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.

Genomic selection (GS) is a potential pathway to accelerate genetic gain for perennial ryegrass (Lolium perenne L.). The main objectives of the present study were to investigate the level of genetic gain and accuracy by applying GS in commercial perennial ryegrass breeding programs. Different scenarios were compared to a conventional breeding program. Simulated scenarios differed in the method of selection and structure of the breeding program. Two scenarios (Phen-Y12 and Phen) for phenotypic selection and three scenarios (GS-Y12, GS and GS-SP) were considered for genomic breeding schemes. All breeding schemes were simulated for 25 cycles. The amount of genetic gain achieved was different across scenarios. Compared to phenotypic scenarios, GS scenarios resulted in substantially larger genetic gain for the simulated traits. This was mainly due to more efficient selection of plots and single plants based on genomic estimated breeding values. Also, GS allows for reduction in waiting time for the availability of the superior genetic materials from previous cycles, which led to at least a doubling or a trebling of genetic gain compared to the traditional program. Reduction in additive genetic variance levels were higher with GS scenarios than with phenotypic selection. The results demonstrated that implementation of GS in ryegrass breeding is possible and presents an opportunity to make very significant improvements in genetic gains.

Conventional wheat-breeding programs involve crossing parental lines and subsequent selfing of the offspring for several generations to obtain inbred lines. Such a breeding program takes more than 8 years to develop a variety. Although wheat-breeding programs have been running for many years, genetic gain has been limited. However, the use of genomic information as selection criterion can increase selection accuracy and that would contribute to increased genetic gain. The main objective of this study was to quantify the increase in genetic gain by implementing genomic selection in traditional wheat-breeding programs. In addition, we investigated the effect of genetic correlation between different traits on genetic gain. A stochastic simulation was used to evaluate wheat-breeding programs that run simultaneously for 25 years with phenotypic or genomic selection. Genetic gain and genetic variance of wheat-breeding program based on phenotypes was compared to the one with genomic selection. Genetic gain from the wheat-breeding program based on genomic estimated breeding values (GEBVs) has tripled compared to phenotypic selection. Genomic selection is a promising strategy for improving genetic gain in wheat-breeding programs.

Chapter 1 General introduction11 Potato: origin and importancePotato (Solanum tuberosum L.) is a staple food with great economic value that ranks as the fourth most important food crop in the world. Globally potato is cultivated on 19 million hectare, being 8 th in terms of area under cultivation and with an estimated 325 million tons of annual production (Food and Agricultural Organization of the United Nations, 2012).Potato production provides food, employment and income as a cash crop (Scott et al. 2000).Potatoes have a high productivity per unit area with relatively little water consumption and take a short production time, thus being a candidate crop for food security.The cultivated potato S. tuberosum is autotetraploid (2n=4x=48). The domestication of potato dates back 6000 years in the central Andes, which is present day southern Peru and northern Bolivia, when the native people started to select wild potato species for human use (Spooner et al. 2005). The modern cultivated potato (Solanum tuberosum) was domesticated from wild potato species of the Solanum brevicaule complex (Spooner et al. 2005). The genus Solanum has over 220 wild tuber bearing potato species and seven cultivated potato species (Hawkes and Jackson 1992). The variation in ploidy level is one of the most important features in potato taxonomy. The chromosome numbers in the wild species vary from diploid (2n=2x=24), triploid (2n=2x=36), tetraploid (2n=4x=48), pentaploid (2n=5x=60), to hexaploid (2n=6x=72), while in cultivated potatoes this ranges from diploid to pentaploid. The majority of the diploid species are self incompatible while tetraploids are self compatible allopolyploids with disomic inheritance (Hawkes 1990). Wild and cultivated potato genetic resources provide a variety of reproductive and genetic features associated with species differentiation and breeding applications.Cultivated potatoes can be classified as landraces or improved varieties. Landraces are native varieties still grown in South America today while improved varieties are grown around the world. Landrace potato cultivars are native to two areas in South America: the upland Andes from eastern Venezuela to northern Argentina and the lowlands of south central Chile (Ames and Spooner 2008). It was in the year 1557 that potato was first introduced to Europe (Ríos et al. 2007). The origin of the "European" potato is disputed with two competing hypotheses, one suggesting its origin from the Andes while another one suggests it to originate from lowland Chile. For the last 60 years it was accepted that European potato could have an Andean origin but recent studies suggest the European 12 potatoes most likely came from both Andean and Chilean landraces (Ríos et al. 2007). By the 1700s, potato cultivation was widespread in Europe and its worldwide cultivation began soon after (Hawkes and Francisco Ortega 1993). The Irish potato famine caused by potato late blight disease, Phytophtera infestans, caused widespread famine and migration in Europe beginning in 1845. Late blig...

28Genomic selection (GS) is a potential pathway to accelerate genetic gain for perennial 29 ryegrass (Lolium perenne L.). The main objectives of the present study were to 30 investigate the level of genetic gain and accuracy by applying GS in commercial 31 perennial ryegrass breeding programs. Different scenarios were compared to a 32 conventional breeding program. Simulated scenarios differed in the method of selection 33 and structure of the breeding program. Two scenarios (Phen-Y12 and Phen) for 34 phenotypic selection and three scenarios (GS-Y12, GS and GS-SP) were considered for 35 genomic breeding schemes. All breeding schemes were simulated for 25 cycles. The 36 amount of genetic gain achieved was different across scenarios. Compared to 37 phenotypic scenarios, GS scenarios resulted in a significantly larger genetic gain for the 38 simulated traits. This was mainly due to more efficient selection of plots and single 39 plants based on GEBV. Also, GS allows for reduction in cycle time, which led to at 40 least a doubling or a trebling of genetic gain compared to the traditional program. 41Reduction in additive genetic variance levels were higher with GS scenarios than with 42 phenotypic selection. The results demonstrated that implementation of GS in ryegrass 43 breeding is possible and presents an opportunity to make very significant improvements 44 in genetic gains. 45 47 48 49 50Perennial ryegrass (Lolium perenne L.) is one of the most cultivated forage species in 51 temperate grasslands, mainly farmed for its re-growth capacity after defoliation, and 52 high value as feed for ruminants, due to palatability, digestibility, and nutritive contents 53 (Wilkins, 1991; Fulkerson et al., 1994; Tallowin et al., 1995). Perennial ryegrass is an 54 obligate allogamous species with genetic gametophytic self-incompatibility, and is bred 55 in genetically heterogeneous families (Cornish et al., 1979). 56Recurrent selection is currently the most common strategy employed in ryegrass 57 breeding. Such selection mainly rely on phenotypic records for key traits, combined 58 with pedigree and progeny information (Humphreys, 2005). A breeding cycle may 59 include several selection steps based on information on individual plants and/or plots. 60 Breeding cycles are typically long (10-14 yr.), because phenotypes for many key traits 61 (such as dry matter yield and persistency) can only be reliably measured in plot 62 conditions over multiple years, required to assess the effects of competition among 63 plants (Hayes et al., 2013) and to control for genotype by environment (including year) 64 interactions. The most efficient conventional selection schemes for ryegrass achieve an 65 approximate genetic gain of between 0.5 and 0.7% per year for dry matter yield 66 (Wilkins and Humphreys, 2003). 67 Genomic selection (GS) is a potential pathway to accelerate genetic gain for perennial 68 ryegrass by reducing the length of the breeding cycle as well as increasing selection 69 accuracy (Meuwissen et al., 2001; Hayes et al., 20...