Genetics of Atopic Dermatitis: From DNA Sequence to Clinical Relevance

Atopic dermatitis (AD) is a complex disease that is thought to be triggered by environmental factors in genetically susceptible individuals. Twin studies have estimated the heritability of AD to be approximately 75%, with the null (loss-of-function) mutations of the gene encoding filaggrin (FLG) (chromosome 1q21.3) as the strongest known genetic risk factor. The discovery of the filaggrin gene was important in the emerging model for AD pathogenesis, combining skin barrier function with adaptive and innate immunity. Assisted by the recent development of large-scale high-throughput genomics, more than 30 genetic loci have been linked to AD across different populations. Identification of these loci, together with functional studies, has already provided new insights into disease biology and identified novel drug targets. Further, these susceptibility loci are laying the groundwork for phenome-wide association studies to test their multiple phenotype relationships and application of Mendelian randomization to investigate causal relationships. Despite many known genes, a majority of the genetic risk for AD is yet unexplored. Therefore, studies investigating refined phenotype groups, low-frequency and rare genetic variation, gene-gene and/or gene-environment interactions, epigenetic mechanisms and data from multi-omics technologies are warranted. In this review, we describe genetic discoveries for AD, including results from candidate gene studies, studies of AD-like genetic diseases, genome-wide association studies and genetic sequencing studies. We explain how some of these genetic discoveries have unraveled new mechanistic insights into the pathogenesis of AD and exemplify how personal genetic data could be used for preventive strategies and a tailored treatment regimen (i.e., precision medicine).

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Twelve years of GWAS discoveries for osteoporosis and related traits: advances, challenges and applications

Bone Research ◽

10.1038/s41413-021-00143-3 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Xiaowei Zhu ◽

Weiyang Bai ◽

Houfeng Zheng

Keyword(s):

Drug Targets ◽

Complex Disease ◽

Association Studies ◽

Osteoporotic Fractures ◽

Disease Prediction ◽

Genome Wide Association Studies ◽

Mineral Density ◽

Small Effect Size ◽

Endocrine Status ◽

Meta Analyses

AbstractOsteoporosis is a common skeletal disease, affecting ~200 million people around the world. As a complex disease, osteoporosis is influenced by many factors, including diet (e.g. calcium and protein intake), physical activity, endocrine status, coexisting diseases and genetic factors. In this review, we first summarize the discovery from genome-wide association studies (GWASs) in the bone field in the last 12 years. To date, GWASs and meta-analyses have discovered hundreds of loci that are associated with bone mineral density (BMD), osteoporosis, and osteoporotic fractures. However, the GWAS approach has sometimes been criticized because of the small effect size of the discovered variants and the mystery of missing heritability, these two questions could be partially explained by the newly raised conceptual models, such as omnigenic model and natural selection. Finally, we introduce the clinical use of GWAS findings in the bone field, such as the identification of causal clinical risk factors, the development of drug targets and disease prediction. Despite the fruitful GWAS discoveries in the bone field, most of these GWAS participants were of European descent, and more genetic studies should be carried out in other ethnic populations to benefit disease prediction in the corresponding population.

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An atlas of polygenic risk score associations to highlight putative causal relationships across the human phenome

10.1101/467910 ◽

2018 ◽

Cited By ~ 1

Author(s):

Tom G. Richardson ◽

Sean Harrison ◽

Gibran Hemani ◽

George Davey Smith

Keyword(s):

Web Application ◽

Large Scale ◽

Complex Disease ◽

Association Studies ◽

Risk Scores ◽

Polygenic Risk Score ◽

Genome Wide Association Studies ◽

Genetic Liability ◽

Polygenic Risk ◽

The Uk

AbstractThe age of large-scale genome-wide association studies (GWAS) has provided us with an unprecedented opportunity to evaluate the genetic liability of complex disease using polygenic risk scores (PRS). In this study, we have analysed 162 PRS (P<5×l0 05) derived from GWAS and 551 heritable traits from the UK Biobank study (N=334,398). Findings can be investigated using a web application (http://mrcieu.mrsoftware.org/PRS_atlas/), which we envisage will help uncover both known and novel mechanisms which contribute towards disease susceptibility.To demonstrate this, we have investigated the results from a phenome-wide evaluation of schizophrenia genetic liability. Amongst findings were inverse associations with measures of cognitive function which extensive follow-up analyses using Mendelian randomization (MR) provided evidence of a causal relationship. We have also investigated the effect of multiple risk factors on disease using mediation and multivariable MR frameworks. Our atlas provides a resource for future endeavours seeking to unravel the causal determinants of complex disease.

