- Research Article
- Open Access
Markers associated with heading and aftermath heading in perennial ryegrass full-sib families
© The Author(s) 2016
- Received: 27 April 2016
- Accepted: 1 July 2016
- Published: 16 July 2016
Heading and aftermath heading are important traits in perennial ryegrass because they impact forage quality. So far, genome-wide association analyses in this major forage species have only identified a small number of genetic variants associated with heading date that overall explained little of the variation. Some possible reasons include rare alleles with large phenotypic affects, allelic heterogeneity, or insufficient marker density. We established a genome-wide association panel with multiple genotypes from multiple full-sib families. This ensured alleles were present at the frequency needed to have sufficient statistical power to identify associations. We genotyped the panel via partial genome sequencing and performed genome-wide association analyses with multi-year phenotype data collected for heading date, and aftermath heading.
Genome wide association using a mixed linear model failed to identify any variants significantly associated with heading date or aftermath heading. Our failure to identify associations for these traits is likely due to the extremely low linkage disequilibrium we observed in this population. However, using single marker analysis within each full-sib family we could identify markers and genomic regions associated with heading and aftermath heading. Using the ryegrass genome we identified putative orthologs of key heading genes, some of which were located in regions of marker-trait associations.
Given the very low levels of LD, genome wide association studies in perennial ryegrass populations are going to require very high SNP densities. Single marker analysis within full-sibs enabled us to identify significant marker-trait associations. One of these markers anchored proximal to a putative ortholog of TFL1, homologues of which have been shown to play a key role in continuous heading of some members of the rose family, Rosaceae.
- Aftermath heading
- Genome wide association
- Lolium perenne
- Perennial ryegrass
- Single marker analysis
Perennial ryegrass (Lolium perenne L.) is an important forage species grown in temperate regions of the world where it underpins the dairy and livestock sectors. This is due to a high palatability and digestibility . It also displays relatively rapid establishment and has long growing seasons with relatively high yields in suitable environments . With 38 % of global land area available for agriculture, 70 % is assigned as pastoral agricultural land . In Europe alone 76 million hectares is used as permanent pasture  and in Ireland 80 % agricultural land (3.4 million hectares) is used for pasture, hay and silage where perennial ryegrass is the preferred species .
Heading date is a trait that can have a large effect to the use of perennial ryegrass as a forage . Heading date is associated with a reduction in forage quality [7–9]. The stem and inflorescence formation significantly reduces tiller formation and affects the persistency, digestibility and nutritional value . Perennial ryegrass belongs to the same sub-family (Pooideae) as several other important grain cereals such as barley, oats, rye and wheat [11, 12]. Heading in situations outside of seed production is unwanted as it negatively impacts forage quality by increasing the stem to leaf ratio. Extending the vegetative period would greatly enhance its utility as a forage [13, 14]. Aftermath heading is mainly associated with early heading genotypes, and these tend to show lower persistency and perenniality. There has been limited work done on the genetic control of aftermath heading, and only a single quantitative trait locus (QTL) has been mapped onto linkage group (LG) 6 in an experimental mapping population .
In perennial ryegrass, heading is mainly controlled by three main pathways, namely the vernalization pathway, the photoperiod pathway and the circadian clock. To date many QTL mapping studies have been carried out in perennial ryegrass and major loci involved in the floral transition have been identified [9, 15–24]. QTL for heading date have been detected on all seven LGs of perennial ryegrass, with analogous regions on LG4 and LG7 being linked with large affect QTL across multiple populations . Although genes underlying some of these QTL have been proposed [16, 17] none have been cloned to date.
In addition to within family based QTL analysis, we can also map QTL in populations using genome wide association analysis (GWAS). This offers the benefit of being able to take advantage of historical recombination to more precisely map the QTL region. In the case of a very rapid decay of linkage disequilibrium (LD), the causative quantitative trait nucleotide (QTN) may be elucidated. However, this does necessitate the need for a high marker density. A recent GWAS study of heading date in perennial ryegrass identified markers affecting heading date across 1,000 F2 families . However, the variation explained by the combined marker set was extremely small. LD only extended to very short distances in the study population, and despite using in excess of 0.9 million SNPs the marker density may be insufficient. Alternatively, rare variants affecting the trait may have resulted in low statistical power to identify associations.
