Fartein Ask Torvik’s research while affiliated with University of Oslo and other places

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Publications (22)


The nature of the relation between mental illbeing and wellbeing
  • Preprint

December 2024

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105 Reads

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Tilmann von Soest

Research on mental health has traditionally separated the study of illbeing—disorder and problems—and wellbeing—life satisfaction and positive affect. While previous reviews of studies primarily employing self-report scales indicate that illbeing and wellbeing are distinct yet interconnected constructs, a deeper examination of their relationship is lacking. This perspective article from the PROMENTA Research Center offers a review and synthesis of findings on the joint and distinct nature of illbeing and wellbeing across multiple levels of analysis. Adopting an interdisciplinary approach, we integrate genetic, biological, developmental, psychosocial, societal, cultural, and intervention-based perspectives. Our review reveals substantial genetic overlap and some similar biological underpinnings for illbeing and wellbeing. In contrast, environmental factors and societal changes often have divergent impacts. We propose that future research should systematically assess both positive and negative aspects of mental health to discern their shared and unique determinants, predictors, mechanisms, and consequences.


Mechanisms of partner similarity
Conceptual representations of mechanisms of similarity between partners 1 and 2 in trait A. A illustrates direct assortment. B illustrates indirect assortment based on unknown traits. C illustrates indirect assortment based on known traits. D illustrates social stratification. E illustrates convergence. Several mechanisms could co-exist, and the list does not include complex multivariate assortment.
Prevalence rates by partner health
Prevalence of 10 mental and 10 somatic health conditions among males (n = 93,963) and females (n = 93,963) with unaffected and affected partners, 10 to 5 years before a couple had their first child. Data are presented as the proportions of diagnosed individuals, with error bars indicating 95% confidence intervals. Source data are provided in the source data file.
Within-trait partner correlations with various adjustments
Correlations between female and male partners (n = 93,963 couples) for educational outcomes and 10 mental health and 10 somatic health phenotypes 10 to 5 years before they had their first child and cross-sectionally in 2015–2019. Error bars indicate 95% confidence intervals. Source data are provided in the source data file.
Prospective partner correlations
Within and across-trait partner correlations for educational outcomes, 10 mental health conditions, and 10 somatic health conditions, 10 to 5 years before first child (n = 93,963 couples). Adjusted for age. We tested whether the correlations differ from zero using two-sided z-tests based on the estimated correlations and their standard errors provided by OpenMx. Significant correlations (p < 0.05 after Benjamini-Hochberg adjustment) are shown in black. Exact p-values are provided in the Source Data file.
Prospective partner correlations adjusted for grade point average
Within and across-trait partner correlations for 10 mental health conditions, and 10 somatic health conditions, 10 to 5 years before first child (n = 93,963 couples). Adjusted for age and grade point average. Correlations shown in black have p-values < 0.05 after adjusting for the false discovery rate. We tested whether the correlations differ from zero using two-sided z-tests based on the estimated correlations and their standard errors provided by OpenMx. Significant correlations (p < 0.05 after Benjamini-Hochberg adjustment) are shown in black. Exact p-values are provided in the Source Data file.

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Non-random mating patterns within and across education and mental and somatic health
  • Article
  • Full-text available

December 2024

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35 Reads

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1 Citation

Partners resemble each other in health and education, but studies usually examine one trait at a time in established couples. Using data from all Norwegian first-time parents (N = 187,926) between 2016–2020, we analyse grade point average at age 16, educational attainment, and medical records of 10 mental and 10 somatic health conditions measured 10 to 5 years before childbirth. We find stronger partner similarity in mental (median r = 0.14) than in somatic health conditions (median r = 0.04), with ubiquitous cross-trait correlations in mental health (median r = 0.13). High grade point average or education is associated with better partner mental (median r = −0.16) and somatic (median r = −0.08) health. Elevated mental health correlations (median r = 0.25) in established couples indicate convergence. Analyses of siblings and in-laws suggest that health similarity is influenced by indirect assortment based on related traits. Adjusting for grade point average or education reduces partner health correlations by 30–40%. These findings have implications for the distribution of risk factors among children, genetic studies, and intergenerational transmission.

