- Methodology article
- Open Access
Prioritization of candidate genes in QTL regions based on associations between traits and biological processes
© Bargsten et al.; licensee BioMed Central Ltd. 2014
- Received: 15 September 2014
- Accepted: 10 November 2014
- Published: 10 December 2014
Elucidation of genotype-to-phenotype relationships is a major challenge in biology. In plants, it is the basis for molecular breeding. Quantitative Trait Locus (QTL) mapping enables to link variation at the trait level to variation at the genomic level. However, QTL regions typically contain tens to hundreds of genes. In order to prioritize such candidate genes, we show that we can identify potentially causal genes for a trait based on overrepresentation of biological processes (gene functions) for the candidate genes in the QTL regions of that trait.
The prioritization method was applied to rice QTL data, using gene functions predicted on the basis of sequence- and expression-information. The average reduction of the number of genes was over ten-fold. Comparison with various types of experimental datasets (including QTL fine-mapping and Genome Wide Association Study results) indicated both statistical significance and biological relevance of the obtained connections between genes and traits. A detailed analysis of flowering time QTLs illustrates that genes with completely unknown function are likely to play a role in this important trait.
Our approach can guide further experimentation and validation of causal genes for quantitative traits. This way it capitalizes on QTL data to uncover how individual genes influence trait variation.
- Quantitative trait locus
- Candidate gene prioritization
- Gene function prediction
The elucidation of genotype-to-phenotype relationships remains a major challenge in biology. The causal relationship between variation of a trait-of-interest and genotypic differences is important for understanding genome evolution and functioning. In plants, it is the basis for developing targeted strategies in molecular breeding ,. Technological developments in high-throughput phenotyping and next generation sequencing (NGS) are revolutionizing the scale of determination of phenotypes and genotypes ,.
A current bottleneck is the integration of all these data to unravel the molecular mechanisms behind traits-of-interest. Quantitative Trait Locus (QTL) mapping is an attractive approach to link genetic determinants to phenotypes -. In combination with physical maps, QTL studies have identified numerous genomic regions of various plants responsible for variation in particular traits. QTL analyses often are the primer to candidate gene mapping , but experimental approaches to identify the causal genes underlying a QTL are labor-intensive, time-consuming and expensive . The limited number of crosses that can reasonably be performed leads to a low number of recombinations, which in turn means that QTLs are generally mapped with a low resolution: QTL regions typically contain tens to hundreds of genes.
Therefore, methods that help prioritizing QTL candidate genes using a computational approach would be very helpful in unraveling genotype-to-phenotype relationships. Such prioritization is well developed in human disease genetics, where several criteria, such as the putative deleteriousness of a variant, evolutionary conservation, and known biological pathways, are taken into account -. However, in plant biology and breeding, QTL candidate gene prioritization is much less developed. One approach consists of using genes previously identified as influencing the trait under study and test whether these explain a QTL ,, but this approach is limited to existing knowledge about genotype-to-phenotype relationships. Other approaches focus on integrating and visualizing existing information for prioritization - or merely give an overview of previously determined QTL candidate genes ,. Little use has been made of biological pathways or predicted gene functions -.
As an alternative experimental approach, genome-wide association studies (GWAS), which take advantage of historical recombination events, are able to increase resolution. However, GWAS can suffer from problems such as confounding due to genetic background, or diminishing power to find associations for rare alleles . Moreover, existing diversity in a population available for GWAS analysis need not be relevant for a trait-of-interest.
We applied this method in rice (Oryza sativa), chosen because of the large amount of QTL data available . For a series of traits, we demonstrate the performance of candidate gene prioritization by comparing predictions with sets of genes known to be involved in the traits analyzed. On average, for 153 rice traits, a ten-fold reduction in the number of candidate genes was obtained by our prioritization. These results enable to capitalize on QTL data to uncover how individual genes influence trait variation.
From traits to genes
For 231 traits, QTL intervals reported as significant were extracted from the rice Gramene QTL compendium . Genes in the QTL intervals were obtained from rice genome build 2009-01-MSU downloaded from Gramene . To prevent too large regions to be used, a cutoff on maximum number of genes for a QTL interval was set to 450 genes; QTL regions containing more genes were excluded. This was based on testing the number of associations obtained for various size cutoffs (Additional file 1, SI Text).
Linking genes to function
To predict gene functions (biological processes), BMRF - was applied using the PlaNet coexpression network  in combination with Argot2  as recently described . We compared the prioritization results obtained with these annotations with alternative existing function annotation from phytozome .
Linking traits to function
For a set of genes contained in QTL regions associated with a particular trait, the occurrence of associated Gene Ontology BP terms was compared with the overall occurrence of these terms in the respective genome. To assess statistical significance, Fisher exact tests were applied as implemented in the R-function fisher.exact . To adjust for multiple testing, a multiple testing correction was applied with the Benjamini-Hochberg method as implemented in the R-function p.adjust .
