Two novel and adjacent genomics and candidate genes for bacterial wilt resistance were identified on chromosome B02 in peanut variety Zhonghua 6 using both traditional QTL mapping and QTL-seq methods.Arachis hypogaea) is an important oilseed crop worldwide. Utilization of genetic resistance is the most economic and effective approach to control bacterial wilt, one of the most devastating plant diseases, in peanut production. To accelerate the genetic improvement of bacterial wilt resistance (BWR) in peanut breeding programs, quantitative trait locus (QTL) mapping has been conducted for two resistant varieties. In this context, we deployed linkage mapping as well as sequencing-based mapping approach, QTL-seq, to identify genomic regions and candidate genes for BWR in another highly resistant variety Zhonghua 6. The recombination inbred line population (268 progenies) from the cross Xuhua 13 × Zhonghua 6 was used in BWR evaluation across five environments. QTL mapping using both SSR- and SNP-based genetic maps identified a stable QTL (qBWRB02-1) on chromosome B02 with 37.79–78.86% phenotypic variation explained (PVE) across five environments. The QTL-seq facilitated further dissection of qBWRB02-1 into two adjacent genomic regions, qBWRB02-1-1 (2.81–4.24 Mb) and qBWRB02-1-2 (6.54–8.75 Mb). Mapping of newly developed Kompetitive allele-specific PCR (KASP) markers on the genetic map confirmed their stable expressions across five environments. The effects of qBWRB02-1-1 (49.43–68.86% PVE) were much higher than qBWRB02-1-2 (3.96–6.48% PVE) and other previously reported QTLs. Nineteen putative candidate genes affected by 49 non-synonymous SNPs were identified for qBWRB02-1-1, and ten of them were predicted to code for disease resistance proteins. The major and stable QTL qBWRB02-1-1 and validated KASP markers could be deployed in genomics-assisted breeding (GAB) to develop improved peanut varieties with enhanced BWR.Peanut (The online version of this article (10.1007/s00122-020-03537-9) contains supplementary material, which is available to authorized users. Arachis hypogaea L.), also known as groundnut, is an important legume crop grown in more than 100 countries with 47.10 million tonnes global production in 2017  population derived from the crossing of Yueyou 92 and susceptible variety Xinhuixiaoli. The resistance in Yueyou 92 is from a Chinese landrace Xiekangqing, which is a major resistance source utilized for bacterial wilt disease in Chinese breeding programs   population (268 progenies) developed from the cross of Xuhua 13 (highly susceptible) × Zhonghua 6 was used to discover the genetic basis of BWR in Zhonghua 6. More SSR loci were added to the previously published SSR-based genetic map , upon which a SNP-based genetic map was constructed with 2595 SNPs, spanning 2465.62 cM with an average inter-marker distance of 0.95 cM  of the RIL population (268 lines) were used for phenotyping BWR. The F9 generation of the RIL population (268 lines) was used to extract DNA for the genotyping of newly developed Kompetitive Allele-Specific PCR (KASP) markers.The improved peanut variety Xuhua 13 is highly susceptible to bacterial wilt disease, while Zhonghua 6 has been highly and stably resistant since it was released. A RIL population (268 lines) was developed from the cross Xuhua 13 × Zhonghua 6 following single seed decent (SSD) method , 2016 (F7 generation) and 2017 (F8 generation) as well as in Nanchong during 2015 (F6 generation) and 2017 (F8 generation). These five environments were designated as HA2015, HA2016, HA2017, NC2015 and NC2017, respectively. The percentage of plants that were not killed by bacterial wilt disease during the entire period was used to indicate the resistance and referred as survival rate  and Nanchong, China . These disease nurseries, in which bacterial wilt disease naturally and evenly occurs across years, were utilized in the BWR evaluation of our previous study  influenced BWR (Table S1). Broad-sense heritability for BWR was estimated to be 71.07% based on plot mean and 93.72% based on entry mean, indicating strong control by genetic factors.The resistant parent Zhonghua 6 (RP) showed significantly higher survival rates (86.75% on average) than the susceptible parent Xuhua 13 (SP) (13.69% on average) across five environments  were successfully added to the previously reported SSR-based genetic map  while the two minor QTLs, qBWRB02-2 and qBWRA07, were only expressed in the HA2017 environment (3.78–4.31% PVE). According to e-PCR locations of the four flanking SSR markers  on the genome assembly of Fuhuasheng  on chromosome B02  of the mapping population in five environments. Through composite interval mapping (CIM), one major QTL was identified on chromosomes B02 while two minor QTLs were identified on B02 and A07 (Table 87 RILs oqBWRB02-1 on chromosome B02 (37.79–53.85% PVE), which was estimated to be located in a 7.51 Mb interval (0.59–8.45 Mb) through BLASTn the RAD tags of five flanking SNPs  to the genome sequence of Fuhuasheng  on chromosome B02  across five environments, 25 RILs with survival rates of 5.99–14.37% and 25 RILs with survival rates of 84.93–95.42% were used to prepare the susceptible bulk (SB) and resistant bulk (RB), respectively  and 3.54 Mb (6.11–9.65 Mb) intervals, respectively  to the genome assembly of Fuhuasheng  to the genome assembly of Fuhuasheng, which was then referred to as the RP assembly . The reads of the RB and SB were also mapped to the RP assembly and achieved 97.20% coverage and 38.19 mean read depth for the RB while 97.19% coverage and 37.12 mean read depth for the SB Table .qBWRB02-1. Therefore, two adjacent genomic regions were selected as the candidates for BWR, i.e., 2.81–4.24 Mb and 6.54–8.75 Mb on B02 for further detailed analysis, and they were refereed as qBWRB02-1-1 and qBWRB02-1-2, respectively . Similarly, 100 SNPs with the top ΔSNP-indexes for qBWRB02-1-2 were targeted for KASP marker development, and 15 KASP markers were successfully developed for qBWRB02-1-2 (Table S8).To validate qBWRB02-1-1 and qBWRB02-1-2. Based on the results of the 30 markers, a genetic map was constructed with map length of 19.60 cM (Table S9). Single-marker analysis (SMA) confirmed the significant associations between BWR and the 30 markers across five environments (Table S9). Among the 13 markers for qBWRB02-1-1, the KASP marker CM014326.1_4160298 was the most significant, with LOD scores of 36.02–54.64. Among the 17 markers for qBWRB02-1-2, the KASP marker CM014326.1_6709822 was the most significant, with LOD scores of 15.19–22.97. CIM analysis indicated that the major and stable effects (49.43–59.90% PVE) of qBWRB02-1-1 while the minor effects (3.96–6.35% PVE) of qBWRB02-1-2 across five environments . Function annotation analysis of the 205 SNPs found that 30 SNPs were intergenic while the other 175 SNPs were located in genic regions of 50 putative genes. Among the 175 SNPs, 61 were located in 5 kb upstream, 3 in 5′ UTR, 23 intronic, 13 synonymous, 49 non-synonymous (1 resulted in stop codon) and 26 in 5 kb downstream. The 49 non-synonymous SNPs affected 16 putative candidate genes for BWR . The candidate gene Ahy_B02g057524 codes for protein ENHANCED DISEASE RESISTANCE. The candidate genes Ahy_B02g057494 and Ahy_B02g057523 code for pentatricopeptide repeat superfamily protein. The candidate genes Ahy_B02g057498 and Ahy_B02g057528 code for serine threonine-protein phosphatase 7 long form homolog and hydroquinone glucosyltransferase, respectively. However, the candidate gene Ahy_B02g057521 codes for protein with unknown function.The 1.43 Mb genomic region of qBWRB02-1-2 had 464 effective SNPs. Of the 464 SNPs, 433 SNPs were identified irrespective of parent used as reference; however, 12 SNPs were specifically identified when SP assembly was used as reference while 19 SNPs were specifically identified when RP assembly was used as reference (Table S11-12). Function annotation analysis of the 464 SNPs found that 160 SNPs were intergenic while the other 304 