Systematic rare variant analyses identify RAB32 as a susceptibility gene for familial Parkinson's disease.

Despite substantial progress, causal variants are identified only for a minority of familial Parkinson's disease (PD) cases, leaving high-risk pathogenic variants unidentified1,2. To identify such variants, we uniformly processed exome sequencing data of 2,184 index familial PD cases and 69,775 controls. Exome-wide analyses converged on RAB32 as a novel PD gene identifying c.213C > G/p.S71R as a high-risk variant presenting in ~0.7% of familial PD cases while observed in only 0.004% of controls (odds ratio of 65.5). This variant was confirmed in all cases via Sanger sequencing and segregated with PD in three families. RAB32 encodes a small GTPase known to interact with LRRK2 (refs. 3,4). Functional analyses showed that RAB32 S71R increases LRRK2 kinase activity, as indicated by increased autophosphorylation of LRRK2 S1292. Here our results implicate mutant RAB32 in a key pathological mechanism in PD-LRRK2 kinase activity5-7-and thus provide novel insights into the mechanistic connections between RAB family biology, LRRK2 and PD risk.

Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a lifetime risk of one in 350 people and an unmet need for disease-modifying therapies. We conducted a cross-ancestry genome-wide association study (GWAS) including 29,612 patients with ALS and 122,656 controls, which identified 15 risk loci. When combined with 8,953 individuals with whole-genome sequencing (6,538 patients, 2,415 controls) and a large cortex-derived expression quantitative trait locus (eQTL) dataset (MetaBrain), analyses revealed locus-specific genetic architectures in which we prioritized genes either through rare variants, short tandem repeats or regulatory effects. ALS-associated risk loci were shared with multiple traits within the neurodegenerative spectrum but with distinct enrichment patterns across brain regions and cell types. Of the environmental and lifestyle risk factors obtained from the literature, Mendelian randomization analyses indicated a causal role for high cholesterol levels. The combination of all ALS-associated signals reveals a role for perturbations in vesicle-mediated transport and autophagy and provides evidence for cell-autonomous disease initiation in glutamatergic neurons.

Detection of long repeat expansions from PCR-free whole-genome sequence data.

Identifying large expansions of short tandem repeats (STRs), such as those that cause amyotrophic lateral sclerosis (ALS) and fragile X syndrome, is challenging for short-read whole-genome sequencing (WGS) data. A solution to this problem is an important step toward integrating WGS into precision medicine. We developed a software tool called ExpansionHunter that, using PCR-free WGS short-read data, can genotype repeats at the locus of interest, even if the expanded repeat is larger than the read length. We applied our algorithm to WGS data from 3001 ALS patients who have been tested for the presence of the C9orf72 repeat expansion with repeat-primed PCR (RP-PCR). Compared against this truth data, ExpansionHunter correctly classified all (212/212, 95% CI [0.98, 1.00]) of the expanded samples as either expansions (208) or potential expansions (4). Additionally, 99.9% (2786/2789, 95% CI [0.997, 1.00]) of the wild-type samples were correctly classified as wild type by this method with the remaining three samples identified as possible expansions. We further applied our algorithm to a set of 152 samples in which every sample had one of eight different pathogenic repeat expansions, including those associated with fragile X syndrome, Friedreich's ataxia, and Huntington's disease, and correctly flagged all but one of the known repeat expansions. Thus, ExpansionHunter can be used to accurately detect known pathogenic repeat expansions and provides researchers with a tool that can be used to identify new pathogenic repeat expansions.

Non-additive genome-wide association scan reveals a new gene associated with habitual coffee consumption.

