Genetic risk for neuroticism predicts emotional health depending on childhood adversity

AbstractBackgroundExisting evidence for gene × environment interaction (G × E) in neuroticism largely relies on candidate gene studies, although neuroticism is highly polygenic. This study aimed to investigate the long-term associations between polygenic risk scores for neuroticism (PRSN), objective childhood adversity and their interplay on emotional health aspects such as neuroticism itself, depressive symptoms, anxiety symptoms, loneliness and life satisfaction.MethodsThe sample consisted of reared-apart (TRA) and reared-together (TRT) middle- and old age twins (N= 699; median age at separation = 2). PRSNwere created under ninepvalue cut-off thresholds (pT-s) and thepTwith the highest degree of neuroticism variance explained was chosen for subsequent analyses. Linear regressions were used to assess the associations between PRSN, childhood adversity (being reared apart) and emotional health. G × E was further investigated using a discordant twin design.ResultsPRSNexplained up to 1.7% (pT< 0.01) of phenotypic neuroticism in the total sample. Analyses across two separation groups revealed substantial heterogeneity in the variance explained by PRSN; 4.3% was explained in TRT, but almost no effect was observed in TRA. Similarly, PRSNexplained 4% and 1.7% of the variance in depressive symptoms and loneliness, respectively, only in TRT. A significant G × E interaction was identified for depressive symptoms.ConclusionsBy taking advantage of a unique sample of adopted twins, we demonstrated the presence of G × E in neuroticism and emotional health using PRSNand childhood adversity. Our results may indicate that genome-wide association studies are detecting genetic main effects associated with neuroticism, but not those susceptible to early environmental influences.

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Genome-wide by environment interaction studies (GWEIS) of depressive symptoms and psychosocial stress in UK Biobank and Generation Scotland

10.1101/479691 ◽

2018 ◽

Cited By ~ 1

Author(s):

Aleix Arnau-Soler ◽

Erin Macdonald-Dunlop ◽

Mark J. Adams ◽

Toni-Kim Clarke ◽

Donald J. MacIntyre ◽

...

Keyword(s):

Depressive Symptoms ◽

Association Studies ◽

Joint Effect ◽

Risk Scores ◽

Environment Interaction ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Interaction Studies ◽

Genome Wide ◽

Generation Scotland

ABSTRACTStress is associated with poorer physical and mental health. To improve our understanding of this link, we performed genome-wide association studies (GWAS) of depressive symptoms and genome-wide by environment interaction studies (GWEIS) of depressive symptoms and stressful life events (SLE) in two UK population cohorts (Generation Scotland and UK Biobank). No SNP was individually significant in either GWAS, but gene-based tests identified six genes associated with depressive symptoms in UK Biobank (DCC, ACSS3, DRD2, STAG1, FOXP2 and KYNU; p < 2.77×10-6). Two SNPs with genome-wide significant GxE effects were identified by GWEIS in Generation Scotland: rs12789145 (53kb downstream PIWIL4; p = 4.95×10-9; total SLE) and rs17070072 (intronic to ZCCHC2; p = 1.46×10-8; dependent SLE). A third locus upstream CYLC2 (rs12000047 and rs12005200, p < 2.00×10-8; dependent SLE) when the joint effect of the SNP main and GxE effects was considered. GWEIS gene-based tests identified: MTNR1B with GxE effect with dependent SLE in Generation Scotland; and PHF2 with the joint effect in UK Biobank (p < 2.77×10-6). Polygenic risk scores (PRS) analyses incorporating GxE effects improved the prediction of depressive symptom scores, when using weights derived from either the UK Biobank GWAS of depressive symptoms (p = 0.01) or the PGC GWAS of major depressive disorder (p = 5.91×10-3). Using an independent sample, PRS derived using GWEIS GxE effects provided evidence of shared aetiologies between depressive symptoms and schizotypal personality, heart disease and COPD. Further such studies are required and may result in improved treatments for depression and other stress-related conditions.

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Association between polygenic risk score of Alzheimer’s disease and plasma phosphorylated tau in individuals from the Alzheimer’s Disease Neuroimaging Initiative

Alzheimer s Research & Therapy ◽

10.1186/s13195-020-00754-8 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Anna Zettergren ◽

◽

Jodie Lord ◽

Nicholas J. Ashton ◽

Andrea L. Benedet ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Association Studies ◽

