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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.

Size distribution of B73 and F2-specific sequences. All PAVs exhibit similar size distribution except F2 unanchored PAVs, which are more numerous and include larger sequences. (PDF 28 kb)

Example of PAVs genotyping using resequencing data. Classification of PAVs in inbred line EP1 based on information from 20X sequencing depth. Each dot represents one PAV. Green and red dots correspond to PAVs that are confidently classified as present and absent, respectively (80% of all PAVs). Black dots correspond to unclassified PAVs (20% of all PAVs). (PDF 279 kb)

Background Insertions/deletions (indels), and more specifically presence/absence variations (PAVs) are pervasive in maize and have strong functional and phenotypic effect by removing or modifying drastically genes. Genotyping of such variants on large panels remains poorly addressed, while necessary for approaches such as association mapping or genomic selection. Results We have developed a new high throughput and cost-effective tool to genotype indel. We first identified 141,000 indels by aligning reads from the B73 line against the genome of three temperate maize inbred lines (F2, PH207, and C103) and reciprocally. Next, we designed an Affymetrix® Axiom® array to target these indels with a combination of probes selected at breakpoint sites (13%) and/or within the indel sequence either at polymorphic (25%) or non-polymorphic sites (63%) sites. The final array design is constituted of 662,772 probes and targets 105,927 indels including PAVs, ranging from 35bp to 129kbp. After Affymetrix® quality control, we successfully genotyped 89,393 indels (84%) on 445 maize DNA samples with 479,027 probes (72%). A principal coordinate analysis on dissimilarity estimated from a subset of 57,824 indels on 362 inbred lines is consistent with the structure obtained using 50K SNP arrays. Conclusions We efficiently genotyped thousands of small to large indels on a large number of individuals using a new Affymetrix® Axiom® array. This powerful tool opens the way to studying the contribution of indels to trait variation and heterosis in maize. The approach is easily extendable to other species and should contribute to decipher the biological impact of indels at a larger scale.

Classification of PAVs with mapped short-reads count (see Methods). (PDF 224 kb)

Bioinformatics workflow of pan-genome sequence building. All steps are described in details in the method section. (PDF 702 kb)

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

Several methods can be used to study genome evolution. Most of the time, genome evolution is studied through nucleotide sequence polymorphism. However, in some species, mutation rate is low and polymorphisms are mainly caused by chromosomal rearrangements. In such a case, chromosomal rearrangement is the only informative marker to study genome evolution. In this study, we focused on plant mitochondrial genome evolution at the structural level. Plant mitochondrial genomes have been described as highly rearranged, but no study has been conducted on their rearrangement evolution. We chose to analyze the diversity of plant mitochondrial genomes at the intraspecific level to work on a short evolutive scale, limiting rearrangement events among genomes. The study was conducted on two species : Zea mays and Beta vulgaris. Moreover, besides structural polymorphisms, plant mitochondrial genomes contain large number of duplicated elements which are not taken into account by rearrangement tools if orthologous and paralogous relations are not established. Based on the hypothesis that the duplicated elements were caused by tandem duplication events, we proposed a new approach to find, sort and differentiate duplicated elements. This method led to phylogenies based on rearrangement events consistent with phylogenies based on nucleotide sequences. The comparison of genome evolution between maize and beet allowed us to show the existence of different evolution histories and mechanisms between these two species. We also observed evolutionary differences at the intraspecific level, raising the question of sampling strategy when genomes are compared at the interspecific level.; L'étude de l'évolution des génomes peut être abordée par différentes stratégies. Généralement, les analyses reposent sur les polymorphismes de séquences. Cependant, il existe des génomes dont le taux de mutation est très faible et dont la principale source de polymorphisme provient de l'arrangement différent de leurs gènes le long des chromosomes. Les événements de réarrangements chromosomiques deviennent alors les seuls marqueurs utilisables pour retracer l'évolution de ces génomes. Nous nous sommes intéressés dans ce travail à l'analyse de l'évolution des génomes mitochondriaux d'espèces végétales au niveau de leur structure. En effet, ces génomes sont caractérisés par un faible taux de mutation et un taux élevé de réarrangements. Cette étude s'est portée à un niveau intraspécifique afin de limiter le nombre de réarrangements à analyser et sur deux espèces : Zea mays, le maïs, et Beta vulgaris, la betterave. Il s'avère, qu'en plus du polymorphisme de structure, ces génomes contiennent un grand nombre d'éléments dupliqués. Or les outils d'analyse d'événements de réarrangements ne permettent pas d'inclure les événements de duplication autrement qu'en distinguant les paralogues des orthologues, ce qu'il est particulièrement difficile à réaliser ici, du fait que les dupliqués sont identiques en séquence. Nous avons ici établi une stratégie basée sur l'hypothèse que les éléments dupliqués proviennent de duplications en tandem, permettant la reconnaissance, le tri et la distinction des éléments dupliqués. Cette méthode nous a conduits à proposer une histoire évolutive basée sur des réarrangements congruente avec les phylogénies de séquences. Les comparaisons entre génomes mitochondriaux de maïs et betteraves nous ont permis de montrer que des mécanismes évolutifs différents sont à l'origine de la diversité génomique observée. Nous avons également observé des différences évolutives entre les génomes à un niveau intraspécifique soulevant le problème d'échantillonnage lorsque l'on veut comparer des génomes à un niveau interspécifique.

