Organ specificity and transcriptional control of metabolic routes revealed by expression QTL profiling of source-sink tissues in a segregating potato population
© Kloosterman et al; licensee BioMed Central Ltd. 2011
Received: 14 July 2011
Accepted: 7 February 2012
Published: 7 February 2012
With the completion of genome sequences belonging to some of the major crop plants, new challenges arise to utilize this data for crop improvement and increased food security. The field of genetical genomics has the potential to identify genes displaying heritable differential expression associated to important phenotypic traits. Here we describe the identification of expression QTLs (eQTLs) in two different potato tissues of a segregating potato population and query the potato genome sequence to differentiate between cis- and trans-acting eQTLs in relation to gene subfunctionalization.
Leaf and tuber samples were analysed and screened for the presence of conserved and tissue dependent eQTLs. Expression QTLs present in both tissues are predominantly cis-acting whilst for tissue specific QTLs, the percentage of trans-acting QTLs increases. Tissue dependent eQTLs were assigned to functional classes and visualized in metabolic pathways. We identified a potential regulatory network on chromosome 10 involving genes crucial for maintaining circadian rhythms and controlling clock output genes. In addition, we show that the type of genetic material screened and sampling strategy applied, can have a high impact on the output of genetical genomics studies.
Identification of tissue dependent regulatory networks based on mapped differential expression not only gives us insight in tissue dependent gene subfunctionalization but brings new insights into key biological processes and delivers targets for future haplotyping and genetic marker development.
The field of associative genomics or genetical genomics attempts to combine the heritability of generated ~ omics data with phenotypic variation through genetic marker associations . The aimed outcome of a genetical genomics study is to define genomic regions that control the expression of single or multiple genes, metabolites and/or proteins (eQTLs, mQTLs and pQTLs, respectively). The amount of population wide ~ omics data for non-model species has increased dramatically over the last few years as costs for data generation have decreased and computational bottlenecks have been largely overcome. Successful application of genetical genomics has been demonstrated for a number of plant species including Arabidopsis, barley, wheat, eucalyptus and poplar [2–8]. In particular, studies with the model plant Arabidopsis have been crucial for the development of the concept of genetical genomics and have pushed software development forward to cope with the increasing amount of data generated by the different profiling platforms available [1, 9]. Associations between ~ omics data, markers and phenotypes have highlighted the overall complexity of plant trait diversity and have led to the reconstruction of a number of regulatory networks including flowering , glucosinolates , flavonoids  and carotenoids . The identification of eQTL hotspots may indicate the presence of major regulatory switches controlling the expression of many genes directly or indirectly . The existence of eQTL hotspots was confirmed in a number of studies were an enrichment of gene functional categories was found for several of the identified regions [14, 15]. However, network modelling still heavily relies on a-prior knowledge of the pathways targeted .
Despite the high potential of genetical genomics studies in plants, the association of differential gene expression or metabolite concentration with the phenotypic variation does not always disclose the responsible genetic factors, due to genetic linkage of additional genes with the genomic region. Identification of causative polymorphisms is important for the development of applicable genetic markers. For genetical genomics experiments, factors like population size, marker density, sampling strategy, environmental factors and screening platform used, greatly influence the outcome and the ability to detect small additive biological effects [reviewed in [17, 18]]. The high costs for population-wide screening often limit genetical genomics experiments to a single tissue or developmental stage. Several studies indicate that the genetic architecture of gene expression is highly divergent between organs and/or developmental stages which will be reflected in eQTL tissue specificity [5, 19–21]. In poplar it was found that less than one-third of genes with eQTLs have co-localizing eQTLs when comparing two different organs . Gene duplication events and diversification through mutations, referred to as sub- and neofunctionalization, underlie gene expression differences and are hypothesized to be the driving forces for obtaining novel gene functions and consequently evolution of plant development [22, 23].
Potato cultivars are highly heterozygous tetraploid outcrossing plants, which complicates genetic analysis aimed at understanding the molecular mechanisms underlying trait variation. Genetic studies therefore are often performed in diploid genotypes and populations. Precision breeding relies on the identification of causative polymorphisms in genes responsible for trait variation. Once a gene has been assigned a regulatory function for a targeted trait, the allelic diversity of the gene in tetraploid cultivars and wild type accessions can be determined and potentially exploited in marker assisted breeding .
The genome sequence of a dihaploid potato clone (DM) has recently been completed  and this will facilitate the rapid identification and cloning of genes associated to trait variation and thus haplotyping efforts. Genetical genomics experiments in potato opens up a new dimension with the availability of the genome sequence. With the physical as well as the genetic map location of many genes known, QTL intervals can be more easily screened for candidate genes. Here we present the first genetical genomics study in potato with eQTL analysis in both leaves and tuber. A comparison between leaf and tuber tissues reveals conservation as well as tissue dependent variation of gene expression. Gene interactions and potential regulatory networks are identified and discussed.
