Ammar Al‐Chalabi

The paper identifies seven sex-dependent differentially methylated positions in ALS blood samples, suggesting sex-specific biological mechanisms in the disease.

This paper conducts a meta-analysis to determine the lower limit of ATXN2 repeat expansions associated with ALS risk, finding that ≥30 repeats define significant risk.

The paper identifies FGF14 GAA repeat expansions in ALS patients, showing enrichment compared to controls and suggesting a broader pathogenic role in motor neuron disease.

The paper quantifies exosomal HERV-K transcripts in ALS patients and controls, finding elevated pol expression that could serve as a biomarker for treatment monitoring.

This study identifies rare and low-frequency non-coding variants at the SCN5A locus associated with Brugada syndrome using whole-genome sequencing and functional assays.

The paper evaluates gene expression-based ALS subtypes across brain and blood tissues using machine learning, finding three subtypes with consistent classification in both tissues.

This study identifies digenic inheritance of SPG7 and AFG3L2 mutations as a cause of motor neuron and cerebellar disorders in 17 patients.

This study benchmarks TE detection tools on long-read data, showing significant variation in performance and highlighting tool-specific strengths and limitations.

The paper measures the functional impact of over two thousand SOD1 missense variants using saturation mutagenesis and cell-based assays, reclassifying 41% of previously uncertain variants as pathogenic.

This study compares Oxford Nanopore long-read sequencing with Illumina short-read sequencing and microarrays across 14 human genomes, finding LRS has competitive accuracy for SNVs and superior performance for structural variants.

The paper calculates the risk of developing rare neurodegenerative diseases following genetic screening, showing low risks for some variants and emphasizing the need for high sensitivity and specificity.

This paper develops a deep learning model using SNP microarray data to predict ALS risk with high accuracy across diverse populations and genotyping platforms.

The paper investigates oligogenic risk in ALS using genome sequencing data, finding that multiple rare variants in known ALS genes increase disease risk but not clinical outcomes.

This study uses GWAS summary statistics to identify shared genetic loci across neuropsychiatric diseases and finds HLA region associations with potential shared causal variants.

The paper identifies sex-associated DNA methylation differences in ALS using a large-scale epigenome-wide association study and links them to survival outcomes.

GenoPred is a pipeline that generates interpretable polygenic scores by integrating GWAS summary statistics and genotype data, supporting diverse populations and improving reproducibility.

This study uses GWAS and functional genomics to identify ALS risk genes, repurpose drugs, and improve risk prediction using polytranscriptomic scores.

The paper introduces predicTTE, a tool for time-to-event prediction in neurological diseases, demonstrating its utility in genetic discovery and personalized medicine.

The paper identifies sex-specific risk loci for ALS, including MEF2C, which is upregulated in female ALS motor neurons, and an X-chromosome locus at IL1RAPL2.

The paper introduces a method to compress genetic data using autoencoders that leverage linkage disequilibrium structure, achieving high reconstruction accuracy while reducing data size.

This study analyzes genetic and clinical data from PLS patients to assess the role of genetic testing and identify potential disease-associated variants.

Mutafy identifies high-quality mutant protein structures from the PDB for given human genes, annotates them with biomedical data, and provides interactive visualization.

This study investigates the role of Neanderthal introgression and natural selection in the heritability of neurodegenerative diseases and finds no significant contribution from either.

The paper evaluates the variability in ALS clinical genetic testing panels globally, finding inconsistent gene inclusion and potential diagnostic challenges.

The paper analyzes CAV1 and CAV2 expression in ALS brain tissue and finds higher expression in ALS patients, associated with increased survival and slower disease progression.

The paper evaluates bioinformatics tools for detecting human endogenous retroviral insertions in short-read genome sequencing data, finding performance variability across datasets and recommending a consensus approach.

This study uses molecular dynamics and clinical data to show structural and dynamic differences between WTL and MBR SOD1 variants, linking these to distinct ALS clinical outcomes.

This paper introduces a method to estimate penetrance for autosomal dominant traits using population data and family history, validated through simulations and case studies.

The paper develops TWAS models using eQTL summary statistics to predict gene expression and identifies novel schizophrenia associations.

This study identifies NEFH tail domain mutations as risk factors for ALS through meta-analysis and variant burden analysis in case-control studies and whole-genome sequencing data.