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Torvik FA, Sunde HF, Cheesman R, Eftedal NH, Keller MC, Ystrom E, Eilertsen EM. Non-random Mating Patterns in Education, Mental, and Somatic Health: A Population Study on Within- and Cross-Trait Associations. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.11.27.23299055. [PMID: 38077008 PMCID: PMC10705620 DOI: 10.1101/2023.11.27.23299055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Partners resemble each other on many traits, such as health and education. The traits are usually studied one by one in data from established couples and with potential participation bias. We studied all Norwegian parents who had their first child between 2016 and 2020 (N=187,926) and the siblings of these parents. We analysed grade point averages at age 16 (GPA), educational attainment (EA), and medical records with diagnostic data on 10 mental and 10 somatic health conditions measured 10 to 5 years before childbirth. We found stronger partner similarity in mental (median r=0.14) than in somatic health conditions (median r=0.04), with ubiquitous cross-trait correlations for mental health conditions (median r=0.13). GPA correlated 0.43 and EA 0.47 between partners. High GPA or EA was associated with better mental (median r=-0.16) and somatic (median r=-0.08) health in partners. Elevated correlations for mental health (median r=0.25) in established couples indicated convergence. Analyses of siblings and in-laws revealed deviations from direct assortment, suggesting instead indirect assortment based on related traits. Adjusting for GPA and EA reduced partner correlations in health with 30-40%. This has implications for the distribution of risk factors among children, for genetic studies, and for studies of intergenerational transmission.
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Affiliation(s)
- Fartein Ask Torvik
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Hans Fredrik Sunde
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Rosa Cheesman
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | | | - Matthew C. Keller
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, USA
| | - Eivind Ystrom
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Espen Moen Eilertsen
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
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2
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Herrera-Luis E, Benke K, Volk H, Ladd-Acosta C, Wojcik GL. Gene-environment interactions in human health. Nat Rev Genet 2024:10.1038/s41576-024-00731-z. [PMID: 38806721 DOI: 10.1038/s41576-024-00731-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/03/2024] [Indexed: 05/30/2024]
Abstract
Gene-environment interactions (G × E), the interplay of genetic variation with environmental factors, have a pivotal impact on human complex traits and diseases. Statistically, G × E can be assessed by determining the deviation from expectation of predictive models based solely on the phenotypic effects of genetics or environmental exposures. Despite the unprecedented, widespread and diverse use of G × E analytical frameworks, heterogeneity in their application and reporting hinders their applicability in public health. In this Review, we discuss study design considerations as well as G × E analytical frameworks to assess polygenic liability dependent on the environment, to identify specific genetic variants exhibiting G × E, and to characterize environmental context for these dynamics. We conclude with recommendations to address the most common challenges and pitfalls in the conceptualization, methodology and reporting of G × E studies, as well as future directions.
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Affiliation(s)
- Esther Herrera-Luis
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kelly Benke
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Heather Volk
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Christine Ladd-Acosta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Genevieve L Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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3
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Frach L, Barkhuizen W, Allegrini AG, Ask H, Hannigan LJ, Corfield EC, Andreassen OA, Dudbridge F, Ystrom E, Havdahl A, Pingault JB. Examining intergenerational risk factors for conduct problems using polygenic scores in the Norwegian Mother, Father and Child Cohort Study. Mol Psychiatry 2024; 29:951-961. [PMID: 38225381 PMCID: PMC11176059 DOI: 10.1038/s41380-023-02383-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 12/07/2023] [Accepted: 12/13/2023] [Indexed: 01/17/2024]
Abstract
The aetiology of conduct problems involves a combination of genetic and environmental factors, many of which are inherently linked to parental characteristics given parents' central role in children's lives across development. It is important to disentangle to what extent links between parental heritable characteristics and children's behaviour are due to transmission of genetic risk or due to parental indirect genetic influences via the environment (i.e., genetic nurture). We used 31,290 genotyped mother-father-child trios from the Norwegian Mother, Father and Child Cohort Study (MoBa), testing genetic transmission and genetic nurture effects on conduct problems using 13 polygenic scores (PGS) spanning psychiatric conditions, substance use, education-related factors, and other risk factors. Maternal or self-reports of conduct problems at ages 8 and 14 years were available for up to 15,477 children. We found significant genetic transmission effects on conduct problems for 12 out of 13 PGS at age 8 years (strongest association: PGS for smoking, β = 0.07, 95% confidence interval = [0.05, 0.08]) and for 4 out of 13 PGS at age 14 years (strongest association: PGS for externalising problems, β = 0.08, 95% confidence interval = [0.05, 0.11]). Conversely, we did not find genetic nurture effects for conduct problems using our selection of PGS. Our findings provide evidence for genetic transmission in the association between parental characteristics and child conduct problems. Our results may also indicate that genetic nurture via traits indexed by our polygenic scores is of limited aetiological importance for conduct problems-though effects of small magnitude or effects via parental traits not captured by the included PGS remain a possibility.
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Affiliation(s)
- Leonard Frach
- Department of Clinical, Educational & Health Psychology, Division of Psychology & Language Sciences, Faculty of Brain Sciences, University College London, London, UK.
| | - Wikus Barkhuizen
- Department of Clinical, Educational & Health Psychology, Division of Psychology & Language Sciences, Faculty of Brain Sciences, University College London, London, UK
| | - Andrea G Allegrini
- Department of Clinical, Educational & Health Psychology, Division of Psychology & Language Sciences, Faculty of Brain Sciences, University College London, London, UK
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Helga Ask
- Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Laurie J Hannigan
- Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Elizabeth C Corfield
- Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Ole A Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Frank Dudbridge
- Department of Population Health Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester, UK
| | - Eivind Ystrom
- Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Alexandra Havdahl
- Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Jean-Baptiste Pingault
- Department of Clinical, Educational & Health Psychology, Division of Psychology & Language Sciences, Faculty of Brain Sciences, University College London, London, UK
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
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4
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Sunde HF, Eftedal NH, Cheesman R, Corfield EC, Kleppesto TH, Seierstad AC, Ystrom E, Eilertsen EM, Torvik FA. Genetic similarity between relatives provides evidence on the presence and history of assortative mating. Nat Commun 2024; 15:2641. [PMID: 38531929 PMCID: PMC10966108 DOI: 10.1038/s41467-024-46939-9] [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: 08/10/2023] [Accepted: 03/13/2024] [Indexed: 03/28/2024] Open
Abstract
Assortative mating - the non-random mating of individuals with similar traits - is known to increase trait-specific genetic variance and genetic similarity between relatives. However, empirical evidence is limited for many traits, and the implications hinge on whether assortative mating has started recently or many generations ago. Here we show theoretically and empirically that genetic similarity between relatives can provide evidence on the presence and history of assortative mating. First, we employed path analysis to understand how assortative mating affects genetic similarity between family members across generations, finding that similarity between distant relatives is more affected than close relatives. Next, we correlated polygenic indices of 47,135 co-parents from the Norwegian Mother, Father, and Child Cohort Study (MoBa) and found genetic evidence of assortative mating in nine out of sixteen examined traits. The same traits showed elevated similarity between relatives, especially distant relatives. Six of the nine traits, including educational attainment, showed greater genetic variance among offspring, which is inconsistent with stable assortative mating over many generations. These results suggest an ongoing increase in familial similarity for these traits. The implications of this research extend to genetic methodology and the understanding of social and economic disparities.
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Affiliation(s)
- Hans Fredrik Sunde
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway.
- Department of Psychology, University of Oslo, Oslo, Norway.
| | | | - Rosa Cheesman
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Elizabeth C Corfield
- Nic Waals Institute, Lovisenberg Diakonale Hospital, Oslo, Norway
- PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Thomas H Kleppesto
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
| | | | - Eivind Ystrom
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Espen Moen Eilertsen
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Fartein Ask Torvik
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
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5
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Havdahl A, Hughes AM, Sanderson E, Ask H, Cheesman R, Reichborn-Kjennerud T, Andreassen OA, Corfield EC, Hannigan L, Magnus P, Njølstad PR, Stoltenberg C, Torvik FA, Brandlistuen R, Smith GD, Ystrom E, Davies NM. Intergenerational effects of parental educational attainment on parenting and childhood educational outcomes: Evidence from MoBa using within-family Mendelian randomization. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.22.23285699. [PMID: 36865116 PMCID: PMC9980223 DOI: 10.1101/2023.02.22.23285699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
The intergenerational transmission of educational attainment from parents to their children is one of the most important and studied relationships in social science. Longitudinal studies have found strong associations between parents' and their children's educational outcomes, which could be due to the effects of parents. Here we provide new evidence about whether parents' educational attainment affects their parenting behaviours and children's early educational outcomes using within-family Mendelian randomization and data from 40,879 genotyped parent-child trios from the Norwegian Mother, Father and Child Cohort (MoBa) study. We found evidence suggesting that parents' educational attainment affects their children's educational outcomes from age 5 to 14. More studies are needed to provide more samples of parent-child trios and assess the potential consequences of selection bias and grandparental effects.
