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Trejo S. Exploring the Fetal Origins Hypothesis Using Genetic Data. SOCIAL FORCES; A SCIENTIFIC MEDIUM OF SOCIAL STUDY AND INTERPRETATION 2024; 102:1555-1581. [PMID: 38638179 PMCID: PMC11021852 DOI: 10.1093/sf/soae018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 10/12/2023] [Accepted: 11/23/2023] [Indexed: 04/20/2024]
Abstract
Birth weight is a robust predictor of valued life course outcomes, emphasizing the importance of prenatal development. But does birth weight act as a proxy for environmental conditions in utero, or do biological processes surrounding birth weight themselves play a role in healthy development? To answer this question, we leverage variation in birth weight that is, within families, orthogonal to prenatal environmental conditions: one's genes. We construct polygenic scores in two longitudinal studies (Born in Bradford, N = 2008; Wisconsin Longitudinal Study, N = 8488) to empirically explore the molecular genetic correlates of birth weight. A 1 standard deviation increase in the polygenic score is associated with an ~100-grams increase in birth weight and a 1.4 pp (22 percent) decrease in low birth weight probability. Sibling comparisons illustrate that this association largely represents a causal effect. The polygenic score-birth weight association is increased for children who spend longer in the womb and whose mothers have higher body mass index, though we find no differences across maternal socioeconomic status. Finally, the polygenic score affects social and cognitive outcomes, suggesting that birth weight is itself related to healthy prenatal development.
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Affiliation(s)
- Sam Trejo
- Princeton University, Department of Sociology and Office of Population Research, United States
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2
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Xu Y, Sun Z, Jonaitis E, Deming Y, Lu Q, Johnson SC, Engelman CD. Mid-to-Late Life Healthy Lifestyle Modifies Genetic Risk for Longitudinal Cognitive Aging among Asymptomatic Individuals. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.26.24307953. [PMID: 38853902 PMCID: PMC11160812 DOI: 10.1101/2024.05.26.24307953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
IMPORTANCE Genetic and lifestyle factors contribute to an individual's risk of developing Alzheimer's disease. However, it is unknown whether and how adherence to healthy lifestyles can mitigate the genetic risk of Alzheimer's. OBJECTIVE The aim of this study is to investigate whether adherence to healthy lifestyles can modify the impact of genetic predisposition to Alzheimer's disease on later-life cognitive decline. DESIGN SETTING AND PARTICIPANTS This prospective cohort study included 891 adults of European ancestry, aged 40 to 65, who were without dementia and had complete healthy-lifestyle and cognition data during the follow-up. Participants joined the Wisconsin Registry for Alzheimer's Prevention (WRAP) beginning in 2001. We conducted replication analyses using a subsample with similar baseline age range from the Health and Retirement Study (HRS). EXPOSURES We assessed participants' exposures using a continuous non-APOE polygenic risk score for Alzheimer's, a binary indicator for APOE-ε4 carrier status, and a weighted healthy-lifestyle score, including factors such as no current smoking, regular physical activity, healthy diet, light to moderate alcohol consumption, and frequent cognitive activities. MAIN OUTCOMES AND MEASURES We z-standardized cognitive scores for global (Preclinical Alzheimer's Cognitive Composite score 3 - PACC3) and domain-specific assessments (delayed recall and immediate learning). RESULTS We followed 891 individuals for up to 10 years (mean [SD] baseline age, 58 [6] years, 31% male, 38% APOE-ε4 carriers). After false discovery rate (FDR) correction, we found statistically significant PRS × lifestyle × age interactions on preclinical cognitive decline but the evidence is stronger among APOE-ε4 carriers. Among APOE-ε4 carriers, PRS-related differences in overall and memory-related domains between people scoring 0-1 and 4-5 regarding healthy lifestyles became evident around age 67 after FDR correction. These findings were robust across several sensitivity analyses and were replicated in the population-based HRS. CONCLUSION A favorable lifestyle can mitigate the genetic risk associated with current known non-APOE genetic variants for longitudinal cognitive decline, and these protective effects are particularly pronounced among APOE-ε4 carriers.
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Affiliation(s)
- Yuexuan Xu
- G.H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University
| | - Zhongxuan Sun
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison
| | - Erin Jonaitis
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison
| | - Yuetiva Deming
- G.H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison
| | - Sterling C. Johnson
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison
| | - Corinne D. Engelman
- G.H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University
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3
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Willems YE, Raffington L, Ligthart L, Pool R, Hottenga JJ, Finkenauer C, Bartels M. No gene by stressful life events interaction on individual differences in adults' self-control. Front Psychiatry 2024; 15:1388264. [PMID: 38693999 PMCID: PMC11061522 DOI: 10.3389/fpsyt.2024.1388264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 04/03/2024] [Indexed: 05/03/2024] Open
Abstract
Background Difficulty with self-control, or the ability to alter impulses and behavior in a goal-directed way, predicts interpersonal conflict, lower socioeconomic attainments, and more adverse health outcomes. Etiological understanding, and intervention for low self-control is, therefore, a public health goal. A prominent developmental theory proposes that individuals with high genetic propensity for low self-control that are also exposed to stressful environments may be most at-risk of low levels of self-control. Here we examine if polygenic measures associated with behaviors marked by low self-control interact with stressful life events in predicting self-control. Methods Leveraging molecular data from a large population-based Dutch sample (N = 7,090, Mage = 41.2) to test for effects of genetics (i.e., polygenic scores for ADHD and aggression), stressful life events (e.g., traffic accident, violent assault, financial problems), and a gene-by-stress interaction on self-control (measured with the ASEBA Self-Control Scale). Results Both genetics (β =.03 -.04, p <.001) and stressful life events (β = .11 -.14, p <.001) were associated with individual differences in self-control. We find no evidence of a gene-by-stressful life events interaction on individual differences in adults' self-control. Conclusion Our findings are consistent with the notion that genetic influences and stressful life events exert largely independent effects on adult self-control. However, the small effect sizes of polygenic scores increases the likelihood of null results. Genetically-informed longitudinal research in large samples can further inform the etiology of individual differences in self-control from early childhood into later adulthood and its downstream implications for public health.
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Affiliation(s)
- Yayouk Eva Willems
- Max Planck Institute for Human Development, Max Planck Research Group Biosocial – Biology, Social Disparities, and Development, Berlin, Germany
| | - Laurel Raffington
- Max Planck Institute for Human Development, Max Planck Research Group Biosocial – Biology, Social Disparities, and Development, Berlin, Germany
| | - Lannie Ligthart
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Rene Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Catrin Finkenauer
- Department of Interdisciplinary Social Science, Universiteit Utrecht, Utrecht, Netherlands
| | - Meike Bartels
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Amsterdam Public Health Research Institute, Amsterdam University Medical Centres, Amsterdam, Netherlands
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Furuya S, Zheng F, Lu Q, Fletcher JM. Separating Scarring Effect and Selection of Early-Life Exposures With Genetic Data. Demography 2024; 61:363-392. [PMID: 38482998 DOI: 10.1215/00703370-11239766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
Causal life course research examining consequences of early-life exposures has largely relied on associations between early-life environments and later-life outcomes using exogenous environmental shocks. Nonetheless, even with (quasi-)randomized early-life exposures, these associations may reflect not only causation ("scarring") but also selection (i.e., which members are included in data assessing later life). Investigating this selection and its impacts on estimated effects of early-life conditions has, however, often been ignored because of a lack of pre-exposure data. This study proposes an approach for assessing and correcting selection, separately from scarring, using genetic measurements. Because genetic measurements are determined at the time of conception, any associations with early-life exposures should be interpreted as selection. Using data from the UK Biobank, we find that in utero exposure to a higher area-level infant mortality rate is associated with genetic predispositions correlated with better educational attainment and health. These findings point to the direction and magnitude of selection from this exposure. Corrections for this selection in examinations of effects of exposure on later educational attainment suggest underestimates of 26-74%; effects on other life course outcomes also vary across selection correction methods.
