Starch phosphorylation in potato tubers is influenced by allelic variation in the genes encoding glucan water dikinase, starch branching enzymes I and II, and starch synthase III.

Starch phosphorylation is an important aspect of plant metabolism due to its role in starch degradation. Moreover, the degree of phosphorylation of starch determines its physicochemical properties and is therefore relevant for industrial uses of starch. Currently, starch is chemically phosphorylated to increase viscosity and paste stability. Potato cultivars with elevated starch phosphorylation would make this process unnecessary, thereby bestowing economic and environmental benefits. Starch phosphorylation is a complex trait which has been previously shown by antisense gene repression to be influenced by a number of genes including those involved in starch synthesis and degradation. We have used an association mapping approach to discover genetic markers associated with the degree of starch phosphorylation. A diverse collection of 193 potato lines was grown in replicated field trials, and the levels of starch phosphorylation at the C6 and C3 positions of the glucosyl residues were determined by mass spectrometry of hydrolyzed starch from tubers. In addition, the potato lines were genotyped by amplicon sequencing and microsatellite analysis, focusing on candidate genes known to be involved in starch synthesis. As potato is an autotetraploid, genotyping included determination of allele dosage. Significant associations (p < 0.001) were found with SNPs in the glucan water dikinase (GWD), starch branching enzyme I (SBEI) and the starch synthase III (SSIII) genes, and with a SSR allele in the SBEII gene. SNPs in the GWD gene were associated with C6 phosphorylation, whereas polymorphisms in the SBEI and SBEII genes were associated with both C6 and C3 phosphorylation and the SNP in the SSIII gene was associated with C3 phosphorylation. These allelic variants have potential as genetic markers for starch phosphorylation in potato.

Introduction

Starch is the major storage carbohydrate in plants and comprises most of the dry matter in potato tubers. Starch represents the largest source of carbohydrates in human food and has a range of industrial uses. Starch is made up of chains of α-D-glucose units which can be unbranched (amylose) or branched (amylopectin). The native starches of almost all plants are phosphorylated, the extent of which varies between plant species, and is greater in potato starch than in cereal starches. The degree of phosphorylation of starch determines its physicochemical properties and is therefore relevant for industrial uses of starch, which include the manufacturing of paper, adhesives, textiles, and foods (Kraak, 1992). Phosphorylation increases the hydration capacity of starch pastes, thereby influencing peak viscosity, gel-forming capacity, swelling power and paste stability (Vikso-Nielsen et al., 2001; Jobling, 2004). Starch phosphorylation is also an important aspect of plant metabolism due to its role in starch degradation (Lorberth et al., 1998). Phosphorylation occurs only in the amylopectin fraction of starch. The glucosyl residues that comprise amylopectin can be phosphorylated at either the C6 or C3 position (Tabata and Hizukuri, 1971). In potato tuber starch, C6 phosphorylation predominates, making up 70–80% of the phosphorylation, while C3 makes up 20–30% (Bay-Smidt et al., 1994; Haebel et al., 2008; Carpenter et al., 2012).

A number of genes have been shown by genetic modification experiments to have a role in starch phosphorylation in potato. The GWD (or R1) gene encodes the protein glucan/water dikinase (GWD) which catalyzes the addition of phosphate groups to the C6 position of the glycosyl residues (Vikso-Nielsen et al., 2001). Down-regulation of the GWD gene resulted in decreased C6 phosphorylation of potato starch (Lorberth et al., 1998; Vikso-Nielsen et al., 2001; Wickramasinghe, 2009). Potato also has a PWD gene which encodes phosphoglucan/water dikinase (PWD) (Orzechowski et al., 2012). Down-regulation of PWD reduced C3 phosphorylation in Arabidopsis (Ritte et al., 2006) showing that PWD adds phosphate at the C3 position. C3 phosphorylation is dependent on prior C6 phosphorylation by GWD (Baunsgaard et al., 2005). Starch phosphorylation is also influenced by the activity of the genes encoding starch branching enzymes. Antisense inhibition of the genes encoding the starch branching enzymes SBEI and SBEII, either singly or together, resulted in an increase in the phosphorus content of the starch (Safford et al., 1998; Schwall et al., 2000) and similarly an increase in the C6 phosphorylation (Wischmann et al., 2005; Wickramasinghe, 2009). This was accompanied by a decrease in the amylopectin content and an increase in the length of amylopectin branches (Schwall et al., 2000; Wischmann et al., 2005; Wickramasinghe, 2009). There is also evidence that the starch synthase genes may influence starch phosphorylation in potato. Suppression of the granule-bound starch synthase gene (GBSS) caused a small increase in C6 phosphorylation and an increase in amylopectin content (Kozlov et al., 2007), while reduction in the activity of starch synthase II (SSII) caused a 40% reduction in C6 phosphorylation (Kossmann et al., 1999). Antisense suppression of the starch synthase I gene (SSI) had no significant effect on starch phosphorylation (Kossmann et al., 1999). Additionally, reduction in the activity of starch synthases II and/or III (SSII, SSIII) resulted in changes to gelatinization behavior and amylopectin structure of the starch, characteristics which are associated with starch phosphorylation (Edwards et al., 1999). In this case the phosphorylation was not determined. Several of these genes have also been associated with the degree of starch phosphorylation in potato through candidate gene mapping approaches, specifically the GWD, SSII, and SSIII genes (Uitdewilligen, 2012; Werij et al., 2012).

