A Role for Gene-Environment Interactions in Autism Spectrum Disorder Is Supported by Variants in Genes Regulating the Effects of Exposure to Xenobiotics.

Heritability estimates support the contribution of genetics and the environment to the etiology of Autism Spectrum Disorder (ASD), but a role for gene-environment interactions is insufficiently explored. Genes involved in detoxification pathways and physiological permeability barriers (e.g., blood-brain barrier, placenta and respiratory airways), which regulate the effects of exposure to xenobiotics during early stages of neurodevelopment when the immature brain is extremely vulnerable, may be particularly relevant in this context. Our objective was to identify genes involved in the regulation of xenobiotic detoxification or the function of physiological barriers (the XenoReg genes) presenting predicted damaging variants in subjects with ASD, and to understand their interaction patterns with ubiquitous xenobiotics previously implicated in this disorder. We defined a panel of 519 XenoReg genes through literature review and database queries. Large ASD datasets were inspected for in silico predicted damaging Single Nucleotide Variants (SNVs) (N = 2,674 subjects) or Copy Number Variants (CNVs) (N = 3,570 subjects) in XenoReg genes. We queried the Comparative Toxicogenomics Database (CTD) to identify interaction pairs between XenoReg genes and xenobiotics. The interrogation of ASD datasets for variants in the XenoReg gene panel identified 77 genes with high evidence for a role in ASD, according to pre-specified prioritization criteria. These include 47 genes encoding detoxification enzymes and 30 genes encoding proteins involved in physiological barrier function, among which 15 are previous reported candidates for ASD. The CTD query revealed 397 gene-environment interaction pairs between these XenoReg genes and 80% (48/60) of the analyzed xenobiotics. The top interacting genes and xenobiotics were, respectively, CYP1A2, ABCB1, ABCG2, GSTM1, and CYP2D6 and benzo-(a)-pyrene, valproic acid, bisphenol A, particulate matter, methylmercury, and perfluorinated compounds. Individuals carrying predicted damaging variants in high evidence XenoReg genes are likely to have less efficient detoxification systems or impaired physiological barriers. They can therefore be particularly susceptible to early life exposure to ubiquitous xenobiotics, which elicit neuropathological mechanisms in the immature brain, such as epigenetic changes, oxidative stress, neuroinflammation, hypoxic damage, and endocrine disruption. As exposure to environmental factors may be mitigated for individuals with risk variants, this work provides new perspectives to personalized prevention and health management policies for ASD.

How personalised medicine will transform healthcare by 2030: the ICPerMed vision.

This commentary presents the vision of the International Consortium for Personalised Medicine (ICPerMed) on how personalised medicine (PM) will lead to the next generation of healthcare by 2030. This vision focuses on five perspectives: individual and public engagement, involvement of health professionals, implementation within healthcare systems, health-related data, and the development of sustainable economic models that allow improved therapy, diagnostic and preventive approaches as new healthcare concepts for the benefit of the public. We further identify four pillars representing transversal issues that are crucial for the successful implementation of PM in all perspectives. The implementation of PM will result in more efficient and equitable healthcare, access to modern healthcare methods, and improved control by individuals of their own health data, as well as economic development in the health sector.

Identification of biological mechanisms underlying a multidimensional ASD phenotype using machine learning.

The complex genetic architecture of Autism Spectrum Disorder (ASD) and its heterogeneous phenotype makes molecular diagnosis and patient prognosis challenging tasks. To establish more precise genotype-phenotype correlations in ASD, we developed a novel machine-learning integrative approach, which seeks to delineate associations between patients' clinical profiles and disrupted biological processes, inferred from their copy number variants (CNVs) that span brain genes. Clustering analysis of the relevant clinical measures from 2446 ASD cases in the Autism Genome Project identified two distinct phenotypic subgroups. Patients in these clusters differed significantly in ADOS-defined severity, adaptive behavior profiles, intellectual ability, and verbal status, the latter contributing the most for cluster stability and cohesion. Functional enrichment analysis of brain genes disrupted by CNVs in these ASD cases identified 15 statistically significant biological processes, including cell adhesion, neural development, cognition, and polyubiquitination, in line with previous ASD findings. A Naive Bayes classifier, generated to predict the ASD phenotypic clusters from disrupted biological processes, achieved predictions with a high precision (0.82) but low recall (0.39), for a subset of patients with higher biological Information Content scores. This study shows that milder and more severe clinical presentations can have distinct underlying biological mechanisms. It further highlights how machine-learning approaches can reduce clinical heterogeneity by using multidimensional clinical measures, and establishes genotype-phenotype correlations in ASD. However, predictions are strongly dependent on patient's information content. Findings are therefore a first step toward the translation of genetic information into clinically useful applications, and emphasize the need for larger datasets with very complete clinical and biological information.

