Contributions to genomic selection and association mapping in structured and admixed populations : application to maize

The advent of molecular markers (SNPs) has revolutionized quantitative genetics methods by enabling the identification of regions involved in the genetic determinism of traits (QTLs) thanks to association studies (GWAS), or the prediction of the performance of individuals using genomic information (GS). The stratification of populations into genetic groups is common in animal and plant breeding. This structure can impact GWAS and GS methods through group differences in QTL allele frequencies and effects, as well as in linkage disequilibrium (LD) between SNP and QTL.During this thesis, two maize diversity panels were used, presenting different levels of structuration: the "Amaizing Dent" panel representing the diversity of dent lines used in Europe and the "Flint-Dent" panel including dent, flint and admixed lines between these two groups.In GS, the impact of genetic structure on genomic prediction accuracy was evaluated in the first panel for productivity and phenology traits. This study highlighted the interest of a training population (TS) whose constitution in terms of genetic groups is similar to that of the population to be predicted. Assembling the different groups within a multi-group TS appears as an effective solution to predict a broad spectrum of genetic diversity. A priori indicators of genomic prediction accuracy, based on the coefficient of determination, were also evaluated and highlighted a variable efficiency depending on the group and the trait.A new GWAS methodology was then developed to study the heterogeneity of the allele effects captured by SNPs depending on the group. The integration of admixed individuals to such analyses allows to disentangle the factors causing the heterogeneity of allele effects across groups: local genomic difference (related to LD or group-specific mutation) or epistatic interactions between the QTL and the genetic background. This methodology was applied to the "Flint-Dent" panel for flowering time. QTLs have been detected as presenting group-specific effects interacting or not with the genetic background. QTLs with an original profile have been highlighted, including known loci such as Vgt1, Vgt2 or Vgt3. Significant directional epistasis has also been demonstrated using admixed individuals and supported the existence of epistatic interactions with the genetic background for this trait.Based on the existence of such heterogeneity of allele effects, we have developed two genomic prediction models named Multi-group Admixed GBLUP (MAGBLUP). Both model group-specific QTL effects and are suited to the prediction of admixed individuals. The first allows the identification the additional genetic variance created by the admixture (segregation variance), while the second allows the evaluations of the degree of conservation of SNP allele effects across groups. These two models showed a certain interest compared to standard models to predict simulated traits, but it was more limited on real traits.Finally, the interest of admixed individuals in multi-group TS was evaluated using the second panel. Although their interest has been clearly demonstrated for simulated traits, more variable results have been observed with the real traits, which can be explained by the presence of interactions with the genetic background.The new methods and the use of admixed individuals open interesting lines of research for quantitative genetics studies in structured population.