Quality analysis of F2 WGA in a gene-rich region. A. A MUMmer alignment showing that more than 44% of a 112.2 kb F2 BAC sequence from the Bronze locus is covered by 3 scaffolds from the F2 WGA. B. Nearly all genes of this region are assembled into one single scaffold while the two other scaffolds cover mostly non-genic DNA and TE. Gaps indicated by a star (A) or an orange box (B) delineate contigs of each scaffold. All contigs are correctly ordered and oriented even in non-genic regions. (PDF 464 kb)

Quality analysis of F2 WGA in a gene-rich region. A. A MUMmer alignment showing that more than 44% of a 112.2 kb F2 BAC sequence from the Bronze locus is covered by 3 scaffolds from the F2 WGA. B. Nearly all genes of this region are assembled into one single scaffold while the two other scaffolds cover mostly non-genic DNA and TE. Gaps indicated by a star (A) or an orange box (B) delineate contigs of each scaffold. All contigs are correctly ordered and oriented even in non-genic regions. (PDF 464 kb)

Quality analysis of F2 WGA in a transposon-rich region. A. A MUMmer alignment showing that a unique scaffold from the F2 WGA covers 62% of a 74.7 kb F2 BAC sequence from a gene-free region. B. While a 20 kb region is absent from the assembly, contigs delineated by gaps (indicated by a star (A) or an orange box (B)) are correctly ordered and oriented showing that the F2 WGA is able to correctly covers large regions encompassing mainly TEs. (PDF 245 kb)

Quality analysis of F2 WGA in a transposon-rich region. A. A MUMmer alignment showing that more than 80% of a 116.7 kb F2 BAC sequence is covered by 5 scaffolds from the F2 WGA. B. With no exception, contigs delineated by gaps (indicated by a star (A) or an orange box (B)) are correctly ordered and oriented showing that the F2 WGA is able to correctly covers large regions encompassing TEs. (PDF 615 kb)

Quality analysis of F2 WGA in a transposon-rich region. A. A MUMmer alignment showing that a unique scaffold from the F2 WGA covers 62% of a 74.7 kb F2 BAC sequence from a gene-free region. B. While a 20 kb region is absent from the assembly, contigs delineated by gaps (indicated by a star (A) or an orange box (B)) are correctly ordered and oriented showing that the F2 WGA is able to correctly covers large regions encompassing mainly TEs. (PDF 245 kb)

Long-jump read coverage of F2 WGA and F2-specific sequences. F2 WGA was evaluated by mapping reads from a F2 3 kb mate-pair Illumina library (24X) on F2 scaffolds. Both reads of pairs were mapped independently with SMALT 0.7.4. A. Insert size distribution after mapping. Only pairs exhibiting an insert size ranging from 1.6 to 3 kb were considered as correctly mapped and used for subsequent evaluation. B. Scaffold coverage versus scaffold size. Coverage computation is based on properly mapped long jump reads with insert size ranging from 1.6 kb to 3 kb. As expected, small scaffolds are less efficiently covered by long jump reads. C. F2-specific scaffolds fraction shows an enrichment in well-covered scaffolds (> 70% coverage) compared to F2 WGA (B). (PDF 8588 kb)

Long-jump read coverage of F2 WGA and F2-specific sequences. F2 WGA was evaluated by mapping reads from a F2 3 kb mate-pair Illumina library (24X) on F2 scaffolds. Both reads of pairs were mapped independently with SMALT 0.7.4. A. Insert size distribution after mapping. Only pairs exhibiting an insert size ranging from 1.6 to 3 kb were considered as correctly mapped and used for subsequent evaluation. B. Scaffold coverage versus scaffold size. Coverage computation is based on properly mapped long jump reads with insert size ranging from 1.6 kb to 3 kb. As expected, small scaffolds are less efficiently covered by long jump reads. C. F2-specific scaffolds fraction shows an enrichment in well-covered scaffolds (> 70% coverage) compared to F2 WGA (B). (PDF 8588 kb)

Quality analysis of F2 WGA in a transposon-rich region. A. A MUMmer alignment showing that more than 80% of a 116.7 kb F2 BAC sequence is covered by 5 scaffolds from the F2 WGA. B. With no exception, contigs delineated by gaps (indicated by a star (A) or an orange box (B)) are correctly ordered and oriented showing that the F2 WGA is able to correctly covers large regions encompassing TEs. (PDF 615 kb)