Amyloid Β ◽

Total Sample ◽

Risk Scores ◽

Polygenic Risk Score ◽

Genome Wide Association Studies ◽

Tau Pathology ◽

Polygenic Risk

Abstract Background Recent studies suggest that plasma phosphorylated tau181 (p-tau181) is a highly specific biomarker for Alzheimer’s disease (AD)-related tau pathology. It has great potential for the diagnostic and prognostic evaluation of AD, since it identifies AD with the same accuracy as tau PET and CSF p-tau181 and predicts the development of AD dementia in cognitively unimpaired (CU) individuals and in those with mild cognitive impairment (MCI). Plasma p-tau181 may also be used as a biomarker in studies exploring disease pathogenesis, such as genetic or environmental risk factors for AD-type tau pathology. The aim of the present study was to investigate the relation between polygenic risk scores (PRSs) for AD and plasma p-tau181. Methods Data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) was used to examine the relation between AD PRSs, constructed based on findings in recent genome-wide association studies, and plasma p-tau181, using linear regression models. Analyses were performed in the total sample (n = 818), after stratification on diagnostic status (CU (n = 236), MCI (n = 434), AD dementia (n = 148)), and after stratification on Aβ pathology status (Aβ positives (n = 322), Aβ negatives (n = 409)). Results Associations between plasma p-tau181 and APOE PRSs (p = 3e−18–7e−15) and non-APOE PRSs (p = 3e−4–0.03) were seen in the total sample. The APOE PRSs were associated with plasma p-tau181 in all diagnostic groups (CU, MCI, and AD dementia), while the non-APOE PRSs were associated only in the MCI group. The APOE PRSs showed similar results in amyloid-β (Aβ)-positive and negative individuals (p = 5e−5–1e−3), while the non-APOE PRSs were associated with plasma p-tau181 in Aβ positives only (p = 0.02). Conclusions Polygenic risk for AD including APOE was found to associate with plasma p-tau181 independent of diagnostic and Aβ pathology status, while polygenic risk for AD beyond APOE was associated with plasma p-tau181 only in MCI and Aβ-positive individuals. These results extend the knowledge about the relation between genetic risk for AD and p-tau181, and further support the usefulness of plasma p-tau181 as a biomarker of AD.

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Genetic Risk Factors of Depression

Depression ◽

10.1093/med/9780190929565.003.0003 ◽

2019 ◽

pp. 33-50

Author(s):

Thorhildur Halldorsdottir ◽

Hildur Ýr Hilmarsdottir

Keyword(s):

Genetic Risk ◽

Familial Aggregation ◽

Association Studies ◽

Twin Studies ◽

Risk Scores ◽

Environment Interaction ◽

Genome Wide Association Studies ◽

Gene By Environment Interaction ◽

Genome Wide ◽

Candidate Gene Studies

Research on the genetic underpinnings of depression has rapidly advanced in the past decade. This field of research provides a promising avenue toward improving the diagnosis of, prevention of, and treatment for this devastating disorder. The goal of this chapter is to review the main genetic and gene-by-environment interaction findings on depression. We first describe family and twin studies used to empirically study the familial aggregation of depression. Second, we provide a review of the genome-wide association studies (GWAS) published to date. Building on GWAS findings, we will discuss the use of polygenic risk scores in predicting depression. We also review the most robust candidate gene studies and gene-by-environment interaction studies. Finally, we discuss the clinical implications of the findings and promising strategies for making further progress within this field.

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Genetic architecture of schizophrenia: a review of major advancements

Psychological Medicine ◽

10.1017/s0033291720005334 ◽

2021 ◽

pp. 1-10

Author(s):

Sophie E. Legge ◽

Marcos L. Santoro ◽

Sathish Periyasamy ◽

Adeniran Okewole ◽

Arsalan Arsalan ◽

...

Keyword(s):

Genetic Architecture ◽

Association Studies ◽

Copy Number Variants ◽

Risk Scores ◽

Genome Wide Association Studies ◽

Loss Of Function ◽

Genetic Loci ◽

Significant Enrichment ◽

High Heritability ◽

Coding Variants

Abstract Schizophrenia is a severe psychiatric disorder with high heritability. Consortia efforts and technological advancements have led to a substantial increase in knowledge of the genetic architecture of schizophrenia over the past decade. In this article, we provide an overview of the current understanding of the genetics of schizophrenia, outline remaining challenges, and summarise future directions of research. World-wide collaborations have resulted in genome-wide association studies (GWAS) in over 56 000 schizophrenia cases and 78 000 controls, which identified 176 distinct genetic loci. The latest GWAS from the Psychiatric Genetics Consortium, available as a pre-print, indicates that 270 distinct common genetic loci have now been associated with schizophrenia. Polygenic risk scores can currently explain around 7.7% of the variance in schizophrenia case-control status. Rare variant studies have implicated eight rare copy-number variants, and an increased burden of loss-of-function variants in SETD1A, as increasing the risk of schizophrenia. The latest exome sequencing study, available as a pre-print, implicates a burden of rare coding variants in a further nine genes. Gene-set analyses have demonstrated significant enrichment of both common and rare genetic variants associated with schizophrenia in synaptic pathways. To address current challenges, future genetic studies of schizophrenia need increased sample sizes from more diverse populations. Continued expansion of international collaboration will likely identify new genetic regions, improve fine-mapping to identify causal variants, and increase our understanding of the biology and mechanisms of schizophrenia.

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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 ◽

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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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 ◽

Genome Wide Association Studies ◽

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.

