Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by aberrations in the genome. While several disea

Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype

Motivation: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by aberrations in the genome. While several disease-causing variants have been identified, a major part of heritability remains unexplained. ALS is believed to have a complex genetic basis where non-additive combinations of variants constitute disease, which cannot be picked up using the linear models employed in classical genotype-phenotype association studies. Deep learning on the other hand is highly promising for identifying such complex relations. We therefore developed a deep-learning based approach for the classification of ALS patients versus healthy individuals from the Dutch cohort of the Project MinE dataset. Based on recent insight that regulatory regions harbor the majority of disease-associated variants, we employ a two-step approach: first promoter regions that are likely associated to ALS are identified, and second individuals are classified based on their genotype in the selected genomic regions. Both steps employ a deep convolutional neural network. The network architecture accounts for the structure of genome data by applying convolution only to parts of the data where this makes sense from a genomics perspective. Results: Our approach identifies potentially ALS-associated promoter regions, and generally outperforms other classification methods. Test results support the hypothesis that non-additive combinations of variants contribute to ALS. Architectures and protocols developed are tailored toward processing population-scale, whole-genome data. We consider this a relevant first step toward deep learning assisted genotype-phenotype association in whole genome-sized data

Comparative interactomics analysis of different ALS-associated proteins identifies converging molecular pathways

Amyotrophic lateral sclerosis (ALS) is a devastating neurological disease with no effective treatment available. An increasing number of genetic causes of ALS are being identified, but how these genetic defects lead to motor neuron degeneration and to which extent they affect common cellular pathways remains incompletely understood. To address these questions, we performed an interactomic analysis to identify binding partners of wild-type (WT) and ALS-associated mutant versions of ATXN2, C9orf72, FUS, OPTN, TDP-43 and UBQLN2 in neuronal cells. This analysis identified several known but also many novel binding partners of these proteins

Analysis of shared common genetic risk between amyotrophic lateral sclerosis and epilepsy

Peer reviewe

A replication study of genetic risk loci for ischemic stroke in a Dutch population: A case-control study

We aimed to replicate reported associations of 10 SNPs at eight distinct loci with overall ischemic stroke (IS) and its subtypes in an independent cohort of Dutch IS patients. We included 1,375 IS patients enrolled in a prospective multicenter hospital-based cohort in the Netherlands, and 1,533 population-level controls of Dutch descent. We tested these SNPs for association with overall IS and its subtypes (large artery atherosclerosis, small vessel disease and cardioembolic stroke (CE), as classified by TOAST) using an additive multivariable logistic regression model, adjusting for age and sex. We obtained odds ratios (OR) with 95% confidence intervals (95% CI) for the risk allele of each SNP analyzed and exact p-values by permutation. We confirmed the association at 4q25 (PITX2) (OR 1.43; 95% CI, 1.13-1.81, p = 0.029) and 16q22 (ZFHX3) (OR 1.62; 95% CI, 1.26-2.07, p = 0.001) as risk loci for CE. Locus 16q22 was also associated with overall IS (OR 1.24; 95% CI, 1.08-1.42, p = 0.016). Other loci previously associated with IS and/or its subtypes were not confirmed. In conclusion, we validated two loci (4q25, 16q22) associated with CE. In addition, our study may suggest that the association of locus 16q22 may not be limited to CE, but also includes overall IS

Facial onset sensory and motor neuronopathy: new cases, cognitive changes and pathophysiology

Purpose of review To improve our clinical understanding of facial onset sensory and motor neuronopathy (FOSMN). Recent findings We identified 29 new cases and 71 literature cases, resulting in a cohort of 100 patients with FOSMN. During follow-up, cognitive and behavioral changes became apparent in 8 patients, suggesting that changes within the spectrum of frontotemporal dementia (FTD) are a part of the natural history of FOSMN. Another new finding was chorea, seen in 6 cases. Despite reports of autoantibodies, there is no consistent evidence to suggest an autoimmune pathogenesis. Four of 6 autopsies had TAR DNA-binding protein (TDP) 43 pathology. Seven cases had genetic mutations associated with neurodegenerative diseases. Summary FOSMN is a rare disease with a highly characteristic onset and pattern of disease progression involving initial sensory disturbances, followed by bulbar weakness with a cranial to caudal spread of pathology. Although not conclusive, the balance of evidence suggests that FOSMN is most likely to be a TDP-43 proteinopathy within the amyotrophic lateral sclerosis–FTD spectrum

Genome-wide identification of genes regulating DNA methylation using genetic anchors for causal inference

BACKGROUND: DNA methylation is a key epigenetic modification in human development and disease, yet there is limited understanding of its highly coordinated regulation. Here, we identify 818 genes that affect DNA methylation patterns in blood using large-scale population genomics data. RESULTS: By employing genetic instruments as causal anchors, we establish directed associations between gene expression and distant DNA methylation levels, while ensuring specificity of the associations by correcting for linkage disequilibrium and pleiotropy among neighboring genes. The identified genes are enriched for transcription factors, of which many consistently increased or decreased DNA methylation levels at multiple CpG sites. In addition, we show that a substantial number of transcription factors affected DNA methylation at their experimentally determined binding sites. We also observe genes encoding proteins with heterogenous functions that have widespread effects on DNA methylation, e.g., NFKBIE, CDCA7(L), and NLRC5, and for several examples, we suggest plausible mechanisms underlying their effect on DNA methylation. CONCLUSION: We report hundreds of genes that affect DNA methylation and provide key insights in the principles underlying epigenetic regulation

Occupational exposure to gases/fumes and mineral dust affect DNA methylation levels of genes regulating expression

Many workers are daily exposed to occupational agents like gases/fumes, mineral dust or biological dust, which could induce adverse health effects. Epigenetic mechanisms, such as DNA methylation, have been suggested to play a role. We therefore aimed to identify differentially methylated regions (DMRs) upon occupational exposures in never-smokers and investigated if these DMRs associated with gene expression levels. To determine the effects of occupational exposures independent of smoking, 903 never-smokers of the LifeLines cohort study were included. We performed three genome-wide methylation analyses (Illumina 450 K), one per occupational exposure being gases/fumes, mineral dust and biological dust, using robust linear regression adjusted for appropriate confounders. DMRs were identified using comb-p in Python. Results were validated in the Rotterdam Study (233 never-smokers) and methylation-expression associations were assessed using Biobank-based Integrative Omics Study data (n = 2802). Of the total 21 significant DMRs, 14 DMRs were associated with gases/fumes and 7 with mineral dust. Three of these DMRs were associated with both exposures (RPLP1 and LINC02169 (2x)) and 11 DMRs were located within transcript start sites of gene expression regulating genes. We replicated two DMRs with gases/fumes (VTRNA2-1 and GNAS) and one with mineral dust (CCDC144NL). In addition, nine gases/fumes DMRs and six mineral dust DMRs significantly associated with gene expression levels. Our data suggest that occupational exposures may induce differential methylation of gene expression regulating genes and thereby may induce adverse health effects. Given the millions of workers that are exposed daily to occupational exposures, further studies on this epigenetic mechanism and health outcomes are warranted