Birdshot chorioretinopathy (BSCR) is a rare form of autoimmune uveitis that can lead to severe visual impairment. Intriguingly, >95% of cases carry the HLA-A29 allele, which defines the strongest documented HLA association for a human disease. We have conducted a genome-wide association study in 96 Dutch and 27 Spanish cases, and 398 unrelated Dutch and 380 Spanish controls. Fine-mapping the primary MHC association through high-resolution imputation at classical HLA loci, identified HLA-A*29:02 as the principal MHC association (odds ratio (OR) = 157.5, 95% CI 91.6-272.6, P = 6.6 × 10(-74)). We also identified two novel susceptibility loci at 5q15 near ERAP2 (rs7705093; OR = 2.3, 95% CI 1.7-3.1, for the T allele, P = 8.6 × 10(-8)) and at 14q32.31 in the TECPR2 gene (rs150571175; OR = 6.1, 95% CI 3.2-11.7, for the A allele, P = 3.2 × 10(-8)). The association near ERAP2 was confirmed in an independent British case-control samples (combined meta-analysis P = 1.7 × 10(-9)). Functional analyses revealed that the risk allele of the polymorphism near ERAP2 is strongly associated with high mRNA and protein expression of ERAP2 in B cells. This study further defined an extremely strong MHC risk component in BSCR, and detected evidence for a novel disease mechanism that affects peptide processing in the endoplasmic reticulum.

Several genome-wide association studies (GWAS) have been published on various complex diseases. Although, new loci are found to be associated with these diseases, still only very little of the genetic risk for these diseases can be explained. As GWAS are still underpowered to find small main effects, and gene-gene interactions are likely to play a role, the data might currently not be analyzed to its full potential. In this study, we evaluated alternative methods to study GWAS data. Instead of focusing on the single nucleotide polymorphisms (SNPs) with the highest statistical significance, we took advantage of prior biological information and tried to detect overrepresented pathways in the GWAS data. We evaluated whether pathway classification analysis can help prioritize the biological pathways most likely to be involved in the disease etiology. In this study, we present the various benefits and limitations of pathway-classification tools in analyzing GWAS data. We show multiple differences in outcome between pathway tools analyzing the same dataset. Furthermore, analyzing randomly selected SNPs always results in significantly overrepresented pathways, large pathways have a higher chance of becoming statistically significant and the bioinformatics tools used in this study are biased toward detecting well-defined pathways. As an example, we analyzed data from two GWAS on type 2 diabetes (T2D): the Diabetes Genetics Initiative (DGI) and the Wellcome Trust Case Control Consortium (WTCCC). Occasionally the results from the DGI and the WTCCC GWAS showed concordance in overrepresented pathways, but discordance in the corresponding genes. Thus, incorporating gene networks and pathway classification tools into the analysis can point toward significantly overrepresented molecular pathways, which cannot be picked up using traditional single-locus analyses. However, the limitations discussed in this study, need to be addressed before these methods can be widely used. Genet. Epidemiol. 33:419-431, 2009.

Despite large-scale genome-wide association studies (GWAS), the underlying genes for schizophrenia are largely unknown. Additional approaches are therefore required to identify the genetic background of this disorder. Here we report findings from a large gene expression study in peripheral blood of schizophrenia patients and controls. We applied a systems biology approach to genome-wide expression data from whole blood of 92 medicated and 29 antipsychotic-free schizophrenia patients and 118 healthy controls. We show that gene expression profiling in whole blood can identify twelve large gene co-expression modules associated with schizophrenia. Several of these disease related modules are likely to reflect expression changes due to antipsychotic medication. However, two of the disease modules could be replicated in an independent second data set involving antipsychotic-free patients and controls. One of these robustly defined disease modules is significantly enriched with brain-expressed genes and with genetic variants that were implicated in a GWAS study, which could imply a causal role in schizophrenia etiology. The most highly connected intramodular hub gene in this module (ABCF1), is located in, and regulated by the major histocompatibility (MHC) complex, which is intriguing in light of the fact that common allelic variants from the MHC region have been implicated in schizophrenia. This suggests that the MHC increases schizophrenia susceptibility via altered gene expression of regulatory genes in this network.

Postzygotic mutation is a common phenomenon in SCN1A-related epilepsies. Participants with mosaicism have on average milder phenotypes, suggesting that mosaicism can be a major modifier of SCN1A-related diseases. Detection of mosaicism has important implications for genetic counseling and can be achieved by deep sequencing of unique reads.