FunVar: A systematic pipeline to unravel the convergence patterns of genetic variants in ASD, a paradigmatic complex disease.

In recent years, the technological advances for capturing genetic variation in large populations led to the identification of large numbers of putative or disease-causing variants. However, their mechanistic understanding is lagging far behind and has posed new challenges regarding their relevance for disease phenotypes, particularly for common complex disorders. In this study, we propose a systematic pipeline to infer biological meaning from genetic variants, namely rare Copy Number Variants (CNVs). The pipeline consists of three modules that seek to (1) improve genetic data quality by excluding low confidence CNVs, (2) identify disrupted biological processes, and (3) aggregate similar enriched biological processes terms using semantic similarity. The proposed pipeline was applied to CNVs from individuals diagnosed with Autism Spectrum Disorder (ASD). We found that rare CNVs disrupting brain expressed genes dysregulated a wide range of biological processes, such as nervous system development and protein polyubiquitination. The disrupted biological processes identified in ASD patients were in accordance with previous findings. This coherence with literature indicates the feasibility of the proposed pipeline in interpreting the biological role of genetic variants in complex disease development. The suggested pipeline is easily adjustable at each step and its independence from any specific dataset and software makes it an effective tool in analyzing existing genetic resources. The FunVar pipeline is available at https://github.com/lasigeBioTM/FunVar and includes pre and post processing steps to effectively interpret biological mechanisms of putative disease causing genetic variants.

Identifying disease genes using machine learning and gene functional similarities, assessed through Gene Ontology.

Identifying disease genes from a vast amount of genetic data is one of the most challenging tasks in the post-genomic era. Also, complex diseases present highly heterogeneous genotype, which difficult biological marker identification. Machine learning methods are widely used to identify these markers, but their performance is highly dependent upon the size and quality of available data. In this study, we demonstrated that machine learning classifiers trained on gene functional similarities, using Gene Ontology (GO), can improve the identification of genes involved in complex diseases. For this purpose, we developed a supervised machine learning methodology to predict complex disease genes. The proposed pipeline was assessed using Autism Spectrum Disorder (ASD) candidate genes. A quantitative measure of gene functional similarities was obtained by employing different semantic similarity measures. To infer the hidden functional similarities between ASD genes, various types of machine learning classifiers were built on quantitative semantic similarity matrices of ASD and non-ASD genes. The classifiers trained and tested on ASD and non-ASD gene functional similarities outperformed previously reported ASD classifiers. For example, a Random Forest (RF) classifier achieved an AUC of 0. 80 for predicting new ASD genes, which was higher than the reported classifier (0.73). Additionally, this classifier was able to predict 73 novel ASD candidate genes that were enriched for core ASD phenotypes, such as autism and obsessive-compulsive behavior. In addition, predicted genes were also enriched for ASD co-occurring conditions, including Attention Deficit Hyperactivity Disorder (ADHD). We also developed a KNIME workflow with the proposed methodology which allows users to configure and execute it without requiring machine learning and programming skills. Machine learning is an effective and reliable technique to decipher ASD mechanism by identifying novel disease genes, but this study further demonstrated that their performance can be improved by incorporating a quantitative measure of gene functional similarities. Source code and the workflow of the proposed methodology are available at https://github.com/Muh-Asif/ASD-genes-prediction.

Attitudes of the autism community to early autism research.

Investigation into the earliest signs of autism in infants has become a significant sub-field of autism research. This work invokes specific ethical concerns such as use of 'at-risk' language, communicating study findings to parents and the future perspective of enrolled infants when they reach adulthood. This study aimed to ground this research field in an understanding of the perspectives of members of the autism community. Following focus groups to identify topics, an online survey was distributed to autistic adults, parents of children with autism and practitioners in health and education settings across 11 European countries. Survey respondents (n = 2317) were positively disposed towards early autism research, and there was significant overlap in their priorities for the field and preferred language to describe infant research participants. However, there were also differences including overall less favourable endorsement of early autism research by autistic adults relative to other groups and a dislike of the phrase 'at-risk' to describe infant participants, in all groups except healthcare practitioners. The findings overall indicate that the autism community in Europe is supportive of early autism research. Researchers should endeavour to maintain this by continuing to take community perspectives into account.

