Biology subjects

Kenna, B.

Publications and source records attributed to Kenna, B..

3 recordsLinked to original sources

bioRxiv2024

Sex-specific DNA methylation differences in Amyotrophic lateral sclerosis

Sex is an important covariate in all genetic and epigenetic research due to its role in the incidence, progression and outcome of many phenotypic characteristics and human diseases. Amyotrophic lateral sclerosis (ALS) is a motor neuron disease with a sex bias towards higher incidence in males. Here, we report for the first time a blood-based epigenome-wide association study meta-analysis in 9274 individuals after stringent quality control (5529 males and 3975 females). We identified a total of 226 ALS saDMPs (sex-associated DMPs) annotated to a total of 159 unique genes. These ALS saDMPs were depleted at transposable elements yet significantly enriched at enhancers and slightly enriched at 3UTRs. These ALS saDMPs were enriched for transcription factor motifs such as ESR1 and REST. Moreover, we identified an additional 10 genes associated with ALS saDMPs through chromatin loop interactions, suggesting a potential regulatory role for these saDMPs on distant genes. Furthermore, we investigated the relationship between DNA methylation at specific CpG sites and overall survival in ALS using Cox proportional hazards models. We identified two ALS saDMPs, cg14380013 and cg06729676, that showed significant associations with survival. Overall, our study reports a reliable catalogue of sex-associated ALS saDMPs in ALS and elucidates several characteristics of these sites using a large-scale dataset. This resource will benefit future studies aiming to investigate the role of sex in the incidence, progression and risk for ALS.

genomics↗

bioRxiv2023

Genomewide paired DNA-RNAseq analyses to discover intronic splice mutation hotspots in neurological disorders

Explaining missing heritability in rare disorders requires effective methods to interpret genetic variants. Sequence-to-function models such as SpliceAI support discovery of splice altering variants but filtering their output to identify pathogenic mutations remains challenging. We developed SpliPath to address 2 unmet needs in this process. First, SpliPath links the output of SpliceAI with reference transcriptomics data. This allows users to identify genetic variants that induce unannotated splice isoforms selectively expressed in disease models or patient tissue. Second, SpliPath aggregates variants with similar functional consequences into collapsed splicing quantitative trait loci (csQTLs) for more powerful genetic association analyses. We first used SpliPath to annotate whole genome sequencing (WGS) of 9,467 ALS patients and controls using RNAseq data from an iPSC model of TDP-43 dysfunction. Through this, SpliPath identified 53 variants predicted to enhance cryptic exon (CE) retention events associated with a core ALS pathomechanism. We then applied SpliPath to 294 ALS patients where both WGS and RNAseq were available and discovered missing genetic risk in the known ALS gene KIF5A. This revealed a first of kind intronic mutation hotspot that was validated using minigene reporter assays. Finally, using the same RNAseq data we then predicted 754 candidate csQTL for an independent WGS cohort of 6,625 ALS patients and 2,472 controls. Unbiased genomewide csQTL association testing successfully recovered KIF5A and nominated EPG5 as a potential pathogenic gene. These effects were undetectable using simplistic SpliceAI gene burden tests. Collectively, our study demonstrates the utility of SpliPath for uncovering missing heritability in rare disorders.

bioinformatics↗

bioRxiv2018

The Project MinE databrowser: bringing large-scale whole-genome sequencing in ALS to researchers and the public.

Amyotrophic lateral sclerosis (ALS) is a rapidly progressive fatal neurodegenerative disease affecting 1 in 350 people. The aim of Project MinE is to elucidate the pathophysiology of ALS through whole-genome sequencing at least 15,000 ALS patients and 7,500 controls at 30X coverage. Here, we present the Project MinE data browser (databrowser.projectmine.com). a unique and intuitive one-stop, open-access server that provides detailed information on genetic variation analyzed in a new and still growing set of 4,366 ALS cases and 1,832 matched controls. Through its visual components and interactive design, the browser specifically aims to be a resource to those without a biostatistics background and allow clinicians and preclinical researchers to integrate Project MinE data into their own research. The browser allows users to query a transcript and immediately access a unique combination of detailed (meta)data, annotations and association statistics that would otherwise require analytic expertise and visits to scattered resources.

genomics↗