scholarly journals No Evidence for Passive Gene-Environment Correlation or the Influence of Genetic Risk for Psychiatric Disorders on Adult Body Composition via the Adoption Design

2020 ◽  
Author(s):  
Avina K. Hunjan ◽  
Rosa Cheesman ◽  
Jonathan R. I. Coleman ◽  
Christopher Hübel ◽  
Thalia C. Eley ◽  
...  
Keyword(s):  
Body Composition ◽  
Family Environment ◽  
Genetic Risk ◽  
Genetic Effects ◽  
Risk Scores ◽  
Psychiatric Genetic ◽  
Gene Environment ◽  
Polygenic Scores ◽  
Adult Body ◽  
The Uk

Abstract The relationship between genetic and environmental risk is complex and for many traits, estimates of genetic effects may be inflated by passive gene-environment correlation. This arises because biological offspring inherit both their genotypes and rearing environment from their parents. We tested for passive gene-environment correlation in adult body composition traits using the ‘natural experiment’ of childhood adoption, which removes passive gene-environment correlation within families. Specifically, we compared 6165 adoptees with propensity score matched non-adoptees in the UK Biobank. We also tested whether passive gene-environment correlation inflates the association between psychiatric genetic risk and body composition. We found no evidence for inflation of heritability or polygenic scores in non-adoptees compared to adoptees for a range of body composition traits. Furthermore, polygenic risk scores for anorexia nervosa, attention-deficit/hyperactivity disorder and schizophrenia did not differ in their influence on body composition traits in adoptees and non-adoptees. These findings suggest that passive gene-environment correlation does not inflate genetic effects for body composition, or the influence of psychiatric disorder genetic risk on body composition. Our design does not look at passive gene-environment correlation in childhood, and does not test for ‘pure’ environmental effects or the effects of active and evocative gene-environment correlations, where child genetics directly influences home environment. However, these findings suggest that genetic influences identified for body composition in this adult sample are direct, and not confounded by the family environment provided by biological relatives.

scholarly journals Comparison of Adopted and Nonadopted Individuals Reveals Gene–Environment Interplay for Education in the UK Biobank

2020 ◽  
Vol 31 (5) ◽  
pp. 582-591 ◽  
Author(s):  
Rosa Cheesman ◽  
Avina Hunjan ◽  
Jonathan R. I. Coleman ◽  
Yasmin Ahmadzadeh ◽  
Robert Plomin ◽  
...  
Keyword(s):  
Educational Attainment ◽  
Home Environment ◽  
Family Environment ◽  
Adoptive Parents ◽  
Genetic Influences ◽  
Uk Biobank ◽  
Gene Environment ◽  
Polygenic Scores ◽  
The Family ◽  
The Uk

Polygenic scores now explain approximately 10% of the variation in educational attainment. However, they capture not only genetic propensity but also information about the family environment. This is because of passive gene–environment correlation, whereby the correlation between offspring and parent genotypes results in an association between offspring genotypes and the rearing environment. We measured passive gene–environment correlation using information on 6,311 adoptees in the UK Biobank. Adoptees’ genotypes were less correlated with their rearing environments because they did not share genes with their adoptive parents. We found that polygenic scores were twice as predictive of years of education in nonadopted individuals compared with adoptees ( R2s = .074 vs. .037, p = 8.23 × 10−24). Individuals in the lowest decile of polygenic scores for education attained significantly more education if they were adopted, possibly because of educationally supportive adoptive environments. Overall, these results suggest that genetic influences on education are mediated via the home environment.

scholarly journals Three models for the regulation of polygenic scores in reproduction

2021 ◽  
pp. medethics-2020-106588
Author(s):  
Sarah Munday ◽  
Julian Savulescu
Keyword(s):  
Human Genetics ◽  
Ethical Issues ◽  
Well Being ◽  
Embryo Selection ◽  
Risk Scores ◽  
Regulatory Regime ◽  
Polygenic Scores ◽  
The Usa ◽  
The Uk ◽  
The Impact

