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1.
NPJ Schizophr ; 7(1): 2, 2021 Jan 22.
Artigo em Inglês | MEDLINE | ID: mdl-33483511

RESUMO

To characterise the trait-effects of increased genetic risk for schizophrenia, and highlight potential risk mediators, we test the association between schizophrenia polygenic risk scores (PRSs) and 529 behavioural traits (personality, psychological, lifestyle, nutritional) in the UK Biobank. Our primary analysis is performed on individuals aged 38-71 with no history of schizophrenia or related disorders, allowing us to report the effects of schizophrenia genetic risk in the sub-clinical general population. Higher schizophrenia PRSs were associated with a range of traits, including lower verbal-numerical reasoning (P = 6 × 10-61), higher nervous feelings (P = 1 × 10-46) and higher self-reported risk-taking (P = 3 × 10-38). We follow-up the risk-taking association, hypothesising that the association may be due to a genetic propensity for risk-taking leading to greater migration, urbanicity or drug-taking - reported environmental risk factors for schizophrenia, and all positively associated with risk-taking in these data. Next, to identify potential disorder or medication effects, we compare the PRS-trait associations in the general population to the trait values in 599 medicated and non-medicated individuals diagnosed with schizophrenia in the biobank. This analysis highlights, for example, levels of BMI, physical activity and risk-taking in cases in the opposite directions than expected from the PRS-trait associations in the general population. Our analyses offer simple yet potentially revealing insights into the possible causes of observed trait-disorder associations, which can complement approaches such as Mendelian Randomisation. While we urge caution in causal interpretations in PRS cross-trait studies that are highly powered to detect weak horizontal pleiotropy or population structure, we propose that well-designed polygenic score analyses have the potential to highlight modifiable risk factors that lie on the path between genetic risk and disorder.

2.
J Med Internet Res ; 22(11): e24018, 2020 11 06.
Artigo em Inglês | MEDLINE | ID: mdl-33027032

RESUMO

BACKGROUND: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. OBJECTIVE: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. METHODS: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. RESULTS: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. CONCLUSIONS: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.


Assuntos
Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/mortalidade , Aprendizado de Máquina/normas , Pneumonia Viral/diagnóstico , Pneumonia Viral/mortalidade , Lesão Renal Aguda/epidemiologia , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Betacoronavirus , Estudos de Coortes , Registros Eletrônicos de Saúde , Feminino , Mortalidade Hospitalar , Hospitalização/estatística & dados numéricos , Hospitais , Humanos , Masculino , Pessoa de Meia-Idade , Cidade de Nova Iorque/epidemiologia , Pandemias , Prognóstico , Curva ROC , Medição de Risco/métodos , Medição de Risco/normas , Adulto Jovem
3.
Nat Protoc ; 15(9): 2759-2772, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32709988

RESUMO

A polygenic score (PGS) or polygenic risk score (PRS) is an estimate of an individual's genetic liability to a trait or disease, calculated according to their genotype profile and relevant genome-wide association study (GWAS) data. While present PRSs typically explain only a small fraction of trait variance, their correlation with the single largest contributor to phenotypic variation-genetic liability-has led to the routine application of PRSs across biomedical research. Among a range of applications, PRSs are exploited to assess shared etiology between phenotypes, to evaluate the clinical utility of genetic data for complex disease and as part of experimental studies in which, for example, experiments are performed that compare outcomes (e.g., gene expression and cellular response to treatment) between individuals with low and high PRS values. As GWAS sample sizes increase and PRSs become more powerful, PRSs are set to play a key role in research and stratified medicine. However, despite the importance and growing application of PRSs, there are limited guidelines for performing PRS analyses, which can lead to inconsistency between studies and misinterpretation of results. Here, we provide detailed guidelines for performing and interpreting PRS analyses. We outline standard quality control steps, discuss different methods for the calculation of PRSs, provide an introductory online tutorial, highlight common misconceptions relating to PRS results, offer recommendations for best practice and discuss future challenges.