Here, we have developed an association mapping population of 360 individuals coming from six full-sib families with contrasting primary heading dates. Multiple individuals from each full-sib family were selected to ensure any allele will be present at a frequency suitable for association analysis. However, the low levels of LD across families restricted GWAS at our marker density, and so we performed single marker analysis within each full-sib family separately. Anchoring markers to the perennial ryegrass GenomeZipper [26, 27] allowed us to identify regions containing clusters of associated markers, some of which co-located with genes having a known involvement in controlling heading and aftermath heading.
Phenotypic variation for heading date and aftermath heading
Full-sib family structure
Late X Early
Inter X Late
Profit X Hercules
Jumbo X Tyrone
Late X Late X Late X Late
Late X Late
Late X Late
Inter X Inter
Aftermath heading was scored only in September 2015 using a visual assessment on a scale of 1 (no aftermath-heading) to 9 (extensive aftermath heading). The Pearson’s product-moment correlation between replicates was 0.68 and a 95 % confidence interval of (0.61, 0.73). The difference in aftermath heading scores between replicates was not significant(F (1,685)= 3.385, MSE =21.522, P = 0.07) at α= 0.05. The population mean, sd, and median scores for aftermath heading were 2.7, 2.7 and 1, respectively. We only have a single years data for aftermath heading, however, a recent study of 1453 F 2 families of perennial ryegrass determined heritibalities for aftermath heading that were in line with those determined for heading date . The distribution of scores within each full-sib family, we see that one family (G18) has more variation and a higher propensity for aftermath heading. Taking this family in isolation we looked at the association between heading date and aftermath heading. Using aftermath heading as a response variable in linear regression, we can see that earlier heading individuals tend to have higher aftermath heading (Additional file 1).
Genome wide association analysis
Markers used at different stages of pipeline
We have now established that, in general, marker numbers in the region of 50,000 are going to be unsuitable for GWAS in perennial ryegrass. We believe that our inability to find any significant associations with heading date and aftermath heading was due to low marker density and the extremely low LD in the population. Our population is made up of six full-sib families, and within each family a much higher LD is expected. An alternative approach would be to perform single marker analysis within each full-sib family. There are only 60 genotypes per full-sib family, however, using this approach there is sufficient SNP density to perform a simple marker-trait association analysis within each family separately. This would not enable us to locate the regions directly affecting a phenotype, but would allow us to identify markers linked to QTL.
Single marker analysis in full-sib families
Single marker analysis for aftermath heading
Identifying putative orthologs of key heading genes
Phylogenetic relationships of candidate genes involved in heading
Protein near query
Position on Zipper
Genetic position on Zipper
Additional file 7
Additional file 11
Additional file 6
Additional file 6
Additional file 6
Additional file 8
Additional file 9
Additional file 12
Additional file 10
We identified putative orthologs of the important photo-receptor proteins PHYA (ms_13514 |ref0035067- gene-0.0mRNA), PHYB (ms_4484 |ref0039062-gene-0.0mRNA), PHYC (ms_2801 |ref0025790-gene-0.3mRNA) and CRYTOCHROME 2 (CRY2) (ms_4185 |ref0010917-gene-0.1mRNA) (Fig. 8) (Additional file 6 and 7) and located these on the genetic map via the GenomeZipper (Fig. 5 and Additional file 5). The three Phytochromes, A, B, and C are anchored onto LG4 at different locations. All three are in locations where markers significantly associated with days to heading in one or more full-sib families. CRY2 is located on LG6 at 52.5 cM, in a region where we identified a cluster of markers between 35.9 and 56 cM that were associated with aftermath heading in G18 (Table 3). We also identified putative orthologs to PSEUDO RESPONSE REGULATOR PROTEIN 37 (PRR37) (ms_13366 |ref0021945-gene-0.0mRNA) and SUPRESSOR OF OVEREXPRESSION OF CO 1 (SOC1) (ms_6002 |ref0025562-gene-0.0mRNA) that play important roles in the central circadian clock (Additional file 8 and 9) (Fig. 4). Another important photoperiodic pathway gene is CO, which directly regulates the key floral activator FT (Fig. 8), and we have found a putative ortholog to CO (ms_5059 |ref0019898-gene-0.1mRNA) (Additional file 10) in perennial ryegrass that anchored onto LG7 at 43.5cM (Fig. 6).
