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.

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.

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.

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.

Kalirin: a novel genetic risk factor for ischemic stroke.

Cerebrovascular and cardiovascular diseases are the leading causes of death and disability worldwide. They are complex disorders resulting from the interplay of genetic and environmental factors, and may share several susceptibility genes. Several recent studies have implicated variants of the Kalirin (KALRN) gene with susceptibility to cardiovascular and metabolic phenotypes, but no studies have yet been performed in stroke patients. KALRN is involved, among others, in the inhibition of inducible nitric oxide synthase, in the regulation of ischemic signal transduction, and in neuronal morphogenesis, plasticity, and stability. The goal of the present study was to determine whether SNPs in the KALRN region on 3q13, which includes the Ropporin gene (ROPN1), predispose to ischemic stroke (IS) in a cohort of Portuguese patients and controls. We genotyped 34 tagging SNPs in the KALRN and ROPN1 chromosomal region on 565 IS patients and 517 unrelated controls, and performed genotype imputation for 405 markers on chromosome 3. We tested the single-marker association of these SNPs with IS. One SNP (rs4499545) in the ROPN1-KALRN intergenic region and two SNPs in KALRN (rs17286604 and rs11712619) showed significant (P < 0.05) allelic and genotypic (unadjusted and adjusted for hypertension, diabetes, and ever smoking) association with IS risk. Thirty-two imputed SNPs also showed an association at P < 0.05, and actual genotyping of three of these polymorphisms (rs7620580, rs6438833, and rs11712039) validated their association. Furthermore, rs11712039 was associated with IS (0.001 < P < 0.01) in a recent well-powered genomewide association study (Ikram et al. 2009). These studies suggest that variants in the KALRN gene region constitute risk factors for stroke and that KALRN may represent a common risk factor for vascular diseases.

Variants of the Matrix Metalloproteinase-2 but not the Matrix Metalloproteinase-9 genes significantly influence functional outcome after stroke.

BACKGROUND: Multiple lines of evidence suggest that genetic factors contribute to stroke recovery. The matrix metalloproteinases -2 (MMP-2) and -9 (MMP-9) are modulators of extracellular matrix components, with important regulatory functions in the Central Nervous System (CNS). Shortly after stroke, MMP-2 and MMP-9 have mainly damaging effects for brain tissue. However, MMPs also have a beneficial activity in angiogenesis and neurovascular remodelling during the delayed neuroinflammatory response phase, thus possibly contributing to stroke functional recovery. METHODS: In the present study, the role of MMP-2 and MMP-9 genetic variants in stroke recovery was investigated in 546 stroke patients. Functional outcome was assessed three months after a stroke episode using the modified Rankin Scale (mRS), and patients were classified in two groups: good recovery (mRS 1). Haplotype tagging single nucleotide polymorphisms (SNPs) in the MMP-2 (N = 21) and MMP-9 (N = 4) genes were genotyped and tested for association with stroke outcome, adjusting for significant non-genetic clinical variables. RESULTS: Six SNPs in the MMP-2 gene were significantly associated with stroke outcome (0.0018

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.

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.

Low frequency of CD4+CD25+ Treg in SLE patients: a heritable trait associated with CTLA4 and TGFbeta gene variants.

