Evaluation of Core Collections with EvaluateCore

Aravind, J.¹, Kaur, V.², Wankhede, D. P.³, and Nanjundan, J.⁴

2023-08-20

1. Division of Germplasm Conservation, ICAR-National Bureau of Plant Genetic Resources, New Delhi.
   2. Division of Germplasm Evaluation, ICAR-National Bureau of Plant Genetic Resources, New Delhi.
      3. Division of Genomic Resources, ICAR-National Bureau of Plant Genetic Resources, New Delhi.
         4. ICAR-Indian Agricultural Research Institute, Regional Station, Wellington, Tamil Nadu.

Overview

A core collection is a “limited set of accessions representing, with minimum repetitiveness, the genetic diversity of a crop species and its wild relatives”(Frankel, 1984).

In case of several large and unwieldy germplasm collections conserved in genebanks, development of several such core collections has facilitated increasing the efficiency of their characterisation and in turn the utilisation.

There are different criteria available for evaluating the core collections by comparing with the original collections from which they have been sampled. The choice of the suitable evaluation criteria should be dictated by the objectives of the core collection. Several such criteria including the distance based metrics described in Odong et al. (2013) have been implemented in EvaluateCore and this document shows how to use them. This document assumes a basic knowledge of R programming language.

Installation

The package can be installed using the following functions:

# Install from CRAN
install.packages('EvaluateCore', dependencies=TRUE)

# Install development version from Github
devtools::install_github("aravind-j/EvaluateCore")

Then the package can be loaded using the function

library(EvaluateCore)

Registered S3 methods overwritten by 'vegan':
  method      from
  plot.rda    klaR
  predict.rda klaR
  print.rda   klaR

--------------------------------------------------------------------------------
Welcome to EvaluateCore version 0.1.3.9000


# To know whats new in this version type:
  news(package='EvaluateCore')
  for the NEWS file.

# To cite the methods in the package type:
  citation(package='EvaluateCore')

# To suppress this message use:
  suppressPackageStartupMessages(library(EvaluateCore))
--------------------------------------------------------------------------------

Version History

The current version of the package is 0.1.3. The previous versions are as follows.

Table 1. Version history of EvaluateCore R package.

Version	Date
0.1.1	2020-06-03
0.1.2	2021-06-12

To know detailed history of changes use news(package='EvaluateCore').

Table 2 : Core evaluation methods implemented in EvaluateCore.

Metric	Criteria/Method	Function	Type of Variable	Type of Core	Type of comparison	Reference
Genetic distance	Average entry-to-nearest-entry distance (\(E-EN\))	dist.evaluate.core	Quantitative & Qualitative	CC–I	Multivariate	Odong et al. (2013)
Genetic distance	Average accession-to-nearest-entry distance (\(A-EN\))	dist.evaluate.core	Quantitative & Qualitative	CC–X	Multivariate	Odong et al. (2013)
Genetic distance	Average entry-to-entry distance (\(E-E\))	dist.evaluate.core	Quantitative & Qualitative	CC–X	Multivariate	Odong et al. (2013)
Mean	Student-Newman-Keuls test	snk.evaluate.core	Quantitative	CC–D	Univariate	Newman (1939); Keuls (1952)
Median	Wilcoxon rank sum test or Mann-Whitney-Wilcoxon test or Mann-Whitney U test	wilcox.evaluate.core	Quantitative	CC–D	Univariate
Mean	Student’s t test	ttest.evaluate.core	Quantitative	CC–D	Univariate
Mean and Variance	Sign test	signtest.evaluate.core	Quantitative	CC–D	Multivariate
Variance	Levene’s test	levene.evaluate.core	Quantitative	CC–D	Univariate
Variability	Interquartile range	iqr.evaluate.core	Quantitative	CC–D	Univariate
Range	Coincidence rate of range	cr.evaluate.core	Quantitative	CC–D	Multivariate
Variance	Variable rate of coefficient of variation	vr.evaluate.core	Quantitative	CC–D	Multivariate
Probability distribution	Quantile-quantile plots	qq.evaluate.core	Quantitative	CC–D	Univariate
Probability distribution	Kullback-Leibler distance	pdfdist.evaluate.core	Quantitative	CC–D	Univariate
Probability distribution	Kolmogorov-Smirnov distance	pdfdist.evaluate.core	Quantitative	CC–D	Univariate
Probability distribution	Anderson-Darling distance	pdfdist.evaluate.core	Quantitative	CC–D	Univariate
Probability distribution	Box-and-whisker plot	box.evaluate.core	Quantitative	CC–D	Univariate
Mean	Percentage of significant differences of mean	percentdiff.evaluate.core	Quantitative	CC–D	Multivariate
Variance	Percentage of significant differences of variance	percentdiff.evaluate.core	Quantitative	CC–D	Multivariate
Mean	Average of absolute differences between means	percentdiff.evaluate.core	Quantitative	CC–D	Multivariate
Variance	Average of absolute differences between variances	percentdiff.evaluate.core	Quantitative	CC–D	Multivariate
Euclidean distance	Percentage difference between the mean squared Euclidean distance	percentdiff.evaluate.core	Quantitative	CC–D	Multivariate
Probability distribution	Principal component analysis	pca.evaluate.core	Quantitative	CC–D	Multivariate
Frequency distribution	Bar plot	bar.evaluate.core	Qualitative	CC–D	Univariate
Frequency distribution	Chi-squared test for homogeneity	chisquare.evaluate.core	Qualitative	CC–D	Univariate
Frequency distribution	Class coverage	coverage.evaluate.core	Qualitative	CC–I	Univariate
Diversity	Shannon-Weaver diversity index	shannon.evaluate.core	Qualitative	CC–I	Univariate
Diversity	Maximum Shannon-Weaver diversity index	shannon.evaluate.core	Qualitative	CC–I	Univariate
Diversity	Shannon Equitability index	shannon.evaluate.core	Qualitative	CC–I	Univariate
Phenotypic correlation	Pearson correlation coefficients	corr.evaluate.core	Quantitative & Qualitative	CC-D	Multivariate
Correlation matrix	Mantel correlation coefficient	corr.evaluate.core	Quantitative & Qualitative	CC-D	Multivariate
Frequency distribution	Stacked frequency distribution histogram	freqdist.evaluate.core	Quantitative & Qualitative	CC-D	Univariate

References

Frankel, O. H. (1984). “Genetic perspective of germplasm conservation,” in Genetic Manipulations: Impact on Man and Society, eds. W. Arber, K. Limensee, W. J. Peacock, and P. Stralinger (Cambridge University Press: Cambridge, UK), 161–170.

Keuls, M. (1952). The use of the „studentized range” in connection with an analysis of variance. Euphytica 1, 112–122. doi:10.1007/BF01908269.

Newman, D. (1939). The distribution of range in samples from a normal population, expressed in terms of an independent estimate of standard deviation. Biometrika 31, 20–30. doi:10.1093/biomet/31.1-2.20.

Odong, T. L., Jansen, J., Eeuwijk, F. A. van, and Hintum, T. J. L. van (2013). Quality of core collections for effective utilisation of genetic resources review, discussion and interpretation. Theoretical and Applied Genetics 126, 289–305. doi:10.1007/s00122-012-1971-y.