scholarly journals Segregation of Familial Risk of Obesity in NHANES Cohort Supports a Major Role for Large Genetic Effects in the Current Obesity Epidemic

2019 ◽  
Author(s):  
Arthur B. Jenkins ◽  
Marijka Batterham ◽  
Lesley V. Campbell
Keyword(s):  
Kernel Smoothing ◽  
Large Population ◽  
Familial Risk ◽  
Population Based ◽  
Genetic Effects ◽  
Binary Marker ◽  
Family History Of Diabetes ◽  
Conditional Quantile ◽  
Adult Body Mass ◽  
Current Trends

AbstractThe continuing increase in many countries in adult body mass index (BMI kg/m2) and its dispersion is contributed to by interaction between genetic susceptibilities and an increasingly obesogenic environment (OE). The determinants of OE-susceptibility are unresolved, due to uncertainty around relevant genetic and environmental architecture. We aimed to test the multi-modal distributional predictions of a Mendelian genetic architecture based on collectively common, but individually rare, large-effect variants and their ability to account for current trends in a large population-based sample. We studied publicly available adult BMI data (n = 9102) from 3 cycles of NHANES (1999, 2005, 2013). A first degree family history of diabetes served as a binary marker (FH0/FH1) of genetic obesity susceptibility. We tested for multi-modal BMI distributions non-parametrically using kernel-smoothing and conditional quantile regression (CQR), obtained parametric fits to a Mendelian model in FH1, and estimated FH x OE interactions in CQR models and ANCOVA models incorporating secular time. Non-parametric distributional analyses were consistent with multi-modality and fits to a Mendelian model in FH1 reliably identified 3 modes. Mode separation accounted for ~40% of BMI variance in FH1 providing a lower bound for the contribution of large effects. CQR identified strong FH x OE interactions and FH1 accounted for ~60% of the secular trends in BMI and its SD in ANCOVA models. Multimodality in the FH effect is inconsistent with a predominantly polygenic, small effect architecture and we conclude that large genetic effects interacting with OE provide a better quantitative explanation for current trends in BMI.

Economics and Genetics

2018 ◽  
Author(s):  
Jason M. Fletcher
Keyword(s):  
Big Data ◽  
Genetic Information ◽  
Predictive Power ◽  
Large Population ◽  
Population Based ◽  
Genetic Effects ◽  
Rapid Expansion ◽  
Economic Research ◽  
Polygenic Scores ◽  
Small Genetic Effects

Two interrelated advances in genetics have occurred which have ushered in the growing field of genoeconomics. The first is a rapid expansion of so-called big data featuring genetic information collected from large population–based samples. The second is enhancements to computational and predictive power to aggregate small genetic effects across the genome into single summary measures called polygenic scores (PGSs). Together, these advances will be incorporated broadly with economic research, with strong possibilities for new insights and methodological techniques.

Genetic Factors Account for Half of the Phenotypic Variance in Liability to Sleep-Related Bruxism in Young Adults: A Nationwide Finnish Twin Cohort Study

2012 ◽  
Vol 15 (6) ◽  
pp. 714-719 ◽  
Author(s):  
Katariina Rintakoski ◽  
Christer Hublin ◽  
Frank Lobbezoo ◽  
Richard J. Rose ◽  
Jaakko Kaprio
Keyword(s):  
Cohort Study ◽  
Gender Difference ◽  
Genetic Factors ◽  
Genetic Model ◽  
Large Population ◽  
Population Based ◽  
Genetic Effects ◽  
Phenotypic Variance ◽  
Birth Cohorts ◽  
Additive Genetic Effects

Objectives: The aim of the present study was to examine the role of genetic and environmental factors in the phenotypic variance of bruxism in a large population-based cohort of young adult twins in Finland.Methods: The material of the present study derives from the FinnTwin16 cohort study consisting of five birth cohorts of twin pairs born in 1975–1979 who completed a questionnaire (at mean age 24, range 23–27 years) with data on frequency of sleep-related bruxism in 2000–2002. We used quantitative genetic modeling, based on the genetic similarity of monozygotic and dizygotic twins, to estimate the most probable genetic model for bruxism, based on decomposition of phenotypic variance into components: additive genetic effects (A), dominant genetic effects (D), and non-shared environmental effects (E).Results: On average, 8.7% experienced bruxism weekly, 23.4% rarely, and 67.9% never, with no significant gender difference (p = .052). The best fitting genetic model for bruxism was the AE-model. Additive genetic effects accounted for 52% (95% CI 0.41–0.62) of the total phenotypic variance. Sex-limitation model revealed no gender differences.Conclusions: Genetic factors account for a substantial proportion of the phenotypic variation of the liability to sleep-related bruxism, with no gender difference in its genetic architecture.