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Polygenic Risk Score of Longevity Predicts Longer Survival Across an Age Continuum

The Journals of Gerontology Series A ◽

10.1093/gerona/glaa289 ◽

2020 ◽

Author(s):

Niccolo’ Tesi ◽

Sven J van der Lee ◽

Marc Hulsman ◽

Iris E Jansen ◽

Najada Stringa ◽

...

Keyword(s):

Older Adults ◽

Cellular Response ◽

Molecular Mechanisms ◽

Association Studies ◽

Risk Scores ◽

Human Longevity ◽

Polygenic Risk Score ◽

Genome Wide Association Studies ◽

Polygenic Risk ◽

Age Related

Abstract Studying the genome of centenarians may give insights into the molecular mechanisms underlying extreme human longevity and the escape of age-related diseases. Here, we set out to construct polygenic risk scores (PRSs) for longevity and to investigate the functions of longevity-associated variants. Using a cohort of centenarians with maintained cognitive health (N = 343), a population-matched cohort of older adults from 5 cohorts (N = 2905), and summary statistics data from genome-wide association studies on parental longevity, we constructed a PRS including 330 variants that significantly discriminated between centenarians and older adults. This PRS was also associated with longer survival in an independent sample of younger individuals (p = .02), leading up to a 4-year difference in survival based on common genetic factors only. We show that this PRS was, in part, able to compensate for the deleterious effect of the APOE-ε4 allele. Using an integrative framework, we annotated the 330 variants included in this PRS by the genes they associate with. We find that they are enriched with genes associated with cellular differentiation, developmental processes, and cellular response to stress. Together, our results indicate that an extended human life span is, in part, the result of a constellation of variants each exerting small advantageous effects on aging-related biological mechanisms that maintain overall health and decrease the risk of age-related diseases.

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Challenges of Adjusting Single-Nucleotide Polymorphism Effect Sizes for Linkage Disequilibrium

Human Heredity ◽

10.1159/000513303 ◽

2021 ◽

pp. 1-11

Author(s):

Valentina Escott-Price ◽

Karl Michael Schmidt

Keyword(s):

Linkage Disequilibrium ◽

Association Studies ◽

Statistical Significance ◽

Ordinary Least Squares ◽

Effect Sizes ◽

Risk Scores ◽

Genome Wide Association Studies ◽

Single Nucleotide ◽

Genome Wide ◽

Tikhonov Regularisation

Background: Genome-wide association studies (GWAS) were successful in identifying SNPs showing association with disease, but their individual effect sizes are small and require large sample sizes to achieve statistical significance. Methods of post-GWAS analysis, including gene-based, gene-set and polygenic risk scores, combine the SNP effect sizes in an attempt to boost the power of the analyses. To avoid giving undue weight to SNPs in linkage disequilibrium (LD), the LD needs to be taken into account in these analyses. Objectives: We review methods that attempt to adjust the effect sizes (β-coefficients) of summary statistics, instead of simple LD pruning. Methods: We subject LD adjustment approaches to a mathematical analysis, recognising Tikhonov regularisation as a framework for comparison. Results: Observing the similarity of the processes involved with the more straightforward Tikhonov-regularised ordinary least squares estimate for multivariate regression coefficients, we note that current methods based on a Bayesian model for the effect sizes effectively provide an implicit choice of the regularisation parameter, which is convenient, but at the price of reduced transparency and, especially in smaller LD blocks, a risk of incomplete LD correction. Conclusions: There is no simple answer to the question which method is best, but where interpretability of the LD adjustment is essential, as in research aiming at identifying the genomic aetiology of disorders, our study suggests that a more direct choice of mild regularisation in the correction of effect sizes may be preferable.

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Stroke Genetics: Turning Discoveries into Clinical Applications

Stroke ◽

10.1161/strokeaha.121.032616 ◽

2021 ◽

Author(s):

Martin Dichgans ◽

Nathalie Beaufort ◽

Stephanie Debette ◽

Christopher D. Anderson

Keyword(s):

Mendelian Randomization ◽

Association Studies ◽

Clinical Applications ◽

Risk Scores ◽

Genome Wide Association Studies ◽

Neuroprotective Agents ◽

Experimental Systems ◽

Genome Wide ◽

Rapid Pace

The field of medical and population genetics in stroke is moving at a rapid pace and has led to unanticipated opportunities for discovery and clinical applications. Genome-wide association studies have highlighted the role of specific pathways relevant to etiologically defined subtypes of stroke and to stroke as a whole. They have further offered starting points for the exploration of novel pathways and pharmacological strategies in experimental systems. Mendelian randomization studies continue to provide insights in the causal relationships between exposures and outcomes and have become a useful tool for predicting the efficacy and side effects of drugs. Additional applications that have emerged from recent discoveries include risk prediction based on polygenic risk scores and pharmacogenomics. Among the topics currently moving into focus is the genetics of stroke outcome. While still at its infancy, this field is expected to boost the development of neuroprotective agents. We provide a brief overview on recent progress in these areas.

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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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