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1.
Blood Adv ; 4(1): 181-190, 2020 Jan 14.
Artigo em Inglês | MEDLINE | ID: mdl-31935283

RESUMO

Persons of African ancestry (AA) have a twofold higher risk for multiple myeloma (MM) compared with persons of European ancestry (EA). Genome-wide association studies (GWASs) support a genetic contribution to MM etiology in individuals of EA. Little is known about genetic risk factors for MM in individuals of AA. We performed a meta-analysis of 2 GWASs of MM in 1813 cases and 8871 controls and conducted an admixture mapping scan to identify risk alleles. We fine-mapped the 23 known susceptibility loci to find markers that could better capture MM risk in individuals of AA and constructed a polygenic risk score (PRS) to assess the aggregated effect of known MM risk alleles. In GWAS meta-analysis, we identified 2 suggestive novel loci located at 9p24.3 and 9p13.1 at P < 1 × 10-6; however, no genome-wide significant association was noted. In admixture mapping, we observed a genome-wide significant inverse association between local AA at 2p24.1-23.1 and MM risk in AA individuals. Of the 23 known EA risk variants, 20 showed directional consistency, and 9 replicated at P < .05 in AA individuals. In 8 regions, we identified markers that better capture MM risk in persons with AA. AA individuals with a PRS in the top 10% had a 1.82-fold (95% confidence interval, 1.56-2.11) increased MM risk compared with those with average risk (25%-75%). The strongest functional association was between the risk allele for variant rs56219066 at 5q15 and lower ELL2 expression (P = 5.1 × 10-12). Our study shows that common genetic variation contributes to MM risk in individuals with AA.

2.
Nat Commun ; 11(1): 27, 2020 Jan 07.
Artigo em Inglês | MEDLINE | ID: mdl-31911640

RESUMO

Impaired lung function is often caused by cigarette smoking, making it challenging to disentangle its role in lung cancer susceptibility. Investigation of the shared genetic basis of these phenotypes in the UK Biobank and International Lung Cancer Consortium (29,266 cases, 56,450 controls) shows that lung cancer is genetically correlated with reduced forced expiratory volume in one second (FEV1: rg = 0.098, p = 2.3 × 10-8) and the ratio of FEV1 to forced vital capacity (FEV1/FVC: rg = 0.137, p = 2.0 × 10-12). Mendelian randomization analyses demonstrate that reduced FEV1 increases squamous cell carcinoma risk (odds ratio (OR) = 1.51, 95% confidence intervals: 1.21-1.88), while reduced FEV1/FVC increases the risk of adenocarcinoma (OR = 1.17, 1.01-1.35) and lung cancer in never smokers (OR = 1.56, 1.05-2.30). These findings support a causal role of pulmonary impairment in lung cancer etiology. Integrative analyses reveal that pulmonary function instruments, including 73 novel variants, influence lung tissue gene expression and implicate immune-related pathways in mediating the observed effects on lung carcinogenesis.

3.
Int J Cancer ; 146(7): 1819-1826, 2020 Apr 01.
Artigo em Inglês | MEDLINE | ID: mdl-31226226

RESUMO

Latinos represent <1% of samples analyzed to date in genome-wide association studies of cancer. The clinical value of genetic information in guiding personalized medicine in populations of non-European ancestry will require additional discovery and risk locus characterization efforts across populations. In the present study, we performed a GWAS of prostate cancer (PrCa) in 2,820 Latino PrCa cases and 5,293 controls to search for novel PrCa risk loci and to examine the generalizability of known PrCa risk loci in Latino men. We also conducted a genetic admixture-mapping scan to identify PrCa risk alleles associated with local ancestry. Genome-wide significant associations were observed with 84 variants all located at the known PrCa risk regions at 8q24 (128.484-128.548) and 10q11.22 (MSMB gene). In admixture mapping, we observed genome-wide significant associations with local African ancestry at 8q24. Of the 162 established PrCa risk variants that are common in Latino men, 135 (83.3%) had effects that were directionally consistent as previously reported, among which 55 (34.0%) were statistically significant with p < 0.05. A polygenic risk model of the known PrCa risk variants showed that, compared to men with average risk (25th-75th percentile of the polygenic risk score distribution), men in the top 10% had a 3.19-fold (95% CI: 2.65, 3.84) increased PrCa risk. In conclusion, we found that the known PrCa risk variants can effectively stratify PrCa risk in Latino men. Larger studies in Latino populations will be required to discover and characterize genetic risk variants for PrCa and improve risk stratification for this population.