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Affiliation(s)
- Alexandra Havdahl
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, BS8 2BN, United Kingdom
- Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Amanda M Hughes
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, BS8 2BN, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom
| | - Eleanor Sanderson
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, BS8 2BN, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom
| | - Helga Ask
- Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Rosa Cheesman
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Ted Reichborn-Kjennerud
- Department of Child Health and Development, Norwegian Institute of Public Health, Oslo, Norway
| | - Ole A. Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Elizabeth C. Corfield
- Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Laurie Hannigan
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, BS8 2BN, United Kingdom
- Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Per Magnus
- Department of Child Health and Development, Norwegian Institute of Public Health, Oslo, Norway
| | - Pål R. Njølstad
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
| | - Camilla Stoltenberg
- Department of Child Health and Development, Norwegian Institute of Public Health, Oslo, Norway
| | - Fartein Ask Torvik
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Ragnhild Brandlistuen
- Department of Child Health and Development, Norwegian Institute of Public Health, Oslo, Norway
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, BS8 2BN, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom
| | - Eivind Ystrom
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Department of Child Health and Development, Norwegian Institute of Public Health, Oslo, Norway
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, BS8 2BN, United Kingdom
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Norway
- Division of Psychiatry, University College London, Maple House, 149 Tottenham Court Rd, London W1T 7NF
- Department of Statistical Sciences, University College London, London WC1E 6BT, UK
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6
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Keller MC. The challenges of sociogenomics make it more, not less, worthy of careful and innovative investigation. Behav Brain Sci 2023; 46:e218. [PMID: 37695010 DOI: 10.1017/s0140525x22002291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Influences on social traits involve a tangled interplay of genetic, social, and environmental factors. Moreover, there is increasing awareness that gene-environment correlations are real and potentially measurable. Such gene-environment correlations can mislead if they are uncontrolled and genetic associations are interpreted as being purely because of direct genetic effects. This complexity is cause for more and better investigation, not a reason to refrain from researching one of the potentially important factors (genetics) influencing trait variation.
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Affiliation(s)
- Matthew C Keller
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
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7
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Tubbs JD, Sham PC. Preliminary Evidence for Genetic Nurture in Depression and Neuroticism Through Polygenic Scores. JAMA Psychiatry 2023; 80:832-841. [PMID: 37285136 PMCID: PMC10248817 DOI: 10.1001/jamapsychiatry.2023.1544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 03/26/2023] [Indexed: 06/08/2023]
Abstract
Importance Modeling genetic nurture (ie, the effects of parental genotypes through influences on the environment experienced by their children) is essential to accurately disentangle genetic and environmental influences on phenotypic variance. However, these influences are often ignored in both epidemiologic and genetic studies of depression. Objective To estimate the association of genetic nurture with depression and neuroticism. Design, Setting, and Participants This cross-sectional study jointly modeled parental and offspring polygenic scores (PGSs) across 9 traits to test for the association of genetic nurture with lifetime broad depression and neuroticism using data from nuclear families in the UK Biobank, with data collected between 2006 and 2019. A broad depression phenotype was measured in 38 702 offspring from 20 905 independent nuclear families, with most of these participants also reporting neuroticism scores. Parental genotypes were imputed from sibships or parent-offspring duos and used to calculate parental PGSs. Data were analyzed between March 2021 and January 2023. Main Outcomes and Measures Estimates of genetic nurture and direct genetic regression coefficients on broad depression and neuroticism. Results This study of 38 702 offspring with data on broad depression (mean [SD] age, 55.5 [8.2] years at study entry; 58% female) found limited preliminary evidence for a statistically significant association of genetic nurture with lifetime depression and neuroticism in adults. The estimated regression coefficient of the parental depression PGS on offspring neuroticism (β = 0.04, SE = 0.02, P = 6.63 × 10-3) was estimated to be approximately two-thirds (66%) that of the offspring's depression PGS (β = 0.06, SE = 0.01, P = 6.13 × 10-11). Evidence for an association between parental cannabis use disorder PGS and offspring depression was also found (β = 0.08, SE = 0.03, P = .02), which was estimated to be 2 times greater than the association between the offspring's cannabis use disorder PGS and their own depression status (β = 0.04, SE = 0.02, P = .07). Conclusions and Relevance The results of this cross-sectional study highlight the potential for genetic nurture to bias results from epidemiologic and genetic studies on depression or neuroticism and, with further replication and larger samples, identify potential avenues for future prevention and intervention efforts.
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Affiliation(s)
- Justin D. Tubbs
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong
| | - Pak C. Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong
- Centre for PanorOmic Sciences, The University of Hong Kong, Hong Kong
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong
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8
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Bruins S, Hottenga JJ, Neale MC, Pool R, Boomsma DI, Dolan CV. Environment-by-PGS Interaction in the Classical Twin Design: An Application to Childhood Anxiety and Negative Affect. MULTIVARIATE BEHAVIORAL RESEARCH 2023:1-13. [PMID: 37439516 PMCID: PMC11157501 DOI: 10.1080/00273171.2023.2228763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/14/2023]
Abstract
One type of genotype-environment interaction occurs when genetic effects on a phenotype are moderated by an environment; or when environmental effects on a phenotype are moderated by genes. Here we outline these types of genotype-environment interaction models, and propose a test of genotype-environment interaction based on the classical twin design, which includes observed genetic variables (polygenic scores: PGSs) that account for part of the genetic variance of the phenotype. We introduce environment-by-PGS interaction and the results of a simulation study to address statistical power and parameter recovery. Next, we apply the model to empirical data on anxiety and negative affect in children. The power to detect environment-by-PGS interaction depends on the heritability of the phenotype, and the strength of the PGS. The simulation results indicate that under realistic conditions of sample size, heritability and strength of the interaction, the environment-by-PGS model is a viable approach to detect genotype-environment interaction. In 7-year-old children, we defined two PGS based on the largest genetic association studies for 2 traits that are genetically correlated to childhood anxiety and negative affect, namely major depression (MDD) and intelligence (IQ). We find that common environmental influences on negative affect are amplified for children with a lower IQ-PGS.
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Affiliation(s)
- Susanne Bruins
- Department of Biological Psychology, Vrije Universiteit
- Amsterdam Public Health research institute
| | | | - Michael C. Neale
- Department of Biological Psychology, Vrije Universiteit
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit
- Amsterdam Public Health research institute
| | - Dorret I. Boomsma
- Department of Biological Psychology, Vrije Universiteit
- Amsterdam Public Health research institute
- Amsterdam Reproduction and Development research institute
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9
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Young AS. Estimation of indirect genetic effects and heritability under assortative mating. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.10.548458. [PMID: 37503158 PMCID: PMC10369881 DOI: 10.1101/2023.07.10.548458] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Both direct genetic effects (effects of alleles in an individual on that individual) and indirect genetic effects - effects of alleles in an individual (e.g. parents) on another individual (e.g. offspring) - can contribute to phenotypic variation and genotype-phenotype associations. Here, we consider a phenotype affected by direct and parental indirect genetic effects under assortative mating at equilibrium. We generalize classical theory to derive a decomposition of the equilibrium phenotypic variance in terms of direct and indirect genetic effect components. We extend this theory to show that popular methods for estimating indirect genetic effects or 'genetic nurture' through analysis of parental and offspring polygenic predictors (called polygenic indices or scores - PGIs or PGSs) are substantially biased by assortative mating. We propose an improved method for estimating indirect genetic effects while accounting for assortative mating that can also correct heritability estimates for bias due to assortative mating. We validate our method in simulations and apply it to PGIs for height and educational attainment (EA), estimating that the equilibrium heritability of height is 0.699 (S.E. = 0.075 ) and finding no evidence for indirect genetic effects on height. We estimate a very high correlation between parents' underlying genetic components for EA, 0.755 (S.E. = 0.035), which is inconsistent with twin based estimates of the heritability of EA, possibly due to confounding in the EA PGI and/or in twin studies. We implement our method in the software package snipar, enabling researchers to apply the method to data including observed and/or imputed parental genotypes. We provide a theoretical framework for understanding the results of PGI analyses and a practical methodology for estimating heritability and indirect genetic effects while accounting for assortative mating.
