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Understanding the mechanisms of adaptation to the environment in cultivated plants is a promising way to meet the challenge of maintaining food security in the context of global warming. In the case of maize, high-throughput sequencing has revealed that structural variations represent a large part of the genome and could have huge phenotypic effects. Among these, Presence Absence Variants (PAVs, which include insertion/deletion of large DNA sequences) may be involved in adaptation of maize to its environment, but their contribution to the genetic determinism of traits and genotype by environment interactions remains largely unknown. To address this issue, we performed a genome-wide association study between two types of polymorphisms, SNPs and InDels, and molecular traits obtained from proteomics and metabolomics analyses to detect quantitative trait loci (QTLs). The genetic panel used for this study was composed of 254 dent inbred lines genotyped with 978,134 SNPs and 72,041 InDels. The latter encompassed from 37 to 129,700 pb, including thousands of PAVs that are not present in the B73 reference genome. Proteins and metabolites were quantified by mass spectrometry in leaf samples from F1 hybrids obtained by crossing the inbred lines with one flint tester line. Hybrid plants were grown under two watering conditions (well-watered and water deficit) in greenhouse. In total, we detected 61,225 QTLs associated with proteome or metabolome variations. Among these, 4,766 QTLs were exclusively detected by InDels. To take into account the difference of marker density between InDels and SNPs, we used a re-sampling approach which showed that there was no difference between InDels and SNPs regarding the number and effect of the QTLs detected . Additionally, the QTLs detected by the two types of polymorphism were equally distributed in the two watering conditions. These preliminary results show that InDels are worse considering to detect new genetic regions of interest. They also suggest that InDels and SNPs equally contribute to molecular trait variation and response to drought stress.

International audience; Understanding the mechanisms of adaptation to the environment in cultivated plants is a promising way to meet the challenge of maintaining food security in the context of global warming. In the case of maize, high-throughput sequencing has revealed that structural variations represent a large part of the genome and could have huge phenotypic effects. Among these, Presence Absence Variants (PAVs) may be involved in adaptation of maize to its environment, but their contribution to the genetic determinism of traits and genotype by environment interactions remains largely unknown. To address this issue, we performed a genome-wide association study between two types of polymorphisms, SNPs and Insetions/Deletions (InDels), and molecular traits obtained from proteomics and metabolomics analyses. The genetic panel used for this study was composed of 254 dent inbred lines genotyped with 978,134 SNPs and 72,041 InDels. The latter encompassed from 37 to 129,700 pb, including thousands of PAVs that are not present in the B73 reference genome. Proteins and metabolites were quantified by mass spectrometry in leaf samples from F1 hybrids obtained by crossing the inbred lines with one flint tester line. Hybrid plants were grown under two watering conditions (well-watered and water deficit) in greenhouse. In total, we detected 61,225 QTLs associated with proteome or metabolome variations. Among these, 4,766 QTLs were exclusively detected by InDels. To take into account the difference of marker density between InDels and SNPs, we used a re-sampling approach which showed that there is no difference for effect size distribution of QTLs between InDels and SNPs and for the number of QTLs detected by InDels or SNPs. Additionally, the QTLs detected by the two types of polymorphism were equally distributed in the two watering conditions. Our results suggest that InDels and SNPs equally contributed to molecular trait variation and response to drought stress.

