Biology subjects

Brooks-Wilson, A.

Publications and source records attributed to Brooks-Wilson, A..

6 recordsLinked to original sources

bioRxiv2026

A New Sparse Bayesian Quantile Neural Network-based Approach and Its Application to Discover Physiological Sweet Spots in the Canadian Longitudinal Study on Aging

Identifying physiological sweet spots (optimal ranges for homeostasis) is essential for precision medicine. However, traditional statistical methods often rely on globally linear or locally jagged models that struggle to capture the smooth, non-linear nature of biological regulation in high-dimensional data. We present the Quantile Feature Selection Network (Q-FSNet), a neural network-based framework that integrates quantile regression, feature selection, and uncertainty estimation to identify biomarkers with sweet spots. Unlike traditional methods, Q-FSNet learns continuous response curves without requiring pre-specified number of change points. We further introduce Quantile Dirichlet Network (Q-DirichNet), a fully Bayesian extension that utilizes Dirichlet priors to automate feature shrinkage. Using data from the Canadian Longitudinal Study on Aging, we identified 25 metabolites with distinct homeostatic ranges for which biological age acceleration is minimized. The metabolites with sweet spots for biological aging include some derived from diet or produced by the gut microbiome; this highlights their potential for knowledge translation and public health impact. Our results, corroborated by existing literature, demonstrate that these sparse neural network-based methods offer a scalable and interpretable tool for discovering metabolic signatures of healthy aging vs. dysregulation in large-scale omics research.

bioinformatics↗

bioRxiv2025

Epigenetic signature of heterogeneity in aging: Findings from the Canadian Longitudinal Study on Aging

Background: Human aging does not follow a single trajectory. Epigenetic changes offer insight into the heterogeneity in aging by reflecting the combined influence of genetic, environmental, and lifestyle factors on the timing and progression of age-related changes beyond what chronological age alone can explain. Recent studies in cancer and aging underscore the importance of methylation variability as a marker of biological dysregulation. Methods: We investigated the role of DNA methylation in aging heterogeneity by performing epigenome-wide differential methylation and variance association analyses in blood samples from 1,445 Canadians aged 45 to 85 from the Canadian Longitudinal Study on Aging. Results: We identified 448 differentially methylated regions and 488 differentially variable regions associated with health decline as measured by the health deficit accumulation Frailty Index, cognitive function, and physical function. These two classes of regions showed minimal overlap, with distinct gene coverage, suggesting that variability contributes a complementary signal to aging heterogeneity. Genes overlapped by differentially methylated regions were enriched for immune and inflammation-related pathways, whereas differentially variable regions highlighted additional localized, CpG-island-enriched signals shared across health domains, consistent with regionally structured rather than diffuse dysregulation. By integrating significant CpGs from both analyses, we constructed an epigenetic biomarker. The biomarker was associated with all-cause mortality and showed higher discrimination than biomarkers constructed from differential methylation or variability alone, with a similar pattern reproduced in the Baltimore Longitudinal Study of Aging. Conclusions: These findings suggest that DNA methylation variability may provide a complementary dimension of epigenetic aging and support further evaluation in larger cohorts with more mortality events.

genomics↗

bioRxiv2023

Statistics to prioritize rare variants in family-based sequencing studies with disease subtypes

Family-based sequencing studies are increasingly used to find rare genetic variants of high risk for disease traits with familial clustering. In some studies, families with multiple disease subtypes are collected and the exomes of affected relatives are sequenced for shared rare variants. Since different families can harbor different causal variants and each family harbors many rare variants, tests to detect causal variants can have low power in this study design. Our goal is rather to prioritize shared variants for further investigation by, e.g., pathway analyses or functional studies. The transmission-disequilibrium test prioritizes variants based on departures from Mendelian transmission in parent-child trios. Extending this idea to families, we propose methods to prioritize rare variants shared in affected relatives with two disease subtypes, with one subtype more heritable than the other. Global approaches condition on a variant being observed in the study and assume a known probability of carrying a causal variant. In contrast, local approaches condition on a variant being observed in specific families to eliminate the carrier probability. Our simulation results indicate that global approaches are robust to mis-specification of the carrier probability and prioritize more effectively than local approaches even when the carrier probability is misspecified.

genetics↗

bioRxiv2019

Common breast cancer risk loci predispose to distinct tumor subtypes

BackgroundGenome-wide association studies (GWAS) have identified multiple common breast cancer susceptibility variants. Many of these variants have differential associations by estrogen receptor (ER), but how these variants relate with other tumor features and intrinsic molecular subtypes is unclear. MethodsAmong 106,571 invasive breast cancer cases and 95,762 controls of European ancestry with data on 173 breast cancer variants identified in previous GWAS, we used novel two-stage polytomous logistic regression models to evaluate variants in relation to multiple tumor features (ER, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and grade) adjusting for each other, and to intrinsic-like subtypes. ResultsEighty-five of 173 variants were associated with at least one tumor feature (false discovery rate <5%), most commonly ER and grade, followed by PR and HER2. Models for intrinsic-like subtypes found nearly all of these variants (83 of 85) associated at P<0.05 with risk for at least one luminal-like subtype, and approximately half (41 of 85) of the variants were associated with risk of at least one non-luminal subtype, including 32 variants associated with triple-negative (TN) disease. Ten variants were associated with risk of all subtypes in different magnitude. Five variants were associated with risk of luminal A-like and TN subtypes in opposite directions. ConclusionThis report demonstrates a high level of complexity in the etiology heterogeneity of breast cancer susceptibility variants and can inform investigations of subtype-specific risk prediction.

genetics↗

bioRxiv2019

SimRVSequences: an R package to simulate genetic sequence data for pedigrees

1SummaryFamily-based studies have several advantages over case-control studies for finding causal rare variants for a disease; these include increased power, smaller sample size requirements, and improved detection of sequencing errors. However, collecting suitable families and compiling their data is time-consuming and expensive. To evaluate methodology to identify causal rare variants in family-based studies, one can use simulated data. For this purpose we present the R package SimRVSequences. Users supply a sample of pedigrees and single-nucleotide variant data from a sample of unrelated individuals representing the pedigree founders. Users may also model genetic heterogeneity among families. For ease of use, SimRVSequences offers methods to import and format single-nucleotide variant data and pedigrees from existing software.\n\nAvailability and ImplementationSimRVSequences is available as a library for R[≥] 3.5.0 on the comprehensive R archive network.

genetics↗

bioRxiv2017

Simulating Pedigrees Ascertained for Multiple Disease-Affected Relatives

Background: Studies that ascertain families containing multiple relatives affected by disease can be useful for identification of causal, rare variants from next-generation sequencing data.\n\nResults: We present the R package SimRVPedigree, which allows researchers to simulate pedigrees ascertained on the basis of multiple, affected relatives. By incorporating the ascertainment process in the simulation, SimRVPedigree allows researchers to better understand the within-family patterns of relationship amongst affected individuals and ages of disease onset.\n\nConclusions: Through simulation, we show that affected members of a family segregating a rare disease variant tend to be more numerous and cluster in relationships more closely than those for sporadic disease. We also show that the family ascertainment process can lead to apparent anticipation in the age of onset. Finally, we use simulation to gain insight into the limit on the proportion of ascertained families segregating a causal variant. SimRVPedigree should be useful to investigators seeking insight into the family-based study design through simulation.

genetics↗