Publications by authors named "Denis Nyaga"

Approximately 200 critically ill infants and children in New Zealand are in high-dependency care, many suspected of having genetic conditions, requiring scalable genomic testing. We adopted an acute care genomics protocol from an accredited laboratory and established a clinical pipeline using Oxford Nanopore Technologies PromethION 2 solo system and Fabric GEM™ software. Benchmarking of the pipeline was performed using Global Alliance for Genomics and Health benchmarking tools and Genome in a Bottle samples (HG002-HG007).

View Article and Find Full Text PDF
Article Synopsis
  • Machine learning (ML) has significant potential in genetics and genomics by analyzing large datasets to improve understanding of diseases and health risks.
  • Caution is essential to avoid biases and misinterpretations that could lead to negative consequences, making it crucial for researchers to grasp the evaluation metrics for ML models.
  • This review outlines key ML metrics for clustering, classification, and regression, discusses their pros and cons, identifies common evaluation pitfalls, and offers examples relevant to genomics.
View Article and Find Full Text PDF

Developmental and epileptic encephalopathies (DEEs) feature altered brain development, developmental delay and seizures, with seizures exacerbating developmental delay. Here we identify a cohort with biallelic variants in DENND5A, encoding a membrane trafficking protein, and develop animal models with phenotypes like the human syndrome. We demonstrate that DENND5A interacts with Pals1/MUPP1, components of the Crumbs apical polarity complex required for symmetrical division of neural progenitor cells.

View Article and Find Full Text PDF
Article Synopsis
  • Sequence-based genetic testing finds causative variants in about 50% of cases of developmental and epileptic encephalopathies (DEEs), but DNA methylation changes in these cases have not been thoroughly explored.
  • This study analyzed genome-wide DNA methylation in blood samples from 582 individuals with unresolved DEEs, identifying rare methylation patterns and potential genetic causes in 12 of these cases.
  • The research highlights the effectiveness of DNA methylation analysis in diagnosing DEEs, showing a 2% diagnostic yield, and provides insights into the CHD2 gene's pathophysiology using advanced sequencing methods.
View Article and Find Full Text PDF

SCN8A variants cause a spectrum of epilepsy phenotypes ranging from self-limited infantile epilepsy (SeLIE) to developmental and epileptic encephalopathy. SeLIE is an infantile onset focal epilepsy, occurring in developmentally normal infants, which often resolves by 3 years. Our aim was to ascertain when epilepsy resolves in SCN8A-SeLIE.

View Article and Find Full Text PDF

Developmental and epileptic encephalopathies (DEEs) are a heterogenous group of epilepsies in which altered brain development leads to developmental delay and seizures, with the epileptic activity further negatively impacting neurodevelopment. Identifying the underlying cause of DEEs is essential for progress toward precision therapies. Here we describe a group of individuals with biallelic variants in and determine that variant type is correlated with disease severity.

View Article and Find Full Text PDF

About 50% of individuals with developmental and epileptic encephalopathies (DEEs) are unsolved following genetic testing. Deep intronic variants, defined as >100 bp from exon-intron junctions, contribute to disease by affecting the splicing of mRNAs in clinically relevant genes. Identifying deep intronic pathogenic variants is challenging and resource intensive, and interpretation is difficult due to limited functional annotations.

View Article and Find Full Text PDF
Article Synopsis
  • Sequence-based genetic testing currently identifies genetic variants in about half of individuals with developmental and epileptic encephalopathies (DEEs), but DNA methylation changes have not been explored in this context.
  • This study analyzed genome-wide DNA methylation in blood samples from 516 individuals with unresolved DEEs, uncovering rare methylation changes that helped identify genetic causes in 10 cases.
  • The findings suggest that DNA methylation analysis can enhance diagnostic accuracy for DEEs, offering a similar increase in yield to traditional genome sequencing techniques.
View Article and Find Full Text PDF

The beta-actin gene (ACTB) encodes a ubiquitous cytoskeletal protein, essential for embryonic development in humans. De novo heterozygous missense variants in the ACTB are implicated in causing Baraitser-Winter cerebrofrontofacial syndrome (BWCFFS; MIM#243310). ACTB pathogenic variants are rarely associated with intestinal malformations.

View Article and Find Full Text PDF

Type 1 diabetes (T1D) etiology is complex. We developed a machine learning approach that ranked the tissue-specific transcription regulatory effects for T1D SNPs and estimated their relative contributions to conversion to T1D by integrating case and control genotypes (Wellcome Trust Case Control Consortium and UK Biobank) with tissue-specific expression quantitative trait loci (eQTL) data. Here we show an eQTL (rs6679677) associated with changes to AP4B1-AS1 transcript levels in lung tissue makes the largest gene regulatory contribution to the risk of T1D development.

View Article and Find Full Text PDF

Assigning function to single nucleotide polymorphisms (SNPs) to understand the mechanisms that link genetic and phenotypic variation and disease is an area of intensive research that is necessary to contribute to the continuing development of precision medicine. However, despite the apparent simplicity that is captured in the name SNP - 'single nucleotide' changes are not easy to functionally characterize. This complexity arises from multiple features of the genome including the fact that function is development and environment specific.

View Article and Find Full Text PDF

There is evidence pointing towards shared etiological features between type 1 diabetes (T1D) and type 2 diabetes (T2D) despite both phenotypes being considered genetically distinct. However, the existence of shared genetic features for T1D and T2D remains complex and poorly defined. To better understand the link between T1D and T2D, we employed an integrated functional genomics approach involving extensive chromatin interaction data (Hi-C) and expression quantitative trait loci (eQTL) data to characterize the tissue-specific impacts of single nucleotide polymorphisms associated with T1D and T2D.

View Article and Find Full Text PDF

Type 1 diabetes (T1D) is a chronic metabolic disorder characterized by the autoimmune destruction of insulin-producing pancreatic islet beta cells in genetically predisposed individuals. Genome-wide association studies (GWAS) have identified over 60 risk regions across the human genome, marked by single nucleotide polymorphisms (SNPs), which confer genetic predisposition to T1D. There is increasing evidence that disease-associated SNPs can alter gene expression through spatial interactions that involve distal loci, in a tissue- and development-specific manner.

View Article and Find Full Text PDF

Type 1 diabetes mellitus (T1D) is a complex autoimmune disorder characterised by loss of the insulin-producing pancreatic beta cells in genetically predisposed individuals, ultimately resulting in insulin deficiency and hyperglycaemia. T1D is most common among children and young adults, and the incidence is on the rise across the world. The aetiology of T1D is hypothesized to involve genetic and environmental factors that result in the T-cell mediated destruction of pancreatic beta cells.

View Article and Find Full Text PDF