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Affiliation(s)
- Shiro Furuya
- Department of Sociology, Center for Demography of Health and Aging, and Center for Demography and Ecology, University of Wisconsin-Madison, Madison, WI, USA
| | - Fengyi Zheng
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Qiongshi Lu
- Center for Demography of Health and Aging, Department of Statistics, and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | - Jason M Fletcher
- Center for Demography of Health and Aging, Center for Demography and Ecology, La Follette School of Public Affairs, Department of Population Health Science, and Department of Agricultural and Applied Economics, University of Wisconsin-Madison, Madison, WI, USA
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Xenaki LA, Dimitrakopoulos S, Selakovic M, Stefanis N. Stress, Environment and Early Psychosis. Curr Neuropharmacol 2024; 22:437-460. [PMID: 37592817 PMCID: PMC10845077 DOI: 10.2174/1570159x21666230817153631] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 01/02/2023] [Accepted: 01/04/2023] [Indexed: 08/19/2023] Open
Abstract
Existing literature provides extended evidence of the close relationship between stress dysregulation, environmental insults, and psychosis onset. Early stress can sensitize genetically vulnerable individuals to future stress, modifying their risk for developing psychotic phenomena. Neurobiological substrate of the aberrant stress response to hypothalamic-pituitary-adrenal axis dysregulation, disrupted inflammation processes, oxidative stress increase, gut dysbiosis, and altered brain signaling, provides mechanistic links between environmental risk factors and the development of psychotic symptoms. Early-life and later-life exposures may act directly, accumulatively, and repeatedly during critical neurodevelopmental time windows. Environmental hazards, such as pre- and perinatal complications, traumatic experiences, psychosocial stressors, and cannabis use might negatively intervene with brain developmental trajectories and disturb the balance of important stress systems, which act together with recent life events to push the individual over the threshold for the manifestation of psychosis. The current review presents the dynamic and complex relationship between stress, environment, and psychosis onset, attempting to provide an insight into potentially modifiable factors, enhancing resilience and possibly influencing individual psychosis liability.
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Affiliation(s)
- Lida-Alkisti Xenaki
- First Department of Psychiatry, School of Medicine, Eginition Hospital, National and Kapodistrian University of Athens, 72 Vas. Sophias Ave., Athens, 115 28, Greece
| | - Stefanos Dimitrakopoulos
- First Department of Psychiatry, School of Medicine, Eginition Hospital, National and Kapodistrian University of Athens, 72 Vas. Sophias Ave., Athens, 115 28, Greece
| | - Mirjana Selakovic
- First Department of Psychiatry, School of Medicine, Eginition Hospital, National and Kapodistrian University of Athens, 72 Vas. Sophias Ave., Athens, 115 28, Greece
| | - Nikos Stefanis
- First Department of Psychiatry, School of Medicine, Eginition Hospital, National and Kapodistrian University of Athens, 72 Vas. Sophias Ave., Athens, 115 28, Greece
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Trevino AD, Jamil B, Su J, Aliev F, Elam KK, Lemery-Chalfant K. Alcohol Use Disorder Polygenic Risk Scores and Trajectories of Early Adolescent Externalizing Behaviors: Examining the Role of Parenting and Family Conflict in the Racially/Ethnically Diverse ABCD Sample. Behav Genet 2024; 54:101-118. [PMID: 37792148 DOI: 10.1007/s10519-023-10155-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 09/08/2023] [Indexed: 10/05/2023]
Abstract
This study examined the independent and interactive effects of alcohol use disorder genome-wide polygenic scores (AUD-PGS) and parenting and family conflict on early adolescent externalizing behaviors. Data were drawn from White (N = 6181, 46.9% female), Black/African American (N = 1784, 50.1% female), and Hispanic/Latinx (N = 2410, 48.0% female) youth from the adolescent brain cognitive development Study (ABCD). Parents reported on youth externalizing behaviors at baseline (T1, age 9/10), 1-year (T2, age 10/11) and 2-year (T3, age 11/12) assessments. Youth reported on parenting and family environment at T1 and provided saliva or blood samples for genotyping. Results from latent growth models indicated that in general externalizing behaviors decreased from T1 to T3. Across all groups, higher family conflict was associated with more externalizing behaviors at T1, and we did not find significant associations between parental monitoring and early adolescent externalizing behaviors. Parental acceptance was associated with lower externalizing behaviors among White and Hispanic youth, but not among Black youth. Results indicated no significant main effect of AUD-PGS nor interaction effect between AUD-PGS and family variables on early adolescent externalizing behaviors. Post hoc exploratory analysis uncovered an interaction between AUD-PGS and parental acceptance such that AUD-PGS was positively associated with externalizing rule-breaking behaviors among Hispanic youth, but only when parental acceptance was very low. Findings highlight the important role of family conflict and parental acceptance in externalizing behaviors among early adolescents, and emphasize the need to examine other developmental pathways underlying genetic risk for AUD across diverse populations.
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Affiliation(s)
- Angel D Trevino
- Department of Psychology, Arizona State University, Phoenix, AZ, USA.
| | - Belal Jamil
- Department of Psychology, Arizona State University, Phoenix, AZ, USA
| | - Jinni Su
- Department of Psychology, Arizona State University, Phoenix, AZ, USA
| | - Fazil Aliev
- Department of Psychiatry, Rutgers University, New Brunswick, NJ, USA
| | - Kit K Elam
- Department of Applied Health Science, Indiana University, Bloomington, IN, USA
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Miao J, Wu Y, Lu Q. Statistical methods for gene-environment interaction analysis. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL STATISTICS 2024; 16:e1635. [PMID: 38699459 PMCID: PMC11064894 DOI: 10.1002/wics.1635] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 09/12/2023] [Indexed: 05/05/2024]
Abstract
Most human complex phenotypes result from multiple genetic and environmental factors and their interactions. Understanding the mechanisms by which genetic and environmental factors interact offers valuable insights into the genetic architecture of complex traits and holds great potential for advancing precision medicine. The emergence of large population biobanks has led to the development of numerous statistical methods aiming at identifying gene-environment interactions (G × E). In this review, we present state-of-the-art statistical methodologies for G × E analysis. We will survey a spectrum of approaches for single-variant G × E mapping, followed by various techniques for polygenic G × E analysis. We conclude this review with a discussion on the future directions and challenges in G × E research.
