Biology subjects

Lucas, A. M.

Publications and source records attributed to Lucas, A. M..

3 recordsLinked to original sources

bioRxiv2021

CellProfiler 4: Improvements in Speed, Utility and Usability

BackgroundImaging data contains a substantial amount of information which can be difficult to evaluate by eye. With the expansion of high throughput microscopy methodologies producing increasingly large datasets, automated and objective analysis of the resulting images is essential to effectively extract biological information from this data. CellProfiler is a free, open source image analysis program which enables researchers to generate modular pipelines with which to process microscopy images into interpretable measurements. ResultsHerein we describe CellProfiler 4, a new version of this software with expanded functionality. Based on user feedback, we have made several user interface refinements to improve the usability of the software. We introduced new modules to expand the capabilities of the software. We also evaluated performance and made targeted optimizations to reduce the time and cost associated with running common large-scale analysis pipelines. ConclusionsCellProfiler 4 provides significantly improved performance in complex workflows compared to previous versions. This release will ensure that researchers will have continued access to CellProfilers powerful computational tools in the coming years.

cell biology↗

bioRxiv2019

Exome-by-phenome-wide rare variant gene burden association with electronic health record phenotypes

BackgroundBy coupling large-scale DNA sequencing with electronic health records (EHR), \"genome-first\" approaches can enhance our understanding of the contribution of rare genetic variants to disease. Aggregating rare, loss-of-function variants in a candidate gene into a \"gene burden\" to test for association with EHR phenotypes can identify both known and novel clinical implications for the gene in human disease. However, this methodology has not yet been applied on both an exome-wide and phenome-wide scale, and the clinical ontologies of rare loss-of-function variants in many genes have yet to be described.\n\nMethodsWe leveraged whole exome sequencing (WES) data in participants (N=11,451) in the Penn Medicine Biobank (PMBB) to address on an exome-wide scale the association of a burden of rare loss-of-function variants in each gene with diverse EHR phenotypes using a phenome-wide association study (PheWAS) approach. For discovery, we collapsed rare (minor allele frequency (MAF) [≤] 0.1%) predicted loss-of-function (pLOF) variants (i.e. frameshift insertions/deletions, gain/loss of stop codon, or splice site disruption) per gene to perform a gene burden PheWAS. Subsequent evaluation of the significant gene burden associations was done by collapsing rare (MAF [≤] 0.1%) missense variants with Rare Exonic Variant Ensemble Learner (REVEL) scores [≥] 0.5 into corresponding yet distinct gene burdens, as well as interrogation of individual low-frequency to common (MAF > 0.1%) pLOF variants and missense variants with REVEL[≥] 0.5. We replicated our findings using the UK Biobanks (UKBB) whole exome sequence dataset (N=49,960).\n\nResultsFrom the pLOF-based discovery phase, we identified 106 gene burdens with phenotype associations at p<10-6 from our exome-by-phenome-wide association studies. Positive-control associations included TTN (cardiomyopathy, p=7.83E-13), MYBPC3 (hypertrophic cardiomyopathy, p=3.48E-15), CFTR (cystic fibrosis, p=1.05E-15), CYP2D6 (adverse effects due to opiates/narcotics, p=1.50E-09), and BRCA2 (breast cancer, p=1.36E-07). Of the 106 genes, 12 gene-phenotype relationships were also detected by REVEL-informed missense-based gene burdens and 19 by single-variant analyses, demonstrating the robustness of these gene-phenotype relationships. Three genes showed evidence of association using both additional methods (BRCA1, CFTR, TGM6), leading to a total of 28 robust gene-phenotype associations within PMBB. Furthermore, replication studies in UKBB validated 30 of 106 gene burden associations, of which 12 demonstrated robustness in PMBB.\n\nConclusionOur study presents 12 exome-by-phenome-wide robust gene-phenotype associations, which include three proof-of-concept associations and nine novel findings. We show the value of aggregating rare pLOF variants into gene burdens on an exome-wide scale for unbiased association with EHR phenotypes to identify novel clinical ontologies of human genes. Furthermore, we show the significance of evaluating gene burden associations through complementary, yet non-overlapping genetic association studies from the same dataset. Our results suggest that this approach applied to even larger cohorts of individuals with WES or whole-genome sequencing data linked to EHR phenotype data will yield many new insights into the relationship of genetic variation and disease phenotypes.

genomics↗

bioRxiv2018

Collective feature selection to identify crucial epistatic variants

BackgroundMachine learning methods have gained popularity and practicality in identifying linear and non-linear effects of variants associated with complex disease/traits. Detection of epistatic interactions still remains a challenge due to the large number of features and relatively small sample size as input, thus leading to the so-called \"short fat data\" problem. The efficiency of machine learning methods can be increased by limiting the number of input features. Thus, it is very important to perform variable selection before searching for epistasis. Many methods have been evaluated and proposed to perform feature selection, but no single method works best in all scenarios. We demonstrate this by conducting two separate simulation analyses to evaluate the proposed collective feature selection approach.\n\nResultsThrough our simulation study we propose a collective feature selection approach to select features that are in the \"union\" of the best performing methods. We explored various parametric, non-parametric, and data mining approaches to perform feature selection. We choose our top performing methods to select the union of the resulting variables based on a user-defined percentage of variants selected from each method to take to downstream analysis. Our simulation analysis shows that non-parametric data mining approaches, such as MDR, may work best under one simulation criteria for the high effect size (penetrance) datasets, while non-parametric methods designed for feature selection, such as Ranger and Gradient boosting, work best under other simulation criteria. Thus, using a collective approach proves to be more beneficial for selecting variables with epistatic effects also in low effect size datasets and different genetic architectures. Following this, we applied our proposed collective feature selection approach to select the top 1% of variables to identify potential interacting variables associated with Body Mass Index (BMI) in ~44,000 samples obtained from Geisingers MyCode Community Health Initiative (on behalf of DiscovEHR collaboration).\n\nConclusionsIn this study, we were able to show that selecting variables using a collective feature selection approach could help in selecting true positive epistatic variables more frequently than applying any single method for feature selection via simulation studies. We were able to demonstrate the effectiveness of collective feature selection along with a comparison of many methods in our simulation analysis. We also applied our method to identify non-linear networks associated with obesity.

bioinformatics↗