Both the regulation of CO and stability of photoreceptors is controlled by GIGANTEA (GI) that is generally believed to be single copy, with a highly conserved role across the angiosperms . We identified a putative ortholog to GI (ms_1276 |ref0038679-gene-0.4mRNA) (Additional file 11) in our analysis that anchored onto LG3 at 29.3cM  (Additional file 3). A scaffold with markers significantly associated with aftermath heading in G18 was anchored to LG3 at 28.8 cM. In addition to genes from the photoperiodic pathway, we identified a putative ortholog of the barley VERNALIZATION 1 (VRN1) (ms_312 |ref002704-gene-0.1mRNA) protein that is involved in the vernalization pathway (Additional file 12). The VRN1 protein is anchored on LG4 at 31.4cM (Fig. 5) in a region with markers significantly associated with days to heading. This was most evident in the full-sib family G11 that was generated from crossing early and late heading populations. It has already been shown that a dominant mutation in VRN1 promoter region is responsible for changes in growth habit of winter wheat to spring wheat . The vernalisation and the photoperiod pathway influence heading by acting on the key floral pathway integrator FT (Fig. 8).
Perennial ryegrass orthologs of FT and TFL1
Apart from floral transition, Arabidopsis FT also mediates stomatal opening . Likewise, TFL1 is also involved in meristematic development and perennial heading . We identified a putative perennial ryegrass ortholog of FT (ms_13332 |ref0029013-gene-0.0mRNA) (Fig. 9) that was anchored to LG7 at 43.6cM, in a region with markers significantly associated with heading (Fig. 6). FT was anchored to the same genetic position (43.6cM), as a previously mapped genetic marker, LpVRN3 . LpVRN3 was designed on a sequence that shared 100 % identity (alignment length of 80.4 %) with the transcript we identified as orthologus to FT. Two genes from the FT/TFL gene family have previously been mapped in perennial ryegrass . Both were mapped in the same experimental population used as the backbone to the GenomeZipper. Using the available genome data we can now better identify the putative perennial ryegrass orthologs to these genes. Based on our phylogentic anlaysis, the genetic marker previously labeled as LpFT is more likely to be an ortholog of TSF (ms_9269 |ref0005840-gene-0.0mRNA) (Fig. 9). TSF is the closet sequence homologue of FT and they have overlapping roles in promoting heading, however it does have a distinct role to play under short day conditions . TSF was anchored to LG7 at 57.2cM (Fig. 6) in a region with markers significantly associated with heading date, particularly in family G11.
The Arabidopsis floral inhibitors, TFL1, BFT and CEN were grouped in a branch separate to FT. We identified putative perennial ryegrass orthologs of TFL1 (ms_821 |ref0016245-gene-0.0mRNA) clustering with barley TFL1 (Fig. 9) that was anchored on the GenomeZipper to LG2 at 79.8cM (Fig. 4). Previously a perennial ryegrass gene with sequence homology to TFL1 was anchored to LG5 at 27.5cM using a transcriptome based genetic map , however, our phylogentic analysis suggests that this is more likely an ortholog of BFT. In Arabidopsis BFT shares highest sequence similarity to TFL1 and functions similar to TFL1 in meristematic development to repress heading . Interestingly, on LG2 markers we identified in single marker analysis for aftermath heading, were located on the GenomeZipper at 80.8cM and 84.2cM. These markers were next to putative perennial ryegrass ortholog of TFL1.
In perennial ryegrass, TFL1 is characterized as a repressor of heading and a regulator of axillary meristem identity . When LpTFL1 was overexpressed in Arabidopsis, plants displayed a delayed heading phenotype and extended vegetative growth . In perennial ryegrass expression level of LpTFL1 was observed in leaves, inflorescence, roots, stem and apex. It was found that after a period of cold (primary induction), expression levels of LpTFL1 reduced, allowing plants to prepare for heading. As the day length and temperature increases (secondary induction), LpTFL1 is upregulated in the apex to promote tillering . Unlike annual grasses that flower once in the season and die after seed production, perennial ryegrass continues to grow even after seed production by maintaining at least one tiller in a vegetative phase. It was shown that tillering in ryegrass is mainly controlled by spatiotemporal regulatory mechanism, by activating certain genes to repress heading in vernalized tillers . Interestingly, mutations in homologues of TFL1 in rose (RoKSN) and woodland strawberry (FvKSN) (both Rosoideae members of the rose family Rosaceae) have been shown to be responsible for continuous heading phenotypes in these species . The putative ortholog of TFL1 identified here, which co-locates with variants for aftermath heading, is an interesting candidate gene for further study of this important forage quality trait.
