Within- and between-group regression for improving the robustness of causal claims in cross-sectional analysis

Genser, Bernd; Teles, Carlos; Barreto, Maurício L.; Fischer, Joachim E.

doi:10.1186/s12940-015-0047-2

Cited by 17 publications

(15 citation statements)

References 33 publications

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“…By including both β W and β B in the same model, the individual estimates are adjusted for, and independent of, the effect of the other estimate. The random effect term , which estimates the difference between each group intercept γ j and the overall intercept α 0 , accounts for the residual structure in the data corresponding to all unaccounted familial factors (both genetic and environmental) that contribute to the trait similarity of the twins 54,55 .…”

Section: Discussionmentioning

confidence: 99%

Comparing within- and between-family polygenic score prediction

Selzam

Ritchie

Pingault

et al. 2019

Preprint

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20Polygenic scores are a popular tool for prediction of complex traits. However, prediction 21 estimates in samples of unrelated participants can include effects of population 22 stratification, assortative mating and environmentally mediated parental genetic effects, a 23 form of genotype-environment correlation (rGE). Comparing genome-wide polygenic score 24 (GPS) predictions in unrelated individuals with predictions between siblings in a within-25 family design is a powerful approach to identify these different sources of prediction. Here, 26we compared within-to between-family GPS predictions of eight life outcomes 27 (anthropometric, cognitive, personality and health) for eight corresponding GPSs. The 28 outcomes were assessed in up to 2,366 dizygotic (DZ) twin pairs from the Twins Early 29Development Study from age 12 to age 21. To account for family clustering, we used mixed-30 effects modelling, simultaneously estimating within-and between-family effects for target-31 and cross-trait GPS prediction of the outcomes. There were three main findings: (1) DZ twin 32 GPS differences predicted DZ differences in height, BMI, intelligence, educational 33 achievement and ADHD symptoms; (2) target and cross-trait analyses indicated that GPS 34 prediction estimates for cognitive traits (intelligence and educational achievement) were on 35 average 60% greater between families than within families, but this was not the case for 36 non-cognitive traits; and (3) this within-and between-family difference for cognitive traits 37 disappeared after controlling for family socio-economic status (SES), suggesting that SES is a 38 source of between-family prediction through rGE mechanisms. These results provide novel 39 insights into the patterns by which rGE contributes to GPS prediction, while ruling out 40 confounding due to population stratification and assortative mating. 41 42 associated with traits other than educational achievement, including intelligence 2,6,7 , 65 socioeconomic status (SES) 8-11 , behaviour problems 12 , mental illness 13 , physical health 13 66 and personality 14,15 , in some cases accounting for as much as or more than the variance in 67 cross-trait associations explained by the target GPS themselves 15,16 . 68 69 However, GWA analyses, and the GPSs derived from them in independent samples, are 70naïve to the pathways that lead from SNPs to trait outcomes 17 . With a focus on prediction, 71 the mechanisms by which polygenic scores relate to phenotypes are left largely unexplored. 72Given the popularity and widespread use of the GPS approach, the interpretation of GPS 73

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Section: Discussionmentioning

confidence: 99%

Comparing within- and between-family polygenic score prediction

Selzam

Ritchie

Pingault

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…The random effect term

, which estimates the difference between each group intercept

and the overall intercept

, accounts for the residual structure in the data corresponding to all unaccounted familial factors (both genetic and environmental) that contribute to the trait similarity of the twins. 61 , 62 …”

Section: Methodsmentioning

confidence: 99%

“…The ICC is the ratio of the between-family (i.e., random intercept) variance over the total variance and is an estimate of how much of the total variation in the outcome is accounted for by family:

where

is the covariance between the family variable, in this case family ID, and the outcome, and

indicates the residual variance capturing within-twin pair differences. The total effect of the relationship between GPS and outcome is the ICC weighted sum of the within- and between-family effects: 62

