Statistical Learning of Large-Scale Genetic Data: How to Run a Genome-Wide Association Study of Gene-Expression Data Using the 1000 Genomes Project Data

Anton Sugolov (), Eric Emmenegger (), Andrew D. Paterson () and Lei Sun ()
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Anton Sugolov: University of Toronto
Eric Emmenegger: University of Toronto
Andrew D. Paterson: University of Toronto
Lei Sun: University of Toronto

Statistics in Biosciences, 2024, vol. 16, issue 1, No 13, 250-264

Abstract: Abstract Teaching statistics through engaging applications to contemporary large-scale datasets is essential to attracting students to the field. To this end, we developed a hands-on, week-long workshop for senior high-school or junior undergraduate students, without prior knowledge in statistical genetics but with some basic knowledge in data science, to conduct their own genome-wide association study (GWAS). The GWAS was performed for open source gene expression data, using publicly available human genetics data. Assisted by a detailed instruction manual, students were able to obtain $$\sim$$ ∼ 1.4 million p-values from a real scientific study, within several days. This early motivation kept students engaged in learning the theories that support their results, including regression, data visualization, results interpretation, and large-scale multiple hypothesis testing. To further their learning motivation by emphasizing the personal connection to this type of data analysis, students were encouraged to make short presentations about how GWAS has provided insights into the genetic basis of diseases that are present in their friends or families. The appended open source, step-by-step instruction manual includes descriptions of the datasets used, the software needed, and results from the workshop. Additionally, scripts used in the workshop are archived on Github and Zenodo to further enhance reproducible research and training.

Keywords: 1000 Genomes Project; Data Visualization; Genome-wide Association Study; Gene Expression; Hands-on Experience; Large-scale Data Analysis; Multiple Hypothesis Testing; Open Resource; Reproducible Research (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12561-023-09375-9

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