Assuntos
Predisposição Genética para Doença/genética , Guias de Prática Clínica como Assunto , Medição de Risco/métodos , Humanos , Controle de Qualidade
4.
medRxiv ; 2020 Apr 23.
Artigo em Inglês | MEDLINE | ID: mdl-32511655

RESUMO

BACKGROUND: The coronavirus 2019 (Covid-19) pandemic is a global public health crisis, with over 1.6 million cases and 95,000 deaths worldwide. Data are needed regarding the clinical course of hospitalized patients, particularly in the United States. Methods Demographic, clinical, and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed Covid-19 between February 27 and April 2, 2020 were identified through institutional electronic health records. We conducted a descriptive study of patients who had in-hospital mortality or were discharged alive. Results A total of 2,199 patients with Covid-19 were hospitalized during the study period. As of April 2nd, 1,121 (51%) patients remained hospitalized, and 1,078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 ug/ml, C-reactive protein was 162 mg/L, and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 ug/ml, C-reactive protein was 79 mg/L, and procalcitonin was 0.09 ng/mL. Conclusions This is the largest and most diverse case series of hospitalized patients with Covid-19 in the United States to date. Requirement of intensive care and mortality were high. Patients who died typically had pre-existing conditions and severe perturbations in inflammatory markers.

6.
Mol Psychiatry ; 25(7): 1430-1446, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-31969693

RESUMO

Depression is more frequent among individuals exposed to traumatic events. Both trauma exposure and depression are heritable. However, the relationship between these traits, including the role of genetic risk factors, is complex and poorly understood. When modelling trauma exposure as an environmental influence on depression, both gene-environment correlations and gene-environment interactions have been observed. The UK Biobank concurrently assessed Major Depressive Disorder (MDD) and self-reported lifetime exposure to traumatic events in 126,522 genotyped individuals of European ancestry. We contrasted genetic influences on MDD stratified by reported trauma exposure (final sample size range: 24,094-92,957). The SNP-based heritability of MDD with reported trauma exposure (24%) was greater than MDD without reported trauma exposure (12%). Simulations showed that this is not confounded by the strong, positive genetic correlation observed between MDD and reported trauma exposure. We also observed that the genetic correlation between MDD and waist circumference was only significant in individuals reporting trauma exposure (rg = 0.24, p = 1.8 × 10-7 versus rg = -0.05, p = 0.39 in individuals not reporting trauma exposure, difference p = 2.3 × 10-4). Our results suggest that the genetic contribution to MDD is greater when reported trauma is present, and that a complex relationship exists between reported trauma exposure, body composition, and MDD.

7.
Int J Epidemiol ; 49(1): 233-243, 2020 02 01.
Artigo em Inglês | MEDLINE | ID: mdl-31074781

RESUMO

BACKGROUND: Maternal pre-pregnancy body mass index (BMI) is positively associated with offspring birth weight (BW) and BMI in childhood and adulthood. Each of these associations could be due to causal intrauterine effects, or confounding (genetic or environmental), or some combination of these. Here we estimate the extent to which the association between maternal BMI and offspring body size is explained by offspring genotype, as a first step towards establishing the importance of genetic confounding. METHODS: We examined the associations of maternal pre-pregnancy BMI with offspring BW and BMI at 1, 5, 10 and 15 years, in three European birth cohorts (n ≤11 498). Bivariate Genomic-relatedness-based Restricted Maximum Likelihood implemented in the GCTA software (GCTA-GREML) was used to estimate the extent to which phenotypic covariance was explained by offspring genotype as captured by common imputed single nucleotide polymorphisms (SNPs). We merged individual participant data from all cohorts, enabling calculation of pooled estimates. RESULTS: Phenotypic covariance (equivalent here to Pearson's correlation coefficient) between maternal BMI and offspring phenotype was 0.15 [95% confidence interval (CI): 0.13, 0.17] for offspring BW, increasing to 0.29 (95% CI: 0.26, 0.31) for offspring 15 year BMI. Covariance explained by offspring genotype was negligible for BW [-0.04 (95% CI: -0.09, 0.01)], but increased to 0.12 (95% CI: 0.04, 0.21) at 15 years, which is equivalent to 43% (95% CI: 15%, 72%) of the phenotypic covariance. Sensitivity analyses using weight, BMI and ponderal index as the offspring phenotype at all ages showed similar results. CONCLUSIONS: Offspring genotype explains a substantial fraction of the covariance between maternal BMI and offspring adolescent BMI. This is consistent with a potentially important role for genetic confounding as a driver of the maternal BMI-offspring BMI association.