As part of the overrepresentation and gene prioritization analysis, three parameters were defined: (1) The False Discovery Rate (FDR) which defines the stringency of the multiple testing correction applied to the results of the Fisher exact test; (2) the minimum fraction of QTL regions for the trait-of-interest in which the BP term should at least occur; this prevents the use of statistically enriched BP terms present only in a small number of QTL regions; and (3) the maximum allowed BP term generality; i.e., only BP terms were used for which not too many genes were annotated genome-wide, to prevent the use of BP terms which are enriched in the QTL regions for a trait but which are very general and not likely to be useful for candidate gene prioritization. In order to find optimal values for these three parameters, the prioritized genes were compared with a set of known causal genes underlying QTLs (Additional file 1: Figure S1). The agreement between the prioritization predictions and the known causal genes was expressed as a p-value, based on comparison of the known causal QTL genes with randomly selected gene sets (see next section). Analyses presented in the paper used the optimized parameter values: FDR = 0.1, occurrence of the BP in at least 50% of the regions, and generality of the BP term not higher than 1%.
To compare the results of this procedure applied to an input set consisting of randomized gene function annotations, predicted gene functions were randomly reassigned to rice genes.
Comparison with experimental datasets and analysis of prioritized candidate genes
Candidate genes occurring in QTL regions were prioritized based on their annotation with at least one of the overrepresented biological processes. To validate these predictions, a set of fine-mapped candidate genes was obtained from the literature. Identifiers of fine-mapped genes were either obtained directly from the publications in which they were reported, or converted using the information from RAP-DB (http://rapdblegacy.dna.affrc.go.jp/download/latest/RAP-MSU.txt.gz).
To assess the significance of fine-mapped gene retention after prioritizing genes, random gene sets were selected out of the QTL regions associated to the various traits; the size of these gene sets for each trait was identical to the number of genes selected by the prioritization approach. This was repeated 1,000 times, and to obtain a p-value, it was counted how many of the random folds retained at least the same number of fine-mapped genes as the number observed with the prioritization approach.
Comparison of prioritized candidate genes with transcription factors was performed using a list of rice transcription factors obtained from http://planttfdb.cbi.edu.cn/download/gene_model_family/Osj . Comparison of predicted candidate genes with rice GWAS data was performed using data from two previous studies ,. For each SNP reported as associated to a trait in those two studies, the three genes located closest to that SNP were considered as potentially causal candidates and were compared with the genes predicted based on QTL gene prioritization.
QTL candidate gene prioritization
Associations between traits and biological processes a
#relevant BP terms b
#BP terms involved
Candidate gene prioritization: comparison with QTL fine-mapping a
Trait and fine-mapped candidate gene
Overrepresented biological processes involved
regulation of flower development
lysine biosynthetic process via diaminopimelate
organic acid catabolic process
Number of spikelets per panicle: LOC_Os01g12160 
systemic acquired resistance
monosaccharide metabolic process
glycolipid biosynthetic process
membrane lipid biosynthetic process
glucose metabolic process
monosaccharide metabolic process
glycolipid biosynthetic process
membrane lipid biosynthetic process
positive regulation of RNA metabolic process
positive regulation of nucleobase-containing compound metabolic process
positive regulation of (macromolecule/cellular) metabolic process
Yield, plant height:
positive regulation of macromolecule/cellular/nitrogen compound biosynthetic process
positive regulation of gene expression
Grain size and quality:
regulation of post-embryonic development
monosaccharide/glucose meta-/catabolic process
hexose catabolic process
alcohol catabolic process
Associations between traits and biological process (BP) terms as defined in the Gene Ontology (GO)  were generated based on overrepresentation of BP terms in the QTL regions associated to a trait. As input BP terms we used our recently presented set of gene function predictions for rice , which consists of 1,767 different BP terms. On average, 23 BP terms occur per gene that can range from very high-level to very specific GO terms, and 494 ± 344 different BP terms occur in a QTL region. In order to focus only on BP terms which are not at a very high-level, a cutoff was applied on the maximum allowed number of genes annotated with a biological process genome-wide. In addition, a second cutoff was applied on the minimum fraction of QTL regions for a trait in which a BP should occur. The reasoning behind this cutoff was that a gene function reoccurring in multiple different QTL regions for the same trait is more relevant for candidate gene prioritization than a gene function that occurs several times in one QTL region for that trait. Values for these cutoffs are described in the Methods section and were obtained using comparison with genes fine-mapped as underlying QTLs.