4.
Can J Urol ; 26(5S2): 31-33, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31629425

RESUMO

Men with germline mutations in DNA repair genes are at an increased risk of prostate cancer. These germline mutations are commonly seen in conjunction with somatic DNA repair gene mutations in prostate tumors. This indicates that men with a personal or family history of prostate cancer-as well as other cancer syndromes arising from mutations in DNA repair genes-should be considered for genetic testing and counseling.

5.
Nat Commun ; 10(1): 3948, 2019 Aug 28.
Artigo em Inglês | MEDLINE | ID: mdl-31462633

RESUMO

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

6.
Nat Commun ; 10(1): 3107, 2019 07 15.
Artigo em Inglês | MEDLINE | ID: mdl-31308362

RESUMO

Here we train cis-regulatory models of prostate tissue gene expression and impute expression transcriptome-wide for 233,955 European ancestry men (14,616 prostate cancer (PrCa) cases, 219,339 controls) from two large cohorts. Among 12,014 genes evaluated in the UK Biobank, we identify 38 associated with PrCa, many replicating in the Kaiser Permanente RPGEH. We report the association of elevated TMPRSS2 expression with increased PrCa risk (independent of a previously-reported risk variant) and with increased tumoral expression of the TMPRSS2:ERG fusion-oncogene in The Cancer Genome Atlas, suggesting a novel germline-somatic interaction mechanism. Three novel genes, HOXA4, KLK1, and TIMM23, additionally replicate in the RPGEH cohort. Furthermore, 4 genes, MSMB, NCOA4, PCAT1, and PPP1R14A, are associated with PrCa in a trans-ethnic meta-analysis (N = 9117). Many genes exhibit evidence for allele-specific transcriptional activation by PrCa master-regulators (including androgen receptor) in Position Weight Matrix, Chip-Seq, and Hi-C experimental data, suggesting common regulatory mechanisms for the associated genes.


Assuntos
Próstata/metabolismo , Neoplasias da Próstata/genética , Estudos de Coortes , Grupo com Ancestrais do Continente Europeu , Perfilação da Expressão Gênica , Humanos , Masculino , Modelos Genéticos , Serina Endopeptidases/genética , Serina Endopeptidases/metabolismo , Transcriptoma
7.
Cancer Res ; 79(13): 3192-3204, 2019 Jul 01.
Artigo em Inglês | MEDLINE | ID: mdl-31101764

RESUMO

Genome-wide association study-identified prostate cancer risk variants explain only a relatively small fraction of its familial relative risk, and the genes responsible for many of these identified associations remain unknown. To discover novel prostate cancer genetic loci and possible causal genes at previously identified risk loci, we performed a transcriptome-wide association study in 79,194 cases and 61,112 controls of European ancestry. Using data from the Genotype-Tissue Expression Project, we established genetic models to predict gene expression across the transcriptome for both prostate models and cross-tissue models and evaluated model performance using two independent datasets. We identified significant associations for 137 genes at P < 2.61 × 10-6, a Bonferroni-corrected threshold, including nine genes that remained significant at P < 2.61 × 10-6 after adjusting for all known prostate cancer risk variants in nearby regions. Of the 128 remaining associated genes, 94 have not yet been reported as potential target genes at known loci. We silenced 14 genes and many showed a consistent effect on viability and colony-forming efficiency in three cell lines. Our study provides substantial new information to advance our understanding of prostate cancer genetics and biology. SIGNIFICANCE: This study identifies novel prostate cancer genetic loci and possible causal genes, advancing our understanding of the molecular mechanisms that drive prostate cancer.

