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1.
Liver Int ; 2021 Feb 16.
Artigo em Inglês | MEDLINE | ID: mdl-33590606

RESUMO

BACKGROUND & AIMS: Primary sclerosing cholangitis (PSC) is a rare bile duct disease strongly associated with inflammatory bowel disease (IBD). Whole-exome sequencing (WES) has contributed to understanding the molecular basis of very early-onset IBD, but rare protein-altering genetic variants have not been identified for early-onset PSC. We performed WES in patients diagnosed with PSC ≤ 12 years to investigate the contribution of rare genetic variants to early-onset PSC. METHODS: In this multicenter study, WES was performed on 87 DNA samples from 29 patient-parent trios with early-onset PSC. We selected rare (minor allele frequency < 2%) coding and splice-site variants that matched recessive (homozygous and compound heterozygous variants) and dominant (de novo) inheritance in the index patients. Variant pathogenicity was predicted by an in-house developed algorithm (GAVIN), and PSC-relevant variants were selected using gene expression data and gene function. RESULTS: In 22 out of 29 trios we identified at least 1 possibly pathogenic variant. We prioritized 36 genes, harboring a total of 54 variants with predicted pathogenic effects. In 18 genes we identified 36 compound heterozygous variants, whereas in the other 18 genes we identified 18 de novo variants. Twelve of 36 candidate risk genes are known to play a role in transmembrane transport, adaptive and innate immunity, and epithelial barrier function. CONCLUSIONS: The 36 candidate genes for early-onset PSC need further verification in other patient cohorts and evaluation of gene function before a causal role can be attributed to its variants.

2.
Bioinformatics ; 2021 Feb 09.
Artigo em Inglês | MEDLINE | ID: mdl-33560296

RESUMO

MOTIVATION: The prediction performance of Cox proportional hazard model suffers when there are only few uncensored events in the training data. RESULTS: We propose a Sparse-Group regularized Cox regression method to improve the prediction performance of large-scale and high-dimensional survival data with few observed events. Our approach is applicable when there is one or more other survival responses that 1. has a large number of observed events; 2. share a common set of associated predictors with the rare event response. This scenario is common in the UK Biobank (Sudlow et al., 2015) dataset where records for a large number of common and less prevalent diseases of the same set of individuals are available. By analyzing these responses together, we hope to achieve higher prediction performance than when they are analyzed individually. To make this approach practical for large-scale data, we developed an accelerated proximal gradient optimization algorithm as well as a screening procedure inspired by Qian et al. (2020). AVAILABILITY: https://github.com/rivas-lab/multisnpnet-Cox. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

3.
Eur J Hum Genet ; 2021 Feb 08.
Artigo em Inglês | MEDLINE | ID: mdl-33558700

RESUMO

Polygenic risk models have led to significant advances in understanding complex diseases and their clinical presentation. While polygenic risk scores (PRS) can effectively predict outcomes, they do not generally account for disease subtypes or pathways which underlie within-trait diversity. Here, we introduce a latent factor model of genetic risk based on components from Decomposition of Genetic Associations (DeGAs), which we call the DeGAs polygenic risk score (dPRS). We compute DeGAs using genetic associations for 977 traits and find that dPRS performs comparably to standard PRS while offering greater interpretability. We show how to decompose an individual's genetic risk for a trait across DeGAs components, with examples for body mass index (BMI) and myocardial infarction (heart attack) in 337,151 white British individuals in the UK Biobank, with replication in a further set of 25,486 non-British white individuals. We find that BMI polygenic risk factorizes into components related to fat-free mass, fat mass, and overall health indicators like physical activity. Most individuals with high dPRS for BMI have strong contributions from both a fat-mass component and a fat-free mass component, whereas a few "outlier" individuals have strong contributions from only one of the two components. Overall, our method enables fine-scale interpretation of the drivers of genetic risk for complex traits.

