ArticleSource code for biology and medicine2019
IPCAPS: an R package for iterative pruning to capture population structure.
Article in Source code for biology and medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure.Briefings in bioinformatics · 2022Pooled it
- Association between human genomes and mycobacterial genomes contributes to severity and transmissibility of pulmonary tuberculosis.iScience · 2026Article
- Pharmacogenomic landscape of the Thai population from genome sequencing of 949 individuals.Scientific reports · 2024Article
- Identification of novel putative alleles related to important agronomic traits of wheat using robust strategies in GWAS.Scientific reports · 2023Article
- Including diverse and admixed populations in genetic epidemiology research.Genetic epidemiology · 2022Article
- A novel pathogenesis concept of biliary atresia approached by combined molecular strategies.PloS one · 2022Article
- Population Structure and Relatedness for Genome-Wide Association Studies.Methods in molecular biology (Clifton, N.J.) · 2022Article
- A different view on fine-scale population structure in Western African populations.Human genetics · 2020Article
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7 authors.
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Abstract
backgroundResolving population genetic structure is challenging, especially when dealing with closely related or geographically confined populations. Although Principal Component Analysis (PCA)-based methods and genomic variation with single nucleotide polymorphisms (SNPs) are widely used to describe shared genetic ancestry, improvements can be made especially when fine-scale population structure is the target.
resultsThis work presents an R package called IPCAPS, which uses SNP information for resolving possibly fine-scale population structure. The IPCAPS routines are built on the iterative pruning Principal Component Analysis (ipPCA) framework that systematically assigns individuals to genetically similar subgroups. In each iteration, our tool is able to detect and eliminate outliers, hereby avoiding severe misclassification errors.
conclusionsIPCAPS supports different measurement scales for variables used to identify substructure. Hence, panels of gene expression and methylation data can be accommodated as well. The tool can also be applied in patient sub-phenotyping contexts. IPCAPS is developed in R and is freely available from http://bio3.giga.ulg.ac.be/ipcaps.
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