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Affiliation(s)
- Jiacheng Miao
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Yixuan Wu
- University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, Wisconsin, USA
- Department of Statistics, University of Wisconsin–Madison, Madison, Wisconsin, USA
- Center for Demography of Health and Aging, University of Wisconsin–Madison, Madison, Wisconsin, USA
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Hannigan LJ, Lund IO, Dahl Askelund A, Ystrom E, Corfield EC, Ask H, Havdahl A. Genotype-environment interplay in associations between maternal drinking and offspring emotional and behavioral problems. Psychol Med 2024; 54:203-214. [PMID: 37929303 DOI: 10.1017/s0033291723003057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2023]
Abstract
BACKGROUND While maternal at-risk drinking is associated with children's emotional and behavioral problems, there is a paucity of research that properly accounts for genetic confounding and gene-environment interplay. Therefore, it remains uncertain what mechanisms underlie these associations. We assess the moderation of associations between maternal at-risk drinking and childhood emotional and behavioral problems by common genetic variants linked to environmental sensitivity (genotype-by-environment [G × E] interaction) while accounting for shared genetic risk between mothers and offspring (GE correlation). METHODS We use data from 109 727 children born to 90 873 mothers enrolled in the Norwegian Mother, Father, and Child Cohort Study. Women self-reported alcohol consumption and reported emotional and behavioral problems when children were 1.5/3/5 years old. We included child polygenic scores (PGSs) for traits linked to environmental sensitivity as moderators. RESULTS Associations between maternal drinking and child emotional (β1 = 0.04 [95% confidence interval (CI) 0.03-0.05]) and behavioral (β1 = 0.07 [0.06-0.08]) outcomes attenuated after controlling for measured confounders and were almost zero when we accounted for unmeasured confounding (emotional: β1 = 0.01 [0.00-0.02]; behavioral: β1 = 0.01 [0.00-0.02]). We observed no moderation of these adjusted exposure effects by any of the PGS. CONCLUSIONS The lack of strong evidence for G × E interaction may indicate that the mechanism is not implicated in this kind of intergenerational association. It may also reflect insufficient power or the relatively benign nature of the exposure in this sample.
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Affiliation(s)
- Laurie John Hannigan
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- MRC (Medical Research Council) Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Ingunn Olea Lund
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Adrian Dahl Askelund
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
| | - Eivind Ystrom
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- PROMENTA, Department of Psychology, University of Oslo, Oslo, Norway
- School of Pharmacy, University of Oslo, Oslo, Norway
| | - Elizabeth C Corfield
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
| | - Helga Ask
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- PROMENTA, Department of Psychology, University of Oslo, Oslo, Norway
| | - Alexandra Havdahl
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- MRC (Medical Research Council) Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- PROMENTA, Department of Psychology, University of Oslo, Oslo, Norway
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Trejo S, Martschenko DO. Beware of the phony horserace between genes and environments. Behav Brain Sci 2023; 46:e228. [PMID: 37695009 DOI: 10.1017/s0140525x22002485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Although Burt provides a valuable critique of the scientific value of integrating genetic data into social science research, she reinforces rather than disrupts the age-old horserace between genetic effects and environmental effects. We must move past this false dichotomy to create a new ontology that recognizes the ways in which genetic and environmental processes are inextricably intertwined.
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Affiliation(s)
- Sam Trejo
- Department of Sociology and Office of Population Research, Princeton University, Princeton, NJ, USA ; www.samtrejo.com
| | - Daphne Oluwaseun Martschenko
- Stanford Center for Biomedical Ethics, Department of Pediatrics, Stanford University, Stanford, CA, USA ; www.daphnemartschenko.com
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Zheng B, Fletcher J, Song J, Lu Q. Analysis of Sex-Specific Gene-by-Cohort and Genetic Correlation-by-Cohort Interaction in Educational and Reproductive Outcomes Using the UK Biobank Data. JOURNAL OF HEALTH AND SOCIAL BEHAVIOR 2023:221465231188166. [PMID: 37572045 DOI: 10.1177/00221465231188166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/14/2023]
Abstract
Synthesizing prior gene-by-cohort (G×C) interaction studies, we theorize that changes in genetic effects by social conditions depend on the level of resource constraints, the distribution and use of resources, structural constraints, and constraints on individual choice. Motivated by the theory, we explored several sex-specific G×C trends across a set of outcomes using 30 birth cohorts of UK Biobank data (N = 400,000). We find that genetic coefficients on years of schooling and secondary educational attainment substantially decrease, but genetic coefficients on college attainments only moderately increase. On the other hand, genetic coefficients for education ranks are stable. Genetic coefficients on reproductive behavior increase for younger cohorts. Additional genetic-correlation-by-cohort analysis shows shifting genetic correlations between education and reproductive behavior. Our results suggest that the G×C patterns are highly heterogenous and that social and genetic factors jointly shape the diversity of human phenotypes.
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Affiliation(s)
- Boyan Zheng
- University of Wisconsin-Madison, Madison, WI, USA
| | | | - Jie Song
- University of Wisconsin-Madison, Madison, WI, USA
| | - Qiongshi Lu
- University of Wisconsin-Madison, Madison, WI, USA
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Urquijo H, Soares AG, Fraser A, Howe LD, Carter AR. Investigating effect modification between childhood maltreatment and genetic risk for cardiovascular disease in the UK Biobank. PLoS One 2023; 18:e0285258. [PMID: 37141292 PMCID: PMC10159177 DOI: 10.1371/journal.pone.0285258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 04/18/2023] [Indexed: 05/05/2023] Open
Abstract
Cardiovascular disease (CVD) is influenced by genetic and environmental factors. Childhood maltreatment is associated with CVD and may modify genetic susceptibility to cardiovascular risk factors. We used genetic and phenotypic data from 100,833 White British UK Biobank participants (57% female; mean age = 55.9 years). We regressed nine cardiovascular risk factors/diseases (alcohol consumption, body mass index [BMI], low-density lipoprotein cholesterol, lifetime smoking behaviour, systolic blood pressure, atrial fibrillation, coronary heart disease, type 2 diabetes, and stroke) on their respective polygenic scores (PGS) and self-reported exposure to childhood maltreatment. Effect modification was tested on the additive and multiplicative scales by including a product term (PGS*maltreatment) in regression models. On the additive scale, childhood maltreatment accentuated the effect of genetic susceptibility to higher BMI (Peffect modification: 0.003). Individuals not exposed to childhood maltreatment had an increase in BMI of 0.12 SD (95% CI: 0.11, 0.13) per SD increase in BMI PGS, compared to 0.17 SD (95% CI: 0.14, 0.19) in those exposed to all types of childhood maltreatment. On the multiplicative scale, similar results were obtained for BMI though these did not withstand to Bonferroni correction. There was little evidence of effect modification by childhood maltreatment in relation to other outcomes, or of sex-specific effect modification. Our study suggests the effects of genetic susceptibility to a higher BMI may be moderately accentuated in individuals exposed to childhood maltreatment. However, gene*environment interactions are likely not a major contributor to the excess CVD burden experienced by childhood maltreatment victims.
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Affiliation(s)
- Helena Urquijo
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Ana Gonçalves Soares
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Abigail Fraser
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Laura D. Howe
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Alice R. Carter
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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12
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BROWN TYSONH, HOMAN PATRICIA. The Future of Social Determinants of Health: Looking Upstream to Structural Drivers. Milbank Q 2023; 101:36-60. [PMID: 37096627 PMCID: PMC10126983 DOI: 10.1111/1468-0009.12641] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 10/15/2022] [Accepted: 01/06/2023] [Indexed: 04/26/2023] Open
Abstract
Policy Points Policies that redress oppressive social, economic, and political conditions are essential for improving population health and achieving health equity. Efforts to remedy structural oppression and its deleterious effects should account for its multilevel, multifaceted, interconnected, systemic, and intersectional nature. The U.S. Department of Health and Human Services should facilitate the creation and maintenance of a national publicly available, user-friendly data infrastructure on contextual measures of structural oppression. Publicly funded research on social determinants of health should be mandated to (a) analyze health inequities in relation to relevant data on structural conditions and (b) deposit the data in the publicly available data repository.