4.
Nat Commun ; 12(1): 350, 2021 01 13.
Artigo em Inglês | MEDLINE | ID: mdl-33441555

RESUMO

Causal inference via Mendelian randomization requires making strong assumptions about horizontal pleiotropy, where genetic instruments are connected to the outcome not only through the exposure. Here, we present causal Graphical Analysis Using Genetics (cGAUGE), a pipeline that overcomes these limitations using instrument filters with provable properties. This is achievable by identifying conditional independencies while examining multiple traits. cGAUGE also uses ExSep (Exposure-based Separation), a novel test for the existence of causal pathways that does not require selecting instruments. In simulated data we illustrate how cGAUGE can reduce the empirical false discovery rate by up to 30%, while retaining the majority of true discoveries. On 96 complex traits from 337,198 subjects from the UK Biobank, our results cover expected causal links and many new ones that were previously suggested by correlation-based observational studies. Notably, we identify multiple risk factors for cardiovascular disease, including red blood cell distribution width.


Assuntos
Bancos de Espécimes Biológicos , Pleiotropia Genética/genética , Estudo de Associação Genômica Ampla/métodos , Herança Multifatorial/genética , Doenças Cardiovasculares/genética , Causalidade , Simulação por Computador , Redes Reguladoras de Genes , Variação Genética , Genótipo , Humanos , Análise da Randomização Mendeliana/métodos , Modelos Teóricos , Fenótipo , Fatores de Risco
5.
Nat Genet ; 53(2): 185-194, 2021 02.
Artigo em Inglês | MEDLINE | ID: mdl-33462484

RESUMO

Clinical laboratory tests are a critical component of the continuum of care. We evaluate the genetic basis of 35 blood and urine laboratory measurements in the UK Biobank (n = 363,228 individuals). We identify 1,857 loci associated with at least one trait, containing 3,374 fine-mapped associations and additional sets of large-effect (>0.1 s.d.) protein-altering, human leukocyte antigen (HLA) and copy number variant (CNV) associations. Through Mendelian randomization (MR) analysis, we discover 51 causal relationships, including previously known agonistic effects of urate on gout and cystatin C on stroke. Finally, we develop polygenic risk scores (PRSs) for each biomarker and build 'multi-PRS' models for diseases using 35 PRSs simultaneously, which improved chronic kidney disease, type 2 diabetes, gout and alcoholic cirrhosis genetic risk stratification in an independent dataset (FinnGen; n = 135,500) relative to single-disease PRSs. Together, our results delineate the genetic basis of biomarkers and their causal influences on diseases and improve genetic risk stratification for common diseases.

6.
PLoS Genet ; 16(11): e1008802, 2020 Nov 23.
Artigo em Inglês | MEDLINE | ID: mdl-33226994

RESUMO

The clinical evaluation of a genetic syndrome relies upon recognition of a characteristic pattern of signs or symptoms to guide targeted genetic testing for confirmation of the diagnosis. However, individuals displaying a single phenotype of a complex syndrome may not meet criteria for clinical diagnosis or genetic testing. Here, we present a phenome-wide association study (PheWAS) approach to systematically explore the phenotypic expressivity of common and rare alleles in genes associated with four well-described syndromic diseases (Alagille (AS), Marfan (MS), DiGeorge (DS), and Noonan (NS) syndromes) in the general population. Using human phenotype ontology (HPO) terms, we systematically mapped 60 phenotypes related to AS, MS, DS and NS in 337,198 unrelated white British from the UK Biobank (UKBB) based on their hospital admission records, self-administrated questionnaires, and physiological measurements. We performed logistic regression adjusting for age, sex, and the first 5 genetic principal components, for each phenotype and each variant in the target genes (JAG1, NOTCH2 FBN1, PTPN1 and RAS-opathy genes, and genes in the 22q11.2 locus) and performed a gene burden test. Overall, we observed multiple phenotype-genotype correlations, such as the association between variation in JAG1, FBN1, PTPN11 and SOS2 with diastolic and systolic blood pressure; and pleiotropy among multiple variants in syndromic genes. For example, rs11066309 in PTPN11 was significantly associated with a lower body mass index, an increased risk of hypothyroidism and a smaller size for gestational age, all in concordance with NS-related phenotypes. Similarly, rs589668 in FBN1 was associated with an increase in body height and blood pressure, and a reduced body fat percentage as observed in Marfan syndrome. Our findings suggest that the spectrum of associations of common and rare variants in genes involved in syndromic diseases can be extended to individual phenotypes within the general population.

