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Background Increasing evidence supports an extensive and complex genetic contribution to PD. Previous genome-wide association studies (GWAS) have shed light on the genetic basis of risk for this disease. However, the genetic determinants of PD age at onset are largely unknown. Objectives To identify the genetic determinants of PD age at onset. Methods Using genetic data of 28,568 PD cases, we performed a genome-wide association study based on PD age at onset. Results We estimated that the heritability of PD age at onset attributed to common genetic variation was similar to 0.11, lower than the overall heritability of risk for PD (similar to 0.27), likely, in part, because of the subjective nature of this measure. We found two genome-wide significant association signals, one at SNCA and the other a protein-coding variant in TMEM175, both of which are known PD risk loci and a Bonferroni-corrected significant effect at other known PD risk loci, GBA, INPP5F/BAG3, FAM47E/SCARB2, and MCCC1. Notably, SNCA, TMEM175, SCARB2, BAG3, and GBA have all been shown to be implicated in alpha-synuclein aggregation pathways. Remarkably, other well-established PD risk loci, such as GCH1 and MAPT, did not show a significant effect on age at onset of PD. Conclusions Overall, we have performed the largest age at onset of PD genome-wide association studies to date, and our results show that not all PD risk loci influence age at onset with significant differences between risk alleles for age at onset. This provides a compelling picture, both within the context of functional characterization of disease-linked genetic variability and in defining differences between risk alleles for age at onset, or frank risk for disease. (c) 2019 International Parkinson and Movement Disorder Society

Recent studies have greatly improved our insight into the genetic background of inflammatory bowel disease (IBD). New high-throughput technologies and large-scale international collaborations have contributed to the identification of 200 independent genetic risk loci for IBD. However, in most of these loci, it is unclear which gene conveys the risk for IBD. More importantly, it is unclear which variant within or near the gene is causal to the disease. Using targeted GWAS, imputation, resequencing of risk loci, and in silico fine-mapping of densely typed loci, several causal variants have been identified in IBD risk genes, and various pathological pathways have been uncovered. Current research in the field of IBD focuses on the effect of these causal variants on gene expression and protein function. However, more elements than only the genome must be taken into account to disentangle the multifactorial pathology of IBD. The genetic risk loci identified to date only explain a small part of genetic variance in disease risk. Currently, large multi-omics studies are incorporating factors ranging from the gut microbiome to the environment. In this review, we present the progress that has been made in IBD genetic research and stress the importance of studying causality to increase our understanding of the pathogenesis of IBD. We highlight important causal genetic variants in the candidate genes NOD2, ATG16L1, IRGM, IL23R, CARD9, RNF186, and PRDM1. We describe their downstream effects on protein function and their direct effects on the gut immune system. Furthermore, we discuss the future role of genetics in unravelling disease mechanisms in IBD. Copyright (C) 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Genome-wide association studies are providing insight into the genetic basis of common complex diseases: more than 1150 genetic loci [2165 unique single nucleotide polymorphisms (SNPs)] have recently been associated to 159 complex diseases. The hunt for genes contributing to immune-related diseases has been particularly successful in celiac disease, for example, with 27 genome-wide significantly associated loci identified so far. One of the current challenges is how to move from a genetic association with a disease to finding disease-associated genes and causal variants, as a step towards understanding the underlying disease process. About 50% of disease-associated SNPs affect the expression of nearby genes (so-called expression quantitative traits loci or eQTLs) and these can provide clues for finding causal variants. Although eQTLs can be useful, fine mapping and sequencing are required to refine the association signal. Ultimately, sophisticated study designs will be needed to find the causal variants involved in complex diseases. In this review, we use celiac disease as an example to describe the different aspects that need to be considered on the path from genetic association to disease-causing variants.

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. Geriet. Epidemiol. 33:419-431, 2009. (C) 2009 Wiley-Liss, Inc.