Assuntos
Peso ao Nascer/genética , Índice de Massa Corporal , Mães , Obesidade/etiologia , Obesidade Pediátrica/genética , Adulto , Criança , Feminino , Humanos , Masculino , Obesidade/genética , Gravidez
8.
Nat Commun ; 10(1): 5765, 2019 12 18.
Artigo em Inglês | MEDLINE | ID: mdl-31852892

RESUMO

Body composition is often altered in psychiatric disorders. Using genome-wide common genetic variation data, we calculate sex-specific genetic correlations amongst body fat %, fat mass, fat-free mass, physical activity, glycemic traits and 17 psychiatric traits (up to N = 217,568). Two patterns emerge: (1) anorexia nervosa, schizophrenia, obsessive-compulsive disorder, and education years are negatively genetically correlated with body fat % and fat-free mass, whereas (2) attention-deficit/hyperactivity disorder (ADHD), alcohol dependence, insomnia, and heavy smoking are positively correlated. Anorexia nervosa shows a stronger genetic correlation with body fat % in females, whereas education years is more strongly correlated with fat mass in males. Education years and ADHD show genetic overlap with childhood obesity. Mendelian randomization identifies schizophrenia, anorexia nervosa, and higher education as causal for decreased fat mass, with higher body fat % possibly being a causal risk factor for ADHD and heavy smoking. These results suggest new possibilities for targeted preventive strategies.


Assuntos
Glicemia/genética , Composição Corporal/genética , Transtornos Mentais/genética , Sobrepeso/genética , Fatores Etários , Comorbidade , Escolaridade , Feminino , Variação Genética , Estudo de Associação Genômica Ampla , Humanos , Masculino , Transtornos Mentais/epidemiologia , Transtornos Mentais/prevenção & controle , Pessoa de Meia-Idade , Herança Multifatorial/genética , Sobrepeso/epidemiologia , Fenótipo , Aptidão Física , Fatores de Risco , Fatores Sexuais
9.
Nat Commun ; 10(1): 4222, 2019 09 17.
Artigo em Inglês | MEDLINE | ID: mdl-31530810

RESUMO

Inversions are one type of structural variants linked to phenotypic differences and adaptation in multiple organisms. However, there is still very little information about polymorphic inversions in the human genome due to the difficulty of their detection. Here, we develop a new high-throughput genotyping method based on probe hybridization and amplification, and we perform a complete study of 45 common human inversions of 0.1-415 kb. Most inversions promoted by homologous recombination occur recurrently in humans and great apes and they are not tagged by SNPs. Furthermore, there is an enrichment of inversions showing signatures of positive or balancing selection, diverse functional effects, such as gene disruption and gene-expression changes, or association with phenotypic traits. Therefore, our results indicate that the genome is more dynamic than previously thought and that human inversions have important functional and evolutionary consequences, making possible to determine for the first time their contribution to complex traits.


Assuntos
Inversão Cromossômica , Evolução Molecular , Genoma Humano , Técnicas de Genotipagem , Humanos , Fenótipo , Polimorfismo de Nucleotídeo Único
10.
Int J Eat Disord ; 52(11): 1205-1223, 2019 11.
Artigo em Inglês | MEDLINE | ID: mdl-31512774

