ADAM-Plant: A Software for Stochastic Simulations of Plant Breeding From Molecular to Phenotypic Level and From Simple Selection to Complex Speed Breeding Programs

Making decisions on plant breeding programs require plant breeders to be able to test different breeding strategies by taking into account all the crucial factors affecting crop genetic improvement. Due to the complexity of the decisions, computer simulation serves as an important tool for researchers and plant breeders. This paper describes ADAM-plant, which is a computer software that models breeding schemes for self-pollinated and cross-pollinated crop plants using stochastic simulation. The program simulates a population of plants and traces the genetic changes in the population under different breeding scenarios. It takes into account different population structures, genomic models, selection (strategies and units) and crossing strategies. It also covers important features e.g., allowing users to perform genomic selection (GS) and speed breeding, simulate genotype-by-environment interactions using multiple trait approach, simulate parallel breeding cycles and consider plot sizes. In addition, the software can be used to simulate datasets produced from very complex breeding program in order to test new statistical methodology to analyze such data. As an example, three wheat-breeding strategies were simulated in the current study: (1) phenotypic selection, (2) GS, and (3) speed breeding with genomic information. The results indicate that the genetic gain can be doubled by GS compared to phenotypic selection and genetic gain can be further increased considerably by speed breeding. In conclusion, ADAM-plant is an important tool for comparing strategies for plant breeding and for estimating the effects of allocation of different resources to the breeding program. In the current study, it was used to compare different methodologies for utilizing genomic information in cereal breeding programs for selection of best-fit breeding strategy as per available resources.

[1]  T. Ostersen,et al.  Most of the long-term genetic gain from optimum-contribution selection can be realised with restrictions imposed during optimisation , 2015, Genetics Selection Evolution.

[2]  M. Goddard,et al.  Prediction of total genetic value using genome-wide dense marker maps. , 2001, Genetics.

[3]  M. Cooper,et al.  Comparison of Two Breeding Strategies by Computer Simulation , 2003 .

[4]  A. C. Sørensen,et al.  ADAM: A computer program to simulate selective-breeding schemes for animals , 2009 .

[5]  I Misztal,et al.  A relationship matrix including full pedigree and genomic information. , 2009, Journal of dairy science.

[6]  G. de los Campos,et al.  Genomic Selection in Plant Breeding: Methods, Models, and Perspectives. , 2017, Trends in plant science.

[7]  Peer Berg,et al.  EVA: realized and predicted optimal genetic contributions. , 2006 .

[8]  A. C. Sørensen,et al.  Mating strategies with genomic information reduce rates of inbreeding in animal breeding schemes without compromising genetic gain , 2016, Animal : an international journal of animal bioscience.

[9]  R. J. Baker,et al.  Estimation of heritability and prediction of selection response in plant populations , 1991 .

[10]  S. Wright,et al.  Evolution in Mendelian Populations. , 1931, Genetics.

[11]  Rita H. Mumm,et al.  The role and basics of computer simulation in support of critical decisions in plant breeding , 2011, Molecular Breeding.

[12]  Karan P. Singh.  Department of Biostatistics , 2002 .

[13]  Paul Nicholson,et al.  Speed breeding in growth chambers and glasshouses for crop breeding and model plant research , 2018, Nature Protocols.

[14]  R. Punnett,et al.  The Genetical Theory of Natural Selection , 1930, Nature.

[15]  N. Wray,et al.  Increasing long-term response to selection , 1994, Genetics Selection Evolution.

[16]  J. Batley,et al.  Speed breeding: a powerful tool to accelerate crop research and breeding , 2017, bioRxiv.

[17]  F. Bassi,et al.  Speed breeding for multiple quantitative traits in durum wheat , 2018, Plant Methods.

[18]  The choice of phenotypes for use of marker assisted selection in dairy cattle. , 2006 .

[19]  M. Lund,et al.  Genomic prediction when some animals are not genotyped , 2010, Genetics Selection Evolution.

[20]  T. Meuwissen.  Genomic selection: marker assisted selection on a genome wide scale. , 2007, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[21]  D. Mather,et al.  GREGOR: Software for Genetic Simulation , 1993 .

[22]  Gregor Gorjanc,et al.  AlphaSim: Software for Breeding Program Simulation , 2016, The plant genome.

[23]  T. Meuwissen.  Maximizing the response of selection with a predefined rate of inbreeding. , 1997, Journal of animal science.

[24]  T. Meuwissen,et al.  Using the Pareto principle in genome-wide breeding value estimation , 2011, Genetics Selection Evolution.

[25]  A. Melchinger,et al.  Accuracy of Genomic Prediction in Synthetic Populations Depending on the Number of Parents, Relatedness, and Ancestral Linkage Disequilibrium , 2016, Genetics.

[26]  D. Mackill,et al.  Marker-assisted selection: an approach for precision plant breeding in the twenty-first century , 2008, Philosophical Transactions of the Royal Society B: Biological Sciences.

[27]  R. Varshney,et al.  Genomic Selection for Crop Improvement , 2017, Springer International Publishing.

[28]  D. Ogunniyan,et al.  Genetic variation, heritability, genetic advance and agronomic character association of yellow elite inbred lines of maize (Zea mays L.) , 2014 .

[29]  Elisabeth Jonas,et al.  Does genomic selection have a future in plant breeding? , 2013, Trends in biotechnology.

[30]  J. Jensen,et al.  Optimizing Training Population Size and Genotyping Strategy for Genomic Prediction Using Association Study Results and Pedigree Information. A Case of Study in Advanced Wheat Breeding Lines , 2017, PloS one.

[31]  R. K. Reddy,et al.  MARKER ASSISTED SELECTION IN DISEASE RESISTANCE BREEDING , 2013 .

[32]  Jean-Luc Jannink,et al.  Genomic selection in plant breeding. , 2014, Methods in molecular biology.