DEVELOPMENT OF A HIGH-THROUGHPUT, LOW-COST SNP GENOTYPING PANEL FOR SUGARCANE BREEDING.

Transcription

1 DEVELOPMENT OF A HIGH-THROUGHPUT, LOW-COST SNP GENOTYPING PANEL FOR SUGARCANE BREEDING By MEREDITH D MCNEIL 1, GEORGE PIPERIDIS 2, SHAMSUL BHUIYAN 3, JINGCHUAN LI 1, XIANMING WEI 2, BERT COLLARD 4, KAREN AITKEN 1 1 CSIRO Agriculture and Food, St Lucia, 2 SRA, Mackay, 3 SRA, Woodford, 4 SRA, Meringa Meredith.Mcneil@csiro.au KEYWORDS: Single Nucleotide Polymorphism, SNP, Marker-assisted Selection, Genotyping. Abstract SUGARCANE (SACCHARUM SPP.) IS one of the world s most important economic crops, grown for its sugar and biofuel production. Investigating genomic sequence variation is critical for identifying alleles contributing to important agronomic traits. The development and delivery of varieties that are higher yielding and disease resistant is one of the main research goals in sugarcane breeding. To achieve this, sugarcane breeding is focusing on genetic improvement programs assisted by single nucleotide polymorphisms (SNP) molecular markers. SNPs are now the molecular marker of choice in animal and crop breeding programs around the world because very large numbers of SNPs per genotype can be accurately screened for, and are amenable to high-throughput screening. However, a particular difficulty when working at the DNA level with sugarcane is its highly complex and polyploid genome. The development of SNP markers in sugarcane can overcome the current limitations as large numbers throughout the genome can be easily screened across many genotypes. Recently, an Affymetrix Axiom 45K SNP chip was developed for sugarcane and has dramatically improved the construction of high-density linkage maps and identification of target QTLs for agronomic traits in sugarcane. Marker-assisted selection (MAS) in plant breeding requires markers that are tightly linked to genes of interest and are costeffective. The use of high-density SNP chips, such as the Axiom sugarcane SNP chip, for MAS is still price-prohibitive in sugarcane breeding. The objective of this study was to develop a low-density, low-cost SNP panel that could be used for selection of disease resistant clones in the breeding program. Initially, we used the 45K sugarcane SNP chip to identify new markers, which were found to be linked to resistance to smut across different genetic backgrounds. A comparison was then made of these SNP markers converted to two different SNP marker technologies, the LGC KASP TM assay and the Fluidigm SNPType TM assay, for the development of a high-throughput, low-cost SNP marker panel for disease resistance in sugarcane. We discuss aspects that should be considered during the design of these SNP genotyping arrays, including the importance of validation of SNP markers in diverse genetic backgrounds, costs of marker implementation and decisions on where to use markers in the breeding program. These results offer promise for making selection during the breeding process more rapid, accurate and less expensive and could result in the faster delivery of new disease resistant varieties to the sugarcane industry. Introduction The fundamental aim of plant breeding is to select specific genotypes with desirable traits and to assemble desirable combinations of genes into new varieties. The entire process involves considerable time (8 10 years) and expense. 304