7.
PLoS Genet ; 16(10): e1009141, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-33095761

RESUMO

The UK Biobank is a very large, prospective population-based cohort study across the United Kingdom. It provides unprecedented opportunities for researchers to investigate the relationship between genotypic information and phenotypes of interest. Multiple regression methods, compared with genome-wide association studies (GWAS), have already been showed to greatly improve the prediction performance for a variety of phenotypes. In the high-dimensional settings, the lasso, since its first proposal in statistics, has been proved to be an effective method for simultaneous variable selection and estimation. However, the large-scale and ultrahigh dimension seen in the UK Biobank pose new challenges for applying the lasso method, as many existing algorithms and their implementations are not scalable to large applications. In this paper, we propose a computational framework called batch screening iterative lasso (BASIL) that can take advantage of any existing lasso solver and easily build a scalable solution for very large data, including those that are larger than the memory size. We introduce snpnet, an R package that implements the proposed algorithm on top of glmnet and optimizes for single nucleotide polymorphism (SNP) datasets. It currently supports ℓ1-penalized linear model, logistic regression, Cox model, and also extends to the elastic net with ℓ1/ℓ2 penalty. We demonstrate results on the UK Biobank dataset, where we achieve competitive predictive performance for all four phenotypes considered (height, body mass index, asthma, high cholesterol) using only a small fraction of the variants compared with other established polygenic risk score methods.

8.
Eur J Hum Genet ; 2020 Sep 01.
Artigo em Inglês | MEDLINE | ID: mdl-32873964

RESUMO

Sex differences have been shown in laboratory biomarkers; however, the extent to which this is due to genetics is unknown. In this study, we infer sex-specific genetic parameters (heritability and genetic correlation) across 33 quantitative biomarker traits in 181,064 females and 156,135 males from the UK Biobank study. We apply a Bayesian Mixture Model, Sex Effects Mixture Model (SEMM), to Genome-wide Association Study summary statistics in order to (1) estimate the contributions of sex to the genetic variance of these biomarkers and (2) identify variants whose statistical association with these traits is sex-specific. We find that the genetics of most biomarker traits are shared between males and females, with the notable exception of testosterone, where we identify 119 female and 445 male-specific variants. These include protein-altering variants in steroid hormone production genes (POR, UGT2B7). Using the sex-specific variants as genetic instruments for Mendelian randomization, we find evidence for causal links between testosterone levels and height, body mass index, waist and hip circumference, and type 2 diabetes. We also show that sex-specific polygenic risk score models for testosterone outperform a combined model. Overall, these results demonstrate that while sex has a limited role in the genetics of most biomarker traits, sex plays an important role in testosterone genetics.

9.
Biostatistics ; 2020 Sep 29.
Artigo em Inglês | MEDLINE | ID: mdl-32989444

RESUMO

We develop a scalable and highly efficient algorithm to fit a Cox proportional hazard model by maximizing the $L^1$-regularized (Lasso) partial likelihood function, based on the Batch Screening Iterative Lasso (BASIL) method developed in Qian and others (2019). Our algorithm is particularly suitable for large-scale and high-dimensional data that do not fit in the memory. The output of our algorithm is the full Lasso path, the parameter estimates at all predefined regularization parameters, as well as their validation accuracy measured using the concordance index (C-index) or the validation deviance. To demonstrate the effectiveness of our algorithm, we analyze a large genotype-survival time dataset across 306 disease outcomes from the UK Biobank (Sudlow and others, 2015). We provide a publicly available implementation of the proposed approach for genetics data on top of the PLINK2 package and name it snpnet-Cox.

