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Identifying factors linked to autism traits in the general population may improve our understanding of the mechanisms underlying divergent neurodevelopment. In this study we assess whether factors increasing the likelihood of childhood autism are related to early autistic trait emergence, or if other exposures are more important. We used data from 536 toddlers from London (UK), collected at birth (gestational age at birth, sex, maternal body mass index, age, parental education, parental language, parental history of neurodevelopmental conditions) and at 18 months (parents cohabiting, measures of socio-economic deprivation, measures of maternal parenting style, and a measure of maternal depression). Autism traits were assessed using the Quantitative Checklist for Autism in Toddlers (Q-CHAT) at 18 months. A multivariable model explained 20% of Q-CHAT variance, with four individually significant variables (two measures of parenting style and two measures of socio-economic deprivation). In order to address variable collinearity we used principal component analysis, finding that a component which was positively correlated with Q-CHAT was also correlated to measures of parenting style and socio-economic deprivation. Our results show that parenting style and socio-economic deprivation correlate with the emergence of autism traits at age 18 months as measured with the Q-CHAT in a community sample. [ABSTRACT FROM AUTHOR]

High cortisol production in Cushing's syndrome leads to fat centralisation. The influence of modest cortisol variations on body shape, however, is less clear. We examined potentially causal associations between morning plasma cortisol and body shape and obesity with inverse-variance weighted random-effects models in a two-sample Mendelian randomisation analysis. We used publicly available summary statistics from the CORtisol NETwork (CORNET) consortium, UK Biobank, and the Genetic Investigation of Anthropometric Traits (GIANT) consortium. Only in women, morning plasma cortisol (proxied by ten genetic polymorphisms) was associated positively with waist size reflected in waist-to-hip index (WHI, 0.035 standard deviation (SD) units change per one SD cortisol increase; 95% confidence interval (0.002–0.067); p = 0.036) and "a body shape index" (ABSI; 0.039 (0.006–0.071); p = 0.021). There was no evidence for associations with hip index (HI) or body mass index (BMI). Among individual polymorphisms, rs7450600 stood out (chromosome 6; Long Intergenic Non-Protein-Coding RNA 473 gene, LINC00473). Morning plasma cortisol proxied by rs7450600 was associated positively with WHI and inversely with HI and BMI in women and men. Our findings support a causal association of higher morning plasma cortisol with larger waist size in women and highlight LINC00473 as a genetic link between morning plasma cortisol and body shape. [ABSTRACT FROM AUTHOR]

Whereas lifestyle-related factors are recognized as snoring risk factors, the role of genetics in snoring remains uncertain. One way to measure the impact of genetic risk is through the use of a polygenic risk score (PRS). In this study, we aimed to investigate whether genetics plays a role in snoring after adjusting for lifestyle factors. Since the effect of polygenic risks may differ across ethnic groups, we calculated the PRS for snoring from the UK Biobank and applied it to a Korean cohort. We sought to evaluate the reproducibility of the UK Biobank PRS for snoring in the Korean cohort and to investigate the interaction of lifestyle factors and genetic risk on snoring in the Korean population. In this study, we utilized a Korean cohort obtained from the Korean Genome Epidemiology Study (KoGES). We computed the snoring PRS for the Korean cohort based on the UK Biobank PRS. We investigated the relationship between polygenic risks and snoring while controlling for lifestyle factors, including sex, age, body mass index (BMI), alcohol consumption, smoking, physical activity, and sleep time. Additionally, we analyzed the interaction of each lifestyle factor and the genetic odds of snoring. We included 3526 snorers and 1939 nonsnorers from the KoGES cohort and found that the PRS, a polygenic risk factor, was an independent factor for snoring after adjusting for lifestyle factors. In addition, among lifestyle factors, higher BMI, male sex, and older age were the strongest lifestyle factors for snoring. In addition, the highest adjusted odds ratio for snoring was higher BMI (OR 1.98, 95% CI 1.76–2.23), followed by male sex (OR 1.54, 95% CI 1.28–1.86), older age (OR 1.23, 95% CI 1.03–1.35), polygenic risks such as higher PRS (OR 1.18, 95% CI 1.08–1.29), drinking behavior (OR 1.18, 95% CI 1.03–1.35), late sleep mid-time (OR 1.17, 95% CI 1.02–1.33), smoking behavior (OR 0.99, 95% CI 0.82–1.19), and lower physical activity (OR 0.92, 95% CI 0.85–1.00). Our study identified that the UK Biobank PRS for snoring was reproducible in the Korean cohort and that genetic risk served as an independent risk factor for snoring in the Korean population. These findings may help to develop personalized approaches to reduce snoring in individuals with high genetic risk. [ABSTRACT FROM AUTHOR]