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Figure 1. Associations between any mental disorder and 16 physical-health conditions in primary-care practices across 14 years. Panel A shows associations (odds ratios and 99.99% confidence intervals) between any mental-disorder diagnosis (a binary variable) and 16 physical-health conditions. The figure is organized from the weakest (left and bottom) to the strongest (right and top) associations. Panel B shows the risk of 16 physical-health conditions among patients who did and did not present with any mental disorder. Inverse probability weighting was used to balance on four demographic variables: age at baseline, sex assigned at birth, educational attainment, and county of residence. Results for specific mental disorders are shown in online Supplementary Figs S1-S7. (A) Associations between any mental disorder and 16 physical-health conditions. (B) Risk of 16 physical-health conditions for patients with and without any mental disorder.
Figure 2. Associations between mental disorders and physical-health conditions. Panel A shows a heatmap of associations (odds ratios) between seven specific mental disorders and 16 physical-health conditions across a 14-year period in primary-care patients. The figure is organized from the strongest (top) to the weakest (bottom) associations with any mental health disorder. Inverse probability weighting was used to balance on four demographic variables: age at baseline, sex assigned at birth, educational attainment, and county of residence. The odds ratios shaded in grey are not statistically significant (i.e. the 99.99% confidence interval includes 1.0). All other odds ratios were statistically significant (i.e. 99.99% confidence interval does not include 1.0.). Panels B and C show multimorbidity associations between variety of different mental health disorders and variety of different multiple physical health conditions. Exact percentages are shown in online Supplementary Tables S7-S8. Inverse probability weighting was used to balance on four demographic variables: age at baseline, sex assigned at birth, educational attainment, and county of residence. (A) Heatmap of associations (odds ratios) between seven specific mental disorders and 16 physical-health conditions across a 14-year period in primary-care patients. (B) Multimorbidity associations between variety of different physical health conditions and variety of different mental health disorders. (C) Multimorbidity associations between variety of different mental health disorders and variety of different multiple physical health conditions. MH, mental health; PH, physical health.
Figure 2. Continued.
Figure 3. Bidirectional associations between any mental disorder and 16 physical-health conditions among primary-care patients across a 14-year observation period. Panel A shows the risk (hazard ratios) of 16 physical health conditions after a diagnosis of any mental health disorder and panel B shows the risk (hazard ratios) of any mental disorder after a diagnosis of 16 physical-health conditions. Inverse probability weights were used to balance on four demographic variables: sex, age, educational attainment, and county of residence. Within each month from January 2006 through December 2019, encounters with primary-care providers were assessed for mental health disorders and physical health conditions. Extended Cox proportional hazards models were used, treating dependent time-to-event variables and independent exposures as recurrent and time-varying, respectively. Results for specific mental disorders are shown in online Supplementary Figs S14-S15. (A) Risk of 16 physical-health conditions after any mental disorder. (B) Risk of any mental disorder after 16 physical-health conditions.
Figure 4. Evaluating surveillance bias in the association between any mental disorder and 16 physical-health conditions among primary-care patients. Panel A shows the distribution of time from first observed diagnosis of any mental disorder to first subsequent diagnosis of 16 physical-health conditions over the 14-year period. Panel B shows the distribution of time from first observed diagnosis of 16 physical-health conditions to first subsequent diagnosis of any mental disorder over the 14-year period. Exact percentages are shown in online Supplementary Tables S9-S10. (A) Timing of 16 physical-health conditions after any mental disorder. (B) Timing of any mental disorder after 16 physical-health conditions.
Co-occurrence between mental disorders and physical diseases: a study of nationwide primary-care medical records