10.
medRxiv ; 2020 Jul 29.
Artigo em Inglês | MEDLINE | ID: mdl-32766602

RESUMO

During COVID19 and other viral pandemics, rapid generation of host and pathogen genomic data is critical to tracking infection and informing therapies. There is an urgent need for efficient approaches to this data generation at scale. We have developed a scalable, high throughput approach to generate high fidelity low pass whole genome and HLA sequencing, viral genomes, and representation of human transcriptome from single nasopharyngeal swabs of COVID19 patients.

11.
Int J Equity Health ; 19(1): 114, 2020 07 06.
Artigo em Inglês | MEDLINE | ID: mdl-32631328

RESUMO

Preliminary reports suggest that the Coronavirus Disease 2019 (COVID- 19) pandemic has led to disproportionate morbidity and mortality among historically disadvantaged populations. We investigate the racial and socioeconomic associations of COVID- 19 hospitalization among 418,794 participants of the UK Biobank, of whom 549 (0.13%) had been hospitalized. Both Black participants (odds ratio 3.7; 95%CI 2.5-5.3) and Asian participants (odds ratio 2.2; 95%CI 1.5-3.2) were at substantially increased risk as compared to White participants. We further observed a striking gradient in COVID- 19 hospitalization rates according to the Townsend Deprivation Index - a composite measure of socioeconomic deprivation - and household income. Adjusting for socioeconomic factors and cardiorespiratory comorbidities led to only modest attenuation of the increased risk in Black participants, adjusted odds ratio 2.4 (95%CI 1.5-3.7). These observations confirm and extend earlier preliminary and lay press reports of higher morbidity in non-White individuals in the context of a large population of participants in a national biobank. The extent to which this increased risk relates to variation in pre-existing comorbidities, differences in testing or hospitalization patterns, or additional disparities in social determinants of health warrants further study.


Assuntos
Grupos de Populações Continentais/estatística & dados numéricos , Infecções por Coronavirus/etnologia , Infecções por Coronavirus/terapia , Disparidades nos Níveis de Saúde , Hospitalização/estatística & dados numéricos , Pneumonia Viral/etnologia , Pneumonia Viral/terapia , Adulto , Idoso , Bancos de Espécimes Biológicos , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Pandemias , Estudos Prospectivos , Fatores Socioeconômicos , Reino Unido/epidemiologia
12.
Gut ; 2020 Jul 10.
Artigo em Inglês | MEDLINE | ID: mdl-32651235

RESUMO

OBJECTIVE: Both the gut microbiome and host genetics are known to play significant roles in the pathogenesis of IBD. However, the interaction between these two factors and its implications in the aetiology of IBD remain underexplored. Here, we report on the influence of host genetics on the gut microbiome in IBD. DESIGN: To evaluate the impact of host genetics on the gut microbiota of patients with IBD, we combined whole exome sequencing of the host genome and whole genome shotgun sequencing of 1464 faecal samples from 525 patients with IBD and 939 population-based controls. We followed a four-step analysis: (1) exome-wide microbial quantitative trait loci (mbQTL) analyses, (2) a targeted approach focusing on IBD-associated genomic regions and protein truncating variants (PTVs, minor allele frequency (MAF) >5%), (3) gene-based burden tests on PTVs with MAF <5% and exome copy number variations (CNVs) with site frequency <1%, (4) joint analysis of both cohorts to identify the interactions between disease and host genetics. RESULTS: We identified 12 mbQTLs, including variants in the IBD-associated genes IL17REL, MYRF, SEC16A and WDR78. For example, the decrease of the pathway acetyl-coenzyme A biosynthesis, which is involved in short chain fatty acids production, was associated with variants in the gene MYRF (false discovery rate <0.05). Changes in functional pathways involved in the metabolic potential were also observed in participants carrying rare PTVs or CNVs in CYP2D6, GPR151 and CD160 genes. These genes are known for their function in the immune system. Moreover, interaction analyses confirmed previously known IBD disease-specific mbQTLs in TNFSF15. CONCLUSION: This study highlights that both common and rare genetic variants affecting the immune system are key factors in shaping the gut microbiota in the context of IBD and pinpoints towards potential mechanisms for disease treatment.

