2024/03/04 by Yunyi Shen, Shen, Yunyi, Lorenzo Masoero +5
Biochemistry, Genetics and Molecular Biology · #Bioinformatics and Genomic Networks #FOS: Biological sciences #FOS: Computer and information sciences #Gene expression and cancer classification #Genetic Associations and Epidemiology #Genomics (q-bio.GN) #Methodology (stat.ME) #Quantitative Methods (q-bio.QM)
paper · doi:10.48550/arxiv.2403.02154
openalex publication_date 2024/03/04 · openalex created_date 2024/03/06 · openalex updated_date 2026/07/28
Collecting genomics data across multiple heterogeneous populations (e.g., across different cancer types) has the potential to improve our understanding of disease. Despite sequencing advances, though, resources often remain a constraint when gathering data. So it would be useful for experimental design if experimenters with access to a pilot study could predict the number of new variants they might expect to find in a follow-up study: both the number of new variants shared between the populations and the total across the populations. While many authors have developed prediction methods for the single-population case, we show that these predictions can fare poorly across multiple populations that are heterogeneous. We prove that, surprisingly, a natural extension of a state-of-the-art single-population predictor to multiple populations fails for fundamental reasons. We provide the first predictor for the number of new shared variants and new total variants that can handle heterogeneity in multiple populations. We show that our proposed method works well empirically using real cancer and population genetics data.