SNPs located in genic region of 112 putative genes. Among the 304 SNPs, 136 were located in 5 kb upstream, 6 in 5′ UTR, 33 intronic, 7 synonymous, 17 non-synonymous (1 resulted in stop codon), 3 in 3′ UTR and 102 in 5 kb downstream. Notably, the 17 non-synonymous SNPs affected 15 putative candidate genes for BWR , while the two minor QTLs were only expressed in single environments (3.78–4.31% PVE). The major QTL qBWRB02-1 was also identified using the SNP-based genetic map, while two additional minor QTLs (3.75–5.88% PVE) were identified in single environments  broad-sense heritability in the RIL population of Yuanza 9102 (resistant) × Xuzhou 68–4 (susceptible)  while qBWRB02-1-2 had minor effects (3.96–6.48% PVE); however, both of them were stable expressed across five environments  of qBWRB02-1-1 were much higher than the previously reported qBW-1 in Yueyou 92 (11.9–21.6% PVE) and qBWRB02.1 in Yuanza 9102 (14.4–29.32% PVE). Therefore, we assumed that qBWRB02-1-1 should be a novel and valuable QTL, and it is possible to further improve resistance level to bacterial wilt disease by pyramiding diverse resistant QTLs/genes from both wild and cultivated peanut.The major QTL qBWRB02-1-1 could be estimated but limited information was provided for the identification of candidate genes. Through QTL-seq, high-density SNPs were identified by resequencing of the parental genotypes and extreme pools. Function annotation analysis of non-synonymous SNPs variations identified 16 putative candidate genes for qBWRB02-1-1  and Ahy_B02g057535 (8). The gene Ahy_B02g057524 codes homolog of EDR4 which plays a negative role in disease resistance Figure S2Graphical presentation of the improved SSR-based genetic maps constructed in the RIL population derived from Xuhua 13 and Zhonghua 6 (PDF 53 kb)Figure S3Genome-wide overview of QTLs for bacterial wilt resistance identified using the improved SSR-based genetic map (TIF 627 kb)Figure S4Genome-wide overview of QTLs for bacterial wilt resistance identified using the SNP-based genetic map (TIF 658 kb)Figure S5Genome-wide SNP-index plots of resistant bulk with the susceptible parent Xuhua 13 as reference (PDF 964 kb)Figure S6Genome-wide SNP-index plots of susceptible bulk with the susceptible parent Xuhua 13 as reference (PDF 871 kb)Figure S7The ΔSNP-index plot obtained by subtraction of susceptible bulk SNP-index from resistant bulk SNP-index using the susceptible parent Xuhua 13 reference (PDF 931 kb)Figure S8Genome-wide SNP-index plots of susceptible bulk with the resistant parent Zhonghua 6 as reference (PDF 1019 kb)Figure S9Genome-wide SNP-index plots of resistant bulk with the resistant parent Zhonghua 6 as reference (PDF 935 kb)Figure S10The ΔSNP-index plot obtained by subtraction of susceptible bulk SNP-index from resistant bulk SNP-index using the resistant parent Zhonghua 6 as reference (PDF 953 kb)Figure S11qBWRB02-1-1. (PDF 407 kb)The conserved domains encoded by the identified candidate genes in the genomic region of Table S1 Result of ANOVA for BWR of the RIL population across five environments. Table S2 Information of 180 SSR markers newly genotyped in the RIL population. Table S3 Genotyping data of the 185 newly genotyped SSR loci in the RIL population. Table S4 Genetic linkage map constructed based on 1002 polymorphic SSR loci in the RIL population. Table S5 Description of the improved genetic map constructed based on SSR markers. Table S6 Details on whole genome re-sequencing data for QTL-seq. Table S7 Chromosome-wise SNPs distribution between extreme bulks for bacterial wilt resistance. Table S8 Details of newly developed KASP markers. Table S9 Single-marker analysis for bacterial wilt resistance using validated KASP markers. Table S10 Phenotypic effect of the two adjacent QTLs on chromosome B02 for bacterial wilt resistance in the RIL population. Table S11 Effective SNPs identified for bacterial wilt resistance using the susceptible parent Xuhua 13 assembly as reference. Table S12 Effective SNPs identified for bacterial wilt resistance using the resistant parent Zhonghua 6 assembly as reference (XLSX 379 kb)Below is the link to the electronic supplementary material.


Plasmodium vivax populations of Papua New Guinea (PNG) for research and surveillance.Genomic surveillance of malaria parasite populations has the potential to inform control strategies and to monitor the impact of interventions. Barcodes comprising large numbers of single nucleotide polymorphism (SNP) markers are accurate and efficient genotyping tools, however may need to be tailored to specific malaria transmission settings, since ‘universal’ barcodes can lack resolution at the local scale. A SNP barcode was developed that captures the diversity and structure of P. vivax genome sequences from PNG, a total of 178 evenly spaced neutral SNPs were selected for development of an amplicon sequencing assay combining a series of multiplex PCRs and sequencing on the Illumina MiSeq platform. For initial testing, 20 SNPs were amplified in a small number of mono- and polyclonal P. vivax infections. The full barcode was then validated by genotyping and population genetic analyses of 94 P. vivax isolates collected between 2012 and 2014 from four distinct catchment areas on the highly endemic north coast of PNG. Diversity and population structure determined from the SNP barcode data was then benchmarked against that of ten microsatellite markers used in previous population genetics studies.Using 20 high-quality PvSalI reference genome with deep coverage  per locus across genotyped samples. Of 178 SNPs assayed, 146 produced high-quality genotypes (minimum coverage = 56X) in more than 85% of P. vivax isolates. No amplification bias was introduced due to either polyclonal infection or whole genome amplification (WGA) of samples before genotyping. Compared to the microsatellite panels, the SNP barcode revealed greater variability in genetic diversity between populations and geographical population structure. The SNP barcode also enabled assignment of genotypes according to their geographic origins with a significant association between genetic distance and geographic distance at the sub-provincial level.From a total of 28,934,460 reads generated from the MiSeq Illumina run, 87% mapped to the High-throughput SNP barcoding can be used to map variation of malaria transmission dynamics at sub-national resolution. The low cost per sample and genotyping strategy makes the transfer of this technology to field settings highly feasible. Plasmodium vivax is the most widely distributed human malaria parasite outside sub-Saharan Africa, accounting for approximately 7.4 million clinical cases per year . For SNP markers the method identified three genetic clusters (K = 3) and two genetic clusters (K = 2) for Microsatellites. However, for Microsatellites K = 2 is a common artifact of the hierarchical clustering algorithm when two very distinct populations are present, so higher K must be observed to identify possible sub-population structure. Figure S7. Pairwise allele sharing of P. vivax genotypes within and between population. a SNP genotype frequency distribution of pairwise allele-sharing (PS). b Microsatellite genotype frequency distribution of pairwise allele-sharing (PS). Black bars indicate within population and grey bars indicate between populations. There was no significant difference in allele sharing within and between parasite populations either SNP barcode or microsatellite markers in north coast of PNG. Figure S8. Kendall’s Tau concordance analysis of genotype allele sharing by SNP and Microsatellite. Each boxplot indicates the proportion of shared alleles between genotypes by SNP. The analysis revealed no significant correlation between the SNP and microsatellite pairwise allele sharing values.Additional file 3: Table S2. Details of 178 SNPs, SNPs included in final panel (n = 146), primers sequences and their multiplex setting.Additional file 4: Table S3. The 83 P. vivax haplotypes used for the final data analysis.


Sugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust resistance is a desirable characteristic due to the large economic impact of the disease. Although marker-assisted selection for rust resistance has been successful, the genes involved are still unknown, and the associated regions vary among cultivars, thus restricting methodological generalization. We used genotyping by sequencing of full-sib progeny to relate genomic regions with brown rust phenotypes. We established a pipeline to identify reliable SNPs in complex polyploid data, which were used for phenotypic prediction via machine learning. We identified 14,540 SNPs, which led to a mean prediction accuracy of 50% when using different models. We also tested feature selection algorithms to increase predictive accuracy, resulting in a reduced dataset with more explanatory power for rust phenotypes. As a result of this approach, we achieved an accuracy of up to 95% with a dataset of 131 SNPs related to brown rust QTL regions and auxiliary genes. Therefore, our novel strategy has the potential to assist studies of the genomic organization of brown rust resistance in sugarcane. Although this crop has great energetic potential, its breeding process has generated high genomic complexity across bred varieties, exceeding that of most if not all other crops3. Modern sugarcane cultivars are derived from a process of hybridization that has occurred over a century between Saccharum spontaneum 6 with highly complex genomic organization1, a polyploid genome 7, a frequent occurrence of aneuploidy at the locus level depending on the number of homologous chromosomes in hybrid cultivars8, an estimated whole-genome size of 10 Gb9, and a high content of repetitive regions (50% of genome size)10. This complexity has challenged the efforts of the scientific community to unravel the genetic architecture of sugarcane in terms of the molecular mechanisms underlying different phenotypes, particularly efforts to detect regions of phenotype–genotype associations.Sugarcane is an important source of income worldwide, especially due to its efficiency in the manufacturing of biofuel and sugar-related products in most tropical and subtropical areas of the world11. One disease with a large impact on sugarcane yield is brown rust, which is caused by Puccinia melanocephala, a fungus that affects foliage and decreases the photosynthetic capacity of sugarcane13. Brown rust infections have already caused large economic losses16. However, disease control has been shown to be successful in sugarcane breeding17, and the planting of cultivars resistant to brown rust is considered the most effective method of controlling this pathogen12. Based on comparisons of the genetic characteristics of the resistant cultivar R570 and other sugarcane varieties17, brown rust resistance was found to be a dominant trait controlled by one or a few genes18, with the presence of two related major genes: Bru119 and Bru220. Bru1 has already been employed in different breeding programs to identify resistant sugarcane genotypes5, using, for instance, the presence of flanking molecular markers for resistance diagnosis across cultivars12.Sugarcane breeding programs are implemented with the intention of releasing new cultivars with interesting agronomic traits, including disease resistance5. Therefore, the exploration of novel genes could contribute to the understanding of this process and in turn overcome the problems associated with reliance on a single gene21. An appropriate strategy for unraveling the genetic architecture and genomic organization of brown rust resistance would be the use of linkage maps followed by quantitative trait locus (QTL) identification. However, existing methodologies for the construction of saturated linkage maps with high resolution are limited for aneuploid species, such as sugarcane23. Using simplification strategies based on the population expected segregation ratio, such as the selection of a subset of single-dose markers, leads to impaired linkage groups and thus compromises the identification of reliable QTLs22. A linkage map depicting QTLs associated with brown rust resistance has been published5, but as observed in previous studies24, adjustments of existing methods resulted in gaps, a poorly saturated map and a large number of unlinked markers, mainly due to the high probability of meiotic behaviors in the cultivars and the aneuploidy of sugarcane22. Different software programs have been developed to build linkage maps for polyploids27; however, none address sugarcane genomic organization.Although there have been several advances in understanding brown rust susceptibility in sugarcane, it is important to consider that pathogens may overcome the resistance of sugarcane varieties, and the use of a single region for resistance examination further increases the probability of vulnerability28. However, resistance differs among cultivars, which can restrict the application of validated linked markers as a general tool for MAS28. Therefore, the identification and characterization of brown rust resistance genes in sugarcane have been slow14, mainly because selection approaches based on QTL mapping overestimate the effect of strong QTLs, while weak QTLs might not be identified30. In general, these methodologies have low power to detect rare variants with phenotypic associations31. Methodologies for addressing sugarcane genomic characteristics are still lacking, and because of the difficulty of accurately selecting QTL regions for MAS, an alternative methodology known as genomic selection (GS) has been developed to identify promising varieties with resistance traits and improve sugarcane breeding programs in terms of time and cost33.The use of Bru1 for marker-assisted selection (MAS) represents a successful application of this methodology in some sugarcane varieties33. In sugarcane breeding programs, the selection of superior genotypes might take more than 12 years34, and GS represents an alternative for improving this process, accelerating the breeding cycle and reducing the time needed to generate diversity35. Due to sugarcane’s genomic complexity, simplified predictive models involving linear regression cannot capture the unknown nonlinear characteristics present in these datasets31, as described for other polyploid species38. To address this issue, machine learning (ML) methodologies represent a promising approach with high accuracy41. Although GS was developed to address the problem of categorizing individuals using different populations, its application in biparental populations is suitable and might be highly efficient due to the significant amount of linkage disequilibrium between loci42, which would facilitate the initial cycles of breeding programs.In general, GS is based on the creation of a predictive model for breeding values built with the entire set of markers using a training and a testing population. This model might be posteriorly applied in a breeding program to select a set of promising individuals7, which might lead to misclassified genotypes. These difficulties in genotyping a population directly impact the estimation of locus effects on model creation43, and this influence is more complex when using nonlinear models with more parameters to be estimated44. An alternative for dealing with erroneous features and additional restrictions for high-dimensional data is feature selection (FS). These techniques aim to reduce the number of single nucleotide polymorphisms (SNPs) in a data set and identify a subset of markers with higher predictive capability by removing markers that are irrelevant/redundant for the phenotype45. These methods are among the most powerful alternatives for building better generalization models46 while avoiding overfitting and the attribution of nongenetic effects to different markers43. With FS, it is possible to reduce marker density and build simpler and more comprehensive models46, thereby increasing predictive power due to the identification of phenotype-associated polymorphisms. A few previous studies applied ML methods to decrease the number of SNP datasets needed for phenotypic predictions49, achieving high accuracy. The identification of such a subset of putative causal polymorphisms is crucial for improving production in plants42 and represents a novel strategy for genomic prediction in sugarcane.In sugarcane, the allele dosages (ADs) of a locus are frequently unknown50 followed by an established bioinformatics pipeline to identify reliable SNPs considering the sugarcane aneuploid condition; (2) creating a ML-based strategy to establish a subset of SNPs with good ability to predict brown rust phenotypes; and (3) examining these polymorphic regions to identify genes and QTL regions. Our study provides a novel methodology that can assist in sugarcane genetic studies and breeding programs to establish a pipeline to infer phenotype-causative regions, which can help unravel sugarcane brown rust resistance molecular mechanisms and identify targets for breeding.Therefore, the objectives of this research were as follows: (1) genotyping a sugarcane full-sib population using a genotyping by sequencing (GBS) protocol51 and mapping studies52.A set of full-sib progeny composed of 219 individuals derived from a biparental cross between the elite clone IACSP95-3018  and the commercial variety IACSP93-3046  was developed by the Sugarcane Breeding Program at the Agronomic Institute of Campinas (IAC). IACSP95-3018 is a promising clone that is used in breeding programs but is susceptible to brown rust. IACSP93-3046 is a variety with good tillering, an erect stool habit and resistance to brown rust. These parents have already been used in transcriptome53. In Ribeirão Preto, four evaluations were performed: (1) November 2005 (plant cane), (2) January 2006 (plant cane), (3) January 2007 (ratoon cane), and (4) March 2007 (ratoon cane). In Piracicaba, the evaluations were conducted in December 2011 (plant cane) and in February 2012 (plant cane).The progeny phenotyped for brown rust symptoms were planted in 2005 at the Sugarcane Breeding Center of the Instituto Agronômico (IAC) located in Ribeirão Preto-SP, Brazil, and again in 2011 in Piracicaba-SP, Brazil, in an augmented block design with five blocks, each containing 44 individuals, plots with 1-m rows and plants spaced 1.5 m apart. Both parents and two varieties (SP81-3250 and RB835486) were included in each replicate as controls. The level of brown rust infection was evaluated using a diagrammatic scale between 1 and 9, with larger values indicating larger percentages of leaf area infection54 following a statistical mixed model:ith genotype, considering the jth block, the rth replicate, the kth location and the mth year of harvest. The trait mean is represented by kth location  calculated based on the mixed model described above using the R package breedR v.0.1256. Heritability  plots together with a Shapiro–Wilk normality test . We also tested normalized values of the brown rust trait created with the R package bestNormalize57. To evaluate the differences among the phenotypic rust groups, we used T-tests of the BLUPs and original values.With these predictions, cluster analysis was performed with the BLUP values. We used complete hierarchical clustering based on pairwise Euclidean distances for visual inspection. The number of appropriate clusters was identified using the K-means algorithm together with (1) the within-cluster