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Affiliation(s)
- Alexander Strudwick Young
- UCLA Anderson School of Management, Los Angeles, CA, USA
- Human Genetics Department, UCLA David Geffen School of Medicine, Los Angeles, CA, US
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10
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Hagenbeek FA, Hirzinger JS, Breunig S, Bruins S, Kuznetsov DV, Schut K, Odintsova VV, Boomsma DI. Maximizing the value of twin studies in health and behaviour. Nat Hum Behav 2023:10.1038/s41562-023-01609-6. [PMID: 37188734 DOI: 10.1038/s41562-023-01609-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 04/19/2023] [Indexed: 05/17/2023]
Abstract
In the classical twin design, researchers compare trait resemblance in cohorts of identical and non-identical twins to understand how genetic and environmental factors correlate with resemblance in behaviour and other phenotypes. The twin design is also a valuable tool for studying causality, intergenerational transmission, and gene-environment correlation and interaction. Here we review recent developments in twin studies, recent results from twin studies of new phenotypes and recent insights into twinning. We ask whether the results of existing twin studies are representative of the general population and of global diversity, and we conclude that stronger efforts to increase representativeness are needed. We provide an updated overview of twin concordance and discordance for major diseases and mental disorders, which conveys a crucial message: genetic influences are not as deterministic as many believe. This has important implications for public understanding of genetic risk prediction tools, as the accuracy of genetic predictions can never exceed identical twin concordance rates.
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Affiliation(s)
- Fiona A Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.
| | - Jana S Hirzinger
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Sophie Breunig
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Psychology & Neuroscience, University of Colorado Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Susanne Bruins
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Dmitry V Kuznetsov
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Faculty of Sociology, Bielefeld University, Bielefeld, Germany
| | - Kirsten Schut
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Nightingale Health Plc, Helsinki, Finland
| | - Veronika V Odintsova
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, the Netherlands
- Department of Psychiatry, University Medical Center of Groningen, University of Groningen, Groningen, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, the Netherlands.
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11
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McAdams TA, Cheesman R, Ahmadzadeh YI. Annual Research Review: Towards a deeper understanding of nature and nurture: combining family-based quasi-experimental methods with genomic data. J Child Psychol Psychiatry 2023; 64:693-707. [PMID: 36379220 PMCID: PMC10952916 DOI: 10.1111/jcpp.13720] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/06/2022] [Indexed: 11/17/2022]
Abstract
Distinguishing between the effects of nature and nurture constitutes a major research goal for those interested in understanding human development. It is known, for example, that many parent traits predict mental health outcomes in children, but the causal processes underlying such associations are often unclear. Family-based quasi-experimental designs such as sibling comparison, adoption and extended family studies have been used for decades to distinguish the genetic transmission of risk from the environmental effects family members potentially have on one another. Recently, these designs have been combined with genomic data, and this combination is fuelling a range of exciting methodological advances. In this review we explore these advances - highlighting the ways in which they have been applied to date and considering what they are likely to teach us in the coming years about the aetiology and intergenerational transmission of psychopathology.
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Affiliation(s)
- Tom A. McAdams
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
- PROMENTA Research Centre, Department of PsychologyUniversity of OsloOsloNorway
| | - Rosa Cheesman
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
- PROMENTA Research Centre, Department of PsychologyUniversity of OsloOsloNorway
| | - Yasmin I. Ahmadzadeh
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
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12
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Axelrud LK, Hoffmann MS, Vosberg DE, Santoro M, Pan PM, Gadelha A, Belangero SI, Miguel EC, Shin J, Thapar A, Smoller JW, Pausova Z, Rohde LA, Keller MC, Paus T, Salum GA. Disentangling the influences of parental genetics on offspring's cognition, education, and psychopathology via genetic and phenotypic pathways. J Child Psychol Psychiatry 2023; 64:408-416. [PMID: 36162806 DOI: 10.1111/jcpp.13708] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/24/2022] [Indexed: 02/04/2023]
Abstract
BACKGROUND Specific pathways of intergenerational transmission of behavioral traits remain unclear. Here, we aim to investigate how parental genetics influence offspring cognition, educational attainment, and psychopathology in youth. METHODS Participants for the discovery sample were 2,189 offspring (aged 6-14 years), 1898 mothers and 1,017 fathers who underwent genotyping, psychiatric, and cognitive assessments. We calculated polygenic scores (PGS) for cognition, educational attainment, attention-deficit hyperactivity disorder (ADHD), and schizophrenia for the trios. Phenotypes studied included educational and cognitive measures, ADHD and psychotic symptoms. We used a stepwise approach and multiple mediation models to analyze the effect of parental PGS on offspring traits via offspring PGS and parental phenotype. Significant results were replicated in a sample of 1,029 adolescents, 363 mothers, and 307 fathers. RESULTS Maternal and paternal PGS for cognition influenced offspring general intelligence and executive function via offspring PGS (genetic pathway) and parental education (phenotypic pathway). Similar results were found for parental PGS for educational attainment and offspring reading and writing skills. These pathways fully explained associations between parental PGS and offspring phenotypes, without residual direct association. Associations with maternal, but not paternal, PGS were replicated. No associations were found between parental PGS for psychopathology and offspring specific symptoms. CONCLUSIONS Our findings indicate that parental genetics influences offspring cognition and educational attainment by genetic and phenotypic pathways, suggesting the expression of parental phenotypes partially explain the association between parental genetic risk and offspring outcomes. Multiple mediations might represent an effective approach to disentangle distinct pathways for intergenerational transmission of behavioral traits.
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Affiliation(s)
- Luiza K Axelrud
- Section on Negative Affect and Social Processes, Department of Psychiatry and Legal Medicine, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.,National Institute of Developmental Psychiatry (INPD, CNPq), São Paulo, Brazil
| | - Maurício S Hoffmann
- Section on Negative Affect and Social Processes, Department of Psychiatry and Legal Medicine, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.,National Institute of Developmental Psychiatry (INPD, CNPq), São Paulo, Brazil.,Department of Neuropsychiatry, Universidade Federal de Santa Maria (UFSM), Santa Maria, Brazil.,Care Policy and Evaluation Centre, London School of Economics and Political Science, London, UK
| | - Daniel E Vosberg
- Research Institute, Hospital for Sick Children, Toronto, ON, Canada.,Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, QC, Canada
| | - Marcos Santoro
- National Institute of Developmental Psychiatry (INPD, CNPq), São Paulo, Brazil.,Department of Morphology and Genetics, Universidade Federal de São Paulo, São Paulo, Brazil.,Department of Psychiatry, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Pedro M Pan
- National Institute of Developmental Psychiatry (INPD, CNPq), São Paulo, Brazil.,Department of Psychiatry, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Ary Gadelha
- National Institute of Developmental Psychiatry (INPD, CNPq), São Paulo, Brazil.,Department of Psychiatry, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Sintia I Belangero
- National Institute of Developmental Psychiatry (INPD, CNPq), São Paulo, Brazil.,Department of Morphology and Genetics, Universidade Federal de São Paulo, São Paulo, Brazil.,Department of Psychiatry, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Euripedes C Miguel
- National Institute of Developmental Psychiatry (INPD, CNPq), São Paulo, Brazil.,Universidade de São Paulo (USP), São Paulo, Brazil
| | - Jean Shin
- Research Institute, Hospital for Sick Children, Toronto, ON, Canada.,Institute of Medical Science, University of Toronto, Toronto, ON, Canada.,Department of Physiology, University of Toronto, Toronto, ON, Canada.,Department of Nutritional Sciences, University of Toronto, Toronto, ON, Canada
| | - Anita Thapar
- Division of Psychological Medicine and Clinical Neurosciences and MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Zdenka Pausova
- Research Institute, Hospital for Sick Children, Toronto, ON, Canada.,Institute of Medical Science, University of Toronto, Toronto, ON, Canada.,Department of Physiology, University of Toronto, Toronto, ON, Canada.,Department of Nutritional Sciences, University of Toronto, Toronto, ON, Canada.,ECOGENE-21, Chicoutimi, QC, Canada
| | - Luis A Rohde
- Section on Negative Affect and Social Processes, Department of Psychiatry and Legal Medicine, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.,National Institute of Developmental Psychiatry (INPD, CNPq), São Paulo, Brazil.,ADHD Outpatient Program, Department of Psychiatry, Clinical Research Center, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Matthew C Keller
- Department of Microbiology, New York University School of Medicine, New York, NY, USA
| | - Tomáš Paus
- Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, QC, Canada.,ECOGENE-21, Chicoutimi, QC, Canada.,Department of Psychology, University of Toronto, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,Department of Psychiatry, Faculty of Medicine, University of Montreal, Montreal, QC, Canada.,Department of Neuroscience, Faculty of Medicine, University of Montreal, Montreal, QC, Canada
| | - Giovanni A Salum
- Section on Negative Affect and Social Processes, Department of Psychiatry and Legal Medicine, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.,National Institute of Developmental Psychiatry (INPD, CNPq), São Paulo, Brazil
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13
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Madrid-Valero JJ, Gregory AM. Behaviour genetics and sleep: A narrative review of the last decade of quantitative and molecular genetic research in humans. Sleep Med Rev 2023; 69:101769. [PMID: 36933344 DOI: 10.1016/j.smrv.2023.101769] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 03/05/2023]
Abstract
During the last decade quantitative and molecular genetic research on sleep has increased considerably. New behavioural genetics techniques have marked a new era for sleep research. This paper provides a summary of the most important findings from the last ten years, on the genetic and environmental influences on sleep and sleep disorders and their associations with health-related variables (including anxiety and depression) in humans. In this review we present a brief summary of the main methods in behaviour genetic research (such as twin and genome-wide association studies). We then discuss key research findings on: genetic and environmental influences on normal sleep and sleep disorders, as well as on the association between sleep and health variables (highlighting a substantial role for genes in individual differences in sleep and their associations with other variables). We end by discussing future lines of enquiry and drawing conclusions, including those focused on problems and misconceptions associated with research of this type. In this last decade our knowledge about genetic and environmental influences on sleep and its disorders has expanded. Both, twin and genome-wide association studies show that sleep and sleep disorders are substantially influenced by genetic factors and for the very first time multiple specific genetic variants have been associated with sleep traits and disorders.
