Russell Lewis McLaughlin

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

The paper identifies shared and distinct genetic loci linking ALS, sFTD, and cognitive traits using genome-wide association data.

This study identifies rare neurological and neurodevelopmental variants in ALS patients, showing associations with onset, survival, and family history patterns across two cohorts.

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 uses genetic data to infer demographic history in Ireland and Britain, finding population bottlenecks, migration patterns, and surname enrichments in specific communities.

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 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.

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 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.

The paper analyzes Dutch population structure using genome-wide data, identifying haplotypic clusters and showing that fine-grained haplotypic covariates reduce GWAS confounding.

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

This paper uses haplotype-based methods to reveal genetic structure in Ireland, showing regional diversity and historical migrations, with implications for genetic association studies.

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.

The paper analyzes ancient genomic data from Portugal to study genetic changes during the Neolithic to Bronze Age transition, finding limited genetic influx and correlations between ancestry and stature.