title: Whole genome prediction of bladder cancer risk with the Bayesian LASSO.
abstract: To build a predictive model for urothelial carcinoma of the bladder (UCB) risk combining both genomic and nongenomic data, 1,127 cases and 1,090 controls from the Spanish Bladder Cancer/EPICURO study were genotyped using the HumanHap 1M SNP array. After quality control filters, genotypes from 475,290 variants were available. Nongenomic information comprised age, gender, region, and smoking status. Three Bayesian threshold models were implemented including: (1) only genomic information, (2) only nongenomic data, and (3) both sources of information. The three models were applied to the whole population, to only nonsmokers, to male smokers, and to extreme phenotypes to potentiate the UCB genetic component. The area under the ROC curve allowed evaluating the predictive ability of each model in a 10-fold cross-validation scenario. Smoking status showed the highest predictive ability of UCB risk (AUCtest = 0.62). On the other hand, the AUC of all genetic variants was poorer (0.53). When the extreme phenotype approach was applied, the predictive ability of the genomic model improved 15%. This study represents a first attempt to build a predictive model for UCB risk combining both genomic and nongenomic data and applying state-of-the-art statistical approaches. However, the lack of genetic relatedness among individuals, the complexity of UCB etiology, as well as a relatively small statistical power, may explain the low predictive ability for UCB risk. The study confirms the difficulty of predicting complex diseases using genetic data, and suggests the limited translational potential of findings from this type of data into public health interventions.
authors.affiliation: Spanish National Cancer Research Center (CNIO), Madrid, Spain.
authors.forename: Evangelina López
authors.initials: EL
authors.lastname: de Maturana
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Chanok
authors.forename: Antoni C
authors.initials: AC
authors.lastname: Picornell
authors.forename: Nathaniel
authors.initials: N
authors.lastname: Rothman
authors.forename: Jesús
authors.initials: J
authors.lastname: Herranz
authors.forename: M Luz
authors.initials: ML
authors.lastname: Calle
authors.forename: Montserrat
authors.initials: M
authors.lastname: García-Closas
authors.forename: Gaëlle
authors.initials: G
authors.lastname: Marenne
authors.forename: Angela
authors.initials: A
authors.lastname: Brand
authors.forename: Adonina
authors.initials: A
authors.lastname: Tardón
authors.forename: Alfredo
authors.initials: A
authors.lastname: Carrato
authors.forename: Debra T
authors.initials: DT
authors.lastname: Silverman
authors.forename: Manolis
authors.initials: M
authors.lastname: Kogevinas
authors.forename: Daniel
authors.initials: D
authors.lastname: Gianola
authors.forename: Francisco X
authors.initials: FX
authors.lastname: Real
authors.forename: Núria
authors.initials: N
authors.lastname: Malats
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D016022:Case-Control Studies; D005260:Female; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D015894:Genome, Human / Q000235:genetics*; D005838:Genotype; D006801:Humans; D008297:Male; D008875:Middle Aged; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D012372:ROC Curve; D012307:Risk Factors; D012907:Smoking / Q000009:adverse effects; D001749:Urinary Bladder Neoplasms / Q000235:genetics*
keywords: Bayesian shrinkage method; area under the ROC curve; genomic predictive model; urothelial carcinoma of the bladder