8.
Clin Pharmacol Ther ; 105(3): 738-745, 2019 03.
Artigo em Inglês | MEDLINE | ID: mdl-30260474

RESUMO

Genome-wide genotyping data are increasingly available for pharmacogenetic association studies, but application of these data for development of prediction models is limited. Prediction methods, such as elastic net regularization, have recently been applied to genetic studies but only limitedly to pharmacogenetic outcomes. An elastic net was applied to a pharmacogenetic study of progression-free survival (PFS) of 468 patients with advanced breast cancer in a clinical trial of paclitaxel, nab-paclitaxel, and ixabepilone. A final model included 13 single nucleotide polymorphisms (SNPs) in addition to clinical covariates (prior taxane status, hormone receptor status, disease-free interval, and presence of visceral metastases) with an area under the curve (AUC) integrated over time of 0.81, an increase compared to an AUC of 0.64 for a model with clinical covariates alone. This model may be of value in predicting PFS with microtubule targeting agents and may inform reverse translational studies to understand differential response to these drugs.


Assuntos
Neoplasias da Mama/diagnóstico , Neoplasias da Mama/genética , Estudo de Associação Genômica Ampla/métodos , Genótipo , Farmacogenética/métodos , Intervalo Livre de Progressão , Adulto , Idoso , Idoso de 80 Anos ou mais , Neoplasias da Mama/tratamento farmacológico , Feminino , Seguimentos , Humanos , Pessoa de Meia-Idade , Testes Farmacogenômicos/métodos , Valor Preditivo dos Testes , Adulto Jovem
10.
Clin Transl Sci ; 12(1): 28-38, 2019 01.
Artigo em Inglês | MEDLINE | ID: mdl-30369069

RESUMO

Our objective was to assess the relationship between hyperbilirubinemia with and without kernicterus and metabolic profile at newborn screening. Included were 1,693,658 infants divided into a training or testing subset in a ratio of 3:1. Forty-two metabolites were analyzed using logistic regression (odds ratios (ORs), area under the receiver operating characteristic curve (AUC), 95% confidence intervals (CIs)). Several metabolite patterns remained consistent across gestational age groups for hyperbilirubinemia without kernicterus. Thyroid stimulating hormone (TSH) and C-18:2 were decreased, whereas tyrosine and C-3 were increased in infants across groupings. Increased C-3 was also observed for kernicterus (OR: 3.17; 95% CI: 1.18-8.53). Thirty-one metabolites were associated with hyperbilirubinemia without kernicterus in the training set. Phenylalanine (OR: 1.91; 95% CI: 1.85-1.97), ornithine (OR: 0.76; 95% 0.74-0.77), and isoleucine + leucine (OR: 0.63; 95% CI: 0.61-0.65) were the most strongly associated. This study showed that newborn metabolic function is associated with hyperbilirubinemia with and without kernicterus.

11.
Biotechniques ; 65(6): 357-360, 2018 12.
Artigo em Inglês | MEDLINE | ID: mdl-30477330

RESUMO

Sample automation and management is increasingly important as the number and size of population-scale and high-throughput projects grow. This is particularly the case in large-scale population studies where sample size is far outpacing the commonly used 96-well plate format. To facilitate management and transfer of samples in this format, we present Samasy, a web-based application for the construction of a sample database, intuitive display of sample and batch information, and facilitation of automated sample transfer or subset. Samasy is designed with ease-of-use in mind, can be quickly set up, and runs in any web browser.


