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Biology subjects

Beesley, L. J.

Publications and source records attributed to Beesley, L. J..

4 recordsLinked to original sources

SARS-CoV-2 variant transition dynamics are associated with vaccination rates, number of co-circulating variants, and natural immunity

BackgroundThroughout the COVID-19 pandemic, the SARS-CoV-2 virus has continued to evolve, with new variants outcompeting existing variants and often leading to different dynamics of disease spread. MethodsIn this paper, we performed a retrospective analysis using longitudinal sequencing data to characterize differences in the speed, calendar timing, and magnitude of 13 SARS-CoV-2 variant waves/transitions for 215 countries and sub-country regions, between October 2020 and October 2022. We then clustered geographic locations in terms of their variant behavior across all Omicron variants, allowing us to identify groups of locations exhibiting similar variant transitions. Finally, we explored relationships between heterogeneity in these variant waves and time-varying factors, including vaccination status of the population, governmental policy, and the number of variants in simultaneous competition. FindingsThis work demonstrates associations between the behavior of an emerging variant and the number of co-circulating variants as well as the demographic context of the population. We also observed an association between high vaccination rates and variant transition dynamics prior to the Mu and Delta variant transitions. InterpretationThese results suggest the behavior of an emergent variant may be sensitive to the immunologic and demographic context of its location. Additionally, this work represents the most comprehensive characterization of variant transitions globally to date. FundingLaboratory Directed Research and Development (LDRD), Los Alamos National Laboratory Research in contextO_ST_ABSEvidence before this studyC_ST_ABSSARS-CoV-2 variants with a selective advantage are continuing to emerge, resulting in variant transitions that can give rise to new waves in global COVID-19 cases and changing dynamics of disease spread. While variant transitions have been well studied individually, more work is needed to better understand how variant transitions have occurred in the past and how properties of these transitions may relate to vaccination rates, natural immunity, and population demographics. Added value of this studyOur retrospective study integrates metadata based on 12.8 million SARS-CoV-2 sequences available through the Global Initiative on Sharing All Influenza Data (GISAID) with clinical and demographic data to characterize heterogeneity in variant waves/transitions across the globe throughout the COVID-19 pandemic. We demonstrate that properties of the variant transitions (e.g., speed, timing, and magnitude of the transition) are associated with vaccination rates, prior COVID-19 cases, and the number of co-circulating variants in competition. Implications of all the available evidenceOur results indicate that there is substantial heterogeneity in how an emerging variant may compete with other viral variants across locations, and suggest that each locations contemporaneous immunologic landscape may play a role in these interactions.

bioinformatics↗

Cancer PRSweb - an Online Repository with Polygenic Risk Scores (PRS) for Major Cancer Traits and Their Phenome-wide Exploration in Two Independent Biobanks

To facilitate scientific collaboration on polygenic risk scores (PRS) research, we created an extensive PRS online repository for 49 common cancer traits integrating freely available genome-wide association studies (GWAS) summary statistics from three sources: published GWAS, the NHGRI-EBI GWAS Catalog, and UK Biobank-based GWAS. Our framework condenses these summary statistics into PRS using various approaches such as linkage disequilibrium pruning / p-value thresholding (fixed or data-adaptively optimized thresholds) and penalized, genome-wide effect size weighting. We evaluated the PRS in two biobanks: the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort at Michigan Medicine, and the population-based UK Biobank (UKB). For each PRS construct, we provide measures on predictive performance, calibration, and discrimination. Besides PRS evaluation, the Cancer-PRSweb platform features construct downloads and phenome-wide PRS association study results (PRS-PheWAS) for predictive PRS. We expect this integrated platform to accelerate PRS-related cancer research.

genetics↗

A Modeling Framework for Exploring Sampling and Observation Process Biases in Genome and Phenome-wide Association Studies using Electronic Health Records

Large-scale agnostic association analyses based on existing observational health care databases such as electronic health records have been a topic of increasing interest in the scientific community. However, particular challenges of non-probability sampling and phenotype misclassification associated with the use of these data sources are often ignored in standard analyses. In general, the extent of the bias that may be introduced by ignoring these factors is unknown. In this paper, we develop a statistical framework for characterizing the degree of bias expected in association studies based on electronic health records when disease status misclassification and the sampling mechanism are ignored. Through a sensitivity analysis type approach, this framework can be used to obtain plausible values for parameters of interest given results obtained from standard naive analysis methods under varying degree of misclassification and sampling biases. We develop an online tool for performing this sensitivity analysis in some special cases that occur frequently. Simulations demonstrate promising properties of the proposed way of characterizing biases. We apply our approach to study bias in genetic association studies using data from the Michigan Genomics Initiative, a longitudinal biorepository effort within Michigan Medicine.

epidemiology↗

Exploring Various Polygenic Risk Scores for Basal Cell Carcinoma, Cutaneous Squamous Cell Carcinoma and Melanoma in the Phenomes of the Michigan Genomics Initiative and the UK Biobank

Polygenic risk scores (PRS) are designed to serve as a single summary measure, condensing information from a large number of genetic variants associated with a disease. They have been used for stratification and prediction of disease risk. The construction of a PRS often depends on the purpose of the study, the available data/summary estimates, and the underlying genetic architecture of a disease. In this paper, we consider several choices for constructing a PRS using summary data obtained from various publicly-available sources including the UK Biobank and evaluate their abilities to predict outcomes derived from electronic health records (EHR). Weexamine the three most common skin cancer subtypes in the USA: basal cellcarcinoma, cutaneous squamous cell carcinoma, and melanoma. The genetic risk profiles of subtypes may consist of both shared and unique elements and we construct PRS to understand the common versus distinct etiology. This study is conducted using data from 30,702 unrelated, genotyped patients of recent European descent from the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort within Michigan Medicine. Using these PRS for various skin cancer subtypes, we conduct a phenome-wide association study (PheWAS) within the MGI data to evaluate their association with secondary traits. PheWAS results are then replicated using population-based UK Biobank data. We develop an accompanying visual catalog called PRSweb that provides detailed PheWAS results and allows users to directly compare different PRS construction methods. The results of this study can provide guidance regarding PRS construction in future PRS-PheWAS studies using EHR data involving disease subtypes.\n\nAuthor summaryIn the study of genetically complex diseases, polygenic risk scores synthesize information from multiple genetic risk factors to provide insight into a patients risk of developing a disease based on his/her genetic profile. These risk scores can be explored in conjunction with health and disease information available in the electronic medical records. They may be associated with diseases that may be related to or precursors of the underlying disease of interest. Limited work is available guiding risk score construction when the goal is to identify associations across the medical phenome. In this paper, we compare different polygenic risk score construction methods in terms of their relationships with the medical phenome. We further propose methods for using these risk scores to decouple the shared and unique genetic profiles of related diseases and to explore related diseases shared and unique secondary associations. Leveraging and harnessing the rich data resources of the Michigan Genomics Initiative, a biorepository effort at Michigan Medicine, and the larger population-based UK Biobank study, we investigated the performance of genetic risk profiling methods for the three most common types of skin cancer: melanoma, basal cell carcinoma and squamous cell carcinoma.

genetics↗