Genetics

Small Magnitude ◽

Gene Environment ◽

Genetic Overlap

An association between type 2 diabetes mellitus (T2DM) and depression has been reported in epidemiological studies. Finding a genetic overlap between T2DM and depression will provide evidence to support a common biological pathway to both disorders. Genetic correlations observed from twin studies indicate that a small magnitude of the variance in liability can be attributed to genetic factors. However, no genetic overlap has been observed between T2DM and depression in genome-wide association studies using both the polygenic score and the linkage disequilibrium score regression approaches. Clarifying the shared heritability between these two complex traits is an important next step towards better therapy and treatment. Another area that needs to be explored is gene–environment interaction, since genotypes can affect an individual’s responses to the environment and environment can differentially affect genotypes expression.

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

Scientific Reports ◽

10.1038/s41598-021-86871-2 ◽

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 ◽

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.

Geographic Confounding in Genome-Wide Association Studies

10.21203/rs.3.rs-362358/v1 ◽

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 ◽

Gene Environment ◽

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.

Geographic Confounding in Genome-Wide Association Studies

10.1101/2021.03.18.435971 ◽

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 ◽

Gene Environment ◽

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.

Investigation of gene–environment interactions in relation to tic severity

Journal of Neural Transmission ◽

10.1007/s00702-021-02396-y ◽

2021 ◽

Author(s):

Mohamed Abdulkadir ◽

Dongmei Yu ◽

Lisa Osiecki ◽

Robert A. King ◽

Thomas V. Fernandez ◽

...

Keyword(s):

Tourette Syndrome ◽

Association Studies ◽

Autism Spectrum ◽

Environment Interaction ◽

Nucleotide Polymorphisms ◽

Linear Regression Models ◽

Compulsive Disorder ◽

Gene Environment ◽

Tic Severity

AbstractTourette syndrome (TS) is a neuropsychiatric disorder with involvement of genetic and environmental factors. We investigated genetic loci previously implicated in Tourette syndrome and associated disorders in interaction with pre- and perinatal adversity in relation to tic severity using a case-only (N = 518) design. We assessed 98 single-nucleotide polymorphisms (SNPs) selected from (I) top SNPs from genome-wide association studies (GWASs) of TS; (II) top SNPs from GWASs of obsessive–compulsive disorder (OCD), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD); (III) SNPs previously implicated in candidate-gene studies of TS; (IV) SNPs previously implicated in OCD or ASD; and (V) tagging SNPs in neurotransmitter-related candidate genes. Linear regression models were used to examine the main effects of the SNPs on tic severity, and the interaction effect of these SNPs with a cumulative pre- and perinatal adversity score. Replication was sought for SNPs that met the threshold of significance (after correcting for multiple testing) in a replication sample (N = 678). One SNP (rs7123010), previously implicated in a TS meta-analysis, was significantly related to higher tic severity. We found a gene–environment interaction for rs6539267, another top TS GWAS SNP. These findings were not independently replicated. Our study highlights the future potential of TS GWAS top hits in gene–environment studies.

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

The Journal of Rheumatology ◽

10.3899/jrheum.101123 ◽

2011 ◽

Vol 38 (3) ◽

pp. 564-566 ◽

Cited By ~ 2

Author(s):

PROTON RAHMAN

Keyword(s):

Psoriatic Arthritis ◽

Annual Meeting ◽

Association Studies ◽

Environment Interaction ◽

Novel Genes ◽

Gene Environment Interaction ◽

Gene Environment ◽

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.