13.
medRxiv ; 2020 May 02.
Artigo em Inglês | MEDLINE | ID: mdl-32511642

RESUMO

Preliminary reports suggest that the Coronavirus Disease 2019 (COVID-19) pandemic has led to disproportionate morbidity and mortality among historically disadvantaged populations. The extent to which these disparities are related to socioeconomic versus biologic factors is largely unknown. We investigate the racial and socioeconomic associations of COVID-19 hospitalization among 418,794 participants of the UK Biobank, of whom 549 (0.13%) had been hospitalized. Both black participants (odds ratio 3.4; 95%CI 2.4-4.9) and Asian participants (odds ratio 2.1; 95%CI 1.5-3.2) were at substantially increased risk as compared to white participants. We further observed a striking gradient in COVID-19 hospitalization rates according to the Townsend Deprivation Index - a composite measure of socioeconomic deprivation - and household income. Adjusting for such factors led to only modest attenuation of the increased risk in black participants, adjusted odds ratio 3.1 (95%CI 2.0-4.8). These observations confirm and extend earlier preliminary and lay press reports of higher morbidity in non-white individuals in the context of a large population of participants in a national biobank. The extent to which this increased risk relates to variation in pre-existing comorbidities, differences in testing or hospitalization patterns, or additional disparities in social determinants of health warrants further study.

14.
PLoS One ; 15(6): e0234647, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32569327

RESUMO

Unstructured clinical narratives are continuously being recorded as part of delivery of care in electronic health records, and dedicated tagging staff spend considerable effort manually assigning clinical codes for billing purposes. Despite these efforts, however, label availability and accuracy are both suboptimal. In this retrospective study, we aimed to automate the assignment of top-level International Classification of Diseases version 9 (ICD-9) codes to clinical records from human and veterinary data stores using minimal manual labor and feature curation. Automating top-level annotations could in turn enable rapid cohort identification, especially in a veterinary setting. To this end, we trained long short-term memory (LSTM) recurrent neural networks (RNNs) on 52,722 human and 89,591 veterinary records. We investigated the accuracy of both separate-domain and combined-domain models and probed model portability. We established relevant baseline classification performances by training Decision Trees (DT) and Random Forests (RF). We also investigated whether transforming the data using MetaMap Lite, a clinical natural language processing tool, affected classification performance. We showed that the LSTM-RNNs accurately classify veterinary and human text narratives into top-level categories with an average weighted macro F1 score of 0.74 and 0.68 respectively. In the "neoplasia" category, the model trained on veterinary data had a high validation accuracy in veterinary data and moderate accuracy in human data, with F1 scores of 0.91 and 0.70 respectively. Our LSTM method scored slightly higher than that of the DT and RF models. The use of LSTM-RNN models represents a scalable structure that could prove useful in cohort identification for comparative oncology studies. Digitization of human and veterinary health information will continue to be a reality, particularly in the form of unstructured narratives. Our approach is a step forward for these two domains to learn from and inform one another.


Assuntos
Mineração de Dados , Medicina Narrativa , Software , Animais , Automação , Bases de Dados como Assunto , Humanos , Reprodutibilidade dos Testes , Especificidade da Espécie
15.
PLoS Genet ; 16(5): e1008682, 2020 05.
Artigo em Inglês | MEDLINE | ID: mdl-32369491