sums of squares and (2) the average silhouette width of clusters, implemented in the R package factoextra v.1.0.658. Genome complexity was reduced via the PstI restriction enzyme for library preparation50. We constructed two 9648-plex libraries from the population consisting of a single sample of each individual, two replicate samples of each parent and one blank sample. Five sequencing runs were performed with the Illumina GAIIx (one in 2015) and Illumina NextSeq  systems.Total genomic DNA samples from parents and 180 progeny were extracted from leaf roll using the CTAB protocol59. Reads resulting in a minimum percent identity of 90% and e-value of 0.01 against PhiX regions were filtered out60. FASTQC61 was used for the initial visualization of nucleotide distributions and their respective qualities, and FastX-Toolkit scripts62 were employed to obtain 90-bp reads with a minimum of 80% of bases with a Q greater than 20. Sample demultiplexing was also performed using the FastX-Toolkit62.PhiX sequences were removed from GBS reads through alignments of raw reads against the PhiX genome using BLASTn63 and Bowtie2 version 2.3.3.164 algorithms to align the filtered reads against the following references: (1) the methyl-filtered (MF) genome of sugarcane cultivar SP70-114365, (2) the sorghum genome from Phytozome v.1366, (3) a sugarcane leaf transcriptome51, (4) the draft genome of the hybrid SP80-328067, (5) the monoploid genome of the R570 variety3, (6) the S. spontaneum genome split into four subsets on the basis of the allele-defined genome, and (7) sequences from the sugarcane expressed sequence tag project (SUCEST)68.We used the BWA-MEM version 0.7.12The performance of each mapping software tool was evaluated according to the percentage of uniquely mapped reads. The individual number of uniquely mapped reads across the population was also analysed and assessed using an alluvial plot for visualization. Individuals with small numbers of reads and high discrepancies with most others (at least a decrease of 70% in the number of reads) were not considered for further analysis. In order to identify the most appropriate reference for SNP calling in sugarcane, we examined the following aspects together: (1) the quantity of uniquely mapped reads; (2) the profiles of sequencing depth across loci; (3) the contiguity of consensus sequences obtained through the read alignments; (4) the capability to comprise the largest amount of consensus sequences formed by the other references; and (5) the number of SNPs identified.69 was used to obtain the profiles of sequencing depth across loci69 in order to examine (2). The consensus sequences (contigs) formed by mapping reads to the different references were retrieved using Stacks version 2.370, and, for aspect (3), we calculated traditional assembly metrics  per 100 kbp, N50/75 and L50/L75) for all the contigs separated by reference using QUAST version 5.0.271. An evaluation of the raw reference sequences was also performed using QUAST version 5.0.2. Additionally, for (4) all contigs were evaluated on the basis of their similarity to the consensus sequences of the other possible references. All correspondences were counted, taking into account the quantity of related contigs. These alignments were obtained through BLASTn59 with stringent parameters to examine real redundancies . We also used the R package circlize72 to visually inspect these redundancies.For (1), we used the results from the most promising mapping tool. SAMtools version 1.674 and Stacks version 2.370 pipelines. We evaluated raw SNPs by comparing them to a dataset with a filter criterion of a maximum of 25% of missing data per locus, considering individual genotypes without a minimum count of 50 reads as missing data.For the last step of reference comparison, we executed the Tassel4-POLY75, SAMtools version 1.669 and FreeBayes version 1.1.0-376. We created a common dataset to be processed by these tools, establishing a pre-processing pipeline according to GATK best practices75. From the mapping results, uniquely mapped reads were selected using SAMtools version 1.669, and with Picard Toolkit77, the following steps were performed: (1) the mapped files from different sequencing experiments were joined into one file per individual; (2) read duplicates were marked; and (3) read group information was added to different files. To produce more accurate results, we used GATK version 3.775 to realign indels and SAMtools version 1.669 to convert mapping formats. Putative SNPs were called using the three different tools and different ploidy configurations with GATK and FreeBayes (even ploidies ranging from 2 to 20). We selected the identified SNPs and evaluated these variants with respect to the quantity of missing data.Together with the Tassel and Stacks results, the best selected reference was used to identify variants through the Haplotype Caller algorithm implemented in Genome Analysis Toolkit (GATK) version 3.778, a Venn diagram was created to evaluate the intersection between SNPs identified by the callers and those identified by the selected reference. Indels were not used for further analyses. Due to sugarcane aneuploidy at the locus level, we genotyped the individuals on the basis of SNP allele proportions, i.e., the ratio between the number of reads for the reference allele and the total number of reads. To increase the reliability of our results, we selected markers called by Tassel and at least one other caller with a minimum count of 50 reads per individual and a maximum of 25% missing data.Using the R package VennDiagram v.1.6.2079 and the VCF2SM pipeline73 were used to estimate the ploidy levels at different loci. Quantitative allele intensities at each locus were estimated for individuals based on read depth73. These values were used to estimate locus ploidies (ranging from 2 to 20). We used the F1 model for population structure due to the usage of a biparental population and did not restrict the posterior probability threshold to capture and analyze all possible configurations produced by the statistical estimate. We also defined the most probable set of loci with a posterior probability greater than 0.8 given the selected ploidy (6 through 14). We compared through treemaps the ploidies estimated for the final dataset of SNPs and for the possible false positives eliminated using the proposed approach.SuperMASSA80, support vector machine (SVM)81, Gaussian process (GP)82, decision tree (DT)83, random forest (RF)84, multilayer perceptron (MLP) neural network85, adaptive boosting (AB)86, and Gaussian naive Bayes (GNB)87 implemented in the scikit-learn v.0.19.0 Python v.3 module88. As a cross-validation strategy, we used a stratified K-fold (k = 4) repeated 100 times for different data configurations. We evaluated the following metrics: (1) accuracy (proportion of correctly classified items), (2) recall/sensitivity , (3) precision , and (4) specificity . The area under the receiver operating characteristic (ROC) curve (AUC) was also calculated for each model and plotted using the Matplotlib v.2.0.2 library89 with Python v.3.Using the identified SNPs as ADs and allele proportions (APs), eight ML algorithms were tested to check their ability to predict the phenotypic rust groups. Missing data were imputed as the means. We tested K-nearest neighbor (KNN)88. We tested the following approaches to obtain feature importance and create subsets of the marker data: (1) gradient tree boosting (FS1)90, (2) L1-based FS through a linear support vector classification system (FS2)81, (3) extremely randomized trees (FS3)91, (4) univariate FS using ANOVA (FS4), and (5) RF (FS5)84. The best genotyping approach for predicting brown rust phenotypic groups was selected by analyzing each prediction measure and counting the percentage of models with the best performance when using ADs or APs in the different established dataset configurations . For evaluating the prediction capability of each FS technique, we combined the calculated metrics with statistical approaches.We also tested FS techniques implemented in the scikit-learn Python v.3 module92 for parametric data and a Wilcoxon test for nonparametric data. We assessed the capability of each FS technique to exceed the confidence intervals for each metric in order to select the most promising strategies. Additionally, for comparing the predictive profiles, we tested the differences in these metrics between the selected FS methods using ANOVA and multiple comparisons by Tukey’s test implemented in the agricolae v.1.3-1 R package93. With the most promising strategies identified, we also evaluated the intersection of these datasets using the R package VennDiagram78.On the evaluation metrics  for each subset of identified markers , we performed a Shapiro-Wilk normality test  and identified confidence intervals for means  using the gmodels v.2.18.1 R package94. These BACs were retrieved from the GenBank database95. We performed comparative alignments of the nine BACs and the selected reference sequences against S. spontaneum1 coding DNA sequences (CDSs) using BLASTn59 with the following parameters: a minimum e-value of 1e−30, a minimum percent identity of 95% and coverage of at least 75% in the query sequence. The distribution of these regions among S. spontaneum chromosomal regions was inferred using the karyoploteR package96.From the SNPs identified by the most promising FS technique, we selected the reference positions to which they belonged and extracted the respective region. To check the distribution of these SNPs in the Bru1 region, we selected nine bacterial artificial chromosomes (BACs) from the sugarcane cultivar R570 that were previously described as belonging to regions containing Bru166 for fourteen different species from the Poaceae family  and Arabidopsis thaliana. Selected S. spontaneum CDSs were aligned against this dataset, enabling the identification of correspondence with Gene Ontology97 (GO) categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthologies98 (KOs). All identified GO categories were used to create a treemap to visualize possible correlated categories in the dataset caused by identified regions using FS and BAC correspondence. This step was performed using the REVIGO tool99.We created a dataset with CDSs extracted from Phytozome v.13The brown rust phenotypic dataset was analyzed as described in Section 2 . After applying filters based on missing data and read depth, we obtained a final set of 14,540 SNPs . These datasets were used to evaluate possible ploidy configurations using SuperMASSA software. This evaluation was performed on (I) 14,540 SNPs representing our final set of markers and (II) 4341 SNPs representing the most likely false-positive SNPs.The intersections between SNPs found with different tools can be visualized in Fig. 7 compared with the second one with 4341 SNPs. The majority of SNPs in the second set were classified as having a ploidy of 20, representing doubtful regions with chances of duplication events and low-quality data7. Therefore, this group of putative molecular markers with higher reliability provided better results in terms of estimated ploidies.Separating SuperMASSA posterior probabilities into three categories  based on their reliability Fig.  classifiTo understand the genotypic associations with different brown rust phenotypes more generally, we chose to perform genotype-phenotype analyses with the phenotypic rust groups identified in