Assuntos
Bases de Dados Genéticas , Técnicas de Genotipagem , Software , Bases de Dados Genéticas/economia , Genótipo , Técnicas de Genotipagem/economia , Humanos , Tamanho da Amostra , Interface Usuário-Computador , Navegador
12.
Genetics ; 210(2): 463-476, 2018 10.
Artigo em Inglês | MEDLINE | ID: mdl-30104420

RESUMO

The genetic etiology of many complex diseases is highly heterogeneous. A complex disease can be caused by multiple mutations within the same gene or mutations in multiple genes at various genomic loci. Although these disease-susceptibility mutations can be collectively common in the population, they are often individually rare or even private to certain families. Family-based studies are powerful for detecting rare variants enriched in families, which is an important feature for sequencing studies due to the heterogeneous nature of rare variants. In addition, family designs can provide robust protection against population stratification. Nevertheless, statistical methods for analyzing family-based sequencing data are underdeveloped, especially those accounting for heterogeneous etiology of complex diseases. In this article, we introduce a random field framework for detecting gene-phenotype associations in family-based sequencing studies, referred to as family-based genetic random field (FGRF). Similar to existing family-based association tests, FGRF could utilize within-family and between-family information separately or jointly to test an association. We demonstrate that FGRF has comparable statistical power with existing methods when there is no genetic heterogeneity, but can improve statistical power when there is genetic heterogeneity across families. The proposed method also shares the same advantages with the conventional family-based association tests (e.g., being robust to population stratification). Finally, we applied the proposed method to a sequencing data from the Minnesota Twin Family Study, and revealed several genes, including SAMD14, potentially associated with alcohol dependence.


Assuntos
Heterogeneidade Genética , Predisposição Genética para Doença , Modelos Genéticos , Taxa de Mutação , Linhagem , Alcoolismo/genética , Algoritmos , Humanos , Fenótipo
13.
Cancer Epidemiol Biomarkers Prev ; 27(10): 1151-1158, 2018 10.
Artigo em Inglês | MEDLINE | ID: mdl-30038050

RESUMO

Background: The genetic etiology of osteosarcoma remains poorly understood despite the publication of a genome-wide association study. Association between HLA genetic variants and risk of several cancers has been observed, but HLA variation is not well captured by standard SNP arrays.Methods: We genotyped 207 Californian pediatric osteosarcoma cases and 696 controls of European ancestry using a custom genome-wide array supplemented with approximately 6,000 additional probes across the MHC region. We subsequently imputed 4-digit classical HLA alleles using a reference panel of 5,225 individuals who underwent high-resolution HLA typing via next-generation sequencing. Case-control comparisons were adjusted for ancestry-informative principal components, and top associations from the discovery analysis underwent replication in an independent dataset of 657 cases and 1,183 controls.Results: Three highly correlated HLA class II variants (r 2 = 0.33-0.98) were associated with osteosarcoma risk in discovery analyses, including HLA-DRB1*0301 (OR = 0.52; P = 3.2 × 10-3), HLA-DQA1*0501 (OR = 0.74; P = 0.031), and HLA-DQB1*0201 (OR = 0.51; P = 2.7 × 10-3). Similar associations were observed in the replication data (P range = 0.011-0.037). Meta-analysis of the two datasets identified HLA-DRB1*0301 as the most significantly associated variant (ORmeta = 0.62; P meta = 1.5 × 10-4), reaching Bonferroni-corrected statistical significance. The meta-analysis also revealed a second significant independent signal at HLA-DQA1*01:01 (ORmeta = 1.33, P meta = 1.2 × 10-3), and a third suggestive association at HLA-DQB1*0302 (ORmeta = 0.73, P meta = 6.4 × 10-3).Conclusions: Multiple independent HLA class II alleles may influence osteosarcoma risk.Impact: Additional work is needed to extend our observations to other patient populations and to clarify the potential causal mechanisms underlying these associations. Understanding immunologic contributions to the etiology of osteosarcoma may inform rational therapeutic targets. Cancer Epidemiol Biomarkers Prev; 27(10); 1151-8. ©2018 AACR.