RESUMO

Protein-altering variants that are protective against human disease provide in vivo validation of therapeutic targets. Here we use genotyping data from UK Biobank (n = 337,151 unrelated White British individuals) and FinnGen (n = 176,899) to conduct a search for protein-altering variants conferring lower intraocular pressure (IOP) and protection against glaucoma. Through rare protein-altering variant association analysis, we find a missense variant in ANGPTL7 in UK Biobank (rs28991009, p.Gln175His, MAF = 0.8%, genotyped in 82,253 individuals with measured IOP and an independent set of 4,238 glaucoma patients and 250,660 controls) that significantly lowers IOP (ß = -0.53 and -0.67 mmHg for heterozygotes, -3.40 and -2.37 mmHg for homozygotes, P = 5.96 x 10-9 and 1.07 x 10-13 for corneal compensated and Goldman-correlated IOP, respectively) and is associated with 34% reduced risk of glaucoma (P = 0.0062). In FinnGen, we identify an ANGPTL7 missense variant at a greater than 50-fold increased frequency in Finland compared with other populations (rs147660927, p.Arg220Cys, MAF Finland = 4.3%), which was genotyped in 6,537 glaucoma patients and 170,362 controls and is associated with a 29% lower glaucoma risk (P = 1.9 x 10-12 for all glaucoma types and also protection against its subtypes including exfoliation, primary open-angle, and primary angle-closure). We further find three rarer variants in UK Biobank, including a protein-truncating variant, which confer a strong composite lowering of IOP (P = 0.0012 and 0.24 for Goldman-correlated and corneal compensated IOP, respectively), suggesting the protective mechanism likely resides in the loss of interaction or function. Our results support inhibition or down-regulation of ANGPTL7 as a therapeutic strategy for glaucoma.


Assuntos
Proteínas Semelhantes a Angiopoietina/genética , Glaucoma/genética , Glaucoma/prevenção & controle , Pressão Intraocular/genética , Polimorfismo de Nucleotídeo Único , Adulto , Idoso , Idoso de 80 Anos ou mais , Bancos de Espécimes Biológicos/estatística & dados numéricos , Estudos de Casos e Controles , Estudos de Coortes , Feminino , Finlândia/epidemiologia , Frequência do Gene , Predisposição Genética para Doença , Genética Populacional , Estudo de Associação Genômica Ampla , Glaucoma/epidemiologia , Humanos , Mutação com Perda de Função/genética , Masculino , Pessoa de Meia-Idade , Mutação de Sentido Incorreto , Reino Unido/epidemiologia
16.
Am J Hum Genet ; 106(5): 611-622, 2020 05 07.
Artigo em Inglês | MEDLINE | ID: mdl-32275883

RESUMO

Population-scale biobanks that combine genetic data and high-dimensional phenotyping for a large number of participants provide an exciting opportunity to perform genome-wide association studies (GWAS) to identify genetic variants associated with diverse quantitative traits and diseases. A major challenge for GWAS in population biobanks is ascertaining disease cases from heterogeneous data sources such as hospital records, digital questionnaire responses, or interviews. In this study, we use genetic parameters, including genetic correlation, to evaluate whether GWAS performed using cases in the UK Biobank ascertained from hospital records, questionnaire responses, and family history of disease implicate similar disease genetics across a range of effect sizes. We find that hospital record and questionnaire GWAS largely identify similar genetic effects for many complex phenotypes and that combining together both phenotyping methods improves power to detect genetic associations. We also show that family history GWAS using cases ascertained on family history of disease agrees with combined hospital record and questionnaire GWAS and that family history GWAS has better power to detect genetic associations for some phenotypes. Overall, this work demonstrates that digital phenotyping and unstructured phenotype data can be combined with structured data such as hospital records to identify cases for GWAS in biobanks and improve the ability of such studies to identify genetic associations.