the clustering analysis. We performed these tests using two different approaches for genomic prediction: ADs obtained with SuperMASSA  and APs calculated based on Tassel output for read counts. With these two different datasets, the FS techniques were applied and generated different sets of SNPs Table .Table 2QThese SNPs were used to predict the phenotypic rust groups using the eight selected ML algorithms in the proposed cross-validation scenario , thus proving its inefficiency in predicting these phenotypes when using the entire set of SNPs. With FS techniques, these values increased but still presented differences between the selected methods. To evaluate the best FS techniques with which to increase predictive capabilities, we determined confidence intervals for all metric means , as shown in Supplementary Table 3%, thus The FS1, FS2 and FS4 methods identified different variants in different scaffolds. However, there were intersections between these sets Fig. . Inter 2The tested ML models had different capabilities of separating the phenotypic groups, and these capabilities changed depending on the dataset used. In addition to using the previous metrics, we chose to evaluate model performance using ROC curves and the respective AUCs. All of these plots are shown in the SI together with the AUC values  9 selected BACs related to Bru1 QTL regions and (B) 146 MF scaffolds identified as important by at least two methods. We identified 373 CDSs using (A) and 240 CDSs using (B). All BACs of (A) had correspondences, and nine scaffolds of (B) did not have relevant alignments. As there was only one CDS in common between (A) and (B), we evaluated the chromosomal location of these CDSs considering the S. spontaneum genomic reference, which is presented in the SI  and (B). The complete discrimination of the identified pathways is shown in the SI. We found 41 associated pathways in (A) and 29 in (B), with 16 in common between these groups. As expected, there was an elevated number of common biological cascades that might be influenced by these regions. The specific pathways found exclusively in group (B) were monoterpenoid biosynthesis, phenylpropanoid biosynthesis, the pentose phosphate pathway, sulfur metabolism, other glycan degradation, fatty acid elongation, basal transcription factors, ubiquitin-mediated proteolysis, various types of N-glycan biosynthesis, tryptophan metabolism, sphingolipid metabolism, carbon metabolism and N-glycan biosynthesis.In relation to metabolic pathways, we selected the 28; however, there is still a gap in the characterization of the wide range of genes involved in the process of infection and how different genomic polymorphisms can influence this phenotype. The adjustments performed on these analyses showed reasonable results, and the identification of these possibly phenotype-causative regions can help unravel sugarcane brown rust resistance molecular mechanisms and the selection of targets for breeding.The organization of the sugarcane genome greatly challenges genetic studies of this species, and alternative approaches must be employed to overcome these difficulties. Here, we developed a novel strategy to address sugarcane genomic specificities and enable the identification of genomic regions related to brown rust resistance through the evaluation of ML predictive performance. The sequencing method, SNP detection process and phenotypic associations were designed to fit these singularities. Sugarcane brown rust susceptibility was previously studied and applied in sugarcane breeding programsFirst, due to the diversity of rust scores (1–9), the variation in rust phenotypes within populations and the qualitative nature of rust phenotypes, we decided to use the two groups identified by the BLUP clustering analysis instead of the raw scores. We were interested in finding markers and genomic regions related to brown rust resistance, and the establishment of these two major groups enabled the identification of resistance categories in the population. As previously described, these phenotypic rust groups presented a high level of differentiation in rust scores, and this contrast in susceptibility may aid in the identification of the most promising plants for sugarcane breeding programs. In addition, the establishment of these groups allowed the use of a wide range of ML strategies.100, for example, designed different probes using in silico approaches. The resulting regions were posteriorly adopted in other studies104 due to the large quantity of markers with sufficient sequencing depth located in genic regions. Another approach is GBS107, which is the preferred genotyping method for plants with some degree of genomic complexity108 mainly due to its simplicity, reproducibility and considerable genome coverage109. In addition, regulatory regions controlling different phenotypes are often located in noncoding DNA, and GBS allows the amplification of such regions50. Herein, we decided to use GBS to obtain a broader set of genomic regions with their respective probabilities of correspondence with rust resistance.In relation to the sugarcane genotyping process, different approaches have been adopted by the scientific community to reduce the genomic complexity of sugarcane and utilize a limited amount of information. Song et al.S. bicolor genome for comparative alignments and the subsequent identification of putative variants with bioinformatic methods. This reference choice is due to sorghum’s phylogenetic proximity to sugarcane107 and, in some cases, probe experimental design104. Despite sorghum’s genome usage, the availability of sugarcane pseudoreferences has provided new genomic tools for scientific research as initially explored by Balsalobre et al.23, who used the sorghum genome, a sugarcane MF genome65, a sugarcane leaf transcriptome51 and SUCEST tags68. However, new sugarcane genomic resources are now available, such as the draft genome of the cultivar SP80-328067, the monoploid genome of the R570 variety3 and the genome of the AP85-441 S. spontaneum cultivar1, which are phylogenetically closer to current sugarcane cultivar resources than are sorghum resources. Therefore, there is a need to explore these new references and check their appropriateness. As there are no previous reports of the usage of these novel references together with sugarcane GBS data, we decided to test them in order to identify the most appropriate reference.Sequencing reads are generally organized by using the 50. However, genome assembly is a difficult task when dealing with repetitive regions and polyploids110. With the aim of reducing possible biases, we decided not to use de novo approaches, which were previously described as inappropriate for sugarcane GBS data105.Although GBS allows a reduction in genomic complexity, we must consider sugarcane singularities to establish an analysis pipeline. In GBS experiments, the consensus of read clusters at cutting sites could be adopted as a reference in cases where there is no appropriate sequence to use100. In terms of MF performance, this may be explained by the experimental procedures of MF sequencing and GBS library preparation. GBS library construction is based on the selection of a subset of genomic regions using methylation-sensitive restriction enzymes, which avoid repetitive regions50. To select our GBS regions, we used the enzyme PstI, which is a methyl-sensitive restriction enzyme, to select hypomethylated DNA111. Similarly, the MF genome was obtained through a process of sequencing where genomic regions were also selected based on hypomethylation65. This approach generated high compatibility between our data and the genomic reference, as observed in the comparative alignments and previous reports23. Although there have been great advances in understanding the sugarcane genome since the S. spontaneum genome became available, we decided to perform our analyses using the sugarcane MF genome to capture the most probable markers and establish a criterion based on data appropriateness. This genomic reference is still at the scaffold level, but as shown in this study, there is a high rate of redundancy among consensus sequences obtained through GBS data alignments with the different references. Due to this observed redundancy, we chose not to use all of the references. In addition to adding redundant markers, it is important to note that these different consensus contigs built based on different references can lead to different alignments of GBS data. These alignments may in turn produce different organizational profiles of read alignments and divergent SNPs. Therefore, we selected the most reference with the best usage of the amount of GBS data as the most appropriate and analyzed the respective SNPs.In our study, the combination of BWA and MF scaffolds had the best performance for GBS data. BWA was previously reported as a sensitive tool for aligning sugarcane reads and retaining a large number of uniquely mapped sequences112. Thus, to increase the reliability of our data, we decided to use other SNP callers  in combination with Tassel, as the usage of SNPs identified by more than one caller is more reliable than the usage of SNPs identified by only one caller113. The intersection between the SNPs identified by at least two tools was established to increase the accuracy of these variants without substantially increasing the number of false negatives. In addition, Tassel was used due to its targeted development for GBS data and preprocessing steps. The Tassel workflow keeps read depths unchanged between the initial mapping and the final data generated for the identified genotypes. In sugarcane, this information is necessary to estimate ADs or calculate APs. Using this intersection approach, we identified the final set of SNPs to be used for our association analyses. Indels, however, were not selected. These variants identified by in silico strategies do not provide reliable information, showing elevated divergence between the existent callers and a probability of producing spurious variants114.A wide range of SNP callers are available. Tassel was developed to handle GBS data and has been widely applied to species with different genomic organizations. Although this tool enables the identification of many SNPs, it was previously described as insufficiently accurate to be used alone23. However, sugarcane aneuploidy together with the common occurrence of duplication events might have influenced the process of estimating locus ploidies and, in turn, the process of categorizing the related dosages through the established filters. In addition, 64% of the identified SNPs were discarded when using this approach for obtaining dosages. Because we would not need to calculate chromosomal distances between loci for linkage map construction, the elevated loss of markers and the reduced performance of ADs in the task of genomic prediction, we decided to continue our analyses with APs. Previous tests of this approach yielded reasonable results104.Using this approach, we found 14,540 putative SNPs. With these regions, we tested two different strategies for genotyping the population at these loci: (1) the usage of ADs estimated with SuperMASSA and (2) the usage of APs calculated based on Tassel output. For SuperMASSA estimations, we kept only SNPs with an estimated ploidy between 6 and 14 (minimum posterior probability of 0.8) due to sugarcane genomic configurations115 represents a challenge in genomic