RESUMO

OBJECTIVE: Clinically, anorexia nervosa (AN) presents with altered body composition. We quantified these alterations and evaluated their relationships with metabolites and hormones in patients with AN longitudinally. METHOD: In accordance with PRISMA guidelines, we conducted 94 meta-analyses on 62 samples published during 1996-2019, comparing up to 2,319 pretreatment, posttreatment, and weight-recovered female patients with AN with up to 1,879 controls. Primary outcomes were fat mass, fat-free mass, body fat percentage, and their regional distribution. Secondary outcomes were bone mineral density, metabolites, and hormones. Meta-regressions examined relationships among those measures and moderators. RESULTS: Pretreatment female patients with AN evidenced 50% lower fat mass (mean difference [MD]: -8.80 kg, 95% CI: -9.81, -7.79, Q = 1.01 × 10-63 ) and 4.98 kg (95% CI: -5.85, -4.12, Q = 1.99 × 10-28 ) lower fat-free mass, with fat mass preferentially stored in the trunk region during early weight restoration (4.2%, 95% CI: -2.1, -6.2, Q = 2.30 × 10-4 ). While the majority of traits returned to levels seen in healthy controls after weight restoration, fat-free mass (MD: -1.27 kg, 95% CI: -1.79, -0.75, Q = 5.49 × 10-6 ) and bone mineral density (MD: -0.10 kg, 95% CI: -0.18, -0.03, Q = 0.01) remained significantly altered. DISCUSSION: Body composition is markedly altered in AN, warranting research into these phenotypes as clinical risk or relapse predictors. Notably, the long-term altered levels of fat-free mass and bone mineral density suggest that these parameters should be investigated as potential AN trait markers. RESUMENOBJETIVO: Clínicamente, la anorexia nervosa (AN) se presenta con alteraciones en la composición corporal. Cuantificamos estas alteraciones y evaluamos longitudinalmente su relación con metabolitos y hormonas en pacientes con AN. MÉTODO: De acuerdo con las pautas PRISMA, realizamos 94 meta-análisis en 62 muestras publicadas entre 1996-2019, comparando hasta 2,319 pacientes mujeres en pre-tratamiento, post-tratamiento, y recuperadas en base al peso con hasta 1,879 controles. Las principales medidas fueron masa grasa, masa libre de grasa, porcentaje de grasa corporal y su distribución regional. Las medidas secundarias fueron densidad mineral ósea, metabolitos y hormonas. Las meta-regresiones examinaron las relaciones entre esas medidas y moderadores. RESULTADOS: Las pacientes femeninas con AN pre-tratamiento mostraron un 50% menos de masa grasa (MD: -8.80 kg, CI 95%: -9.81, -7.79, Q = 1.01 × 10- 63 ) y 4.98 kg (CI 95%: -5.85, -4.12, Q = 1.99 × 10- 28 ) menos de masa libre de grasa, con masa grasa preferentemente almacenada en la región del tronco durante la recuperación temprana del peso (4.2%, CI 95%: -2.1, -6.2, Q = 2.30 × 10- 4 ). Aunque la mayoría de los rasgos regresaron a los niveles vistos en los controles sanos después de la restauración del peso, la masa libre de grasa (MD: -1.27 kg, CI 95%: -1.79, -0.75, Q = 5.49 × 10- 6 ) y la densidad mineral ósea (MD: -0.10 kg, CI 95%: -0.18, -0.03, Q = 0.01) permanecieron significativamente alteradas. DISCUSIÓN: La composición corporal es marcadamente alterada en la AN, lo que garantiza la investigación en estos fenotipos como predictores de riesgo clínico o de recaída. Notablemente, la alteración a largo plazo de los niveles de masa libre de grasa y densidad mineral ósea sugieren que estos parámetros debe ser investigados como potenciales rasgos indicadores de AN.


Assuntos
Anorexia Nervosa/diagnóstico , Composição Corporal/fisiologia , Estudos Transversais , Humanos , Estudos Longitudinais
11.
Am J Hum Genet ; 105(2): 351-363, 2019 08 01.
Artigo em Inglês | MEDLINE | ID: mdl-31303263

RESUMO

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 socio-economic 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.


Assuntos
Transtornos Cognitivos/fisiopatologia , Doenças em Gêmeos/genética , Genes/genética , Herança Multifatorial , Transtornos do Neurodesenvolvimento/etiologia , Polimorfismo de Nucleotídeo Único , Esquizofrenia/fisiopatologia , Adolescente , Adulto , Criança , Cognição/fisiologia , Escolaridade , Família , Feminino , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Genótipo , Humanos , Masculino , Transtornos do Neurodesenvolvimento/patologia , Fenótipo , Adulto Jovem
12.
Gigascience ; 8(7)2019 07 01.
Artigo em Inglês | MEDLINE | ID: mdl-31307061