In this study we did not detect any SNPs significantly associated with heading and aftermath heading in a genome-wide association analysis, most likely due to the rapid decay of LD we observed in the population. However, using single marker analysis within each full-sib family we did identify linked markers, some in regions containing putative orthologs of key heading genes. Interestingly, in a family segregating for aftermath heading, SNPs were anchored proximal to a putative ortholog of TFL1, homologues of which have recently been shown to play a key role in continuous heading/aftermath heading of some Rosaceae species .
Plant material and phenotypic data
The association population consisted of 360 individual plants from six full-sib F2 families created at Oak Park, Carlow, Ireland (60 individuals selected at random from each family) (Table 1). The parents used to create the full-sib families originated from perennial ryegrass varieties (Table 1). Plants were established in the glasshouse and then transplanted into the field in a spaced plant nursery in 2013 at Oak Park, Carlow, Ireland in two replicates. Each replicate consisted of 30 blocks with 2 individuals from each full-sib family within a block. The number of days to heading from April 1 s t was monitored in 2014 and 2015 for each plant. An individual plant was considered as headed, when three or more heads had emerged from the leaf sheath. In the same population aftermath heading was visually scored in the year 2015 on a scale of 1 (no aftermath heading) to 9 (intense aftermath heading) as described in . Using the R package lme4  variance components were estimated for heading date using genotype as a random effect and year as fixed effect. Conditional means were calculated and used for subsequent analysis.
Genotyping full-sib families
We used a genotyping-by-sequencing approach that followed the protocol developed by Elshire et al. . Briefly, genomic DNA was isolated from each individual, digested with ApeKI, samples were grouped into libraries, amplified, and sequenced on an Illumina HiSeq 2000. After sequencing, adaptor contamination was removed with Scythe  with a prior contamination rate set to 0.40. Sickle  was used to trim reads when the average quality score in a sliding window (of 20 bp) fell below a phred score of 20, and reads shorter than 40bp were discarded. The reads were demultiplexed using sabre  and data from each sample was aligned to the perennial ryegrass reference genome  using BWA . The Genome Analysis Tool Kit (GATK)  was used to identify putative variants across the full-sib families, and also within each full-sib family. Only genotype calls with a phred score of 30 (GQ, Genotype Quality), and only variant sites with a mean mapping quality of 30 were retained. In the case of the SNP set across all full-sib families, we used a minimum minor allele frequency threshold of 5 % (Additional file 13). When identifying an SNP set within each full-sib family we used a minimum minor allele frequency threshold of 10 %.
Genome wide association and linkage disequilibrium (LD) analysis
A mixed linear model implemented in the R package GAPIT (Genomic Association and Prediction Integrated Tool)  was used to perform an association analysis. The mixed model accounts for population structure and family relatedness using principal component analysis (PCA) and a kinship matrix calculated by GAPIT with available input genotypic data. To account for multiple testing during association analysis, false discovery rate (FDR)  and Bonferroni correction (0.05/51864)  with an α level of 0.05 was used setting a threshold at 9.8x10 −7. To assess the extent of LD across the full-sib populations we identified SNPs located within a single genomic scaffold, and calculated the inter SNP distance and the squared correlation of the allele counts in Plink 1.9 , based on the maximum likelihood solution to the cubic Eq. .
Pipeline for single marker analysis
A SNP panel was developed for each full-sib family, using a 10 % of minor allele frequency, and subsequent analyis was performed on each of the six full sib families independently. SNPs segregating in a 1:1 ratio were selected, that is homozygous in parent one and heterozygous in parent two. A X 2 test was used to eliminate SNPs that deviated significantly from a 1:1 segregation. We then performed a Kruskal-Wallis test using R  on each marker to check for association with heading date. Using the GenomeZipper [26, 30] we established a putative order for these markers along the genetic map. The median Kruskal-Wallis test statistic was calculated for bins represented by gaps between markers on the genetic linkage map.
Protein datasets and phylogenetic analysis
The query proteins for key heading genes was obtained from Arabidopsis thaliana, rice and barley using the uniport database  (Table 4). The complete protein sets from perennial ryegrass , Arabidopsis , Brachypodium , barley , rice , Sorghum  and maize  were gathered from PLAZA 3.0  and combined into single file to build a BLASTp database. Using each query we performed a BLASTp with an evalue of 10e-10 and parsed the results for hits with at least 60 % coverage and 50 % identity. The sequences were aligned using MUSCLE , an alignment program implemented in MEGA 6.06 . The phylogentic analysis was carried out using the Maximum Likelihood method based on the JTT matrix-based model in MEGA 6.06 [66, 67]. Bootstrap values after 100 replicates are shown next to the branches. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using a JTT model, and then selecting the topology with superior log likelihood value. The tree is mid-point rooted, drawn to scale, with branch lengths measured in the number of substitutions per site.