prediction, particularly due to the difficulty of working with high-dimensional datasets, i.e., the ’large p, small n’ problem116. This increase in the amount of available information makes the task of directly applying these marker data in genomic analyses more difficult and necessitates appropriate preprocessing steps117. In this study, we proposed the use of FS techniques to select a smaller set of SNPs with more predictive power than the entire dataset and closer associations with the brown rust phenotype to assist the identification of regions associated with disease status. This can be considered quite advantageous in the context of genomic selection because the identification of a subset of markers allows a reduction in sequencing costs49. In addition, it has already been demonstrated that for genomic selection, a selected reduced number of SNPs has reasonable reliability119.After establishing the bioinformatics pipeline for identifying and evaluating these regions, we studied the influence of SNP subsets identified by FS techniques on the task of predicting phenotypic rust groups. The amount of data generated by high-throughput sequencing technologies120. This strategy of selecting a subgroup of SNPs with higher predictive power and closeness to the predictive class has already been employed in different contexts122. In this study, we tested five different strategies and found three promising alternatives for executing this methodology. FS1, FS2 and FS4 substantially increased the models’ capabilities of predicting the phenotypic groups as demonstrated in this paper. We believe that this increase in predictive power is due to the identification of regions influencing the phenotype, possibly in QTLs or regulatory genomic elements. As a final strategy for the prediction and selection of these associated regions, we suggest the use of the intersection of these three techniques. This approach enabled the creation of more stable models using different ML algorithms and better accuracies for predicting these phenotypes.The identification of markers related to this phenotype using FS is based on these techniques to provide an interpretable model due to the close relation between trait and genotype; i.e., using the subset of high-density markers might help elucidate the regions most likely to be involved in phenotypic differentiationS. spontaneum CDSs, we identified these regions and compared them with CDSs correlated with BACs developed based on Bru1 regions. A total of 146 different scaffolds were selected as important for this predictive task by at least two methods . Among these sequences, only 9 did not have correspondence with S. spontaneum CDSs, possibly due to the presence of additional noncoding regulatory elements. These regions can be targets of genetic studies due to their relationships with predicted phenotypes. Although there was no considerable intersection between CDSs associated with BACs and the selected scaffolds, we did find consensus in correlated biological functions. This divergence between regions is mainly explained by the differences between the populations used to generate the GBS data and the brown rust QTLs (which were used to select BACs). QTL regions are identified for a specific population, and there might be differences between datasets from different populations, especially for the sugarcane genome. In addition, the creation of sugarcane linkage maps relies on many adaptations of methods, such as the selection of only single-dosage markers23, which might lead to the identification of a restricted set of QTLs and the nonuse of many auxiliary genomic elements.Corroborating this hypothesis, we also found that most of the identified regions containing these SNPs were associated with QTLs with known biological functions, and there were also additional categories known to be correlated with rust resistance. Through comparative alignments between MF scaffolds and 125; DNA topological change is a wider category with different implications in many biological processes, including responses to pathogens126; differences in nitrogen compound transport might be related to the accumulation of this nutrient and its influence on resistance against pathogens127; and phosphatidylinositol-mediated signaling includes important categories that also act on plants’ responses to pathogens125. A considerable number of metabolic pathways related to both BACs and the selected scaffolds were also detected. However, specific pathways were found to be associated with these scaffolds, mainly due to the different roles of the proteins encoded by these identified CDSs and because these pathways were already reported as being associated with plant responses to different pathogens135, further corroborating our findings. The indication of possible mutation events in these regions provides evidence of differences in protein expression and phenotypic characteristics.The exclusive GO categories related to the selected variants have already been reported to be associated with resistance. Sphingolipid metabolism is intimately connected to programmed cell deathThe identified regions with putative variants and high predictive performance for brown rust phenotypic groups can be employed as novel regions to investigate susceptibility-related traits. This proposed strategy can complement traditional methodologies for deciphering sugarcane genomic regions associated with pathogen infection responses and susceptibility. Although these SNPs were identified for only one biparental population, the strategy can be used for different populations, and the genes can be further investigated to validate the influence of the genomic regions on different phenotypes. This study represents an initial step in employing ML and FS strategies in sugarcane genomic studies. We illustrated the great potential of applying these methodologies to predict phenotypes by using a highly complex polyploid species.Supplementary Information.


Acropora, and their symbionts. We designed the array to co-analyze host and symbionts based on bi-allelic single nucleotide polymorphisms (SNP) markers identified from genomic data of the two Caribbean Acropora species as well as their dominant dinoflagellate symbiont, Symbiodinium ‘fitti’. SNPs were selected to resolve multi-locus genotypes of host  and symbionts , distinguish host populations and determine ancestry of coral hybrids between Caribbean acroporids. Pacific acroporids can also be genotyped using a subset of the SNP loci and additional markers enable the detection of symbionts belonging to the genera Breviolum, Cladocopium, and Durusdinium. Analytic tools to produce multi-locus genotypes of hosts based on these SNP markers were combined in a workflow called the Standard Tools for Acroporid Genotyping (STAG). The STAG workflow and database are contained within a customized Galaxy environment , which allows for consistent identification of host genet and symbiont strains and serves as a template for the development of arrays for additional coral genera. STAG data can be used to track temporal and spatial changes of sampled genets necessary for restoration planning and can be applied to downstream genomic analyses. Using STAG, we uncover bi-directional hybridization between and population structure within Caribbean acroporids and detect a cryptic Acroporid species in the Pacific.Standardized identification of genotypes is necessary in animals that reproduce asexually and form large clonal populations such as coral. We developed a high-resolution hybridization-based genotype array coupled with an analysis workflow and database for the most speciose genus of coral,  These methods are widely used in population genomics but have the disadvantage that the SNP loci are anonymous. Thus, there is no guarantee that the same set of SNP loci will be recovered from each sample within an experiment or between experiments, making it more difficult to design standardized workflows. To circumvent this issue, standardized SNP probes can be designed for reproducible genotyping and analysis from hundreds of samples using modified RAD-based approaches like Rapture2, RADcap3, and quaddRAD4 or using hybridization-based SNP genotyping arrays. Hybridization-based SNP arrays tend to have lower error rates then RADseq methods6 and thus increased accuracy of genet identification and tracking. However, both approaches forgo discovery of new SNP loci in favor of assaying a standard set of probes across all samples resulting in some ascertainment bias9.Genotype identification and tracking are required for well-replicated basic research experiments and in applied research such as designing restoration projects. High-resolution genetic tools are necessary for large clonal populations where genets can only be delineated via genotyping. The advent of reduced representation sequencing methods such as Genotype-By-Sequencing (GBS) or Restriction-site Associated DNA Sequencing (RADseq) have made it possible to assay a large number of single-nucleotide polymorphism (SNP) loci in any organism at a reasonable cost10 and provides the underlying framework for Science Gateways. Science Gateways are extensions of cyberinfrastructure, like Galaxy, that focus on a specific scientific communities’ needs by providing digital interfaces of computational resources which lowers the barriers (know-how and cost) often associated with these resources. The use of a standardized workflow within a Scientific Gateway enables scientists and restoration practitioners to accurately match samples to existing genets and strains, discover novel genets/strains and track their fate across years, all from a web browser.When it comes to the analysis of SNP genotyping data, familiarity with computer programming and access to high performance computing is typically required but not always available. Because genotyping arrays contain a known set of SNP loci, standardized workflows can be designed easily. Galaxy is an open source, web-based platform for data-intensive biomedical research15. Over time coral genets can extend over tens of meters consisting of tens to hundreds of colonies18. This leads to considerable variability in genotypic evenness and richness on small spatial scales, ranging from minimal clonal replication to reefs dominated by a single genet 19. The importance of coral genets in explaining variation in growth rates and stress response is becoming increasingly clear24. Further, hermaphroditic corals species like the Caribbean acroporids are mainly self-incompatible, thereby requiring the presence of gametes from different genets for successful sexual reproduction26. For these reasons, identification of genets and preservation of genotypic diversity are conservation priorities27.Corals, like other clonal plant and animal species, reproduce frequently via asexual fragmentation29. Coral species differ in their symbiont specificity, and colonies may house several algal genera within their cells at a given time. Thus, the complex mixtures of coral and algal DNA present challenges and opportunities for the development of high-resolution co-genotyping methods. Microsatellite markers specific for certain species of algae have further revealed subspecies level strain diversity and elucidated the temporal and spatial dynamics of symbiont strain/host genet associations38, but no SNP-based markers are available yet. Given that the algal species associated with a coral colony can