Cross-Lagged Analysis of Interplay Between Differential Traits in Sibling Pairs: Validation and Application to Parenting Behavior and ADHD Symptomatology

Moscati, Arden; Verhulst, Brad; McKee, Kevin L.; Silberg, Judy L.; Eaves, Lindon J.

doi:10.1007/s10519-017-9882-y

Cited by 22 publications

(13 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our results suggest that confounding due to familial effects is unlikely to explain these results, and that they are more likely due to an individual level causal effect of BMI on an individual's risk. Behavioural geneticists have used longitudinal data from samples of twins to understand how different family members affect each other over time 42,43 . Other studies have used animal models to investigate how social genetic effects (i.e.…”

Section: Discussionmentioning

confidence: 99%

“…Contrast effects, which may inflate the estimated contribution of the nonshared environment in twin studies 57 , can be mimicked by parental rating bias 58,59 . However, for biological phenotypes where rating bias is not a concern, Mendelian randomization could be used to study the influence via imitation or contrast of one sibling's genotype on the other's phenotype, sometimes called 'social genetic effects' 44 , thereby adding to work on dynamic interplay between siblings 42,43 .…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses

et al. 2020

View full text Add to dashboard Cite

Estimates from Mendelian randomization studies of unrelated individuals can be biased due to uncontrolled confounding from familial effects. Here we describe methods for withinfamily Mendelian randomization analyses and use simulation studies to show that familybased analyses can reduce such biases. We illustrate empirically how familial effects can affect estimates using data from 61,008 siblings from the Nord-Trøndelag Health Study and UK Biobank and replicated our findings using 222,368 siblings from 23andMe. Both Mendelian randomization estimates using unrelated individuals and within family methods reproduced established effects of lower BMI reducing risk of diabetes and high blood pressure. However, while Mendelian randomization estimates from samples of unrelated individuals suggested that taller height and lower BMI increase educational attainment, these effects were strongly attenuated in within-family Mendelian randomization analyses. Our findings indicate the necessity of controlling for population structure and familial effects in Mendelian randomization studies.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Behavioural geneticists have used longitudinal data from samples of twins to understand how different family members affect each other over time. 41,42 Other studies have used animal models to investigate how "social genetic effects" (i.e. indirect or dynastic effects) can affect health outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…55,56 However, for biological phenotypes where rating bias is not a concern, Mendelian randomization could be used to study the influence via imitation or contrast of one sibling's genotype on the other's phenotype, sometimes called 'social genetic effects', 43 thereby adding to work on dynamic interplay between siblings. 41,42 Dynastic effects and assortative mating may cause bias in GWAS. 14 If a GWAS is aiming to estimate the causal effect of variants on a given phenotype, then samples of unrelated individuals may produce biased estimates and potentially spurious findings.…”

Section: Discussionmentioning

confidence: 99%

Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases

Brumpton

Sanderson

Hartwig

et al. 2019

Preprint

107

View full text Add to dashboard Cite

154Abstract Mendelian randomization (MR) is a widely-used method for causal inference using genetic data.Mendelian randomization studies of unrelated individuals may be susceptible to bias from family structure, for example, through dynastic effects which occur when parental genotypes directly affect offspring phenotypes. Here we describe methods for within-family Mendelian randomization and through simulations show that family-based methods can overcome bias due to dynastic effects. We illustrate these issues empirically using data from 61,008 siblings from the UK Biobank and Nord-Trøndelag Health Study. Both within-family and populationbased Mendelian randomization analyses reproduced established effects of lower BMI reducing risk of diabetes and high blood pressure. However, while MR estimates from population-based samples of unrelated individuals suggested that taller height and lower BMI increase educational attainment, these effects largely disappeared in within-family MR analyses. We found differences between population-based and within-family based estimates, indicating the importance of controlling for family effects and population structure in Mendelian randomization studies.

show abstract

“…We should note that the joint consideration of twin and longitudinal data can be applied in many creative ways, for example to leverage longitudinal sibling based designs to infer the development of gene-environment covariance via phenotypic transmission (De Kort et al, 2014. Others have applied longitudinal difference scoring to infer causal relations within a cross-lagged framework (Moscati et al, 2018, Ritchie et al, 2015. Each of these methods leverages the familial relationships in slightly different ways and highlights the immense value of longitudinal twin and family studies.…”

Section: Discussionmentioning

confidence: 99%

Developmental co-occurrence of psychopathology dimensions in childhood

Allegrini¹,

T²,

Boomsma³

et al. 2021

Preprint

View full text Add to dashboard Cite

BackgroundComorbidity between psychopathologies may be attributed to genetic and environmentaldifferences between people as well as causal processes within individuals, where onepathology increases risk for another. Disentangling between-person (co)variance fromwithin-person processes of psychopathology dimensions across childhood could shed light ondevelopmental causes of comorbid mental health problems. Cross-sectional data as well asstandard models to investigate lagged effects conflate between-person and within-personprocesses. This makes it difficult to distinguish time invariant overarching (confounding)factors from temporal directed effects. Additionally, more recent random effects modelsmake no allowance for direct temporal effects from one person to another. Here, we aim todetermine whether and to what extent directional relationships between psychopathologydimensions within-person, and between individuals within families, play a role inmultivariate comorbidity.MethodsWe investigated longitudinal data on measures of common psychopathologies fromchildhood to early adolescence (age 7 to 12), jointly estimating between-person and withinpersonprocesses across time. We conducted random intercepts cross-lagged panel model(RI-CLPM) analyses to unravel the longitudinal co-occurrence of child psychopathologydimensions, and developed an extension of the model to estimate sibling effects withinfamily(wfRI-CLPM). Analyses were separately conducted in two large population-basedcohorts, the Twin Early Developmental Study and the Netherlands Twin Register, includingparent-rated measures of child problem behaviours based on the SDQ and CBCL scalesrespectively.ResultsWe found evidence for strong between-person effects underlying the positive intercorrelationbetween problem behaviours across time. We further identified time-varying within-personprocesses accounting for an increasing amount of trait variance overtime, up to 11% forattention problems in TEDS, and up to 18% for attention problems and social problems inNTR. Lastly, by accommodating family-level data, we found evidence for reciprocaldirectional influences within sib-pairs longitudinally, from externalizing to internalizingproblems, after accounting for similarities that arise through shared (genetic orenvironmental) influences.Co-occurrence of psychopathology dimensions in childhoodConclusionsOur results indicate that within-person processes partly explain the co-occurrence ofpsychopathology dimensions in childhood, and within families, suggesting that both shouldbe taken into account in developmental models of comorbidity.

show abstract

Cross-Lagged Analysis of Interplay Between Differential Traits in Sibling Pairs: Validation and Application to Parenting Behavior and ADHD Symptomatology

Cited by 22 publications

References 21 publications

Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses

Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses

Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases

Developmental co-occurrence of psychopathology dimensions in childhood

Contact Info

Product

Resources

About