Assuntos
Doença/genética , Estudo de Associação Genômica Ampla , Fenótipo , Asma/genética , Bases de Dados Factuais , Feminino , Genética Médica , Genótipo , Humanos , Masculino , Neoplasias/genética , Reino Unido
17.
Mol Psychiatry ; 25(10): 2422-2430, 2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-30610202

RESUMO

Suicide accounts for nearly 800,000 deaths per year worldwide with rates of both deaths and attempts rising. Family studies have estimated substantial heritability of suicidal behavior; however, collecting the sample sizes necessary for successful genetic studies has remained a challenge. We utilized two different approaches in independent datasets to characterize the contribution of common genetic variation to suicide attempt. The first is a patient reported suicide attempt phenotype asked as part of an online mental health survey taken by a subset of participants (n = 157,366) in the UK Biobank. After quality control, we leveraged a genotyped set of unrelated, white British ancestry participants including 2433 cases and 334,766 controls that included those that did not participate in the survey or were not explicitly asked about attempting suicide. The second leveraged electronic health record (EHR) data from the Vanderbilt University Medical Center (VUMC, 2.8 million patients, 3250 cases) and machine learning to derive probabilities of attempting suicide in 24,546 genotyped patients. We identified significant and comparable heritability estimates of suicide attempt from both the patient reported phenotype in the UK Biobank (h2SNP = 0.035, p = 7.12 × 10-4) and the clinically predicted phenotype from VUMC (h2SNP = 0.046, p = 1.51 × 10-2). A significant genetic overlap was demonstrated between the two measures of suicide attempt in these independent samples through polygenic risk score analysis (t = 4.02, p = 5.75 × 10-5) and genetic correlation (rg = 1.073, SE = 0.36, p = 0.003). Finally, we show significant but incomplete genetic correlation of suicide attempt with insomnia (rg = 0.34-0.81) as well as several psychiatric disorders (rg = 0.26-0.79). This work demonstrates the contribution of common genetic variation to suicide attempt. It points to a genetic underpinning to clinically predicted risk of attempting suicide that is similar to the genetic profile from a patient reported outcome. Lastly, it presents an approach for using EHR data and clinical prediction to generate quantitative measures from binary phenotypes that can improve power for genetic studies.

18.
PLoS Med ; 16(12): e1002982, 2019 12.
Artigo em Inglês | MEDLINE | ID: mdl-31821322

RESUMO

BACKGROUND: Lifestyle interventions to reduce body mass index (BMI) are critical public health strategies for type 2 diabetes prevention. While weight loss interventions have shown demonstrable benefit for high-risk and prediabetic individuals, we aimed to determine whether the same benefits apply to those at lower risk. METHODS AND FINDINGS: We performed a multi-stratum Mendelian randomization study of the effect size of BMI on diabetes odds in 287,394 unrelated individuals of self-reported white British ancestry in the UK Biobank, who were recruited from across the United Kingdom from 2006 to 2010 when they were between the ages of 40 and 69 years. Individuals were stratified on the following diabetes risk factors: BMI, diabetes family history, and genome-wide diabetes polygenic risk score. The main outcome measure was the odds ratio of diabetes per 1-kg/m2 BMI reduction, in the full cohort and in each stratum. Diabetes prevalence increased sharply with BMI, family history of diabetes, and genetic risk. Conversely, predicted risk reduction from weight loss was strikingly similar across BMI and genetic risk categories. Weight loss was predicted to substantially reduce diabetes odds even among lower-risk individuals: for instance, a 1-kg/m2 BMI reduction was associated with a 1.37-fold reduction (95% CI 1.12-1.68) in diabetes odds among non-overweight individuals (BMI < 25 kg/m2) without a family history of diabetes, similar to that in obese individuals (BMI ≥ 30 kg/m2) with a family history (1.21-fold reduction, 95% CI 1.13-1.29). A key limitation of this analysis is that the BMI-altering DNA sequence polymorphisms it studies represent cumulative predisposition over an individual's entire lifetime, and may consequently incorrectly estimate the risk modification potential of weight loss interventions later in life. CONCLUSIONS: In a population-scale cohort, lower BMI was consistently associated with reduced diabetes risk across BMI, family history, and genetic risk categories, suggesting all individuals can substantially reduce their diabetes risk through weight loss. Our results support the broad deployment of weight loss interventions to individuals at all levels of diabetes risk.