Definition of a putative pathological region in PARK2 associated with autism spectrum disorder through in silico analysis of its functional structure.

OBJECTIVE: The PARK2 gene encodes Parkin, a component of a multiprotein E3 ubiquitin ligase complex that targets substrate proteins for proteasomal degradation. PARK2 mutations are frequently associated with Parkinson's disease, but structural alterations have also been described in patients with neurodevelopmental disorders (NDD), suggesting a pathological effect ubiquitous to neurodevelopmental and neurodegenerative brain processes. The present study aimed to define the critical regions for NDD within PARK2. MATERIALS AND METHODS: To clarify PARK2 involvement in NDDs, we examined the frequency and location of copy number variants (CNVs) identified in patients from our sample and reported in the literature and relevant databases, and compared with control populations. RESULTS: Overall, the frequency of PARK2 CNVs was higher in controls than in NDD cases. However, closer inspection of the CNV location in PARK2 showed that the frequency of CNVs targeting the Parkin C-terminal, corresponding to the ring-between-ring (RBR) domain responsible for Parkin activity, is significantly higher in NDD cases than in controls. In contrast, CNVs targeting the N-terminal of Parkin, including domains that regulate ubiquitination activity, are very common both in cases and in controls. CONCLUSION: Although PARK2 may be a pathological factor for NDDs, likely not all variants are pathogenic, and a conclusive assessment of PARK2 variant pathogenicity requires an accurate analysis of their location within the coding region and encoded functional domains.

Low-frequency and common genetic variation in ischemic stroke: The METASTROKE collaboration.

OBJECTIVE: To investigate the influence of common and low-frequency genetic variants on the risk of ischemic stroke (all IS) and etiologic stroke subtypes. METHODS: We meta-analyzed 12 individual genome-wide association studies comprising 10,307 cases and 19,326 controls imputed to the 1000 Genomes (1 KG) phase I reference panel. We selected variants showing the highest degree of association (p < 1E-5) in the discovery phase for replication in Caucasian (13,435 cases and 29,269 controls) and South Asian (2,385 cases and 5,193 controls) samples followed by a transethnic meta-analysis. We further investigated the p value distribution for different bins of allele frequencies for all IS and stroke subtypes. RESULTS: We showed genome-wide significance for 4 loci: ABO for all IS, HDAC9 for large vessel disease (LVD), and both PITX2 and ZFHX3 for cardioembolic stroke (CE). We further refined the association peaks for ABO and PITX2. Analyzing different allele frequency bins, we showed significant enrichment in low-frequency variants (allele frequency <5%) for both LVD and small vessel disease, and an enrichment of higher frequency variants (allele frequency 10% and 30%) for CE (all p < 1E-5). CONCLUSIONS: Our findings suggest that the missing heritability in IS subtypes can in part be attributed to low-frequency and rare variants. Larger sample sizes are needed to identify the variants associated with all IS and stroke subtypes.

The Autism Simplex Collection: an international, expertly phenotyped autism sample for genetic and phenotypic analyses.