Results and discussion
QTL analysis and distribution
Expression profiles of leaf and tuber RNA extracts were obtained from two independent experiments. For tuber profiling, each sample was assayed twice in a two-colour dye experiment, allowing cross-validation of the observed variation and subsequent filtering based on correlation scores (Methods). The total number of array features exhibiting significant expression that could be used for QTL mapping was higher for leaf (22,193) than tuber samples (19,956). A wide overlap of expressed genes (19,590 features) was found between both organs, which was somewhat surprising considering their highly contrasting functions in terms of architecture and morphology, environmental exposure and developmental stage. However, the organs harvested consist of a multitude of different cell types thereby reducing the overall transcriptomic complexity and no distinction between basal or high gene expression was made. Similarly, the potato microarray is based on available EST sequences where temporal and spatially restricted transcripts have a lower change of being captured and thus not profiled when using the microarray.
QTL analysis was performed based on the R/qtl package that allows high-throughput QTL mapping in an outbreeding species using a single integrated genetic map (Methods). A total of 17,764 QTLs were identified as significant for leaf and tuber data combined, corrected for gene redundancy present on the array where possible (i.e. similar blast scores for independent array features against the potato genome gene prediction sequences v3.2).
Overview of eQTL analysis results based on obtained population-wide gene expression profiles for potato tuber and leaf tissue
Nr of QTLS per Feature*
Total nr of QTLs
Nr of Features
QTL Expl. Var. %
Nr of QTLs Tuber
Nr of QTLs Leaf
Nr of eQTLs
Leaf + Tuber eQTL
Leaf Unique eQTL
Tuber unique eQTL
Cis vs. Trans-acting eQTls
One of the most exciting outputs from large-scale eQTL studies in species with a full genome sequence available is the ability to distinguish cis- and trans-acting regulation of gene expression, which holds the potential to reveal regulatory networks. With the availability of the potato draft genome sequence we were able to distinguish cis- and trans-acting QTLs for most genes. EST based unigenes representing the features on the array were blasted against the genome scaffolds as described in Methods. Putative chromosome locations could be assigned to the vast majority of genes (92%). Genes that could not be assigned a chromosome location were either similar to multiple regions on the genome, had similarity scores below the threshold, or represented significant allelic diversity.
Identified QTLs on the same linkage group as their physical map position are identified as cis-acting while QTLs on different linkage groups are defined as trans-acting. Our results show that almost twice as many cis-eQTLs were identified in comparison to trans-acting eQTLs for leaf and tuber data (Table 1b; > 10% explained variance). This may indicate that in potato there is a strong preference for cis- over trans-acting transcriptional control, which is in contrast with observations made in other studies where trans-acting QTLs are generally overrepresented [2, 4, 5, 28]. Although the low mapping resolution and accompanying wide eQTL confidence intervals does not allow distinction between trans- or cis-eQTL for genes mapped on the same linkage group, this in itself cannot explain the large differences observed.
Using the threshold for significance, we have classified genes in groups based on the amount of explained variance. Interesting to note is the reduction in the ratio between the numbers of cis- and trans-acting QTLs with increased explained variance (Table 1b). Several studies have reported a similar increase in local (cis) over trans-acting regulation, indicating that on average, cis-regulation results in stronger differential expression and thus genetic variation in comparison to trans-regulation [3, 4, 29–31].
In addition to the major QTL peak on chromosome 5, further comparison of trans-acting QTL distribution between leaf and tuber data reveals few other overlapping regions of high QTL frequency in comparison to the distribution plots containing all QTLs. Overlapping trans-eQTL hotspots for both tissues can be found on LG 5 and 10 and to a lesser extent on LG 3, 9 and 11.
A total of 5,818 overlapping QTLs (spanning the same genomic region) were found in leaf and tuber, with only 401 features (6.9%) exhibiting trans-acting transcriptional control (Table 1c). It has previously been suggested that trans-acting QTLs are more often tissue specific [5, 28]. In this study, we find a similar result when considering only those QTLs that were uniquely found in one of the two tissues (leaf or tuber) for which the percentages of trans-acting QTLs are elevated to 34.8% for leaf and 48.8% for tuber, respectively. Trans-acting QTLs with different eQTLs identified for the same gene in the two tissues, may be crucial in establishing organ dependent transcriptional networks. The different mRNA extraction methods used, or differences in mRNA extractability for both organs, is not likely to impact on the identification of genetic regulation of gene expression.