Coffee is one of the most consumed beverages world-wide and one of the primary sources of caffeine intake. Given its important health and economic impact, the underlying genetics of its consumption has been widely studied. Despite these efforts, much has still to be uncovered. In particular, the use of non-additive genetic models may uncover new information about the genetic variants driving coffee consumption. We have conducted a genome-wide association study in two Italian populations using additive, recessive and dominant models for analysis. This has uncovered a significant association in the PDSS2 gene under the recessive model that has been replicated in an independent cohort from the Netherlands (ERF). The identified gene has been shown to negatively regulate the expression of the caffeine metabolism genes and can thus be linked to coffee consumption. Further bioinformatics analysis of eQTL and histone marks from Roadmap data has evidenced a possible role of the identified SNPs in regulating PDSS2 gene expression through enhancers present in its intron. Our results highlight a novel gene which regulates coffee consumption by regulating the expression of the genes linked to caffeine metabolism. Further studies will be needed to clarify the biological mechanism which links PDSS2 and coffee consumption.

A Genome-Wide Association Study in isolated populations reveals new genes associated to common food likings.

Food preferences are the first factor driving food choice and thus nutrition. They involve numerous different senses such as taste and olfaction as well as various other factors such as personal experiences and hedonistic aspects. Although it is clear that several of these have a genetic basis, up to now studies have focused mostly on the effects of polymorphisms of taste receptor genes. Therefore, we have carried out one of the first large scale (4611 individuals) GWAS on food likings assessed for 20 specific food likings belonging to 4 different categories (vegetables, fatty, dairy and bitter). A two-step meta-analysis using three different isolated populations from Italy for the discovery step and two populations from The Netherlands and Central Asia for replication, revealed 15 independent genome-wide significant loci (p < 5 × 10(-8)) for 12 different foods. None of the identified genes coded for either taste or olfactory receptors suggesting that genetics impacts in determining food likings in a much broader way than simple differences in taste perception. These results represent a further step in uncovering the genes that underlie liking of common foods that in the end will greatly help understanding the genetics of human nutrition in general.

JAG: A Computational Tool to Evaluate the Role of Gene-Sets in Complex Traits.

Gene-set analysis has been proposed as a powerful tool to deal with the highly polygenic architecture of complex traits, as well as with the small effect sizes typically found in GWAS studies for complex traits. We developed a tool, Joint Association of Genetic variants (JAG), which can be applied to Genome Wide Association (GWA) data and tests for the joint effect of all single nucleotide polymorphisms (SNPs) located in a user-specified set of genes or biological pathway. JAG assigns SNPs to genes and incorporates self-contained and/or competitive tests for gene-set analysis. JAG uses permutation to evaluate gene-set significance, which implicitly controls for linkage disequilibrium, sample size, gene size, the number of SNPs per gene and the number of genes in the gene-set. We conducted a power analysis using the Wellcome Trust Case Control Consortium (WTCCC) Crohn's disease data set and show that JAG correctly identifies validated gene-sets for Crohn's disease and has more power than currently available tools for gene-set analysis. JAG is a powerful, novel tool for gene-set analysis, and can be freely downloaded from the CTG Lab website.

Genome-wide association analysis on five isolated populations identifies variants of the HLA-DOA gene associated with white wine liking.

Wine is the most popular alcoholic beverage around the world and because of its importance in society has been widely studied. Understanding what drives its flavor has been a quest for decades but much is still unknown and will be determined at least in part by individual taste preferences. Recently studies in the genetics of taste have uncovered the role of different genes in the determination of food preferences giving new insight on its physiology. In this context we have performed a genome-wide association study on red and white wine liking using three isolated populations collected in Italy, and replicated our results on two additional populations coming from the Netherland and Central Asia for a total of 3885 samples. We have found a significant association (P=2.1 × 10(-8)) between white wine liking and rs9276975:C>T a polymorphism in the HLA-DOA gene encoding a non-canonical MHC II molecule, which regulates other MHC II molecules. The same association was also found with red wine liking (P=8.3 × 10(-6)). Sex-separated analysis have also revealed that the effect of HLA-DOA is twice as large in women as compared to men suggesting an interaction between this polymorphism and gender. Our results are one of the first examples of genome-wide association between liking of a commonly consumed food and gene variants. Moreover, our results suggest a role of the MHC system in the determination of food preferences opening new insight in this field in general.