2 Given the extent and complexity of selection required in sugarcane breeding programs and the number and size of populations, new tools that may assist breeders in plant selection have great potential. There are a number of fundamental advantages of marker-assisted selection (MAS) over conventional phenotypic selection that can be exploited by breeders to accelerate the breeding process. For example, with sugarcane breeding, conventional selection of traits like disease resistance, such as smut, occurs later in the selection program, due to the need to phenotype the plants at maturity in the field. This results in carrying a proportion of susceptible genotypes in early generation populations (genetic load). This is a process that takes many months and requires a large amount of land and money. By using MAS, most of these susceptible genotypes could be discarded, such as at the seedling stage, and the efficiency of selection would be greatly enhanced per dollar invested, allowing increased genetic gain for other traits. Markers can also be used as a replacement for phenotyping, which allows selection in off-season nurseries, making it more cost-effective to grow more generations per year. Also, phenotypic screening for smut resistance requires replicate plants (12 to 18) of the same genotype because single-plant selection is unreliable due to environmental factors. With MAS, individual plants can be selected based on their genotype. One of the drawbacks with the use of markers is that marker-trait association can be broken by recombination if the marker is not tightly linked, leading to false positives. This can be difficult in polyploid crops such as sugarcane, as there can be many regions of small effects that contribute to the trait. However, disease resistance has been a trait that has been useful in the implementation of markers in crops such as wheat and rice, as large effects across small genomic regions have been demonstrated. SNP markers have emerged as the marker of choice for identifying marker-trait associations in plant breeding (Cavanagh et al., 2013; Thomson, 2014), animal breeding (Dekkers, 2012), and human genetic studies for identifying disease loci (Carlson et al., 2004). A major factor in the advantages of SNP markers for flexibility, speed and costeffectiveness is the range of genotyping platforms available to address a variety of needs for different marker densities and costs per sample (Thomson, 2014). SNP genotyping platforms have expanded at a rapid rate from the initial gel-based methods to high throughput marker technologies based on highly multiplexed fixed SNP arrays or chips that enable running of hundreds of samples per day at low-cost. The expansion of the field has led to large-scale commercial investment by life science companies, with the end result that the cost per sample has decreased to the point where it is significantly cheaper to genotype than to phenotype. The early successes with high-throughput SNP genotyping relied on fixed sets of SNP markers assayed using microarrays (Matsuzaki et al., 2004; Shen et al., 2005). By carefully selecting informative, evenly spaced SNPs across the genome, these fixed SNP arrays are powerful tools for genome-wide association studies (GWA) and diversity analysis, as has been achieved in rice using Illumina 1536 and 50K SNP arrays (Chen et al., 2014) and an Affymetrix 44K SNP chip (Zhao et al., 2011). More recently, an Illumina Infinium 6K SNP chip in rice was designed to achieve highdensity genome-wide scans at a reasonable cost per sample ($80 per sample; gsl.irri.org) that could also target functional genes in addition to genome-wide loci (Yu et al., 2014). In wheat, a 9K and 90K SNP chip has been developed at an estimated cost of $60 per sample for GWA and screening of this chip is offered as a service by Australian Genome Research Facility (AGRF) (Cavanagh et al., 2013; Wang et al., 2014). A high density array, Affymetrix Axiom sugarcane 45K SNP chip has been developed for sugarcane, which uses large numbers of SNP markers randomly distributed throughout the genome to screen for many different traits, and has been used successfully to generate an improved genetic map for sugarcane (Aitken et al., 2014; Aitken et al., 2016). Due to the polyploid genome of 305

3 sugarcane, low dose SNPs targeted to the S. officinarum genome, in 16 selected sugarcane cultivars used to develop the SNP chip, were targeted for SNP marker development. The disadvantages of a fixed SNP array chip are that it is expensive to design a custom SNP array, once the chip is manufactured the SNPs are fixed and unable to be changed and a large initial commitment is needed to get volume discounts to make them more cost-effective (Thomson, 2014). A flexible SNP panel array has an advantage of being able to mix and match different SNPs for each set of samples screened. This is useful if, as germplasm is screened, certain SNPs will no longer be useful (lose the marker-trait association) and different SNPs will need to be added to the SNP panel array. Also, once genomic regions have been identified linked to trait of interest, it is not necessary to screen the entire genome with thousands of SNPs each time you wish to select for this particular region. Following the identification of GWA for particular traits such as disease resistance in sugarcane parental populations, the next stage involves the validation and replication of smaller, relevant subsets of SNPs against targeted SNPs that need to be genotyped against a large set of clones. The 45K Axiom SNP chip developed for sugarcane is valuable for parental improvement but is not cost effective for screening large numbers of clones (Aitken et al., 2016). Clearly, there is a need for scalable and cost-effective platform technologies capable of analysing flexible sample sizes on a specific set of SNPs and suitable for validation of SNPs identified from disease association studies. Two PCR-based fluorescently-labeled SNP assay technologies, such as the LGC KASP TM assay and the Fluidigm SNPType TM assay meet these criteria (Wang et al., 2009; Semagn et al., 2014). Fluidigm Dynamic Arrays is a technology where microfluidics with a miniaturised complex fluid-handling system is used on the chip, making it a lab-on-a-chip device, thereby further reducing costs of PCR consumables. The costs per sample can be reduced to $9 per sample, depending on number of SNP markers and number of samples screened. In choosing the best SNP technology to use for the development of SNP markers and ultimately to be used in marker implementation, the following variables need to be considered: throughput of the marker system; data turnaround; time the assay takes to run; ease of use of the assay and whether specialised skill is required; performance of the assay (in terms of sensitivity, reliability, reproducibility, and accuracy); flexibility (genotyping few samples with many SNPs or few SNPs with many samples); number of markers generated per run (whether the assay is uniplex or allows multiplex, can reduce costs); assay development requirements and genotyping cost per sample or data point. The throughput of each SNP technology is displayed in the graph (Figure. 1). Fig. 1 Different platforms for genotyping, showing their relative high-throughput in terms of number of samples and assays that can be used in a single run. (Figure modified from LGC Genomics and Gupta et al., 2013). 306

