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.
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.
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.
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
predicted damaging Single Nucleotide Variants (SNVs) (
= 2,674 subjects) or Copy Number Variants (CNVs) (
= 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,
,
,
,
, and
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.
To investigate the influence of common and low-frequency genetic variants on the risk of ischemic stroke (all IS) and etiologic stroke subtypes.
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.
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).
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.
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•For functional inference of rare CNVs, FunVar includes pre and post processing of CNVs.•Putative disease-causing variants aggregate in disease related biological processes.•Rare CNVs ...from ASD cases disrupt neural mechanisms e.g. nervous system development.
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.
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.
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.
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.
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.
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) or poor 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.
Six SNPs in the MMP-2 gene were significantly associated with stroke outcome (0.0018<P < 0.0415), two of which survived the Bonferroni correction for multiple testing. In the subset of ischemic stroke patients, association of five of these SNPs remained positive (0.0042<P < 0.0306). No significant associations were found for the MMP-9 gene.
The results presented strongly indicate that MMP-2 genetic variants are an important mediator of functional outcome after stroke.
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