RESUMO

BACKGROUND: Polygenic risk score (PRS) analyses have become an integral part of biomedical research, exploited to gain insights into shared aetiology among traits, to control for genomic profile in experimental studies, and to strengthen causal inference, among a range of applications. Substantial efforts are now devoted to biobank projects to collect large genetic and phenotypic data, providing unprecedented opportunity for genetic discovery and applications. To process the large-scale data provided by such biobank resources, highly efficient and scalable methods and software are required. RESULTS: Here we introduce PRSice-2, an efficient and scalable software program for automating and simplifying PRS analyses on large-scale data. PRSice-2 handles both genotyped and imputed data, provides empirical association P-values free from inflation due to overfitting, supports different inheritance models, and can evaluate multiple continuous and binary target traits simultaneously. We demonstrate that PRSice-2 is dramatically faster and more memory-efficient than PRSice-1 and alternative PRS software, LDpred and lassosum, while having comparable predictive power. CONCLUSION: PRSice-2's combination of efficiency and power will be increasingly important as data sizes grow and as the applications of PRS become more sophisticated, e.g., when incorporated into high-dimensional or gene set-based analyses. PRSice-2 is written in C++, with an R script for plotting, and is freely available for download from http://PRSice.info.


Assuntos
Estudo de Associação Genômica Ampla/métodos , Herança Multifatorial , Software , Animais , Big Data , Humanos , Locos de Características Quantitativas
13.
Am J Med Genet B Neuropsychiatr Genet ; 180(6): 428-438, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-30593698

RESUMO

Anorexia nervosa (AN) occurs nine times more often in females than in males. Although environmental factors likely play a role, the reasons for this imbalanced sex ratio remain unresolved. AN displays high genetic correlations with anthropometric and metabolic traits. Given sex differences in body composition, we investigated the possible metabolic underpinnings of female propensity for AN. We conducted sex-specific GWAS in a healthy and medication-free subsample of the UK Biobank (n = 155,961), identifying 77 genome-wide significant loci associated with body fat percentage (BF%) and 174 with fat-free mass (FFM). Partitioned heritability analysis showed an enrichment for central nervous tissue-associated genes for BF%, which was more prominent in females than males. Genetic correlations of BF% and FFM with the largest GWAS of AN by the Psychiatric Genomics Consortium were estimated to explore shared genomics. The genetic correlations of BF%male and BF%female with AN differed significantly from each other (p < .0001, δ = -0.17), suggesting that the female preponderance in AN may, in part, be explained by sex-specific anthropometric and metabolic genetic factors increasing liability to AN.


Assuntos
Anorexia Nervosa/genética , Anorexia Nervosa/metabolismo , Composição Corporal/genética , Tecido Adiposo/metabolismo , Adulto , Anorexia Nervosa/fisiopatologia , Índice de Massa Corporal , Estudos de Casos e Controles , Bases de Dados Genéticas , Feminino , Predisposição Genética para Doença/genética , Estudo de Associação Genômica Ampla/métodos , Genômica/métodos , Humanos , Masculino , Pessoa de Meia-Idade , Fenótipo , Fatores Sexuais
14.
PLoS Genet ; 14(11): e1007757, 2018 11.
Artigo em Inglês | MEDLINE | ID: mdl-30457987