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Affiliation(s)
- Juan J Madrid-Valero
- Department of Health Psychology, Faculty of Health Sciences, University of Alicante, Spain.
| | - Alice M Gregory
- Department of Psychology, Goldsmiths, University of London, London, United Kingdom
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14
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Moen GH, Nivard M, Bhatta L, Warrington NM, Willer C, Åsvold BO, Brumpton B, Evans DM. Using Genomic Structural Equation Modeling to Partition the Genetic Covariance Between Birthweight and Cardiometabolic Risk Factors into Maternal and Offspring Components in the Norwegian HUNT Study. Behav Genet 2023; 53:40-52. [PMID: 36322199 PMCID: PMC9823066 DOI: 10.1007/s10519-022-10116-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 08/18/2022] [Accepted: 09/26/2022] [Indexed: 11/07/2022]
Abstract
The Barker Hypothesis posits that adverse intrauterine environments result in fetal growth restriction and increased risk of cardiometabolic disease through developmental compensations. Here we introduce a new statistical model using the genomic SEM software that is capable of simultaneously partitioning the genetic covariation between birthweight and cardiometabolic traits into maternally mediated and offspring mediated contributions. We model the covariance between birthweight and later life outcomes, such as blood pressure, non-fasting glucose, blood lipids and body mass index in the Norwegian HUNT study, consisting of 15,261 mother-eldest offspring pairs with genetic and phenotypic data. Application of this model showed some evidence for maternally mediated effects of systolic blood pressure on offspring birthweight, and pleiotropy between birthweight and non-fasting glucose mediated through the offspring genome. This underscores the importance of genetic links between birthweight and cardiometabolic phenotypes and offer alternative explanations to environmentally based hypotheses for the phenotypic correlation between these variables.
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Affiliation(s)
- Gunn-Helen Moen
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway. .,Institute of Molecular Biosciences, The University of Queensland, Brisbane, Australia. .,Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, NTNU, Norwegian University of Science and Technology, Trondheim, Norway. .,Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK. .,The University of Queensland Diamantina Institute, The University of Queensland, 4102, Woolloongabba, QLD, Australia.
| | - Michel Nivard
- grid.12380.380000 0004 1754 9227Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands ,grid.16872.3a0000 0004 0435 165XAmsterdam Public Health Research Institute, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Laxmi Bhatta
- grid.5947.f0000 0001 1516 2393Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Nicole M Warrington
- grid.1003.20000 0000 9320 7537Institute of Molecular Biosciences, The University of Queensland, Brisbane, Australia ,grid.5947.f0000 0001 1516 2393Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, NTNU, Norwegian University of Science and Technology, Trondheim, Norway ,grid.1003.20000 0000 9320 7537The University of Queensland Diamantina Institute, The University of Queensland, 4102 Woolloongabba, QLD Australia
| | - Cristen Willer
- grid.214458.e0000000086837370Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA ,grid.214458.e0000000086837370Department of Internal Medicine, University of Michigan, Ann Arbor, MI US ,grid.214458.e0000000086837370Department of Human Genetics, University of Michigan, Ann Arbor, USA
| | - Bjørn Olav Åsvold
- grid.5947.f0000 0001 1516 2393Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, NTNU, Norwegian University of Science and Technology, Trondheim, Norway ,grid.52522.320000 0004 0627 3560Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway ,grid.5947.f0000 0001 1516 2393Department of Public Health and Nursing, HUNT Research Centre, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ben Brumpton
- grid.5947.f0000 0001 1516 2393Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, NTNU, Norwegian University of Science and Technology, Trondheim, Norway ,grid.5947.f0000 0001 1516 2393Department of Public Health and Nursing, HUNT Research Centre, NTNU, Norwegian University of Science and Technology, Trondheim, Norway ,grid.52522.320000 0004 0627 3560Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - David M. Evans
- grid.1003.20000 0000 9320 7537Institute of Molecular Biosciences, The University of Queensland, Brisbane, Australia ,grid.1003.20000 0000 9320 7537The University of Queensland Diamantina Institute, The University of Queensland, 4102 Woolloongabba, QLD Australia ,grid.5337.20000 0004 1936 7603Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
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15
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Bogdan R, Hatoum AS, Johnson EC, Agrawal A. The Genetically Informed Neurobiology of Addiction (GINA) model. Nat Rev Neurosci 2023; 24:40-57. [PMID: 36446900 PMCID: PMC10041646 DOI: 10.1038/s41583-022-00656-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/19/2022] [Indexed: 11/30/2022]
Abstract
Addictions are heritable and unfold dynamically across the lifespan. One prominent neurobiological theory proposes that substance-induced changes in neural circuitry promote the progression of addiction. Genome-wide association studies have begun to characterize the polygenic architecture undergirding addiction liability and revealed that genetic loci associated with risk can be divided into those associated with a general broad-spectrum liability to addiction and those associated with drug-specific addiction risk. In this Perspective, we integrate these genomic findings with our current understanding of the neurobiology of addiction to propose a new Genetically Informed Neurobiology of Addiction (GINA) model.
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Affiliation(s)
- Ryan Bogdan
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA.
| | - Alexander S Hatoum
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
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16
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Balbona JV, Kim Y, Keller MC. The estimation of environmental and genetic parental influences. Dev Psychopathol 2022; 34:1-11. [PMID: 36524242 PMCID: PMC10272284 DOI: 10.1017/s0954579422000761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Parents share half of their genes with their children, but they also share background social factors and actively help shape their child's environment - making it difficult to disentangle genetic and environmental causes of parent-offspring similarity. While adoption and extended twin family designs have been extremely useful for distinguishing genetic and nongenetic parental influences, these designs entail stringent assumptions about phenotypic similarity between relatives and require samples that are difficult to collect and therefore are typically small and not publicly shared. Here, we describe these traditional designs, as well as modern approaches that use large, publicly available genome-wide data sets to estimate parental effects. We focus in particular on an approach we recently developed, structural equation modeling (SEM)-polygenic score (PGS), that instantiates the logic of modern PGS-based methods within the flexible SEM framework used in traditional designs. Genetically informative designs such as SEM-PGS rely on different and, in some cases, less rigid assumptions than traditional approaches; thus, they allow researchers to capitalize on new data sources and answer questions that could not previously be investigated. We believe that SEM-PGS and similar approaches can lead to improved insight into how nature and nurture combine to create the incredible diversity underlying human behavior.
