ALSgeneScanner: a pipeline for the analysis and interpretation of DNA sequencing data of ALS patients

Calculating variant penetrance from family history of disease and average family size in population-scale data

BACKGROUND

Genetic penetrance is the probability of a phenotype when harbouring a particular pathogenic variant. Accurate penetrance estimates are important across biomedical fields including genetic counselling, disease research, and gene therapy. However, existing approaches for penetrance estimation require, for instance, large family pedigrees or availability of large databases of people affected and not affected by a disease.

METHODS

We present a method for penetrance estimation in autosomal dominant phenotypes. It examines the distribution of a variant among people affected (cases) and unaffected (controls) by a phenotype within population-scale data and can be operated using cases only by considering family disease history. It is validated through simulation studies and candidate variant-disease case studies.

RESULTS

Our method yields penetrance estimates which align with those obtained via existing approaches in the Parkinson's disease LRRK2 gene and pulmonary arterial hypertension BMPR2 gene case studies. In the amyotrophic lateral sclerosis case studies, examining penetrance for variants in the SOD1 and C9orf72 genes, we make novel penetrance estimates which correspond closely to understanding of the disease.

CONCLUSIONS

The present approach broadens the spectrum of traits for which reliable penetrance estimates can be obtained. It has substantial utility for facilitating the characterisation of disease risks associated with rare variants with an autosomal dominant inheritance pattern. The yielded estimates avoid any kinship-specific effects and can circumvent ascertainment biases common when sampling rare variants among control populations.

Motor neuron-derived induced pluripotent stem cells as a drug screening platform for amyotrophic lateral sclerosis

The development of cell culture models that recapitulate the etiology and features of nervous system diseases is central to the discovery of new drugs and their translation onto therapies. Neuronal tissues are inaccessible due to skeletal constraints and the invasiveness of the procedure to obtain them. Thus, the emergence of induced pluripotent stem cell (iPSC) technology offers the opportunity to model different neuronal pathologies. Our focus centers on iPSCs derived from amyotrophic lateral sclerosis (ALS) patients, whose pathology remains in urgent need of new drugs and treatment. In this sense, we aim to revise the process to obtain motor neurons derived iPSCs (iPSC-MNs) from patients with ALS as a drug screening model, review current 3D-models and offer a perspective on bioinformatics as a powerful tool that can aid in the progress of finding new pharmacological treatments.

The third international hackathon for applying insights into large-scale genomic composition to use cases in a wide range of organisms

In October 2021, 59 scientists from 14 countries and 13 U.S. states collaborated virtually in the Third Annual Baylor College of Medicine & DNANexus Structural Variation hackathon. The goal of the hackathon was to advance research on structural variants (SVs) by prototyping and iterating on open-source software. This led to nine hackathon projects focused on diverse genomics research interests, including various SV discovery and genotyping methods, SV sequence reconstruction, and clinically relevant structural variation, including SARS-CoV-2 variants. Repositories for the projects that participated in the hackathon are available at https://github.com/collaborativebioinformatics.

Structural variation analysis of 6,500 whole genome sequences in amyotrophic lateral sclerosis

There is a strong genetic contribution to Amyotrophic lateral sclerosis (ALS) risk, with heritability estimates of up to 60%. Both Mendelian and small effect variants have been identified, but in common with other conditions, such variants only explain a little of the heritability. Genomic structural variation might account for some of this otherwise unexplained heritability. We therefore investigated association between structural variation in a set of 25 ALS genes, and ALS risk and phenotype. As expected, the repeat expansion in the C9orf72 gene was identified as associated with ALS. Two other ALS-associated structural variants were identified: inversion in the VCP gene and insertion in the ERBB4 gene. All three variants were associated both with increased risk of ALS and specific phenotypic patterns of disease expression. More than 70% of people with respiratory onset ALS harboured ERBB4 insertion compared with 25% of the general population, suggesting respiratory onset ALS may be a distinct genetic subtype.

DGLinker: flexible knowledge-graph prediction of disease-gene associations

As a result of the advent of high-throughput technologies, there has been rapid progress in our understanding of the genetics underlying biological processes. However, despite such advances, the genetic landscape of human diseases has only marginally been disclosed. Exploiting the present availability of large amounts of biological and phenotypic data, we can use our current understanding of disease genetics to train machine learning models to predict novel genetic factors associated with the disease. To this end, we developed DGLinker, a webserver for the prediction of novel candidate genes for human diseases given a set of known disease genes. DGLinker has a user-friendly interface that allows non-expert users to exploit biomedical information from a wide range of biological and phenotypic databases, and/or to upload their own data, to generate a knowledge-graph and use machine learning to predict new disease-associated genes. The webserver includes tools to explore and interpret the results and generates publication-ready figures. DGLinker is available at https://dglinker.rosalind.kcl.ac.uk. The webserver is free and open to all users without the need for registration.

Genome-wide Meta-analysis Finds the ACSL5-ZDHHC6 Locus Is Associated with ALS and Links Weight Loss to the Disease Genetics

We meta-analyze amyotrophic lateral sclerosis (ALS) genome-wide association study (GWAS) data of European and Chinese populations (84,694 individuals). We find an additional significant association between rs58854276 spanning ACSL5-ZDHHC6 with ALS (p = 8.3 × 10-9), with replication in an independent Australian cohort (1,502 individuals; p = 0.037). Moreover, B4GALNT1, G2E3-SCFD1, and TRIP11-ATXN3 are identified using a gene-based analysis. ACSL5 has been associated with rapid weight loss, as has another ALS-associated gene, GPX3. Weight loss is frequent in ALS patients and is associated with shorter survival. We investigate the effect of the ACSL5 and GPX3 single-nucleotide polymorphisms (SNPs), using longitudinal body composition and weight data of 77 patients and 77 controls. In patients' fat-free mass, although not significant, we observe an effect in the expected direction (rs58854276: -2.1 ± 1.3 kg/A allele, p = 0.053; rs3828599: -1.0 ± 1.3 kg/A allele, p = 0.22). No effect was observed in controls. Our findings support the increasing interest in lipid metabolism in ALS and link the disease genetics to weight loss in patients.

A Knowledge-Based Machine Learning Approach to Gene Prioritisation in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis is a neurodegenerative disease of the upper and lower motor neurons resulting in death from neuromuscular respiratory failure, typically within two to five years of first symptoms. Several rare disruptive gene variants have been associated with ALS and are responsible for about 15% of all cases. Although our knowledge of the genetic landscape of this disease is improving, it remains limited. Machine learning models trained on the available protein-protein interaction and phenotype-genotype association data can use our current knowledge of the disease genetics for the prediction of novel candidate genes. Here, we describe a knowledge-based machine learning method for this purpose. We trained our model on protein-protein interaction data from IntAct, gene function annotation from Gene Ontology, and known disease-gene associations from DisGeNet. Using several sets of known ALS genes from public databases and a manual review as input, we generated a list of new candidate genes for each input set. We investigated the relevance of the predicted genes in ALS by using the available summary statistics from the largest ALS genome-wide association study and by performing functional and phenotype enrichment analysis. The predicted sets were enriched for genes associated with other neurodegenerative diseases known to overlap with ALS genetically and phenotypically, as well as for biological processes associated with the disease. Moreover, using ALS genes from ClinVar and our manual review as input, the predicted sets were enriched for ALS-associated genes (ClinVar p = 0.038 and manual review p = 0.060) when used for gene prioritisation in a genome-wide association study.