Sociology, Genetics, and the Coming of Age of Sociogenomics

Annual Review of Sociology ◽

10.1146/annurev-soc-121919-054756 ◽

2020 ◽

Vol 46 (1) ◽

pp. 553-581 ◽

Cited By ~ 1

Author(s):

Melinda C. Mills ◽

Felix C. Tropf

Keyword(s):

Coming Of Age ◽

Association Studies ◽

Molecular Genetic ◽

Genetic Correlations ◽

Genetic Research ◽

Well Being ◽

Environment Interaction ◽

Molecular Genetic Data ◽

Polygenic Scores

Recent years have seen the birth of sociogenomics via the infusion of molecular genetic data. We chronicle the history of genetics, focusing particularly on post-2005 genome-wide association studies, the post-2015 big data era, and the emergence of polygenic scores. We argue that understanding polygenic scores, including their genetic correlations with each other, causation, and underlying biological architecture, is vital. We show how genetics can be introduced to understand a myriad of topics such as fertility, educational attainment, intergenerational social mobility, well-being, addiction, risky behavior, and longevity. Although models of gene-environment interaction and correlation mirror agency and structure models in sociology, genetics is yet to be fully discovered by this discipline. We conclude with a critical reflection on the lack of diversity, nonrepresentative samples, precision policy applications, ethics, and genetic determinism. We argue that sociogenomics can speak to long-standing sociological questions and that sociologists can offer innovative theoretical, measurement, and methodological innovations to genetic research.

Comparison of methods for estimating genetic correlation between complex traits using GWAS summary statistics

10.1101/2020.10.12.336867 ◽

2020 ◽

Cited By ~ 1

Author(s):

Yiliang Zhang ◽

Youshu Cheng ◽

Wei Jiang ◽

Yixuan Ye ◽

Qiongshi Lu ◽

...

Keyword(s):

Genetic Correlation ◽

Complex Traits ◽

Association Studies ◽

Genetic Correlations ◽

Real Data ◽

Estimation Methods ◽

Easy Access ◽

Summary Statistics ◽

Correlation Estimation

AbstractGenetic correlation is the correlation of additive genetic effects on two phenotypes. It is an informative metric to quantify the overall genetic similarity between complex traits, which provides insights into their polygenic genetic architecture. Several methods have been proposed to estimate genetic correlations based on data collected from genome-wide association studies (GWAS). Due to the easy access of GWAS summary statistics and computational efficiency, methods only requiring GWAS summary statistics as input have become more popular than methods utilizing individual-level genotype data. Here, we present a benchmark study for different summary-statistics-based genetic correlation estimation methods through simulation and real data applications. We focus on two major technical challenges in estimating genetic correlation: marker dependency caused by linkage disequilibrium (LD) and sample overlap between different studies. To assess the performance of different methods in the presence of these two challenges, we first conducted comprehensive simulations with diverse LD patterns and sample overlaps. Then we applied these methods to real GWAS summary statistics for a wide spectrum of complex traits. Based on these experiments, we conclude that methods relying on accurate LD estimation are less robust in real data applications compared to other methods due to the imprecision of LD obtained from reference panels. Our findings offer a guidance on how to appropriately choose the method for genetic correlation estimation in post-GWAS analysis in interpretation.

Joint Genome-Wide Association Study Identifies Twenty-One Novel Loci for Age at Menarche and Highlights Its Causal Association with Other Complex Diseases

10.21203/rs.3.rs-955340/v1 ◽

2021 ◽

Author(s):

Gui-Juan Feng ◽

Qian Xu ◽

Jing-Jing Ni ◽

Shan-Shan Yang ◽

Bai-Xue Han ◽

...

Keyword(s):