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Comment on ‘Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets’ by Lam et al.

Twin Research and Human Genetics ◽

10.1017/thg.2018.12 ◽

2018 ◽

Vol 21 (2) ◽

pp. 84-88 ◽

Cited By ~ 6

Author(s):

W. David Hill

Keyword(s):

Genetic Information ◽

Drug Targets ◽

Large Scale ◽

Association Studies ◽

Meta Analysis ◽

Genetic Correlations ◽

Data Sets ◽

Genome Wide Association Studies ◽

Nootropic Drug ◽

Genome Wide

Intelligence and educational attainment are strongly genetically correlated. This relationship can be exploited by Multi-Trait Analysis of GWAS (MTAG) to add power to Genome-wide Association Studies (GWAS) of intelligence. MTAG allows the user to meta-analyze GWASs of different phenotypes, based on their genetic correlations, to identify association's specific to the trait of choice. An MTAG analysis using GWAS data sets on intelligence and education was conducted by Lam et al. (2017). Lam et al. (2017) reported 70 loci that they described as ‘trait specific’ to intelligence. This article examines whether the analysis conducted by Lam et al. (2017) has resulted in genetic information about a phenotype that is more similar to education than intelligence.

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Dissociable influences of APOE ε4 and polygenic risk of AD dementia on amyloid and cognition

Neurology ◽

10.1212/wnl.0000000000005415 ◽

2018 ◽

Vol 90 (18) ◽

pp. e1605-e1612 ◽

Cited By ~ 24

Author(s):

Tian Ge ◽

Mert R. Sabuncu ◽

Jordan W. Smoller ◽

Reisa A. Sperling ◽

Elizabeth C. Mormino ◽

...

Keyword(s):

Cognitive Decline ◽

Genetic Risk ◽

Large Scale ◽

Association Studies ◽

Specific Effect ◽

Hippocampal Volume ◽

Risk Scores ◽

Hippocampal Atrophy ◽

Genome Wide Association Studies ◽

Polygenic Risk

ObjectiveTo investigate the effects of genetic risk of Alzheimer disease (AD) dementia in the context of β-amyloid (Aβ) accumulation.MethodsWe analyzed data from 702 participants (221 clinically normal, 367 with mild cognitive impairment, and 114 with AD dementia) with genetic data and florbetapir PET available. A subset of 669 participants additionally had longitudinal MRI scans to assess hippocampal volume. Polygenic risk scores (PRSs) were estimated with summary statistics from previous large-scale genome-wide association studies of AD dementia. We examined relationships between APOE ε4 status and PRS with longitudinal Aβ and cognitive and hippocampal volume measurements.ResultsAPOE ε4 was strongly related to baseline Aβ, whereas only weak associations between PRS and baseline Aβ were present. APOE ε4 was additionally related to greater memory decline and hippocampal atrophy in Aβ+ participants. When APOE ε4 was controlled for, PRS was related to cognitive decline in Aβ+ participants. Finally, PRSs were associated with hippocampal atrophy in Aβ− participants and weakly associated with baseline hippocampal volume in Aβ+ participants.ConclusionsGenetic risk factors of AD dementia demonstrate effects related to Aβ, as well as synergistic interactions with Aβ. The specific effect of faster cognitive decline in Aβ+ individuals with higher genetic risk may explain the large degree of heterogeneity in cognitive trajectories among Aβ+ individuals. Consideration of genetic variants in conjunction with baseline Aβ may improve enrichment strategies for clinical trials targeting Aβ+ individuals most at risk for imminent cognitive decline.

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Chemical Genetic Validation of GWAS-derived Disease Loci

Current Bioinformatics ◽

10.2174/1574893616999210120182030 ◽

2021 ◽

Vol 16 ◽

Author(s):

Yuan Quan ◽

Hong-Yu Zhang

Keyword(s):