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Armstrong-Carter E, Bush NR, Boyce WT, Obradović J. Cortisol response marks biological sensitivity to kindergartners' social hierarchies for emerging school engagement. Dev Psychobiol 2023; 65:e22373. [PMID: 36811375 DOI: 10.1002/dev.22373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 12/05/2022] [Accepted: 01/03/2023] [Indexed: 02/18/2023]
Abstract
This longitudinal study investigated how kindergartners' position in the classroom social hierarchy and cortisol response relate to their change in school engagement across the first year of kindergarten (N = 332, M = 5.3 years, 51% boys, 41% White, 18% Black). We used naturalistic classroom observations of social hierarchy positions, laboratory-based challenges to elicit salivary cortisol response, and teacher, parent, and child reports of emotional engagement with school. Robust, clustered regression models revealed that in the fall, lower cortisol response (but not social hierarchy position) was associated with greater school engagement. However, by spring, significant interactions emerged. Highly reactive, subordinate children showed increases in school engagement from fall to spring of the kindergarten year, whereas highly reactive, dominant children showed decreases in school engagement. This is some of the first evidence that higher cortisol response marks biological sensitivity to early peer-based social contexts.
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Affiliation(s)
- Emma Armstrong-Carter
- The University of California, Berkeley, Berkeley, California.,The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Nicole R Bush
- University of California at San Francisco, San Francisco, California
| | - W Thomas Boyce
- University of California at San Francisco, San Francisco, California
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Wang Z, Shi W, Carroll RJ, Chatterjee N. Joint Modeling of Gene-Environment Correlations and Interactions using Polygenic Risk Scores in Case-Control Studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.14.528572. [PMID: 36824704 PMCID: PMC9948994 DOI: 10.1101/2023.02.14.528572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Polygenic risk scores (PRS) are rapidly emerging as aggregated measures of disease-risk associated with many genetic variants. Understanding the interplay of PRS with environmental factors is critical for interpreting and applying PRS in a wide variety of settings. We develop an efficient method for simultaneously modeling gene-environment correlations and interactions using PRS in case-control studies. We use a logistic-normal regression modeling framework to specify the disease risk and PRS distribution in the underlying population and propose joint inference across the two models using the retrospective likelihood of the case-control data. Extensive simulation studies demonstrate the flexibility of the method in trading-off bias and efficiency for the estimation of various model parameters compared to the standard logistic regression or a case-only analysis for gene-environment interactions, or a control-only analysis for gene-environment correlations. Finally, using simulated case-control datasets within the UK Biobank study, we demonstrate the power of the proposed method for its ability to recover results from the full prospective cohort for the detection of an interaction between long-term oral contraceptive use and PRS on the risk of breast cancer. This method is computationally efficient and implemented in a user-friendly R package.
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Affiliation(s)
- Ziqiao Wang
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Wen Shi
- McKusick-Nathans Institute, Department of Genetic Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Raymond J. Carroll
- Department of Statistics, Texas A&M University, College Station, TX, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
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15
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Fritzsche MC, Akyüz K, Cano Abadía M, McLennan S, Marttinen P, Mayrhofer MT, Buyx AM. Ethical layering in AI-driven polygenic risk scores-New complexities, new challenges. Front Genet 2023; 14:1098439. [PMID: 36816027 PMCID: PMC9933509 DOI: 10.3389/fgene.2023.1098439] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 01/04/2023] [Indexed: 01/27/2023] Open
Abstract
Researchers aim to develop polygenic risk scores as a tool to prevent and more effectively treat serious diseases, disorders and conditions such as breast cancer, type 2 diabetes mellitus and coronary heart disease. Recently, machine learning techniques, in particular deep neural networks, have been increasingly developed to create polygenic risk scores using electronic health records as well as genomic and other health data. While the use of artificial intelligence for polygenic risk scores may enable greater accuracy, performance and prediction, it also presents a range of increasingly complex ethical challenges. The ethical and social issues of many polygenic risk score applications in medicine have been widely discussed. However, in the literature and in practice, the ethical implications of their confluence with the use of artificial intelligence have not yet been sufficiently considered. Based on a comprehensive review of the existing literature, we argue that this stands in need of urgent consideration for research and subsequent translation into the clinical setting. Considering the many ethical layers involved, we will first give a brief overview of the development of artificial intelligence-driven polygenic risk scores, associated ethical and social implications, challenges in artificial intelligence ethics, and finally, explore potential complexities of polygenic risk scores driven by artificial intelligence. We point out emerging complexity regarding fairness, challenges in building trust, explaining and understanding artificial intelligence and polygenic risk scores as well as regulatory uncertainties and further challenges. We strongly advocate taking a proactive approach to embedding ethics in research and implementation processes for polygenic risk scores driven by artificial intelligence.
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Affiliation(s)
- Marie-Christine Fritzsche
- Institute of History and Ethics in Medicine, TUM School of Medicine, Technical University of Munich, Munich, Germany,Department of Science, Technology and Society (STS), School of Social Sciences and Technology, Technical University of Munich, Munich, Germany,*Correspondence: Marie-Christine Fritzsche,
| | - Kaya Akyüz
- Biobanking and Biomolecular Resources Research Infrastructure Consortium - European Research Infrastructure Consortium (BBMRI-ERIC), Graz, Austria,Department of Science and Technology Studies, University of Vienna, Vienna, Austria
| | - Mónica Cano Abadía
- Biobanking and Biomolecular Resources Research Infrastructure Consortium - European Research Infrastructure Consortium (BBMRI-ERIC), Graz, Austria
| | - Stuart McLennan
- Institute of History and Ethics in Medicine, TUM School of Medicine, Technical University of Munich, Munich, Germany,Department of Science, Technology and Society (STS), School of Social Sciences and Technology, Technical University of Munich, Munich, Germany
| | - Pekka Marttinen
- Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland
| | - Michaela Th. Mayrhofer
- Biobanking and Biomolecular Resources Research Infrastructure Consortium - European Research Infrastructure Consortium (BBMRI-ERIC), Graz, Austria
| | - Alena M. Buyx
- Institute of History and Ethics in Medicine, TUM School of Medicine, Technical University of Munich, Munich, Germany,Department of Science, Technology and Society (STS), School of Social Sciences and Technology, Technical University of Munich, Munich, Germany
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16
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Judd N, Sauce B, Klingberg T. Schooling substantially improves intelligence, but neither lessens nor widens the impacts of socioeconomics and genetics. NPJ SCIENCE OF LEARNING 2022; 7:33. [PMID: 36522329 PMCID: PMC9755250 DOI: 10.1038/s41539-022-00148-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
Schooling, socioeconomic status (SES), and genetics all impact intelligence. However, it is unclear to what extent their contributions are unique and if they interact. Here we used a multi-trait polygenic score for cognition (cogPGS) with a quasi-experimental regression discontinuity design to isolate how months of schooling relate to intelligence in 6567 children (aged 9-11). We found large, independent effects of schooling (β ~ 0.15), cogPGS (β ~ 0.10), and SES (β ~ 0.20) on working memory, crystallized (cIQ), and fluid intelligence (fIQ). Notably, two years of schooling had a larger effect on intelligence than the lifetime consequences, since birth, of SES or cogPGS-based inequalities. However, schooling showed no interaction with cogPGS or SES for the three intelligence domains tested. While schooling had strong main effects on intelligence, it did not lessen, nor widen the impact of these preexisting SES or genetic factors.
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Affiliation(s)
- Nicholas Judd
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden.