The past few years have brought significant breakthroughs in understanding human genetics. This knowledge has been used to develop ‘polygenic scores’ (or ‘polygenic risk scores’) which provide probabilistic information about the development of polygenic conditions such as diabetes or schizophrenia. They are already being used in reproduction to select for embryos at lower risk of developing disease. Currently, the use of polygenic scores for embryo selection is subject to existing regulations concerning embryo testing and selection. Existing regulatory approaches include ‘disease-based' models which limit embryo selection to avoiding disease characteristics (employed in various formats in Australia, the UK, Italy, Switzerland and France, among others), and 'laissez-faire' or 'libertarian' models, under which embryo testing and selection remain unregulated (as in the USA). We introduce a novel 'Welfarist Model' which limits embryo selection according to the impact of the predicted trait on well-being. We compare the strengths and weaknesses of each model as a way of regulating polygenic scores. Polygenic scores create the potential for existing embryo selection technologies to be used to select for a wider range of predicted genetically influenced characteristics including continuous traits. Indeed, polygenic scores exist to predict future intelligence, and there have been suggestions that they will be used to make predictions within the normal range in the USA in embryo selection. We examine how these three models would apply to the prediction of non-disease traits such as intelligence. The genetics of intelligence remains controversial both scientifically and ethically. This paper does not attempt to resolve these issues. However, as with many biomedical advances, an effective regulatory regime must be in place as soon as the technology is available. If there is no regulation in place, then the market effectively decides ethical issues.

scholarly journals Parkinson’s disease determinants, prediction and gene–environment interactions in the UK Biobank

2020 ◽  
Vol 91 (10) ◽  
pp. 1046-1054 ◽  
Author(s):  
Benjamin Meir Jacobs ◽  
Daniel Belete ◽  
Jonathan Bestwick ◽  
Cornelis Blauwendraat ◽  
Sara Bandres-Ciga ◽  
...  
Keyword(s):  
Risk Factors ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Family History ◽  
Genetic Risk ◽  
Positive Family History ◽  
Risk Scores ◽  
Uk Biobank ◽  
Gene Environment ◽  
History Of

ObjectiveTo systematically investigate the association of environmental risk factors and prodromal features with incident Parkinson’s disease (PD) diagnosis and the interaction of genetic risk with these factors. To evaluate whether existing risk prediction algorithms are improved by the inclusion of genetic risk scores.MethodsWe identified individuals with an incident diagnosis of PD (n=1276) and controls (n=500 406) in UK Biobank. We determined the association of risk factors with incident PD using adjusted logistic regression models. We constructed polygenic risk scores (PRSs) using external weights and selected the best PRS from a subset of the cohort (30%). The PRS was used in a separate testing set (70%) to examine gene–environment interactions and compare predictive models for PD.ResultsStrong evidence of association (false discovery rate <0.05) was found between PD and a positive family history of PD, a positive family history of dementia, non-smoking, low alcohol consumption, depression, daytime somnolence, epilepsy and earlier menarche. Individuals with the highest 10% of PRSs had increased risk of PD (OR 3.37, 95% CI 2.41 to 4.70) compared with the lowest risk decile. A higher PRS was associated with earlier age at PD diagnosis and inclusion of the PRS in the PREDICT-PD algorithm led to a modest improvement in model performance. We found evidence of an interaction between the PRS and diabetes.InterpretationHere, we used UK Biobank data to reproduce several well-known associations with PD, to demonstrate the validity of a PRS and to demonstrate a novel gene–environment interaction, whereby the effect of diabetes on PD risk appears to depend on background genetic risk for PD.

scholarly journals Testing Familial Transmission of Smoking With Two Different Research Designs

2017 ◽  
Vol 20 (7) ◽  
pp. 836-842 ◽  
Author(s):  
Jorien L Treur ◽  
Karin J H Verweij ◽  
Abdel Abdellaoui ◽  
Iryna O Fedko ◽  
Eveline L de Zeeuw ◽  
...  
Keyword(s):  
The Netherlands ◽  
Family Environment ◽  
Genetic Risk ◽  
Genetic Factors ◽  
Environmental Influence ◽  
Smoking Initiation ◽  
Risk Scores ◽  
Environment Interaction ◽  
Polygenic Risk ◽  
Children Of Twins