In the last decades, the rapid development of genome sequencing allowed to identify structural variations in many species. In maize, thousands of large insertions and deletions (InDels) from few bp to hundreds of Kbp were discovered by comparing the reference genome B73 and many other resequenced genomes. These InDel sequences can carry genes and therefore be involved in phenotypic variation by changing the gene composition between individuals, but their effect on phenotype was not well studied. The aim of this thesis was to study the contribution of InDels to adaptation, phenotypic variations and hybrid performances in maize. We developed an Affymetrix® Axiom® genotyping array that allowed to genotype 105,947 InDels sequences ranging from 35bp to 129,7Kbp of size. 79,969 out 105,947 sequences of these InDels were not present in B73 reference genome and have been discovered by assembling three genomes (F2, C103, and PH207). We selected 61,492 polymorphic InDels to genotype a 362 maize inbred lines panel representing a broad range of diversity to study the contribution of InDels to genetic diversity, adaptation and trait variation. We also assembled one million of SNPs from two genotyping arrays and genotyping by sequencing to study the complementarity between InDels and SNPs. Genetic structuration and relatedness between inbred lines displayed by SNPs or by InDels were highly similar suggesting that almost all indels and SNPs followed a similar evolutionary trajectory. 51% of InDels were not in high linkage disequilibrium (LD>0.8) with any nearby SNP suggesting that the effect of these InDels was not be well captured using this density of SNP. Thanks to InDels, we detected 13 new quantitative trait loci (QTLs) among 294 QTLs identified for 23 traits by a genome wide association studies (GWAS). Similarly, 56 out 188 regions under selection between tropical, dent and flint maize lines were identified by InDels leading to an enrichment of genomic regions under selection detected by InDels compared to SNPs. These InDels include genes involved in tolerance to biotic and abiotic stress and/or adaptive traits as flowering time. Accordingly, the highest number of associated InDels was found for flowering time. These results suggest that InDels were often involved in adaptation and stress tolerance. In order to study the effect of InDels on hybrid performances, we analyzed a panel of 287 hybrids derived from the crossing of 210 maize temperate inbred lines from the previous panel. We decomposed the variance of female flowering (FF), plant height (PH) and grain yield (GY) by distinguishing the additive and dominant genetic effects. We observed the highest dominance and genotype by environment effects for GY and the lowest for FF. We performed GWAS on this panel by testing additive and dominance effects of 51,844 InDels and 469,267 SNPs on these three traits in 4 different environment combinations. We identified 78 and 133 QTLs with an additive and dominance effect, respectively including 6 and 11 QTLs discovered only by InDels. 83% of all QTLs were found with only one environment combination. One QTL for GY detected with InDels was located in a large cluster of InDels on chromosome 6, previously identified to have a strong effect on GY in heat conditions. We finally used InDels and/or SNPs genotyping to predict hybrid performances. Whereas including a dominance effect in genomic prediction models increased by 1.5 to 5.6% predictive abilities (PA) for GY, including InDels genotyping did not increased PA.; Le récent développement des méthodes de séquençage permet aujourd’hui d’identifier des variations structurales chez de nombreuses espèces. Chez le maïs, des milliers de grandes insertions et délétions (InDel) de quelques pb à plusieurs centaines de Kbp ont été découvertes entre le génome de référence B73 et de nombreux autres génomes reséquencés. Ces InDel peuvent changer la composition des gènes entre les individus et donc être impliquées dans la variation du phénotype, mais cet effet sur le phénotype reste mal connu. L’objectif de cette thèse était d'étudier la contribution des InDel à l'adaptation, aux variations phénotypiques et aux performances hybrides chez le maïs. Nous avons développé une puce de génotypage des InDel Affymetrix® Axiom® capable de génotyper 105 927 InDel de 35bp à 129,7Kbp. 79 969 de ces InDel ont leur séquences absentes du génome de référence B73 et ont été identifiées par l’assemblage 3 génomes (F2, C103, and PH207). Nous avons sélectionné 61 492 InDel polymorphiques pour génotyper 362 lignées de maïs représentant une large gamme de diversité pour étudier la contribution des InDel à la diversité génétique, l’adaptation et la variation des caractères. Nous avons également génotypé 1 million de SNP à partir de deux puces de génotypage et du génotypage par séquençage pour étudier la complémentarité entre les InDel et les