Drug Target ◽

Large Scale ◽

Target Genes ◽

Association Studies ◽

Chemical Genetics ◽

Genome Wide Association Studies ◽

Loss Of Function ◽

Chemical Genetic ◽

Disease Loci ◽

Underlying Pathogenesis

Background: Genome-wide association studies (GWAS) have opened the door to unprecedented large-scale identification of susceptibility loci for human diseases and traits. However, it is still a great challenge to validate these loci and elucidate how these sequence variants give rise to the genetic and phenotypic changes. Because many drug targets are genetic disease genes and the general drug mode of action (MoA, agonist or antagonist) is in line with the consequence of target gene mutations (loss-of-function (LOF) or gain-of-function (GOF)), here we propose a chemical genetic method to address the above issues of GWAS. Objective: This study intends to use chemical genetics information to validate GWAS-derived disease loci and interpret their underlying pathogenesis. Method: We conducted a comprehensive comparative analysis on GWAS data and drug/target information (chemical genetics information). Results: We have identified hundreds of GWAS-derived disease loci which are linked to drug target genes and have matched disease traits and drug indications. It is interesting to note that more than 40% genes have been recognized as disorder factors, indicating the potential power of chemical genetic validation. The pathogenesis of these loci was inferred by corresponding drug MoA. Some inferences were supported by prior experimental observations; some were interpreted in terms of microRNA regulation, codon usage bias, and transcriptional regulation, in particular the transcription factorbinding affinity variation induced by disease-causing mutations. Conclusion: In summary, chemical genetics information is useful to validate GWAS-derived disease loci and to interpret their underlying pathogenesis as well, which has important implications not only in medical genetics but also in methodology evaluation of GWAS.

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On Zebrafish Disease Models and Matters of the Heart

Biomedicines ◽

10.3390/biomedicines7010015 ◽

2019 ◽

Vol 7 (1) ◽

pp. 15 ◽

Cited By ~ 10

Author(s):

Panagiota Giardoglou ◽

Dimitris Beis

Keyword(s):

Heart Development ◽

Large Scale ◽

Complex Disease ◽

Genetic Manipulation ◽

Association Studies ◽

Population Analysis ◽

Genome Wide Association Studies ◽

Cardiovascular Development ◽

Regulatory Pathways ◽

Functional Genomic Analysis

Coronary artery disease (CAD) is the leading form of cardiovascular disease (CVD), which is the primary cause of mortality worldwide. It is a complex disease with genetic and environmental risk factor contributions. Reports in human and mammalian models elucidate age-associated changes in cardiac function. The diverse mechanisms involved in cardiac diseases remain at the center of the research interest to identify novel strategies for prevention and therapy. Zebrafish (Danio rerio) have emerged as a valuable vertebrate model to study cardiovascular development over the last few decades. The facile genetic manipulation via forward and reverse genetic approaches combined with noninvasive, high-resolution imaging and phenotype-based screening has provided new insights to molecular pathways that orchestrate cardiac development. Zebrafish can recapitulate human cardiac pathophysiology due to gene and regulatory pathways conservation, similar heart rate and cardiac morphology and function. Thus, generations of zebrafish models utilize the functional analysis of genes involved in CAD, which are derived from large-scale human population analysis. Here, we highlight recent studies conducted on cardiovascular research focusing on the benefits of the combination of genome-wide association studies (GWAS) with functional genomic analysis in zebrafish. We further summarize the knowledge obtained from zebrafish studies that have demonstrated the architecture of the fundamental mechanisms underlying heart development, homeostasis and regeneration at the cellular and molecular levels.

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A genome-wide association study of serum proteins reveals shared loci with common diseases

10.1101/2021.07.02.450858 ◽

2021 ◽

Author(s):

Alexander Gudjonsson ◽

Valborg Gudmundsdottir ◽

Gisli T Axelsson ◽

Elias F Gudmundsson ◽

Brynjolfur G Jonsson ◽

...

Keyword(s):

Genetic Association ◽

Genetic Variants ◽

Large Scale ◽

Complex Disease ◽

Genome Wide Association Study ◽

Serum Proteins ◽

Association Studies ◽

Loss Of Function ◽

Protein Levels ◽

A Genome

With the growing number of genetic association studies, the genotype-phenotype atlas has become increasingly more complex, yet the functional consequences of most disease associated alleles is not understood. The measurement of protein level variation in solid tissues and biofluids integrated with genetic variants offers a path to deeper functional insights. Here we present a large-scale proteogenomic study in 5,368 individuals, revealing 4,113 independent associations between genetic variants and 2,099 serum proteins, of which 37% are previously unreported. The majority of both cis- and trans-acting genetic signals are unique for a single protein, although our results also highlight numerous highly pleiotropic genetic effects on protein levels and demonstrate that a proteins genetic association profile reflects certain characteristics of the protein, including its location in protein networks, tissue specificity and intolerance to loss of function mutations. Integrating protein measurements with deep phenotyping of the cohort, we observe substantial enrichment of phenotype associations for serum proteins regulated by established GWAS loci, and offer new insights into the interplay between genetics, serum protein levels and complex disease.

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AncestryDNA COVID-19 Host Genetic Study Identifies Three Novel Loci

10.1101/2020.10.06.20205864 ◽

2020 ◽

Cited By ~ 1

Author(s):

Genevieve H.L. Roberts ◽

Danny S. Park ◽

Marie V. Coignet ◽

Shannon R. McCurdy ◽

Spencer C. Knight ◽

...