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Affiliation(s)
- Jared V. Balbona
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO 80303, USA
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO 80303, USA
| | - Yongkang Kim
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO 80303, USA
| | - Matthew C. Keller
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO 80303, USA
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO 80303, USA
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17
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Announcement of the Fulker Award for a Paper Published in Behavior Genetics, Volume 51, 2021. Behav Genet 2022. [PMID: 36181570 PMCID: PMC9525935 DOI: 10.1007/s10519-022-10115-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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18
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Estimating effects of parents' cognitive and non-cognitive skills on offspring education using polygenic scores. Nat Commun 2022; 13:4801. [PMID: 35999215 PMCID: PMC9399113 DOI: 10.1038/s41467-022-32003-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 07/12/2022] [Indexed: 12/12/2022] Open
Abstract
Understanding how parents' cognitive and non-cognitive skills influence offspring education is essential for educational, family and economic policy. We use genetics (GWAS-by-subtraction) to assess a latent, broad non-cognitive skills dimension. To index parental effects controlling for genetic transmission, we estimate indirect parental genetic effects of polygenic scores on childhood and adulthood educational outcomes, using siblings (N = 47,459), adoptees (N = 6407), and parent-offspring trios (N = 2534) in three UK and Dutch cohorts. We find that parental cognitive and non-cognitive skills affect offspring education through their environment: on average across cohorts and designs, indirect genetic effects explain 36-40% of population polygenic score associations. However, indirect genetic effects are lower for achievement in the Dutch cohort, and for the adoption design. We identify potential causes of higher sibling- and trio-based estimates: prenatal indirect genetic effects, population stratification, and assortative mating. Our phenotype-agnostic, genetically sensitive approach has established overall environmental effects of parents' skills, facilitating future mechanistic work.
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19
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Hwang LD, Moen GH, Evans DM. Using adopted individuals to partition indirect maternal genetic effects into prenatal and postnatal effects on offspring phenotypes. eLife 2022; 11:e73671. [PMID: 35822614 PMCID: PMC9323003 DOI: 10.7554/elife.73671] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 07/11/2022] [Indexed: 11/13/2022] Open
Abstract
Maternal genetic effects can be defined as the effect of a mother's genotype on the phenotype of her offspring, independent of the offspring's genotype. Maternal genetic effects can act via the intrauterine environment during pregnancy and/or via the postnatal environment. In this manuscript, we present a simple extension to the basic adoption design that uses structural equation modelling (SEM) to partition maternal genetic effects into prenatal and postnatal effects. We examine the power, utility and type I error rate of our model using simulations and asymptotic power calculations. We apply our model to polygenic scores of educational attainment and birth weight associated variants, in up to 5,178 adopted singletons, 943 trios, 2687 mother-offspring pairs, 712 father-offspring pairs and 347,980 singletons from the UK Biobank. Our results show the expected pattern of maternal genetic effects on offspring birth weight, but unexpectedly large prenatal maternal genetic effects on offspring educational attainment. Sensitivity and simulation analyses suggest this result may be at least partially due to adopted individuals in the UK Biobank being raised by their biological relatives. We show that accurate modelling of these sorts of cryptic relationships is sufficient to bring type I error rate under control and produce asymptotically unbiased estimates of prenatal and postnatal maternal genetic effects. We conclude that there would be considerable value in following up adopted individuals in the UK Biobank to determine whether they were raised by their biological relatives, and if so, to precisely ascertain the nature of these relationships. These adopted individuals could then be incorporated into informative statistical genetics models like the one described in our manuscript to further elucidate the genetic architecture of complex traits and diseases.
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Affiliation(s)
- Liang-Dar Hwang
- Institute for Molecular Bioscience, The University of QueenslandBrisbaneAustralia
| | - Gunn-Helen Moen
- Institute for Molecular Bioscience, The University of QueenslandBrisbaneAustralia
- The University of Queensland Diamantina Institute, The University of QueenslandBrisbaneAustralia
- Institute for Clinical Medicine, Faculty of Medicine, University of OsloOsloNorway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and TechnologyTrondheimNorway
- Population Health Science, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - David M Evans
- Institute for Molecular Bioscience, The University of QueenslandBrisbaneAustralia
- The University of Queensland Diamantina Institute, The University of QueenslandBrisbaneAustralia
- MRC Integrative Epidemiology Unit, University of BristolBristolUnited Kingdom
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20
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Evaluating indirect genetic effects of siblings using singletons. PLoS Genet 2022; 18:e1010247. [PMID: 35797272 PMCID: PMC9262210 DOI: 10.1371/journal.pgen.1010247] [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: 07/14/2021] [Accepted: 05/10/2022] [Indexed: 12/23/2022] Open
Abstract
Estimating effects of parental and sibling genotypes (indirect genetic effects) can provide insight into how the family environment influences phenotypic variation. There is growing molecular genetic evidence for effects of parental phenotypes on their offspring (e.g. parental educational attainment), but the extent to which siblings affect each other is currently unclear. Here we used data from samples of unrelated individuals, without (singletons) and with biological full-siblings (non-singletons), to investigate and estimate sibling effects. Indirect genetic effects of siblings increase (or decrease) the covariance between genetic variation and a phenotype. It follows that differences in genetic association estimates between singletons and non-singletons could indicate indirect genetic effects of siblings if there is no heterogeneity in other sources of genetic association between singletons and non-singletons. We used UK Biobank data to estimate polygenic score (PGS) associations for height, BMI and educational attainment in self-reported singletons (N = 50,143) and non-singletons (N = 328,549). The educational attainment PGS association estimate was 12% larger (95% C.I. 3%, 21%) in the non-singleton sample than in the singleton sample, but the height and BMI PGS associations were consistent. Birth order data suggested that the difference in educational attainment PGS associations was driven by individuals with older siblings rather than firstborns. The relationship between number of siblings and educational attainment PGS associations was non-linear; PGS associations were 24% smaller in individuals with 6 or more siblings compared to the rest of the sample (95% C.I. 11%, 38%). We estimate that a 1 SD increase in sibling educational attainment PGS corresponds to a 0.025 year increase in the index individual's years in schooling (95% C.I. 0.013, 0.036). Our results suggest that older siblings may influence the educational attainment of younger siblings, adding to the growing evidence that effects of the environment on phenotypic variation partially reflect social effects of germline genetic variation in relatives.
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21
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Mendelian imputation of parental genotypes improves estimates of direct genetic effects. Nat Genet 2022; 54:897-905. [PMID: 35681053 PMCID: PMC9197765 DOI: 10.1038/s41588-022-01085-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 04/28/2022] [Indexed: 01/22/2023]
Abstract
Effects estimated by genome-wide association studies (GWASs) include effects of alleles in an individual on that individual (direct genetic effects), indirect genetic effects (for example, effects of alleles in parents on offspring through the environment) and bias from confounding. Within-family genetic variation is random, enabling unbiased estimation of direct genetic effects when parents are genotyped. However, parental genotypes are often missing. We introduce a method that imputes missing parental genotypes and estimates direct genetic effects. Our method, implemented in the software package snipar (single-nucleotide imputation of parents), gives more precise estimates of direct genetic effects than existing approaches. Using 39,614 individuals from the UK Biobank with at least one genotyped sibling/parent, we estimate the correlation between direct genetic effects and effects from standard GWASs for nine phenotypes, including educational attainment (r = 0.739, standard error (s.e.) = 0.086) and cognitive ability (r = 0.490, s.e. = 0.086). Our results demonstrate substantial confounding bias in standard GWASs for some phenotypes.