Association mapping is an increasingly popular approach for identifying loci linked to traits of commercial interest in plants (D'hoop et al., 2008). This approach has been used in potatoes for a variety of traits such as cold-induced sweetening (Baldwin et al., 2011), disease resistance (Simko et al., 2004; Pajerowska-Mukhtar et al., 2009), tuber bruising and enzymatic discoloration (Urbany et al., 2011) and other tuber characteristics (D'hoop et al., 2008; Li et al., 2008; Schreiber et al., 2014). Association mapping employs collections of existing germplasm, thus avoiding the need to select and cross parents, and propagate the offspring. If a suitably diverse collection is used, the gene-trait associations which are identified will be applicable over a broad range of cultivars, and may identify desirable alleles which are not present in current breeding programs. Association mapping can also confer a higher resolution than QTL mapping, depending on the extent of linkage disequilibrium, (Flint-Garcia et al., 2003; Gaut and Long, 2003) such that it works well with a candidate gene approach, where genotyping focuses on genes that are known or suspected to be associated with the trait of interest. Association mapping can be confounded by population structure or kinship within the collection, generating false positive results (Flint-Garcia et al., 2003). Therefore, the relatedness of individuals within the collection needs to be determined and the population structure and kinship taken into account (Yu et al., 2006; Stich and Melchinger, 2009), both of which add complexity to the analysis.

The potato (Solanum tuberosum) is a highly heterozygous, autotetraploid, outcrossing species which is propagated clonally. The breeding history of the cultivated potato has created a situation in which many current cultivars from around the world are related by a complex network of relationships. Genes from wild species (S. vernei, S. demissum) have been introduced during the introgression of disease resistance genes to try to overcome disease problems in commercial cultivars (Pajerowska-Mukhtar et al., 2009). Traditional lines of uncertain origin also exist. Autotetraploidy adds complexity to genetic analysis as many more combinations of alleles are possible than in a diploid. For example, a biallelic locus can have up to five different genotypes, two homozygous and three heterozygous, depending on the number of copies of each allele. The number of copies of each allele can affect the phenotype in an additive or dominant fashion, or somewhere between (Gallais, 2003). Therefore, the allele dosage is an important aspect of the data and should, if possible, be included.

Here, we report an association mapping analysis of starch phosphorylation in potato in which mass spectrometry was used to determine the C6 and C3 phosphorylation of tuber starch from two field trials, and genotyping was focused on eight candidate genes. The association analysis was performed using a mixed model which included population structure and kinship estimates derived from molecular markers. This study differs from previous reports which identified genetic markers associated with potato starch phosphorylation (Uitdewilligen, 2012; Werij et al., 2012) in that we have measured both C3 and C6 phosphorylation separately and have chosen a different selection of candidate genes. Our results validate some of the previous reports and also provide new insights.

Materials and methods

Plant material

A diverse collection of 193 tetraploid potato lines (mainly Solanum tuberosum but including lines with introgression from other species) was grown in field trials at Lincoln, Canterbury, New Zealand, during two consecutive seasons (2011 and 2012). Tubers were planted in October and harvested in March, and the plants were irrigated as required. Climate data for the growing seasons were obtained from the National Climate Database maintened by NIWA (National Institute of Water and Atmospheric Research), New Zealand. The lines and their origins are listed in Supplementary Data 1.

