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Shared genetic aetiology of puberty timing between sexes and with health-related outcomes.

ABSTRACT: Understanding of the genetic regulation of puberty timing has come largely from studies of rare disorders and population-based studies in women. Here, we report the largest genomic analysis for puberty timing in 55,871 men, based on recalled age at voice breaking. Analysis across all genomic variants reveals strong genetic correlation (0.74, P=2.7 × 10(-70)) between male and female puberty timing. However, some loci show sex-divergent effects, including directionally opposite effects between sexes at the SIM1/MCHR2 locus (Pheterogeneity=1.6 × 10(-12)). We find five novel loci for puberty timing (P<5 × 10(-8)), in addition to nine signals in men that were previously reported in women. Newly implicated genes include two retinoic acid-related receptors, RORB and RXRA, and two genes reportedly disrupted in rare disorders of puberty, LEPR and KAL1. Finally, we identify genetic correlations that indicate shared aetiologies in both sexes between puberty timing and body mass index, fasting insulin levels, lipid levels, type 2 diabetes and cardiovascular disease.

SUBMITTER: Day FR

PROVIDER: S-EPMC4667609 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Shared genetic aetiology of puberty timing between sexes and with health-related outcomes.

Day Felix R FR Bulik-Sullivan Brendan B Hinds David A DA Finucane Hilary K HK Murabito Joanne M JM Tung Joyce Y JY Ong Ken K KK Perry John R B JRB

Nature communications 20151109

Understanding of the genetic regulation of puberty timing has come largely from studies of rare disorders and population-based studies in women. Here, we report the largest genomic analysis for puberty timing in 55,871 men, based on recalled age at voice breaking. Analysis across all genomic variants reveals strong genetic correlation (0.74, P=2.7 × 10(-70)) between male and female puberty timing. However, some loci show sex-divergent effects, including directionally opposite effects between sex ...[more]

PMID: 26548314

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Project description:BackgroundSchizophrenia commonly co-occurs with cardiometabolic and inflammation-related traits. It is unclear to what extent the comorbidity could be explained by shared genetic aetiology.MethodsWe used GWAS data to estimate shared genetic aetiology between schizophrenia, cardiometabolic, and inflammation-related traits: fasting insulin (FI), fasting glucose, glycated haemoglobin, glucose tolerance, type 2 diabetes (T2D), lipids, body mass index (BMI), coronary artery disease (CAD), and C-reactive protein (CRP). We examined genome-wide correlation using linkage disequilibrium score regression (LDSC); stratified by minor-allele frequency using genetic covariance analyzer (GNOVA); then refined to locus-level using heritability estimation from summary statistics (ρ-HESS). Regions with local correlation were used in hypothesis prioritization multi-trait colocalization to examine for colocalisation, implying common genetic aetiology.ResultsWe found evidence for weak genome-wide negative correlation of schizophrenia with T2D (rg = -0.07; 95% C.I., -0.03,0.12; P = .002) and BMI (rg = -0.09; 95% C.I., -0.06, -0.12; P = 1.83 × 10-5). We found a trend of evidence for positive genetic correlation between schizophrenia and cardiometabolic traits confined to lower-frequency variants. This was underpinned by 85 regions of locus-level correlation with evidence of opposing mechanisms. Ten loci showed strong evidence of colocalization. Four of those (rs6265 (BDNF); rs8192675 (SLC2A2); rs3800229 (FOXO3); rs17514846 (FURIN)) are implicated in brain-derived neurotrophic factor (BDNF)-related pathways.ConclusionsLDSC may lead to downwardly-biased genetic correlation estimates between schizophrenia, cardiometabolic, and inflammation-related traits. Common genetic aetiology for these traits could be confined to lower-frequency common variants and involve opposing mechanisms. Genes related to BDNF and glucose transport amongst others may partly explain the comorbidity between schizophrenia and cardiometabolic disorders.

