Biology subjects

Norris, E. T.

Publications and source records attributed to Norris, E. T..

4 recordsLinked to original sources

bioRxiv2026

Epidemiology of Legionella: Genome-bAsed Typing (el_gato) - a new bioinformatic tool for identifying sequence-based types of Legionella pneumophila from whole genome sequencing data

Sequence-based typing (SBT) via Sanger sequencing has been the standard for describing Legionella pneumophila relationships for two decades. SBT involves sequencing seven loci, identifying alleles using the United Kingdom Health Security Agency (UKHSA) database, and inferring the corresponding sequence type (ST). While similar SBT approaches for other organisms can be easily adapted to whole genome sequencing (WGS), L. pneumophila presents two known challenges for this adaptation: multiple copies of one locus (mompS) and extensive heterogeneity in a second locus (neuA/neuAh). Although several computational methods have been proposed to address these issues, a WGS-based replacement with equal resolution to traditional SBT has been elusive. To address this gap, we developed el_gato (Epidemiology of Legionella: Genome-bAsed Typing; https://github.com/CDCgov/el_gato), which offers several advantages over existing methods: (1) a novel approach for resolving multiple mompS alleles identified in the same isolate, (2) the ability to capture diverse neuA/neuAh alleles, (3) fast runtime with an average of 27.7 seconds per sample, (4) easy installation via Bioconda or Docker and (5) an updated database as of March 2025. el_gato works with either paired-end short reads or genome assemblies, performing more accurately with paired-end short reads at least 250 base pairs (bp) in length. We compared el_gato against two other in silico SBT tools ("mompS", hereafter referred to as mompS tool and "legsta") using a dataset of 441 isolates with sequence types (STs) previously determined by Sanger-based sequencing. el_gato correctly identified the ST for 98.9% of the test isolates, compared to 95.2% for the mompS tool and 42.2% for legsta, demonstrating a significant improvement compared to the mompS tool (adjusted p = 1.06e-3) and legsta (adjusted p = 4.24e-55) in ST identification. Furthermore, el_gatos determination of ST was not significantly different from Sanger sequencing (adjusted p = 0.442). In summary, el_gato significantly improves in silico SBT and given its growing adoption, is poised to support the public health community.

bioinformatics↗

bioRxiv2022

Rye: genetic ancestry inference at biobank scale

Biobank projects around the world are generating genomic data for many thousands and even millions of individuals. Computational methods are needed to handle these massive data sets, including tools for genetic ancestry (GA) inference. Current methods for GA inference are generally accurate, but they are slow and do not scale to biobank-size genomic datasets. Here we present Rye - a new algorithm for GA inference at biobank scale. We compare the accuracy and runtime performance of Rye to the widely used RFMix and ADMIXTURE programs, and we apply it to a dataset of 488,221 genome-wide variant samples from the UK Biobank. Rye infers GA based on principal component analysis (PCA) of genomic variant samples from ancestral reference populations and query individuals. The algorithms accuracy is powered by Metropolis-Hastings optimization and its speed is provided by non-negative least squares (NNLS) regression. Rye produces highly accurate GA estimates for three-way admixed populations - African, European, and Native American - compared to RFMix and ADMIXTURE (R2 = 0.998 - 1.00), and shows 50x runtime improvement compared to ADMIXTURE on the UK Biobank dataset. Rye analysis of UK Biobank samples demonstrates how it can be used to infer GA at different levels of relatedness. We discuss user consideration and options for the use of Rye; the program and its documentation are distributed on the GitHub repository: https://github.com/healthdisparities/rye.

bioinformatics↗

bioRxiv2019

Admixture-enabled selection for rapid adaptive evolution in the Americas

BackgroundAdmixture occurs when previously isolated populations come together and exchange genetic material. We hypothesized that admixture can enable rapid adaptive evolution in human populations by introducing novel genetic variants (haplotypes) at intermediate frequencies, and we tested this hypothesis via the analysis of whole genome sequences sampled from admixed Latin American populations in Colombia, Mexico, Peru, and Puerto Rico.\n\nResultsOur screen for admixture-enabled selection relies on the identification of loci that contain more or less ancestry from a given source population than would be expected given the genome-wide ancestry frequencies. We employed a combined evidence approach to evaluate levels of ancestry enrichment at (1) single loci across multiple populations and (2) multiple loci that function together to encode polygenic traits. We found cross-population signals of African ancestry enrichment at the major histocompatibility locus on chromosome 6, consistent with admixture-enabled selection for enhanced adaptive immune response. Several of the human leukocyte antigen genes at this locus (HLA-A, HLA-DRB51 and HLA-DRB5) showed independent evidence of positive selection prior to admixture, based on extended haplotype homozygosity in African populations. A number of traits related to inflammation, blood metabolites, and both the innate and adaptive immune system showed evidence of admixture-enabled polygenic selection in Latin American populations.\n\nConclusionsThe results reported here, considered together with the ubiquity of admixture in human evolution, suggest that admixture serves as a fundamental mechanism that drives rapid adaptive evolution in human populations.

genomics↗

bioRxiv2017

Assortative mating on ancestry-variant traits in admixed Latin American populations

BackgroundAssortative mating is a universal feature of human societies, and individuals from ethnically diverse populations are known to mate assortatively based on similarities in genetic ancestry. However, little is currently known regarding the exact phenotypic cues, or their underlying genetic architecture, which inform ancestry-based assortative mating.\n\nResultsWe developed a novel approach, using genome-wide analysis of ancestry-specific haplotypes, to evaluate ancestry-based assortative mating on traits whose expression varies among the three continental population groups - African, European, and Native American - that admixed to form modern Latin American populations. Application of this method to genome sequences sampled from Colombia, Mexico, Peru, and Puerto Rico revealed widespread ancestry-based assortative mating. We discovered a number of anthropometric traits (body mass, height, facial development and waist-hip ratio) and neurological attributes (educational attainment and schizophrenia) that serve as phenotypic cues for ancestry-based assortative mating. Major histocompatibility complex (MHC) loci show population-specific patterns of both assortative and disassortative mating in Latin America. Ancestry-based assortative mating in the populations analyzed here appears to be driven primarily by African ancestry.\n\nConclusionsThis study serves as an example of how population genomic analyses can yield novel insights into human behavior.

genomics↗