Assuntos
Neoplasias Ósseas/patologia , Genes MHC da Classe II , Predisposição Genética para Doença , Osteossarcoma/patologia , Polimorfismo de Nucleotídeo Único , Adulto , Neoplasias Ósseas/genética , Estudos de Casos e Controles , Feminino , Seguimentos , Genótipo , Humanos , Masculino , Metanálise como Assunto , Osteossarcoma/genética , Prognóstico , Adulto Jovem
14.
Clin Cancer Res ; 24(21): 5250-5260, 2018 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-30021908

RESUMO

Purpose: PD-1/L1 axis-directed therapies produce clinical responses in a subset of patients; therefore, biomarkers of response are needed. We hypothesized that quantifying key immunosuppression mechanisms within the tumor microenvironment by multiparameter algorithms would identify strong predictors of anti-PD-1 response.Experimental Design: Pretreatment tumor biopsies from 166 patients treated with anti-PD-1 across 10 academic cancer centers were fluorescently stained with multiple markers in discovery (n = 24) and validation (n = 142) cohorts. Biomarker-positive cells and their colocalization were spatially profiled in pathologist-selected tumor regions using novel Automated Quantitative Analysis algorithms. Selected biomarker signatures, PD-1/PD-L1 interaction score, and IDO-1/HLA-DR coexpression were evaluated for anti-PD-1 treatment outcomes.Results: In the discovery cohort, PD-1/PD-L1 interaction score and/or IDO-1/HLA-DR coexpression was strongly associated with anti-PD-1 response (P = 0.0005). In contrast, individual biomarkers (PD-1, PD-L1, IDO-1, HLA-DR) were not associated with response or survival. This finding was replicated in an independent validation cohort: patients with high PD-1/PD-L1 and/or IDO-1/HLA-DR were more likely to respond (P = 0.0096). These patients also experienced significantly improved progression-free survival (HR = 0.36; P = 0.0004) and overall survival (HR = 0.39; P = 0.0011). In the combined cohort, 80% of patients exhibiting higher levels of PD-1/PD-L1 interaction scores and IDO-1/HLA-DR responded to PD-1 blockers (P = 0.000004). In contrast, PD-L1 expression was not predictive of survival.Conclusions: Quantitative spatial profiling of key tumor-immune suppression pathways by novel digital pathology algorithms could help more reliably select melanoma patients for PD-1 monotherapy. Clin Cancer Res; 24(21); 5250-60. ©2018 AACR.


Assuntos
Antígeno B7-H1/metabolismo , Antígenos HLA-DR/metabolismo , Indolamina-Pirrol 2,3,-Dioxigenase/metabolismo , Melanoma/metabolismo , Melanoma/mortalidade , Receptor de Morte Celular Programada 1/metabolismo , Adulto , Idoso , Antineoplásicos Imunológicos/farmacologia , Antineoplásicos Imunológicos/uso terapêutico , Biomarcadores Tumorais , Biópsia , Linhagem Celular Tumoral , Feminino , Humanos , Imuno-Histoquímica , Masculino , Melanoma/tratamento farmacológico , Melanoma/patologia , Pessoa de Meia-Idade , Modelos Biológicos , Metástase Neoplásica , Estadiamento de Neoplasias , Prognóstico , Ligação Proteica , Retratamento , Resultado do Tratamento
15.
PLoS Genet ; 14(2): e1007139, 2018 02.
Artigo em Inglês | MEDLINE | ID: mdl-29432419

RESUMO

Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy). For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes) that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov Chain Monte Carlo (MCMC) technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes can produce higher accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected six independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis and Peripheral Vascular Disease. We provide an R-package 'CPBayes' implementing the proposed method.