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22
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Howe LJ, Nivard MG, Morris TT, Hansen AF, Rasheed H, Cho Y, Chittoor G, Ahlskog R, Lind PA, Palviainen T, van der Zee MD, Cheesman R, Mangino M, Wang Y, Li S, Klaric L, Ratliff SM, Bielak LF, Nygaard M, Giannelis A, Willoughby EA, Reynolds CA, Balbona JV, Andreassen OA, Ask H, Baras A, Bauer CR, Boomsma DI, Campbell A, Campbell H, Chen Z, Christofidou P, Corfield E, Dahm CC, Dokuru DR, Evans LM, de Geus EJC, Giddaluru S, Gordon SD, Harden KP, Hill WD, Hughes A, Kerr SM, Kim Y, Kweon H, Latvala A, Lawlor DA, Li L, Lin K, Magnus P, Magnusson PKE, Mallard TT, Martikainen P, Mills MC, Njølstad PR, Overton JD, Pedersen NL, Porteous DJ, Reid J, Silventoinen K, Southey MC, Stoltenberg C, Tucker-Drob EM, Wright MJ, Hewitt JK, Keller MC, Stallings MC, Lee JJ, Christensen K, Kardia SLR, Peyser PA, Smith JA, Wilson JF, Hopper JL, Hägg S, Spector TD, Pingault JB, Plomin R, Havdahl A, Bartels M, Martin NG, Oskarsson S, Justice AE, Millwood IY, Hveem K, Naess Ø, Willer CJ, Åsvold BO, Koellinger PD, Kaprio J, Medland SE, Walters RG, Benjamin DJ, Turley P, Evans DM, Davey Smith G, Hayward C, Brumpton B, Hemani G, Davies NM. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects. Nat Genet 2022; 54:581-592. [PMID: 35534559 PMCID: PMC9110300 DOI: 10.1038/s41588-022-01062-7] [Citation(s) in RCA: 121] [Impact Index Per Article: 60.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 03/25/2022] [Indexed: 02/01/2023]
Abstract
Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects.
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Affiliation(s)
- Laurence J Howe
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Michel G Nivard
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
| | - Tim T Morris
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ailin F Hansen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Humaira Rasheed
- Medical Research Council Integrative Epidemiology Unit at the 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
| | - Yoonsu Cho
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Geetha Chittoor
- Department of Population Health Sciences, Geisinger Health, Danville, PA, USA
| | - Rafael Ahlskog
- Department of Government, Uppsala University, Uppsala, Sweden
| | - Penelope A Lind
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
- School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Australia
| | - Teemu Palviainen
- Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
| | - Matthijs D van der Zee
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
| | - Rosa Cheesman
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Social Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, UK
| | - Yunzhang Wang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Lucija Klaric
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Scott M Ratliff
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Marianne Nygaard
- The Danish Twin Registry, Department of Public Health, University of Southern Denmark, Odense, Denmark
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
| | | | | | - Chandra A Reynolds
- Department of Psychology, University of California, Riverside, Riverside, CA, USA
| | - Jared V Balbona
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Ole A Andreassen
- NORMENT Centre, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Helga Ask
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
| | - Aris Baras
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Christopher R Bauer
- BioMarin Pharmaceutical Inc., Novato, CA, USA
- Biomedical and Translational Informatics, Geisinger Health, Danville, PA, USA
| | - Dorret I Boomsma
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health (APH) and Amsterdam Reproduction and Development (AR&D), Amsterdam, the Netherlands
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Harry Campbell
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | | | - Elizabeth Corfield
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | | | - Deepika R Dokuru
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Luke M Evans
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
- Department of Ecology & Evolutionary Biology, University of Colorado at Boulder, Boulder, CO, USA
| | - Eco J C de Geus
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, the Netherlands
| | - Sudheer Giddaluru
- Institute of Health and Society, University of Oslo, Oslo, Norway
- Norwegian Institute of Public Health, Oslo, Norway
| | - Scott D Gordon
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - K Paige Harden
- Department of Psychology and Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - W David Hill
- Lothian Birth Cohorts Group, Department of Psychology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Amanda Hughes
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Shona M Kerr
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Yongkang Kim
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Hyeokmoon Kweon
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Antti Latvala
- Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
- Institute of Criminology and Legal Policy, Faculty of Social Sciences, University of Helsinki, Helsinki, Finland
| | - Deborah A Lawlor
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Bristol NIHR Biomedical Research Centre, Bristol, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Per Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Skøyen, Oslo, Norway
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Travis T Mallard
- Department of Psychology and Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Pekka Martikainen
- Population Research Unit, Faculty of Social Sciences, University of Helsinki, Helsinki, Finland
- The Max Planck Institute for Demographic Research, Rostock, Germany
- Department of Public Health Sciences, Stockholm University, Stockholm, Sweden
| | - Melinda C Mills
- Leverhulme Centre for Demographic Science, University of Oxford, Oxford, UK
| | - Pål Rasmus Njølstad
- Department of Clinical Science, University of Bergen, Bergen, Norway
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
| | | | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | | | - Karri Silventoinen
- Population Research Unit, Faculty of Social Sciences, University of Helsinki, Helsinki, Finland
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Camilla Stoltenberg
- Norwegian Institute of Public Health, Oslo, Norway
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
| | - Elliot M Tucker-Drob
- Department of Psychology and Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Margaret J Wright
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | | | | | - John K Hewitt
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Matthew C Keller
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Michael C Stallings
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - James J Lee
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Kaare Christensen
- The Danish Twin Registry, Department of Public Health, University of Southern Denmark, Odense, Denmark
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
- Department of Clinical Biochemistry and Pharmacology, Odense University Hospital, Odense, Denmark
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - James F Wilson
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Jean-Baptiste Pingault
- Social Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - Robert Plomin
- Social Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Alexandra Havdahl
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Meike Bartels
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
| | - Nicholas G Martin
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Sven Oskarsson
- Department of Government, Uppsala University, Uppsala, Sweden
| | - Anne E Justice
- Department of Population Health Sciences, Geisinger Health, Danville, PA, USA
| | - Iona Y Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
| | - Øyvind Naess
- Institute of Health and Society, University of Oslo, Oslo, Norway
- Norwegian Institute of Public Health, Oslo, Norway
| | - Cristen J Willer
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Bjørn Olav Åsvold
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
- Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Philipp D Koellinger
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI, USA
| | - Jaakko Kaprio
- Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
| | - Sarah E Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Australia
- School of Psychology, University of Queensland, Brisbane, Queensland, Australia
| | - Robin G Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Daniel J Benjamin
- UCLA Anderson School of Management, Los Angeles, CA, USA
- Human Genetics Department, UCLA David Geffen School of Medicine, Gonda (Goldschmied) Neuroscience and Genetics Research Center, Los Angeles, CA, USA
- National Bureau of Economic Research, Cambridge, MA, USA
| | - Patrick Turley
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
- Department of Economics, University of Southern California, Los Angeles, CA, USA
| | - David M Evans
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- University of Queensland Diamantina Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Ben Brumpton
- Medical Research Council Integrative Epidemiology Unit at the 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.
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway.
| | - Gibran Hemani
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit at the 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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23
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Okbay A, Wu Y, Wang N, Jayashankar H, Bennett M, Nehzati SM, Sidorenko J, Kweon H, Goldman G, Gjorgjieva T, Jiang Y, Hicks B, Tian C, Hinds DA, Ahlskog R, Magnusson PKE, Oskarsson S, Hayward C, Campbell A, Porteous DJ, Freese J, Herd P, Watson C, Jala J, Conley D, Koellinger PD, Johannesson M, Laibson D, Meyer MN, Lee JJ, Kong A, Yengo L, Cesarini D, Turley P, Visscher PM, Beauchamp JP, Benjamin DJ, Young AI. Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. Nat Genet 2022; 54:437-449. [PMID: 35361970 PMCID: PMC9005349 DOI: 10.1038/s41588-022-01016-z] [Citation(s) in RCA: 198] [Impact Index Per Article: 99.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 01/20/2022] [Indexed: 12/14/2022]
Abstract
We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57.
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Affiliation(s)
- Aysu Okbay
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
| | - Yeda Wu
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Nancy Wang
- National Bureau of Economic Research, Cambridge, MA, USA
| | | | | | | | - Julia Sidorenko
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Hyeokmoon Kweon
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Grant Goldman
- National Bureau of Economic Research, Cambridge, MA, USA
| | | | | | | | | | | | - Rafael Ahlskog
- Department of Government, Uppsala University, Uppsala, Sweden
| | - Patrik K E Magnusson
- Swedish Twin Registry, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Sven Oskarsson
- Department of Government, Uppsala University, Uppsala, Sweden
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
- Usher Institute, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Jeremy Freese
- Department of Sociology, Stanford University, Stanford, CA, USA
| | - Pamela Herd
- McCourt School of Public Policy, Georgetown University, Washington, DC, USA
| | - Chelsea Watson
- UCLA Anderson School of Management, Los Angeles, CA, USA
| | - Jonathan Jala
- UCLA Anderson School of Management, Los Angeles, CA, USA
| | - Dalton Conley
- Department of Sociology, Princeton University, Princeton, NJ, USA
| | - Philipp D Koellinger
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Robert M. La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI, USA
| | - Magnus Johannesson
- Department of Economics, Stockholm School of Economics, Stockholm, Sweden
| | - David Laibson
- Department of Economics, Harvard University, Cambridge, MA, USA
| | - Michelle N Meyer
- Center for Translational Bioethics and Health Care Policy, Geisinger Health System, Danville, PA, USA
| | - James J Lee
- Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Augustine Kong
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Loic Yengo
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - David Cesarini
- National Bureau of Economic Research, Cambridge, MA, USA
- Department of Economics, New York University, New York, NY, USA
- Center for Experimental Social Science, New York University, New York, NY, USA
| | - Patrick Turley
- Department of Economics, University of Southern California, Los Angeles, CA, USA
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.
| | - Jonathan P Beauchamp
- Interdisciplinary Center for Economic Science and Department of Economics, George Mason University, Fairfax, VA, USA
| | - Daniel J Benjamin
- National Bureau of Economic Research, Cambridge, MA, USA.