Association analysis of bitter receptor genes in five isolated populations identifies a significant correlation between TAS2R43 variants and coffee liking.

Coffee, one of the most popular beverages in the world, contains many different physiologically active compounds with a potential impact on people's health. Despite the recent attention given to the genetic basis of its consumption, very little has been done in understanding genes influencing coffee preference among different individuals. Given its markedly bitter taste, we decided to verify if bitter receptor genes (TAS2Rs) variants affect coffee liking. In this light, 4066 people from different parts of Europe and Central Asia filled in a field questionnaire on coffee liking. They have been consequently recruited and included in the study. Eighty-eight SNPs covering the 25 TAS2R genes were selected from the available imputed ones and used to run association analysis for coffee liking. A significant association was detected with three SNP: one synonymous and two functional variants (W35S and H212R) on the TAS2R43 gene. Both variants have been shown to greatly reduce in vitro protein activity. Surprisingly the wild type allele, which corresponds to the functional form of the protein, is associated to higher liking of coffee. Since the hTAS2R43 receptor is sensible to caffeine, we verified if the detected variants produced differences in caffeine bitter perception on a subsample of people coming from the FVG cohort. We found a significant association between differences in caffeine perception and the H212R variant but not with the W35S, which suggests that the effect of the TAS2R43 gene on coffee liking is mediated by caffeine and in particular by the H212R variant. No other significant association was found with other TAS2R genes. In conclusion, the present study opens new perspectives in the understanding of coffee liking. Further studies are needed to clarify the role of the TAS2R43 gene in coffee hedonics and to identify which other genes and pathways are involved in its genetics.

The PinkThing for analysing ChIP profiling data in their genomic context.

BACKGROUND: Current epigenetic research makes frequent use of whole-genome ChIP profiling for determining the in vivo binding of proteins, e.g. transcription factors and histones, to DNA. Two important and recurrent questions for these large scale analyses are: 1) What is the genomic distribution of a set of binding sites? and 2) Does this genomic distribution differ significantly from another set of sites? FINDINGS: We exemplify the functionality of the PinkThing by analysing a ChIP profiling dataset of cohesin binding sites. We show the subset of cohesin sites with no CTCF binding have a characteristic genomic distribution different from the set of all cohesin sites. CONCLUSIONS: The PinkThing is a web application for fast and easy analysis of the context of genomic loci, such as peaks from ChIP profiling experiments. The output of the PinkThing analysis includes: categorisation of position relative to genes (intronic, exonic, 5' near, 3' near 5' far, 3' far and distant), distance to the closest annotated 3' and 5' end of genes, direction of transcription of the nearest gene, and the option to include other genomic elements like ESTs and CpG islands. The PinkThing enables easy statistical comparison between experiments, i.e. experimental versus background sets, reporting over- and underrepresentation as well as p-values for all comparisons. Access and use of the PinkThing is free and open (without registration) to all users via the website: http://pinkthing.cmbi.ru.nl

MetiTree: a web application to organize and process high-resolution multi-stage mass spectrometry metabolomics data.

UNLABELLED: Identification of metabolites using high-resolution multi-stage mass spectrometry (MS(n)) data is a significant challenge demanding access to all sorts of computational infrastructures. MetiTree is a user-friendly, web application dedicated to organize, process, share, visualize and compare MS(n) data. It integrates several features to export and visualize complex MS(n) data, facilitating the exploration and interpretation of metabolomics experiments. A dedicated spectral tree viewer allows the simultaneous presentation of three related types of MS(n) data, namely, the spectral data, the fragmentation tree and the fragmentation reactions. MetiTree stores the data in an internal database to enable searching for similar fragmentation trees and matching against other MS(n) data. As such MetiTree contains much functionality that will make the difficult task of identifying unknown metabolites much easier. AVAILABILITY: MetiTree is accessible at http://www.MetiTree.nl. The source code is available at https://github.com/NetherlandsMetabolomicsCentre/metitree/wiki.