Functional classification of cis- and trans-acting eQTLs in both leaf and tuber (overlap) or uniquely present in leaf or tuber
Cell cycle and organisation
Amino acid metabolism
Minor CHO metabolism
Major CHO metabolism
Identification of trans-regulatory networks
The strategy of deductive reasoning as used in the above examples requires a priori knowledge of the pathways and the potential interactors. For a model species such as Arabidopsis there is a wealth of scientific evidence on gene function, expression patterns and protein interactions that can be integrated in genetical genomics studies. Still, many gene functions are not yet resolved. For the majority of other plant species the annotation of biological functions of genes is often based on sequence similarity alone, which can generate various assumptions lacking scientific support. Genetical genomics studies can provide evidence supporting proposed functions of genes through newly found interactions and co-regulation of genes. This approach will lead to better understanding of transcriptional regulation, and may lead to identification of key regulatory genes underlying trait QTLs.
Heterozygosity based QTLs (false cis-eQTLs)
Based on sequence homology of the representative unigenes to the recently published potato genome sequence, physical map positions can be attributed to a large portion of the features present on the array. However, due to the heterozygous nature of potato, the sequences of genotypes are often relatively divergent from the published genome sequence, which makes distinguishing between allelic variation, different gene family members or multi-copy genes distributed throughout the genome not straightforward. SNP frequency estimations in potato range from 1 SNP per 29 bp, found in a single phase comparison targeting 6.6 Mb of sequence , to 1 SNP every 87 bp between pairs of randomly selected sequences . The potato unigene set used for microarray oligo design  brings forth similar issues, as ESTs used in the assembly originate from various tetraploid cultivars containing allelic variation. Hence, unigenes may contain allelic variation or represent different alleles of the same gene. Consequently, designed oligonucleotides based on these sequences can potentially act as allelic discriminants depending on the genotype used for array hybridizations. Hughes et al.  showed that even a single nucleotide mismatch can reduce signal strength up to 50% depending on the position of the mismatch. To assess allele hybridization specificity within the C × E population, we hybridized genomic DNA of both parental lines on the POCI microarray array (Methods). For 94% of the potato oligonucleotides on the array, hybridization ratios between the C and E parent could be obtained of which only 1921 features showed signal strength variation larger than 33% between both parents (Additional file 4). Of the identified cis-eQTLs, only 391 and 385 features in tuber and leaf data respectively, result in both a cis-eQTL and a differential genomic hybridization ratio (> 33%). These cis-eQTLs are likely to be false eQTLs since variation in observed signal strength may not reflect actual expression differences. Although the percentage of potential false eQTLs (4.7%) appears to be low considering SNP frequency in potato, the actual percentage may be higher due to several factors. Firstly, the C × E population is the result of a backcross, giving rise to a common allele present in both parental lines and for this common allele no specificity can be discriminated when hybridizing parental genomic DNA. Within the population however, any oligonucleotide with unique specificity for the common allele (a) will segregate in an expected 1:2:1 (aa:a-:--) ratio, resulting in differential signals that can be interpreted as expression differences. Secondly, in cases for which gene expression is relatively stable across members of the population, hybridization specificity among alleles can result in the identification of eQTLs that are based on much smaller differences than can be statistically inferred based on genomic hybridization signals. Thirdly, oligonucleotides for the array were designed based on coding sequences and when these oligonucleotides span intron-exon splicing junctions, hybridization with genomic DNA may be hindered. The use of high coverage RNAseq data would bypass most of these issues as SNP calling would allow allele distinction , while read counts will enable comparison of expression levels of alleles and the detection of eQTLs.
In this paper we describe the identification of expression QTLs (eQTLs) in two different tissues of one of the most important food crops in the world; potato. With the completed potato genome sequence, population-wide differential gene expression can be queried to differentiate between cis- and trans-acting eQTLs. Overlapping QTLs present in both tissues are predominantly cis-acting while for tissue specific QTLs, the percentage of trans-acting QTLs increases. The type of genetic material screened and sampling strategy applied in genetical genomics studies has a high impact on the output of a genetical genomics study. Interesting regulatory networks have been identified on chromosome 10 associated to the photosystem and circadian clock control. Identification of key regulatory genes and networks unique to either source or sink tissues not only gives us insight in tissue dependent gene subfunctionalization but will also greatly enhance the identification of the causative polymorphism(s) underlying important trait QTLs.
Ninety-six individuals, including the parental clones, of a diploid backcross population (C × E) were used in this study. This population is derived from an original cross between potato clones C (USW533.7) and E (77.2102.37) and is described in detail in . Tuber samples originate from a field experiment, grown in repeats during the normal potato-growing season in the Netherlands (April-September). Mature tubers were collected from three plants and representative samples were mechanically peeled and immediately frozen in liquid nitrogen before being ground into a fine powder and stored at -80°C. Tuber total RNA was extracted from the 96 samples using the hot phenol method described previously , DNAseI treated and purified using Qiagen RNeasy columns (Qiagen). Tubers of the C × E population were planted in soil filled pots in the greenhouse and grown until stolon emergence after which fully open but still growing young leaves were collected from two replicates and immediately frozen in liquid nitrogen. Leaf RNA was isolated using KingFisher Flex system and the MagMAX™-96 total RNA isolation kit according to the manufacturer's instruction and DNAseI treated prior to labelling.