BACKGROUND: There is an urgent need for expanding and enhancing autism spectrum disorder (ASD) samples, in order to better understand causes of ASD. METHODS: In a unique public-private partnership, 13 sites with extensive experience in both the assessment and diagnosis of ASD embarked on an ambitious, 2-year program to collect samples for genetic and phenotypic research and begin analyses on these samples. The program was called The Autism Simplex Collection (TASC). TASC sample collection began in 2008 and was completed in 2010, and included nine sites from North America and four sites from Western Europe, as well as a centralized Data Coordinating Center. RESULTS: Over 1,700 trios are part of this collection, with DNA from transformed cells now available through the National Institute of Mental Health (NIMH). Autism Diagnostic Interview-Revised (ADI-R) and Autism Diagnostic Observation Schedule-Generic (ADOS-G) measures are available for all probands, as are standardized IQ measures, Vineland Adaptive Behavioral Scales (VABS), the Social Responsiveness Scale (SRS), Peabody Picture Vocabulary Test (PPVT), and physical measures (height, weight, and head circumference). At almost every site, additional phenotypic measures were collected, including the Broad Autism Phenotype Questionnaire (BAPQ) and Repetitive Behavior Scale-Revised (RBS-R), as well as the non-word repetition scale, Communication Checklist (Children's or Adult), and Aberrant Behavior Checklist (ABC). Moreover, for nearly 1,000 trios, the Autism Genome Project Consortium (AGP) has carried out Illumina 1 M SNP genotyping and called copy number variation (CNV) in the samples, with data being made available through the National Institutes of Health (NIH). Whole exome sequencing (WES) has been carried out in over 500 probands, together with ancestry matched controls, and this data is also available through the NIH. Additional WES is being carried out by the Autism Sequencing Consortium (ASC), where the focus is on sequencing complete trios. ASC sequencing for the first 1,000 samples (all from whole-blood DNA) is complete and data will be released in 2014. Data is being made available through NIH databases (database of Genotypes and Phenotypes (dbGaP) and National Database for Autism Research (NDAR)) with DNA released in Dist 11.0. Primary funding for the collection, genotyping, sequencing and distribution of TASC samples was provided by Autism Speaks and the NIH, including the National Institute of Mental Health (NIMH) and the National Human Genetics Research Institute (NHGRI). CONCLUSIONS: TASC represents an important sample set that leverages expert sites. Similar approaches, leveraging expert sites and ongoing studies, represent an important path towards further enhancing available ASD samples.

Protein interaction networks reveal novel autism risk genes within GWAS statistical noise.

Genome-wide association studies (GWAS) for Autism Spectrum Disorder (ASD) thus far met limited success in the identification of common risk variants, consistent with the notion that variants with small individual effects cannot be detected individually in single SNP analysis. To further capture disease risk gene information from ASD association studies, we applied a network-based strategy to the Autism Genome Project (AGP) and the Autism Genetics Resource Exchange GWAS datasets, combining family-based association data with Human Protein-Protein interaction (PPI) data. Our analysis showed that autism-associated proteins at higher than conventional levels of significance (P<0.1) directly interact more than random expectation and are involved in a limited number of interconnected biological processes, indicating that they are functionally related. The functionally coherent networks generated by this approach contain ASD-relevant disease biology, as demonstrated by an improved positive predictive value and sensitivity in retrieving known ASD candidate genes relative to the top associated genes from either GWAS, as well as a higher gene overlap between the two ASD datasets. Analysis of the intersection between the networks obtained from the two ASD GWAS and six unrelated disease datasets identified fourteen genes exclusively present in the ASD networks. These are mostly novel genes involved in abnormal nervous system phenotypes in animal models, and in fundamental biological processes previously implicated in ASD, such as axon guidance, cell adhesion or cytoskeleton organization. Overall, our results highlighted novel susceptibility genes previously hidden within GWAS statistical "noise" that warrant further analysis for causal variants.

Hope for GWAS: relevant risk genes uncovered from GWAS statistical noise.

Hundreds of genetic variants have been associated to common diseases through genome-wide association studies (GWAS), yet there are limits to current approaches in detecting true small effect risk variants against a background of false positive findings. Here we addressed the missing heritability problem, aiming to test whether there are indeed risk variants within GWAS statistical noise and to develop a systematic strategy to retrieve these hidden variants. Employing an integrative approach, which combines protein-protein interactions with association data from GWAS for 6 common diseases, we found that associated-genes at less stringent significance levels (p < 0.1) with any of these diseases are functionally connected beyond noise expectation. This functional coherence was used to identify disease-relevant subnetworks, which were shown to be enriched in known genes, outperforming the selection of top GWAS genes. As a proof of principle, we applied this approach to breast cancer, supporting well-known breast cancer genes, while pinpointing novel susceptibility genes for experimental validation. This study reinforces the idea that GWAS are under-analyzed and that missing heritability is rather hidden. It extends the use of protein networks to reveal this missing heritability, thus leveraging the large investment in GWAS that produced so far little tangible gain.

Recurrent duplications of the annexin A1 gene (ANXA1) in autism spectrum disorders.