Two replicate plots of each of 192 test lines were planted as well as 48 plots of a control line (“Red Rascal”). Each plot consisted of six plants, planted in two adjacent rows. The 432 plots were planted in a 72 × 6 plot array, with a full replicate (including 24 controls) in each of two adjacent blocks. The position of test lines within the layout was derived from a resolvable block design with blocks of eight, created with CycDesign (Cycsoftware, 2009). Control plots were added to this base design, such that each column of plots (six columns) contained eight control plots, and each set of 9rows of plots (eight sets; 72 rows in all) contained six controls, with a maximum of one control per row.

At maturity, a sample of six typical tubers from the two plants in the middle of the plot was harvested for phenotyping. Cylindrical samples were taken through the middle of each tuber using an 11 mm cork borer. The six cores from each plot were pooled and freeze dried. Samples were processed in plot order, in batches of 36 for starch extraction and 72 for phosphorylation measurement.

Starch phosphorylation assays

Starch was extracted from freeze-dried tuber core samples. Starch samples (1 mg) were hydrolyzed with trifluoroacetic acid and prepared as described previously (Carpenter et al., 2012). The glucose, glucose 6-phosphate and glucose 3-phosphate content of the hydrolysates were determined by mass spectrometry using external standards for quantitation (Carpenter et al., 2012). The glucose content gave an accurate estimation of the amount of starch hydrolyzed, and was therefore used to convert the glucose 6-phosphate and glucose 3-phosphate measurements to C6 and C3 phosphorylation in nmol/mg starch.

Genotyping

Genomic DNA was isolated from young leaf tissue using a cetyl trimethylammonium bromide (CTAB) method (Timmerman et al., 1993). The potato lines were genotyped using 21 SSR markers for determination of population structure. Markers were developed for the eight candidate genes using a range of methods including amplicon sequencing, SSR analysis, and by amplification of a region containing an insertion/deletion polymorphism.

SSR markers for population structure were amplified by PCR using previously published primers (Milbourne et al., 1998; Feingold et al., 2005) as shown in Supplementary Data 2. These were selected so that there was at least one SSR marker per chromosome, and in most cases two per chromosome. The forward primer of each pair was modified by the addition of a 20 bp tail with the sequence gacgttgtaaaacgacggcc at the 5′ end, enabling the addition during PCR of a fluorescently labeled “tail primer” (gacgttgtaaaacgacg) (Schuelke, 2000) labeled with FAM (6-carboxy-fluorescine), HEX (hexachloro-6-carboxy-fluorescine) or NED (Applied Biosystems, Carlsbad, California, USA). A short 4 bp “pigtail” gttt was added to the 5′ end of the reverse primer to reduce the appearance of stutter bands (Brownstein et al., 1996). Optimal annealing temperatures and MgCl2 concentrations for the PCR reactions were determined empirically as the reported conditions did not always produce satisfactory results. The 15 μl PCRs contained 75 mM TrisHCl pH8.8, 20 mM (NH4)2SO4, 0.01% Tween® 20, 0.2 mM each dNTP, 1 μM each primer, 0.02 μM tail primer, 0.75U Taq (Thermo Fisher Scientific, Waltham, MA, USA), approximately 10 ng genomic DNA and 2.5–4.0 mM MgCl2 as shown in Supplementary Data 2. The PCR conditions were one cycle of 95°C for 1 min, 40 cycles of 95°C for 30 s, annealing at the temperature shown in Supplementary Data 2 for 30s, 72°C for 1 min, then one cycle of 72°C for 7 min. PCR products from two to three SSRs were combined in groups such that both amplicon sizes and fluorescent labels differed within the group, and separated by capilliary electrophoresis using an ABI PRISM 3130xl Genetic Analyzer (Applied Biosystems, Carlsbad, California, USA) with the GeneScan 400HD ROX size standard (Applied Biosystems, Carlsbad, California, USA).

Four SSR markers in the candidate genes were also characterized: the STGBSS and STWAX-2 primers (Ghislain et al., 2004) are described in Supplementary Data 2 and the novel SBEII and GWD SSR primers are shown in Supplementary Data 3. PCR of the candidate gene SSRs was performed as described above, except that the PCR for the GWD and SBEII SSRs used 2.5 mM MgCl2 and annealing at 58°C.