RESUMO

The parental feeding practices (PFPs) of excessive restriction of food intake ('restriction') and pressure to increase food consumption ('pressure') have been argued to causally influence child weight in opposite directions (high restriction causing overweight; high pressure causing underweight). However child weight could also 'elicit' PFPs. A novel approach is to investigate gene-environment correlation between child genetic influences on BMI and PFPs. Genome-wide polygenic scores (GPS) combining BMI-associated variants were created for 10,346 children (including 3,320 DZ twin pairs) from the Twins Early Development Study using results from an independent genome-wide association study meta-analysis. Parental 'restriction' and 'pressure' were assessed using the Child Feeding Questionnaire. Child BMI standard deviation scores (BMI-SDS) were calculated from children's height and weight at age 10. Linear regression and fixed family effect models were used to test between- (n = 4,445 individuals) and within-family (n = 2,164 DZ pairs) associations between the GPS and PFPs. In addition, we performed multivariate twin analyses (n = 4,375 twin pairs) to estimate the heritabilities of PFPs and the genetic correlations between BMI-SDS and PFPs. The GPS was correlated with BMI-SDS (ß = 0.20, p = 2.41x10-38). Consistent with the gene-environment correlation hypothesis, child BMI GPS was positively associated with 'restriction' (ß = 0.05, p = 4.19x10-4), and negatively associated with 'pressure' (ß = -0.08, p = 2.70x10-7). These results remained consistent after controlling for parental BMI, and after controlling for overall family contributions (within-family analyses). Heritabilities for 'restriction' (43% [40-47%]) and 'pressure' (54% [50-59%]) were moderate-to-high. Twin-based genetic correlations were moderate and positive between BMI-SDS and 'restriction' (rA = 0.28 [0.23-0.32]), and substantial and negative between BMI-SDS and 'pressure' (rA = -0.48 [-0.52 - -0.44]. Results suggest that the degree to which parents limit or encourage children's food intake is partly influenced by children's genetic predispositions to higher or lower BMI. These findings point to an evocative gene-environment correlation in which heritable characteristics in the child elicit parental feeding behaviour.


Assuntos
Comportamento Alimentar , Interação Gene-Ambiente , Variação Genética , Índice de Massa Corporal , Criança , Feminino , Estudo de Associação Genômica Ampla , Humanos , Masculino , Modelos Genéticos , Fenótipo , Gêmeos
15.
Commun Biol ; 1: 163, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30320231

RESUMO

Psychiatric disorders are thought to have a complex genetic pathology consisting of interplay of common and rare variation. Traditionally, pedigrees are used to shed light on the latter only, while here we discuss the application of polygenic risk scores to also highlight patterns of common genetic risk. We analyze polygenic risk scores for psychiatric disorders in a large pedigree (n ~ 260) in which 30% of family members suffer from major depressive disorder or bipolar disorder. Studying patterns of assortative mating and anticipation, it appears increased polygenic risk is contributed by affected individuals who married into the family, resulting in an increasing genetic risk over generations. This may explain the observation of anticipation in mood disorders, whereby onset is earlier and the severity increases over the generations of a family. Joint analyses of rare and common variation may be a powerful way to understand the familial genetics of psychiatric disorders.

16.
Artigo em Inglês | MEDLINE | ID: mdl-30047479

RESUMO

BACKGROUND: A recent large-scale mega genome-wide association study identified, for the first time, genetic variants at 12 loci significantly associated with attention-deficit/hyperactivity disorder (ADHD). In this study we use a powerful polygenic approach, with polygenic scores derived from the genome-wide association study, to investigate the etiological overlap between ADHD and frequently co-occurring traits and disorders. METHODS: Polygenic risk scores for ADHD derived from the mega genome-wide association study (20,183 cases and 35,191 control subjects) were computed in a large-scale adult population sample (N = 135,726) recruited by the UK Biobank. Regression analyses were conducted to investigate whether polygenic risk for ADHD is associated with related traits and disorders in this population sample. The effects of sex were investigated via inclusion of an interaction term in the models. RESULTS: Polygenic risk for ADHD significantly and positively predicted body mass index (R2 = .45%; p = 5 × 10-129), neuroticism (R2 = .09%; p = 2 × 10-24), depression (R2 = .11%; p = 2 × 10-13), anxiety (R2 = .06%; p = 3 × 10-4), risk taking (R2 = .12%; p = 9 × 10-25), alcohol intake (R2 = .09%; p = 8 × 10-29), smoking (R2 = .33%; p = 4 × 10-21), alcohol dependency (R2 = .21%; p = 5 × 10-6), and negatively predicted verbal-numerical reasoning (R2 = .38%; p = 5 × 10-36). Polygenic risk scores did not significantly predict schizophrenia or bipolar disorder, although this may be because of the small number of diagnostic cases. We found no interaction effects between polygenic risk for ADHD and sex on any phenotypes. CONCLUSIONS: Our findings suggest that common genetic variation underlying risk for clinically diagnosed ADHD also contributes to higher body mass index, neuroticism, anxiety and depressive disorders, alcohol and nicotine use, risk taking, and lower general cognitive ability in the general population. These findings suggest that the co-occurrence of several traits with ADHD is partly explained by the same common genetic variants.