BACKGROUND: CD4+CD25+ regulatory T cells play an essential role in maintaining immune homeostasis and preventing autoimmunity. Therefore, defects in Treg development, maintenance or function have been associated with several human autoimmune diseases including Systemic Lupus Erythematosus (SLE), a systemic autoimmune disease characterized by loss of tolerance to nuclear components and significantly more frequent in females. RESULTS: To investigate the involvement of Treg in SLE pathogenesis, we determined the frequency of CD4+CD25+CD45RO+ T cells, which encompass the majority of Treg activity, in the PBMC of 148 SLE patients (76 patients were part of 54 families), 166 relatives and 117 controls. SLE patients and their relatives were recruited in several Portuguese hospitals and through the Portuguese Lupus Association. Control individuals were blood donors recruited from several regional blood donor centers. Treg frequency was significantly lower in SLE patients than healthy controls (z = -6.161, P < 0.00001) and intermediate in the relatives' group. Remarkably, this T cell subset was also lower in females, most strikingly in the control population (z = 4.121, P < 0.001). We further ascertained that the decreased frequency of Treg in SLE patients resulted from the specific reduction of bona fide FOXP3+CD4+CD25+ Treg. Treg frequency was negatively correlated with SLE activity index (SLEDAI) and titers of serum anti-dsDNA antibodies. Both Treg frequency and disease activity were modulated by IVIg treatment in a documented SLE case. The segregation of Treg frequency within the SLE families was indicative of a genetic trait. Candidate gene analysis revealed that specific variants of CTLA4 and TGFbeta were associated with the decreased frequency of Treg in PBMC, while FOXP3 gene variants were associated with affection status, but not with Treg frequency. CONCLUSION: SLE patients have impaired Treg production or maintenance, a trait strongly associated with SLE disease activity and autoantibody titers, and possibly resulting from the inability to convert FOXP3+CD25- into FOXP3+CD25+ T cells. Treg frequency is highly heritable within SLE families, with specific variants of the CTLA4 and TGFbeta genes contributing to this trait, while FOXP3 contributes to SLE through mechanisms not involving a modulation of Treg frequency. These findings establish that the genetic components in SLE pathogenesis include genes related to Treg generation or maintenance.

Linkage disequilibrium and diversity for three genomic regions in Azoreans and mainland Portuguese.

Studies on linkage disequilibrium (LD) across the genome and populations have been used in recent years with the main objective of improving gene mapping of complex traits. Here, we characterize the patterns of genetic diversity of HLA loci and evaluate LD (D') extent in three genomic regions: Xq13.3, NRY and HLA. In addition, we examine the distribution of DXS1225-DXS8082 haplotype diversity in Azoreans and mainland Portuguese. Allele distribution has demonstrated that the São Miguel population is genetically very diverse; haplotype analysis revealed 100% discriminatory power for X- and Y-markers and 94.3% for HLA markers. Standardized multiallelic D' in these three genomic regions shows values lower than 0.33, thereby suggesting there is no extensive LD in the São Miguel population. Data regarding the distribution of DXS1225-DXS8082 haplotypes indicate that there are no significant differences among all the populations studied, (Azorean geographical groups, the Azores archipelago and mainland Portugal). Moreover, in these as well as in other European populations, the most frequent DXS1225-DXS8082 haplotype is 210-219. Even though São Miguel islanders and Azoreans do not constitute isolated populations and show LD for only very short physical distances, certain characteristics, such as the absence of genetic structure, the same environment and the possibility of constructing extensive pedigrees through church and civil records, offer an opportunity for dissecting the genetic background of complex diseases in these populations.

Association of the alpha4 integrin subunit gene (ITGA4) with autism.

In the present work, we provide further evidence for the involvement of the integrin alpha-4 precursor gene (ITGA4) in the etiology of autism, by replicating previous findings of a genetic association with autism in various independent populations. The ITGA4 gene maps to the autism linkage region on 2q31-33 and is therefore a plausible positional candidate. We tested eight single nucleotide polymorphisms (SNPs) in the ITGA4 gene region for association with autism in a sample of 164 nuclear families. Evidence for association was found for the rs155100 marker (P = 0.019) and for a number of specific marker haplotypes containing this SNP (0.00053 < P < 0.022). alpha4 integrins are known to play a key role in neuroinflammatory processes, which are hypothesized to contribute to autism. In this study, an association was found between the ITGA4 rs1449263 marker and levels of a serum autoantibody directed to brain tissue, which was previously shown to be significantly more frequent in autistic patients than in age-matched controls in our population. This result suggests that the ITGA4 gene could be involved in a neuroimmune process thought to occur in autistic patients and, together with previous findings, offers a new perspective on the role of integrins in the etiology of autism to which little attention has been paid so far.

Mitochondrial haplogroup H1 is protective for ischemic stroke in Portuguese patients.