Microarray hybridizations and data processing
All tuber samples were labelled with both Cy3 and Cy5-dye using the Low RNA Input Linear Amplification Kit, PLUS, Two-colour (Agilent technologies) according to the manufacturer's protocol starting with 2 μg of purified total RNA. Leaf samples were labelled with Cy3-dye following the same protocol as the tuber samples but starting amounts were 200 ng of purified total RNA. Hybridization and washing was performed according to the Agilent's two-colour hybridization protocol with the following change: 1 μg of labelled Cy5 and/or Cy3 cRNA was used as input in the hybridization mixture. Slides were scanned on the Agilent DNA Microarray Scanner and data extracted using the feature extraction software package (v126.96.36.199) using a standard two-colour protocol. Genes which show consistent low expression were removed and data sets were independently normalized using the quantile normalization procedure (mean) available in Genstat® 11.1. As tuber samples were measured twice, only genes with a Pearson correlation coefficient higher than 0.8 across the 94 individuals between the Cy3 and Cy5 datasets were included resulting in 19,956 features. For calculation of genome distribution in tubers the Cy3 data was used. All raw and normalized data files are available as Additional files 5, 6, 7, 8, 9, 10, 11, 12, 13,& 14 including annotation and genome mapping information. All normalized expression data for leaf and tuber samples have been deposited in ArrayExpress (E-MTAB-808, E-MTAB-701).
Genomic DNA hybridizations were performed after labelling 800 ng of genomic DNA from both parents (C and E) in duplo, using the BioPrime® labelling kit (Invitrogen) with a modified dNTPmix (1.2 mM of dATP, dGTP, dTTP, 0,6 mM dCTP and 5 ul dCTP-Cy3 or dCTP-cy5 Agilent technologies) and incubated for 16 h at 37°. Labelled samples were purified using PureLink™ PCR purification System (Invitrogen) and heat fragmented for 30 min resulting in fragments ranging from ~150 to 600 bp. 1 ug of labelled samples (cy3 and cy5) were hybridized at 65° for 17 h in a standard swop-dye experiment using the independently labelled samples. Washing and scanning of the slides was carried out as described for the gene expression experiments. Feature normalization and ratio calculations were carried out using default method available in Agilent Feature extraction software package (v188.8.131.52). Features showing consistent differential expression (> 33%) between both parents are presented in Additional file 4.
Genetic map and linkage to the potato genome
The genetic map used in all QTL studies was generated using mapping software Joinmap 4.0®  and is a modified version of an earlier C × E genetic map , including all sequence based SNP markers. Additional markers were obtained from the tuber expression data set, in cases where both cy3 and cy5 hybridization signals could be unambiguously scored as present or absent. Marker names originating from tuber expression data have POCI as a prefix. All sequence based markers present in the map are linked to EST contigs or EST singletons and these sequences were blasted against the potato genome scaffolds (v3.4). Segregating markers with unique and significant genome scaffold hits (> 90% homology) could be subsequently linked to the physical genome map (Additional file 15). The C × E generated genetic map was validated aligning the sequence based markers along the draft scaffold/pseudo-molecules available from the PGSC website . The potato oligo (60-mer) microarray (POCI) used in the experiments, contains 42,034 features based on a potato unigene set . To allow discrimination between cis and trans-eQTLs all unigenes were blasted against the genome scaffolds sequences, predicted Coding sequences (CDS) and predicted gene regions (including 5' and 3'-UTR's). Features with a unique and significant hit were assigned to genome scaffolds for which the majority has chromosome information  and results are presented in Additional file 16. Identified QTLs on the same linkage group as their physical map position are identified as cis-acting while QTLs on different linkage groups are defined as trans-acting. Features on the array for which no physical map position could be assigned are classified as unknown.
We have used the main module of the R/qtl package, and optimized it for high-throughput QTL mapping in outcrossing species such as potato. The R-script checks the ratio of missing values before automatically converting JoinMap segregation scores to R/qtl scores after which the QTL mapping is performed . The genotypic scores and genetic map order used in this study are available in Additional file 15 and 17. The program relies on R/qtl for the QTL mapping where QTL information is extracted from the summary.scanone method and the explained variance of the QTL is computed for each QTL using the makeqtl and the fitqtl functions. The QTL interval is computed using LOD interval method. The default analysis performs a QTL mapping using the Haley-Knott regression with a mapping step size of 5 cM. The LOD threshold used to determine the QTLs is calculated using Li & Ji algorithm . The number of simulation replicates to perform for the sim.geno function is default to 16 and the n.draws parameter allows further adjustment. For tuber and leaf expression data QTL analysis was run using the LOD interval method with default step size and significance LOD threshold of 4.35.
