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PloS one2017; 12(5); e0177638; doi: 10.1371/journal.pone.0177638

Identification of key contributors in complex population structures.

Abstract: Evaluating the genetic contribution of individuals to population structure is essential to select informative individuals for genome sequencing, genotype imputation and to ascertain complex population structures. Existing methods for the selection of informative individuals for genomic imputation solely focus on the identification of key ancestors, which can lead to a loss of phasing accuracy of the reference population. Currently many methods are independently applied to investigate complex population structures. Based on the Eigenvalue Decomposition (EVD) of a genomic relationship matrix we describe a novel approach to evaluate the genetic contribution of individuals to population structure. We combined the identification of key contributors with model-based clustering and population network visualization into an integrated three-step approach, which allows identification of high-resolution population structures and substructures around such key contributors. The approach was applied and validated in four disparate datasets including a simulated population (5,100 individuals and 10,000 SNPs), a highly structured experimental sheep population (1,421 individuals and 44,693 SNPs) and two large complex pedigree populations namely horse (1,077 individuals and 38,124 SNPs) and cattle (2,457 individuals and 45,765 SNPs). In the simulated and experimental sheep dataset, our method, which is unsupervised, successfully identified all known key contributors. Applying our three-step approach to the horse and cattle populations, we observed high-resolution population substructures including the absence of obvious important key contributors. Furthermore, we show that compared to commonly applied strategies to select informative individuals for genotype imputation including the computation of marginal gene contributions (Pedig) and the optimization of genetic relatedness (Rel), the selection of key contributors provided the highest phasing accuracies within the selected reference populations. The presented approach opens new perspectives in the characterization and informed management of populations in general, and in areas such as conservation genetics and selective animal breeding in particular, where assessing the genetic contribution of influential and admixed individuals is crucial for research and management applications. As such, this method provides a valuable complement to common applied tools to visualize complex population structures and to select individuals for re-sequencing.
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Publication Date: 2017-05-16 PubMed ID: 28520805PubMed Central: PMC5433729DOI: 10.1371/journal.pone.0177638Google Scholar: Lookup
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  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research article explores a novel method to evaluate individuals’ genetic contribution to population structures. The researchers developed an integrated three-step approach, combining key contributor identification, population network visualization, and model-based clustering. The method was tested on four varied datasets, including simulated, experimental, and complex pedigree populations. The study suggests that this approach could offer more accurate insights for complex population structures, which may be beneficial for conservation genetics and selective animal breeding.

Novel Approach to Evaluate Genetic Contribution

  • The paper presents a unique strategy to assess the genetic contribution of individuals within population structures. This new approach was built on the Eigenvalue Decomposition (EVD) of a genomic relationship matrix, and it combines three different methods: key contributor identification, model-based clustering, and population network visualization.
  • Existing methods primarily focus on identifying key ancestors, which often leads to reduced phasing accuracy. This new approach aims to overcome these limitations by offering a comprehensive, multi-step way to analyze and comprehend complex population structures.
  • The researchers suggested that their unsupervised method could identify high-resolution population structures and substructures around key contributors. This kind of detailed analysis could provide deeper insights and a better understanding of studied populations.

Method Testing and Validation

  • The new method was tested and validated using four disparate datasets, including a simulated population, a highly structured experimental sheep population, and two complex pedigree populations (horse and cattle).
  • In the simulated and experimental sheep dataset, the method successfully identified all known key contributors, proving its efficacy and accuracy.
  • In the horse and cattle populations, the method detected high-resolution population substructures. It was observed that there had been an absence of broadly important key contributors within these populations.

Applications and Potential Benefits

  • The researchers propose that their method can provide new perspectives in the management and characterization of populations, especially in the fields of selective animal breeding and conservation genetics.
  • The method is seen as particularly suitable for scenarios where evaluating the genetic contribution of influential and mixed individuals is critical for research and management applications.
  • The study claims that their three-step approach resulted in higher phasing accuracies within the selected reference populations, compared to common strategies for selecting informative individuals for genotype imputation.
  • As a complement to common tools for visualizing complex population structures and selecting individuals for re-sequencing, this method is said to be valuable and potentially game-changing.

Cite This Article

APA
Neuditschko M, Raadsma HW, Khatkar MS, Jonas E, Steinig EJ, Flury C, Signer-Hasler H, Frischknecht M, von Niederhäusern R, Leeb T, Rieder S. (2017). Identification of key contributors in complex population structures. PLoS One, 12(5), e0177638. https://doi.org/10.1371/journal.pone.0177638

Publication

PloS one
ISSN: 1932-6203
NlmUniqueID: 101285081
Country: United States
Language: English
Volume: 12
Issue: 5
Pages: e0177638
PII: e0177638

Researcher Affiliations

Neuditschko, Markus
  • Agroscope, Swiss National Stud Farm, Avenches, Switzerland.
  • Reprogen - Animal Bioscience Group, Faculty of Veterinary Science, University of Sydney, Camden, Australia.
Raadsma, Herman W
  • Reprogen - Animal Bioscience Group, Faculty of Veterinary Science, University of Sydney, Camden, Australia.
Khatkar, Mehar S
  • Reprogen - Animal Bioscience Group, Faculty of Veterinary Science, University of Sydney, Camden, Australia.
Jonas, Elisabeth
  • Reprogen - Animal Bioscience Group, Faculty of Veterinary Science, University of Sydney, Camden, Australia.
  • SLU, Department of Animal Breeding and Genetics, Uppsala, Sweden.
Steinig, Eike J
  • College of Marine and Environmental Sciences, James Cook University, Townsville, Australia.
Flury, Christine
  • School of Agricultural Forest and Food Sciences, Bern University of Applied Sciences, Zollikofen, Switzerland.
Signer-Hasler, Heidi
  • School of Agricultural Forest and Food Sciences, Bern University of Applied Sciences, Zollikofen, Switzerland.
Frischknecht, Mirjam
  • Agroscope, Swiss National Stud Farm, Avenches, Switzerland.
  • Institute of Genetics, Vetsuisse Faculty, University of Bern, Bern, Switzerland.
von Niederhäusern, Ruedi
  • Agroscope, Swiss National Stud Farm, Avenches, Switzerland.
Leeb, Tosso
  • Institute of Genetics, Vetsuisse Faculty, University of Bern, Bern, Switzerland.
Rieder, Stefan
  • Agroscope, Swiss National Stud Farm, Avenches, Switzerland.

MeSH Terms

  • Algorithms
  • Animals
  • Cattle
  • Computer Simulation
  • Genetics, Population
  • Horses
  • Models, Genetic
  • Reproducibility of Results
  • Sheep
  • Workflow

Conflict of Interest Statement

The authors have declared that no competing interests exist.

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Citations

This article has been cited 8 times.
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