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An important research problem is feature selection, which ranks candidate variables based on their relevance to the outcome variable and retains those that satisfy a chosen criterion. This article proposes a computationally efficient variable selection method based on principal component analysis tailored to a binary classification problem or case\u2010control study. This method is accessible and is suitable for the analysis of high\u2010dimensional datasets. We demonstrate the superior performance of our method through extensive simulations. A semi\u2010real gene expression dataset, a challenging childhood acute lymphoblastic leukemia gene expression study, and a GWAS that attempts to identify single\u2010nucleotide polymorphisms (SNPs) associated with rice grain length further demonstrate the usefulness of our method in genomic applications. We expect our method to accurately identify important features and reduce the False Discovery Rate (fdr) by accounting for the correlation between variables and by de\u2010noising data in the training phase, which also makes it robust to mild outliers in the training data. Our method is almost as fast as univariate filters, so it allows valid statistical inference. The ability to make such inferences sets this method apart from most current multivariate statistical tools designed for today's high\u2010dimensional data.<\/jats:p>","DOI":"10.1002\/sim.70110","type":"journal-article","created":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T09:16:33Z","timestamp":1748942193000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Using a Supervised Principal Components Analysis for Variable Selection in High\u2010Dimensional Datasets Reduces False Discovery Rates"],"prefix":"10.1002","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9476-6837","authenticated-orcid":false,"given":"Insha","family":"Ullah","sequence":"first","affiliation":[{"name":"Research School of Finance, Actuarial Studies &amp; Statistics, ANU College of Business &amp; Economics The Australian National University  Canberra Australian Capital Territory 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