- Cognitive Neuroscience Department, Donders Institute for Brain, Cognition, and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Bruno Sauce
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Torkel Klingberg
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
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17
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Novembre J, Stein C, Asgari S, Gonzaga-Jauregui C, Landstrom A, Lemke A, Li J, Mighton C, Taylor M, Tishkoff S. Addressing the challenges of polygenic scores in human genetic research. Am J Hum Genet 2022; 109:2095-2100. [PMID: 36459976 PMCID: PMC9808501 DOI: 10.1016/j.ajhg.2022.10.012] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
The genotyping of millions of human samples has made it possible to evaluate variants across the human genome for their possible association with risks for numerous diseases and other traits by using genome-wide association studies (GWASs). The associations between phenotype and genotype found in GWASs make possible the construction of polygenic scores (PGSs), which aim to predict a trait or disease outcome in an individual on the basis of their genotype (in the disease case, the term polygenic risk score [PRS] is often used). PGSs have shown promise for studying the biology of complex traits and as a tool for evaluating individual disease risks in clinical settings. Although the quantity and quality of data to compute PGSs are increasing, challenges remain in the technical aspects of developing PGSs and in the ethical and social issues that might arise from their use. This ASHG Guidance emphasizes three major themes for researchers working with or interested in the application of PGSs in their own research: (1) developing diverse research cohorts; (2) fostering robustness in the development, application, and interpretation of PGSs; and (3) improving the communication of PGS results and their implications to broad audiences.
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Affiliation(s)
- John Novembre
- Professional Practice and Social Implications Committee Polygenic Scores Guidance Writing Group, American Society of Human Genetics, Rockville MD, USA,Department of Human Genetics, University of Chicago, Chicago, IL, USA,Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA,Corresponding author
| | - Catherine Stein
- Professional Practice and Social Implications Committee Polygenic Scores Guidance Writing Group, American Society of Human Genetics, Rockville MD, USA,Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA,Corresponding author
| | - Samira Asgari
- Professional Practice and Social Implications Committee Polygenic Scores Guidance Writing Group, American Society of Human Genetics, Rockville MD, USA,Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Claudia Gonzaga-Jauregui
- Professional Practice and Social Implications Committee Polygenic Scores Guidance Writing Group, American Society of Human Genetics, Rockville MD, USA,International Laboratory for Human Genome Research, Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Andrew Landstrom
- Professional Practice and Social Implications Committee Polygenic Scores Guidance Writing Group, American Society of Human Genetics, Rockville MD, USA,Department of Pediatrics, Division of Cardiology, Duke University School of Medicine, Durham, NC, USA
| | - Amy Lemke
- Professional Practice and Social Implications Committee Polygenic Scores Guidance Writing Group, American Society of Human Genetics, Rockville MD, USA,Norton Children’s Research Institute, affiliated with the University of Louisville School of Medicine, Louisville, KY, USA
| | - Jun Li
- Professional Practice and Social Implications Committee Polygenic Scores Guidance Writing Group, American Society of Human Genetics, Rockville MD, USA,Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Chloe Mighton
- Professional Practice and Social Implications Committee Polygenic Scores Guidance Writing Group, American Society of Human Genetics, Rockville MD, USA,Genomics Health Services Research Program, St. Michael’s Hospital, Unity Health Toronto, Toronto, ON, Canada,Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Matthew Taylor
- Professional Practice and Social Implications Committee Polygenic Scores Guidance Writing Group, American Society of Human Genetics, Rockville MD, USA,Adult Medical Genetics Program, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Sarah Tishkoff
- Professional Practice and Social Implications Committee Polygenic Scores Guidance Writing Group, American Society of Human Genetics, Rockville MD, USA,Department of Genetics, Center for Global Genomics and Health Equity, University of Pennsylvania, Philadelphia, PA, USA,Department of Biology, Center for Global Genomics and Health Equity, University of Pennsylvania, Philadelphia, PA, USA
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18
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Domingue BW, Kanopka K, Trejo S, Rhemtulla M, Tucker-Drob EM. Ubiquitous bias and false discovery due to model misspecification in analysis of statistical interactions: The role of the outcome's distribution and metric properties. Psychol Methods 2022:2023-06135-001. [PMID: 36201820 PMCID: PMC10369499 DOI: 10.1037/met0000532] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Studies of interaction effects are of great interest because they identify crucial interplay between predictors in explaining outcomes. Previous work has considered several potential sources of statistical bias and substantive misinterpretation in the study of interactions, but less attention has been devoted to the role of the outcome variable in such research. Here, we consider bias and false discovery associated with estimates of interaction parameters as a function of the distributional and metric properties of the outcome variable. We begin by illustrating that, for a variety of noncontinuously distributed outcomes (i.e., binary and count outcomes), attempts to use the linear model for recovery leads to catastrophic levels of bias and false discovery. Next, focusing on transformations of normally distributed variables (i.e., censoring and noninterval scaling), we show that linear models again produce spurious interaction effects. We provide explanations offering geometric and algebraic intuition as to why interactions are a challenge for these incorrectly specified models. In light of these findings, we make two specific recommendations. First, a careful consideration of the outcome's distributional properties should be a standard component of interaction studies. Second, researchers should approach research focusing on interactions with heightened levels of scrutiny. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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Affiliation(s)
- Benjamin W. Domingue
- Graduate School of Education, Stanford University & Center for Population Health Sciences, Stanford Medicine
| | | | - Sam Trejo
- Department of Sociology & Office of Population Research, Princeton University
| | | | - Elliot M. Tucker-Drob
- Department of Psychology & Population Research Center, University of Texas at Austin
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19
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Cheesman R, Eilertsen EM, Ayorech Z, Borgen NT, Andreassen OA, Larsson H, Zachrisson H, Torvik FA, Ystrom E. How interactions between ADHD and schools affect educational achievement: a family-based genetically sensitive study. J Child Psychol Psychiatry 2022; 63:1174-1185. [PMID: 35789088 PMCID: PMC9796390 DOI: 10.1111/jcpp.13656] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/19/2022] [Indexed: 01/01/2023]
Abstract
BACKGROUND Children with ADHD tend to achieve less than their peers in school. It is unknown whether schools moderate this association. Nonrandom selection of children into schools related to variations in their ADHD risk poses a methodological problem. METHODS We linked data on ADHD symptoms of inattention and hyperactivity and parent-child ADHD polygenic scores (PGS) from the Norwegian Mother, Father, and Child Cohort Study (MoBa) to achievement in standardised tests and school identifiers. We estimated interactions of schools with individual differences between students in inattention, hyperactivity, and ADHD-PGS using multilevel models with random slopes for ADHD effects on achievement over schools. In our PGS analyses, we adjust for parental selection of schools by adjusting for parental ADHD-PGS (a within-family PGS design). We then tested whether five school sociodemographic measures explained any interactions. RESULTS Analysis of up to 23,598 students attending 2,579 schools revealed interactions between school and ADHD effects on achievement. The variability between schools in the effects of inattention, hyperactivity and within-family ADHD-PGS on achievement was 0.08, 0.07 and 0.05 SDs, respectively. For example, the average effect of inattention on achievement was β = -0.23 (SE = 0.009), but in 2.5% of schools with the weakest effects, the value was -0.07 or less. ADHD has a weaker effect on achievement in higher-performing schools. Schools make more of a difference to the achievements of students with higher levels of ADHD, explaining over four times as much variance in achievement for those with high versus average inattention symptoms. School sociodemographic measures could not explain the ADHD-by-school interactions. CONCLUSIONS Although ADHD symptoms and genetic risk tend to hinder achievement, schools where their effects are weaker do exist. Differences between schools in support for children with ADHD should be evened out.