The SSR data for candidate genes and for population structure were analyzed using GeneMarker v1.85 (SoftGenetics, State College Pennsylvania, USA) to identify alleles by product size, while allele copy number was estimated by peak area. When four alleles were present, these were scored as a single copy of each. When a single allele was present, this was scored as four copies of that allele. When two alleles were present, this was scored as 2:2, 3:1 or 2:1 depending on the relative peak areas (a ratio of 2:1 inferred that one allele was null). Where three alleles were present, the largest peak was scored as two copies and the other two as a single copy, except where the three peaks were all of a similar area in which case a null allele was assumed and the three alleles were scored as one copy each.

The eight candidate genes were resequenced to provide sequence data for optimal design of primers to be used for genotyping. The candidate genes were amplified as fragments of 4–15 kb from genomic DNA from four potato lines (4164A3, “Brodick,” V201, Vtn62-33-3). The PCR was performed using the Expand long template PCR system (Roche, Basel, Switzerland) according to the manufacturers' instructions, and using the primers shown in Supplementary Data 3. One of the genes, SBEII, was amplified in two pieces as it was not possible to amplify the complete 20 kb gene. The products were normalized to ensure equal representation of all genes, then fragmented and used to construct four bar-coded libraries which were sequenced on a Genome Sequencer FLX instrument (Roche, Basel, Switzerland). The resulting sequence reads were aligned to reference sequences using gsMapper software (Roche, Basel, Switzerland), which identified a list of SNPs (Supplementary Data 4). The SNP positions were converted to a GFF file which was imported into Geneious Pro v5.3.6 (Biomatters, Auckland, New Zealand) where primers for amplicon sequencing were designed so as to avoid having SNPs in the primer binding site.

Primers (shown in Supplementary Data 3) were designed to amplify regions of 300–700 bp from the candidate genes for sequencing. The primers were designed using Primer 3 (Koressaar and Remm, 2007) with an optimal annealing temperature of 60°C and using the default parameters. The 15 μl PCRs contained 75 mM TrisHCl pH8.8, 20 mM (NH4)2SO4, 0.01% Tween 20, 2.5 mM MgCl2, 0.2 mM each dNTP, 1μM each primer, 0.75U Taq (Thermo Fisher Scientific, Waltham, MA, USA), and approximately 10 ng genomic DNA. The PCR conditions were one cycle of 95°C for 2 min, 40 cycles of 95°C for 30 s, 58°C for 30s, 72°C for 1 min, then one cycle of 72°C for 7 min. Unincorporated primers were removed from the PCR products using exonuclease I and shrimp alkaline phosphatase (Ibrahim et al., 2001). The PCR products were sequenced in both directions using the amplification primers, except in the case of SBEII where a different forward primer (Supplementary Data 3) was used for sequencing in order to avoid an insertion/deletion polymorphism. Sanger sequencing was performed using an ABI BigDye terminator cycle sequencing kit and an ABI PRISM 3130xl Genetic Analyzer (Applied Biosystems, Carlsbad, California, USA). SNPs were detected by aligning sequences using Geneious Pro v5.3.6 (Biomatters, Auckland, New Zealand). The SNP allele dosage was estimated using the DAx data analysis and acquisition software (Van Mierlo Software Consultancy, Eindhoven, The Netherlands). Allele dosages were determined separately for both forward and reverse sequences and then the two results were compared. When discrepancies occurred, the result with the higher sequence quality was used.

A region containing an insertion/deletion polymorphism (InDel) located 0.5 kb upstream of the ATG start codon of the GBSS gene was amplified using cdf1 and cdf2 primers (van de Wal et al., 2001). PCR conditions were as for amplicon sequencing except that the annealing temperature was 55°C. PCR products were separated by electrophoresis on a 2% agarose gel and visualized by ethidium bromide staining. Relative band intensity of heterozygotes was determined using GelQuantNET software (http://biochemlabsolutions.com/GelQuantNET.html) and used to group the heterozygotes into three genotypes.

Statistical analysis of phenotypic data

The C6 and C3 phosphorylation (nmol/mg) for each of the two field trials were analyzed separately. For each type of phosphorylation, mean values were calculated which were adjusted to compensate for spatial variation in the field trial and any local trends in processing (such as a tendency to systematically increase or decrease within a run of the mass spectrometer), using the “Red Rascal” control data. The data were analyzed using a mixed model approach, with the model fitted with restricted maximum likelihood (REML, Payne et al., 2011) as implemented in Genstat (Genstat Committee, 2013). The potato line was included as a fixed effect whereas other potentially important effects were included as random effects. For both C6 and C3 phosphorylation, an AR(1) × AR(1) (Verbyla et al., 1999) correlation model was fitted to account for local trends in the data. In addition, random effects were required for the sets of 72 plots (corresponding to the six columns of plots as in the field layout) and also for the batches of 36 plots used for starch extraction. Overall differences between lines were assessed with an F-test, using the Kenward-Roger estimation for the denominator degrees of freedom (Kenward and Roger, 1997).