4 TaqMan offers low to medium throughput (96 to 384 well PCR plate), and Fluidigm SNPType TM and KASP TM assays are similar with medium to high throughput. Fluidigm system is fixed in that SNPType TM assays can be run on a 48.48, and IFC arrays (number of samples/number of SNP assays) resulting in 2304, 9216 and 4608 assays respectively in one run. KASP TM assays offer the flexibility of running samples on 96, 384 and 1592 PCR plates in a single run, with the ability to increase this by doing multiple runs in one. In this paper, we will present the current progress towards the development of a panel of SNPs targeted to disease resistance, based on the flexible SNP marker platforms, LGC KASP TM or Fluidigm SNPType TM, and some of the applications of this SNP panel in the sugarcane breeding program. Methods Assessment of SNP genotyping using the LGC KASP TM and Fluidigm SNPType TM assays A preliminary analysis was carried out based on the genotyping results from the Axiom sugarcane 45k SNP chip screened across sugarcane clones, using a mixed-model approach (Wei et al., 2006; Wei et al., 2010). A large number SNP markers were found to be associated to smut at a significance level of greater than 0.05, many more than would be expected by random chance. Four of the most significantly associated SNP markers were chosen to be converted to a more flexible SNP platform for this study (Table 1). Table 1 Top 4 SNP markers associated with smut resistance from Axiom sugarcane 45k SNP chip screened across diverse sugarcane germplasm. The p-value represents the probability of the association of the marker with smut disease by chance. SNP marker AX AX AX AX p-value 1.53e e e e-09 Sequence of the sugarcane genome surrounding these SNPs was used to design primers for a KASP TM assay and Fluidigm SNPType TM assay. As LGC Genomics offers the ability to run SNP marker assays from DNA extraction through to data collection, a small pilot validation project was carried out using the KASP technology in collaboration with LGC Genomics to assess the viability of using the KASP system for the development of the disease resistance SNP mini-chip. Forty DNA samples selected from the 2000 sugarcane clones screened against the SNP chip were sent to LGC Genomics labs in UK and the KASP TM assay for the 4 chosen SNPs was done. For the SNPType TM assay, the 40 sugarcane DNA samples were amplified using primers flanking the SNP sequence using a Specific Target Amplification (STA) protocol (Wang et al., 2009). The amplified samples were diluted five-fold after STA and then genotyped on a 48.48CS dynamic array on a Fluidigm BioMark HD system. Figure 2 shows the genotyping results from STA DNA for SNP assay AX Results and discussion We have estimated the genotyping concordance between the results from the SNPType TM assay, the KASP TM assay and the corresponding data for the selected SNPs from the Axiom sugarcane SNP 45k chip. Figure 3 shows that for the SNPType TM assay our genotyping results displayed a concordance greater than 80% for all 4 SNPs, whereas the KASP TM assay showed a concordance of between 67% (AX ) and 77% (AX ). In fact, three of the KASP markers (AX , AX , AX ) could only detect the cluster containing one allele and were unable to detect the alternative allele cluster or the cluster containing both alleles. The remaining KASP marker, AX (targeting SNP A/G), could detect the cluster containing only the A allele and the cluster containing the A and G alleles but could not detect the cluster containing only the G allele. 307

