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.

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.

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.

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.