November 2024

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20 Reads

Psychological Medicine

Background Mental disorders and physical-health conditions frequently co-occur, impacting treatment outcomes. While most prior research has focused on single pairs of mental disorders and physical-health conditions, this study explores broader associations between multiple mental disorders and physical-health conditions. Methods Using the Norwegian primary-care register, this population-based cohort study encompassed all 2 203 553 patients born in Norway from January 1945 through December 1984, who were full-time residents from January 2006 until December 2019 (14 years; 363 million person-months). Associations between seven mental disorders (sleep disturbance, anxiety, depression, acute stress reaction, substance-use disorders, phobia/compulsive disorder, psychosis) and 16 physical-health conditions were examined, diagnosed according to the International Classification of Primary Care. Results Of 112 mental-disorder/physical-health condition pairs, 96% of associations yielded positive and significant ORs, averaging 1.41 and ranging from 1.05 (99.99% CI 1.00–1.09) to 2.38 (99.99% CI 2.30–2.46). Across 14 years, every mental disorder was associated with multiple different physical-health conditions. Across 363 million person-months, having any mental disorder was associated with increased subsequent risk of all physical-health conditions (HRs:1.40 [99.99% CI 1.35–1.45] to 2.85 [99.99% CI 2.81–2.89]) and vice versa (HRs:1.56 [99.99% CI 1.54–1.59] to 3.56 [99.99% CI 3.54–3.58]). Associations were observed in both sexes, across age groups, and among patients with and without university education. Conclusions The breadth of associations between virtually every mental disorder and physical-health condition among patients treated in primary care underscores a need for integrated mental and physical healthcare policy and practice. This remarkable breadth also calls for research into etiological factors and underlying mechanisms that can explain it.


Estimated number of young adult deaths across sex, quartiles of school performance, quartiles of parental income and parental education
a–c, The predicted number of young adult deaths between age 16 and 30 years from a Cox regression, estimated separately for each sex and using dummy variables for each quartile of GPA (a), quartile of parental income (b) and highest level of parental education, LowUni, Bachelor's degree; HighUni, Master's degree or higher (c). The error bars represent the 95% CI. NQ1 = 235,560, NQ2 = 247,635, NQ3 = 250,937 and NQ4 = 252,560 for a. NQ1 = 223,642, NQ2 = 253,079, NQ3 = 256,366,642 and NQ4 = 253,486 for b. Note that the slight imbalance in number of observations was due to the quartiles being created on the basis of all children in a given birth cohort, also including those with missing GPA. Nprimary = 90,758, Nsecondary = 427,823, NLowUni = 340,020 and NHighUni = 127,972 for c. The corresponding tables are presented in Supplementary Tables 2–4.
Estimated HRs of young adult deaths across sex, school performance, parental income and parental education
The HRs from the Cox regression models were estimated separately for each sex and use dummy variables for each quartile of school performance, each quartile of parental income and each highest level of parental education; Nboys = 505,216 and Ngirls = 481,357. The grey solid line represents an HR of 1, indicating no difference in hazard compared to the highest level within each variable category. The error bars represent the 95% CI. See Supplementary Table 5 for a corresponding table, also including models with parental income, parental education and GPA, separately. To examine the validity of the proportionality assumption, we also present results for GPA quartiles using a median split of the time window (Supplementary Fig. 4).
Number of young adult deaths across sex and quartiles of school performance, events per 10,000
See Supplementary Table 8 for a corresponding table and Supplementary Table 1 for the details on the encoding of the different causes of death.
Estimated HRs for external causes of young adult death among lowest quartile of GPA across sex
The HRs from the Cox regression models are estimated using young adult death as the outcome and the lowest quartile of school performance as the binary covariate, separately for each sex and cause of death within the category ‘External causes of injury and poisoning’; Nboys = 505,216 and Ngirls = 481,357. The grey solid line represents an HR of 1, indicating no difference in hazard compared to quartiles two to four of school performance. The error bars represent the 95% CI (see Supplementary Table 9 for a corresponding table).
School performance and the social gradient in young adult death in Norway