BACKGROUND: Validating the potential pathogenicity of copy number variants (CNVs) identified in genome-wide studies of autism spectrum disorders (ASD) requires detailed assessment of case/control frequencies, inheritance patterns, clinical correlations, and functional impact. Here, we characterize a small recurrent duplication in the annexin A1 (ANXA1) gene, identified by the Autism Genome Project (AGP) study. METHODS: From the AGP CNV genomic screen in 2,147 ASD individuals, we selected for characterization an ANXA1 gene duplication that was absent in 4,964 population-based controls. We further screened the duplication in a follow-up sample including 1,496 patients and 410 controls, and evaluated clinical correlations and family segregation. Sequencing of exonic/downstream ANXA1 regions was performed in 490 ASD patients for identification of additional variants. RESULTS: The ANXA1 duplication, overlapping the last four exons and 3'UTR region, had an overall prevalence of 11/3,643 (0.30%) in unrelated ASD patients but was not identified in 5,374 controls. Duplication carriers presented no distinctive clinical phenotype. Family analysis showed neuropsychiatric deficits and ASD traits in multiple relatives carrying the duplication, suggestive of a complex genetic inheritance. Sequencing of exonic regions and the 3'UTR identified 11 novel changes, but no obvious variants with clinical significance. CONCLUSIONS: We provide multilevel evidence for a role of ANXA1 in ASD etiology. Given its important role as mediator of glucocorticoid function in a wide variety of brain processes, including neuroprotection, apoptosis, and control of the neuroendocrine system, the results add ANXA1 to the growing list of rare candidate genetic etiological factors for ASD.

Dysfunction of the Heteromeric KV7.3/KV7.5 Potassium Channel is Associated with Autism Spectrum Disorders.

Heterozygous mutations in the KCNQ3 gene on chromosome 8q24 encoding the voltage-gated potassium channel KV7.3 subunit have previously been associated with rolandic epilepsy and idiopathic generalized epilepsy (IGE) including benign neonatal convulsions. We identified a de novo t(3;8) (q21;q24) translocation truncating KCNQ3 in a boy with childhood autism. In addition, we identified a c.1720C > T [p.P574S] nucleotide change in three unrelated individuals with childhood autism and no history of convulsions. This nucleotide change was previously reported in patients with rolandic epilepsy or IGE and has now been annotated as a very rare SNP (rs74582884) in dbSNP. The p.P574S KV7.3 variant significantly reduced potassium current amplitude in Xenopus laevis oocytes when co-expressed with KV7.5 but not with KV7.2 or KV7.4. The nucleotide change did not affect trafficking of heteromeric mutant KV7.3/2, KV7.3/4, or KV7.3/5 channels in HEK 293 cells or primary rat hippocampal neurons. Our results suggest that dysfunction of the heteromeric KV7.3/5 channel is implicated in the pathogenesis of some forms of autism spectrum disorders, epilepsy, and possibly other psychiatric disorders and therefore, KCNQ3 and KCNQ5 are suggested as candidate genes for these disorders.

Genetic risk factors for ischaemic stroke and its subtypes (the METASTROKE collaboration): a meta-analysis of genome-wide association studies.

BACKGROUND: Various genome-wide association studies (GWAS) have been done in ischaemic stroke, identifying a few loci associated with the disease, but sample sizes have been 3500 cases or less. We established the METASTROKE collaboration with the aim of validating associations from previous GWAS and identifying novel genetic associations through meta-analysis of GWAS datasets for ischaemic stroke and its subtypes. METHODS: We meta-analysed data from 15 ischaemic stroke cohorts with a total of 12 389 individuals with ischaemic stroke and 62 004 controls, all of European ancestry. For the associations reaching genome-wide significance in METASTROKE, we did a further analysis, conditioning on the lead single nucleotide polymorphism in every associated region. Replication of novel suggestive signals was done in 13 347 cases and 29 083 controls. FINDINGS: We verified previous associations for cardioembolic stroke near PITX2 (p=2·8×10(-16)) and ZFHX3 (p=2·28×10(-8)), and for large-vessel stroke at a 9p21 locus (p=3·32×10(-5)) and HDAC9 (p=2·03×10(-12)). Additionally, we verified that all associations were subtype specific. Conditional analysis in the three regions for which the associations reached genome-wide significance (PITX2, ZFHX3, and HDAC9) indicated that all the signal in each region could be attributed to one risk haplotype. We also identified 12 potentially novel loci at p<5×10(-6). However, we were unable to replicate any of these novel associations in the replication cohort. INTERPRETATION: Our results show that, although genetic variants can be detected in patients with ischaemic stroke when compared with controls, all associations we were able to confirm are specific to a stroke subtype. This finding has two implications. First, to maximise success of genetic studies in ischaemic stroke, detailed stroke subtyping is required. Second, different genetic pathophysiological mechanisms seem to be associated with different stroke subtypes. FUNDING: Wellcome Trust, UK Medical Research Council (MRC), Australian National and Medical Health Research Council, National Institutes of Health (NIH) including National Heart, Lung and Blood Institute (NHLBI), the National Institute on Aging (NIA), the National Human Genome Research Institute (NHGRI), and the National Institute of Neurological Disorders and Stroke (NINDS).