influence the colony’s physiology, it is also of interest to researchers and practitioners to identify the dominant and any background symbionts in coral samples.Tropical corals frequently house single-celled photosynthetic algae in the family Symbiodiniaceae that provide the majority of the hosts organic carbonCorals often occur in remote locations without access to molecular laboratory and computation facilities or require special export permits to transport tissue samples to well-equipped facilities. Thus, we aimed to develop a genotyping array designed for instruments available at most major hospitals around the world. Genotyping arrays can be processed by a sequencing facility with user supplied tissue 43. Because of drastic population declines, they are listed as threatened under the U.S. Endangered species act, making them the focal species in reef restoration efforts across the Caribbean. Promoting genotypic diversity within nurseries and outplanting sites is a management priority for these species. We present a ~ 30k SNP genotyping array that identifies host and symbiont genotypes, coral hybrid status and background symbiont genera. The array can be analyzed cost-effectively in a standardized manner using the Standard Tools for Acroporid Genotyping (STAG) within a Galaxy environment  using two variant callers, samtools mpileup44 that uses likelihood scores and freebayes45 that uses Bayesian posterior probabilities for variant calls. After Affymetrix filtered the 34,783 coral loci, the final array contained 32,124 loci with 53,579 probes, broken down into 25,889 fixed, 17,803 population and 9,887 A. palmata variable probes . Applying similar filtering methods to identify so-called ‘fixed’ differences between strains and populations as was done in the coral, we were left with only a small fraction of SNPs. Given the status of the S. ‘fitti’ genome analysis at the time of the array design, we submitted more probes from the first comparison than the latter . Those loci were mostly found in the intergenic regions of the Symbiodinium genomes , population (n = 304) and genera (n = 54) categories (Table When comparing two deeply-sequenced es Table .n = 293 samples) were re-analyzed together using the ‘Best Practices Workflow’ (BPW). The recommended probes were reduced further after removing probes that matched draft genome assemblies of A. palmata , A. cervicornis , A. tenuis47, A. hyacinthus47, and A. millepora48 with high homology , were not classified as Poly High Resolution, and had limited resolution outside of Florida samples  from all the other samples. This resulted in 531 symbiont genotyping probes for downstream analysis.The recommended coral probes from the first plate were designated as the genotyping probes for the Caribbean acroporids in all subsequent analyses Fig. . For theThe genotype success for each plate is presented in Table A. palmata R2 = 0.4507, p = 8.142e−14; A. cervicornis R2 = 0.8223, p < 2.2e−16; Fig. 50.Four hundred seventy-nine corals (out of 520) were successfully genotyped using the genotyping probe set Table a in the p = 2.81e−13). Plate 9SR22843, which contained the technical replicates, had larger average pairwise genetic distances between ramets and technical replicates within the plate compared to three of the other plates .Technical variation between replicate runs of the same DNA was low with an average genetic distance of 0.0053 ± 0.0015 between technical replicates  and A. cervicornis (n = 64 samples), respectively. The ancestry of each sample was assessed assuming two source populations for the full dataset and two to ten populations for each species separately. For K = 2 of the entire dataset, the two species clearly separate with the hybrids having mixed ancestry  were assigned to one of the parental species instead.The genetic species assignment was based on 9,072 fixed probes. The proportion of ancestry from each parental species was calculated for each sample and used to identify hybrids Fig. . There wrid Fig. . Based oS. ‘fitti’ strains were identified based on a genetic distance threshold of 0.0018. We call these putative strains based on the limited a priori information available for setting the strain detection threshold. Enriched symbiont DNA and mixed DNA extractions from the same tissue shared the same strain ID as did technical replicates of the same DNA extractions from the same ramet  were used to determine likely colonization status for the known and unknown samples  and the other contained host samples that were A. cervicornis (n = 2), A. digitifera (n = 8), and A. millepora (n = 5). Finally, there were 49 samples with Durisdinium . Samples containing either Cladocopium or Durisdinium failed the S. ‘fitti’ genotyping analysis.In addition to multiple strains of es Table , but eaces Table , decisioes Table a and ranes Table b was 98.A. hyacinthus, 28,395 matched A. millepora and 14,399 matched A. tenuis. Given that our probes were designed using the genome assembly of A. digitifera and that they had high homology to other species, we tested whether we could find a conserved set of probes across the Pacific acroproids for future genotyping studies. The Pacific samples were run separately for each species in the genotyping mode in the Axiom Analysis Suite to get the recommended probe sets. This analysis did not enforce a dish-quality threshold. A total of 15,717, 21,520 and 7,275 probes were recommended for A. digitifera (n = 9 samples), A. millepora (n = 5 samples) and A. muricata , respectively. Only those probes that were recommended for all three species were used for further analysis  and differed only at two probes  workflow produces multi-locus genotypes for coral hosts and their algal symbionts. In the workflow, new user-supplied samples are compared to previously genotyped samples and their results contribute to the growing STAG database Fig. . This ar25. The STAG workflow uses 61% of the coral loci to produce the host genotype , stored as cold as possible and extracted using the Qiagen DNeasy tissue extraction kit. DNA requirements are modest for the Axiom SNP array. Adult tissue, single larva and concentrated sperm were successfully genotyped in samples with DNA concentrations as low as 63 pg/µl, although higher concentrations are recommended. While high-quality, non-degraded DNA provided the best results, moderately degraded samples (i.e. extractions that show a dense band of high molecular weight DNA with some smearing across size ranges) were also successfully genotyped. DNA requirements with respect to quality and quantity are thus comparable to RADseq and whole genome sequencing techniques.A. palmata and A. cervicornis differ in the scale of dispersal with A. cervicornis showing higher levels of population subdivision across the Caribbean and North Atlantic compared to A. palmata57. A. palmata stands were found to be structured into two long-separated East/West populations based on microsatellite data58, but additional samples from the Mesoamerican Reef Tract57 and the development of SNP markers59 resulted in the discovery of further population structure. Our results from a limited number of geographic locations identified three populations in A. palmata consistent with the previous study by Devlin-Durante and Baums59, recovering the East/West divide with additional substructure between Puerto Rico and Curacao in the East. We also recovered three populations in A. cervicornis, but with substructure detected between the Western Caribbean populations of Florida and Belize.A. palmata in the field and two classified A. palmata identified as hybrid in the field). While F1 hybrids are more common, later generation backcrosses do occur42 albeit the direction of introgression has been debated61. Here, we identified one later generation hybrid that was classified as a putative backcross A. palmata  and offshore (= Cladocopium) A. muricata samples were consistent with a recent study by Hoadley, et al.65, although this taxon of Cladocopium (Cladocopium_2) was distinctly different from the other Cladocopium taxa (Cladocopium) containing both Caribbean and Pacific hosts  that were only slightly lower than those observed between A. palmata and A. cervicornis (0.684 ± 0.051), suggesting potential cryptic speciation between the two divergent A. muricata populations. Polyphyletic relationships between conspecific A. muricata have been observed66 as well as lack of fertilization among morphotypes67. To establish a robust threshold for genet assignment in Pacific acroporids, it will be necessary to add additional samples to the STAGdb that have also been genotyped using highly polymorphic microsatellite markers. However, because of the large ascertainment bias inherent in applying probes designed for Caribbean acroporids to long-separated Pacific species, population genetic models and models designed to detect loci under selection should not be applied to this data.Application of the current array to non-target Pacific acroporid species is possible when the sole intent is to delineate genets as is often required in restoration settings. Using a common set of acroporid probes, we genotyped three additional acroporid species and could identify members of the same genet. Interestingly, we observed genetic distances between Acropora spp. and symbiont strains of the Caribbean species. These markers and analysis tools can be used for basic research questions such as gene by environment interactions, hybridization history, or identification of loci under selection. Genetic linkage maps can be generated and inbreeding levels, and relatedness questions can be addressed. Because of the low error rate, the SNP array is particularly suited for the detection of somatic mutation, which are expected to be common in the large, old genets that are now dominating Caribbean Acropora populations. Restoration practitioners can use the information to design propagule transfer zones and choose genets for nursery rearing.The combination of the tools presented here provides reliable, standardized identification of host genotypes in diverse A. digitifera, and of those 1.6 million high-quality SNPs varied between the Caribbean acroporids46. To create a conservative set of SNP, we additionally called variants with freebayes v1.1.0-50-g61527c545 using the same alignment file from the previous study and identified shared SNPs between the two variant callers with vcf-compare v0.1.14-12-gcdb80b868. From these shared SNPs, they were further refined into three informative categories: fixed, population and variable. The “fixed” SNPs are those variants where all 21 individuals of a given species share a nucleotide and the other 21 individuals of the other species share a different nucleotide. The fixed SNPs were filtered to a sample read depth of ≥ 3 and a minimum distance of 500 bp. We also retained those that we previously defined as PCR-ready . Population SNPs were identified based on pairwise comparisons of the four different collection sites  using bedtools getfasta70. These 71 nucleotide (71mer) candidate sequences were filtered through a series of similarity searches to reduce non-specific sequence capture. First, the sequences were compared to the A. digitifera genome assembly using BLAST v2.6.0  to determine whether redundant genomic targets were present. Sequences were discarded that had a ≥ 30 bp match with more than one genomic location. To check for repetitive probes, a same-strand self-analysis was