scholarly journals Genetic Risk for Smoking: Disentangling Interplay Between Genes and Socioeconomic Status

2021 ◽  
Author(s):  
Joëlle A. Pasman ◽  
Perline A. Demange ◽  
Sinan Guloksuz ◽  
A. H. M. Willemsen ◽  
Abdel Abdellaoui ◽  
...  
Keyword(s):  
Mental Health ◽  
Socioeconomic Status ◽  
The Netherlands ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Genetic Liability ◽  
Neighborhood Socioeconomic Status ◽  
Gene Environment ◽  
Genome Wide ◽  
Polygenic Scores

AbstractThis study aims to disentangle the contribution of genetic liability, educational attainment (EA), and their overlap and interaction in lifetime smoking. We conducted genome-wide association studies (GWASs) in UK Biobank (N = 394,718) to (i) capture variants for lifetime smoking, (ii) variants for EA, and (iii) variants that contribute to lifetime smoking independently from EA (‘smoking-without-EA’). Based on the GWASs, three polygenic scores (PGSs) were created for individuals from the Netherlands Twin Register (NTR, N = 17,805) and the Netherlands Mental Health Survey and Incidence Study-2 (NEMESIS-2, N = 3090). We tested gene–environment (G × E) interactions between each PGS, neighborhood socioeconomic status (SES) and EA on lifetime smoking. To assess if the PGS effects were specific to smoking or had broader implications, we repeated the analyses with measures of mental health. After subtracting EA effects from the smoking GWAS, the SNP-based heritability decreased from 9.2 to 7.2%. The genetic correlation between smoking and SES characteristics was reduced, whereas overlap with smoking traits was less affected by subtracting EA. The PGSs for smoking, EA, and smoking-without-EA all predicted smoking. For mental health, only the PGS for EA was a reliable predictor. There were suggestions for G × E for some relationships, but there were no clear patterns per PGS type. This study showed that the genetic architecture of smoking has an EA component in addition to other, possibly more direct components. PGSs based on EA and smoking-without-EA had distinct predictive profiles. This study shows how disentangling different models of genetic liability and interplay can contribute to our understanding of the etiology of smoking.

scholarly journals The Genetic Basis of Hypertriglyceridemia

2021 ◽  
Vol 23 (8) ◽  
Author(s):  
Germán D. Carrasquilla ◽  
Malene Revsbech Christiansen ◽  
Tuomas O. Kilpeläinen
Keyword(s):  
Genetic Basis ◽  
Association Studies ◽  
Cumulative Effect ◽  
Genome Wide Association Studies ◽  
Genetic Loci ◽  
Risk Variants ◽  
Genome Wide ◽  
Severe Hypertriglyceridemia ◽  
Increased Risk ◽  
Polygenic Scores

Abstract Purpose of Review Hypertriglyceridemia is a common dyslipidemia associated with an increased risk of cardiovascular disease and pancreatitis. Severe hypertriglyceridemia may sometimes be a monogenic condition. However, in the vast majority of patients, hypertriglyceridemia is due to the cumulative effect of multiple genetic risk variants along with lifestyle factors, medications, and disease conditions that elevate triglyceride levels. In this review, we will summarize recent progress in the understanding of the genetic basis of hypertriglyceridemia. Recent Findings More than 300 genetic loci have been identified for association with triglyceride levels in large genome-wide association studies. Studies combining the loci into polygenic scores have demonstrated that some hypertriglyceridemia phenotypes previously attributed to monogenic inheritance have a polygenic basis. The new genetic discoveries have opened avenues for the development of more effective triglyceride-lowering treatments and raised interest towards genetic screening and tailored treatments against hypertriglyceridemia. Summary The discovery of multiple genetic loci associated with elevated triglyceride levels has led to improved understanding of the genetic basis of hypertriglyceridemia and opened new translational opportunities.

scholarly journals Phenotypic Annotation: Using Polygenic Scores to Translate Discoveries From Genome-Wide Association Studies From the Top Down

2019 ◽  
Vol 28 (1) ◽  
pp. 82-90 ◽  
Author(s):  
Daniel W. Belsky ◽  
K. Paige Harden
Keyword(s):  
Educational Attainment ◽  
Association Studies ◽  
Science Research ◽  
Well Being ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Research Activities ◽  
Genome Wide ◽  
Polygenic Scores ◽  
Methodological Considerations

Genome-wide association studies (GWASs) have identified specific genetic variants associated with complex human traits and behaviors, such as educational attainment, mental disorders, and personality. However, small effect sizes for individual variants, uncertainty regarding the biological function of discovered genotypes, and potential “outside-the-skin” environmental mechanisms leave a translational gulf between GWAS results and scientific understanding that will improve human health and well-being. We propose a set of social, behavioral, and brain-science research activities that map discovered genotypes to neural, developmental, and social mechanisms and call this research program phenotypic annotation. Phenotypic annotation involves (a) elaborating the nomological network surrounding discovered genotypes, (b) shifting focus from individual genes to whole genomes, and (c) testing how discovered genotypes affect life-span development. Phenotypic-annotation research is already advancing the understanding of GWAS discoveries for educational attainment and schizophrenia. We review examples and discuss methodological considerations for psychologists taking up the phenotypic-annotation approach.