Assuntos
Teorema de Bayes , Estudos de Associação Genética/métodos , Estudos de Associação Genética/estatística & dados numéricos , Predisposição Genética para Doença , Fenótipo , Estudos de Casos e Controles , Estudos de Coortes , Predisposição Genética para Doença/epidemiologia , Humanos , Cadeias de Markov , Método de Monte Carlo
17.
Bioinformatics ; 34(6): 936-942, 2018 03 15.
Artigo em Inglês | MEDLINE | ID: mdl-29106441

RESUMO

Motivation: As whole-genome tumor sequence and biological annotation datasets grow in size, number and content, there is an increasing basic science and clinical need for efficient and accurate data management and analysis software. With the emergence of increasingly sophisticated data stores, execution environments and machine learning algorithms, there is also a need for the integration of functionality across frameworks. Results: We present orchid, a python based software package for the management, annotation and machine learning of cancer mutations. Building on technologies of parallel workflow execution, in-memory database storage and machine learning analytics, orchid efficiently handles millions of mutations and hundreds of features in an easy-to-use manner. We describe the implementation of orchid and demonstrate its ability to distinguish tissue of origin in 12 tumor types based on 339 features using a random forest classifier. Availability and implementation: Orchid and our annotated tumor mutation database are freely available at https://github.com/wittelab/orchid. Software is implemented in python 2.7, and makes use of MySQL or MemSQL databases. Groovy 2.4.5 is optionally required for parallel workflow execution. Contact: JWitte@ucsf.edu. Supplementary information: Supplementary data are available at Bioinformatics online.


Assuntos
Mutação , Neoplasias/genética , Análise de Sequência de DNA/métodos , Software , Aprendizado de Máquina Supervisionado , Feminino , Humanos , Masculino , Neoplasias/classificação
18.
J Allergy Clin Immunol ; 141(3): 991-1001, 2018 03.
Artigo em Inglês | MEDLINE | ID: mdl-29030101

RESUMO

BACKGROUND: Peanut allergy (PA) is a complex disease with both environmental and genetic risk factors. Previously, PA loci were identified in filaggrin (FLG) and HLA in candidate gene studies, and loci in HLA were identified in a genome-wide association study and meta-analysis. OBJECTIVE: We sought to investigate genetic susceptibility to PA. METHODS: Eight hundred fifty cases and 926 hyper-control subjects and more than 7.8 million genotyped and imputed single nucleotide polymorphisms (SNPs) were analyzed in a genome-wide association study to identify susceptibility variants for PA in the Canadian population. A meta-analysis of 2 phenotypes (PA and food allergy) was conducted by using 7 studies from the Canadian, American (n = 2), Australian, German, and Dutch (n = 2) populations. RESULTS: An SNP near integrin α6 (ITGA6) reached genome-wide significance with PA (P = 1.80 × 10-8), whereas SNPs associated with Src kinase-associated phosphoprotein 1 (SKAP1), matrix metallopeptidase 12 (MMP12)/MMP13, catenin α3 (CTNNA3), rho GTPase-activating protein 24 (ARHGAP24), angiopoietin 4 (ANGPT4), chromosome 11 open reading frame (C11orf30/EMSY), and exocyst complex component 4 (EXOC4) reached a threshold suggestive of association (P ≤ 1.49 × 10-6). In the meta-analysis of PA, loci in or near ITGA6, ANGPT4, MMP12/MMP13, C11orf30, and EXOC4 were significant (P ≤ 1.49 × 10-6). When a phenotype of any food allergy was used for meta-analysis, the C11orf30 locus reached genome-wide significance (P = 7.50 × 10-11), whereas SNPs associated with ITGA6, ANGPT4, MMP12/MMP13, and EXOC4 and additional C11orf30 SNPs were suggestive (P ≤ 1.49 × 10-6). Functional annotation indicated that SKAP1 regulates expression of CBX1, which colocalizes with the EMSY protein coded by C11orf30. CONCLUSION: This study identifies multiple novel loci as risk factors for PA and food allergy and establishes C11orf30 as a risk locus for both PA and food allergy. Multiple genes (C11orf30/EMSY, SKAP1, and CTNNA3) identified by this study are involved in epigenetic regulation of gene expression.


