel drug targets for AN treatment.

Phenome-Wide Association Studies

Genome-wide association studies (GWAS) have made clear that single-nucleotide variants (SNVs) that occur at multiple locations across the genome can be associated with a specific condition or trait, also known as a phenotype. Phenome-wide association studies (PheWAS) invert the idea of a GWAS by searching for phenotypes associated with specific SNVs across the range of thousands of human phenotypes, or the “phenome” (Figure). Analogous to GWAS, PheWAS have shown that specific genetic variations may be associated with multiple conditions and traits.

STXBP6 and B3GNT6 Genes are Associated With Selective IgA Deficiency

Immunoglobulin A Deficiency (IgAD) is a polygenic primary immune deficiency, with a strong genetic association to the human leukocyte antigen (HLA) region. Previous genome-wide association studies (GWAS) have identified five non-HLA risk loci (IFIH1, PVT1, ATG13-AMBRA1, AHI1 and CLEC16A). In this study, we investigated the genetic interactions between different HLA susceptibility haplotypes and non-MHC genes in IgAD. To do this, we stratified IgAD subjects and healthy controls based on HLA haplotypes (N = 10,993), and then performed GWAS to identify novel genetic regions contributing to IgAD susceptibility. After replicating previously published HLA risk haplotypes, we compared individuals carrying at least one HLA risk allele (HLA-B*08:01-DRB1*03:01-DQB1*02:01 or HLA-DRB1*07:01-DQB1*02:02 or HLA-DRB1*01-DQB1*05:01) with individuals lacking an HLA risk allele. Subsequently, we stratified subjects based on the susceptibility alleles/haplotypes and performed gene-based association analysis using 572,856 SNPs and 24,125 genes. A significant genome-wide association in STXBP6 (rs4097492; p = 7.63 × 10^-9) was observed in the cohort carrying at least one MHC risk allele. We also identified a significant gene-based association for B3GNT6 (P _Gene = 2.1 × 10^-6) in patients not carrying known HLA susceptibility alleles. Our findings indicate that the etiology of IgAD differs depending on the genetic background of HLA susceptibility haplotypes.

Maturation and application of phenome-wide association studies

In the past 10 years since its introduction, phenome-wide association studies (PheWAS) have uncovered novel genotype-phenotype relationships. Along the way, PheWAS have evolved in many aspects as a study design with the expanded availability of large data repositories with genome-wide data linked to detailed phenotypic data. Advancement in methods, including algorithms, software, and publicly available integrated resources, makes it feasible to more fully realize the potential of PheWAS, overcoming the previous computational and analytical limitations. We review here the most recent improvements and notable applications of PheWAS since the second half of the decade from its inception. We also note the challenges that remain embedded along the entire PheWAS analytical pipeline that necessitate further development of tools and resources to further advance the understanding of the complex genetic architecture underlying human diseases and traits.