Gene ontology and eQTL visualization
Gene ontology (GO) assignment for all the potato micro-array features has been described previously (Kloosterman et al. 2008). GO information was downloaded through Agilent E-array published designs (https://earray.chem.agilent.com/earray/). The number of array features having eQTLs (cis or trans) were counted per GO group and totals are listed. To reduce the number of classes, GO identifiers targeting the same metabolic routes were in some cases merged (Table 2). GO classification of the POCI microarray has been previously linked to the expression data visualization tool MapMan (Kloosterman et al. 2008). MapMan is a user-driven tool that normally displays large datasets (e.g. gene expression data arrays) onto diagrams of metabolic pathways or other processes (http://mapman.gabipd.org/web/guest/mapman). Instead of visualizing gene expression data we display the presence of tissue specific eQTLs (eQTL present in leaf = blue box; eQTL present in tuber = red box). In this manner a quick overview can be obtained for each pre-designed metabolic route and genes can be identified that are differentially expressed within a segregating population (Figure 3). Additional information on gene name and function can be obtained for each box when running the MapMan software (publicly available).
Single nucleotide polymorphism
Quantitative trait loci
Logarithm of odds
We would like to thank Robert Rap for his help in developing the R/qtl package for high throughput QTL analysis in potato. The work presented was carried out and funded by the EU-SOL project (PL 016214-2 EU-SOL) and the research program of the Centre of BioSystems Genomics (CBSG), which is part of The Netherlands Genomics Initiative/Netherlands Organization for Scientific Research.
- Jansen RC, Nap JP: Genetical genomics: the added value from segregation. Trends Genet. 2001, 17 (7): 388-391. 10.1016/S0168-9525(01)02310-1.PubMedView ArticleGoogle Scholar
- West MA, Kim K, Kliebenstein DJ, van Leeuwen H, Michelmore RW, Doerge RW, St Clair DA: Global eQTL mapping reveals the complex genetic architecture of transcript-level variation in Arabidopsis. Genetics. 2007, 175 (3): 1441-1450.PubMedPubMed CentralView ArticleGoogle Scholar
- Potokina E, Druka A, Luo Z, Wise R, Waugh R, Kearsey M: Gene expression quantitative trait locus analysis of 16 000 barley genes reveals a complex pattern of genome-wide transcriptional regulation. Plant J. 2008, 53 (1): 90-101. 10.1111/j.1365-313X.2007.03315.x.PubMedView ArticleGoogle Scholar
- Keurentjes JJ, Fu J, Terpstra IR, Garcia JM, van den Ackerveken G, Snoek LB, Peeters AJ, Vreugdenhil D, Koornneef M, Jansen RC: Regulatory network construction in Arabidopsis by using genome-wide gene expression quantitative trait loci. Proc Natl Acad Sci USA. 2007, 104 (5): 1708-1713. 10.1073/pnas.0610429104.PubMedPubMed CentralView ArticleGoogle Scholar
- Drost DR, Benedict CI, Berg A, Novaes E, Novaes CR, Yu Q, Dervinis C, Maia JM, Yap J, Miles B, et al: Diversification in the genetic architecture of gene expression and transcriptional networks in organ differentiation of Populus. Proc Natl Acad Sci USA. 2010, 107 (18): 8492-8497. 10.1073/pnas.0914709107.PubMedPubMed CentralView ArticleGoogle Scholar
- Jordan MC, Somers DJ, Banks TW: Identifying regions of the wheat genome controlling seed development by mapping expression quantitative trait loci. Plant Biotechnol J. 2007, 5 (3): 442-453. 10.1111/j.1467-7652.2007.00253.x.PubMedView ArticleGoogle Scholar
- Kirst M, Basten CJ, Myburg AA, Zeng ZB, Sederoff RR: Genetic architecture of transcript-level variation in differentiating xylem of a eucalyptus hybrid. Genetics. 2005, 169 (4): 2295-2303. 10.1534/genetics.104.039198.PubMedPubMed CentralView ArticleGoogle Scholar
- DeCook R, Lall S, Nettleton D, Howell SH: Genetic regulation of gene expression during shoot development in Arabidopsis. Genetics. 2006, 172 (2): 1155-1164.PubMedPubMed CentralView ArticleGoogle Scholar
- Fu J, Swertz MA, Keurentjes JJ, Jansen RC: MetaNetwork: a computational protocol for the genetic study of metabolic networks. Nat Protoc. 2007, 2 (3): 685-694. 10.1038/nprot.2007.96.PubMedView ArticleGoogle Scholar
- Keurentjes JJ: Genetical metabolomics: closing in on phenotypes. Curr Opin Plant Biol. 2009, 12 (2): 223-230. 10.1016/j.pbi.2008.12.003.PubMedView ArticleGoogle Scholar
- Morreel K, Goeminne G, Storme V, Sterck L, Ralph J, Coppieters W, Breyne P, Steenackers M, Georges M, Messens E, et al: Genetical metabolomics of flavonoid biosynthesis in Populus: a case study. Plant J. 2006, 47 (2): 224-237. 10.1111/j.1365-313X.2006.02786.x.PubMedView ArticleGoogle Scholar
- Acharjee A, Kloosterman B, de Vos RCH, Werij JS, Bachem CWB, Visser RGF, Maliepaard C: Data integration and network reconstruction with ~ omics data using Random Forest regression in potato. Analytica Chimica Acta. corrected proof.