GWAS, genome wide association study; QTL, quantitative trait loci; LD, linkage disequilibrium; LG, linkage group
SKA is supported by a Teagasc PhD Walsh Fellowship. SLB is supported by an EU Marie-Sklodowska-Curie Fellowship (H2020-MSCA-IF: 658031). The study was funded through a DAFM project (RSF 14/S/819) and Teagasc core funding. The funders played no role in design of the study, collection of the data, analysis, and interpretation of data and in writing the manuscript.
Availability of data and materials
The datasets supporting the conclusions of this article are included within the article and its additional files.
DM, PC, SB, TRH conceived and designed the study. SKA, JV and SLB performed the data analysis. SKA, and SLB drafted the initial paper. SKA, SLB, DM, PC, JV, TRH, and SB contributed to interpretation of data and preparation of the final manuscript. All authors read and approved the final version.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
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- McGrath S, Hodkinson TR, Barth S. Extremely high cytoplasmic diversity in natural and breeding populations of Lolium (Poaceae). Heredity. 2007; 99(5):531–44.View ArticlePubMedGoogle Scholar
- Wilken D. Lolium. The Jepson Manual. Higher Plants of California, University of California Press, Berkeley.1993; p. 1400.Google Scholar
- Gagic M, Faville M, Kardailsky I, Putterill J. Comparative genomics and functional characterisation of the gigantea gene from the temperate forage perennial ryegrass Lolium perenne. Plant Mol Biol Rep. 2015; 33(4):1098–106.View ArticleGoogle Scholar
- Fischer G, Prieler S, van Velthuizen H, Berndes G, Faaij A, Londo M, de Wit M. Biofuel production potentials in europe: Sustainable use of cultivated land and pastures, part ii: Land use scenarios. Biomass Bioenergy. 2010; 34(2):173–87.View ArticleGoogle Scholar
- O’Donoghue C, Creamer R, Crosson P, Curran T, Donnellan T, Farrelly N, Fealy R, French P, Geoghegan C, Green S, et al. Drivers of agricultural land use change in Ireland to 2025. Teagasc; 2015. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.704.6065&rep=rep1&type=pdf.
- McGrath S, Hodkinson TR, Charles T, Zen D, Barth S. Variation in inflorescence characters and inflorescence development in ecotypes and cultivars of Lolium perenne L. Grass Forage Sci. 2010; 65(4):398–409.View ArticleGoogle Scholar
- Smit H, Tas B, Taweel H, Tamminga S, Elgersma A. Effects of perennial ryegrass Lolium perenne L. cultivars on herbage production, nutritional quality and herbage intake of grazing dairy cows. Grass Forage Sci. 2005; 60(3):297–309.View ArticleGoogle Scholar
- Humphreys MW, Yadav R, Cairns AJ, Turner L, Humphreys J, Skøt L. A changing climate for grassland research. New Phytologist. 2006; 169(1):9–26.View ArticlePubMedGoogle Scholar
- Yamada T, Jones E, Cogan N, Vecchies A, Nomura T, Hisano H, Shimamoto Y, Smith K, Hayward M, Forster J. Qtl analysis of morphological, developmental, and winter hardiness-associated traits in perennial ryegrass. Crop Sci. 2004; 44(3):925–35.View ArticleGoogle Scholar
- Valk P, Proveniers M, Pertijs J, Lamers J, Dun C, Smeekens J. Late heading of perennial ryegrass caused by introducing an Arabidopsis homeobox gene. Plant Breed. 2004; 123(6):531–5.View ArticleGoogle Scholar
- Wilkins P. Breeding perennial ryegrass for agriculture. Euphytica. 1991; 52(3):201–14.View ArticleGoogle Scholar
- Devos KM, Gale MD. Comparative genetics in the grasses. Plant Mol Biol. 1997; 35(1–2):3–15.View ArticlePubMedGoogle Scholar
- Casler M, Vogel K. Accomplishments and impact from breeding for increased forage nutritional value. Crop Sci. 1999; 39(1):12–20.View ArticleGoogle Scholar