Project description:Aims/hypothesisType 2 diabetes is associated with cognitive impairments, but it is unclear whether common genetic factors influence both type 2 diabetes risk and cognition.MethodsUsing data from 1892 Mexican-American individuals from extended pedigrees, including 402 with type 2 diabetes, we examined possible pleiotropy between type 2 diabetes and cognitive functioning, as measured by a comprehensive neuropsychological test battery.ResultsNegative phenotypic correlations (ρp) were observed between type 2 diabetes and measures of attention (Continuous Performance Test [CPT d']: ρp = -0.143, p = 0.001), verbal memory (California Verbal Learning Test [CVLT] recall: ρp = -0.111, p = 0.004) and face memory (Penn Face Memory Test [PFMT]: ρp = -0.127, p = 0.002; PFMT Delayed: ρp = -0.148, p = 2 × 10-4), replicating findings of cognitive impairment in type 2 diabetes. Negative genetic correlations (ρg) were also observed between type 2 diabetes and measures of attention (CPT d': ρg = -0.401, p = 0.001), working memory (digit span backward test: ρg = -0.380, p = 0.005), and face memory (PFMT: ρg = -0.476, p = 2 × 10-4; PFMT Delayed: ρg = -0.376, p = 0.005), suggesting that the same genetic factors underlying risk for type 2 diabetes also influence poor cognitive performance in these domains. Performance in these domains was also associated with type 2 diabetes risk using an endophenotype ranking value approach. Specifically, on measures of attention (CPT d': β = -0.219, p = 0.005), working memory (digit span backward: β = -0.326, p = 0.035), and face memory (PFMT: β = -0.171, p = 0.023; PFMT Delayed: β = -0.215, p = 0.005), individuals with type 2 diabetes showed the lowest performance, while unaffected/unrelated individuals showed the highest performance, and those related to an individual with type 2 diabetes performed at an intermediate level.Conclusions/interpretationThese findings suggest that cognitive impairment may be a useful endophenotype of type 2 diabetes and, therefore, help to elucidate the pathophysiological underpinnings of this chronic disease.Data availabilityThe data analysed in this study is available in dbGaP: www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001215.v2.p2.

Project description:Puberty is marked by substantial increases and emerging sex differences in psychological disorders and risky behaviors. However, few studies have examined these effects beyond adolescence, and the previous literature has been dominated by samples of White girls. The current study examines the broadest known set of health sequelae related to traditional pubertal markers and peer-relative pubertal timing in a representative sample of 14,545 U.S. youth from the National Longitudinal Study of Adolescent to Adult Health. Maturational timing was assessed by age at menarche for girls and physical development for boys (e.g., facial hair, voice change), and then categorized as early (1 SD below mean), on-time, or late (1 SD above mean) within-sex. Early and late peer-relative timing was assessed by a self-report of looking "much older" or "much younger" than one's peers. We examined psychological (depressive symptoms, antisocial behavior), behavioral (number of sex partners, drug use, physical activity, screen time, sleep hours), and physical health (self-reported health, BMI) outcomes during adolescence and young adulthood in a series of sex-stratified regression analyses using survey weights and a comprehensive set of sociodemographic covariates. Results indicated that, overall, earlier pubertal timing (i.e., maturational timing and peer-relative timing) put both girls and boys at risk during adolescence, while later timing was protective. However, longitudinal models revealed mixed results. For instance, early maturational timing was associated with higher young adult BMI (girls: β = 0.139, p < .01; boys: β = 0.107, p < .01), but later timing for boys was associated with both risky (e.g., more screen time; β = 0.125, p < .05) and health promoting (e.g., more sleep; β = .296, p < .01) behaviors. Analysis of this holistic set of outcomes with sex differences in mind allows for more careful evidence-based recommendations for adolescent health promotion.

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Shared genetic aetiology of puberty timing between sexes and with health-related outcomes.

Publications

Shared genetic aetiology of puberty timing between sexes and with health-related outcomes.

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