Genome-wide meta-analysis for severe diabetic retinopathy.

Diabetic retinopathy is a leading cause of blindness. The purpose of this study is to identify novel genetic loci associated with the sight threatening complications of diabetic retinopathy. We performed a meta-analysis of genome-wide association data for severe diabetic retinopathy as defined by diabetic macular edema or proliferative diabetic retinopathy in unrelated cases ascertained from two large, type I diabetic cohorts: the Genetics of Kidney in Diabetes (GoKinD) and the Epidemiology of Diabetes Intervention and Control Trial (EDIC) studies. Controls were other diabetic subjects in the cohort. A combined total of 2829 subjects (973 cases, 1856 controls) were studied on 2 543 887 single nucleotide polymorphisms (SNPs). Subjects with nephropathy were excluded in a sub-analysis of 281 severe retinopathy cases. We also performed an association analysis of 1390 copy number variations (CNVs) using tag SNPs. No associations were significant at a genome-wide level after correcting for multiple measures. The meta-analysis did identify several associations that can be pursued in future replication studies, including an intergenic SNP, rs476141, on chromosome 1 (P-value 1.2 × 10(-7)). The most interesting signal from the CNV analysis came from the sub-group analysis without nephropathy subjects and is rs10521145 (P-value 3.4 × 10(-6)) in the intron of CCDC101, a histone acetyltransferase. This SNP tags the copy number region CNVR6685.1 on chromosome 16 at 28.5 Mb, a gain/loss site. In summary, this study nominates several novel genetic loci associated with the sight-threatening complications of diabetic retinopathy and anticipates future large-scale consortium-based validation studies.

Spoiling the whole bunch: quality control aimed at preserving the integrity of high-throughput genotyping.

False-positive or false-negative results attributable to undetected genotyping errors and confounding factors present a constant challenge for genome-wide association studies (GWAS) given the low signals associated with complex phenotypes and the noise associated with high-throughput genotyping. In the context of the genetics of kidneys in diabetes (GoKinD) study, we identify a source of error in genotype calling and demonstrate that a standard battery of quality-control (QC) measures is not sufficient to detect and/or correct it. We show that, if genotyping and calling are done by plate (batch), even a few DNA samples of marginally acceptable quality can profoundly alter the allele calls for other samples on the plate. In turn, this leads to significant differential bias in estimates of allele frequency between plates and, potentially, to false-positive associations, particularly when case and control samples are not sufficiently randomized to plates. This problem may become widespread as investigators tap into existing public databases for GWAS control samples. We describe how to detect and correct this bias by utilizing additional sources of information, including raw signal-intensity data.

Replication analysis for severe diabetic retinopathy.

PURPOSE: The purpose of this study is to attempt to replicate the top single nucleotide polymorphism (SNP) associations from a previous genome-wide association study (GWAS) for the sight-threatening complications of diabetic retinopathy in an independent cohort of diabetic subjects from the Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR). METHODS: This study included 469 type 1 diabetic, Caucasian subjects from WESDR. Cases (n = 208) were defined by prior laser treatment for either proliferative diabetic retinopathy or diabetic macular edema. Controls (n = 261) were all other subjects in the cohort. Three hundred eighty-nine SNPs were tested for association using the Illumina GoldenGate custom array. A retinopathy-only subanalysis was conducted in 437 subjects by removing those with end-stage renal disease. Evaluation for association between cases and controls was conducted by using chi-square tests. A combined analysis incorporated the results from WESDR with the prior GWAS. RESULTS: No associations were significant at a genome-wide level. The analysis did identify SNPs that can be pursued in future replication studies. The top association was at rs4865047, an intronic SNP, in the gene CEP135 (P value 2.06 × 10(-5)). The top association from the subanalysis was at rs1902491 (P value 2.81 × 10(-5)), a SNP that sits upstream of the gene NPY2R. CONCLUSIONS: This study nominates several novel genetic loci that may be associated with severe diabetic retinopathy. In order to confirm these findings, replication and extension in additional cohorts will be necessary as susceptibility alleles for diabetic retinopathy appear to be of modest effect.

Biomolecular Systems of Disease Buried Across Multiple GWAS Unveiled by Information Theory and Ontology.

A key challenge for genome-wide association studies (GWAS) is to understand how single nucleotide polymorphisms (SNPs) mechanistically underpin complex diseases. While this challenge has been addressed partially by Gene Ontology (GO) enrichment of large list of host genes of SNPs prioritized in GWAS, these enrichment have not been formally evaluated. Here, we develop a novel computational approach anchored in information theoretic similarity, by systematically mining lists of host genes of SNPs prioritized in three adult-onset diabetes mellitus GWAS. The "gold-standard" is based on GO associated with 20 published diabetes SNPs' host genes and on our own evaluation. We computationally identify 69 similarity-predicted GO independently validated in all three GWAS (FDR<5%), enriched with those of the gold-standard (odds ratio=5.89, P=4.81e-05), and these terms can be organized by similarity criteria into 11 groupings termed "biomolecular systems". Six biomolecular systems were corroborated by the gold-standard and the remaining five were previously uncharacterized. http://lussierlab.org/publications/ITS-GWAS.