Abstract Introduction Classical twin studies show that smoking is heritable. To determine if shared family environment plays a role in addition to genetic factors, and if they interact (G×E), we use a children-of-twins design. In a second sample, we measure genetic influence with polygenic risk scores (PRS) and environmental influence with a question on exposure to smoking during childhood. Methods Data on smoking initiation were available for 723 children of 712 twins from the Netherlands Twin Register (64.9% female, median birth year 1985). Children were grouped in ascending order of risk, based on smoking status and zygosity of their twin-parent and his/her co-twin: never smoking twin-parent with a never smoking co-twin; never smoking twin-parent with a smoking dizygotic co-twin; never smoking twin-parent with a smoking monozygotic co-twin; and smoking twin-parent with a smoking or never smoking co-twin. For 4072 participants from the Netherlands Twin Register (67.3% female, median birth year 1973), PRS for smoking were computed and smoking initiation, smoking heaviness, and exposure to smoking during childhood were available. Results Patterns of smoking initiation in the four group children-of-twins design suggested shared familial influences in addition to genetic factors. PRS for ever smoking were associated with smoking initiation in all individuals. PRS for smoking heaviness were associated with smoking heaviness in individuals exposed to smoking during childhood, but not in non-exposed individuals. Conclusions Shared family environment influences smoking, over and above genetic factors. Genetic risk of smoking heaviness was only important for individuals exposed to smoking during childhood, versus those not exposed (G×E). Implications This study adds to the very few existing children-of-twins (CoT) studies on smoking and combines a CoT design with a second research design that utilizes polygenic risk scores and data on exposure to smoking during childhood. The results show that shared family environment affects smoking behavior over and above genetic factors. There was also evidence for gene–environment interaction (G×E) such that genetic risk of heavy versus light smoking was only important for individuals who were also exposed to (second-hand) smoking during childhood. Together, these findings give additional incentive to recommending parents not to expose their children to cigarette smoking.

Chromosomal scale length variation of germline DNA can predict individual cancer risk

2018 ◽  
Author(s):  
Chris Toh ◽  
James P. Brody
Keyword(s):  
Cancer Risk ◽  
Genetic Risk ◽  
Germ Line ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Length Variation ◽  
Uk Biobank ◽  
Genetic Risk Scores ◽  
The Uk ◽  
Scale Length

AbstractInherited factors are thought to be responsible for a substantial fraction of many different forms of cancer. However, individual cancer risk cannot currently be well quantified by analyzing germ line DNA. Most analyses of germline DNA focus on the additive effects of single nucleotide polymorphisms (SNPs) found. Here we show that chromosomal-scale length variation of germline DNA can be used to predict whether a person will develop cancer. In two independent datasets, the Cancer Genome Atlas (TCGA) project and the UK Biobank, we could classify whether or not a patient had a certain cancer based solely on chromosomal scale length variation. In the TCGA data, we found that all 32 different types of cancer could be predicted better than chance using chromosomal scale length variation data. We found a model that could predict ovarian cancer in women with an area under the receiver operator curve, AUC=0.89. In the UK Biobank data, we could predict breast cancer in women with an AUC=0.83. This method could be used to develop genetic risk scores for other conditions known to have a substantial genetic component and complements genetic risk scores derived from SNPs.

scholarly journals Child-evoked maternal negativity from 9 to 27 months: Evidence of gene–environment correlation and its moderation by marital distress

2014 ◽  
Vol 27 (4pt1) ◽  
pp. 1251-1265 ◽  
Author(s):  
R. M. Pasco Fearon ◽  
David Reiss ◽  
Leslie D. Leve ◽  
Daniel S. Shaw ◽  
Laura V. Scaramella ◽  
...  
Keyword(s):  
Early Development ◽  
Family Environment ◽  
Genetic Risk ◽  
Adoptive Parents ◽  
Self Report ◽  
Adoptive Parent ◽  
Birth Mother ◽  
Adoptive Family ◽  
Gene Environment ◽  
Maternal Negativity