Association Study ◽

Complex Traits ◽

Causal Effect ◽

Association Studies ◽

Genetic Correlations ◽

Genome Wide Association ◽

Age At Menarche ◽

Luteinizing Hormone Level ◽

Abstract Age at menarche (AAM) is a sign of puberty of females. It is a heritable trait associated with various adult diseases. However, the genetic mechanism that determines AAM and links it to disease risk is poorly understood. Aiming to uncover the genetic basis for AAM, we conducted a joint association study in up to 438,089 participants from 3 genome-wide association studies of European and East Asian ancestries. Twenty-one novel genomic loci were identified at the genome-wide significance level. Besides, we observed significant genetic correlations between AAM and 67 complex traits, and the highest genetic correlation was observed between AAM and body mass index (rg=-0.19, P=6.11×10−31). Latent causal variable analyses demonstrate that there is a genetically causal effect of AAM on high blood pressure (GCP=0.47, P=0.02), forced vital capacity (GCP=0.63, P=0.02), age at first live birth (GCP=0.51, P=0.03), impedance of right arm (GCP=0.41, P<1×10-7) and right leg fat percentage (GCP=-0.10, P=0.02), etc. Enrichment analysis identified 5 enriched tissues and 51 enriched gene sets. Four of the five enriched tissues were related to the nervous system, including the hypothalamus middle, hypothalamo hypophyseal system, neurosecretory systems and hypothalamus. The fifth tissue was the retina in the sensory organ. The most significant gene set was the ‘decreased circulating luteinizing hormone level’ (P=2.45×10-6). Our findings may provide useful insights that elucidate the mechanisms determining AAM and the genetic interplay between AAM and some traits of women.

Local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits

10.1101/2020.05.08.084475 ◽

2020 ◽

Cited By ~ 5

Author(s):

Yiliang Zhang ◽

Qiongshi Lu ◽

Yixuan Ye ◽

Kunling Huang ◽

Wei Liu ◽

...

Keyword(s):

Correlation Analysis ◽

Genetic Correlation ◽

Complex Traits ◽

Association Studies ◽

Genetic Correlations ◽

Autism Spectrum ◽

Accurate Estimation ◽

Unified Framework ◽

Genomic Regions

AbstractLocal genetic correlation quantifies the genetic similarity of complex traits in specific genomic regions, which could shed unique light on etiologic sharing and provide additional mechanistic insights into the genetic basis of complex traits compared to global genetic correlation. However, accurate estimation of local genetic correlation remains challenging, in part due to extensive linkage disequilibrium in local genomic regions and pervasive sample overlap across studies. We introduce SUPERGNOVA, a unified framework to estimate both global and local genetic correlations using summary statistics from genome-wide association studies. Through extensive simulations and analyses of 30 complex traits, we demonstrate that SUPERGNOVA substantially outperforms existing methods and identifies 150 trait pairs with significant local genetic correlations. In particular, we show that the positive, consistently-identified, yet paradoxical genetic correlation between autism spectrum disorder and cognitive performance could be explained by two etiologically-distinct genetic signatures with bidirectional local genetic correlations. We believe that statistically-rigorous local genetic correlation analysis could accelerate progress in complex trait genetics research.

Genetics of generalized anxiety disorder and related traits

Dialogues in Clinical Neuroscience ◽

10.31887/dcns.2017.19.2/kdomschke ◽

2017 ◽

Vol 19 (2) ◽

pp. 159-168 ◽

Cited By ~ 13

Keyword(s):

Anxiety Disorder ◽

Generalized Anxiety Disorder ◽

Reuptake Inhibitor ◽

Stressful Life Events ◽

Association Studies ◽

Twin Studies ◽

Generalized Anxiety ◽

Environment Interaction ◽

Clinical Genetic

This review serves as a systematic guide to the genetics of generalized anxiety disorder (GAD) and further focuses on anxiety-relevant endophenotypes, such as pathological worry fear of uncertainty, and neuroticism. We inspect clinical genetic evidence for the familialityl heritability of GAD and cross-disorder phenotypes based on family and twin studies. Recent advances of linkage studies, genome-wide association studies, and candidate gene studies (eg, 5-HTT, 5-HT1A, MAOA, BDNF) are outlined. Functional and structural neuroimaging and neurophysiological readouts relating to peripheral stress markers and psychophysiology are further integrated, building a multilevel disease framework. We explore etiologic factors in gene-environment interaction approaches investigating childhood trauma, environmental adversity, and stressful life events in relation to selected candidate genes (5-HTT, NPSR1, COMT, MAOA, CRHR1, RGS2), Additionally, the pharmacogenetics of selective serotonin reuptake inhibitor/serotonin-norepinephrine reuptake inhibitor treatment are summarized (5-HTT, 5-HT2A, COMT, CRHR1). Finally, GAD and trait anxiety research challenges and perspectives in the field of genetics, including epigenetics, are discussed.