Nature Human Behaviour

Young adults from low socioeconomic backgrounds face an increased risk of early mortality. Here we utilize population-wide data from 17 Norwegian birth cohorts (N = 986,573) to assess whether this risk gradient was explained by early-life educational performance, specifically grade point average at 16 years of age. We show that the gradients in both parental education and income largely disappeared when adjusting for school performance in the models. Specifically, among boys, those with the lowest parental education had an unadjusted hazard ratio (HR) of 2.04 (95% confidence interval (CI) 1.86–2.22) compared with peers with the highest parental education, while for girls, the HR was 1.64 (95% CI 1.35–1.93). After adjusting for school performance, these estimates dropped to 0.99 (95% CI 0.79–1.19) for boys and 0.87 (95% CI 0.55–1.19) for girls. Similarly, the mortality risk for those from the lowest parental income quartile decreased from 1.79 (95% CI 1.67–1.91) to 1.25 (95% CI 1.12–1.38) for boys and from 1.63 (95% CI 1.44–1.83) to 1.24 (95% CI 1.03–1.46) for girls. Low educational performance remained strongly associated with early mortality in analyses accounting for unobserved heterogeneity at the family level; boys with a grade point average in the lowest quartile had an HR of 3.04 (95% CI 2.38–3.89), while for girls, the HR was 1.79 (95% CI 1.22–2.63). External causes of death, particularly accidents and poisoning, were most overrepresented among individuals with poor school performance.


Parenthood, Mental Disorders, and Symptoms Through Adulthood: A Total Population Study

November 2024

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41 Reads

During recent decades, parenthood has declined in many Western countries. Simultaneously, mental disorders have become more prevalent. We investigated the link between parenthood and mental health in the entire Norwegian-born population aged 31 to 80 from 2006 to 2019 (n=2,234,087). We used logistic regression models on national register data and included sibling- and twin-matched analyses to address unobserved confounders. Parenthood was associated with a lower risk of mental disorders, including depressive and anxiety disorders. For symptoms related to mental disorders, fathers had a reduced risk, while mothers had a slightly elevated risk. Mental health disparities between parents and non-parents were greater among men than among women and persisted across adulthood, before reducing at older ages. Our main findings were largely consistent in sibling- and twin-matched designs. The disparity between parents and non-parents increased over the study period, suggesting stronger selection into parenthood. Our findings highlight parenthood as a significant indicator of mental health inequalities, with its importance growing over time.


Figure 1: A simplified version of the kinship-based model, showing the relationship between a parental and offspring phenotype (omitting the effects of co-parents and assortative mating). Rectangles represent observed variables and circles represent latent variables. All latent variables have unit variances. The parent-offspring correlation is thought to result from direct phenotypic transmission (í µí¼Ž í µí± 2 í µí±, green line), passive genetic transmission ((í µí±Ž 1 í µí±Ž 1 ′ )/2, light blue line), and passive environmental transmission (í µí± 1 í µí± 1 ′ , dark blue line). A = Additive Genetic Effects, C = Shared Environmental Effects, E = Non-Shared Environmental Effects. The model is identified by including multiple children and multiple nuclear families linked by monozygotic twins and full siblings. The full biometric model is presented in Supplementary Fig. S3. See Supplementary Table S5 for a complete overview of the parameters in the model and Supplementary Table S6 for the derived equations for the different covariances.
Figure 2: Psychiatric disorders across age and parental income quartile. 95% CIs are too small to be visible in this figure. The top panels present results for paternal income quartile (A: prevalences, B: prevalence ratios), and the bottom panels present results for maternal income quartile (C: prevalences, D: prevalence ratios).
Figure 4: Polyserial correlations between parental or avuncular (uncle or aunt) income rank and offspring psychiatric disorder, stratified by age group and family type (i.e., whether uncle/aunt is a monozygotic twin (MZ), dizygotic twin (DZ), or a full sibling (FS) of the parent). The y-axis is reversed to make interpretation more intuitive. The numbers behind this figure are listed in Supplementary Table S4.
Figure 5: The decomposition (incl. 95% CIs) of the correlation between parental income rank and any offspring psychiatric disorders across offspring age groups. The correlations are assumed to reflect phenotypic transmission (i.e., direct effects of parental income rank, green), passive genetic transmission (light blue), and passive environmental transmission (dark blue). The total correlation is the sum of each component. The yaxis is reversed to make interpretation easier, so that a taller column means a larger negative correlation (lower income rank → higher risk of psychiatric disorders). Results are also presented in Supplementary Table S9.
table 40 ,
Parental income and psychiatric disorders from age 10 to 40: a genetically informative population study