It follows from Equation 3 that the total effect ranges between

and

. If the relationship between GPS and outcome is mostly due to individual-level variation, the ICC approximates 0 and the total effect will be close to

.…”

Section: Methodsmentioning

confidence: 99%

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Comparing Within- and Between-Family Polygenic Score Prediction

Selzam

Ritchie

Pingault

et al. 2019

The American Journal of Human Genetics

235

259

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Polygenic scores are a popular tool for prediction of complex traits. However, prediction estimates in samples of unrelated participants can include effects of population stratification, assortative mating, and environmentally mediated parental genetic effects, a form of genotype-environment correlation (rGE). Comparing genome-wide polygenic score (GPS) predictions in unrelated individuals with predictions between siblings in a within-family design is a powerful approach to identify these different sources of prediction. Here, we compared within-to between-family GPS predictions of eight outcomes (anthropometric, cognitive, personality, and health) for eight corresponding GPSs. The outcomes were assessed in up to 2,366 dizygotic (DZ) twin pairs from the Twins Early Development Study from age 12 to age 21. To account for family clustering, we used mixed-effects modeling, simultaneously estimating within-and between-family effects for target-and cross-trait GPS prediction of the outcomes. There were three main findings: (1) DZ twin GPS differences predicted DZ differences in height, BMI, intelligence, educational achievement, and ADHD symptoms; (2) target and cross-trait analyses indicated that GPS prediction estimates for cognitive traits (intelligence and educational achievement) were on average 60% greater between families than within families, but this was not the case for non-cognitive traits; and (3) much of this within-and between-family difference for cognitive traits disappeared after controlling for family socioeconomic status (SES), suggesting that SES is a major source of between-family prediction through rGE mechanisms. These results provide insights into the patterns by which rGE contributes to GPS prediction, while ruling out confounding due to population stratification and assortative mating.

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“…To our knowledge, this is the first study to specifically examine the relation of PFAS with CHD in adults with diabetes. Although PFAS have been linked to a number of adverse health outcomes, including dyslipidemia,(2, 3) these compounds have also been inversely associated with other cardiovascular disease risk factors such as CRP (25) and kidney function. (17) In the current study, PFAS showed a significant, inverse relationship with CHD; this association persisted after controlling for kidney function and other potentially confounding factors, and remained after stratification by chronic kidney disease status.…”

Section: Discussionmentioning

confidence: 99%

Perfluoroalkyl substances are inversely associated with coronary heart disease in adults with diabetes

Honda-Kohmo

Hutcheson

Innes

et al. 2019

Journal of Diabetes and its Complications

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Aims-Perfluoroalkyl substances (PFAS) are environmentally and biologically persistent synthetic environmental contaminants linked to adverse health outcomes. Though null to modest inverse relationships between PFAS and coronary heart disease (CHD) have been reported, studies regarding relationships in high risk populations such as those with diabetes are sparse. We investigated the relationship of PFAS with CHD in persons with diabetes. Methods-Data on 5,270 adults, aged ≥20 years, with diabetes were obtained from the C8 Health Project. Four PFAS were investigated separately: perfluorohexane sulfonate (PFHxS), perfluoroctanoic acid (PFOA), perfluorooctane sulfonate (PFOS), and perfluoronanaoic acid (PFNA). Results-In logistic regression analyses adjusting for age, sex, diabetes duration, BMI, smoking, lipids, WBC, CRP, eGFR, uric acid, hemoglobin and iron, all PFAS were inversely associated with CHD, ORs (95% CIs

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Within- and between-group regression for improving the robustness of causal claims in cross-sectional analysis

Cited by 17 publications

References 33 publications

Comparing within- and between-family polygenic score prediction

Comparing within- and between-family polygenic score prediction

Comparing Within- and Between-Family Polygenic Score Prediction

Perfluoroalkyl substances are inversely associated with coronary heart disease in adults with diabetes

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