SNP. Qu’ils soient calculés avec les InDel ou les SNP la structuration génétique et les valeurs d’apparentement entre les lignées sont très similaires, ce qui suggère que la plupart des InDel ont suivi la même trajectoire évolutive que les SNP. 51% des InDel ne sont pas en déséquilibre de liaison élevé (>0.8) avec aucun SNP proche donc l’effet de ces InDel n’est donc a priori pas capturé pas des SNP à cette densité. Parmi les 294 régions génomiques associées au phénotype (QTL), 13 nouveaux QTL ont été détectés grâce aux InDel par rapport aux SNP par une approche de génétique d’association (GA). Nous avons détecté un enrichissement en InDel sous sélection entre les lignées tropicales, cornées et dentées par rapport aux SNP, avec 56 sur 188 régions sous sélection détectées avec les InDel. Ces régions contiennent des gènes impliqués dans l’adaptation et/ou la tolérance aux stress. De plus, le plus grand nombre d’associations a été découvert pour la floraison, caractère adaptatif chez le maïs. Ces résultats suggèrent que les InDel sont plus souvent impliquées dans l’adaptation et la tolérance aux stress. Nous avons enfin testé l’effet des InDel sur les performances hybride en analysant un panel de 287 hybrides issus du croisement de 210 lignées tempérées du panel précédent. Nous avons décomposé la variance des performances hybrides en distinguant les effets de dominance et d’additivité pour la floraison femelle (FF), la hauteur (PH) et le rendement (GY). La plus forte part de dominance et d’interaction génotype-environnement a été observée pour le GY et la plus faible pour la FF. L’effet additif et de dominance de 51,844 InDel et 469 267 SNP a été testé pour 4 combinaisons d’environnements par une approche de GA. 78 et 133 QTL avec un effet additif et dominant respectivement ont été identifiés, dont 6 et 11 avec des InDel. 83% de ces QTL ont été identifiés dans une seule combinaison d’environnements. Un des QTL de rendement identifié avec des InDel est situé dans un large cluster d’InDel sur le chromosome 6 et colocalise avec un QTL déjà identifié avec des SNP avec un effet fort dans l’augmentation du rendement sous des températures élevées. L’ajout de l’effet de dominance en plus de l’effet additif permet d’augmenter la précision des prédictions génomiques jusqu’à 5,6% pour le rendement. Cependant, l’ajout du génotypage des InDel en plus de celui des SNP n’a pas permis d’améliorer les prédictions des phénotypes hybrides.

In the last decades, the rapid development of genome sequencing allowed to identify structural variations in many species. In maize, thousands of large insertions and deletions (InDels) from few bp to hundreds of Kbp were discovered by comparing the reference genome B73 and many other resequenced genomes. These InDel sequences can carry genes and therefore be involved in phenotypic variation by changing the gene composition between individuals, but their effect on phenotype was not well studied. The aim of this thesis was to study the contribution of InDels to adaptation, phenotypic variations and hybrid performances in maize. We developed an Affymetrix® Axiom® genotyping array that allowed to genotype 105,947 InDels sequences ranging from 35bp to 129,7Kbp of size. 79,969 out 105,947 sequences of these InDels were not present in B73 reference genome and have been discovered by assembling three genomes (F2, C103, and PH207). We selected 61,492 polymorphic InDels to genotype a 362 maize inbred lines panel representing a broad range of diversity to study the contribution of InDels to genetic diversity, adaptation and trait variation. We also assembled one million of SNPs from two genotyping arrays and genotyping by sequencing to study the complementarity between InDels and SNPs. Genetic structuration and relatedness between inbred lines displayed by SNPs or by InDels were highly similar suggesting that almost all indels and SNPs followed a similar evolutionary trajectory. 51% of InDels were not in high linkage disequilibrium (LD>0.8) with any nearby SNP suggesting that the effect of these InDels was not be well captured using this density of SNP. Thanks to InDels, we detected 13 new quantitative trait loci (QTLs) among 294 QTLs identified for 23 traits by a genome wide association studies (GWAS). Similarly, 56 out 188 regions under selection between tropical, dent and flint maize lines were identified by InDels leading to an enrichment of genomic regions under selection detected by InDels compared to SNPs. These InDels include genes involved in tolerance to biotic and abiotic stress and/or adaptive traits as flowering time. Accordingly, the highest number of associated InDels was found for flowering time. These results suggest that InDels were often involved in adaptation and stress tolerance. In order to study the effect of InDels on hybrid performances, we analyzed a panel of 287 hybrids derived from the crossing of 210 maize temperate inbred lines from the previous panel. We decomposed the variance of female flowering (FF), plant height (PH) and grain yield (GY) by distinguishing the additive and dominant genetic effects. We observed the highest dominance and genotype by environment effects for GY and the lowest for FF. We performed GWAS on this panel by testing additive and dominance effects of 51,844 InDels and 469,267 SNPs on these three traits in 4 different environment combinations. We identified 78 and 133 QTLs with an additive and dominance effect, respectively including 6 and 11 QTLs discovered only by InDels. 