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Affiliation(s)
- Rosa Cheesman
- PROMENTA Research Center, Department of PsychologyUniversity of OsloOsloNorway
| | - Espen M. Eilertsen
- PROMENTA Research Center, Department of PsychologyUniversity of OsloOsloNorway,Centre for Fertility and HealthNorwegian Institute of Public HealthOsloNorway
| | - Ziada Ayorech
- PROMENTA Research Center, Department of PsychologyUniversity of OsloOsloNorway
| | | | - Ole A. Andreassen
- NORMENT, Division of Mental Health and AddictionOslo University HospitalOsloNorway,Institute of Clinical MedicineUniversity of OsloOsloNorway
| | - Henrik Larsson
- Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden,School of Medical SciencesÖrebro UniversityÖrebroSweden
| | | | - Fartein A. Torvik
- PROMENTA Research Center, Department of PsychologyUniversity of OsloOsloNorway,Centre for Fertility and HealthNorwegian Institute of Public HealthOsloNorway
| | - Eivind Ystrom
- PROMENTA Research Center, Department of PsychologyUniversity of OsloOsloNorway,Department of Mental DisordersNorwegian Institute of Public HealthOsloNorway
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20
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Plomin R, Gidziela A, Malanchini M, von Stumm S. Gene-environment interaction using polygenic scores: Do polygenic scores for psychopathology moderate predictions from environmental risk to behavior problems? Dev Psychopathol 2022; 34:1-11. [PMID: 36148872 PMCID: PMC7613991 DOI: 10.1017/s0954579422000931] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The DNA revolution has energized research on interactions between genes and environments (GxE) by creating indices of G (polygenic scores) that are powerful predictors of behavioral traits. Here, we test the extent to which polygenic scores for attention-deficit/hyperactivity disorder and neuroticism moderate associations between parent reports of their children's environmental risk (E) at ages 3 and 4 and teacher ratings of behavior problems (hyperactivity/inattention, conduct problems, emotional symptoms, and peer relationship problems) at ages 7, 9 and 12. The sampling frame included up to 6687 twins from the Twins Early Development Study. Our analyses focused on relative effect sizes of G, E and GxE in predicting behavior problems. G, E and GxE predicted up to 2%, 2% and 0.4%, respectively, of the variance in externalizing behavior problems (hyperactivity/inattention and conduct problems) across ages 7, 9 and 12, with no clear developmental trends. G and E predictions of emotional symptoms and peer relationship problems were weaker. A quarter (12 of 48) of our tests of GxE were nominally significant (p = .05). Increasing the predictive power of G and E would enhance the search for GxE.
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Affiliation(s)
- Robert Plomin
- Institute of Psychiatry, Psychology and Neuroscience, King’s
College London, London, UK
| | - Agnieszka Gidziela
- School of Biological and Behavioural Sciences, Queen Mary University
of London, London, UK
| | - Margherita Malanchini
- School of Biological and Behavioural Sciences, Queen Mary University
of London, London, UK
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21
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Guo G, Lin MJ, Harris KM. Socioeconomic and genomic roots of verbal ability from current evidence. NPJ SCIENCE OF LEARNING 2022; 7:22. [PMID: 36085328 PMCID: PMC9463438 DOI: 10.1038/s41539-022-00137-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 08/03/2022] [Indexed: 06/15/2023]
Abstract
This research examines how the human genome and SES jointly and interactively shape verbal ability among youth in the U.S. The youth are aged 12-18 when the study starts. The research draws on findings from the latest GWAS as well as a rich set of longitudinal SES measures at individual, family and neighborhood levels from Add Health (N = 7194). Both SES and genome measures predict verbal ability well separately and jointly. More interestingly, the inclusion of both sets of predictors in the same model corrects for about 20% upward bias in the effect of the education PGS, and implies that about 20-30% of the effects of parental SES are not environmental, but parentally genomic. The three incremental R2s that measure the relative contributions of the two PGSs, the genomic component in parental SES, and the environmental component in parental SES are estimated to be about 1.5%, 1.5%, and 7.8%, respectively. The total environmental R2 and the total genomic R2 are, thus, 7.8% and 3%, respectively. These findings confirm the importance of SES environment and also pose challenges to traditional social-science research. Not only does an individual's genome have an important direct influence on verbal ability, parental genomes also influence verbal ability through parental SES. The decades-long blueprint of including SES in a model and interpreting their effects as those of SES needs to be amended accordingly. A straightforward solution is to routinely collect DNA data for large social-science studies granted that the primary purpose is to understand social and environmental influences.
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Affiliation(s)
- Guang Guo
- Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Meng-Jung Lin
- Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kathleen Mullan Harris
- Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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22
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Thomas NS, Barr P, Aliev F, Stephenson M, Kuo SIC, Chan G, Dick DM, Edenberg HJ, Hesselbrock V, Kamarajan C, Kuperman S, Salvatore JE. Principal Component Analysis Reduces Collider Bias in Polygenic Score Effect Size Estimation. Behav Genet 2022; 52:268-280. [PMID: 35674916 PMCID: PMC10103419 DOI: 10.1007/s10519-022-10104-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 04/11/2022] [Accepted: 04/23/2022] [Indexed: 11/02/2022]
Abstract
In this study, we test principal component analysis (PCA) of measured confounders as a method to reduce collider bias in polygenic association models. We present results from simulations and application of the method in the Collaborative Study of the Genetics of Alcoholism (COGA) sample with a polygenic score for alcohol problems, DSM-5 alcohol use disorder as the target phenotype, and two collider variables: tobacco use and educational attainment. Simulation results suggest that assumptions regarding the correlation structure and availability of measured confounders are complementary, such that meeting one assumption relaxes the other. Application of the method in COGA shows that PC covariates reduce collider bias when tobacco use is used as the collider variable. Application of this method may improve PRS effect size estimation in some cases by reducing the effect of collider bias, making efficient use of data resources that are available in many studies.
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Affiliation(s)
- Nathaniel S Thomas
- Department of Psychology, Virginia Commonwealth University, Box 842018, 23284-2018, Richmond, VA, United States.
| | - Peter Barr
- Department of Psychiatry & Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, New Jersey, United States
| | - Fazil Aliev
- Department of Psychology, Virginia Commonwealth University, Box 842018, 23284-2018, Richmond, VA, United States
| | - Mallory Stephenson
- Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, Virginia, United States
| | - Sally I-Chun Kuo
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey, United States
| | - Grace Chan
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, Connecticut, United States
- Department of Psychiatry, University of Iowa, Carver College of Medicine, Iowa City, Iowa, United States
| | - Danielle M Dick
- Department of Psychology, Virginia Commonwealth University, Box 842018, 23284-2018, Richmond, VA, United States
- Department of Human & Molecular Genetics, Virginia Commonwealth University, Richmond, Virginia, United States
| | - Howard J Edenberg
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, United States
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, United States
| | - Victor Hesselbrock
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, Connecticut, United States
| | - Chella Kamarajan
- Department of Psychiatry & Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, New Jersey, United States
| | - Samuel Kuperman
- Department of Psychiatry, University of Iowa, Carver College of Medicine, Iowa City, Iowa, United States
| | - Jessica E Salvatore
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey, United States
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23
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Polygenic risk scores: improving the prediction of future disease or added complexity? Br J Gen Pract 2022; 72:396-398. [PMID: 35902257 PMCID: PMC9343049 DOI: 10.3399/bjgp22x720437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
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24
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Johnson R, Sotoudeh R, Conley D. Polygenic Scores for Plasticity: A New Tool for Studying Gene-Environment Interplay. Demography 2022; 59:1045-1070. [PMID: 35553650 DOI: 10.1215/00703370-9957418] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Fertility, health, education, and other outcomes of interest to demographers are the product of an individual's genetic makeup and their social environment. Yet, gene × environment (G×E) research deploys a limited toolkit on the genetic side to study the gene-environment interplay, relying on polygenic scores (PGSs) that reflect the influence of genetics on levels of an outcome. In this article, we develop a genetic summary measure better suited for G×E research: variance polygenic scores (vPGSs), which are PGSs that reflect genetic contributions to plasticity in outcomes. First, we use the UK Biobank (N ∼ 408,000 in the analytic sample) and the Health and Retirement Study (N ∼ 5,700 in the analytic sample) to compare four approaches to constructing PGSs for plasticity. The results show that widely used methods for discovering which genetic variants affect outcome variability fail to serve as distinctive new tools for G×E. Second, using the PGSs 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 useful new tool for population scientists studying the interplay of nature and nurture and for population-based studies that are releasing PGSs to applied researchers.
