Impact and interpretation of different measures of genetic diversity in inbred lines applied to crop breeding
Main Article Content
Abstract
The application of molecular genetics in crop breeding has grown significantly, largely due to the success of molecular breeding, which utilizes genotype-based approaches to achieve substantial genetic improvements with favorable cost-effectiveness. A successful molecular breeding strategy involves thorough genotyping, enabling detailed genetic characterization of target germplasm, including genetic diversity analysis, relationships, and population structure. Genotype-based methods, favored for their stability and independence from environmental factors, are preferred over phenotype-based approaches. Genetic diversity is assessed by comparing individual genotypes within and across populations, using statistical methods to calculate genetic distances or similarities. This study focuses on establishing a unified framework to compare and evaluate common similarity measures and their relation to distance metrics, specifically in diploid inbred lines genotyped with biallelic SNPs, for use in genetic improvement efforts.
Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
1. Aronszajn, N. Theory of reproducing kernels. Transactions of the American mathematical society 68, 337–404 (1950). https://doi.org/10.2307/1990404
2. Cavalli-Sforza, L. L. The history and geography of human genes 428 (1994).
3. Cox, T. F. & Cox, M. A. Multidimensional scaling 328 (CRC press, 2000).
4. Fernando, R., Cheng, H, Sun, X & Garrick, D. A comparison of identity-by-descent and identity-by-state matrices that are used for genetic evaluation and estimation of variance components. Journal of Animal Breeding and Genetics 134, 213–223 (2017). https://doi.org/10.1111/jbg.12275
5. Goodman, M. M. & Stuber, C. W. Races of maize. VI. Isozyme variation among races of maize in Bolivia. (1983).
6. Habier, D., Fernando, R. L.&Dekkers, J. C. M. The impact of genetic relationship information on genome-assisted breeding values. Genetics 177, 2389–2397 (2007). https://doi.org/10.1534/genetics.107.081190
7. Hein, M. & Bousquet, O. Hilbertian metrics and positive definite kernels on probability measures in International Workshop on Artificial Intelligence and Statistics, 136-143 (2005).
8. Meuwissen, T. H., Hayes, B. J. & Goddard, M. Prediction of total genetic value using genomewide dense marker maps. Genetics 157, 1819–1829 (2001). https://doi.org/10.1093/genetics/157.4.1819
9. Mohammadi, S. A.& Prasanna, B. Analysis of genetic diversity in crop plants—salient statistical tools and considerations. Crop science 43, 1235–1248 (2003). https://doi.org/10.2135/cropsci2003.1235
10. Mondini, L., Noorani, A. & Pagnotta, M. A. Assessing plant genetic diversity by molecular tools. Diversity 1, 19–35 (2009). https://doi.org/10.3390/d1010019
11. Nei, M. Genetic distance between populations. The American Naturalist 106, 283–292 (1972). https://doi.org/10.1086/282771
12. Nei, M., Tajima, F. & Tateno, Y. Accuracy of estimated phylogenetic trees from molecular data: II. Gene frequency data. Journal of molecular evolution 19, 153–170 (1983). https://doi.org/10.1007/bf02300753
13. Nejati-Javaremi, A, Smith, C & Gibson, J. Effect of total allelic relationship on accuracy of evaluation and response to selection. Journal of animal science 75, 1738–1745 (1997). https://doi.org/10.2527/1997.7571738x
14. Patterson, N., Price, A. L. & Reich, D. Population structure and eigenanalysis. PLoS genetics 2, e190 (2006). https://doi.org/10.1371/journal.pgen.0020190
15. Reif, J. C., Melchinger, A. E. & Frisch, M. Genetical and mathematical properties of similarity and dissimilarity coefficients applied in plant breeding and seed bank management. Crop science 45, 1–7 (2005). https://doi.org/10.2135/cropsci2005.0001
16. VanRaden, P. & Tooker, M. Genetic evaluations using combined data from all breeds and crossbred cows in Crossbreeding of Dairy Cattle: The Science and the Impact. 4th Biennial WE Petersen Symposium, University of Minnesota, St. Paul. http://www. ansci. umn. edu/petersen_symposium/petersen2007.htm Accessed March 19, 23-28 (2007).
17. VanRaden, P. M. Efficient methods to compute genomic predictions. Journal of dairy science 91, 4414–4423 (2008). https://doi.org/10.3168/jds.2007-0980
18. VanRaden, P. Efficient estimation of breeding values from dense genomic data. J Dairy Sci 90, 374–375 (2007).
19. Venables, W. N. & Ripley, B. D. Modern applied statistics with S-PLUS 400 (Springer Science & Business Media, 2013). https://doi.org/10.1007/978-1-4757-3121-7
20. Warburton, M. & Crossa, J. Data analysis in the CIMMYT applied biotechnology center: for fingerprinting and genetic diversity studies 2002.
21. Weir, B. S. & Cockerham, C. Genetic data analysis II: Methods for discrete population genetic data. Sinauer Assoc. Inc., Sunderland, MA, USA (1996).
22. Wright, S. Evolution and genetics of populations vol IV. (The University of Chicago Press, 1978).
23. Wright, S. Coefficients of inbreeding and relationship. The American Naturalist 56, 330–338 (1922). http://www.jstor.org/stable/2456273.
24. Zambelli, A. The importance of deep genotyping in crop breeding. Journal of Basic and Applied Genetics 34, 47–56 (2023). https://doi.org/10.35407/bag.2023.34.01.02