Whereas lifestyle-related factors are recognized as snoring risk factors, the role of genetics in snoring remains uncertain. One way to measure the impact of genetic risk is through the use of a polygenic risk score (PRS). In this study, we aimed to investigate whether genetics plays a role in snoring after adjusting for lifestyle factors. Since the effect of polygenic risks may differ across ethnic groups, we calculated the PRS for snoring from the UK Biobank and applied it to a Korean cohort. We sought to evaluate the reproducibility of the UK Biobank PRS for snoring in the Korean cohort and to investigate the interaction of lifestyle factors and genetic risk on snoring in the Korean population. In this study, we utilized a Korean cohort obtained from the Korean Genome Epidemiology Study (KoGES). We computed the snoring PRS for the Korean cohort based on the UK Biobank PRS. We investigated the relationship between polygenic risks and snoring while controlling for lifestyle factors, including sex, age, body mass index (BMI), alcohol consumption, smoking, physical activity, and sleep time. Additionally, we analyzed the interaction of each lifestyle factor and the genetic odds of snoring. We included 3526 snorers and 1939 nonsnorers from the KoGES cohort and found that the PRS, a polygenic risk factor, was an independent factor for snoring after adjusting for lifestyle factors. In addition, among lifestyle factors, higher BMI, male sex, and older age were the strongest lifestyle factors for snoring. In addition, the highest adjusted odds ratio for snoring was higher BMI (OR 1.98, 95% CI 1.76–2.23), followed by male sex (OR 1.54, 95% CI 1.28–1.86), older age (OR 1.23, 95% CI 1.03–1.35), polygenic risks such as higher PRS (OR 1.18, 95% CI 1.08–1.29), drinking behavior (OR 1.18, 95% CI 1.03–1.35), late sleep mid-time (OR 1.17, 95% CI 1.02–1.33), smoking behavior (OR 0.99, 95% CI 0.82–1.19), and lower physical activity (OR 0.92, 95% CI 0.85–1.00). Our study identified that the UK Biobank PRS for snoring was reproducible in the Korean cohort and that genetic risk served as an independent risk factor for snoring in the Korean population. These findings may help to develop personalized approaches to reduce snoring in individuals with high genetic risk. [ABSTRACT FROM AUTHOR]

Unlike conventional epidemiological studies that use observational data to estimate "associations" between risk factors and disease, the science of causal inference has identified situations where causal estimates can be made from observational data, using results such as the "backdoor criteria". Here these results are combined with established epidemiological methods, to calculate simple population attribution fractions that estimate the causal influence of risk factors on disease incidence, and can be estimated using conventional proportional hazards methods. A counterfactual argument gives an attribution fraction for individuals. Causally meaningful attribution fractions cannot be constructed for all risk factors or confounders, but they can for the important established risk factors of smoking and body mass index (BMI). Using the new results, the causal attribution of smoking and BMI to the incidence of 226 diseases in the UK Biobank are estimated, and summarised in terms of disease chapters from the International Classification of Diseases (ICD-10). The diseases most strongly attributed to smoking and BMI are identified, finding 11 with attribution fractions greater than 0.5, and a small number with protective associations. The results provide new tools to quantify the causal influence of risk factors such as smoking and BMI on disease, and survey the causal influence of smoking and BMI on the landscape of disease incidence in the UK Biobank population. [ABSTRACT FROM AUTHOR]