83% of all QTLs were found with only one environment combination. One QTL for GY detected with InDels was located in a large cluster of InDels on chromosome 6, previously identified to have a strong effect on GY in heat conditions. We finally used InDels and/or SNPs genotyping to predict hybrid performances. Whereas including a dominance effect in genomic prediction models increased by 1.5 to 5.6% predictive abilities (PA) for GY, including InDels genotyping did not increased PA.; Le récent développement des méthodes de séquençage permet aujourd’hui d’identifier des variations structurales chez de nombreuses espèces. Chez le maïs, des milliers de grandes insertions et délétions (InDel) de quelques pb à plusieurs centaines de Kbp ont été découvertes entre le génome de référence B73 et de nombreux autres génomes reséquencés. Ces InDel peuvent changer la composition des gènes entre les individus et donc être impliquées dans la variation du phénotype, mais cet effet sur le phénotype reste mal connu. L’objectif de cette thèse était d'étudier la contribution des InDel à l'adaptation, aux variations phénotypiques et aux performances hybrides chez le maïs. Nous avons développé une puce de génotypage des InDel Affymetrix® Axiom® capable de génotyper 105 927 InDel de 35bp à 129,7Kbp. 79 969 de ces InDel ont leur séquences absentes du génome de référence B73 et ont été identifiées par l’assemblage 3 génomes (F2, C103, and PH207). Nous avons sélectionné 61 492 InDel polymorphiques pour génotyper 362 lignées de maïs représentant une large gamme de diversité pour étudier la contribution des InDel à la diversité génétique, l’adaptation et la variation des caractères. Nous avons également génotypé 1 million de SNP à partir de deux puces de génotypage et du génotypage par séquençage pour étudier la complémentarité entre les InDel et les SNP. Qu’ils soient calculés avec les InDel ou les SNP la structuration génétique et les valeurs d’apparentement entre les lignées sont très similaires, ce qui suggère que la plupart des InDel ont suivi la même trajectoire évolutive que les SNP. 51% des InDel ne sont pas en déséquilibre de liaison élevé (>0.8) avec aucun SNP proche donc l’effet de ces InDel n’est donc a priori pas capturé pas des SNP à cette densité. Parmi les 294 régions génomiques associées au phénotype (QTL), 13 nouveaux QTL ont été détectés grâce aux InDel par rapport aux SNP par une approche de génétique d’association (GA). Nous avons détecté un enrichissement en InDel sous sélection entre les lignées tropicales, cornées et dentées par rapport aux SNP, avec 56 sur 188 régions sous sélection détectées avec les InDel. Ces régions contiennent des gènes impliqués dans l’adaptation et/ou la tolérance aux stress. De plus, le plus grand nombre d’associations a été découvert pour la floraison, caractère adaptatif chez le maïs. Ces résultats suggèrent que les InDel sont plus souvent impliquées dans l’adaptation et la tolérance aux stress. Nous avons enfin testé l’effet des InDel sur les performances hybride en analysant un panel de 287 hybrides issus du croisement de 210 lignées tempérées du panel précédent. Nous avons décomposé la variance des performances hybrides en distinguant les effets de dominance et d’additivité pour la floraison femelle (FF), la hauteur (PH) et le rendement (GY). La plus forte part de dominance et d’interaction génotype-environnement a été observée pour le GY et la plus faible pour la FF. L’effet additif et de dominance de 51,844 InDel et 469 267 SNP a été testé pour 4 combinaisons d’environnements par une approche de GA. 78 et 133 QTL avec un effet additif et dominant respectivement ont été identifiés, dont 6 et 11 avec des InDel. 83% de ces QTL ont été identifiés dans une seule combinaison d’environnements. Un des QTL de rendement identifié avec des InDel est situé dans un large cluster d’InDel sur le chromosome 6 et colocalise avec un QTL déjà identifié avec des SNP avec un effet fort dans l’augmentation du rendement sous des températures élevées. L’ajout de l’effet de dominance en plus de l’effet additif permet d’augmenter la précision des prédictions génomiques jusqu’à 5,6% pour le rendement. Cependant, l’ajout du génotypage des InDel en plus de celui des SNP n’a pas permis d’améliorer les prédictions des phénotypes hybrides.

