Computational analysis of 10,860 phenotypic annotations in individuals with SCN2A-related disorders.

in Genetics in medicine : official journal of the American College of Medical Genetics (2021), 23(7), 1263-1272

PURPOSE: Pathogenic variants in SCN2A cause a wide range of neurodevelopmental phenotypes. Reports of genotype-phenotype correlations are often anecdotal, and the available phenotypic data have not been ... [more ▼]

PURPOSE: Pathogenic variants in SCN2A cause a wide range of neurodevelopmental phenotypes. Reports of genotype-phenotype correlations are often anecdotal, and the available phenotypic data have not been systematically analyzed. METHODS: We extracted phenotypic information from primary descriptions of SCN2A-related disorders in the literature between 2001 and 2019, which we coded in Human Phenotype Ontology (HPO) terms. With higher-level phenotype terms inferred by the HPO structure, we assessed the frequencies of clinical features and investigated the association of these features with variant classes and locations within the Na(V)1.2 protein. RESULTS: We identified 413 unrelated individuals and derived a total of 10,860 HPO terms with 562 unique terms. Protein-truncating variants were associated with autism and behavioral abnormalities. Missense variants were associated with neonatal onset, epileptic spasms, and seizures, regardless of type. Phenotypic similarity was identified in 8/62 recurrent SCN2A variants. Three independent principal components accounted for 33% of the phenotypic variance, allowing for separation of gain-of-function versus loss-of-function variants with good performance. CONCLUSION: Our work shows that translating clinical features into a computable format using a standardized language allows for quantitative phenotype analysis, mapping the phenotypic landscape of SCN2A-related disorders in unprecedented detail and revealing genotype-phenotype correlations along a multidimensional spectrum. [less ▲]

Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes.

in Nature genetics (2018), 50(4), 524-537

Stroke has multiple etiologies, but the underlying genes and pathways are largely unknown. We conducted a multiancestry genome-wide-association meta-analysis in 521,612 individuals (67,162 cases and 454 ... [more ▼]

Stroke has multiple etiologies, but the underlying genes and pathways are largely unknown. We conducted a multiancestry genome-wide-association meta-analysis in 521,612 individuals (67,162 cases and 454,450 controls) and discovered 22 new stroke risk loci, bringing the total to 32. We further found shared genetic variation with related vascular traits, including blood pressure, cardiac traits, and venous thromboembolism, at individual loci (n = 18), and using genetic risk scores and linkage-disequilibrium-score regression. Several loci exhibited distinct association and pleiotropy patterns for etiological stroke subtypes. Eleven new susceptibility loci indicate mechanisms not previously implicated in stroke pathophysiology, with prioritization of risk variants and genes accomplished through bioinformatics analyses using extensive functional datasets. Stroke risk loci were significantly enriched in drug targets for antithrombotic therapy. [less ▲]

Shared genetic regulatory networks for cardiovascular disease and type 2 diabetes in multiple populations of diverse ethnicities in the United States.

in PLoS genetics (2017), 13(9), 1007040

Cardiovascular diseases (CVD) and type 2 diabetes (T2D) are closely interrelated complex diseases likely sharing overlapping pathogenesis driven by aberrant activities in gene networks. However, the ... [more ▼]

Cardiovascular diseases (CVD) and type 2 diabetes (T2D) are closely interrelated complex diseases likely sharing overlapping pathogenesis driven by aberrant activities in gene networks. However, the molecular circuitries underlying the pathogenic commonalities remain poorly understood. We sought to identify the shared gene networks and their key intervening drivers for both CVD and T2D by conducting a comprehensive integrative analysis driven by five multi-ethnic genome-wide association studies (GWAS) for CVD and T2D, expression quantitative trait loci (eQTLs), ENCODE, and tissue-specific gene network models (both co-expression and graphical models) from CVD and T2D relevant tissues. We identified pathways regulating the metabolism of lipids, glucose, and branched-chain amino acids, along with those governing oxidation, extracellular matrix, immune response, and neuronal system as shared pathogenic processes for both diseases. Further, we uncovered 15 key drivers including HMGCR, CAV1, IGF1 and PCOLCE, whose network neighbors collectively account for approximately 35% of known GWAS hits for CVD and 22% for T2D. Finally, we cross-validated the regulatory role of the top key drivers using in vitro siRNA knockdown, in vivo gene knockout, and two Hybrid Mouse Diversity Panels each comprised of >100 strains. Findings from this in-depth assessment of genetic and functional data from multiple human cohorts provide strong support that common sets of tissue-specific molecular networks drive the pathogenesis of both CVD and T2D across ethnicities and help prioritize new therapeutic avenues for both CVD and T2D. [less ▲]

