Statistical learning of large-scale genetic data: How to run a genome-wide association study of gene-expression data using the 1000 GenomesProject data

Sugolov, A.; Emmenegger, E.; Sun, L.; Paterson, A. D.

doi:10.1101/2022.09.03.506492

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bioRxiv · 10.1101/2022.09.03.506492

Statistical learning of large-scale genetic data: How to run a genome-wide association study of gene-expression data using the 1000 GenomesProject data

Sugolov, A.·Emmenegger, E.·Sun, L.·Paterson, A. D.

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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 studies (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 [~]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 and/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 Zenodo to further enhance reproducible research and training.

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Sugolov, A., Emmenegger, E., Sun, L., Paterson, A. D.. 2022-09-06. Statistical learning of large-scale genetic data: How to run a genome-wide association study of gene-expression data using the 1000 GenomesProject data. https://doi.org/10.1101/2022.09.03.506492

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