Genome wide association studies (GWAS) have focused on elucidating the genetic architecture of complex traits by assessing single variant effects in additive genetic models, albeit explaining a fraction of the trait heritability. Epistasis has recently emerged as one of the intrinsic mechanisms that could explain part of this missing heritability. We conducted epistasis analysis for genome-wide body mass index (BMI) associated SNPs in Alzheimer's Disease Neuroimaging Initiative (ADNI) and followed up top significant interacting SNPs for replication in the UK Biobank imputed genotype dataset. We report two pairwise epistatic interactions, between rs2177596 (RHBDD1) and rs17759796 (MAPK1), rs1121980 (FTO) and rs6567160 (MC4R), obtained from a consensus of nine different epistatic approaches. Gene interaction maps and tissue expression profiles constructed for these interacting loci highlights co-expression, co-localisation, physical interaction, genetic interaction, and shared pathways emphasising the neuronal influence in obesity and implicating concerted expression of associated genes in liver, pancreas, and adipose tissues insinuating to metabolic abnormalities characterized by obesity. Detecting epistasis could thus be a promising approach to understand the effect of simultaneously interacting multiple genetic loci in disease aetiology, beyond single locus effects. [ABSTRACT FROM AUTHOR]

Associations of liver, metabolic, and inflammatory biomarkers in blood with body shape are unclear, because waist circumference (WC) and hip circumference (HC) are dependent on overall body size, resulting in bias. We have used the allometric "a body shape index" (ABSI = WC(mm) ∗ Weight(kg)-2/3 ∗ Height(m)5/6) and hip index (HIwomen = HC(cm) ∗ Weight(kg)-0.482 ∗ Height(cm)0.310, HImen = HC(cm) ∗ Weight(kg)-2/5 ∗ Height(cm)1/5), which are independent of body mass index (BMI) by design, in multivariable linear regression models for 121,879 UK Biobank men and 135,559 women. Glucose, glycated haemoglobin (HbA1c), triglycerides, low-density-lipoprotein cholesterol, apolipoprotein-B, alanine aminotransferase (ALT), gamma-glutamyltransferase, and lymphocytes were associated positively with BMI and ABSI but inversely with HI. High-density-lipoprotein cholesterol and apolipoprotein-A1 were associated inversely with BMI and ABSI but positively with HI. Lipid-related biomarkers and ALT were associated only with HI in obese men. C-reactive protein, neutrophils, monocytes, and alkaline phosphatase were associated positively, while bilirubin was associated inversely, with BMI and ABSI but not with HI. Associations were consistent within the clinical reference ranges but were lost or changed direction for low or high biomarker levels. Our study confirms associations with waist and hip size, independent of BMI, for metabolic biomarkers but only with waist size for inflammatory biomarkers, suggesting different contribution of the mechanistic pathways related to body shape. [ABSTRACT FROM AUTHOR]

Genome wide association studies (GWAS) have focused on elucidating the genetic architecture of complex traits by assessing single variant effects in additive genetic models, albeit explaining a fraction of the trait heritability. Epistasis has recently emerged as one of the intrinsic mechanisms that could explain part of this missing heritability. We conducted epistasis analysis for genome-wide body mass index (BMI) associated SNPs in Alzheimer's Disease Neuroimaging Initiative (ADNI) and followed up top significant interacting SNPs for replication in the UK Biobank imputed genotype dataset. We report two pairwise epistatic interactions, between rs2177596 (RHBDD1) and rs17759796 (MAPK1), rs1121980 (FTO) and rs6567160 (MC4R), obtained from a consensus of nine different epistatic approaches. Gene interaction maps and tissue expression profiles constructed for these interacting loci highlights co-expression, co-localisation, physical interaction, genetic interaction, and shared pathways emphasising the neuronal influence in obesity and implicating concerted expression of associated genes in liver, pancreas, and adipose tissues insinuating to metabolic abnormalities characterized by obesity. Detecting epistasis could thus be a promising approach to understand the effect of simultaneously interacting multiple genetic loci in disease aetiology, beyond single locus effects. [ABSTRACT FROM AUTHOR]