Analysis of population structure and kinship

Population structure of the collection of 192 potato lines was explored by analyzing 21 SSR markers using the program STRUCTURE v2.3 (Pritchard et al., 2000). An admixture model was used, with the number of subpopulations, K, set from 1 to 20 for each of three repetitions. For each run, both the burn-in and the iteration number were set to 100,000. The mean and standard deviation of Ln P(D) were calculated and the best estimate of K was determined by the method of Evanno et al. (2005). A Q matrix was generated for the best estimate of K and used in the association analysis. The Q matrix contained, for each potato line, its estimated membership of each subpopulation. A kinship matrix (K) was calculated from the SSR data according to Loiselle et al. (1995) using the software package SPAGeDI v1.3 (Hardy and Vekemans, 2002), with negative kinship values set to zero.

Association analysis

A mixed model analysis was used to test for association between phosphorylation phenotypes and SNP/SSR/InDel genotypes following the approach of Pajerowska-Mukhtar et al. (2009) Population structure (Q) and kinship (K) are included in the model to decrease the rate of discovery of false positives (Yu et al., 2006). The genotype and subpopulation membership (Q) were incorporated in the model as fixed effects, with genotype as a factor (number of copies of each allele) and subpopulation membership as a variate. Kinship and residual were included as random effects. The C6 and C3 phosphorylation for each of the two field trials were analyzed separately. The analysis was performed using ASReml-R (Butler et al., 2009).

To address the problem of false discovery when multiple tests are performed simultaneously, q-values were calculated using the QVALUE package implemented in R with the default range of lambda values and the bootstrap method (Storey and Tibshirani, 2003). The p-values from the four trait data sets (C6 and C3, both years) were combined for the q-value calculation.

Amino acid substitutions resulting from the DNA polymorphisms that were found to be significantly associated with starch phosphorylation were analyzed using PROVEAN software (Choi et al., 2012). This predicted whether an amino acid substitution would affect the biological function of a protein, by referring to the variation observed in homologous sequences.

Results

Starch phosphorylation

The adjusted means for C6 phosphorylation varied from 7.9 to 23.6 nmol/mg in samples from the 2011 field trial, and 12.2 to 33.8 nmol/mg in 2012. C3 phosphorylation means were in the range 2.3–7.2 nmol/mg in 2011 and 2.2–7.5 nmol/mg in 2012. For each field trial, the C6 and C3 phosphorylation of the 193 lines approximated a normal distribution. C6 and C3 phosphorylation were highly correlated in both years, with correlation coefficients r = 0.81 for 2011 and r = 0.86 for 2012. C6 and C3 phosphorylation results from the 2012 field trial are shown in Figure 1. The adjusted means produced by the first and second field trials were moderately correlated with r = 0.72 for C6 and r = 0.62 for C3, as shown in Figure 2. The proportion of the phosphorylation occurring at the C6 position was 69–82% in 2011 and 80–87% in 2012.

Figure 1

Comparison between the adjusted means of C3 and C6 phosphorylation from the 2011 and 2012 field trials. The correlation coefficients r were 0.81 and 0.86 respectively.

Figure 2

Comparison between the two field trials: adjusted means of C6 and C3 phosphorylation of starch (nmol/mg) from potato tubers. The correlation coefficients r were 0.72 for C6 and 0.62 for C3.

Climate data for Lincoln for the period 1 November to 30 March revealed a mean temperature of 16°C for the 2011 growing season and 14.5°C for 2012. The mean temperature for each month of the 2011 growing season was higher than the corresponding month of the 2012 season.

Population structure

SSR allele size and dosage were scored for 21 loci which spanned all 12 potato chromosomes. There were between three and 17 alleles per locus giving a total of 173 alleles. Analysis of the SSR genotype data using STRUCTURE software revealed a complex population structure. There was no clearly defined number of sub-populations, as indicated by the continual increase in log likelihood Ln P(D) (Figure 3A). However the slope of the graph decreases markedly after K = 4, indicating that four subpopulations may be the most useful estimate to use. The Delta K method, which targets a change in slope combined with low variance (Evanno et al., 2005), also indicated four subpopulations to be the best representation of the population structure (Figure 3B).

