1
|
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.
Collapse
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
| |
Collapse
|
2
|
Ahlqvist VH, Madley-Dowd P, Ly A, Rast J, Lundberg M, Jónsson-Bachmann E, Berglind D, Rai D, Magnusson C, Lee BK. Bias amplification of unobserved confounding in pharmacoepidemiological studies using indication-based sampling. Pharmacoepidemiol Drug Saf 2023; 32:886-897. [PMID: 36919941 PMCID: PMC11168799 DOI: 10.1002/pds.5614] [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: 09/15/2022] [Revised: 03/08/2023] [Accepted: 03/09/2023] [Indexed: 03/16/2023]
Abstract
PURPOSE Estimating causal effects in observational pharmacoepidemiology is a challenging task, as it is often plagued by confounding by indication. Restricting the sample to those with an indication for drug use is a commonly performed procedure; indication-based sampling ensures that the exposed and unexposed are exchangeable on the indication-limiting the potential for confounding by indication. However, indication-based sampling has received little scrutiny, despite the hazards of exposure-related covariate control. METHODS Using simulations of varying levels of confounding and applied examples we describe bias amplification under indication-based sampling. RESULTS We demonstrate that indication-based sampling in the presence of unobserved confounding can give rise to bias amplification, a self-inflicted phenomenon where one inflates pre-existing bias through inappropriate covariate control. Additionally, we show that indication-based sampling generally leads to a greater net bias than alternative approaches, such as regression adjustment. Finally, we expand on how bias amplification should be reasoned about when distinct clinically relevant effects on the outcome among those with an indication exist (effect-heterogeneity). CONCLUSION We conclude that studies using indication-based sampling should have robust justification - and that it should by no means be considered unbiased to adopt such approaches. As such, we suggest that future observational studies stay wary of bias amplification when considering drug indications.
Collapse
Affiliation(s)
- Viktor H. Ahlqvist
- Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
| | - Paul Madley-Dowd
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Amanda Ly
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Jessica Rast
- A.J. Drexel Autism Institute, Drexel University, Philadelphia, PA, USA
| | - Michael Lundberg
- Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
| | | | - Daniel Berglind
- Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
- Centre for Epidemiology and Community Medicine, Region Stockholm, Stockholm, Sweden
| | - Dheeraj Rai
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- NIHR Biomedical Research Centre, University of Bristol, Bristol, United Kingdom
- Avon and Wiltshire Partnership NHS Mental Health Trust, Bristol, United Kingdom
| | - Cecilia Magnusson
- Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
- Centre for Epidemiology and Community Medicine, Region Stockholm, Stockholm, Sweden
| | - Brian K. Lee
- Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
- A.J. Drexel Autism Institute, Drexel University, Philadelphia, PA, USA
- Department of Epidemiology and Biostatistics, Drexel University School of Public Health, Philadelphia, PA, USA
| |
Collapse
|
3
|
Costantini I, Sallis H, Tilling K, Major‐Smith D, Pearson RM, Kounali D. Childhood trajectories of internalising and externalising problems associated with a polygenic risk score for neuroticism in a UK birth cohort study. JCPP ADVANCES 2023; 3:e12141. [PMID: 37431323 PMCID: PMC10241477 DOI: 10.1002/jcv2.12141] [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] [Received: 08/25/2022] [Accepted: 12/14/2022] [Indexed: 02/25/2023] Open
Abstract
Background Neuroticism represents a personality disposition towards experiencing negative emotions more frequently and intensely. Longitudinal studies suggest that neuroticism increases risk of several psychological problems. Improved understanding of how this trait manifests in early life could help inform preventative strategies in those liable to neuroticism. Methods This study explored how a polygenic risk score for neuroticism (NEU PRS) is expressed from infancy to late childhood across various psychological outcomes using multivariable linear and ordinal regression models. In addition, we employed a three-level mixed-effect model to characterise child internalising and externalising trajectories and estimate how a child PRS associated with both their overall levels and rates of change in 5279 children aged 3-11 in the Avon Longitudinal Study of Parents and Children cohort. Results We found evidence that the NEU PRS was associated with a more emotionally sensitive temperament in early infancy in addition to higher emotional and behavioural problems and a higher risk of meeting diagnostic criteria for a variety of clinical disorders, particularly anxiety disorders, in childhood. The NEU PRS was associated with overall levels of internalising and externalising trajectories, with a larger magnitude of association on the internalising trajectory. The PRS was also associated with slower rates of reduction of internalising problems across childhood. Conclusions Our findings using a large, well-characterised birth cohort study suggest that phenotypic manifestations of a PRS for adult neuroticism can be detected as early as in infancy and that this PRS associates with several mental health problems and differences in emotional trajectories across childhood.
