 was analysed, undiluted and also diluted in PBST, for MM by the competitive ELISA described below. Extract supernatants were stored overnight at 4°C before analysis or for longer-term storage at -20°C. Independent extractions were made from two (2005) or three (2006) replicates of all 188 I 9 S population genotypes and all three replicates of 285 P 9 O population genotypes sampled in May 2011 and September 2011.

Competitive ELISA
The presence of endophyte mycelia in sample extracts was indicated by inhibition of specific antibody binding to coating antigen. All assay procedures were carried out at 21°C. Freeze-dried coating antigen previously prepared from E. festucae var. lolii mycelium following the method of Ball et al. (1995) was diluted (20 lg/mL) in standard ELISA coating buffer, carbonate/bicarbonate buffer (50 mM, pH 9.6), and microtiter plates coated (100 lL/well). After incubation for 16 h, plates were washed four times with PBST and blocked for 1 h with 1 % BSA (bovine serum albumin) in PBST (200 lL/well). This was followed by four washes with PBST.
Standards and samples were preincubated with the anti-endophyte antibody. ELISA standard curves were prepared for each assay using E. festucae var. lolii endophyte prepared by Ball et al. (1995) as the reference standard. Standard was diluted in PBST to 200 lg/mL, and further 2.5-fold serial dilutions were made in PBST to give 10 standards (0.05-200 lg/ mL). To each tube, 150 lL of standard or sample extract was added, followed by 150 lL of diluted anti-E. festucae var. lolii antibodies. Antibodies were diluted in 1 % BSA in PBST such that the maximum absorbance (A max ) in the assay, in the absence of analyte, was approximately 1.0 absorbance. After tubes were incubated for 2 h, 100 lL of the standard or sample extract and antibody mixture were added to each well on blocked plates. All standards and samples were analysed in duplicate wells. Variation within each plate (intra-assay) was determined by preparing duplicate extracts for two of the samples analysed on each plate. Between-plate variation (inter-assay) was determined by analysing the same positive control sample on each plate.
The absorbance of wells was determined at 450 nm using a Versamax microplate reader (Molecular Devices Corporation, USA). Data analysis was performed using SOFTmax PRO data analysis software (Molecular Devices Corporation). Curve fits of mean absorbance versus the logarithm of the analyte concentration were performed by four-parameter curve fit. Results were reported as E. festucae var. lolii immunoreactive equivalents (IRE) in mg/g DW.

ELISA development and validation
Perennial ryegrass samples that had been previously analysed by immunoblot (Simpson et al. 2012) and shown to be free of endophyte were milled, and the effects of dilution of endophyte-free grass extract on ELISA A max were studied for each sample extracting system investigated. The system selected was the one requiring the least dilution of grass extracts to remove inhibition of A max and giving the best assay signal (inhibition of colour development) with endophytecontaining samples extracted in the same system and at the same dilution. Minimum sample size giving maximum extraction of immunoreactivity was also determined (data not presented).
Within-plate variation was determined by duplicate extraction and analysis of six replicates of a single sample on the same plate. Between-plate variation was determined by extraction and analysis of a sample on 12 different occasions.
Metabolite profiling of PER and EGV in population I 9 S Analysis of I 9 S ryegrass herbage samples for endophyte alkaloid secondary metabolites PER (m/z 248) and EGV (m/z 534) was completed using direct infusion mass spectrometry metabolite profiling procedures (DIMSMS) as described in Koulman et al. (2009). The polar isopropanol-water (1:1) extraction solvent used extracts PER and EVG efficiently but does not extract lolitrem B (Cao et al. 2008); therefore, the latter alkaloid was not assessed in this study. Data from 2005 were generated using an untargeted metabolite profiling DIMSMS method described by Koulman et al. (2007). The accuracy of DIMSMS estimation of peramine quantity was validated by LC-MS on a subset of samples and for ergovaline by HPLC with fluorescence detection (Koulman et al. 2009). Due to low throughput of the DIMSMS method, no more than two clonal replicates per genotype were analysed from the 2005 data set. Data for 2006 were generated using a targeted DIMS(MS) approach that more than doubles the throughput for specific compounds (Koulman et al. 2009). This was more resource efficient and enabled all three clonal replicates to be analysed. Data were reported as normalised intensity units (NIU).
Quantitation of NFL in population P 9 O Analysis of 4275 P 9 O herbage samples for NFL (five time points x three replicates 9 285 F 1 progeny genotypes) was conducted using a new extraction methodology and competitive ELISA (L Briggs et al., unpublished data) to be described in a future publication. Briefly, freeze-dried and milled herbage was extracted with PBS for 1 h, microtitre plates were coated with hemisuccinyl loline-ovalbumin coating conjugate, and BSA was used as a blocking agent. ELISA standards were prepared with NFL as the reference standard (working range 0.1-400 ng/mL), and results were reported in NFL IRE lg/g DW. After 300-and 600-fold dilution of extracts, the limit of quantitation for NFL was 3 lg/g in dried herbage.