- Frank A, Hofmann L. Light quality and stem numbers in cool-season forage grasses. Crop Sci. 1994; 34(2):468–73.View ArticleGoogle Scholar
- Byrne S, Guiney E, Barth S, Donnison I, Mur LA, Milbourne D. Identification of coincident qtl for days to heading, spike length and spikelets per spike in Lolium perenne L.Euphytica. 2009; 166(1):61–70.View ArticleGoogle Scholar
- Armstead IP, Turner LB, Farrell M, Skøt L, Gomez P, Montoya T, Donnison IS, King IP, Humphreys MO. Synteny between a major heading-date qtl in perennial ryegrass (Lolium perenne L.) and the hd3 heading-date locus in rice. Theor Appl Genet. 2004; 108(5):822–8.View ArticlePubMedGoogle Scholar
- Jensen LB, Andersen JR, Frei U, Xing Y, Taylor C, Holm PB, Lübberstedt T. Qtl mapping of vernalization response in perennial ryegrass (Lolium perenne L.) reveals co-location with an orthologue of wheat vrn1. Theor Appl Genet. 2005; 110(3):527–36.View ArticlePubMedGoogle Scholar
- Armstead I, Turner L, Marshall A, Humphreys M, King I, Thorogood D. Identifying genetic components controlling fertility in the outcrossing grass species perennial ryegrass (Lolium perenne) by quantitative trait loci analysis and comparative genetics. New Phytologist. 2008; 178(3):559–71.View ArticlePubMedGoogle Scholar
- Barre P, Moreau L, Mi F, Turner L, Gastal F, Julier B, Ghesquière M. Quantitative trait loci for leaf length in perennial ryegrass (Lolium perenne L.)Grass Forage Sci. 2009; 64(3):310–21.View ArticleGoogle Scholar
- Studer B, Jensen LB, Hentrup S, Brazauskas G, Kölliker R, Lübberstedt T. Genetic characterisation of seed yield and fertility traits in perennial ryegrass (Lolium perenne L.)Theor Appl Genet. 2008; 117(5):781–91.View ArticlePubMedGoogle Scholar
- Skøt L, Humphreys MO, Armstead I, Heywood S, Skøt KP, Sanderson R, Thomas ID, Chorlton KH, Hamilton NRS. An association mapping approach to identify flowering time genes in natural populations of Lolium perenne (L.)Mol Breed. 2005; 15(3):233–45.View ArticleGoogle Scholar
- Skøt L, Humphreys J, Humphreys MO, Thorogood D, Gallagher J, Sanderson R, Armstead IP, Thomas ID. Association of candidate genes with flowering time and water-soluble carbohydrate content in Lolium perenne (L.)Genetics. 2007; 177(1):535–47.View ArticlePubMedPubMed CentralGoogle Scholar
- Shinozuka H, Cogan NO, Spangenberg GC, Forster JW. Quantitative trait locus (qtl) meta-analysis and comparative genomics for candidate gene prediction in perennial ryegrass (Lolium perenne L.)BMC Genetics. 2012; 13(1):1.View ArticleGoogle Scholar
- Andersen JR, Jensen LB, Asp T, Lübberstedt T. Vernalization response in perennial ryegrass (Lolium perenne L.) involves orthologues of diploid wheat (Triticum monococcum) vrn1 and rice (Oryza sativa) hd1. Plant Mol Biol. 2006; 60(4):481–94.View ArticlePubMedGoogle Scholar
- Fè D, Cericola F, Byrne S, Lenk I, Ashraf BH, Pedersen MG, Roulund N, Asp T, Janss L, Jensen CS, et al. Genomic dissection and prediction of heading date in perennial ryegrass. BMC Genomics. 2015; 16(1):1.View ArticleGoogle Scholar
- Pfeifer M, Martis M, Asp T, Mayer KF, Lübberstedt T, Byrne S, Frei U, Studer B. The perennial ryegrass genomezipper: targeted use of genome resources for comparative grass genomics. Plant Physiol. 2013; 161(2):571–82.View ArticlePubMedGoogle Scholar
- Studer B, Byrne S, Nielsen RO, Panitz F, Bendixen C, Islam MS, Pfeifer M, Lübberstedt T, Asp T. A transcriptome map of perennial ryegrass (Lolium perenne L.)BMC Genomics. 2012; 13(1):140.View ArticlePubMedPubMed CentralGoogle Scholar
- Bates D, Maechler M, Bolker B, Walker S. Fitting Linear Mixed-Effects Models Using lme4.Journal of Statistical Software; 67(1):1–48. doi:10.18637/jss.v067.i01.