Collapse
Affiliation(s)
- Ilaria Costantini
- Centre for Academic Mental HealthUniversity of BristolBristolUK
- Department of Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUK
| | - Hannah Sallis
- Centre for Academic Mental HealthUniversity of BristolBristolUK
- Department of Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUK
- Medical Research Council (MRC) Integrative Epidemiology UnitUniversity of BristolBristolUK
- School of Psychological ScienceUniversity of BristolBristolUK
| | - Kate Tilling
- Department of Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUK
- Medical Research Council (MRC) Integrative Epidemiology UnitUniversity of BristolBristolUK
| | - Daniel Major‐Smith
- Department of Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUK
- Medical Research Council (MRC) Integrative Epidemiology UnitUniversity of BristolBristolUK
- Centre for Academic Child HealthBristol Medical SchoolUniversity of BristolBristolUK
| | - Rebecca M. Pearson
- Centre for Academic Mental HealthUniversity of BristolBristolUK
- Department of Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUK
- Medical Research Council (MRC) Integrative Epidemiology UnitUniversity of BristolBristolUK
- Department of PsychologyManchester Metropolitan UniversityManchesterUK
| | - Daphne‐Zacharenia Kounali
- Centre for Academic Mental HealthUniversity of BristolBristolUK
- Department of Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUK
| |
Collapse
|
4
|
Bond TA, Richmond RC, Karhunen V, Cuellar-Partida G, Borges MC, Zuber V, Couto Alves A, Mason D, Yang TC, Gunter MJ, Dehghan A, Tzoulaki I, Sebert S, Evans DM, Lewin AM, O'Reilly PF, Lawlor DA, Järvelin MR. Exploring the causal effect of maternal pregnancy adiposity on offspring adiposity: Mendelian randomisation using polygenic risk scores. BMC Med 2022; 20:34. [PMID: 35101027 PMCID: PMC8805234 DOI: 10.1186/s12916-021-02216-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 12/13/2021] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Greater maternal adiposity before or during pregnancy is associated with greater offspring adiposity throughout childhood, but the extent to which this is due to causal intrauterine or periconceptional mechanisms remains unclear. Here, we use Mendelian randomisation (MR) with polygenic risk scores (PRS) to investigate whether associations between maternal pre-/early pregnancy body mass index (BMI) and offspring adiposity from birth to adolescence are causal. METHODS We undertook confounder adjusted multivariable (MV) regression and MR using mother-offspring pairs from two UK cohorts: Avon Longitudinal Study of Parents and Children (ALSPAC) and Born in Bradford (BiB). In ALSPAC and BiB, the outcomes were birthweight (BW; N = 9339) and BMI at age 1 and 4 years (N = 8659 to 7575). In ALSPAC only we investigated BMI at 10 and 15 years (N = 4476 to 4112) and dual-energy X-ray absorptiometry (DXA) determined fat mass index (FMI) from age 10-18 years (N = 2659 to 3855). We compared MR results from several PRS, calculated from maternal non-transmitted alleles at between 29 and 80,939 single nucleotide polymorphisms (SNPs). RESULTS MV and MR consistently showed a positive association between maternal BMI and BW, supporting a moderate causal effect. For adiposity at most older ages, although MV estimates indicated a strong positive association, MR estimates did not support a causal effect. For the PRS with few SNPs, MR estimates were statistically consistent with the null, but had wide confidence intervals so were often also statistically consistent with the MV estimates. In contrast, the largest PRS yielded MR estimates with narrower confidence intervals, providing strong evidence that the true causal effect on adolescent adiposity is smaller than the MV estimates (Pdifference = 0.001 for 15-year BMI). This suggests that the MV estimates are affected by residual confounding, therefore do not provide an accurate indication of the causal effect size. CONCLUSIONS Our results suggest that higher maternal pre-/early-pregnancy BMI is not a key driver of higher adiposity in the next generation. Thus, they support interventions that target the whole population for reducing overweight and obesity, rather than a specific focus on women of reproductive age.