Keyword(s):

Risk Factors ◽

Genetic Risk ◽

Large Scale ◽

Association Studies ◽

Genetic Risk Factors ◽

Human Infection ◽

Nasopharyngeal Swab ◽

Genome Wide Association Studies ◽

Health Crisis ◽

Host Genetic

AbstractHuman infection with SARS-CoV-2, the causative agent of COVID-19, leads to a remarkably diverse spectrum of outcomes, ranging from asymptomatic to fatal. Recent reports suggest that both clinical and genetic risk factors may contribute to COVID-19 susceptibility and severity. To investigate genetic risk factors, we collected over 500,000 COVID-19 survey responses between April and May 2020 with accompanying genetic data from the AncestryDNA database. We conducted sex-stratified and meta-analyzed genome-wide association studies (GWAS) for COVID-19 susceptibility (positive nasopharyngeal swab test, ncases=2,407) and severity (hospitalization, ncases=250). The severity GWAS replicated associations with severe COVID-19 near ABO and SLC6A20 (P<0.05). Furthermore, we identified three novel loci with P<5×10−8. The strongest association was near IVNS1ABP, a gene involved in influenza virus replication1, and was associated only in males. The other two novel loci harbor genes with established roles in viral replication or immunity: SRRM1 and the immunoglobulin lambda locus. We thus present new evidence that host genetic variation likely contributes to COVID-19 outcomes and demonstrate the value of large-scale, self-reported data as a mechanism to rapidly address a health crisis.

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Identifying nootropic drug targets via large-scale cognitive GWAS and transcriptomics

Neuropsychopharmacology ◽

10.1038/s41386-021-01023-4 ◽

2021 ◽

Author(s):

Max Lam ◽

Chia-Yen Chen ◽

Tian Ge ◽

Yan Xia ◽

David W. Hill ◽

...

Keyword(s):

Drug Targets ◽

Large Scale ◽

Association Studies ◽

Drug Repurposing ◽

Genome Wide Association Studies ◽

Data Set ◽

Nootropic Drug ◽

Gene Sets ◽

Genome Wide ◽

Novel Drug

AbstractBroad-based cognitive deficits are an enduring and disabling symptom for many patients with severe mental illness, and these impairments are inadequately addressed by current medications. While novel drug targets for schizophrenia and depression have emerged from recent large-scale genome-wide association studies (GWAS) of these psychiatric disorders, GWAS of general cognitive ability can suggest potential targets for nootropic drug repurposing. Here, we (1) meta-analyze results from two recent cognitive GWAS to further enhance power for locus discovery; (2) employ several complementary transcriptomic methods to identify genes in these loci that are credibly associated with cognition; and (3) further annotate the resulting genes using multiple chemoinformatic databases to identify “druggable” targets. Using our meta-analytic data set (N = 373,617), we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging our transcriptomic and chemoinformatic databases, we identified 16 putative genes targeted by existing drugs potentially available for cognitive repurposing.

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Expanding the drug discovery space with predicted metabolite–target interactions

Communications Biology ◽

10.1038/s42003-021-01822-x ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Andrea Nuzzo ◽

Somdutta Saha ◽

Ellen Berg ◽

Channa Jayawickreme ◽

Joel Tocker ◽

...

Keyword(s):

Protein Interactions ◽

Drug Targets ◽

Large Scale ◽

Association Studies ◽

Human Microbiome ◽

Human Microbiome Project ◽

Host Protein ◽

Multiple Drug ◽

Genome Wide Association Studies

AbstractMetabolites produced in the human gut are known modulators of host immunity. However, large-scale identification of metabolite–host receptor interactions remains a daunting challenge. Here, we employed computational approaches to identify 983 potential metabolite–target interactions using the Inflammatory Bowel Disease (IBD) cohort dataset of the Human Microbiome Project 2 (HMP2). Using a consensus of multiple machine learning methods, we ranked metabolites based on importance to IBD, followed by virtual ligand-based screening to identify possible human targets and adding evidence from compound assay, differential gene expression, pathway enrichment, and genome-wide association studies. We confirmed known metabolite–target pairs such as nicotinic acid–GPR109a or linoleoyl ethanolamide–GPR119 and inferred interactions of interest including oleanolic acid–GABRG2 and alpha-CEHC–THRB. Eleven metabolites were tested for bioactivity in vitro using human primary cell-types. By expanding the universe of possible microbial metabolite–host protein interactions, we provide multiple drug targets for potential immune-therapies.

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