Assuntos
Transtorno do Deficit de Atenção com Hiperatividade/genética , Transtorno do Deficit de Atenção com Hiperatividade/fisiopatologia , Transtornos Mentais/genética , Transtornos Mentais/fisiopatologia , Adulto , Transtorno do Deficit de Atenção com Hiperatividade/epidemiologia , Comorbidade , Bases de Dados Factuais , Feminino , Pleiotropia Genética , Estudo de Associação Genômica Ampla , Humanos , Masculino , Transtornos Mentais/epidemiologia , Herança Multifatorial , Fenótipo , Risco , Reino Unido/epidemiologia
17.
Nat Rev Genet ; 19(9): 566-580, 2018 09.
Artigo em Inglês | MEDLINE | ID: mdl-29872216

RESUMO

Causal inference is essential across the biomedical, behavioural and social sciences.By progressing from confounded statistical associations to evidence of causal relationships, causal inference can reveal complex pathways underlying traits and diseases and help to prioritize targets for intervention. Recent progress in genetic epidemiology - including statistical innovation, massive genotyped data sets and novel computational tools for deep data mining - has fostered the intense development of methods exploiting genetic data and relatedness to strengthen causal inference in observational research. In this Review, we describe how such genetically informed methods differ in their rationale, applicability and inherent limitations and outline how they should be integrated in the future to offer a rich causal inference toolbox.


Assuntos
Mineração de Dados/métodos , Bases de Dados Genéticas , Genótipo , Técnicas de Genotipagem , Humanos
18.
Nat Commun ; 9(1): 260, 2018 01 17.
Artigo em Inglês | MEDLINE | ID: mdl-29343764

RESUMO

Vitamin D is a steroid hormone precursor that is associated with a range of human traits and diseases. Previous GWAS of serum 25-hydroxyvitamin D concentrations have identified four genome-wide significant loci (GC, NADSYN1/DHCR7, CYP2R1, CYP24A1). In this study, we expand the previous SUNLIGHT Consortium GWAS discovery sample size from 16,125 to 79,366 (all European descent). This larger GWAS yields two additional loci harboring genome-wide significant variants (P = 4.7×10-9 at rs8018720 in SEC23A, and P = 1.9×10-14 at rs10745742 in AMDHD1). The overall estimate of heritability of 25-hydroxyvitamin D serum concentrations attributable to GWAS common SNPs is 7.5%, with statistically significant loci explaining 38% of this total. Further investigation identifies signal enrichment in immune and hematopoietic tissues, and clustering with autoimmune diseases in cell-type-specific analysis. Larger studies are required to identify additional common SNPs, and to explore the role of rare or structural variants and gene-gene interactions in the heritability of circulating 25-hydroxyvitamin D levels.


Assuntos
Grupo com Ancestrais do Continente Europeu/genética , Proteínas de Transporte Vesicular/genética , Vitamina D/análogos & derivados , Amidoidrolases/genética , Doenças Autoimunes/genética , Estudos de Coortes , Feminino , Estudo de Associação Genômica Ampla , Humanos , Masculino , Polimorfismo de Nucleotídeo Único , Vitamina D/sangue
20.
Hypertension ; 2017 Jul 24.
Artigo em Inglês | MEDLINE | ID: mdl-28739976

RESUMO

Elevated blood pressure is a major risk factor for cardiovascular disease and has a substantial genetic contribution. Genetic variation influencing blood pressure has the potential to identify new pharmacological targets for the treatment of hypertension. To discover additional novel blood pressure loci, we used 1000 Genomes Project-based imputation in 150 134 European ancestry individuals and sought significant evidence for independent replication in a further 228 245 individuals. We report 6 new signals of association in or near HSPB7, TNXB, LRP12, LOC283335, SEPT9, and AKT2, and provide new replication evidence for a further 2 signals in EBF2 and NFKBIA Combining large whole-blood gene expression resources totaling 12 607 individuals, we investigated all novel and previously reported signals and identified 48 genes with evidence for involvement in blood pressure regulation that are significant in multiple resources. Three novel kidney-specific signals were also detected. These robustly implicated genes may provide new leads for therapeutic innovation.

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