Biology subjects

McAuley, E.

Publications and source records attributed to McAuley, E..

4 recordsLinked to original sources

bioRxiv2026

Expanded unbiased population-genomic summary statistics in pixy

Estimates of population-genetic summary statistics are often computed from variant-only VCFs, which commonly omit invariant sites (i.e. sites with only homozygous reference genotypes across all samples). However, these sites are critical for correctly estimating per site statistics such as nucleotide diversity ({pi}) and between population divergence (dxy). Our software pixy addressed this issue by providing support for computing statistics directly from "all-sites" VCFs that encode invariant positions explicitly (Korunes and Samuk 2021). pixy has since been widely adopted and used in a large variety of population genetic studies. Here we present a major update to pixy, which expands the original tool in four major areas. First, we provide implementations of new estimators of Wattersons {theta} and Tajimas D that are unbiased with respect to missing data. Secondly, we provide support for arbitrary ploidy and multiallelic sites. Third, we introduce a variety of optimizations, including multicore execution and a roughly order-of-magnitude reduction in per-worker memory footprint. Finally, we have broadly modernized our code base, test suite, and community contribution pathway. We validate these new features on simulated polyploid and multiallelic datasets, as well as two empirical datasets (one diploid and one autotetraploid), and benchmark the new multicore scaling. We position pixy relative to contemporary tools and discuss shared limitations of VCF-based estimators. By integrating support for diverse ploidies, allelic complexity, and genome-wide scale in one workflow, pixy provides the population genetics community with a straightforward tool for estimates of key summary statistics that are unbiased with respect to missing data. pixy remains completely open-source (MIT-licensed) and easily installable with the conda package management system.

bioinformatics↗

bioRxiv2021

Brain structure and function predict adherence to an exercise intervention in older adults

Individualized and precision medicine approaches to exercise for cognitive and brain health in aging have the potential to improve intervention efficacy. Predicting adherence to an exercise intervention in older adults prior to its commencement will allow for adaptive and optimized approaches that could save time (no need to demonstrate failure before changing course) and money (cost of de-implementing approaches that do not work for certain individuals) which ultimately could improve health outcomes (e.g., preventative medicine approaches prior to the onset of symptoms). Individual differences in brain structure and function in older adults are potential proxies of brain and brain reserve or maintenance and may provide strong predictions of adherence. We hypothesized that brain-based measures would predict adherence to a six-month randomized controlled trial of exercise in older adults, alone and in combination with psychosocial, cognitive and health measures. In 131 older adults (aged 65.79 {+/-} 4.65 years, 63% female) we found, using regularized elastic net regression within a nested cross-validation framework, that brain structure (cortical thickness and cortical surface area) in somatosensory, inferior temporal, and inferior frontal regions and functional connectivity (degree count) in primary information processing (somatosensory, visual), executive control, default, and attentional networks, predicted exercise adherence (R2 = 0.15, p < 0.001). Traditional survey and clinical measures such as gait and walking self-efficacy, biological sex and perceived stress also predicted adherence (R2 = 0.06, p = 0.001) but a combined multimodal model achieved the highest predictive strength (R2 = 0.22, p <0.001). Neuroimaging features alone can predict adherence to a structured group-based exercise intervention in older adults which suggests there is substantial utility of these measures for future research into precision medicine approaches. The best performing model contained multimodal features suggesting that each modality provided independent relevant information in the prediction of exercise adherence.

neuroscience↗

bioRxiv2021

Resting State Functional Connectivity Predicts Future Changes in Sedentary Behavior

Information about a persons available energy resources is integrated in daily behavioral choices that weigh motor costs against expected rewards. It has been posited that humans have an innate attraction towards effort minimization and that executive control is required to overcome this prepotent disposition. With sedentary behaviors increasing at the cost of millions of dollars spent in health care and productivity losses due to physical inactivity-related deaths, understanding the predictors of sedentary behaviors will improve future intervention development and precision medicine approaches. In 64 healthy older adults participating in a 6-month aerobic exercise intervention, we use neuroimaging (resting state functional connectivity), baseline measures of executive function and accelerometer measures of time spent sedentary to predict future changes in objectively measured time spent sedentary in daily life. Using cross-validation and bootstrap resampling, our results demonstrate that functional connectivity between 1) the anterior cingulate cortex and the supplementary motor area and 2) the right anterior insula and the left temporoparietal/temporooccipital junction, predict changes in time spent sedentary, whereas baseline cognitive, behavioral and demographic measures do not. Previous research has shown activation in and between the anterior cingulate and supplementary motor area as well as in the right anterior insula during effort avoidance and tasks that integrate motor costs and reward benefits in effort-based decision making. Our results add important knowledge toward understanding mechanistic associations underlying complex sedentary behaviors.

neuroscience↗

bioRxiv2020

Rate volatility and asymmetric segregation diversify mutation burden in mutator cells.

Mutations that compromise mismatch repair (MMR) or DNA polymerase exonuclease domains produce mutator phenotypes capable of fueling cancer evolution. Tandem defects in these pathways dramatically increase mutation rate. Here, we model how mutator phenotypes expand genetic heterogeneity in budding yeast cells using a single-cell resolution approach that tallies all replication errors arising from individual divisions. The distribution of count data from cells lacking MMR and polymerase proofreading was broader than expected for a single rate, consistent with volatility of the mutator phenotype. The number of mismatches that segregated to the mother and daughter cells after the initial round of replication co-varied, suggesting that mutagenesis in each division is governed by a different underlying rate. The distribution of "fixed" mutation counts that cells inherit is further broadened by an unequal sharing of mutations due to semiconservative replication and Mendelian segregation. Modeling suggests that this asymmetric segregation may diversify mutation burden in mutator-driven tumors.

cancer biology↗