- Fè D, Pedersen MG, Jensen CS, Jensen J. Genetic and environmental variation in a commercial breeding program of perennial ryegrass. Crop Sci. 2015; 55(2):631–40.View ArticleGoogle Scholar
- Byrne SL, Nagy I, Pfeifer M, Armstead I, Swain S, Studer B, Mayer K, Campbell JD, Czaban A, Hentrup S, et al. A synteny-based draft genome sequence of the forage grass Lolium perenne. Plant J. 2015; 84(4):816–26.View ArticlePubMedGoogle Scholar
- Andrés F, Coupland G. The genetic basis of flowering responses to seasonal cues. Nat Rev Genet. 2012; 13(9):627–39.View ArticlePubMedGoogle Scholar
- Kaul S, Koo HL, Jenkins J, Rizzo M, Rooney T, Tallon LJ, Feldblyum T, Nierman W, Benito MI, Lin X, et al. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature. 2000; 408(6814):796–815.View ArticleGoogle Scholar
- Vogel JP, Garvin DF, Mockler TC, Schmutz J, Rokhsar D, Bevan MW, Barry K, Lucas S, Harmon-Smith M, Lail K, et al. Genome sequencing and analysis of the model grass Brachypodium distachyon. Nature. 2010; 463(7282):763–8.View ArticleGoogle Scholar
- Consortium IBGS, et al. A physical, genetic and functional sequence assembly of the barley genome. Nature. 2012; 491(7426):711–6.Google Scholar
- Schnable PS, Ware D, Fulton RS, Stein JC, Wei F, Pasternak S, Liang C, Zhang J, Fulton L, Graves TA, et al. The b73 maize genome: complexity, diversity, and dynamics. Science. 2009; 326(5956):1112–5.View ArticlePubMedGoogle Scholar
- Paterson AH, Bowers JE, Bruggmann R, Dubchak I, Grimwood J, Gundlach H, Haberer G, Hellsten U, Mitros T, Poliakov A, et al. The Sorghum bicolor genome and the diversification of grasses. Nature. 2009; 457(7229):551–6.View ArticlePubMedGoogle Scholar
- Project IRGS, et al. The map-based sequence of the rice genome. Nature. 2005; 436(7052):793–800.View ArticleGoogle Scholar
- Gabaldón T. Large-scale assignment of orthology: back to phylogenetics. Genome Biol. 2008; 9(10):235.View ArticlePubMedPubMed CentralGoogle Scholar
- Mishra P, Panigrahi KC. Gigantea–an emerging story. Front Plant Sci. 2015; 6:8.View ArticlePubMedPubMed CentralGoogle Scholar
- Loukoianov A, Yan L, Blechl A, Sanchez A, Dubcovsky J. Regulation of vrn-1 vernalization genes in normal and transgenic polyploid wheat. Plant Physiol. 2005; 138(4):2364–373.View ArticlePubMedPubMed CentralGoogle Scholar
- Karlgren A, Gyllenstrand N, Källman T, Sundström JF, Moore D, Lascoux M, Lagercrantz U. Evolution of the pebp gene family in plants: functional diversification in seed plant evolution. Plant Physiol. 2011; 156(4):1967–77.View ArticlePubMedPubMed CentralGoogle Scholar
- Kobayashi Y, Kaya H, Goto K, Iwabuchi M, Araki T. A pair of related genes with antagonistic roles in mediating flowering signals. Science. 1999; 286(5446):1960–62.View ArticlePubMedGoogle Scholar
- Peng FY, Hu Z, Yang RC. Genome-wide comparative analysis of flowering-related genes in Arabidopsis, wheat, and barley. Int J Plant Genomics. 2015;2015.Google Scholar
- Kinoshita T, Ono N, Hayashi Y, Morimoto S, Nakamura S, Soda M, Kato Y, Ohnishi M, Nakano T, Inoue S-I, et al. Flowering locus t regulates stomatal opening. Curr Biol. 2011; 21(14):1232–8.View ArticlePubMedGoogle Scholar
- Wang R, Albani MC, Vincent C, Bergonzi S, Luan M, Bai Y, Kiefer C, Castillo R, Coupland G. Aa tfl1 confers an age-dependent response to vernalization in perennial Arabis alpina. Plant Cell. 2011; 23(4):1307–21.View ArticlePubMedPubMed CentralGoogle Scholar
- Yamaguchi A, Kobayashi Y, Goto K, Abe M, Araki T. Twin sister of ft (tsf) acts as a floral pathway integrator redundantly with ft. Plant Cell Physiol. 2005; 46(8):1175–89.View ArticlePubMedGoogle Scholar
- Yoo SJ, Chung KS, Jung SH, Yoo SY, Lee JS, Ahn JH. Brother of ft and tfl1 (bft) has tfl1-like activity and functions redundantly with tfl1 in inflorescence meristem development in Arabidopsis. Plant J. 2010; 63(2):241–53.View ArticlePubMedGoogle Scholar
- Jensen CS, Salchert K, Nielsen KK. A terminal flower1-like gene from perennial ryegrass involved in floral transition and axillary meristem identity. Plant Physiol. 2001; 125(3):1517–28.View ArticlePubMedPubMed CentralGoogle Scholar
- Williamson ML. Differential responses of tillers to floral induction in perennial ryegrass (Lolium perenne L.) : implications for perenniality. Massey University. Palmerston North, New Zealand. 2008. http://mro.massey.ac.nz/handle/10179/842.
