Key message: For genomic selection in clonal breeding programs to be 14 effective, crossing parents should be selected based on genomic predicted cross 15 performance unless dominance is negligible. Genomic prediction of cross performance 16 enables a balanced exploitation of the additive and dominance value simultaneously. A 17 two-part breeding program with parent selection based on genomic predicted cross 18 performance to rapidly drive population improvement has great potential to improve 19 breeding clonally propagated crops. 20 Abstract 21For genomic selection in clonal breeding programs to be effective, crossing 22 parents should be selected based on genomic predicted cross performance unless 23 dominance is negligible. Genomic prediction of cross performance enables a balanced 24 exploitation of the additive and dominance value simultaneously. Here, we compared 25 different strategies for the implementation of genomic selection in clonal plant breeding 26 programs. We used stochastic simulations to evaluate six combinations of three 27 breeding programs and two parent selection methods. The three breeding programs 28 included i) a breeding program that introduced genomic selection in the first clonal 29 testing stage, and ii) two variations of a two-part breeding program with one and three 30 crossing cycles per year, respectively. The two parent selection methods were i) 31 selection of parents based on genomic estimated breeding values, and ii) selection of 32 parents based on genomic predicted cross performance. Selection of parents based on 33 genomic predicted cross performance produced faster genetic gain than selection of 34 parents based on genomic estimated breeding values because it substantially reduced 35 inbreeding when the dominance degree increased. The two-part breeding programs 36 with one and three crossing cycles per year using genomic prediction of cross 37 performance always produced the most genetic gain unless dominance was negligible. 38We conclude that i) in clonal breeding programs with genomic selection, parents should 39 be selected based on genomic predicted cross performance, and ii) a two-part breeding 40 program with parent selection based on genomic predicted cross performance to rapidly 41 drive population improvement has great potential to improve breeding clonally 42 propagated crops. 43 additive genetic effects in a given breeding population. As a result, heterozygosity is 55 reduced. Although selection for the genomic estimated breeding value will increase the 56 additive value over time, it may lead to a reduction of the dominance value, unless 57 dominance is negligible. In the long term, using the genomic estimated breeding value 58 to select new parents in breeding programs which deliver outbred varieties, such as in 59 clonal plant breeding programs, might not be the optimal method to use in order to 60 maximize the total genetic value of the breeding population in a sustainable fashion. 61Many major food crops, including nearly all types of fruit and all importa...

13Background: Genetic evaluation is a central component of a breeding program. In advanced 14 economies, most genetic evaluations depend on large quantities of data that are recorded on 15 commercial farms. Large herd sizes and widespread use of artificial insemination create strong 16 genetic connectedness that enables the genetic and environmental effects of an individual 17 animal's phenotype to be accurately separated. In contrast to this, herds are neither large nor 18 have strong genetic connectedness in smallholder dairy production systems of many low to 19 middle-income countries (LMIC). This limits genetic evaluation, and furthermore, the pedigree 20 information needed for traditional genetic evaluation is typically unavailable. Genomic 21 information keeps track of shared haplotypes rather than shared relatives. This information 22 could capture and strengthen genetic connectedness between herds and through this may enable 23 genetic evaluations for LMIC smallholder dairy farms. The objective of this study was to use 24 simulation to quantify the power of genomic information to enable genetic evaluation under 25 such conditions. 26 Results:The results from this study show: (i) the genetic evaluation of phenotyped cows using 27 genomic information had higher accuracy compared to pedigree information across all 28 breeding designs; (ii) the genetic evaluation of phenotyped cows with genomic information 29 and modelling herd as a random effect had higher or equal accuracy compared to modelling 30 herd as a fixed effect; (iii) the genetic evaluation of phenotyped cows from breeding designs 31 with strong genetic connectedness had higher accuracy compared to breeding designs with 32 weaker genetic connectedness; (iv) genomic prediction of young bulls was possible using 33 marker estimates from the genetic evaluations of their phenotyped dams. For example, the 34 accuracy of genomic prediction of young bulls from an average herd size of 1 (=1.58) was 35 0.40 under a breeding design with 1,000 sires mated per generation and a training set of 8,000 36 phenotyped and genotyped cows. 37Conclusions: This study demonstrates the potential of genomic information to be an enabling 38 technology in LMIC smallholder dairy production systems by facilitating genetic evaluations 39 with in-situ records collected from farms with herd sizes of four cows or less. Across a range 40 of breeding designs, genomic data enabled accurate genetic evaluation of phenotyped cows and 41 genomic prediction of young bulls using data sets that contained small herds with weak genetic 42 connections. The use of smallholder dairy data in genetic evaluations would enable the 43 establishment of breeding programs to improve in-situ germplasm and, if required, would 44 enable the importation of the most suitable external germplasm. This could be individually 45 tailored for each target environment. Together this would increase the productivity, 46 profitability and sustainability of LMIC smallholder dairy production systems. However, data 47 col...

Advances in sequencing technologies mean that insights into crop diversification aiding future breeding can now be explored in crops beyond major staples. For the first time, we use a genome assembly of finger millet, an allotetraploid orphan crop, to analyze DArTseq single nucleotide polymorphisms (SNPs) at the sub-genome level. A set of 8,778 SNPs and 13 agronomic traits characterizing a broad panel of 423 landrace accessions from Africa and Asia suggested the crop has undergone complex, context-specific diversification consistent with a long domestication history. Both Principal Component Analysis and Discriminant Analysis of Principal Components of SNPs indicated four groups of accessions that coincided with the principal geographic areas of finger millet cultivation. East Africa, the considered origin of the crop, appeared the least genetically diverse. A Principal Component Analysis of phenotypic data also indicated clear geographic differentiation, but different relationships among geographic areas than genomic data. Neighbour-joining trees of sub-genomes A and B showed different features which further supported the crop’s complex evolutionary history. Our genome-wide association study indicated only a small number of significant marker-trait associations. We applied then clustering to marker effects from a ridge regression model for each trait which revealed two clusters of different trait complexity, with days to flowering and threshing percentage among simple traits, and finger length and grain yield among more complex traits. Our study provides comprehensive new knowledge on the distribution of genomic and phenotypic variation in finger millet, supporting future breeding intra- and inter-regionally across its major cultivation range.Core ideas8,778 SNPs and 13 agronomic traits characterized a panel of 423 finger millet landraces.4 clusters of accessions coincided with major geographic areas of finger millet cultivation.A comparison of phenotypic and genomic data indicated a complex diversification history.This was confirmed by the analysis of allotetraploid finger millet’s separate sub-genomes.Comprehensive new knowledge for intra- and inter-regional breeding is provided.