5 a) b) Fig. 2 a) A 48.48CS dynamic array showing the position of the sample inlets (48) and the assay inlets (48) (Wang et al., 2009) b) Software generated scatter plot from Fluidigm BioMark HD system for 40 sugarcane samples in SNPType TM assay AX (targeting SNP C/G) with genotype calls automatically; green cluster shows genotypes containing only G allele, blue cluster shows genotypes containing G and C alleles, red cluster shows genotype containing only C allele, black dot NTC (no template control). Fig. 3 Comparison of genotyping results from SNP markers converted to Fluidigm SNPType TM assay (orange) and LGC KASP TM assay (blue). The percentage concordance of genotyping results from both assays with the Axiom sugarcane 45K SNP chip results is shown on the Y axis. As sugarcane is a complex polyploid, it appears the KASP TM technology in the instance of the 4 KASP markers is unable to detect the chromosomal segment on the sugarcane genome that contains the targeted SNP. The SNPType TM assay displayed better concordance with the Axiom sugarcane 45K SNP chip genotyping (AX displayed only one difference from the Axiom sugarcane 45K SNP chip) and this may be due to the pre-amplification step (STA) that assists in the detection of the chromosomal segment on the sugarcane genome on which the SNP resides. As a result of the lack of concordance between the Axiom sugarcane 45K SNP genotyping and KASP TM marker system, further validation work is required. Subsequent validation work on these markers, in collaboration with LGC Genomics, resulted in the amplicons from the 4 KASP markers, AX , AX , AX and AX , being amplified and sequenced to determine whether it is possible to identify chromosome-specific alleles on which the SNP resides. Initial results indicate a pre-amplification step has improved the concordance of the 308

6 genotyping of the new KASP assays with the Axiom sugarcane 45K SNP chip genotyping results. The lack of complete concordance between the two genotyping technologies and the Axiom sugarcane 45K SNP chip highlights the difficulty in converting markers from the Axiom sugarcane 45K SNP chip to a different SNP marker system such as KASP TM or SNPType TM assays in sugarcane. However, the chemistry between the two SNP genotyping technologies is different, KASP TM and SNPType TM technology employs a PCR-based allele-specific amplification of the SNP, while the Axiom SNP technology is allele-specific hybridisation based. Application of the SNP panel in the SRA sugarcane breeding program Figure 4 displays graphically the SRA breeding program and, as an example, where phenotyping for disease resistance (e.g. smut or pachymetra) is carried out. The place where SNP markers could add the best value for selection is displayed at the points indicated by the red arrows. This involves points where it is most expensive to phenotype and involves large numbers of sugarcane clones. As was shown in the validation of the SNP markers, the SNP markers identified will not be able to predict disease resistance to 100% accuracy and therefore would not be able to substitute for phenotyping for disease resistance. However, the SNP markers would enable an increase in the likely number of sugarcane clones containing the disease resistance, thereby increasing the pool of genotypes with increased resistance for breeders to choose from to select the best performing clones for cultivar release. This would result in a cost-benefit for the sugarcane breeders as it would increase the chance of selecting a cultivar with the desired disease resistance. Fig. 4 Figure showing the SRA sugarcane breeding program (adapted from Park et al., 2007). The number of sugarcane clones run through the program is shown. Phenotyping for disease resistance (e.g. smut or Pachymetra) could occur at the stages as indicated by the blue arrows. Potential stages for marker implementation are indicated by the red arrows. 309

