scholarly journals Genotypes and Phenotypes: A Search for Influential Genes in Diabetic Retinopathy

2020 ◽  
Vol 21 (8) ◽  
pp. 2712
Author(s):  
Andrea P. Cabrera ◽  
Rushi N. Mankad ◽  
Lauren Marek ◽  
Ryan Das ◽  
Sampath Rangasamy ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Complex Traits ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Metabolic Dysfunction ◽  
Duration Of Diabetes ◽  
Gene Environment ◽  
Genome Wide ◽  
Novel Biomarkers

Although gene–environment interactions are known to play an important role in the inheritance of complex traits, it is still unknown how a genotype and the environmental factors result in an observable phenotype. Understanding this complex interaction in the pathogenesis of diabetic retinopathy (DR) remains a big challenge as DR appears to be a disease with heterogenous phenotypes with multifactorial influence. In this review, we examine the natural history and risk factors related to DR, emphasizing distinct clinical phenotypes and their natural course in retinopathy. Although there is strong evidence that duration of diabetes and metabolic factors play a key role in the pathogenesis of DR, accumulating new clinical studies reveal that this disease can develop independently of duration of diabetes and metabolic dysfunction. More recently, studies have emphasized the role of genetic factors in DR. However, linkage analyses, candidate gene studies, and genome-wide association studies (GWAS) have not produced any statistically significant results. Our recently initiated genomics study, the Diabetic Retinopathy Genomics (DRGen) Study, aims to examine the contribution of rare and common variants in the development DR, and how they can contribute to clinical phenotype, rate of progression, and response to available therapies. Our preliminary findings reveal a novel set of genetic variants associated with proangiogenic and inflammatory pathways that may contribute to DR pathogenesis. Further investigation of these variants is necessary and may lead to development of novel biomarkers and new therapeutic targets in DR.

scholarly journals Geographic Confounding in Genome-Wide Association Studies

2021 ◽  
Author(s):  
Abdel Abdellaoui ◽  
Karin Verweij ◽  
Michel G Nivard
Keyword(s):  
Educational Attainment ◽  
Social Stratification ◽  
Complex Traits ◽  
Association Studies ◽  
Genetic Correlations ◽  
Geographic Region ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Gene Environment ◽  
Genome Wide

Abstract Gene-environment correlations can bias associations between genetic variants and complex traits in genome-wide association studies (GWASs). Here, we control for geographic sources of gene-environment correlation in GWASs on 56 complex traits (N = 69,772–271,457). Controlling for geographic region significantly decreases heritability signals for SES-related traits, most strongly for educational attainment and income, indicating that socio-economic differences between regions induce gene-environment correlations that become part of the polygenic signal. For most other complex traits investigated, genetic correlations with educational attainment and income are significantly reduced, most significantly for traits related to BMI, sedentary behavior, and substance use. Controlling for current address has greater impact on the polygenic signal than birth place, suggesting both active and passive sources of gene-environment correlations. Our results show that societal sources of social stratification that extend beyond families introduce regional-level gene-environment correlations that affect GWAS results.

scholarly journals Geographic Confounding in Genome-Wide Association Studies

2021 ◽  
Author(s):  
Abdel Abdellaoui ◽  
Karin J.H. Verweij ◽  
Michel G. Nivard
Keyword(s):  
Educational Attainment ◽  
Social Stratification ◽  
Complex Traits ◽  
Association Studies ◽  
Genetic Correlations ◽  
Geographic Region ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Gene Environment ◽  
Genome Wide

Gene-environment correlations can bias associations between genetic variants and complex traits in genome-wide association studies (GWASs). Here, we control for geographic sources of gene-environment correlation in GWASs on 56 complex traits (N=69,772-271,457). Controlling for geographic region significantly decreases heritability signals for SES-related traits, most strongly for educational attainment and income, indicating that socio-economic differences between regions induce gene-environment correlations that become part of the polygenic signal. For most other complex traits investigated, genetic correlations with educational attainment and income are significantly reduced, most significantly for traits related to BMI, sedentary behavior, and substance use. Controlling for current address has greater impact on the polygenic signal than birth place, suggesting both active and passive sources of gene-environment correlations. Our results show that societal sources of social stratification that extend beyond families introduce regional-level gene-environment correlations that affect GWAS results.

scholarly journals The role of gene expression on human sexual dimorphism: too early to call

2020 ◽  
Author(s):  
Eleonora Porcu ◽  
Annique Claringbould ◽  
Kaido Lepik ◽  
Tom G. Richardson ◽  
Federico A. Santoni ◽  
...  
Keyword(s):  
Sexual Dimorphism ◽  
Complex Traits ◽  
Causal Effect ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
A Genome ◽  
Specific Trait ◽  
Trait Associations

