Biology subjects

Belmont, J. W.

Publications and source records attributed to Belmont, J. W..

3 recordsLinked to original sources

bioRxiv2024

The Polygenic Score - Rare Variant Causal Pivot: A Conditional Approach to Discovery in Complex Disease Genetics

We present the Causal Pivot (CP) as a structural causal model (SCM) for analyzing genetic heterogeneity in complex diseases. The CP leverages one established causal factor to detect the contribution of a second suspected cause. Specifically, polygenic risk scores (PRS) serve as known causes, while rare variants (RV) or RV ensembles are evaluated as candidate causes. The CP incorporates outcome-induced association by conditioning on disease status. We derive a conditional maximum likelihood procedure for binary and quantitative traits and develop the Causal Pivot Likelihood Ratio Test (CP-LRT) to detect causal signals. Through simulations, we demonstrate the CP-LRTs robust power and superior error control compared to alternatives. We apply the CP-LRT to UK Biobank (UKB) data, analyzing three exemplar diseases: hypercholesterolemia (HC, LDL-c [≥] 4.9 mmol/L; nc=24,656), breast cancer (BC, ICD10 C50; nc=12,479), and Parkinsons disease (PD, ICD10 G20; nc=2,940). For PRS, we utilize UKB-derived values, and for RVs, we analyze ClinVar pathogenic/likely pathogenic variants and loss-of-function mutations in disease-relevant genes: LDLR for HC, BRCA1 for BC, and GBA for PD. Significant CP-LRT signals were detected for all three diseases. Cross-disease and synonymous variant analyses serve as controls. We further develop ancestry adjustment using matching and inverse probability weighting, and we extend the CP to examine oligogenic burden in the lysosomal storage pathway for PD. The CP reveals an approach to address heterogeneity and is an extensible method for inference and discovery in complex disease genetics.

genetics↗

bioRxiv2018

Microdeletions excluding YWHAE and PAFAH1B1 cause a unique leukoencephalopathy and hypermobility syndrome: Further delineation of the 17p13.3 microdeletion spectrum

PurposeBrain malformations are caused by 17p13.3 deletions: lissencephaly with deletions of the larger Miller-Dieker syndrome region or smaller deletions of only PAFAH1B1, as well as various abnormalities, including white matter changes, in the distinct syndrome due to deletions including YWHAE and CRK but sparing PAFAH1B1. We sought to understand the significance of 17p13.3 deletions between the YWHAE/CRK and PAFAH1B1 loci.\n\nMethodsWe analyzed the clinical features of five individuals from four families with 17p13.3 deletions between and not including YWHAE/CRK and PAFAH1B1 identified among individuals undergoing clinical chromosomal microarray testing.\n\nResultsFour individuals from three families have multi-focal white matter lesions and hypermobile joints, while a fifth had a normal MRI. A combination of our patients and a review of those in the literature with white matter changes and deletions in this chromosomal region narrows the overlapping region for this brain phenotype to [~]345 kb, including 11 RefSeq genes, with RTN4RL1 haploinsufficiency as the best candidate for causing this.\n\nConclusionWhile previous literature has hypothesized dysmorphic features and white matter changes related to YWHAE, our cohort contributes evidence to the presence of additional genes within 17p13.3 required for proper brain development.

genetics↗

bioRxiv2017

Capture-based DNA methylation sequencing facilitates diagnosis and reveals potential pathogenic mechanisms in teratogenic diabetes exposure

Diabetic embryopathy (DE) describes a spectrum of birth defects associated with a teratogenic exposure to maternal diabetes in utero. These defects strongly overlap the phenotypes of known genetic syndromes; however, the pathogenic mechanisms underlying DE remain uncertain and there are no definitive tests that distinguish the diagnosis. Here, we explore the potential of DNA methylation as both a diagnostic biomarker and a means of informing disease pathogenesis in DE. Capture-based bisulfite sequencing was used to compare patterns of DNA methylation at 2,800,516 sites genome-wide in seven DE neonates and 11 healthy neonates, including five with in utero diabetes exposure. DE infants had significantly lower global DNA methylation (ANOVA, Tukey HSD p=0.045) than diabetes-unexposed, healthy controls (UH), with multiple sites showing large (mean methylation difference = 16.6%) and significant (p<0.001) differential methylation between the two groups. We found that a subset of 237 highly differentially methylated loci could accurately distinguish DE infants from both UH and diabetes-exposed healthy infants (sensitivity 80% -100%). Differentially methylated sites were enriched in intergenic (p<3.52x10-15) and intronic (p<0.001) regions found proximal to genes either associated with Mendelian syndromes that overlap the DE phenotype (e.g. TRIO, ANKRD11), or known to influence early organ development (e.g. BRAX1, RASA3). Further, by integrating information on cis-sequence variation, we found that 39.3% of loci with evidence for allele-specific methylation also showed differential methylation between DE and controls. Our study suggests a role for aberrant DNA methylation and cis-sequence variation in the pathogenesis of DE, and highlights the diagnostic potential of DNA methylation for teratogenic birth defects.

genetics↗