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Affiliation(s)
- Rebecca Johnson
- McCourt School of Public Policy, Georgetown University, Washington, DC, USA
| | | | - Dalton Conley
- Department of Sociology and Office of Population Research, Princeton University, Princeton, NJ, USA
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25
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Fuentes-Paez G, Escaramís G, Aguilar-Lacasaña S, Andrusaityte S, Brantsæter AL, Casas M, Charles MA, Chatzi L, Lepeule J, Grazuleviciene R, Gützkow KB, Heude B, Maitre L, Ruiz-Arenas C, Sunyer J, Urquiza J, Yang TC, Wright J, Vrijheid M, Vilor-Tejedor N, Bustamante M. Study of the Combined Effect of Maternal Tobacco Smoking and Polygenic Risk Scores on Birth Weight and Body Mass Index in Childhood. Front Genet 2022; 13:867611. [PMID: 35646076 PMCID: PMC9133473 DOI: 10.3389/fgene.2022.867611] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 04/04/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Maternal smoking during pregnancy has adverse health effects on the offspring, including lower birth weight and increased risk for obesity. These outcomes are also influenced by common genetic polymorphisms. We aimed to investigate the combined effect of maternal smoking during pregnancy and genetic predisposition on birth weight and body mass index (BMI)-related traits in 1,086 children of the Human Early Life Exposome (HELIX) project.Methods: Maternal smoking during pregnancy was self-reported. Phenotypic traits were assessed at birth or at the age of 8 years. Ten polygenic risk scores (PRSs) per trait were calculated using the PRSice v2 program. For birth weight, we estimated two sets of PRSs based on two different base GWAS summary statistics: PRS-EGG, which includes HELIX children, and PRS-PanUK, which is completely independent. The best PRS per trait (highest R2) was selected for downstream analyses, and it was treated in continuous or categorized into three groups. Multivariate linear regression models were applied to evaluate the association of the explanatory variables with the traits of interest. The combined effect was evaluated by including an interaction term in the regression models and then running models stratified by the PRS group.Results: BMI-related traits were correlated among them but not with birth weight. A similar pattern was observed for their PRSs. On average, the PRSs explained ∼4% of the phenotypic variation, with higher PRS values related to higher trait values (p-value <5.55E-08). Sustained maternal smoking was associated with lower birth weight and higher BMI and related traits (p-value <2.99E-02). We identified a gene by environment (GxE) interaction for birth weight between sustained maternal smoking and the PRS-EGG in three groups (p-value interaction = 0.01), which was not replicated with the PRS-PanUK (p-value interaction = 0.341). Finally, we did not find any statistically significant GxE interaction for BMI-related traits (p-value interaction >0.237).Conclusion: Sustained maternal smoking and the PRSs were independently associated with birth weight and childhood BMI-related traits. There was low evidence of GxE interactions.
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Affiliation(s)
- Georgina Fuentes-Paez
- Endocrine Regulatory Genomics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Geòrgia Escaramís
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Departament de Biomedicina, Institut de Neurociències, Universitat de Barcelona (UB), Barcelona, Spain
| | - Sofía Aguilar-Lacasaña
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Childhood and Environment, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Sandra Andrusaityte
- Department of Environmental Sciences, Vytautas Magnus University, Kaunas, Lithuania
| | - Anne Lise Brantsæter
- Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Maribel Casas
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Childhood and Environment, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Marie-Aline Charles
- Université de Paris Cité, Inserm, INRAE, Centre of Research in Epidemiology and StatisticS (CRESS), Paris, France
| | - Leda Chatzi
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Johanna Lepeule
- Inserm, CNRS, Team of Environmental Epidemiology Applied to Development and Respiratory Health, Institute for Advanced Biosciences, University Grenoble Alpes, Grenoble, France
| | | | - Kristine B. Gützkow
- Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Barbara Heude
- Université de Paris Cité, Inserm, INRAE, Centre of Research in Epidemiology and StatisticS (CRESS), Paris, France
| | - Léa Maitre
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Childhood and Environment, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Carlos Ruiz-Arenas
- Genetics Unit, Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Enfermedades Raras (CIBERER), Barcelona, Spain
| | - Jordi Sunyer
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Childhood and Environment, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Fundació Institut Mar D'Investigacions Mèdiques (IMIM), Barcelona, Spain
| | - Jose Urquiza
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Childhood and Environment, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Tiffany C. Yang
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, United Kingdom
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, United Kingdom
| | - Martine Vrijheid
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Childhood and Environment, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Natàlia Vilor-Tejedor
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, Netherlands
| | - Mariona Bustamante
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Childhood and Environment, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- *Correspondence: Mariona Bustamante,
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Genetically-predicted trait-BMI, everyday discrimination and life satisfaction among older U.S. adults. ADAPTIVE HUMAN BEHAVIOR AND PHYSIOLOGY 2022. [DOI: 10.1007/s40750-022-00189-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Carter AR, Harrison S, Gill D, Davey Smith G, Taylor AE, Howe LD, Davies NM. Educational attainment as a modifier for the effect of polygenic scores for cardiovascular risk factors: cross-sectional and prospective analysis of UK Biobank. Int J Epidemiol 2022; 51:885-897. [PMID: 35134953 PMCID: PMC9189971 DOI: 10.1093/ije/dyac002] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 01/06/2022] [Indexed: 01/22/2023] Open
Abstract
Background Understanding the interplay between educational attainment and genetic predictors of cardiovascular risk may improve our understanding of the aetiology of educational inequalities in cardiovascular disease. Methods In up to 320 120 UK Biobank participants of White British ancestry (mean age = 57 years, female 54%), we created polygenic scores for nine cardiovascular risk factors or diseases: alcohol consumption, body mass index, low-density lipoprotein cholesterol, lifetime smoking behaviour, systolic blood pressure, atrial fibrillation, coronary heart disease, type 2 diabetes and stroke. We estimated whether educational attainment modified genetic susceptibility to these risk factors and diseases. Results On the additive scale, higher educational attainment reduced genetic susceptibility to higher body mass index, smoking, atrial fibrillation and type 2 diabetes, but increased genetic susceptibility to higher LDL-C and higher systolic blood pressure. On the multiplicative scale, there was evidence that higher educational attainment increased genetic susceptibility to atrial fibrillation and coronary heart disease, but little evidence of effect modification was found for all other traits considered. Conclusions Educational attainment modifies the genetic susceptibility to some cardiovascular risk factors and diseases. The direction of this effect was mixed across traits considered and differences in associations between the effect of the polygenic score across strata of educational attainment was uniformly small. Therefore, any effect modification by education of genetic susceptibility to cardiovascular risk factors or diseases is unlikely to substantially explain the development of inequalities in cardiovascular risk.