Assuntos
Epigênese Genética , Loci Gênicos , Estudo de Associação Genômica Ampla , Proteínas de Neoplasias/genética , Proteínas Nucleares/genética , Hipersensibilidade a Amendoim/genética , Polimorfismo de Nucleotídeo Único , Proteínas Repressoras/genética , Feminino , Humanos , Masculino , Hipersensibilidade a Amendoim/epidemiologia , Hipersensibilidade a Amendoim/metabolismo , Fosfoproteínas/biossíntese , Fosfoproteínas/genética , Fatores de Risco , alfa Catenina/biossíntese , alfa Catenina/genética
19.
Cancer Epidemiol Biomarkers Prev ; 27(1): 75-85, 2018 01.
Artigo em Inglês | MEDLINE | ID: mdl-29150481

RESUMO

Background: There exists compelling evidence that some genetic variants are associated with the risk of multiple cancer sites (i.e., pleiotropy). However, the biological mechanisms through which the pleiotropic variants operate are unclear.Methods: We obtained all cancer risk associations from the National Human Genome Research Institute-European Bioinformatics Institute GWAS Catalog, and correlated cancer risk variants were clustered into groups. Pleiotropic variant groups and genes were functionally annotated. Associations of pleiotropic cancer risk variants with noncancer traits were also obtained.Results: We identified 1,431 associations between variants and cancer risk, comprised of 989 unique variants associated with 27 unique cancer sites. We found 20 pleiotropic variant groups (2.1%) composed of 33 variants (3.3%), including novel pleiotropic variants rs3777204 and rs56219066 located in the ELL2 gene. Relative to single-cancer risk variants, pleiotropic variants were more likely to be in genes (89.0% vs. 65.3%, P = 2.2 × 10-16), and to have somewhat larger risk allele frequencies (median RAF = 0.49 versus 0.39, P = 0.046). The 27 genes to which the pleiotropic variants mapped were suggestive for enrichment in response to radiation and hypoxia, alpha-linolenic acid metabolism, cell cycle, and extension of telomeres. In addition, we observed that 8 of 33 pleiotropic cancer risk variants were associated with 16 traits other than cancer.Conclusions: This study identified and functionally characterized genetic variants showing pleiotropy for cancer risk.Impact: Our findings suggest biological pathways common to different cancers and other diseases, and provide a basis for the study of genetic testing for multiple cancers and repurposing cancer treatments. Cancer Epidemiol Biomarkers Prev; 27(1); 75-85. ©2017 AACR.


Assuntos
Pleiotropia Genética , Neoplasias/genética , Biomarcadores Tumorais/genética , Feminino , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Humanos , Masculino , Polimorfismo de Nucleotídeo Único , Fatores de Risco
20.
Am J Epidemiol ; 186(7): 762-770, 2017 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-28978192

RESUMO

The analysis of gene-environment interaction (G×E) may hold the key for further understanding the etiology of many complex traits. The current availability of high-volume genetic data, the wide range in types of environmental data that can be measured, and the formation of consortiums of multiple studies provide new opportunities to identify G×E but also new analytical challenges. In this article, we summarize several statistical approaches that can be used to test for G×E in a genome-wide association study. These include traditional models of G×E in a case-control or quantitative trait study as well as alternative approaches that can provide substantially greater power. The latest methods for analyzing G×E with gene sets and with data in a consortium setting are summarized, as are issues that arise due to the complexity of environmental data. We provide some speculation on why detecting G×E in a genome-wide association study has thus far been difficult. We conclude with a description of software programs that can be used to implement most of the methods described in the paper.


Assuntos
Doença/etiologia , Interação Gene-Ambiente , Estudo de Associação Genômica Ampla/métodos , Modelos Genéticos , Modelos Estatísticos , Software , Teorema de Bayes , Doença/genética , Predisposição Genética para Doença , Humanos , Modelos Logísticos , Análise de Sequência de DNA
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