- Breitling R, Li Y, Tesson BM, Fu J, Wu C, Wiltshire T, Gerrits A, Bystrykh LV, de Haan G, Su AI, et al: Genetical genomics: spotlight on QTL hotspots. PLoS Genet. 2008, 4 (10): e1000232-10.1371/journal.pgen.1000232.PubMedPubMed CentralView ArticleGoogle Scholar
- Terpstra IR, Snoek LB, Keurentjes JJ, Peeters AJ, van den Ackerveken G: Regulatory network identification by genetical genomics: signaling downstream of the Arabidopsis receptor-like kinase ERECTA. Plant Physiol. 2010, 154 (3): 1067-1078. 10.1104/pp.110.159996.PubMedPubMed CentralView ArticleGoogle Scholar
- Wu C, Delano DL, Mitro N, Su SV, Janes J, McClurg P, Batalov S, Welch GL, Zhang J, Orth AP, et al: Gene set enrichment in eQTL data identifies novel annotations and pathway regulators. PLoS Genet. 2008, 4 (5): e1000070-10.1371/journal.pgen.1000070.PubMedPubMed CentralView ArticleGoogle Scholar
- Kliebenstein DJ, West MA, van Leeuwen H, Loudet O, Doerge RW, St Clair DA: Identification of QTLs controlling gene expression networks defined a priori. BMC Bioinformatics. 2006, 7: 308-10.1186/1471-2105-7-308.PubMedPubMed CentralView ArticleGoogle Scholar
- Druka A, Druka I, Centeno AG, Li H, Sun Z, Thomas WT, Bonar N, Steffenson BJ, Ullrich SE, Kleinhofs A, et al: Towards systems genetic analyses in barley: Integration of phenotypic, expression and genotype data into GeneNetwork. BMC Genet. 2008, 9: 73-PubMedPubMed CentralView ArticleGoogle Scholar
- Kliebenstein D: Quantitative genomics: analyzing intraspecific variation using global gene expression polymorphisms or eQTLs. Annu Rev Plant Biol. 2009, 60: 93-114. 10.1146/annurev.arplant.043008.092114.PubMedView ArticleGoogle Scholar
- Gerrits A, Li Y, Tesson BM, Bystrykh LV, Weersing E, Ausema A, Dontje B, Wang X, Breitling R, Jansen RC, et al: Expression quantitative trait loci are highly sensitive to cellular differentiation state. PLoS Genet. 2009, 5 (10): e1000692-10.1371/journal.pgen.1000692.PubMedPubMed CentralView ArticleGoogle Scholar
- Potokina E, Druka A, Luo Z, Moscou M, Wise R, Waugh R, Kearsey M: Tissue-dependent limited pleiotropy affects gene expression in barley. Plant J. 2008, 56 (2): 287-296. 10.1111/j.1365-313X.2008.03601.x.PubMedView ArticleGoogle Scholar
- Sergeeva LI, Vonk J, Keurentjes JJ, van der Plas LH, Koornneef M, Vreugdenhil D: Histochemical analysis reveals organ-specific quantitative trait loci for enzyme activities in Arabidopsis. Plant Physiol. 2004, 134 (1): 237-245. 10.1104/pp.103.027615.PubMedPubMed CentralView ArticleGoogle Scholar
- Zahn LM, Leebens-Mack JH, Arrington JM, Hu Y, Landherr LL, de Pamphilis CW, Becker A, Theissen G, Ma H: Conservation and divergence in the AGAMOUS subfamily of MADS-box genes: evidence of independent sub- and neofunctionalization events. Evol Dev. 2006, 8 (1): 30-45. 10.1111/j.1525-142X.2006.05073.x.PubMedView ArticleGoogle Scholar
- Lynch M, Force A: The probability of duplicate gene preservation by subfunctionalization. Genetics. 2000, 154 (1): 459-473.PubMedPubMed CentralGoogle Scholar
- Li L, Paulo MJ, Strahwald J, Lubeck J, Hofferbert HR, Tacke E, Junghans H, Wunder J, Draffehn A, van Eeuwijk F, et al: Natural DNA variation at candidate loci is associated with potato chip color, tuber starch content, yield and starch yield. Theor Appl Genet. 2008, 116 (8): 1167-1181. 10.1007/s00122-008-0746-y.PubMedPubMed CentralView ArticleGoogle Scholar
- Xu X, Pan S, Cheng S, Zhang B, Mu D, Ni P, Zhang G, Yang S, Li R, Wang J, et al: Genome sequence and analysis of the tuber crop potato. Nature. 2011, 475 (7355): 189-195. 10.1038/nature10158.PubMedView ArticleGoogle Scholar