BACKGROUND: The genetic contribution to stroke is well established but it has proven difficult to identify the genes and the disease-associated alleles mediating this effect, possibly because only nuclear genes have been intensely investigated so far. Mitochondrial DNA (mtDNA) has been implicated in several disorders having stroke as one of its clinical manifestations. The aim of this case-control study was to assess the contribution of mtDNA polymorphisms and haplogroups to ischemic stroke risk. METHODS: We genotyped 19 mtDNA single nucleotide polymorphisms (SNPs) defining the major European haplogroups in 534 ischemic stroke patients and 499 controls collected in Portugal, and tested their allelic and haplogroup association with ischemic stroke risk. RESULTS: Haplogroup H1 was found to be significantly less frequent in stroke patients than in controls (OR = 0.61, 95% CI = 0.45-0.83, p = 0.001), when comparing each clade against all other haplogroups pooled together. Conversely, the pre-HV/HV and U mtDNA lineages emerge as potential genetic factors conferring risk for stroke (OR = 3.14, 95% CI = 1.41-7.01, p = 0.003, and OR = 2.87, 95% CI = 1.13-7.28, p = 0.021, respectively). SNPs m.3010G>A, m.7028C>T and m.11719G>A strongly influence ischemic stroke risk, their allelic state in haplogroup H1 corroborating its protective effect. CONCLUSION: Our data suggests that mitochondrial haplogroup H1 has an impact on ischemic stroke risk in a Portuguese sample.

Evaluation of linkage disequilibrium on the Xq13.3 region: comparison between the Azores islands and mainland Portugal.

The design of genetic studies of complex diseases is dependent on the extent and distribution of linkage disequilibrium (LD) across the genome in different populations. Here, we characterize the extent of LD in the Azores (Western, Central, and Eastern island groups) and mainland Portugal populations. LD was evaluated in the Xq13.3 region by genotyping eight STR markers spanning 20.9 Mb. Standardized multiallelic disequilibrium coefficient (D') analysis indicates that the Western group presents higher values when compared with the Central and Eastern groups. However, all island groups show values of D' lower than 0.5 and 0.33, suggesting no extensive LD in these populations. Taken together, the data show that the Azorean population presents a lower D' (0.142) than mainland Portugal (0.226). Although, both populations do not show extensive LD, the easy reconstruction of large pedigrees in the Azorean population is a valuable resource for the fine mapping of disease genes.

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.

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.

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.

Brief report: High frequency of biochemical markers for mitochondrial dysfunction in autism: no association with the mitochondrial aspartate/glutamate carrier SLC25A12 gene.

In the present study we confirm the previously reported high frequency of biochemical markers of mitochondrial dysfunction, namely hyperlactacidemia and increased lactate/pyruvate ratio, in a significant fraction of 210 autistic patients. We further examine the involvement of the mitochondrial aspartate/glutamate carrier gene (SLC25A12) in mitochondrial dysfunction associated with autism. We found no evidence of association of the SLC25A12 gene with lactate and lactate/pyruvate distributions or with autism in 241 nuclear families with one affected individual. We conclude that while mitochondrial dysfunction may be one of the most common medical conditions associated with autism, variation at the SLC25A12 gene does not explain the high frequency of mitochondrial dysfunction markers and is not associated with autism in this sample of autistic patients.

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.

Autoantibody repertoires to brain tissue in autism nuclear families.

The hypothesis of an immune dysfunction in autism spectrum disorders has previously been put forward without, however, compelling evidence of a direct relation to its etiology or pathogenesis. To further understand if autoimmunity could play a significant role in autism, we analyzed autoantibody repertoires to brain tissue extract in the plasma of 171 autism children, their parents, and 54 controls, by quantitative immunoblotting. Multiparametric analysis revealed significant differences between patients and controls, and showed that one single reactivity in Section 32 of the blot had the most power to discriminate between these samples. Family correlation coefficients and heritability estimates did not provide any evidence that this reactivity was genetically determined. While the molecular weight of the target protein suggested that it might be an isoform of Myelin Basic Protein (MBP), inhibition assays with human MBP argued against this hypothesis. The study evidences the widespread occurrence of autoreactivities to brain tissue in autism patients, which may represent the immune system's neuroprotective response to a previous brain injury occurred during neurodevelopment. The molecular identification of the target protein in Section 32 will contribute to the understanding of the role of immune responses against brain antigens in autistic patients.

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.