performed using blastn .For each SNP, 35 bp of identical flanking sequence between the species was pulled from the A. digitifera. We required a sample read depth of ≥ 6 reads with a minimum distance of 1,000 bp between SNPs and no repetitive or ambiguous bases in the 35 bp flanking sequence. We discarded probes that had significant overlap to the array probes  and randomly selected 3,000 to act as the background probes.In addition to the SNP probes, we identified non-polymorphic sequences from acroporids by extracting high-quality SNPs that were identical between the two Caribbean acroporids and different from S. tridacnidorum (NCBI: GCA_003297005.171) or partial assembly of the predominant symbiont of A. palmata and A. cervicornis, S. ‘fitti’ , both of which belong to the genus Symbiodinium (ITS2-clade A3). Only 15–25% of the reads mapped to the symbiont genomes, reducing our ability to identify comparable number of SNPs in the symbiont as the coral. Some of the Symbiodinium SNPs were identified by comparing only the deep-coverage metagenome sequences of A. palmata and A. cervicornis to the genome assembly of S. tridacnidorum71. These SNPs were identified as fixed between the two representative Florida acroporids sampled, but it was unclear if they were fixed between the symbiont strains of the two coral species across their geographic range, just in Florida or just between these two samples. The other Symbiodinium SNPs were identified by mapping the 42 re-sequenced genome samples to a draft genome assembly of S. ‘fitti’ and processed as described in Kitchen, et al.46. High-quality SNPs were had a quality phred score > 200 and no more than 20% missing data at a given site among all samples. The 71 bp flanking sequences were filtered through the series of blast homology searches in the same manner as the coral SNPs described above. Finally, to confirm that the probes designed for the host and symbiont did not overlap, the final set of both groups were compared to each other using blastn with an e-value threshold of 1e−13. Symbiodinium non-polymorphic SNVs were identified from extracted genomic regions from the S. ‘fitti’ scaffolds with the highest gene coverage for the A. palmata and A. cervicornis samples. After searching the non-polymorphic probes against each other using blastn , a random subset of 3,000 probes was selected.SNP discovery in the symbionts was accomplished by comparing our genome samples to two reference genomes, either the assembly of cultured Symbiodinium, Breviolum, Cladocopium and Durisdinium and can be distinguished by genetic markers. These loci include ribosomal , mitochondrial (COI and cob), chloroplast (cp23S and psbA) and nuclear (elongation factor 2) markers using sequences from previously published studies76. Sequence accessions are provided in Supplemental Table Symbiodinium, Breviolum, Cladocopium and Durusdinium for each locus was aligned with MUSCLE in Mega X77. SNPs were identified based on their ability to distinguish genera with enough conserved flanking sequence for probe design  and 2,661 symbiont SNPs were submitted for review by Affymetrix . Final probe construction was completed by their bioinformatics team Table . The fin79 was used to compute three posterior cluster locations  based on pre-positioned genotype cluster locations called priors. Genotype calls were made by identifying the intensity distribution, or cluster, each sample most likely belongs to with a confidence score (1 – posterior probability of the sample assignment to genotype cluster). In the case of the symbionts, because they are haploid, the algal genotyping probes were treated as mitochondrial probes with only homozygous AA or BB allele calls being valid. Five of the symbiont genera probes allowed for three clusters when multiple alleles were predicted to separate different genera  were processed on the GeneTitan . The raw data was analyzed using the Axiom ‘Best Practices Workflow’ (BPW) in the Axiom Analysis Suite software  for each of the five runs separately for the coral and algal probe sets, with default quality filtering thresholds. Important thresholds that identify low sample quality include the dish quality, which is the signal of the non-polymorphic probes from one individual to the next, and call rate, which is the proportion of assigned genotypes for an individual out of all tested probes. The Bayesian clustering algorithm BRLMM-Pra Table .Following the analysis of the five plates, the performance of each probe was classified into six categories based on their separation of genotype clusters with SNPpolisher  Table . These chttps://github.com/gregvonkuster/galaxy_tools/tree/master/tools/corals). Following the BPW, the genotypes were converted https://github.com/freeseek/gtc2vcf, filtered and combined with user-supplied metadata into a VCF , which are inputs for the Coral Multilocus Genotype tool executed through the R environment80. The VCF file was imported and converted into the genind format by the package vcfR v1.8.081. The genind contains the individual genotypes that is then converted into a genclone format utilized by poppr v2.8.3 for clone identification83. A distance matrix is calculated within poppr using the Prevosti’s absolute genetic distance84, or the number of allelic differences between two individuals. From the distance matrix, known clone mates (ramets of the same genet) or replicate extractions from the same sample , whereas samples without matches to previously genotyped samples were assigned new genet IDs. The representative sample of the new genet ID was identified using the clonecorrect function in poppr. A series of tables were generated from the analysis and imported into the respective database tables using the Update STAG Database tool , was used to identify populations with ADMIXTURE v1.3.086 outside of the Galaxy portal. Plate 9SR22844 was excluded due to higher percentage of missing data  and the percentage of homozygous alleles matching each species in the fixed probe set was calculated. A sample was identified as A. palmata or A. cervicornis if more than 85% of the fixed alleles match the respective species. Hybrid samples were identified as having 40% or greater heterozygosity.Genotypes were extracted from the VCF file using the https://coralsnp.science.psu.edu/galaxy) enables streamlined analysis of the Affymetrix genotype data described above to ultimately provide the user with a genet ID, converted raw genotype data, sample relatedness and hybrid status  provides various views of the data, and additional reports will be added over time.The Galaxy Scientific Gateway called CoralSNP , executes the CoralSNP workflow .The straightforward process is shown in Figure ool Fig. b, which low Fig. a and updlow Fig.  with theEnsure Synced tool confirms that the data contained within these two components is synchronized and creates backup copies of the VCF file and the database before updating either component. The Galaxy CoralSNP environment contains an independent tool named Export All Sample Data, which produces a tabular dataset consisting of all samples and associated metadata in the STAG database. This dataset can be saved locally for analysis within other environments. The dataset that contains all previously genotyped samples can also be downloaded from the Galaxy Data Library, providing more options for additional analyses outside of Galaxy.It is imperative that the previously genotyped samples contained within this VCF file are synchronized with the previously genotyped sample records contained within the STAG database. The https://github.com/gregvonkuster/galaxy_tools/blob/master/galaxy/README. The CoralSNP workflow requires access to a dataset that contains all previously genotyped samples stored in a Galaxy Data Library . The CoralSNP Science Gateway is hosted on a high-performance compute cluster environment managed by the Information Technology VM Hosting team at Pennsylvania State University.All the code and configuration files needed for hosting a local Galaxy CoralSNP instance are available in GitHub, and the instructions for configuring the environment are here n = 531, Table Coral Multilocus Genotype tool. Notably, the ploidy was set to haploid. Because there was limited a priori information on the symbiont stains from microsatellite data, the distance threshold was set based on farthest and nearest threshold calculated by cutoff_predictor in poppr. Symbiont strains were given strain IDs in the format of SG0000.The symbiont genotype data was analyzed in a similar manner to the coral data, but outside the Galaxy environment. Symbiont genotyping probes were identified from the BPW of all five plates after additional filtering to remove host contamination and low-resolution probes (S. ‘fitti’ (n = 17 samples with multiple strains and n = 11 samples with a single strain). The remaining samples were the test set (n = 265). The two data sets were centered and scaled prior to analysis. The five classification tests included supervised learning models such as linear discriminant analysis (LDA) (MASS v7.3-51.4 R package89), decision tree (rpart v4.1-15 R package90 and rpart.plot v3.0.8 R package91), random forest (caret v6.0–84 R package92), naïve Bayes (caret v6.0–84 R package92), and semi-supervised learning model using k nearest-neighbor masking 30% of the training data (SSC v2.0.0 R package93). All tests, except for the LDA, were resampled three times with tenfold cross-validation to evaluate model fit. The results of the five tests are presented as the percent of multiple strain assignment for each genotyped sample.For multiple vs. single strain detection from a single coral sample, five classification methods were used based on signal intensities of the filtered genotyping probes for samples assigned a strain ID. The intensities for each allele of each probe was extracted from the raw CEL file using Axiom Analysis Suite. Samples with prior symbiont genotyping from 12 to 13 microsatellites were used as the training set for all classification models where any sample with more than one allele per microsatellite marker was considered as containing multiple strains of A. cervicornis with Cladocopium: n = 294, A. cervicornis with Durusdinium: n = 2, Pacific acroporids with Cladocopium: n = 20 and A. muricata with Durusdinium: n = 565). Probes were filtered based on their recommended status (Table n = 18) was extracted for all samples regardless of their genotype status using the Axiom Analysis Suite. The data was split into 80% for training and 20% for testing. Cross-validation was performed on the decision tree and random forest models as described above.The background genera were assigned based on the fit of three of the classification tests above: LDA, decision tree and random forest. All samples and probes were first visualized in the Axiom Analysis Suite software to identify patterns in samples with known background symbiont populations  and how to extract DNA (10.17504/protocols.io.bgjqjumw). The code for the new tools developed for this study are available at https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/corals and https://github.com/gregvonkuster/galaxy_tools/tree/master/galaxy. Sequences for the genome samples are available on NCBI under SRA project SRP149363. The coral probe annotation is provided in Supplemental File The Galaxy CoralSNP analysis environment and database reports are available at Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.