AbstractThe genetic underpinning of sexual dimorphism is very poorly understood. The prevalence of many diseases differs between men and women, which could be in part caused by sex-specific genetic effects. Nevertheless, only a few published genome-wide association studies (GWAS) were performed separately in each sex. The reported enrichment of expression quantitative trait loci (eQTLs) among GWAS–associated SNPs suggests a potential role of sex-specific eQTLs in the sex-specific genetic mechanism underlying complex traits.To explore this scenario, we performed a genome-wide analysis of sex-specific whole blood RNA-seq eQTLs from 3,447 individuals. Among 9 million SNP-gene pairs showing sex-combined associations, we found 18 genes with significant sex-specific cis-eQTLs (FDR 5%). Our phenome-wide association study of the 18 top sex-specific eQTLs on >700 traits unraveled that these eQTLs do not systematically translate into detectable sex-specific trait-associations. Power analyses using real eQTL- and causal effect sizes showed that millions of samples would be necessary to observe sex-specific trait associations that are fully driven by sex-specific cis-eQTLs. Compensatory effects may further hamper their detection. In line with this observation, we confirmed that the sex-specific trait-associations detected so far are not driven by sex-specific cis-eQTLs.

scholarly journals Do Genomic Factors Play a Role in Diabetic Retinopathy?

2020 ◽  
Vol 9 (1) ◽  
pp. 216 ◽  
Author(s):  
Andrea P. Cabrera ◽  
Finny Monickaraj ◽  
Sampathkumar Rangasamy ◽  
Sam Hobbs ◽  
Paul McGuire ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Proliferative Diabetic Retinopathy ◽  
Clinical Evidence ◽  
Association Studies ◽  
Lipid Level ◽  
Genome Wide Association Studies ◽  
Long Duration ◽  
Genome Wide ◽  
Novel Biomarkers ◽  
Candidate Gene Studies

Although there is strong clinical evidence that the control of blood glucose, blood pressure, and lipid level can prevent and slow down the progression of diabetic retinopathy (DR) as shown by landmark clinical trials, it has been shown that these factors only account for 10% of the risk for developing this disease. This suggests that other factors, such as genetics, may play a role in the development and progression of DR. Clinical evidence shows that some diabetics, despite the long duration of their diabetes (25 years or more) do not show any sign of DR or show minimal non-proliferative diabetic retinopathy (NPDR). Similarly, not all diabetics develop proliferative diabetic retinopathy (PDR). So far, linkage analysis, candidate gene studies, and genome-wide association studies (GWAS) have not produced any statistically significant results. We recently initiated a genomics study, the Diabetic Retinopathy Genetics (DRGen) Study, to examine the contribution of rare and common variants in the development of different phenotypes of DR, as well as their responsiveness to anti-VEGF treatment in diabetic macular edema (DME). Our preliminary findings reveal a novel set of genetic variants involved in the angiogenesis and inflammatory pathways that contribute to DR progression or protection. Further investigation of variants can help to develop novel biomarkers and lead to new therapeutic targets in DR.

scholarly journals Integrating gene expression with summary association statistics to identify susceptibility genes for 30 complex traits

2016 ◽  
Author(s):  
Nicholas Mancuso ◽  
Huwenbo Shi ◽  
Pagé Goddard ◽  
Gleb Kichaev ◽  
Alexander Gusev ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Correlation ◽  
Complex Traits ◽  
Association Studies ◽  
Susceptibility Genes ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Causal Variants

AbstractAlthough genome-wide association studies (GWASs) have identified thousands of risk loci for many complex traits and diseases, the causal variants and genes at these loci remain largely unknown. We leverage recently introduced methods to integrate gene expression measurements from 45 expression panels with summary GWAS data to perform 30 transcriptome-wide association studies (TWASs). We identify 1,196 susceptibility genes whose expression is associated with these traits; of these, 168 reside more than 0.5Mb away from any previously reported GWAS significant variant, thus providing new risk loci. Second, we find 43 pairs of traits with significant genetic correlation at the level of predicted expression; of these, 8 are not found through genetic correlation at the SNP level. Third, we use bi-directional regression to find evidence for BMI causally influencing triglyceride levels, and triglyceride levels causally influencing LDL. Taken together, our results provide insights into the role of expression to susceptibility of complex traits and diseases.

scholarly journals The PAGE Study: How Genetic Diversity Improves Our Understanding of the Architecture of Complex Traits

2017 ◽  
Author(s):  
Genevieve L Wojcik ◽  
Mariaelisa Graff ◽  
Katherine K Nishimura ◽  
Ran Tao ◽  
Jeffrey Haessler ◽  
...  
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Genome Wide ◽  
Size Heterogeneity ◽  
Gwas Catalog ◽  
Health Applications

Summary/AbstractGenome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development, and clinical guidelines. However, the dominance of European-ancestry populations in GWAS creates a biased view of the role of human variation in disease, and hinders the equitable translation of genetic associations into clinical and public health applications. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioral phenotypes in 49,839 non-European individuals. Using strategies designed for analysis of multi-ethnic and admixed populations, we confirm 574 GWAS catalog variants across these traits, and find 38 secondary signals in known loci and 27 novel loci. Our data shows strong evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts, and insights into clinical implications. We strongly advocate for continued, large genome-wide efforts in diverse populations to reduce health disparities.