Collapse
Affiliation(s)
- Tom A Bond
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK.
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia.
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Rebecca C Richmond
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ville Karhunen
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Center for Life-course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
| | - Gabriel Cuellar-Partida
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia
- 23andMe, Inc., Sunnyvale, CA, USA
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Verena Zuber
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Alexessander Couto Alves
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Dan Mason
- Born in Bradford, Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Tiffany C Yang
- Born in Bradford, Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Marc J Gunter
- Section of Nutrition and Metabolism, IARC, Lyon, France
| | - Abbas Dehghan
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Sylvain Sebert
- Center for Life-course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - David M Evans
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Alex M Lewin
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Paul F O'Reilly
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Marjo-Riitta Järvelin
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Center for Life-course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Unit of Primary Care, Oulu University Hospital, Oulu, Finland
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
| |
Collapse
|
5
|
Wu Y, Zhong X, Lin Y, Zhao Z, Chen J, Zheng B, Li JJ, Fletcher JM, Lu Q. Estimating genetic nurture with summary statistics of multigenerational genome-wide association studies. Proc Natl Acad Sci U S A 2021; 118:e2023184118. [PMID: 34131076 PMCID: PMC8237646 DOI: 10.1073/pnas.2023184118] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Marginal effect estimates in genome-wide association studies (GWAS) are mixtures of direct and indirect genetic effects. Existing methods to dissect these effects require family-based, individual-level genetic, and phenotypic data with large samples, which is difficult to obtain in practice. Here, we propose a statistical framework to estimate direct and indirect genetic effects using summary statistics from GWAS conducted on own and offspring phenotypes. Applied to birth weight, our method showed nearly identical results with those obtained using individual-level data. We also decomposed direct and indirect genetic effects of educational attainment (EA), which showed distinct patterns of genetic correlations with 45 complex traits. The known genetic correlations between EA and higher height, lower body mass index, less-active smoking behavior, and better health outcomes were mostly explained by the indirect genetic component of EA. In contrast, the consistently identified genetic correlation of autism spectrum disorder (ASD) with higher EA resides in the direct genetic component. A polygenic transmission disequilibrium test showed a significant overtransmission of the direct component of EA from healthy parents to ASD probands. Taken together, we demonstrate that traditional GWAS approaches, in conjunction with offspring phenotypic data collection in existing cohorts, could greatly benefit studies on genetic nurture and shed important light on the interpretation of genetic associations for human complex traits.
