title: From disease association to risk assessment: an optimistic view from genome-wide association studies on type 1 diabetes.
abstract: Genome-wide association studies (GWAS) have been fruitful in identifying disease susceptibility loci for common and complex diseases. A remaining question is whether we can quantify individual disease risk based on genotype data, in order to facilitate personalized prevention and treatment for complex diseases. Previous studies have typically failed to achieve satisfactory performance, primarily due to the use of only a limited number of confirmed susceptibility loci. Here we propose that sophisticated machine-learning approaches with a large ensemble of markers may improve the performance of disease risk assessment. We applied a Support Vector Machine (SVM) algorithm on a GWAS dataset generated on the Affymetrix genotyping platform for type 1 diabetes (T1D) and optimized a risk assessment model with hundreds of markers. We subsequently tested this model on an independent Illumina-genotyped dataset with imputed genotypes (1,008 cases and 1,000 controls), as well as a separate Affymetrix-genotyped dataset (1,529 cases and 1,458 controls), resulting in area under ROC curve (AUC) of approximately 0.84 in both datasets. In contrast, poor performance was achieved when limited to dozens of known susceptibility loci in the SVM model or logistic regression model. Our study suggests that improved disease risk assessment can be achieved by using algorithms that take into account interactions between a large ensemble of markers. We are optimistic that genotype-based disease risk assessment may be feasible for diseases where a notable proportion of the risk has already been captured by SNP arrays.
authors.affiliation: Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, United States of America.
authors.forename: Zhi
authors.initials: Z
authors.lastname: Wei
authors.forename: Kai
authors.initials: K
authors.lastname: Wang
authors.forename: Hui-Qi
authors.initials: HQ
authors.lastname: Qu
authors.forename: Haitao
authors.initials: H
authors.lastname: Zhang
authors.forename: Jonathan
authors.initials: J
authors.lastname: Bradfield
authors.forename: Cecilia
authors.initials: C
authors.lastname: Kim
authors.forename: Edward
authors.initials: E
authors.lastname: Frackleton
authors.forename: Cuiping
authors.initials: C
authors.lastname: Hou
authors.forename: Joseph T
authors.initials: JT
authors.lastname: Glessner
authors.forename: Rosetta
authors.initials: R
authors.lastname: Chiavacci
authors.forename: Charles
authors.initials: C
authors.lastname: Stanley
authors.forename: Dimitri
authors.initials: D
authors.lastname: Monos
authors.forename: Struan F A
authors.initials: SF
authors.lastname: Grant
authors.forename: Constantin
authors.initials: C
authors.lastname: Polychronakos
authors.forename: Hakon
authors.initials: H
authors.lastname: Hakonarson
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001185:Artificial Intelligence; D030541:Databases, Genetic; D003922:Diabetes Mellitus, Type 1 / Q000235:genetics*; D005819:Genetic Markers; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis; D020641:Polymorphism, Single Nucleotide; D012372:ROC Curve; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*
chemical_list: D005819:Genetic Markers