AbstractPast research has documented pervasive genetic influences on emotional and behavioral disturbance across the life span and on liability to adult psychiatric disorder. Increasingly, interest is turning to mechanisms of gene–environment interplay in attempting to understand the earliest manifestations of genetic risk. We report findings from a prospective adoption study, which aimed to test the role of evocative gene–environment correlation in early development. Included in the study were 561 infants adopted at birth and studied between 9 and 27 months, along with their adoptive parents and birth mothers. Birth mother psychiatric diagnoses and symptoms scales were used as indicators of genetic influence, and multiple self-report measures were used to index adoptive mother parental negativity. We hypothesized that birth mother psychopathology would be associated with greater adoptive parent negativity and that such evocative effects would be amplified under conditions of high adoptive family adversity. The findings suggested that genetic factors associated with birth mother externalizing psychopathology may evoke negative reactions in adoptive mothers in the first year of life, but only when the adoptive family environment is characterized by marital problems. Maternal negativity mediated the effects of genetic risk on child adjustment at 27 months. The results underscore the importance of genetically influenced evocative processes in early development.

scholarly journals Inclusion of Variants Discovered from Diverse Populations Improves Polygenic Risk Score Transferability

2020 ◽  
Author(s):  
Taylor B. Cavazos ◽  
John S. Witte
Keyword(s):  
Genetic Risk ◽  
African Ancestry ◽  
European Ancestry ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Genetic Risk Prediction ◽  
Causal Variants ◽  
The Uk ◽  
The Relationship

ABSTRACTThe majority of polygenic risk scores (PRS) have been developed and optimized in individuals of European ancestry and may have limited generalizability across other ancestral populations. Understanding aspects of PRS that contribute to this issue and determining solutions is complicated by disease-specific genetic architecture and limited knowledge of sharing of causal variants and effect sizes across populations. Motivated by these challenges, we undertook a simulation study to assess the relationship between ancestry and the potential bias in PRS developed in European ancestry populations. Our simulations show that the magnitude of this bias increases with increasing divergence from European ancestry, and this is attributed to population differences in linkage disequilibrium and allele frequencies of European discovered variants, likely as a result of genetic drift. Importantly, we find that including into the PRS variants discovered in African ancestry individuals has the potential to achieve unbiased estimates of genetic risk across global populations and admixed individuals. We confirm our simulation findings in an analysis of HbA1c, asthma, and prostate cancer in the UK Biobank. Given the demonstrated improvement in PRS prediction accuracy, recruiting larger diverse cohorts will be crucial—and potentially even necessary—for enabling accurate and equitable genetic risk prediction across populations.

scholarly journals Comparison of adopted and non-adopted individuals reveals gene-environment interplay for education in the UK Biobank

2019 ◽  
Author(s):  
Rosa Cheesman ◽  
Avina Hunjan ◽  
Jonathan R. I. Coleman ◽  
Yasmin Ahmadzadeh ◽  
Robert Plomin ◽  
...  
Keyword(s):  
Home Environment ◽  
Adoptive Parents ◽  
Genetic Influences ◽  
Uk Biobank ◽  
Rearing Environment ◽  
Individual Level ◽  
Gene Environment ◽  
Polygenic Scores ◽  
The Uk ◽  
Difference Test

AbstractIndividual-level polygenic scores can now explain ∼10% of the variation in number of years of completed education. However, associations between polygenic scores and education capture not only genetic propensity but information about the environment that individuals are exposed to. This is because individuals passively inherit effects of parental genotypes, since their parents typically also provide the rearing environment. In other words, the strong correlation between offspring and parent genotypes results in an association between the offspring genotypes and the rearing environment. This is termed passive gene-environment correlation. We present an approach to test for the extent of passive gene-environment correlation for education without requiring intergenerational data. Specifically, we use information from 6311 individuals in the UK Biobank who were adopted in childhood to compare genetic influence on education between adoptees and non-adopted individuals. Adoptees’ rearing environments are less correlated with their genotypes, because they do not share genes with their adoptive parents. We find that polygenic scores are twice as predictive of years of education in non-adopted individuals compared to adoptees (R2= 0.074 vs 0.037, difference test p= 8.23 × 10−24). We provide another kind of evidence for the influence of parental behaviour on offspring education: individuals in the lowest decile of education polygenic score attain significantly more education if they are adopted, possibly due to educationally supportive adoptive environments. Overall, these results suggest that genetic influences on education are mediated via the home environment. As such, polygenic prediction of educational attainment represents gene-environment correlations just as much as it represents direct genetic effects.