- Keiner R, Kaiser H, Nakajima K, Hashimoto T, Drager B: Molecular cloning, expression and characterization of tropinone reductase II, an enzyme of the SDR family in Solanum tuberosum (L.). Plant Mol Biol. 2002, 48 (3): 299-308. 10.1023/A:1013315110746.PubMedView ArticleGoogle Scholar
- Wang P, Dawson JA, Keller MP, Yandell BS, Thornberry NA, Zhang BB, Wang IM, Schadt EE, Attie AD, Kendziorski C: A model selection approach for expression quantitative trait loci (eQTL) mapping. Genetics. 2011, 187 (2): 611-621. 10.1534/genetics.110.122796.PubMedPubMed CentralView ArticleGoogle Scholar
- Grieve IC, Dickens NJ, Pravenec M, Kren V, Hubner N, Cook SA, Aitman TJ, Petretto E, Mangion J: Genome-wide co-expression analysis in multiple tissues. PLoS One. 2008, 3 (12): e4033-10.1371/journal.pone.0004033.PubMedPubMed CentralView ArticleGoogle Scholar
- Hughes KA, Ayroles JF, Reedy MM, Drnevich JM, Rowe KC, Ruedi EA, Caceres CE, Paige KN: Segregating variation in the transcriptome: cis regulation and additivity of effects. Genetics. 2006, 173 (3): 1347-1355. 10.1534/genetics.105.051474.PubMedPubMed CentralView ArticleGoogle Scholar
- Brem RB, Kruglyak L: The landscape of genetic complexity across 5,700 gene expression traits in yeast. Proc Natl Acad Sci USA. 2005, 102 (5): 1572-1577. 10.1073/pnas.0408709102.PubMedPubMed CentralView ArticleGoogle Scholar
- Petretto E, Mangion J, Dickens NJ, Cook SA, Kumaran MK, Lu H, Fischer J, Maatz H, Kren V, Pravenec M, et al: Heritability and tissue specificity of expression quantitative trait loci. PLoS Genet. 2006, 2 (10): e172-10.1371/journal.pgen.0020172.PubMedPubMed CentralView ArticleGoogle Scholar
- Celis-Gamboa C, Struik PC, Jacobsen E, Visser RGF: Temporal dynamics of tuber formation and related processes in a crossing population of potato (Solanum tuberosum). Ann Appl Biol. 2003, 143 (2): 175-186. 10.1111/j.1744-7348.2003.tb00284.x.View ArticleGoogle Scholar
- Edwards EJ, Saint RE, Cobb AH: Is there a link between greening and light-enhanced glycoalkaloid accumulation in potato (Solanum tuberosumL) tubers?. J Sci Food Agric. 1998, 76 (3): 327-333. 10.1002/(SICI)1097-0010(199803)76:3<327::AID-JSFA934>3.0.CO;2-G.View ArticleGoogle Scholar
- Griffiths DW, Dale MFB, Bain H: The effect of cultivar, maturity and storage on photo-induced changes in the total glycoalkaloid and chlorophyll contents of potato (Solanum tuberosum). Plant Science. 1994, 98 (1): 103-109. 10.1016/0168-9452(94)90153-8.View ArticleGoogle Scholar
- Percival GC: The influence of light upon glycoalkaloid and chlorophyll accumulation in potato tubers (Solanum tuberosum L.). Plant Science. 1999, 145 (2): 99-107. 10.1016/S0168-9452(99)00081-3.View ArticleGoogle Scholar
- McClung CR: A modern circadian clock in the common angiosperm ancestor of monocots and eudicots. BMC Biol. 2010, 8: 55-10.1186/1741-7007-8-55.PubMedPubMed CentralView ArticleGoogle Scholar
- Schaffer R, Ramsay N, Samach A, Corden S, Putterill J, Carre IA, Coupland G: The late elongated hypocotyl mutation of Arabidopsis disrupts circadian rhythms and the photoperiodic control of flowering. Cell. 1998, 93 (7): 1219-1229. 10.1016/S0092-8674(00)81465-8.PubMedView ArticleGoogle Scholar
- Nakamichi N, Kiba T, Henriques R, Mizuno T, Chua NH, Sakakibara H: PSEUDO-RESPONSE REGULATORS 9, 7, and 5 are transcriptional repressors in the Arabidopsis circadian clock. Plant Cell. 2010, 22 (3): 594-605. 10.1105/tpc.109.072892.PubMedPubMed CentralView ArticleGoogle Scholar
- Nakamichi N, Kita M, Ito S, Yamashino T, Mizuno T: PSEUDO-RESPONSE REGULATORS, PRR9, PRR7 and PRR5, together play essential roles close to the circadian clock of Arabidopsis thaliana. Plant Cell Physiol. 2005, 46 (5): 686-698. 10.1093/pcp/pci086.PubMedView ArticleGoogle Scholar
