Biology subjects

Ojewunmi, O.

Publications and source records attributed to Ojewunmi, O..

3 recordsLinked to original sources

bioRxiv2025

Multi-ancestry fine-mapping accounting for ancestral and environmental heterogeneity improves resolution and interpretation

Amongst genome-wide association studies (GWAS) across diverse populations, allelic effect heterogeneity may arise due to differences in genetic ancestry and environmental exposures. This heterogeneity impacts the refinement of sets of potential causal variants underlying genetic associations through statistical fine-mapping. We introduce two multi-ancestry fine-mapping methods, MR-MEGAfm and env-MR-MEGAfm, allowing for multiple causal variants in a genomic region. Both methods integrate GWAS summary statistics and differing linkage disequilibrium from multiple cohorts; env-MR-MEGAfm additionally incorporates summary-level environmental covariates. Through simulations, we show that, when allelic heterogeneity is correlated with environmental exposures and ancestry, env-MR-MEGAfm yields improved resolution over MR-MEGAfm and similar resolution to SuSiEx. In twelve sex-stratified African GWAS of low-density lipoprotein cholesterol in 19,589 individuals, MR-MEGAfm and env-MR-MEGAFM (adjusting for urban status) identify five variants with posterior probability > 0.5 within two loci. One variant showed heterogeneity only due to ancestry, while three showed heterogeneity due only to urban status.

genetics↗

bioRxiv2024

Accounting for heterogeneity due to environmental sources in meta-analysis of genome-wide association studies

Meta-analysis of genome-wide association studies (GWAS) across diverse populations offers power gains to identify loci associated with complex traits and diseases. Often heterogeneity in effect sizes across populations will be correlated with genetic ancestry and environmental exposures (e.g. lifestyle factors). We present an environment-adjusted meta-regression model (env-MR-MEGA) to detect genetic associations by adjusting for and quantifying environmental and ancestral heterogeneity between populations. In simulations, env-MR-MEGA had similar or greater association power than MR-MEGA, with notable gains when the environmental factor had a greater correlation with the trait than ancestry. In our analysis of low-density lipoprotein cholesterol in [~]19,000 individuals across twelve sex-stratified GWAS from Africa, adjusting for sex, BMI, and urban status, we identified additional heterogeneity beyond ancestral effects for nine variants. Env-MR-MEGA provides an approach to account for environmental effects using summary-level data, making it a useful tool for meta-analyses without the need to share individual-level data.

genetics↗

bioRxiv2021

COPILOT: a Containerised wOrkflow for Processing ILlumina genOtyping daTa

BackgroundThe Illumina genotyping microarrays generate data in image format, which is processed by the platform-specific software GenomeStudio, followed by an array of complex bioinformatics analyses. This process can be time-consuming, lead to reproducibility errors, and be a daunting task for novice bioinformaticians. ResultsHere we introduce the COPILOT (Containerised wOrkflow for Processing ILlumina genOtyping daTa) protocol, which provides an in-depth and clear guide to process raw Illumina genotype data in GenomeStudio, followed by a containerised workflow to automate an array of complex bioinformatics analyses involved in a GWAS quality control (QC). The COPILOT protocol was applied to two independent cohorts consisting of 2791 and 479 samples genotyped on the Infinium Global Screening (GSA) array with Multi-disease (MD) drop-in (~750,000 markers) and the Infinium H3Africa consortium array (~2,200,000 markers) respectively. Following the COPILOT protocol, an average sample quality improvement of 1.24% was observed across sample call rates, with notable improvement for low-quality samples. For example, from the 3270 samples processed, 141 samples had an initial sample call rate below 98%, averaging 96.6% (95% CI 95.6-97.7%), which is considered below the acceptable sample call rate threshold for a typical GWAS analysis. However, following the COPILOT protocol, all 141 samples had a call rate above 98% after QC and averaged 99.6% (95% CI 99.5-99.7%). In addition, the COPILOT pipeline automatically identified potential data issues, including gender discrepancies, heterozygosity outliers, related individuals, and population outliers through ancestry estimation. ConclusionsThe COPILOT protocol makes processing Illumina genotyping data transparent, effortless and reproducible. The container is deployable on multiple platforms, improves data quality, and the end product is analysis-ready PLINK formatted data, with a comprehensive and interactive summary report to guide the user for further data analyses.

genomics↗