Associations between Body Composition and Mortality: A Large Population-Based Cohort Study

2020 ◽  
Author(s):  
Fu-Rong Li ◽  
Pei-Liang Chen ◽  
Xin Cheng ◽  
Hai-Lian Yang ◽  
Wen-Fang Zhong ◽  
...  
Keyword(s):  
Body Composition ◽  
Cohort Study ◽  
Large Population ◽  
Population Based

Fracture Risk in Patients with Myasthenia Gravis: A Population-Based Cohort Study

2021 ◽  
pp. 1-8
Author(s):  
Charles Kassardjian ◽  
Jessica Widdifield ◽  
J. Michael Paterson ◽  
Alexander Kopp ◽  
Chenthila Nagamuthu ◽  
...  
Keyword(s):  
Myasthenia Gravis ◽  
Lower Risk ◽  
Cox Regression ◽  
Large Population ◽  
Population Based ◽  
Osteoporosis Prevention ◽  
Inception Cohort ◽  
Hazard Ratios ◽  
Multiple Covariates ◽  
Region Of Residence

Background: Prednisone is a common treatment for myasthenia gravis (MG), and osteoporosis is a known potential risk of chronic prednisone therapy. Objective: Our aim was to evaluate the risk of serious fractures in a population-based cohort of MG patients. Methods: An inception cohort of patients with MG was identified from administrative health data in Ontario, Canada between April 1, 2002 and December 31, 2015. For each MG patient, we matched 4 general population comparators based on age, sex, and region of residence. Fractures were identified through emergency department and hospitalization data. Crude overall rates and sex-specific rates of fractures were calculated for the MG and comparator groups, as well as rates of specific fractures. Adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox regression. Results: Among 3,823 incident MG patients (followed for a mean of 5 years), 188 (4.9%) experienced a fracture compared with 741 (4.8%) fractures amongst 15,292 matched comparators. Crude fracture rates were not different between the MG cohort and matched comparators (8.71 vs. 7.98 per 1000 patient years), overall and in men and women separately. After controlling for multiple covariates, MG patients had a significantly lower risk of fracture than comparators (HR 0.74, 95% CI 0.63–0.88). Conclusions: In this large, population-based cohort of incident MG patients, MG patients were at lower risk of a major fracture than comparators. The reasons for this finding are unclear but may highlight the importance osteoporosis prevention.

scholarly journals Estimating the asymptomatic proportion of SARS-CoV-2 infection in the general population: Analysis of nationwide serosurvey data in the Netherlands

2021 ◽  
Author(s):  
Scott A. McDonald ◽  
Fuminari Miura ◽  
Eric R. A. Vos ◽  
Michiel van Boven ◽  
Hester E. de Melker ◽  
...  
Keyword(s):  
The Netherlands ◽  
Population Analysis ◽  
Large Population ◽  
Population Based ◽  
Attributable Risk ◽  
Case Definition ◽  
Aggregated Data ◽  
Age Dependent ◽  
Additive Regression ◽  
June July

Abstract Background The proportion of SARS-CoV-2 positive persons who are asymptomatic—and whether this proportion is age-dependent—are still open research questions. Because an unknown proportion of reported symptoms among SARS-CoV-2 positives will be attributable to another infection or affliction, the observed, or 'crude' proportion without symptoms may underestimate the proportion of persons without symptoms that are caused by SARS-CoV-2 infection. Methods Based on two rounds of a large population-based serological study comprising test results on seropositivity and self-reported symptom history conducted in April/May and June/July 2020 in the Netherlands (n = 7517), we estimated the proportion of reported symptoms among those persons infected with SARS-CoV-2 that is attributable to this infection, where the set of relevant symptoms fulfills the ECDC case definition of COVID-19, using inferential methods for the attributable risk (AR). Generalised additive regression modelling was used to estimate the age-dependent relative risk (RR) of reported symptoms, and the AR and asymptomatic proportion (AP) were calculated from the fitted RR. Results Using age-aggregated data, the 'crude' AP was 37% but the model-estimated AP was 65% (95% CI 63–68%). The estimated AP varied with age, from 74% (95% CI 65–90%) for < 20 years, to 61% (95% CI 57–65%) for the 50–59 years age-group. Conclusion Whereas the 'crude' AP represents a lower bound for the proportion of persons infected with SARS-CoV-2 without COVID-19 symptoms, the AP as estimated via an attributable risk approach represents an upper bound. Age-specific AP estimates can inform the implementation of public health actions such as targetted virological testing and therefore enhance containment strategies.

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