TY - JOUR
T1 - From disease association to risk assessment
T2 - An optimistic view from genome-wide association studies on type 1 diabetes
AU - Wei, Zhi
AU - Wang, Kai
AU - Qu, Hui Qi
AU - Zhang, Haitao
AU - Bradfield, Jonathan
AU - Kim, Cecilia
AU - Frackleton, Edward
AU - Hou, Cuiping
AU - Glessner, Joseph T.
AU - Chiavacci, Rosetta
AU - Stanley, Charles
AU - Monos, Dimitri
AU - Grant, Struan F.A.
AU - Polychronakos, Constantin
AU - Hakonarson, Hakon
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2009/10
Y1 - 2009/10
N2 - 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 -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.
AB - 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 -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.
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U2 - 10.1371/journal.pgen.1000678
DO - 10.1371/journal.pgen.1000678
M3 - Article
C2 - 19816555
AN - SCOPUS:73449129712
VL - 5
JO - PLoS Genetics
JF - PLoS Genetics
SN - 1553-7390
IS - 10
M1 - e1000678
ER -