ReviewHuman genomics2018
Nonparametric approaches for population structure analysis.
Review in Human genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Optimizing phenotypic data analysis strategies to enhance genomic prediction in winter wheat breeding.Frontiers in plant science · 2026Article
- Decoding the DNA of Anatolian water buffalo by genome-wide discoveries for body size and ultrasound carcass traits.BMC veterinary research · 2025Article
- Advances in Whole Genome Sequencing: Methods, Tools, and Applications in Population Genomics.International journal of molecular sciences · 2025Review
- Article
- The genetic status and rescue measure for a geographically isolated population of Amur tigers.Scientific reports · 2024Article
- Genetic Diversity within a Collection of Italian Maize Inbred Lines: A Resource for Maize Genomics and Breeding.Plants (Basel, Switzerland) · 2024Article
- Phenotypic plasticity and genetic diversity shed light on endemism of rareEcology and evolution · 2023Article
- Evaluation of the Genetic Diversity, Population Structure and Selection Signatures of Three Native Chinese Pig Populations.Animals : an open access journal from MDPI · 2023Article
- Racial disparities in minimally invasive esophagectomy and gastrectomy for upper GI malignancies.Surgical endoscopy · 2022Article
- A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci.Scientific reports · 2022Article
- Quantitative evaluation of nonlinear methods for population structure visualization and inference.G3 (Bethesda, Md.) · 2022Article
- Whole Transcriptome Sequencing Unveils the Genomic Determinants of Putative Somaclonal Variation in Mint (International journal of molecular sciences · 2022Article
- A Deep Learning Approach to Population Structure Inference in Inbred Lines of Maize.Frontiers in genetics · 2020Article
- Heterogeneity in the distribution of 159 drug-response related SNPs in world populations and their genetic relatedness.PloS one · 2020Article
- Genomic biosurveillance of forest invasive alien enemies: A story written in code.Evolutionary applications · 2020Review
Corrections and comments
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The analysis of population structure has many applications in medical and population genetic research. Such analysis is used to provide clear insight into the underlying genetic population substructure and is a crucial prerequisite for any analysis of genetic data. The analysis involves grouping individuals into subpopulations based on shared genetic variations. The most widely used markers to study the variation of DNA sequences between populations are single nucleotide polymorphisms. Data preprocessing is a necessary step to assess the quality of the data and to determine which markers or individuals can reasonably be included in the analysis. After preprocessing, several methods can be utilized to uncover population substructure, which can be categorized into two broad approaches: parametric and nonparametric. Parametric approaches use statistical models to infer population structure and assign individuals into subpopulations. However, these approaches suffer from many drawbacks that make them impractical for large datasets. In contrast, nonparametric approaches do not suffer from these drawbacks, making them more viable than parametric approaches for analyzing large datasets. Consequently, nonparametric approaches are increasingly used to reveal population substructure. Thus, this paper reviews and discusses the nonparametric approaches that are available for population structure analysis along with some implications to resolve challenges.
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