Biology subjects

Mary, H.

Publications and source records attributed to Mary, H..

2 recordsLinked to original sources

bioRxiv2022

A meta-analysis of genome-wide association studies identifies new genetic loci associated with all-cause and vascular dementia

Dementia is multifactorial with Alzheimer (AD) and vascular (VaD) pathologies making the largest contributions. Genome-wide association studies (GWAS) have identified over 70 genetic risk loci for AD but the genomic determinants of other dementias, including VaD remain understudied. We hypothesize that common forms of dementia will share genetic risk factors and conducted the largest GWAS to date of "all-cause dementia" (ACD) and examined the genetic overlap with VaD. Our dataset includes 809,299 individuals from European, African, Asian, and Hispanic ancestries with 46,902 and 8,702 cases of ACD and VaD, respectively. We replicated known AD loci at genome-wide significance for both ACD and VaD and conducted bioinformatic analyses to prioritize genes that are likely functionally relevant, and shared with closely related traits and risk factors. For ACD, novel loci identified were associated with energy transport (SEMA4D), neuronal excitability (ANO3), amyloid deposition in the brain (RBFOX1), and MRI markers of small vessel disease (HBEGF). Novel VaD loci were associated with hypertension, diabetes, and neuron maintenance (SPRY2, FOXA2, AJAP1, and PSMA3). Our study identified genetic risks underlying all-cause dementia, demonstrating overlap with neurodegenerative processes, vascular risk factors (Type-II diabetes, blood pressure, lipid) and cerebral small vessel disease. These novel insights could lead to new prevention and treatment strategies for all dementias.

genetics↗

bioRxiv2019

Kappa (κ): Analysis of Curvature in Biological Image Data using B-splines

Curvature is a central morphological feature of tissues, cells, and sub-cellular structures. A challenge for computational biology is to measure the curvature of these structures from biological image data. We present an open-source Fiji plugin for measuring curvature using B-splines. The plugin is named Kappa after the Greek symbol for curvature,{kappa} . Kappa is semi-automated: users create an initialization curve by a point-click method, and the initialization curve is fit to the underlying data using an iterative minimization algorithm. We demonstrate Kappas applicability on images of cytoskeletal filaments in vitro, the cell wall of budding yeast, and whole worms moving in an agar dish. In order to verify the accuracy and precision of Kappa, we created a bank of synthetic images of known curvature using sine waves and golden spirals, which we digitized with different signal-to-noise ratios (SNR), pixel sizes, and point-spread functions (PSF). For synthetic images with characteristics similar to real data, the measured curvatures of those images show a high correlation with the theoretical curvatures. Our fitting algorithms perform better with higher SNR, smaller pixel sizes, and especially PSFs equivalent to super-resolution microscopy data (surprise, surprise). Kappa is freely available under the MIT license for simple integration into Fiji-based workflows. The source code and documentation can be found on GitHub at https://github.com/brouhardlab/Kappa.

bioinformatics↗