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

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 ∼ 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.

Accurate Estimation of Context-Dependent False Discovery Rates in Top-Down Proteomics

Within the last several years, top-down proteomics has emerged as a high throughput technique for protein and proteoform identification. This technique has the potential to identify and characterize thousands of proteoforms within a single study, but the absence of accurate false discovery rate (FDR) estimation could hinder the adoption and consistency of top-down proteomics in the future. In automated identification and characterization of proteoforms, FDR calculation strongly depends on the context of the search. The context includes MS data quality, the database being interrogated, the search engine, and the parameters of the search. Particular to top-down proteomics-there are four molecular levels of study: proteoform spectral match (PrSM), protein, isoform, and proteoform. Here, a context-dependent framework for calculating an accurate FDR at each level was designed, implemented, and validated against a manually curated training set with 546 confirmed proteoforms. We examined several search contexts and found that an FDR calculated at the PrSM level under-reported the true FDR at the protein level by an average of 24-fold. We present a new open-source tool, the TDCD_FDR_Calculator, which provides a scalable, context-dependent FDR calculation that can be applied post-search to enhance the quality of results in top-down proteomics from any search engine.