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Affiliation(s)
- Alice R Carter
- MRC Integrative Epidemiology Unit, University of Bristol Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Sean Harrison
- MRC Integrative Epidemiology Unit, University of Bristol Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Dipender Gill
- Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George’s, University of London, London, UK
- Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George’s University Hospitals NHS Foundation Trust, London, UK
- Novo Nordisk Research Centre Oxford, Old Road Campus, Oxford, UK
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol, UK
| | - Amy E Taylor
- MRC Integrative Epidemiology Unit, University of Bristol Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol, UK
| | - Laura D Howe
- MRC Integrative Epidemiology Unit, University of Bristol Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Neil M Davies
- MRC Integrative Epidemiology Unit, University of Bristol Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
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Domingue BW, Kanopka K, Mallard TT, Trejo S, Tucker-Drob EM. Modeling Interaction and Dispersion Effects in the Analysis of Gene-by-Environment Interaction. Behav Genet 2021; 52:56-64. [PMID: 34855050 DOI: 10.1007/s10519-021-10090-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 10/28/2021] [Indexed: 11/25/2022]
Abstract
Genotype-by-environment interaction (GxE) studies probe heterogeneity in response to risk factors or interventions. Popular methods for estimation of GxE examine multiplicative interactions between individual genetic and environmental measures. However, risk factors and interventions may modulate the total variance of an epidemiological outcome that itself represents the aggregation of many other etiological components. We expand the traditional GxE model to directly model genetic and environmental moderation of the dispersion of the outcome. We derive a test statistic, [Formula: see text], for inferring whether an interaction identified between individual genetic and environmental measures represents a more general pattern of moderation of the total variance in the phenotype by either the genetic or the environmental measure. We validate our method via extensive simulation, and apply it to investigate genotype-by-birth year interactions for Body Mass Index (BMI) with polygenic scores in the Health and Retirement Study (N = 11,586) and individual genetic variants in the UK Biobank (N = 380,605). We find that changes in the penetrance of a genome-wide polygenic score for BMI across birth year are partly representative of a more general pattern of expanding BMI variation across generations. Three individual variants found to be more strongly associated with BMI among later born individuals, were also associated with the magnitude of variability in BMI itself within any given birth year, suggesting that they may confer general sensitivity of BMI to a range of unmeasured factors beyond those captured by birth year. We introduce an expanded GxE regression model that explicitly models genetic and environmental moderation of the dispersion of the outcome under study. This approach can determine whether GxE interactions identified are specific to the measured predictors or represent a more general pattern of moderation of the total variance in the outcome by the genetic and environmental measures.
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Affiliation(s)
- Benjamin W Domingue
- Graduate School of Education, Stanford University and Center for Population Health Sciences, Stanford Medicine, Stanford, USA.
| | - Klint Kanopka
- Graduate School of Education, Stanford University, Stanford, USA
| | - Travis T Mallard
- Department of Psychology, University of Texas at Austin, Austin, USA
| | - Sam Trejo
- Department of Sociology and Office of Population Research, Princeton University, Princeton, USA
| | - Elliot M Tucker-Drob
- Department of Psychology and Population Research Center, University of Texas at Austin, Austin, USA.
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Uchikoshi F, Conley D. Gene-environment Interactions and School Tracking during Secondary Education: Evidence from the U.S. RESEARCH IN SOCIAL STRATIFICATION AND MOBILITY 2021; 76:100628. [PMID: 35185239 PMCID: PMC8849562 DOI: 10.1016/j.rssm.2021.100628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
There is much evidence to suggest that family background and the context of secondary education both contribute to the formation of educational inequalities. Meanwhile, our knowledge about the role of ability in generating class differences in educational outcomes is still limited. By deploying genetic data that allow us to measure at least part of "innate" ability inherited through biological mechanisms from parents, this study examines how such abilities are associated with educational tracking outcomes among U.S. high schoolers. This study also details our understanding of the role of nature and nurture in the educational attainment processes by testing for gene-environment interactions-that is, a joint, mutually moderating effect of one's genetic potential and one's environment (e.g., family background or school context) on phenotypic outcomes (educational tracking). Using the National Longitudinal Study of Adolescent to Adult Health that collects a unique set of demographic, educational, and genetic characteristics of students, we report the following results: First, a positive association between the genetic potential for educational attainment and taking advanced courses holds even after controlling for previous course tracking measures. Second, results provide suggestive evidence that parental SES amplifies the association between one's genetic potential for educational attainment and mathematics tracking. In contrast to the argument by some stratification scholars that places primary emphasis on the role of social background for the reproduction of educational stratification, the present findings imply that we need to fully consider the role of genetic inheritance for educational stratification in addition to social origin.
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Herd P, Mills MC, Dowd JB. Reconstructing Sociogenomics Research: Dismantling Biological Race and Genetic Essentialism Narratives. JOURNAL OF HEALTH AND SOCIAL BEHAVIOR 2021; 62:419-435. [PMID: 34100668 DOI: 10.1177/00221465211018682] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
We detail the implications of sociogenomics for social determinants research. We focus on education and race because of how early twentieth-century scientific eugenic thinking facilitated a range of racist and eugenic policies, most of which helped justify and pattern racial and educational morbidity and mortality disparities that remain today, and are central to sociological research. Consequently, we detail the implications of sociogenomics research by unpacking key controversies and opportunities in sociogenomics as they pertain to the understanding of racial and educational inequalities. We clarify why race is not a valid biological or genetic construct, the ways that environments powerfully shape genetic influence, and risks linked to this field of research. We argue that sociologists can usefully engage in genetics research, a domain dominated by psychologists and behaviorists who, given their focus on individuals, have mostly not examined the role of history and social structure in shaping genetic influence.
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31
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Schmitz LL, Goodwin J, Miao J, Lu Q, Conley D. The impact of late-career job loss and genetic risk on body mass index: Evidence from variance polygenic scores. Sci Rep 2021; 11:7647. [PMID: 33828129 PMCID: PMC8027610 DOI: 10.1038/s41598-021-86716-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 03/16/2021] [Indexed: 02/02/2023] Open
Abstract
Unemployment shocks from the COVID-19 pandemic have reignited concerns over the long-term effects of job loss on population health. Past research has highlighted the corrosive effects of unemployment on health and health behaviors. This study examines whether the effects of job loss on changes in body mass index (BMI) are moderated by genetic predisposition using data from the U.S. Health and Retirement Study (HRS). To improve detection of gene-by-environment (G × E) interplay, we interacted layoffs from business closures-a plausibly exogenous environmental exposure-with whole-genome polygenic scores (PGSs) that capture genetic contributions to both the population mean (mPGS) and variance (vPGS) of BMI. Results show evidence of genetic moderation using a vPGS (as opposed to an mPGS) and indicate genome-wide summary measures of phenotypic plasticity may further our understanding of how environmental stimuli modify the distribution of complex traits in a population.
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Affiliation(s)
- Lauren L Schmitz
- Robert M. La Follette School of Public Affairs, University of Wisconsin-Madison, 1225 Observatory Drive, Madison, WI, 53706, USA.
| | - Julia Goodwin
- Department of Sociology, University of Wisconsin-Madison, Madison, WI, USA
| | - Jiacheng Miao
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
- Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA
| | - Dalton Conley
- Department of Sociology, Princeton University & NBER, Princeton, NJ, USA
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Armstrong‐Carter E, Wertz J, Domingue BW. Genetics and Child Development: Recent Advances and Their Implications for Developmental Research. CHILD DEVELOPMENT PERSPECTIVES 2021. [DOI: 10.1111/cdep.12400] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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