Additional file 1: Table S1. Summary of sequencing data used during the assembly process provided by ALLPATHS-LG.Table S2. Classification by the Affymetrix® pipeline of 84,994 BP probes based on cluster number, separation, variance, and call rate. A) Probes recommended for genotyping, B) Probes not recommended for genotyping. Table S3. Classification by the Affymetrix® pipeline of 163,278 OTV probes based on cluster number, separation, variance, and call rate. A) Probes recommended for genotyping B) Probes not recommended probes for genotyping. Table S4. Classification by the Affymetrix® pipeline of 414,500 MONO probes, based on cluster number, separation, variance, and call rate. A) Probes recommended for genotyping B) Probes not recommended for genotyping. Table S5. Effect of probe number within InDels on average percentage of missing data, of genotypes absent and genotypes not fully concordant. Table S6. Simulation of genotyping error rates for 362 lines and 10,000 InDels called by various numbers of probes with a probe genotyping error rate ranging from 1 to 10%. Table S7. Comparison of reproducibility between 5 DNA replicates of hybrid F1 according to probes type and observed clustering. Table S8. Mendelian inheritance of 12 hybrids F1 derived from 9 different parental inbred lines for 46,382 BP probes passing Affymetrix quality control and polymorphic. Table S9. Comparison of the reproducibility of InDels and SNP genotyping between 13 maize varieties replicated on 50 K Illumina SNP and Affymetrix® Axiom® InDel arrays.

International audience; Compilation of genetic maps combined to QTL meta-analysis has proven to be a powerful approach contributing to the identification of candidate genes underlying quantitative traits. One of the most interesting properties of meta-QTL (or consensus QTL) is its confidence interval (IC) often shorter than IC of corresponding QTLs, decreasing the number of candidate gene to consider. As map compilation and QTL meta-analysis do not rely on genotyping raw data or trait measure, they can be easily achieved even if user holds maps from the literature or genetic databases.BioMercator was the first software offering a complete set of algorithms and visualization tool covering all steps required to perform QTL meta-analysis. The fourth version of BioMercator propose additional methods and improve graphical representation of large datasets. In this version, user may import sequence and genome annotations datasets within the software in order to display and mine functional annotation related to QTL and meta-QTL. In order to improve candidates genes detection, we aim to inculde genetic association approach in the release of BioMercator. Association genetics allow to build a relationship between molecular polymorphism and phenotypic variation so, Genome-Wide Association Studies (GWAS) present a good potential for QTL's sharpening. We integrated GWAS results in Biomercator and provided new functionalities to display and exploit them.

National audience; Large extent of Copy Number Variants (CNV), i.e changes in the copy number of genes between individuals, have been recently highlighted in maize using Comparative Genomic Hybridization array or massive parallel sequencing. However, their contribution to genetic diversity and to traits variation remains mostly unknown since these technologies are very expensive and labor demanding. To address these issues, we developed an original approach based on Affymetrix Axiom technology, to genotype an extreme form of CNV called Present Absent Variant (PAV) in maize. PAV was defined as DNA sequence >1kbp that is present in some individuals but absent from others. Using our high throughtput genotyping array, we genotyped 60 026 PAV on 356 inbred lines from an association panel representing worldwide maize genetic diversity. This panel has been previously genotyped using 50k SNP Illumina Infinium array and phenotyped for 29 agronomic traits related to yield, phenology and plant architecture. We analyzed and compared how PAV and SNP polymorphisms were globally structured in these panel by analyzing relatedness and genetic structuration. We observed that genetic structuration and relatedness obtained using PAV were globally similar to those obtained with SNP. We analyzed extent of linkage disequilibrium (LD) between SNP and PAV. We observed that LD were less extended between PAV and SNP than between SNP suggesting that the effect of these polymorphisms on traits could be not easily captured by linkage disequilibrium with SNP from 50K array. We performed a genome wide association study on 29 agronomic traits and identified several PAV significantly associated with different agronomic traits.

National audience