Genome-wide genotyping data renew knowledge on genetic diversity of a worldwide alfalfa collection and give insights on genetic control of phenology traits.

China's and Europe's dependence on imported protein is a threat to the food self-sufficiency of these regions. It could be solved by growing more legumes, including alfalfa that is the highest protein producer under temperate climate. To create productive and high-value varieties, the use of large genetic diversity combined with genomic evaluation could improve current breeding programs. To study alfalfa diversity, we have used a set of 395 alfalfa accessions (i.e. populations), mainly from Europe, North and South America and China, with fall dormancy ranging from 3 to 7 on a scale of 11. Five breeders provided materials (617 accessions) that were compared to the 400 accessions. All accessions were genotyped using Genotyping-by-Sequencing (GBS) to obtain SNP allele frequency. These genomic data were used to describe genetic diversity and identify genetic groups. The accessions were phenotyped for phenology traits (fall dormancy and flowering date) at two locations (Lusignan in France, Novi Sad in Serbia) from 2018 to 2021. The QTL were detected by a Multi-Locus Mixed Model (mlmm). Subsequently, the quality of the genomic prediction for each trait was assessed. Cross-validation was used to assess the quality of prediction by testing GBLUP, Bayesian Ridge Regression (BRR), and Bayesian Lasso methods. A genetic structure with seven groups was found. Most of these groups were related to the geographical origin of the accessions and showed that European and American material is genetically distinct from Chinese material. Several QTL associated with fall dormancy were found and most of these were linked to genes. In our study, the infinitesimal methods showed a higher prediction quality than the Bayesian Lasso, and the genomic prediction achieved high (>0.75) predicting abilities in some cases. Our results are encouraging for alfalfa breeding by showing that it is possible to achieve high genomic prediction quality.

Gene Introgression in Weeds Depends on Initial Gene Location in the Crop: Brassica napus-Raphanus raphanistrum Model.

The effect of gene location within a crop genome on its transfer to a weed genome remains an open question for gene flow assessment. To elucidate this question, we analyzed advanced generations of intergeneric hybrids, derived from an initial pollination of known oilseed rape varieties (Brassica napus, AACC, 2n = 38) by a local population of wild radish (Raphanus raphanistrum, RrRr, 2n = 18). After five generations of recurrent pollination, 307 G5 plants with a chromosome number similar to wild radish were genotyped using 105 B. napus specific markers well distributed along the chromosomes. They revealed that 49.8% of G5 plants carried at least one B. napus genomic region. According to the frequency of B. napus markers (0-28%), four classes were defined: Class 1 (near zero frequency), with 75 markers covering ∼70% of oilseed rape genome; Class 2 (low frequency), with 20 markers located on 11 genomic regions; Class 3 (high frequency), with eight markers on three genomic regions; and Class 4 (higher frequency), with two adjacent markers detected on A10. Therefore, some regions of the oilseed rape genome are more prone than others to be introgressed into wild radish. Inheritance and growth of plant progeny revealed that genomic regions of oilseed rape could be stably introduced into wild radish and variably impact the plant fitness (plant height and seed number). Our results pinpoint that novel technologies enabling the targeted insertion of transgenes should select genomic regions that are less likely to be introgressed into the weed genome, thereby reducing gene flow.

Spatial heterogeneity in landscape structure influences dispersal and genetic structure: empirical evidence from a grasshopper in an agricultural landscape.

Dispersal may be strongly influenced by landscape and habitat characteristics that could either enhance or restrict movements of organisms. Therefore, spatial heterogeneity in landscape structure could influence gene flow and the spatial structure of populations. In the past decades, agricultural intensification has led to the reduction in grassland surfaces, their fragmentation and intensification. As these changes are not homogeneously distributed in landscapes, they have resulted in spatial heterogeneity with generally less intensified hedged farmland areas remaining alongside streams and rivers. In this study, we assessed spatial pattern of abundance and population genetic structure of a flightless grasshopper species, Pezotettix giornae, based on the surveys of 363 grasslands in a 430-km² agricultural landscape of western France. Data were analysed using geostatistics and landscape genetics based on microsatellites markers and computer simulations. Results suggested that small-scale intense dispersal allows this species to survive in intensive agricultural landscapes. A complex spatial genetic structure related to landscape and habitat characteristics was also detected. Two P. giornae genetic clusters bisected by a linear hedged farmland were inferred from clustering analyses. This linear hedged farmland was characterized by high hedgerow and grassland density as well as higher grassland temporal stability that were suspected to slow down dispersal. Computer simulations demonstrated that a linear-shaped landscape feature limiting dispersal could be detected as a barrier to gene flow and generate the observed genetic pattern. This study illustrates the relevance of using computer simulations to test hypotheses in landscape genetics studies.