Obsessive-compulsive disorder (OCD) is a common, debilitating neuropsychiatric illness with complex genetic etiology. The International OCD Foundation Genetics Collaborative (IOCDF-GC) is a multi-national collaboration established to discover the genetic variation predisposing to OCD. A set of individuals affected with DSM-IV OCD, a subset of their parents, and unselected controls, were genotyped with several different Illumina SNP microarrays. After extensive data cleaning, 1,465 cases, 5,557 ancestry-matched controls and 400 complete trios remained, with a common set of 469,410 autosomal and 9,657 X-chromosome SNPs. Ancestry-stratified case-control association analyses were conducted for three genetically-defined subpopulations and combined in two meta-analyses, with and without the trio-based analysis. In the case-control analysis, the lowest two p-values were located within DLGAP1 (p=2.49×10-6 and p=3.44×10-6), a member of the neuronal postsynaptic density complex. In the trio analysis, rs6131295, near BTBD3, exceeded the genome-wide significance threshold with a p-value=3.84 × 10-8. However, when trios were meta-analyzed with the combined case-control samples, the p-value for this variant was 3.62×10-5, losing genome-wide significance. Although no SNPs were identified to be associated with OCD at a genome-wide significant level in the combined trio-case-control sample, a significant enrichment of methylation-QTLs (p<0.001) and frontal lobe eQTLs (p=0.001) was observed within the top-ranked SNPs (p<0.01) from the trio-case-control analysis, suggesting these top signals may have a broad role in gene expression in the brain, and possibly in the etiology of OCD.

Tourette Syndrome (TS) is a developmental disorder that has one of the highest familial recurrence rates among neuropsychiatric diseases with complex inheritance. However, the identification of definitive TS susceptibility genes remains elusive. Here, we report the first genome-wide association study (GWAS) of TS in 1285 cases and 4964 ancestry-matched controls of European ancestry, including two European-derived population isolates, Ashkenazi Jews from North America and Israel, and French Canadians from Quebec, Canada. In a primary meta-analysis of GWAS data from these European ancestry samples, no markers achieved a genome-wide threshold of significance (p<5 × 10−8); the top signal was found in rs7868992 on chromosome 9q32 within COL27A1 (p=1.85 × 10−6). A secondary analysis including an additional 211 cases and 285 controls from two closely-related Latin-American population isolates from the Central Valley of Costa Rica and Antioquia, Colombia also identified rs7868992 as the top signal (p=3.6 × 10−7 for the combined sample of 1496 cases and 5249 controls following imputation with 1000 Genomes data). This study lays the groundwork for the eventual identification of common TS susceptibility variants in larger cohorts and helps to provide a more complete understanding of the full genetic architecture of this disorder.

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.

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.

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.