Nonparametric approaches for population structure analysis

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.

Source and spread dynamics of mountain pine beetle in central Alberta, Canada

“…Principal component analysis groups specimens based on the extent of covariance in their associated data, and often individuals that are closely related can confound population structure due to their high rate of covariance (Alhusain and Hafez 2018). Although our sampling procedure was designed to reduce the incidence of multiple specimens from a single family, data visualisation using principal component analysis indicated tight clustering patterns for two groups, consisting of a total of seven individuals, that were consistent with family-level structure (Price et al 2010; Supplemental materials, Fig.…”

Heterogeneity in the distribution of 159 drug-response related SNPs in world populations and their genetic relatedness

“…For example, discrepant responses to 5-Fluorouracil (5-FU) among different ethnicities of the South Asian population were attributed to genetic variations in the DPYD gene [21]. Analysis of population specific genetic structure, therefore, has many applications in medical and population genetic research as well as ensuring drug efficacy and development of pharmacogenetic tests [8,22,23].…”

A Deep Learning Approach to Population Structure Inference in Inbred Lines of Maize

“…Population structure analysis is a major area of interest within the field of genetics and bioinformatics (Alhusain and Hafez, 2018). In this sense, several bioinformatics methods have been developed to examine the population structure in genetically diverse plant germplasm based on high-throughput genomic data.…”

“…Another successful approach to infer population structure has been implemented in the ADMIXTURE software (Alexander et al, 2009;Alexander and Lange, 2011), a maximum-likelihood-based method that updates the loglikelihood as it converges on a solution for the ancestry proportions and allele frequencies that maximize the likelihood function (Alexander and Lange, 2011). Other authors have emphasized the use of non-parametric methods such as K-means (KM) and hierarchical clustering (HC) (Bouaziz et al, 2012;Meirmans, 2012;Alhusain and Hafez, 2018). KM and HC approaches correspond to machine learning (ML) methods that do not require the assumptions of the Hardy-Weinberg principle and use external dimension reduction techniques, such as principal component analysis (PCA) (Kobak and Berens, 2019), commonly used in several data-intensive biological fields.…”