Compensatory T-cell regulation in unaffected relatives of SLE patients, and opposite IL-2/CD25-mediated effects suggested by coreferentiality modeling.

In human systemic lupus erythematosus (SLE), diverse autoantibodies accumulate over years before disease manifestation. Unaffected relatives of SLE patients frequently share a sustained production of autoantibodies with indiscriminable specificity, usually without ever acquiring the disease. We studied relations of IgG autoantibody profiles and peripheral blood activated regulatory T-cells (aTregs), represented by CD4(+)CD25(bright) T-cells that were regularly 70-90% Foxp3(+). We found consistent positive correlations of broad-range as well as specific SLE-associated IgG with aTreg frequencies within unaffected relatives, but not patients or unrelated controls. Our interpretation: unaffected relatives with shared genetic factors compensated pathogenic effects by aTregs engaged in parallel with the individual autoantibody production. To study this further, we applied a novel analytic approach named coreferentiality that tests the indirect relatedness of parameters in respect to multivariate phenotype data. Results show that independently of their direct correlation, aTreg frequencies and specific SLE-associated IgG were likely functionally related in unaffected relatives: they significantly parallelled each other in their relations to broad-range immunoblot autoantibody profiles. In unaffected relatives, we also found coreferential effects of genetic variation in the loci encoding IL-2 and CD25. A model of CD25 functional genetic effects constructed by coreferentiality maximization suggests that IL-2-CD25 interaction, likely stimulating aTregs in unaffected relatives, had an opposed effect in SLE patients, presumably triggering primarily T-effector cells in this group. Coreferentiality modeling as we do it here could also be useful in other contexts, particularly to explore combined functional genetic effects.

TTC7B emerges as a novel risk factor for ischemic stroke through the convergence of several genome-wide approaches.

We hereby propose a novel approach to the identification of ischemic stroke (IS) susceptibility genes that involves converging data from several unbiased genetic and genomic tools. We tested the association between IS and genes differentially expressed between cases and controls, then determined which data mapped to previously reported linkage peaks and were nominally associated with stroke in published genome-wide association studies. We first performed gene expression profiling in peripheral blood mononuclear cells of 20 IS cases and 20 controls. Sixteen differentially expressed genes mapped to reported whole-genome linkage peaks, including the TTC7B gene, which has been associated with major cardiovascular disease. At the TTC7B locus, 46 tagging polymorphisms were tested for association in 565 Portuguese IS cases and 520 controls. Markers nominally associated in at least one test and defining associated haplotypes were then examined in 570 IS Spanish cases and 390 controls. Several polymorphisms and haplotypes in the intron 5-intron 6 region of TTC7B were also associated with IS risk in the Spanish and combined data sets. Multiple independent lines of evidence therefore support the role of TTC7B in stroke susceptibility, but further work is warranted to identify the exact risk variant and its pathogenic potential.

Variants within the nitric oxide synthase 1 gene are associated with stroke susceptibility.

OBJECTIVE: Animal studies have allowed important insights into the role of the nitric oxide synthase (NOS) enzymes in atherosclerosis and hypertension, as well as in stroke. In this study we tested the hypothesis that the NOS1 and NOS3 genes, respectively encoding neuronal NOS (nNOS) and endothelial NOS (eNOS), influence stroke susceptibility and outcome after a stroke event. METHODS: We conducted a case-control association study in 551 ischemic stroke patients and 530 controls to assess the role of NOS1 and NOS3 variants in stroke susceptibility. The same genes were tested for association with stroke outcome in a subset of 431 patients. RESULTS: Four NOS1 single nucleotide polymorphisms (SNPs) (rs2293050, rs2139733, rs7308402 and rs1483757) and four haplotypes were significantly associated with stroke susceptibility after adjusting for demographic, clinical and life-style risk factors, and correcting for multiple testing using the false discovery rate (FDR) method (SNPs: 0.004<(uncorrected)P<0.007 and 0.036

Replication of the CELSR1 association with ischemic stroke in a Portuguese case-control cohort.