Genetics of PTSD

2016 ◽  
Author(s):  
Jennifer A. Sumner ◽  
Angela C. Bustamante ◽  
Karestan C. Koenen ◽  
Monica Uddin
Keyword(s):  
Association Studies ◽  
Molecular Genetic ◽  
Critical Role ◽  
Trauma Exposure ◽  
Genome Wide Association Studies ◽  
Risk And Resilience ◽  
New Developments ◽  
Gene Environment ◽  
Genome Wide

Trauma exposure and PTSD are heritable. However, the mechanisms of risk and resilience following trauma exposure are not yet well understood, suggesting that investigations into the genetic architecture of PTSD have much to contribute. This chapter reviews the rapidly growing literature on molecular genetic risk factors for PTSD, including findings from candidate gene and genome-wide association studies. Given the critical role of trauma exposure in the onset of PTSD, it also discusses gene-environment interplay, and highlights some recent findings from epigenetic studies. The chapter concludes by summarizing considerations for the field as it continues to move forward, and discusses exciting new developments in the search for genetic markers for PTSD.

Genome-Wide Association Studies Identify Two Novel Loci Conferring Susceptibility to Diabetic Retinopathy in Japanese Patients with Type 2 Diabetes

2021 ◽  
Author(s):  
Minako Imamura ◽  
Atsushi Takahashi ◽  
Masatoshi Matsunami ◽  
Momoko Horikoshi ◽  
Minoru Iwata ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Diabetic Retinopathy ◽  
Association Studies ◽  
Meta Analysis ◽  
Japanese Patients ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Stage 1

Abstract Several reports have suggested that genetic susceptibility contributes to the development and progression of diabetic retinopathy. We aimed to identify genetic loci that confer susceptibility to diabetic retinopathy in Japanese patients with type 2 diabetes. We analysed 5 790 508 single nucleotide polymorphisms (SNPs) in 8880 Japanese patients with type 2 diabetes, 4839 retinopathy cases and 4041 controls, as well as 2217 independent Japanese patients with type 2 diabetes, 693 retinopathy cases, and 1524 controls. The results of these two genome-wide association studies (GWAS) were combined with an inverse variance meta-analysis (Stage-1), followed by de novo genotyping for the candidate SNP loci (p < 1.0 × 10−4) in an independent case–control study (Stage-2, 2260 cases and 723 controls). After combining the association data (Stage-1 and -2) using meta-analysis, the associations of two loci reached a genome-wide significance level: rs12630354 near STT3B on chromosome 3, p = 1.62 × 10−9, odds ratio (OR) = 1.17, 95% confidence interval (CI) 1.11–1.23, and rs140508424 within PALM2 on chromosome 9, p = 4.19 × 10−8, OR = 1.61, 95% CI 1.36–1.91. However, the association of these two loci were not replicated in Korean, European, or African American populations. Gene-based analysis using Stage-1 GWAS data identified a gene-level association of EHD3 with susceptibility to diabetic retinopathy (p = 2.17 × 10−6). In conclusion, we identified two novel SNP loci, STT3B and PALM2, and a novel gene, EHD3, that confers susceptibility to diabetic retinopathy; however, further replication studies are required to validate these associations.

scholarly journals Family-based gene-environment interaction using sequence kernel association test (FGE-SKAT) for complex quantitative traits

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chao-Yu Guo ◽  
Reng-Hong Wang ◽  
Hsin-Chou Yang
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Association Test ◽  
Whole Genome Sequence ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Sequence Kernel Association Test ◽  
Gene Environment ◽  
Family Based

AbstractAfter the genome-wide association studies (GWAS) era, whole-genome sequencing is highly engaged in identifying the association of complex traits with rare variations. A score-based variance-component test has been proposed to identify common and rare genetic variants associated with complex traits while quickly adjusting for covariates. Such kernel score statistic allows for familial dependencies and adjusts for random confounding effects. However, the etiology of complex traits may involve the effects of genetic and environmental factors and the complex interactions between genes and the environment. Therefore, in this research, a novel method is proposed to detect gene and gene-environment interactions in a complex family-based association study with various correlated structures. We also developed an R function for the Fast Gene-Environment Sequence Kernel Association Test (FGE-SKAT), which is freely available as supplementary material for easy GWAS implementation to unveil such family-based joint effects. Simulation studies confirmed the validity of the new strategy and the superior statistical power. The FGE-SKAT was applied to the whole genome sequence data provided by Genetic Analysis Workshop 18 (GAW18) and discovered concordant and discordant regions compared to the methods without considering gene by environment interactions.

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