7 Conclusions The development of a panel of SNP markers targeted to smut resistance is possible through using flexible SNP technology, such as the Fluidigm SNPType TM assay. A flexible SNP array will allow breeders to complement existing tools for the selection of sugarcane clones containing high priority traits of interest. A key feature is to demonstrate that the use of a SNP marker panel targeted to disease resistance in sugarcane breeding will result in further cost efficiencies. Also, validation of the SNP marker panel in diverse germplasm is required to ensure that there has been no breakdown of the linkage between the markers and trait. Once this has been established, a SNP marker panel will allow target genotypes to be more effectively selected, enabling certain traits to be 'fast-tracked', resulting in quicker clone development and variety release. Acknowledgements We acknowledge Sugar Research Australia for providing the funding for this study under project 2015/025, Barry Croft for providing information and comments on this paper, and the SRA breeding program for providing the germplasm for this study. REFERENCES Aitken KS, McNeil MD, Hermann S, Bundock PC, Kilian A, Heller-Uszynska K, Henry RJ, Li J (2014) A comprehensive genetic map of sugarcane that provides enhanced map coverage and integrates high-throughput Diversity Array Technology (DArT) markers. BMC Genomics 15(1), Aitken KS, Farmer A, Berkman P, Muller C, Wei X, Demano E, Jackson PA, Magwire M, Dietrich B, Kota R (2016) Generation of a 345K sugarcane SNP chip. Proceedings of the International Society of Sugar Cane Technologists 29, 1 7. Carlson CS, Eberle MA, Kruglyak L, Nickerson DA (2004) Mapping complex disease loci in whole-genome association studies. Nature 429(6990), Cavanagh CR, Chao S, Wang S, Huang BE, et al (2013) Genome-wide comparative diversity uncovers multiple targets of selection for improvement in hexaploid wheat landraces and cultivars. Proceedings of the National Academy of Sciences 110(20), Chen H, Xie W, He H, Yu H, Chen W, Li J, Yu R, Yao Y, Zhang W, He Y (2014) A high-density SNP genotyping array for rice biology and molecular breeding. Molecular Plant 7, Dekkers, JCM (2012) Application of genomic tools to animal breeding. Current Genomics 13(3), Gupta PK, Rustgi S, Mir RR (2013) Array-based high-throughput DNA markers and genotyping platforms for cereal genetics and genomics. In Cereal genomics II (Eds PK Gupta and RK Varsheny) pp (Springer Netherlands). Matsuzaki H, Dong S, Loi H, Di X, Liu G, Hubbell E, Law J, Berntsen T, Chadha M (2004) Genotyping over SNPs on a pair of oligonucleotide arrays. Nature Methods 1, Park S, Jackson P, Berding N, Inman-Bamber G (2007) Conventional breeding practices within the Australian sugarcane breeding program. Proceedings of the Australian Society of Sugar Cane Technologists 29 (Electronic format). Semagn K, Babu R, Hearne S, Olsen M (2014) Single nucleotide polymorphism genotyping using Kompetitive Allele Specific PCR (KASP): overview of the technology and its application in crop improvement. Molecular Breeding 33, Shen R, Fan JB, Campbell D, Chang W, Chen J, Doucet D, Yeakley J, Bibikova M, Garcia EW, McBride C (2005) High-throughput SNP genotyping on universal bead arrays. Mutation Research 573, Thomson MJ (2014) High-throughput SNP genotyping to accelerate Crop Improvement. Plant Breeding and Biotechnology 2(3),

8 Wang J, Lin M, Crenshaw A, Hutchinson A, Hicks B, Yeager M, Berndt S, Huang WY, Hayes RB, Chanock SJ, Jones RC, Ramakrishnan R (2009) High-throughput single nucleotide polymorphism genotyping using nanofluidic Dynamic Arrays. BMC Genomics 10, Wang S, Wong D, Forrest K, Allen A, Chao S, Huang BE, Maccaferri M, Salvi S, Milner SG, et al (2014) Characterisation of polyploid wheat genomic diversity using a high-density single nucleotide polymorphism array. Plant Biotechnology Journal 12, Wei X, Jackson PA, McIntyre CL, Aitken KS, Croft B (2006) Associations between DNA markers and resistance to diseases in sugarcane and effects of population substructure. Theoretical and Applied Genetics 114, Wei X, Jackson PA, Hermann S, Kilian A, Heller-Uszynska K, Deomano E (2010) Simultaneously accounting for population structure, genotype by environment interaction, and spatial variation in marker-trait associations in sugarcane. Genome 53(11), Yu H, Xie W, Li J, Zhou F, Zhang Q (2014) A whole-genome SNP array (RICE6K) for genomic breeding in rice. Plant Biotechnology Journal 12, Zhao K, Tung CW, Eizenga GC, Wright MH, Ali ML, Price AH, Norton GJ, Islam MR, Reynolds A, Mezey J, McClung AM, Bustamante CD, McCouch SR (2011) Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nature Communications 2,