Statistical analysis of phenotypic data
Alkaloid and MM data from the population I 9 S and P 9 O data sets were analysed using the variance component analysis procedure, residual maximum likelihood (REML) option, in GenStat (2006). A completely random linear model was used in the analysis using the REML algorithm. The final, adjusted genotypic means were based on best linear unbiased predictors (BLUPs). Estimation of the significance of the genotypic variance component for each trait was based on the loglikelihood ratio test. The linear models also included genotype-by-year and genotype-by-month interaction effects in the analysis of the I 9 S and P 9 O data sets, respectively. The variance components generated from the REML analysis were used to estimate clonal mean repeatability (R c,) the upper limit of the degree of genetic determination (Falconer 1989), for the traits PER, EGV, NFL and MM. The model R 1 c ¼ r 2 g = r 2 g þ r 2 e =n r was used for calculating clonal mean repeatability using individual year (I 9 S) or month (P 9 O) trait data, where r 2 g is the genotypic component of variance, r 2 e the residual variance and n r the number of replications.
The model R 2 c ¼ r 2 g = r 2 g þ r 2 gy =n y þ r 2 e =n r was based on variance components generated from the genotype-by-year analysis in I 9 S, where r 2 gy is the genotype-by-year component of variance and n y is the number of years. Similarly, the model R 3 c ¼ r 2 g = r 2 g þ r 2 gm =n m þ r 2 e =n r was based on variance components from the genotype-by-month analysis in P 9 O, where r 2 gm is the genotype-by-month component of variance and n m the number of months. Pearson correlations amongst traits were obtained using GenStat (2012).
Multiple QTL mapping analysis in population I 9 S An existing integrated genetic linkage map of the I 9 S population, based on EST-derived simple sequence repeat (SSR) and sequence-tagged site (STS) markers (Sartie et al. 2011), was used for QTL analysis. This map was augmented for the current study by a further 30 ryegrass and tall fescue SSR markers (Khaembah et al. 2013).
QTL analysis was performed with MapQTL Ò 4.0 software (Van Ooijen et al. 2002) under the CP population mode, using 2005 and 2006 BLUPs for MM, PER and EGV from n = 188 F 1 progeny. Additionally, QTL analysis was conducted using BLUPs, generated from a genotype-by-year analysis, integrating values for traits MM and PER across both years (designated MM int and PER int, respectively). Interval mapping was performed first, and then the estimated positions and magnitude of QTL were refined using the multiple QTL mapping (MQM) module, as described in Sartie et al. (2011). Permutation testing (n = 2000) was performed for each measurement to establish logarithm-of-odds (LOD) thresholds for QTL declaration at a linkage group-or genome-wide significance of P B 0.05 (Churchill and Doerge 1994). QTL position was described by LOD peak position and 2-LOD support intervals. An additional criterion for declaration of a significant QTL was the presence of markers within the 2-LOD support interval that were significant by Kruskal-Wallis analysis, as executed in MapQTL Ò 4.0. QTL naming convention is q-trait-year-linkage group, where trait is MM, PER or EGV, year is 05 (2005) or 06 (2006), and linkage group (