Biology subjects

McGuirl, M. R.

Publications and source records attributed to McGuirl, M. R..

2 recordsLinked to original sources

bioRxiv2019

Hierarchical clustering of gene-level association statistics reveals shared and differential genetic architecture among traits in the UK Biobank

Genome-wide association (GWA) studies have generally focused on a single phenotype of interest. Emerging biobanks that pair genotype data from thousands of individuals with phenotype data using medical records or surveys enable testing for genetic associations in each phenotype assayed. However, methods for characterizing shared genetic architecture among multiple traits are lagging behind. Here, we present a new method, Ward clustering to identify Internal Node branch length outliers using Gene Scores (WINGS), for characterizing shared and divergent genetic architecture among multiple phenotypes. The objective of WINGS (freely available at https://github.com/ramachandran-lab/PEGASUS-WINGS) is to identify groups of phenotypes, or \"clusters\", that share a core set of genes enriched for mutations in cases. We show in simulations that WINGS can reliably detect phenotype clusters across a range of percent shared architecture and number of phenotypes included. We then use the gene-level association test PEGASUS with WINGS to characterize shared genetic architecture among 87 case-control and seven quantitative phenotypes in 349,468 unrelated European-ancestry individuals from the UK Biobank. We identify 10 significant phenotype clusters that contain two to eight phenotypes. One significant cluster of seven immunological phenotypes is driven by seven genes; these genes have each been associated with two or more of those same phenotypes in past publications. WINGS offers a precise and efficient new application of Ward hierarchical clustering to generate hypotheses regarding shared genetic architecture among phenotypes in the biobank era.

genomics↗

bioRxiv2018

Fast Estimation of Recombination Rates Using Topological Data Analysis

Accurate estimation of recombination rates is critical for studying the origins and maintenance of genetic diversity. Because the inference of recombination rates under a full evolutionary model is computationally expensive, an alternative approach using topological data analysis (TDA) has been proposed. Previous TDA methods used information contained solely in the first Betti number ({beta}1)of the cloud of genomes, which relates to the number of loops that can be detected within a genealogy. While these methods are considerably less computationally intensive than current biological model-based methods, these explorations have proven difficult to connect to the theory of the underlying biological process of recombination, and consequently have unpredictable behavior under different perturbations of the data. We introduce a new topological feature with a natural connection to coalescent models, which we call{psi} . We show that{psi} and {beta}1 are differentially affected by changes to the structure of the data and use them in conjunction to provide a robust, efficient, and accurate estimator of recombination rates, TREE. Compared to previous TDA methods, TREE more closely approximates of the results of commonly used model-based methods. These characteristics make TREE well suited as a first-pass estimator of recombination rate heterogeneity or hotspots throughout the genome. In addition, we present novel arguments relating {beta}1 to population genetic models; our work justifies the use of topological statistics as summaries of distributions of genome sequences and describes a new, unintuitive relationship between topological summaries of distance and the footprint of recombination on genome sequences.

evolutionary biology↗