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Additional file 11: Figure S8. Log scale QQ-plots corresponding to association analysis performed independently on each trait and repetition. Three different models (i.e. EMMA, LFMM, MLM) taking into account relatedness and/or structure were used for association and ANOVA was used as a benchmark. For a given trait, the transformed data were used if at least one of the replicates failed to reach normality, otherwise non-transformed data were used.

Additional file 8: Figure S5. Correlations between traits and structure. For each ancestry group (A1 to A4), we made a Spearman’s rank correlation test and plotted it as a correlogram. Colors sign the intensity of the correlation and white stars were added when the correlation was significant given the threshold retained, i.e. either a p-value cutoff of 0.01 (a) or a False Discovery Rate of 5% (b).

Additional file 2:Figure S1. Positions of accessions with resistance alleles of RYMV1, RYMV2 and NLRRYMV3 genes on the genetic diversity tree. Susceptible accessions are colored in dark grey and accessions not evaluated for resistance in light grey. Adapted from the genetic tree of Orjuela et al. [20]. Figure S2. Characteristics of primers and amplified fragments for markers or Sanger sequencing. Genes are represented as grey boxes for exons and grey lines for introns. Primers are represented as triangles and the numbers below the triangle refer to the corresponding sequences. (a, b, c) Blue traits represent fragments that were amplified and then sequenced with the primers colored in red. (c) Amplification fragments corresponding to the CAPS or dCAPS markers designed on the CPR5–1 gene are represented as green traits. Additional information on these markers is provided in Additional file 2: Table S7. (d) Position of T-DNA insertions in the CPR5–1 gene in lines 3A-06612 and 3D-01842 are indicated. The T-DNA-specific and gene-specific primers used for sequencing the T-DNA flanking site and genotyping for the presence/absence of insertions are indicated in brown and blue, respectively. Table S7. Characteristics of CAPS and dCAPS markers. Marker names indicate whether there are CAPS or dCAPS markers and which alleles of the CPR5–1 gene they target. The bracketed number before the primer sequences refer to the reference of primers in Additional file 2: Figure S2. The size of the fragments expected in plants with the reference haplotype of CG14 (WT) or the alternate haplotypes (R) are indicated, except fragments below 30 bp that are uneasily detected by agarose electrophoresis. The CAPS-CPR5–1-R1 marker had already been described in Orjuela et al. [13].

Additional file 5: Figure S2. Genome wide Linkage disequilibrium (LD) decay.

Additional file 9: Figure S6. Linear scale QQ-plots corresponding to association analysis performed independently on each trait and repetition. Three different methods (EMMA, LFMM, MLM) taking into account relatedness and/or structure were used for association and ANOVA was used as a benchmark. For a given trait, the transformed data were used if at least one of the replicates failed to reach normality, otherwise non-transformed data were used.

The Rice yellow mottle virus (RYMV) is a major constraint for rice production in Africa. Two genes controlling recessive resistances have been previously described : RYMV1 coding for eIF(iso)4G1, a translation initiation factor, and RYMV2, coding for CPR5-1, a probable component of the nuclear pore complex involved in the regulation of defense mechanisms. However, the virus' ability to overcome these resistances highlight the need to characterize new sources of resistance in the African rice species O. glaberrima and O. barthii. Three complementary approaches were carried out in order to identify the genetic factors controlling these resistances.A genetic mapping strategy in bi-parental populations led to the identification of the RYMV3 gene, controlling resistance in the Tog5307 accession and probably also in the Tog5672 accession. It is the first dominant resistance identified in the rice/RYMV pathosystem. RYMV3 mapped in a 15 kb interval in which two genes annotated occur, including one NB-LRR gene.The two other strategies used were based on the utilization of Illumina sequencing data of 163 O. glaberrima accessions and 84 O. barthii accessions. O. glaberrima accessions were phenotyped for both high and partial resistance to RYMV, and the high resistance of a portion of the O. barthii accessions was assessed. Analysis of allelic variability at the previously identified genes led to the identification of a probable new resistance allele at RYMV1 and of six others at RYMV2. These alleles are currently undergoing validation. Furthermore, a genome wide association study was carried out on 125 O. glaberrima accessions, revealing two partial resistance QTLs on chromosomes 6 and 11, including one colocalized with RYMV3.This work has thus allowed the identification of one major resistance gene, of two QTLs and of new resistance alleles, contributing to a better understanding of rice/RYMV interactions and creating new prospects for the breeding for resistant varieties.; Le virus de la panachure jaune (RYMV) est une contrainte majeure pour la riziculture en Afrique. Deux gènes contrôlant des résistances récessives ont précédemment été décrits : RYMV1, qui code pour eIF(iso)4G1, un facteur d’initiation de la traduction et RYMV2, qui code pour CPR5-1, un probable composant du pore nucléaire impliqué dans la régulation des mécanismes de défense. Cependant, la capacité du virus à contourner ces résistances justifie la caractérisation de sources de résistance originales, présentes dans les espèces de riz africain O. glaberrima et O. barthii. Trois approches complémentaires ont été mises en œuvre afin d’identifier les facteurs génétiques contrôlant ces résistances.Une approche de cartographie génétique dans des populations bi-parentales a permis l’identification du gène RYMV3, contrôlant la résistance de l’accession Tog5307 et sans doute également de l’accession Tog5672. Il s’agit de la première résistance dominante identifiée dans le pathosystème riz/RYMV. RYMV3 a été cartographié dans un intervalle de 15 kb où deux gènes sont annotés, dont un gène NB-LRR. Des comparaisons de séquences entre accessions résistantes et accessions sensibles suggèrent que le polymorphisme responsable de la résistance est une mutation ponctuelle dans le domaine LRR du gène NB-LRR.Les deux autres approches ont reposé sur l’exploitation de données de séquençage Illumina de 163 accessions O. glaberrima et 84 accessions O. barthii. Les accessions O. glaberrima ont été phénotypées à la fois pour la résistance élevée et pour la résistance partielle au RYMV, et une partie des accessions O. barthii a été évaluée pour la résistance élevée. L’analyse de la variabilité allélique aux trois gènes majeurs de résistance a permis l’identification d’un probable nouvel allèle de résistance à RYMV1 et de six à RYMV2. Ces allèles sont actuellement en cours de validation. D’autre part, une approche de génétique d’association réalisée sur 125 accessions O. glaberrima a mis en évidence deux QTL de résistance partielle sur les chromosomes 6 et 11, dont l’un colocalise, en première approche, avec le gène RYMV3.Ce travail a ainsi permis l’identification d’un gène majeur, de deux QTL et de nouveaux allèles de résistance qui contribuent à une meilleure compréhension des interactions riz/RYMV et sont utilisables en sélection pour améliorer la durabilité des variétés résistantes.

Additional file 6: Figure S3. Structure of the population. (a) evolution of the cross-entropy criterion with increasing K, (b) bar plot of ancestries membership considering K = 4 ancestral population.

Additional file 7: Figure S4. Geographic distribution of traits. We plotted the mean value of each trait for accessions having sampling location in their passport data.

Additional file 3: Figure S1. Histograms of trait distribution and quantile-quantile plots for each non-transformed and transformed variables.

Additional file 3: Figure S1. Histograms of trait distribution and quantile-quantile plots for each non-transformed and transformed variables.

Additional file 7: Figure S4. Geographic distribution of traits. We plotted the mean value of each trait for accessions having sampling location in their passport data.