scholarly journals Polygenic Scores for Plasticity: A New Tool for Studying Gene-Environment Interplay

2020 ◽  
Author(s):  
Rebecca Johnson ◽  
Ramina Sotoudeh ◽  
Dalton Conley
Keyword(s):  
Education Reform ◽  
Population Based ◽  
Training Set ◽  
Heterogeneous Effects ◽  
Gene Environment ◽  
Polygenic Scores ◽  
Nature And Nurture ◽  
The Uk ◽  
Genetic Contributions ◽  
Retirement Study

AbstractOutcomes of interest to demographers—fertility; health; education—are the product of both an individual’s genetic makeup and his or her social environment. Yet Gene × Environment research (GxE) currently deploys a limited toolkit on the genetic side to study gene-environment interplay: polygenic scores (PGS, or what we call mPGS) that reflect the influence of genetics on levels of an outcome. The purpose of the present paper is to develop a genetic summary measure better suited for GxE research. We develop what we call variance polygenic scores (vPGS), or polygenic scores that reflect genetic contributions to plasticity in outcomes. The first part of the analysis uses the UK Biobank (N ∼ 326,000 in the training set) and the Health and Retirement Study (HRS) (N = 10,524) to compare four approaches for constructing polygenic scores for plasticity. The results show that two widely-used methods for discovering which genetic variants affect outcome variability fail to serve as distinctive new tools for GxE. Then, using the polygenic scores that do capture distinctive genetic contributions to plasticity, we analyze heterogeneous effects of a UK education reform on health and educational attainment. The results show the properties of a new tool useful for population scientists studying the interplay of nature and nurture and for population-based studies that are releasing polygenic scores to applied researchers.

scholarly journals Associations of coffee genetic risk scores with coffee, tea and other beverages in the UK Biobank

2016 ◽  
Author(s):  
Amy E. Taylor ◽  
Marcus R. Munafò
Keyword(s):  
Genetic Risk ◽  
Coffee Consumption ◽  
Risk Scores ◽  
Tea Consumption ◽  
Uk Biobank ◽  
Beverage Consumption ◽  
Dietary Recall ◽  
Genetic Risk Scores ◽  
Decaffeinated Coffee ◽  
The Uk

AbstractBackgroundGenetic variants which determine amount of coffee consumed have been identified in genome-wide association studies (GWAS) of coffee consumption; these may help to further understanding of the effects of coffee on health outcomes. However, there is limited information about how these variants relate to caffeinated beverage consumption more generally.AimsTo improve phenotype definition for coffee consumption related genetic risk scores by testing their association with coffee, tea and other beverages.MethodsWe tested the associations of genetic risk scores for coffee consumption with beverage consumption in 114,316 individuals of European ancestry from the UK Biobank. Drinks were self-reported in a baseline questionnaire and in detailed 24 dietary recall questionnaires in a subset.ResultsGenetic risk scores including two and eight single nucleotide polymorphisms (SNPs) explained up to 0.39%, 0.19% and 0.77% of the variance in coffee, tea and combined coffee and tea consumption respectively. A one standard deviation increase in the 8 SNP genetic risk score was associated with a 0.13 cup per day (95% CI: 0.12, 0.14), 0.12 cup per day (95%CI: 0.11, 0.14) and 0.25 cup per day (95% CI: 0.24, 0.27) increase in coffee, tea and combined tea and coffee consumption, respectively. Genetic risk scores also demonstrated positive associations with both caffeinated and decaffeinated coffee and tea consumption. In 48,692 individuals with dietary recall data, the genetic risk scores were positively associated with coffee and tea, (apart from herbal teas) consumption, but did not show clear evidence for positive associations with other beverages. However, there was evidence that the genetic risk scores were associated with lower daily water consumption and lower overall drink consumption.ConclusionsGenetic risk scores created from variants identified in coffee consumption GWAS associate more broadly with caffeinated beverage consumption and also with decaffeinated coffee and tea consumption.

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