Preservation Analysis of Macrophage Gene Coexpression Between Human and Mouse Identifies PARK2 as a Genetically Controlled Master Regulator of Oxidative Phosphorylation in Humans.

in G3 (Bethesda, Md.) (2016), 6(10), 3361-3371

Macrophages are key players involved in numerous pathophysiological pathways and an in-depth characterization of their gene regulatory networks can help in better understanding how their dysfunction may ... [more ▼]

Macrophages are key players involved in numerous pathophysiological pathways and an in-depth characterization of their gene regulatory networks can help in better understanding how their dysfunction may impact on human diseases. We here conducted a cross-species network analysis of macrophage gene expression data between human and mouse to identify conserved networks across both species, and assessed whether such networks could reveal new disease-associated regulatory mechanisms. From a sample of 684 individuals processed for genome-wide macrophage gene expression profiling, we identified 27 groups of coexpressed genes (modules). Six modules were found preserved (P < 10(-4)) in macrophages from 86 mice of the Hybrid Mouse Diversity Panel. One of these modules was significantly [false discovery rate (FDR) = 8.9 × 10(-11)] enriched for genes belonging to the oxidative phosphorylation (OXPHOS) pathway. This pathway was also found significantly (FDR < 10(-4)) enriched in susceptibility genes for Alzheimer, Parkinson, and Huntington diseases. We further conducted an expression quantitative trait loci analysis to identify SNP that could regulate macrophage OXPHOS gene expression in humans. This analysis identified the PARK2 rs192804963 as a trans-acting variant influencing (minimal P-value = 4.3 × 10(-8)) the expression of most OXPHOS genes in humans. Further experimental work demonstrated that PARK2 knockdown expression was associated with increased OXPHOS gene expression in THP1 human macrophages. This work provided strong new evidence that PARK2 participates to the regulatory networks associated with oxidative phosphorylation and suggested that PARK2 genetic variations could act as a trans regulator of OXPHOS gene macrophage expression in humans. [less ▲]

Prediction of Causal Candidate Genes in Coronary Artery Disease Loci.

in Arteriosclerosis, thrombosis, and vascular biology (2015), 35(10), 2207-17

OBJECTIVE: Genome-wide association studies have to date identified 159 significant and suggestive loci for coronary artery disease (CAD). We now report comprehensive bioinformatics analyses of sequence ... [more ▼]

OBJECTIVE: Genome-wide association studies have to date identified 159 significant and suggestive loci for coronary artery disease (CAD). We now report comprehensive bioinformatics analyses of sequence variation in these loci to predict candidate causal genes. APPROACH AND RESULTS: All annotated genes in the loci were evaluated with respect to protein-coding single-nucleotide polymorphism and gene expression parameters. The latter included expression quantitative trait loci, tissue specificity, and miRNA binding. High priority candidate genes were further identified based on literature searches and our experimental data. We conclude that the great majority of causal variations affecting CAD risk occur in noncoding regions, with 41% affecting gene expression robustly versus 6% leading to amino acid changes. Many of these genes differed from the traditionally annotated genes, which was usually based on proximity to the lead single-nucleotide polymorphism. Indeed, we obtained evidence that genetic variants at CAD loci affect 98 genes which had not been linked to CAD previously. CONCLUSIONS: Our results substantially revise the list of likely candidates for CAD and suggest that genome-wide association studies efforts in other diseases may benefit from similar bioinformatics analyses. [less ▲]