Current Challenges in the Genetics of Psoriatic Arthritis: A Report from the GRAPPA 2009 Annual Meeting

2011 ◽  
Vol 38 (3) ◽  
pp. 564-566 ◽  
Author(s):  
PROTON RAHMAN
Keyword(s):  
Psoriatic Arthritis ◽  
Annual Meeting ◽  
Association Studies ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Novel Genes ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Genome Wide

Psoriasis and psoriatic arthritis (PsA) are heterogeneous diseases. While both have a strong genetic basis, it is strongest for PsA, where fewer investigators are studying its genetics. Over the last year the number of independent genetic loci associated with psoriasis has substantially increased, mostly due to completion of multiple genome-wide association studies (GWAS) in psoriasis. At least 2 GWAS efforts are now under way in PsA to identify novel genes in this disease; a metaanalysis of genome-wide scans and further studies must follow to examine the genetics of disease expression, epistatic interaction, and gene-environment interaction. In the long term, it is anticipated that genome-wide sequencing is likely to generate another wave of novel genes in PsA. At the annual meeting of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) in Stockholm, Sweden, in 2009, members discussed issues and challenges regarding the advancement of the genetics of PsA; results of those discussions are summarized here.

scholarly journals One size does not fit all. Genomics differentiates among binge-eating disorder, bulimia nervosa, and anorexia nervosa

2020 ◽  
Author(s):  
Christopher Hübel ◽  
Mohamed Abdulkadir ◽  
Moritz Herle ◽  
Ruth J.F. Loos ◽  
Gerome Breen ◽  
...  
Keyword(s):  
Eating Disorders ◽  
Anorexia Nervosa ◽  
Eating Disorder ◽  
Bulimia Nervosa ◽  
Binge Eating ◽  
Binge Eating Disorder ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Polygenic Scores

AbstractObjectiveGenome-wide association studies have identified multiple genomic regions associated with anorexia nervosa. Relatively few or no genome-wide studies of other eating disorders, such as bulimia nervosa and binge-eating disorder, have been performed, despite their substantial heritability. Exploratively, we aimed to identify traits that are genetically associated with binge-type eating disorders.MethodWe calculated genome-wide polygenic scores for 269 trait and disease outcomes using PRSice v2.2 and their association with anorexia nervosa, bulimia nervosa, and binge-eating disorder in up to 640 cases and 17,050 controls from the UK Biobank. Significant associations were tested for replication in the Avon Longitudinal Study of Parents and Children (up to 217 cases and 3018 controls).ResultsIndividuals with binge-type eating disorders had higher polygenic scores than controls for other psychiatric disorders, including depression, schizophrenia, and attention deficit hyperactivity disorder, and higher polygenic scores for body mass index.DiscussionOur findings replicate some of the known comorbidities of eating disorders on a genomic level and motivate a deeper investigation of shared and unique genomic factors across the three primary eating disorders.

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 HisCoM-G×E: Hierarchical Structural Component Analysis of Gene-Based Gene–Environment Interactions

2020 ◽  
Vol 21 (18) ◽  
pp. 6724
Author(s):  
Sungkyoung Choi ◽  
Sungyoung Lee ◽  
Iksoo Huh ◽  
Heungsun Hwang ◽  
Taesung Park
Keyword(s):  
Structural Component ◽  
Latent Variable ◽  
Association Studies ◽  
Component Analysis ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Genome Wide ◽  
Missing Heritability Problem

Gene–environment interaction (G×E) studies are one of the most important solutions for understanding the “missing heritability” problem in genome-wide association studies (GWAS). Although many statistical methods have been proposed for detecting and identifying G×E, most employ single nucleotide polymorphism (SNP)-level analysis. In this study, we propose a new statistical method, Hierarchical structural CoMponent analysis of gene-based Gene–Environment interactions (HisCoM-G×E). HisCoM-G×E is based on the hierarchical structural relationship among all SNPs within a gene, and can accommodate all possible SNP-level effects into a single latent variable, by imposing a ridge penalty, and thus more efficiently takes into account the latent interaction term of G×E. The performance of the proposed method was evaluated in simulation studies, and we applied the proposed method to investigate gene–alcohol intake interactions affecting systolic blood pressure (SBP), using samples from the Korea Associated REsource (KARE) consortium data.

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