Assuntos
Diabetes Mellitus Tipo 2/epidemiologia , Herança Multifatorial/genética , Obesidade/epidemiologia , Adulto , Idoso , Bancos de Espécimes Biológicos/estatística & dados numéricos , Índice de Massa Corporal , Feminino , Humanos , Masculino , Análise da Randomização Mendeliana/métodos , Pessoa de Meia-Idade , Fatores de Risco , Reino Unido/epidemiologia
19.
Nat Commun ; 10(1): 4064, 2019 09 06.
Artigo em Inglês | MEDLINE | ID: mdl-31492854

RESUMO

Population-based biobanks with genomic and dense phenotype data provide opportunities for generating effective therapeutic hypotheses and understanding the genomic role in disease predisposition. To characterize latent components of genetic associations, we apply truncated singular value decomposition (DeGAs) to matrices of summary statistics derived from genome-wide association analyses across 2,138 phenotypes measured in 337,199 White British individuals in the UK Biobank study. We systematically identify key components of genetic associations and the contributions of variants, genes, and phenotypes to each component. As an illustration of the utility of the approach to inform downstream experiments, we report putative loss of function variants, rs114285050 (GPR151) and rs150090666 (PDE3B), that substantially contribute to obesity-related traits and experimentally demonstrate the role of these genes in adipocyte biology. Our approach to dissect components of genetic associations across the human phenome will accelerate biomedical hypothesis generation by providing insights on previously unexplored latent structures.


Assuntos
Adipócitos/metabolismo , Bancos de Espécimes Biológicos , Estudos de Associação Genética/métodos , Estudo de Associação Genômica Ampla/métodos , Células 3T3-L1 , Adipócitos/citologia , Animais , Células Cultivadas , Nucleotídeo Cíclico Fosfodiesterase do Tipo 3/genética , Predisposição Genética para Doença/genética , Humanos , Camundongos , Obesidade/genética , Fenótipo , Polimorfismo de Nucleotídeo Único , Reino Unido
20.
Hum Mol Genet ; 28(R2): R162-R169, 2019 11 21.
Artigo em Inglês | MEDLINE | ID: mdl-31363759

RESUMO

Complex diseases such as inflammatory bowel disease (IBD), which consists of ulcerative colitis and Crohn's disease, are a significant medical burden-70 000 new cases of IBD are diagnosed in the United States annually. In this review, we examine the history of genetic variant discovery in complex disease with a focus on IBD. We cover methods that have been applied to microsatellite, common variant, targeted resequencing and whole-exome and -genome data, specifically focusing on the progression of technologies towards rare-variant discovery. The inception of these methods combined with better availability of population level variation data has led to rapid discovery of IBD-causative and/or -associated variants at over 200 loci; over time, these methods have grown exponentially in both power and ascertainment to detect rare variation. We highlight rare-variant discoveries critical to the elucidation of the pathogenesis of IBD, including those in NOD2, IL23R, CARD9, RNF186 and ADCY7. We additionally identify the major areas of rare-variant discovery that will evolve in the coming years. A better understanding of the genetic basis of IBD and other complex diseases will lead to improved diagnosis, prognosis, treatment and surveillance.


Assuntos
Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Doenças Inflamatórias Intestinais/genética , Grupo com Ancestrais do Continente Asiático/genética , Grupo com Ancestrais do Continente Asiático/estatística & dados numéricos , Estudos de Casos e Controles , Grupo com Ancestrais do Continente Europeu/genética , Grupo com Ancestrais do Continente Europeu/estatística & dados numéricos , Ligação Genética , Estudo de Associação Genômica Ampla/história , Estudo de Associação Genômica Ampla/estatística & dados numéricos , História do Século XX , História do Século XXI , Humanos , Doenças Inflamatórias Intestinais/história , Modelos Estatísticos , Polimorfismo de Nucleotídeo Único , Receptores de Interleucina/genética , Sequenciamento Completo do Exoma/estatística & dados numéricos
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