October 2024

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54 Reads

Background: Lower parental income is associated with more psychiatric disorders among offspring, but it is unclear if this association reflects effects of parental income (social causation) or shared risk factors (social selection). Prior research finds contradictory results, which may be due to age differences between the studied offspring. Methods: Here, we studied the entire Norwegian population aged 10 to 40 between 2006 and 2018 (N = 2,468,503). By linking tax registries to administrative health registries, we describe prevalence rates by age, sex, and parental income rank. Next, we used kinship-based models with extended families of twins and siblings to distinguish direct effects of parents from shared genetic and environmental risk factors. Results: We show that lower parental income rank was associated with more psychiatric disorders at all ages from age 10 to 40 (r between -.06 and -.15). The kinship-based models indicated that direct effects of parental income played a large role (38%) in explaining the parent-offspring correlation among adolescents, while shared risk factors accounted for the entire parent-offspring correlation among adults. Conclusion: Our findings indicate that social causation plays a significant role during adolescence, while social selection fully explains the parent-offspring association in adulthood.


Fig. 1 | An analysis of primary-care encounters. a, The International Classification of Primary Care (ICPC-2), which focuses on conditions that are encountered in primary care and which are coded into chapters representing body systems. b, The flow chart of medical conditions studied in this nationwide analysis of health encounters in general practices.
Fig. 3 | Encounter-level analysis: proportion of primary-care encounters devoted to mental health. a, The proportion of 41,616,704 mental-health encounters devoted to each of 24 different mental-health conditions (Supplementary Table 5). b, Mental-health encounters generated by patients at every point over the lifespan.
A nationwide analysis of 350 million patient encounters reveals a high volume of mental-health conditions in primary care

September 2024

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43 Reads

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1 Citation

Nature Mental Health

How many primary-care encounters are devoted to mental-health conditions compared with physical-health conditions? Here we analyzed Norway’s nationwide administrative primary-care records, extracting all doctor–patient encounters occurring during 14 years (2006–2019) for the population aged 0–100 years. Encounters were recorded according to the International Classification of Primary Care. We compared the volume of mental-health encounters against volumes for conditions in multiple different body systems. A total of 4,875,722 patients generated 354,516,291 encounters. One in 9 encounters (11.7%) involved a mental-health condition. Only musculoskeletal conditions accounted for a greater share of primary-care physicians’ attention. The volume of mental-health encounters in primary care equaled encounters for infections, cardiovascular and respiratory conditions and exceeded encounters for pain, injuries, metabolic, digestive, skin, urological, reproductive and sensory conditions. Primary-care physicians frequently treat complex mental-health conditions in patients of every age. These physicians may have a more important role in preventing the escalation of mental-health problems than heretofore appreciated.


