From disease association to risk assessment: an optimistic view from genome-wide association studies on type 1 diabetes.

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.

Genetic variants in major histocompatibility complex-linked genes associate with pediatric liver transplant rejection.

BACKGROUND & AIMS: Limited access to large samples precludes genome-wide association studies of rare but complex traits. To localize candidate genes with family-based genome-wide association, a novel exploratory analysis was first tested on 1774 major histocompatibility complex single nucleotide polymorphisms (SNPs) in 240 DNA samples from 80 children with primary liver transplantation and their biologic parents. METHODS: Initially, 57 SNPs with large differences (P < .05) in minor allele frequencies were selected when parents of children with early rejection (rejectors) were compared with parents of nonrejectors. RESULTS: In hypothesis testing of selected SNPs, the gamete competition statistic identified the minor allele G of the SNP rs9296068, near HLA-DOA, as being significantly different (P = .018) between outcome groups in parent-to-child transmission. Subsequent simple association testing confirmed over- and undertransmission of rs9296068 based on the most significant differences between outcome groups, of 1774 SNPs tested (P = .002), and allele (G) frequencies that were greater among rejectors (51.4% vs 36.8%, respectively, P = .015) and lower among nonrejectors (26.8% vs 36.8%, respectively, P = .074) compared with 400 normal control Caucasian children. In early functional validation, rejectors demonstrated significant repression of the first HLA-DOA exon closest to rs9296068. Also, intragraft B lymphocytes, whose antigen-presenting function is selectively inhibited by HLA-DOA were 3-fold more numerous during rejection among rejectors with the risk allele, than those without. CONCLUSIONS: The minor allele of the SNP rs9296068 is significantly associated with liver transplantation rejection and with enhanced B-lymphocyte participation in rejection, likely because of a dysfunctional HLA-DOA gene product.