OBJECTIVES: Replication of GWAS association findings remains the gold standard for results validation. Our aim was to test the association of four polymorphisms (rs1671021 in LLGL2, rs753307 in RUVBL2, rs6007897 and rs4044210 in CELSR1) previously identified as ischemic stroke (IS) risk factors in a phased GWAS performed on 6341 Japanese individuals [1]. METHODS: These polymorphisms were genotyped in a Portuguese sample of 566 IS cases and 525 controls, and their allele, genotype and haplotype associations were assessed. RESULTS: rs6007897 and rs4044210 in CELSR1 were associated with stroke risk individually (OR[95%CI]=1.43[1.13-1.81], p=0.003 and 1.38[1.09-1.74], p=0.007, respectively), and in combination as a haplotype. These associations remain after correction for multiple testing and in a meta-analysis with the original findings. The other polymorphisms were not associated. CONCLUSIONS: Our study independently confirmed for the first time the association between IS and CELSR1. This finding and the mechanisms by which these genetic variants exert their effects on stroke pathogenesis warrant further replication and investigation.

Variants in the inflammatory IL6 and MPO genes modulate stroke susceptibility through main effects and gene-gene interactions.

There is substantial evidence that inflammation within the central nervous system contributes to stroke risk and recovery. Inflammatory conditions increase stroke risk, and the inflammatory response is of major importance in recovery and healing processes after stroke. We investigated the role of inflammatory genes IL1B, IL6, MPO, and TNF in stroke susceptibility and recovery in a population sample of 672 patients and 530 controls, adjusting for demographic, clinical and lifestyle risk factors, and stroke severity parameters. We also considered the likely complexity of inflammatory mechanisms in stroke, by assessing the combined effects of multiple genes. Two interleukin 6 (IL6) and one myeloperoxidase (MPO) single-nucleotide polymorphisms were significantly associated with stroke risk (0.022<(corrected)P<0.042), highlighting gene variants of low to moderate effect in stroke risk. An epistatic interaction between the IL6 and MPO genes was also identified in association with stroke susceptibility (P=0.031 after 1,000 permutations). In a subset of 546 patients, one IL6 haplotype was associated with stroke outcome at 3 months ((corrected)P=0.024), an intriguing finding warranting further validation. Our findings support the association of the IL6 gene and present novel evidence for the involvement of MPO in stroke susceptibility, suggesting a modulation of stroke risk by main gene effects, clinical and lifestyle factors, and gene-gene interactions.

Association of a genetic variant in the ALOX5AP with higher risk of ischemic stroke: a case-control, meta-analysis and functional study.

BACKGROUND: Variants in the 5-lipoxygenase-activating protein (ALOX5AP) and phosphodiesterase 4D (PDE4D) genes have first been associated with ischemic stroke (IS) through whole-genome linkage screens. However, association studies obtained conflicting results. We aimed to investigate the contribution of selected single nucleotide polymorphisms (SNPs) in these genes for the first time in a large Iberian population. METHODS: A case-control design was used to analyze one SNP in ALOX5AP and five SNPs in PDE4D in a total of 1,092 IS patients and 781 healthy controls of two different subsets from Spain and Portugal. The analysis was adjusted for confounding variables and the results were integrated in a meta-analysis of all case-control studies. In addition, ALOX5AP gene expression levels were determined in controls and IS cases. RESULTS: A first meta-analysis of both subsets showed that the T allele of the SG13S114 SNP in ALOX5AP was a risk factor for IS after Bonferroni correction [OR = 1.22 (1.06-1.40); p = 0.006]. A second meta-analysis of white populations confirmed these results [OR = 1.18 (1.07-1.31); p = 0.001]. ALOX5AP gene expression analysis in a subset of controls and cases revealed that the SG13S114 genotypes modulate mRNA levels of ALOX5AP (p = 0.001) and mRNA levels were higher in IS cases (2.8 +/- 2.4%) than in controls (1.4 +/- 1.3%; p = 0.003). No association of the variants in PDE4D with IS was observed in our study. CONCLUSIONS: The ALOX5AP SG13S114 variant is an independent risk factor for IS in the Iberian population and is associated with ALOX5AP expression levels. The role of this gene in stroke merits further investigation.