Figure 3

Determination of population structure. (A) The mean log likelihood of K (postulated number of subpopulations) for K = 1 to 20, with bars representing ± standard deviation. (B) Delta K for 1 to 20 subpopulations as calculated by Evanno et al. (2005).

The four subpopulations indicated by STRUCTURE reflect the geographical origins of the samples (Supplementary Data 1) to some extent. For example, group 1 includes many old lines of unknown pedigree, including some which are thought to have been among the first potatoes brought to and grown in New Zealand. Most of the lines from North and South America fell into the largest group, group 2. Many of the lines from the UK were in group 3, whereas those from The Netherlands and Germany tended to be in group 4.

A relationship was evident between subpopulation membership and level of phosphorylation as shown for 2011 C6 phosphorylation in Figure 4. Most of the potato lines that had a high level of starch phosphorylation were in group 2. In addition, group 2 had the highest mean C6 phosphorylation. The same relationship was evident for C6 phosphorylation in 2012 and for C3 phosphorylation in both years.

Figure 4

Beanplot showing the degree of C6 phosphorylation in samples from the 2011 field trial, divided into the four subpopulations determined from SSR data using STRUCTURE. The dotted line represents the overall mean; the long lines across each bean are mean values for that subpopulation, whereas the short lines represent individual potato lines. Similar results were obtained for the C6 2012, C3 2011, and C3 2012 datasets.

Genotyping candidate genes

Short amplicons (300–700 bp) from within the candidate genes of 192 potato lines were sequenced in both directions. This included two amplicons for SSII, none from GBSS, and one for each of the other six candidate genes. Analysis of the regions of the sequences which could be reliably determined in both the forward and reverse directions yielded 104 SNPs. The amplicon sizes and number of SNPs per amplicon are shown in Table 1. Four SSR markers were used for candidate gene genotyping, two from the GBSS gene which each had 11 alleles, one from SBEII with 12 alleles and one from GWD with five alleles. An insertion/deletion in the GBSS gene was amplified producing products of 60 and 200 bp, which were easily distinguished on an agarose gel. Image analysis software was effective for determining allele dosage in the heterozygotes. A list of the DNA polymorphisms and their positions on reference genes is given in Supplementary Data 4.

Table 1

Summary of genotyping methods used for each candidate gene.

Candidate gene (GenBank accession No.)	Size of amplicon(s) sequenced (bp)	Number of SNPs	Other markers
SSI (Y10416)	315	5
SSII (X87988)	327, 302	11, 6
SSIII (X94400)	587	14
SBEI (X69805)	600	8
SBEII (AJ000004)	442	12	SSR 12 alleles
GWD (Y09533)	690	32	SSR 5 alleles
PWD (GU045560)	523	16
GBSS (X58453)			SSR (STGBSS) 11 alleles SSR (STWAX-2) 11 alleles InDel 140 bp

DNA polymorphisms were tested individually for association with the C6 and C3 phosphorylation adjusted means for each of the two field trials, the results of which are shown in Figure 5. SNPs in three genes gave association at p < 0.001 as shown in Table 2. Five of these SNPs were in the GWD gene and were associated with C6 phosphorylation (p < 0.001) for the 2012 samples, and of these, three also gave association at p < 0.01 for the 2011 samples. Two SNPs in the SBEI gene were associated with both C6 and C3 phosphorylation. Sbei2ag was associated (p < 0.001) with C6 for both years and C3 2011. Sbei5tc was associated with C3 2011 at p < 0.001 and also with C3 2012 and C6 for both years at p < 0.01. Two SNPs in the SSIII gene were associated with C3 phosphorylation at p < 0.001. Both ssiii7tc and ssiii11gc were associated with C3 phosphorylation in 2012 (p < 0.001) while ssiii11gc was also associated with C3 2011 at a lower level of significance (p < 0.01). Only one of the SSR alleles (sbeii180) gave association results significant at p < 0.001. It was associated with both C6 and C3 phosphorylation in 2011(p < 0.001) and also with C3 in 2012 (p < 0.01). In total, 9 SNPs and one SSR allele gave association results significant at p < 0.001 (Table 2), and another 20 SNPs/SSRs at 0.001