Fig. 1 | Mendelian randomization applied to an intergenerational relationship. Blue lines: genetic nurture. Orange lines: direct genetic transmission. Yellow lines: confounding by environmental factors or ancestry. Dotted lines: pathways accounted for by use of instrumental variables or adjustment. In Mendelian randomization, genetic variants associated with an exposure are used as instrumental variables for the exposure. Given certain assumptions, this avoids confounding by factors which influence both the exposure (here: parental educational attainment) and the outcome (here: children's depressive traits). With intergenerational relationships, estimates can still be biased if genetic variants which influence the exposure in parents also influence the outcome when inherited by children. Withinfamily Mendelian randomization avoids this source of bias by adjusting for the child's genotype. Ancestry can also confound associations of genetic variants with outcomes, so is typically adjusted for via principal components 24 .
Fig. 3 | Associations between mother's and father's years of education and children's traits of depression, anxiety, and ADHD, N = 40,879. Circles: non-genetic multivariable regression adjusting for the child's sex, year of birth, and genotyping covariates. Diamonds: non-genetic multivariable regression adjusting for the child's sex and year of birth, mother's and father's traits of depression and ADHD, mother's and father's smoking status, maternal parity at the child's birth, and genotyping covariates. Squares: Mendelian randomization model adjusting for the child's sex and year of birth, the other parent's education PGI, and genotyping covariates. Triangles: within-family Mendelian randomization, adjusting for the child's own education PGI, sex and year of birth, the other parent's education PGI, and genotyping covariates. All outcomes are standardized; mother's and father's education are in years.
Descriptive characteristics of analytic sample (N = 40,879) a
Parental education and children's depression, anxiety, and ADHD traits, a within-family study in MoBa

July 2024

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91 Reads

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1 Citation

npj Science of Learning

Children born to parents with fewer years of education are more likely to have depression, anxiety, and attention-deficit hyperactivity disorder (ADHD), but it is unclear to what extent these associations are causal. We estimated the effect of parents’ educational attainment on children’s depressive, anxiety, and ADHD traits at age 8 years, in a sample of 40,879 Norwegian children born in 1998–2009 and their parents. We used within-family Mendelian randomization, which employs genetic variants as instrumental variables, and controlled for direct genetic effects by adjusting for children’s polygenic indexes. We found little evidence that mothers’ or fathers’ educational attainment independently affected children’s depressive, anxiety, or ADHD traits. However, children’s own polygenic scores for educational attainment were independently and negatively associated with these traits. Results suggest that differences in these traits according to parents’ education may reflect direct genetic effects more than genetic nurture. Consequences of social disadvantage for children’s mental health may however be more visible in samples with more socioeconomic variation, or contexts with larger socioeconomic disparities than present-day Norway. Further research is required in populations with more educational and economic inequality and in other age groups.


Cognitive Abilities and Educational Attainment as Antecedents of Mental Disorders: A Total Population Study of Males

June 2024

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24 Reads

The positive relation between mental health and educational attainment is well-established, yet the extent to which cognitive abilities influence this gradient or independently predict mental health outcomes remains unclear. In this study, we investigated the association between adolescent cognitive abilities, educational attainment, and adult mental health. Cognitive ability was ascertained in Norwegian military conscript test data (N = 272,351; mean age 17.8 years; males only), whereas mental disorders were ascertained using the Norwegian register of primary care diagnoses received between the age of 36–40. Higher cognitive abilities were associated with a monotonically decreasing risk of developing all the studied mental disorders except bipolar disorder. The association held even when comparing the cognitive abilities of brothers raised in the same family, attesting that cognitive ability and mental disorders are not associated because both arise from the same family background circumstances. Similarly, individuals with higher educational attainment had fewer mental health disorders. The association between low cognitive abilities and the risk of mental disorders was notably stronger in males with low educational attainment, compared to those with high educational attainment. These individuals may be an underutilized target group for mental-disorder prevention.