- Imaizumi T: Arabidopsis circadian clock and photoperiodism: time to think about location. Curr Opin Plant Biol. 2010, 13 (1): 83-89. 10.1016/j.pbi.2009.09.007.PubMedPubMed CentralView ArticleGoogle Scholar
- Martinez-Garcia JF, Virgos-Soler A, Prat S: Control of photoperiod-regulated tuberization in potato by the Arabidopsis flowering-time gene CONSTANS. Proc Natl Acad Sci USA. 2002, 99 (23): 15211-15216. 10.1073/pnas.222390599.PubMedPubMed CentralView ArticleGoogle Scholar
- Navarro C, Abelenda JA, Cruz-Oro E, Cuellar CA, Tamaki S, Silva J, Shimamoto K, Prat S: Control of flowering and storage organ formation in potato by FLOWERING LOCUS T. Nature. 2011, 478 (7367): 119-122. 10.1038/nature10431.PubMedView ArticleGoogle Scholar
- Wang ZY, Tobin EM: Constitutive expression of the CIRCADIAN CLOCK ASSOCIATED 1 (CCA1) gene disrupts circadian rhythms and suppresses its own expression. Cell. 1998, 93 (7): 1207-1217. 10.1016/S0092-8674(00)81464-6.PubMedView ArticleGoogle Scholar
- Sawa M, Nusinow DA, Kay SA, Imaizumi T: FKF1 and GIGANTEA complex formation is required for day-length measurement in Arabidopsis. Science. 2007, 318 (5848): 261-265. 10.1126/science.1146994.PubMedPubMed CentralView ArticleGoogle Scholar
- Simko I, Haynes KG, Jones RW: Assessment of linkage disequilibrium in potato genome with single nucleotide polymorphism markers. Genetics. 2006, 173 (4): 2237-2245. 10.1534/genetics.106.060905.PubMedPubMed CentralView ArticleGoogle Scholar
- Kloosterman B, De Koeyer D, Griffiths R, Flinn B, Steuernagel B, Scholz U, Sonnewald S, Sonnewald U, Bryan GJ, Prat S, et al: Genes driving potato tuber initiation and growth: identification based on transcriptional changes using the POCI array. Funct Integr Genomics. 2008, 8 (4): 329-340. 10.1007/s10142-008-0083-x.PubMedView ArticleGoogle Scholar
- Hughes TR, Mao M, Jones AR, Burchard J, Marton MJ, Shannon KW, Lefkowitz SM, Ziman M, Schelter JM, Meyer MR, et al: Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer. Nat Biotechnol. 2001, 19 (4): 342-347. 10.1038/86730.PubMedView ArticleGoogle Scholar
- Turro E, Su SY, Goncalves A, Coin LJ, Richardson S, Lewin A: Haplotype and isoform specific expression estimation using multi-mapping RNA-seq reads. Genome Biol. 2011, 12 (2): R13-10.1186/gb-2011-12-2-r13.PubMedPubMed CentralView ArticleGoogle Scholar
- Bachem CW, van der Hoeven RS, de Bruijn SM, Vreugdenhil D, Zabeau M, Visser RG: Visualization of differential gene expression using a novel method of RNA fingerprinting based on AFLP: analysis of gene expression during potato tuber development. Plant J. 1996, 9 (5): 745-753. 10.1046/j.1365-313X.1996.9050745.x.PubMedView ArticleGoogle Scholar
- Van Ooijen JW: JoinMap® 4, Software for the Calculation of Genetic Linkage Maps in Experimental Populations. Wageningen: Kyazma B.V.2006.Google Scholar
- Anithakumari AM, Tang J, van Eck HJ, Visser RG, Leunissen JA, Vosman B, van der Linden CG: A pipeline for high throughput detection and mapping of SNPs from EST databases. Mol Breed. 2010, 26 (1): 65-75. 10.1007/s11032-009-9377-5.PubMedPubMed CentralView ArticleGoogle Scholar
- Broman KW, Wu H, Sen S, Churchill GA: R/qtl: QTL mapping in experimental crosses. Bioinformatics. 2003, 19 (7): 889-890. 10.1093/bioinformatics/btg112.PubMedView ArticleGoogle Scholar
- Li J, Ji L: Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity. 2005, 95 (3): 221-227. 10.1038/sj.hdy.6800717.PubMedView ArticleGoogle Scholar
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