Collapse
Affiliation(s)
- Yuchang Wu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706
- Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, WI 53706
| | - Xiaoyuan Zhong
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706
| | - Yunong Lin
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706
- Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706
| | - Zijie Zhao
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706
| | - Jiawen Chen
- Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
| | - Boyan Zheng
- Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, WI 53706
- Department of Sociology, University of Wisconsin-Madison, Madison, WI 53706
| | - James J Li
- Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, WI 53706
- Department of Psychology, University of Wisconsin-Madison, Madison, WI 53706
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53706
| | - Jason M Fletcher
- Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, WI 53706
- Department of Sociology, University of Wisconsin-Madison, Madison, WI 53706
- La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI 53706
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706;
- Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, WI 53706
- Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706
| |
Collapse
|
6
|
Tubbs JD, Hwang LD, Luong J, Evans DM, Sham PC. Modeling Parent-Specific Genetic Nurture in Families with Missing Parental Genotypes: Application to Birthweight and BMI. Behav Genet 2021; 51:289-300. [PMID: 33454873 DOI: 10.1007/s10519-020-10040-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 11/15/2020] [Accepted: 12/15/2020] [Indexed: 12/25/2022]
Abstract
Disaggregation and estimation of genetic effects from offspring and parents has long been of interest to statistical geneticists. Recently, technical and methodological advances have made the genome-wide and loci-specific estimation of direct offspring and parental genetic nurture effects more possible. However, unbiased estimation using these methods requires datasets where both parents and at least one child have been genotyped, which are relatively scarce. Our group has recently developed a method and accompanying software (IMPISH; Hwang et al. in PLoS Genet 16:e1009154, 2020) which is able to impute missing parental genotypes from observed data on sibships and estimate their effects on an offspring phenotype conditional on the effects of genetic transmission. However, this method is unable to disentangle maternal and paternal effects, which may differ in magnitude and direction. Here, we introduce an extension to the original IMPISH routine which takes advantage of all available nuclear families to impute parent-specific missing genotypes and obtain asymptotically unbiased estimates of genetic effects on offspring phenotypes. We apply this this method to data from related individuals in the UK Biobank, showing concordance with previous estimates of maternal genetic effects on offspring birthweight. We also conduct the first GWAS jointly estimating offspring-, maternal-, and paternal-specific genetic effects on body-mass index.
Collapse
Affiliation(s)
- Justin D Tubbs
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
| | - Liang-Dar Hwang
- University of Queensland Diamantina Institute, University of Queensland, Brisbane, Australia
| | - Justin Luong
- University of Queensland Diamantina Institute, University of Queensland, Brisbane, Australia
| | - David M Evans
- University of Queensland Diamantina Institute, University of Queensland, Brisbane, Australia.,Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Pak C Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China. .,Centre for PanorOmic Sciences, University of Hong Kong, Hong Kong, China. .,State Key Laboratory for Cognitive and Brain Sciences, University of Hong Kong, Hong Kong, China. .,The University of Hong Kong, 1/F, Jockey Club Building for Interdisciplinary Research, 5 Sassoon Road, Pokfulam, Hong Kong, China.
| |
Collapse
|
7
|
Estimation of Parental Effects Using Polygenic Scores. Behav Genet 2021; 51:264-278. [PMID: 33387133 PMCID: PMC8093180 DOI: 10.1007/s10519-020-10032-w] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 11/20/2020] [Indexed: 12/11/2022]
Abstract
Offspring resemble their parents for both genetic and environmental reasons. Understanding the relative magnitude of these alternatives has long been a core interest in behavioral genetics research, but traditional designs, which compare phenotypic covariances to make inferences about unmeasured genetic and environmental factors, have struggled to disentangle them. Recently, Kong et al. (2018) showed that by correlating offspring phenotypic values with the measured polygenic score of parents' nontransmitted alleles, one can estimate the effect of "genetic nurture"-a type of passive gene-environment covariation that arises when heritable parental traits directly influence offspring traits. Here, we instantiate this basic idea in a set of causal models that provide novel insights into the estimation of parental influences on offspring. Most importantly, we show how jointly modeling the parental polygenic scores and the offspring phenotypes can provide an unbiased estimate of the variation attributable to the environmental influence of parents on offspring, even when the polygenic score accounts for a small fraction of trait heritability. This model can be further extended to (a) account for the influence of different types of assortative mating, (b) estimate the total variation due to additive genetic effects and their covariance with the familial environment (i.e., the full genetic nurture effect), and (c) model situations where a parental trait influences a different offspring trait. By utilizing structural equation modeling techniques developed for extended twin family designs, our approach provides a general framework for modeling polygenic scores in family studies and allows for various model extensions that can be used to answer old questions about familial influences in new ways.
Collapse
|
8
|
Tubbs JD, Porsch RM, Cherny SS, Sham PC. The Genes We Inherit and Those We Don’t: Maternal Genetic Nurture and Child BMI Trajectories. Behav Genet 2020; 50:310-319. [DOI: 10.1007/s10519-020-10008-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 07/09/2020] [Indexed: 01/06/2023]
|