- UCLA Anderson School of Management, Los Angeles, CA, USA.
- Human Genetics Department, UCLA David Geffen School of Medicine, Los Angeles, CA, USA.
| | - Alexander I Young
- UCLA Anderson School of Management, Los Angeles, CA, USA.
- Human Genetics Department, UCLA David Geffen School of Medicine, Los Angeles, CA, USA.
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24
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Modeling assortative mating and genetic similarities between partners, siblings, and in-laws. Nat Commun 2022; 13:1108. [PMID: 35233010 PMCID: PMC8888605 DOI: 10.1038/s41467-022-28774-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 02/10/2022] [Indexed: 12/17/2022] Open
Abstract
Assortative mating on heritable traits can have implications for the genetic resemblance between siblings and in-laws in succeeding generations. We studied polygenic scores and phenotypic data from pairs of partners (n = 26,681), siblings (n = 2,170), siblings-in-law (n = 3,905), and co-siblings-in-law (n = 1,763) in the Norwegian Mother, Father and Child Cohort Study. Using structural equation models, we estimated associations between measurement error-free latent genetic and phenotypic variables. We found evidence of genetic similarity between partners for educational attainment (rg = 0.37), height (rg = 0.13), and depression (rg = 0.08). Common genetic variants associated with educational attainment correlated between siblings above 0.50 (rg = 0.68) and between siblings-in-law (rg = 0.25) and co-siblings-in-law (rg = 0.09). Indirect assortment on secondary traits accounted for partner similarity in education and depression, but not in height. Comparisons between the genetic similarities of partners and siblings indicated that genetic variances were in intergenerational equilibrium. This study shows genetic similarities between extended family members and that assortative mating has taken place for several generations.
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25
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Pingault JB, Richmond R, Davey Smith G. Causal Inference with Genetic Data: Past, Present, and Future. Cold Spring Harb Perspect Med 2022; 12:a041271. [PMID: 34580080 PMCID: PMC8886738 DOI: 10.1101/cshperspect.a041271] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
The set of methods discussed in this collection has emerged from the convergence of two scientific fields-genetics and causal inference. In this introduction, we discuss relevant aspects of each field and show how their convergence arises from the natural experiments that genetics offer. We present introductory concepts useful to readers unfamiliar with genetically informed methods for causal inference. We conclude that existing applications and foreseeable developments should ensure that we rapidly reap the rewards of this relatively new field, not only in terms of our understanding of human disease and development, but also in terms of tangible translational applications.
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Affiliation(s)
- Jean-Baptiste Pingault
- Division of Psychology and Language Sciences, University College London, London WC1H 0AP United Kingdom
| | - Rebecca Richmond
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 1TH, United Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 1TH, United Kingdom
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26
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Baud A, McPeek S, Chen N, Hughes KA. Indirect Genetic Effects: A Cross-disciplinary Perspective on Empirical Studies. J Hered 2022; 113:1-15. [PMID: 34643239 PMCID: PMC8851665 DOI: 10.1093/jhered/esab059] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Indirect genetic effects (IGE) occur when an individual's phenotype is influenced by genetic variation in conspecifics. Opportunities for IGE are ubiquitous, and, when present, IGE have profound implications for behavioral, evolutionary, agricultural, and biomedical genetics. Despite their importance, the empirical study of IGE lags behind the development of theory. In large part, this lag can be attributed to the fact that measuring IGE, and deconvoluting them from the direct genetic effects of an individual's own genotype, is subject to many potential pitfalls. In this Perspective, we describe current challenges that empiricists across all disciplines will encounter in measuring and understanding IGE. Using ideas and examples spanning evolutionary, agricultural, and biomedical genetics, we also describe potential solutions to these challenges, focusing on opportunities provided by recent advances in genomic, monitoring, and phenotyping technologies. We hope that this cross-disciplinary assessment will advance the goal of understanding the pervasive effects of conspecific interactions in biology.
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Affiliation(s)
- Amelie Baud
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.,the Universitat Pompeu Fabra (UPF), Barcelona,Spain
| | - Sarah McPeek
- the Department of Biology, University of Virginia, Charlottesville, VA 22904, USA
| | - Nancy Chen
- the Department of Biology, University of Rochester, Rochester, NY 14627,USA
| | - Kimberly A Hughes
- the Department of Biological Science, Florida State University, Tallahassee, FL 32303,USA
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27
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Giangrande EJ, Weber RS, Turkheimer E. What Do We Know About the Genetic Architecture of Psychopathology? Annu Rev Clin Psychol 2022; 18:19-42. [DOI: 10.1146/annurev-clinpsy-081219-091234] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In the second half of the twentieth century, twin and family studies established beyond a reasonable doubt that all forms of psychopathology are substantially heritable and highly polygenic. These conclusions were simultaneously an important theoretical advance and a difficult methodological obstacle, as it became clear that heritability is universal and undifferentiated across forms of psychopathology, and the radical polygenicity of genetic effects limits the biological insight provided by genetically informed studies at the phenotypic level. The paradigm-shifting revolution brought on by the Human Genome Project has recapitulated the great methodological promise and the profound theoretical difficulties of the twin study era. We review these issues using the rubric of genetic architecture, which we define as a search for specific genetic insight that adds to the general conclusion that psychopathology is heritable and polygenic. Although significant problems remain, we see many promising avenues for progress. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Evan J. Giangrande
- Department of Psychology, University of Virginia, Charlottesville, Virginia, USA
| | - Ramona S. Weber
- Department of Psychology, University of Virginia, Charlottesville, Virginia, USA
| | - Eric Turkheimer
- Department of Psychology, University of Virginia, Charlottesville, Virginia, USA
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28
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Willoughby EA, McGue M, Iacono WG, Lee JJ. Genetic and environmental contributions to IQ in adoptive and biological families with 30-year-old offspring. INTELLIGENCE 2021; 88. [PMID: 34658462 DOI: 10.1016/j.intell.2021.101579] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
While adoption studies have provided key insights into the influence of the familial environment on IQ scores of adolescents and children, few have followed adopted offspring long past the time spent living in the family home. To improve confidence about the extent to which shared environment exerts enduring effects on IQ, we estimated genetic and environmental effects on adulthood IQ in a unique sample of 486 biological and adoptive families. These families, tested previously on measures of IQ when offspring averaged age 15, were assessed a second time nearly two decades later ( M offspring age = 32 years). We estimated the proportions of the variance in IQ attributable to environmentally mediated effects of parental IQs, sibling-specific shared environment, and gene-environment covariance to be .01 [95% CI .00, .02], .04 [95% CI .00, .15], and .03 [95% CI .00, .07] respectively; these components jointly accounted for 8 percent of the IQ variance in adulthood. The heritability was estimated to be .42 [95% CI .21, .64]. Together, these findings provide further evidence for the predominance of genetic influences on adult intelligence over any other systematic source of variation.