- Iwata H, Gaston A, Remay A, Thouroude T, Jeauffre J, Kawamura K, Oyant LH-S, Araki T, Denoyes B, Foucher F. The tfl1 homologue ksn is a regulator of continuous flowering in rose and strawberry. Plant J. 2012; 69(1):116–25.View ArticlePubMedGoogle Scholar
- Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE. A robust, simple genotyping-by-sequencing (gbs) approach for high diversity species. PLoS ONE. 2011; 6(5):19379.View ArticleGoogle Scholar
- Buffalo:Scythe - A Bayesian adapter trimmer version 0.994 BETA. 2011. https://github.com/vsbuffalo/scythe. Accessed 7 Nov 2015.
- Joshi NA FJ. Sickle - A windowed adaptive trimming tool for FASTQ files using quality. 2011. https://github.com/ucdavis-bioinformatics/sickle. Accessed 7 Nov 2015.
- Joshi NA FJ. Sabre - A barcode demultiplexing and trimming tool for FastQ files. 2011. https://github.com/najoshi/sabre. Accessed 7 Nov 2015.
- Li H, Durbin R. Fast and accurate short read alignment with burrows–wheeler transform. Bioinformatics. 2009; 25(14):1754–60.View ArticlePubMedPubMed CentralGoogle Scholar
- DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, Philippakis AA, Del Angel G, Rivas MA, Hanna M, et al. A framework for variation discovery and genotyping using next-generation dna sequencing data. Nat Genet. 2011; 43(5):491–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Lipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ, Gore MA, Buckler ES, Zhang Z. Gapit: genome association and prediction integrated tool. Bioinformatics. 2012; 28(18):2397–9.View ArticlePubMedGoogle Scholar
- Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodological). 1995; 57(1):289–300.Google Scholar
- Bonferroni CE. Teoria statistica delle classi e calcolo delle probabilita. Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze. 1936; 8:3–62.Google Scholar
- Chang CC, Chow CC, Tellier L, Vattikuti S, Purcell SM, Lee JJ. Second-generation plink: rising to the challenge of larger and richer datasets. Gigascience. 2015; 4(1):1–16.View ArticleGoogle Scholar
- Gaunt TR, Rodríguez S, Day IN. Cubic exact solutions for the estimation of pairwise haplotype frequencies: implications for linkage disequilibrium analyses and a web tool’cubex’. BMC Bioinforma. 2007; 8(1):428.View ArticleGoogle Scholar
- Team RC. A language and environment for statistical computing. Vienna, Austria. 2014; 2015.Google Scholar
- UniProt:a hub for protein information. Nucleic acids research. 2015;43(Database issue):D204-12.Google Scholar
- Proost S, Van Bel M, Vaneechoutte D, Van de Peer Y, Inzé D, Mueller-Roeber B, Vandepoele K. Plaza 3.0: an access point for plant comparative genomics. Nucleic Acids Res. 2015; 43(Database issue):D974-81.View ArticlePubMedGoogle Scholar
- Edgar RC. Muscle: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004; 32(5):1792–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. Mega6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol. 2013; 30(12):2725–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Jones DT, Taylor WR, Thornton JM. The rapid generation of mutation data matrices from protein sequences. Comput Appl biosci CABIOS. 1992; 8(3):275–82.PubMedGoogle Scholar