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Data from: Optimal mating of Pinus taeda L. under different scenarios using differential evolution algorithm

Citation

Goda, Khushi; Isik, Fikret (2022), Data from: Optimal mating of Pinus taeda L. under different scenarios using differential evolution algorithm, Dryad, Dataset, https://doi.org/10.5061/dryad.tdz08kq2r

Abstract

A newly developed software, AgMate, was used to perform optimized mating for monoecious Pinus taeda L. breeding. Using a computational optimization procedure called differential evolution (DE), AgMate was applied under different breeding population sizes scenarios (50, 100, 150, 200, 250) and candidate contribution scenarios (max use of each candidate was set to 1 or 8), to assess its efficiency in maximizing the genetic gain while controlling inbreeding. Real pedigree data set from North Carolina State University Tree Improvement Co-op with 962 Pinus taeda were used to optimize objective functions accounting for coancestry of parents and expected genetic gain and inbreeding of the future progeny. AgMate results were compared with those from another widely used mating software called MateSel (Kinghorn, 1999). For the proposed mating list for 200 progenies, AgMate resulted in an 83.7% increase in genetic gain compared with the candidate population. There was evidence that AgMate performed similarly to MateSel in managing coancestry and expected genetic gain, but MateSel was superior in avoiding inbreeding in proposed mate pairs. The developed algorithm was computationally efficient in maximizing the objective functions and flexible for practical application in monoecious diploid conifer breeding.

Methods

Agmate, an R software that utilizes the Differential Evolution algorithm and Second-order Cone Programming for the optimization of mate pair decisions and contributions for monoecious diploid species like Pinus taeda was used to analyze the data. 

AgMate is a multi-functional software able to

Process pedigree information

  1. Complete and order pedigrees

  2. Generate numerator relationship matrices

  3. Optimize selection and contribution of candidates

  4. Design optimal matings between candidates

  5. Provide visualization of results

Detailed R code is available at https://github.com/khushigoda/AgMate.

The ShinyApp version is available here: https://khushigoda.shinyapps.io/AgMate/

MateSel licensed version was used to analyze the same data to validate AgMate for this study. MateSel is available at http://matesel.une.edu.au/

Funding

North Carolina State University