Figure 2 Correlations between family members' educational attainment (measured as years of education at age 30), stratified by family type (i.e., whether siblings in the parent generation are monozygotic twins, dizygotic twins, or regular full siblings). Error bars are 95% confidence intervals calculated using 1000 bootstrap samples. Inlaws = partner of sibling/twin, Co-inlaws = partner of sibling-in-law.
Figure 3 The iAM-ACE model. The model includes four observed individuals: a set of twins (or siblings) along with their respective partners. Differences in the observed, focal phenotype (denoted í µí±ƒ) are thought to result from additive genetic factors (í µí°´), siblingshared environmental factors (í µí° ¶), twin-shared environmental factors (í µí±‡), and non-shared environmental factors (í µí°¸). Their effects on the focal phenotype are denoted í µí±Ž, í µí±, í µí±¡, and í µí±’, respectively. Partners (i.e., Partner 1 -Twin 1, and Partner 2 -Twin 2) are assorting (í µí¼‡) on a sorting factor (S), which are influenced by the same factors that influence the focal phenotype, albeit with different effects (í µí±Ž ̃,
Understanding indirect assortative mating and its intergenerational consequences

June 2024

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19 Reads

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3 Citations

Partners tend to have similar levels of education. Previous studies indicate that this is likely due to some form of indirect assortative mating but there is not a consistent understanding of this process. Understanding indirect assortment is crucial for resolving the role of nature and nurture in education. We attribute previous inconsistencies to idiosyncratic models and inconsistent use of relevant terms. In this paper, we develop a new framework for understanding indirect assortative mating and provide updated definitions of key terms. We then develop a model (the iAM-ACE-model) that can use partners of twins and siblings to distinguish the degree of assortment on genetic, social, and individual characteristics. We also expand this model to include children of twins and siblings (the iAM-COTS model), allowing us to explain parent-offspring similarity while accounting for indirect assortative mating and gene-environment correlations. We apply the models on educational attainment using 1,529,144 individuals in 209,792 extended families from Norwegian registry data and the Norwegian Twin Registry. The analysis suggests that partner similarity in educational attainment can be explained by strong assortment on sibling-shared environmental factors, with only moderate assortment on genetic factors. The implied genotypic correlation between partners ( r = .33) is comparable to earlier studies, and higher than expected under direct assortment. Most of the parent-offspring correlation ( r = .33) was attributable to passive genetic transmission (62%), with the rest attributable to passive environmental transmission (23%) and direct phenotypic transmission (15%). Environmental transmission was estimated lower in alternative models that assumed direct assortment, but these did not fit the data well.


Citations (12)


... Due to this financial scheme, it is unlikely that visits to the general practitioner go unreported. Another study found that >99% of the population (comprising full-time residents in the study period) had at least one visit to a general practitioner in the study period 29 . For our descriptive analyses, we primarily looked at whether offspring had any psychiatric disorder, indicated by at least one visit that year registered with a diagnostic code (P70 -P99) in the psychological chapter of the ICPC-2. ...

Reference:

Parental income and psychiatric disorders from age 10 to 40: a genetically informative population study
A nationwide analysis of 350 million patient encounters reveals a high volume of mental-health conditions in primary care

Nature Mental Health

... By adjusting such models for the child's own PGS (the transmitted genetic variants), we can estimate of possible parent-driven effects on the child outcome. Such effects have been identified for common genetic variants associated with autism, ADHD, and educational attainment on child neurodevelopmental traits 6 , but with less evidence for other outcomes (e.g., on conduct problems 7 , and of the educational attainment PGS on children's depressive, anxiety, or ADHD traits 8 ). ...

Parental education and children's depression, anxiety, and ADHD traits, a within-family study in MoBa

npj Science of Learning

... One promising methodology for tackling the causation-selection issue is kinship-based models of extended families, such as the children-of-twins model [23][24][25] . Kinship-based models employ differences in genetic relatedness to differentiate genetic and environmental causes of correlations between family members. ...