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Affiliation(s)
- Emily A Willoughby
- University of Minnesota Twin Cities, Department of Psychology 75 E River Rd, Minneapolis, Minnesota 55455
| | - Matt McGue
- University of Minnesota Twin Cities, Department of Psychology 75 E River Rd, Minneapolis, Minnesota 55455
| | - William G Iacono
- University of Minnesota Twin Cities, Department of Psychology 75 E River Rd, Minneapolis, Minnesota 55455
| | - James J Lee
- University of Minnesota Twin Cities, Department of Psychology 75 E River Rd, Minneapolis, Minnesota 55455
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Friedman NP, Banich MT, Keller MC. Twin studies to GWAS: there and back again. Trends Cogn Sci 2021; 25:855-869. [PMID: 34312064 PMCID: PMC8446317 DOI: 10.1016/j.tics.2021.06.007] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 06/25/2021] [Accepted: 06/28/2021] [Indexed: 01/01/2023]
Abstract
The field of human behavioral genetics has come full circle. It began by using twin/family studies to estimate the relative importance of genetic and environmental influences. As large-scale genotyping became cost-effective, genome-wide association studies (GWASs) yielded insights about the nature of genetic influences and new methods that use GWAS data to estimate heritability and genetic correlations invigorated the field. Yet these newer GWAS methods have not replaced twin/family studies. In this review, we discuss the strengths and weaknesses of the two approaches with respect to characterizing genetic and environmental influences, measurement of behavioral phenotypes, and evaluation of causal models, with a particular focus on cognitive neuroscience. This discussion highlights how twin/family studies and GWAS complement and mutually reinforce one another.
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Affiliation(s)
- Naomi P Friedman
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO 80309, USA; Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO 80309, USA.
| | - Marie T Banich
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO 80309, USA; Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO 80309, USA
| | - Matthew C Keller
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO 80309, USA; Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO 80309, USA
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30
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Wang B, Baldwin JR, Schoeler T, Cheesman R, Barkhuizen W, Dudbridge F, Bann D, Morris TT, Pingault JB. Robust genetic nurture effects on education: A systematic review and meta-analysis based on 38,654 families across 8 cohorts. Am J Hum Genet 2021; 108:1780-1791. [PMID: 34416156 PMCID: PMC8456157 DOI: 10.1016/j.ajhg.2021.07.010] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 07/21/2021] [Indexed: 12/20/2022] Open
Abstract
Similarities between parents and offspring arise from nature and nurture. Beyond this simple dichotomy, recent genomic studies have uncovered "genetic nurture" effects, whereby parental genotypes influence offspring outcomes via environmental pathways rather than genetic transmission. Such genetic nurture effects also need to be accounted for to accurately estimate "direct" genetic effects (i.e., genetic effects on a trait originating in the offspring). Empirical studies have indicated that genetic nurture effects are particularly relevant to the intergenerational transmission of risk for child educational outcomes, which are, in turn, associated with major psychological and health milestones throughout the life course. These findings have yet to be systematically appraised across contexts. We conducted a systematic review and meta-analysis to quantify genetic nurture effects on educational outcomes. A total of 12 studies comprising 38,654 distinct parent(s)-offspring pairs or trios from 8 cohorts reported 22 estimates of genetic nurture effects. Genetic nurture effects on offspring's educational outcomes (βgenetic nurture = 0.08, 95% CI [0.07, 0.09]) were smaller than direct genetic effects (βdirect genetic = 0.17, 95% CI [0.13, 0.20]). Findings were largely consistent across studies. Genetic nurture effects originating from mothers and fathers were of similar magnitude, highlighting the need for a greater inclusion of fathers in educational research. Genetic nurture effects were largely explained by observed parental education and socioeconomic status, pointing to their role in environmental pathways shaping child educational outcomes. Findings provide consistent evidence that environmentally mediated parental genetic influences contribute to the intergenerational transmission of educational outcomes, in addition to effects due to genetic transmission.
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Affiliation(s)
- Biyao Wang
- Division of Psychology and Language Sciences, University College London, London WC1H 0AP, UK
| | - Jessie R Baldwin
- Division of Psychology and Language Sciences, University College London, London WC1H 0AP, UK; Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College, London SE5 8AF, UK
| | - Tabea Schoeler
- Division of Psychology and Language Sciences, University College London, London WC1H 0AP, UK
| | - Rosa Cheesman
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College, London SE5 8AF, UK; PROMENTA Research Center, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Wikus Barkhuizen
- Division of Psychology and Language Sciences, University College London, London WC1H 0AP, UK
| | - Frank Dudbridge
- Department of Health Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - David Bann
- Centre for Longitudinal Studies, Social Research Institute, University College London, London WC1H 0AL, UK
| | - Tim T Morris
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
| | - Jean-Baptiste Pingault
- Division of Psychology and Language Sciences, University College London, London WC1H 0AP, UK; Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College, London SE5 8AF, UK.
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31
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Ksinan AJ, Smith RL, Barr PB, Vazsonyi AT. The Associations of Polygenic Scores for Risky Behaviors and Parenting Behaviors with Adolescent Externalizing Problems. Behav Genet 2021; 52:26-37. [PMID: 34333687 DOI: 10.1007/s10519-021-10079-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 07/24/2021] [Indexed: 11/28/2022]
Abstract
The current study focused on longitudinal effects of genetics and parental behaviors and their interplay on externalizing behaviors in a panel study following individuals from adolescence to young adulthood. The nationally representative sample of Add Health participants of European ancestry included N = 4142 individuals, measured on three occasions. Parenting was operationalized as experiences with child maltreatment and maternal closeness. Externalizing problems were operationalized as alcohol use, cannabis use, and antisocial behaviors. Genetic effects were operationalized as a polygenic score (PGS) of risky behaviors. The results showed significant effects for child maltreatment, maternal closeness, and PGS, above and beyond other factors and previous levels of externalizing behaviors. Furthermore, maternal closeness was found to negatively correlate with PGS. No significant interaction effects of parenting and PGS were found. The results underscore the joint independent effects of parenting and genetics on the change in externalizing behaviors from adolescence to young adulthood.
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Affiliation(s)
- Albert J Ksinan
- Department of Family Sciences, University of Kentucky, 160 Funkhouser Dr, Lexington, KY, 40506-0054, USA. .,Research Center for Toxic Compounds (RECETOX), Masaryk University, Kamenice 5, 62500, Brno, Czech Republic.
| | - Rebecca L Smith
- Department of Psychology, Virginia Commonwealth University, Box 842018, Richmond, VA, 23284-2018, USA
| | - Peter B Barr
- Department of Psychology, Virginia Commonwealth University, Box 842018, Richmond, VA, 23284-2018, USA
| | - Alexander T Vazsonyi
- Department of Family Sciences, University of Kentucky, 160 Funkhouser Dr, Lexington, KY, 40506-0054, USA
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32
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Guillermo R, Elena V, Martin K, Chris W. RápidoPGS: A rapid polygenic score calculator for summary GWAS data without a test dataset. Bioinformatics 2021; 37:4444-4450. [PMID: 34145897 PMCID: PMC8652106 DOI: 10.1093/bioinformatics/btab456] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 04/29/2021] [Accepted: 06/17/2021] [Indexed: 11/14/2022] Open
Abstract
Motivation Polygenic scores (PGS) aim to genetically predict complex traits at an individual level. PGS are typically trained on genome-wide association summary statistics and require an independent test dataset to tune parameters. More recent methods allow parameters to be tuned on the training data, removing the need for independent test data, but approaches are computationally intensive. Based on fine-mapping principles, we present RápidoPGS, a flexible and fast method to compute PGS requiring summary-level Genome-wide association studies (GWAS) datasets only, with little computational requirements and no test data required for parameter tuning. Results We show that RápidoPGS performs slightly less well than two out of three other widely used PGS methods (LDpred2, PRScs and SBayesR) for case–control datasets, with median r2 difference: -0.0092, -0.0042 and 0.0064, respectively, but up to 17 000-fold faster with reduced computational requirements. RápidoPGS is implemented in R and can work with user-supplied summary statistics or download them from the GWAS catalog. Availability and implementation Our method is available with a GPL license as an R package from CRAN and GitHub. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Reales Guillermo
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Puddicombe Way, Cambridge, CB2 0AW, UK.,Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 2QQ, UK
| | - Vigorito Elena
- MRC Biostatistics Unit University of Cambridge, School of Clinical Medicine, East Forvie Building, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK
| | - Kelemen Martin
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Puddicombe Way, Cambridge, CB2 0AW, UK.,Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Wallace Chris
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Puddicombe Way, Cambridge, CB2 0AW, UK.,Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 2QQ, UK.,MRC Biostatistics Unit University of Cambridge, School of Clinical Medicine, East Forvie Building, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK
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33
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Evans DM, Medland SE, Prom-Wormley E. Introduction to the Special Issue on Statistical Genetic Methods for Human Complex Traits. Behav Genet 2021; 51:165-169. [PMID: 33864530 DOI: 10.1007/s10519-021-10057-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- David M Evans
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia. .,MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Elizabeth Prom-Wormley
- The Division of Epidemiology, Department of Family Medicine and Population Health, Virginia Commonwealth University, Richmond, USA
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