Christopher E. Shaw

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

The paper develops an allele-selective qPCR assay to quantify X-chromosome inactivation in iPSC models of UBQLN2 ALS/FTD and identifies clones retaining XCI for further study.

The paper develops U7 snRNA therapeutics to correct TDP-43-dependent mis-splicing in ALS, restoring gene expression and neuronal function in human iPSC-derived neurons.

The paper identifies co-segregating RUBCN and KANK1 mutations in a UK ALS family, showing their association with disease and functional impact on cellular processes.

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.

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

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.

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

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 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.

This study identifies 690 candidate ALS genes using a hierarchical Bayesian model, highlighting genetic convergence on the distal axon and functional significance in motor neurons.

The paper identifies a hippocampal protein aggregation signature that distinguishes pathogenic from wildtype UBQLN2 in ALS/FTD cases.

The paper identifies de-novo SPTLC1 mutations in juvenile ALS patients and suggests serine supplementation may alleviate symptoms.

The paper identifies an enrichment of rare protein-truncating variants in ALS patients, highlighting novel genes like DNAJC7 and known ALS genes such as SOD1, NEK1, and FUS.

The paper develops SPiQE, an automated tool to detect and characterize fasciculations in ALS using HDSEMG data, achieving high sensitivity and specificity.

Project MinE analyzes whole-genome sequencing data from ALS patients and controls to study genetic variation and its implications for disease susceptibility.

The paper introduces ExpansionHunter, a tool for detecting long repeat expansions from PCR-free WGS data, achieving high sensitivity and specificity in identifying pathogenic expansions.