Understanding indirect assortative mating and its intergenerational consequences

... This allows for comparing outcomes within siblings who are similar or different on the exposures of interest [51]. However, bias can occur from environmental factors and experiences not shared between siblings [52]. Gene-environment interaction analyses can uncover how genetic predispositions and environmental factors interact to affect outcomes [53]. ...

Familial confounding or measurement error? How to interpret findings from sibling and co-twin control studies

European Journal of Epidemiology

... Advances have contributed to an increased understanding of open science and reproducible research practices by highlighting the value of using large samples sizes and transparent reporting (Larsson, 2022), as well as by clarifying how an increased meta-research literacy may ensure high quality, transparent and reproducible primary data or meta-research products (Fabiano et al., 2024). (Lahti-Pulkkinen et al., 2024), the UK's Millennium Cohort Study (Tsomokos et al., 2024), and large-scale national registers or health care databases (Coughlan et al., 2024;Nordmo et al., 2024). Two papers (Donaldson, Hawkins et al., 2024;Tsomokos et al., 2024) in the September issue of JCPP Advances made the data openly available (in both studies via the UK Data Service) and one paper made the code that was used to generate correlations, models and figures publicly available on their GitHub page (Keijser et al., 2024). ...

The diminishing association between adolescent mental disorders and educational performance from 2006–2019

... Assortment based on heritable mental disorders should lead to genetic correlations between partners, and since the genes are determined before the couples are formed, correlations should be independent of convergence. These studies report null findings for mental disorders 8,9 , except for schizophrenia 10 . Such findings could imply that mental health does not influence partner selection. ...

Genetic similarity between relatives provides evidence on the presence and history of assortative mating

... Assortment based on heritable mental disorders should lead to genetic correlations between partners, and since the genes are determined before the couples are formed, correlations should be independent of convergence. These studies report null findings for mental disorders 8,9 , except for schizophrenia 10 . Such findings could imply that mental health does not influence partner selection. ...

The structure of psychiatric comorbidity without selection and assortative mating

Translational Psychiatry

... A false negative is therefore highly likely. Reports of smaller but non-zero phenotypic correlations prior to partner formation suggests that both convergence and assortment play an important role 43,44 . ...

Non-random Mating Patterns in Education, Mental, and Somatic Health: A Population Study on Within- and Cross-Trait Associations

... According to the 2021 World ADHD Federation International Consensus Statement, ADHD affects 5.9% of adolescents and 2.5% of adults worldwide . Although the pathogenesis of ADHD has not yet been clarified, existing studies suggest that it is caused by a synergistic combination of various genetic-environmental factors (Faraone et al., 2021;Demontis et al., 2023;Kleppesto et al., 2024). Furthermore, these studies indicate that the pathogenesis and development of ADHD are closely related to several genetic, neurodevelopmental, familial, and social factors. ...

Intergenerational transmission of ADHD behaviors: genetic and environmental pathways

Psychological Medicine

... 7(Núm. 16) (Edición Diciembre 2024).ISSN: 2697-3626 ¿Cómo Afecta el Alcohol a mi Rendimiento Académico?: La Autopercepción de Adolescentes Ecuatorianos significativos en el rendimiento académico y en la salud en general(Hjarnaa et al., 2023).En este estudio, se observó que el 61.5% de los estudiantes no consumía alcohol, lo cual es consistente con investigaciones que sugieren que la regulación parental y los programas preventivos efectivos pueden reducir el consumo de alcohol en adolescentes. Sin embargo, un 19% reportó consumir alcohol de 2 a 4 veces al mes, y un 10.5% con mayor frecuencia, lo que destaca la influencia de factores sociales y la aceptación del consumo en esta etapa de la vida(Panes et al., 2023). ...

Alcohol Intake and Academic Performance and Dropout in High School: A Prospective Cohort Study in 65,233 Adolescents
  • Citing Article
  • September 2023

Journal of Adolescent Health