For a given trait, we calculated overrepresentation of BP terms associated with all genes in all QTL regions (i.e. all candidate genes) as follows. From all candidate genes for the trait under investigation we determined the number of genes annotated with a particular BP term. This number was compared with the number of genes annotated with that same BP term in the whole genome. Enrichment was assessed using a Fisher exact test with multiple testing correction after testing for all traits and all biological processes. Within each QTL region for a given trait, genes associated with the overrepresented BP terms for that trait were identified as the candidate genes that are the most likely causal genes for that trait; we will refer to these as ‘prioritized candidate genes’. Because biological processes are intermediate in the process of candidate gene prioritization in this approach, we first discuss the biological processes selected, and then present the results of candidate gene prioritization based on these.
Analysis of the association of traits with biological process terms
Inspection of these associations based on prior knowledge or through relevant literature shows that several connections were evident. These include the term ‘catabolic processes’ found for yield related traits; for the trait days to maturity, ‘carpel development’; for leaf height, ‘regulation of cell cycle process’; and for root activity both ‘organ development’ and ‘negative regulator of cell cycle’. Associations confirmed in literature include the link between the trait potassium uptake and glucose/galactose-related processes: potassium deficiency led to the inhibition of glycolysis and a build-up of root sugar levels in Arabidopsis . For the yield trait ‘harvest index’ (weight of the harvested grain as percentage of total plant weight), the link with the BP ‘response to brassinosteroid stimulus’ is confirmed by the fact that manipulation of brassinosteroid level or brassinosteroid sensitivity influences yield .
To assess the significance of the obtained number of associations the procedure was repeated after randomly reassigning biological processes to genes. In this way no biological process-trait associations were obtained. In addition, we considered whether there is added value of using our BMRF function annotations for candidate gene prioritization compared to using alternative existing annotations. We found that existing rice gene function annotations resulted in less than half the number of associations obtained with our approach (data not shown). This confirms that our gene function annotation better enables to find associations between traits and BP terms. This is in line with the performance observed for our set of predictions, when comparing with experimentally determined gene functions . This comparison indicated they were of high quality, demonstrating the added value of integrating sequence- and expression information for gene function prediction .
The associations between traits and overrepresented biological processes allow narrowing down the number of candidate genes for a trait in a QTL region: genes associated with those BPs constitute the potentially causal genes. In total, for 153 traits, 6,175 prioritized candidate genes were obtained (Additional file 2: Table S2; see also www.ab.wur.nl/bmrftrait which allows to search on gene or trait), involving 1,120 different QTL regions. This involved a more than ten-fold reduction in the number of candidate genes: averaged over the traits, 9% ± 5% of QTL candidate genes were prioritized. Per QTL region, the average number of prioritized genes was 13 ± 13 which is indeed an over ten-fold reduction compared to the above-mentioned number of 140 ± 121 candidate genes per input QTL region. We assessed the relevance of the prioritization in several ways.
First, a simulation analysis indicated that overrepresented biological processes allow to preferentially select, i.e. prioritize, relevant candidate genes. Upon randomly adding genes to the set of genes present in the QTL regions for a trait, the enrichment analysis tends to identify genes that occur in the original QTL regions and not randomly added genes (Additional file 1). This shows that our prioritization protocol can do away with deliberately added noise.
Second, we compared the prioritization results with a set of genes in rice that were experimentally validated by QTL fine-mapping as truly causal gene for the trait-of-interest. To do so, fine-mapping results for various traits obtained from literature were matched to traits in the Gramene QTL database. This established a test set of 16 genes that should be prioritized in the analysis. Of these 16 genes, 8 were indeed prioritized by our approach (Table 2, Additional file 3: Table S3). The percentage of correctly prioritized candidate genes (8/16, 50%) is much higher than the above mentioned percentage of genes that is prioritized using our approach (9%). Hence, prioritization based on BP term overrepresentation reduces the number of candidate genes over tenfold while at the same time the loss of validated causal genes is only twofold. Compared with randomly selected gene sets, this is very significant (p < 0.001). Note that the set of fine-mapped causal genes used in this comparison was also used in setting the two cutoff values applied in our prioritization method (see above). Hence, this dataset does not constitute independent validation of our method. However, irrespective of the exact cutoff values chosen, prioritization results were always significant, except for a very high value of the cutoff on the fraction of QTL regions in which a prioritized BP should occur (>90%; Additional file 1: Figure S1). Changing the values of the applied cutoffs would allow to recover more truly causal genes, but at the expense of also obtaining a larger set of prioritized candidate genes overall. For example, when the cutoff on the maximum allowed percentage of genes annotated with a biological process genome-wide would be set to 20% instead of the chosen value of 1%, we would recover 13 out of 16 genes (80%) instead of 8 out of 16 (50%). However, with this setting, the average percentage of prioritized genes would be 25% (instead of 9%).
Note that uncertainty in the set of causal genes that we use as reference set will lead to an underestimate of the performance of our method in correctly prioritizing fine-mapped genes. There are at least three sources of such uncertainty. First, traits mentioned in the literature for which fine-mapped genes were found, were matched to traits in the rice QTL compendium available. However, in most cases, the trait was not exactly the same trait as the one for which fine-mapping was performed (Additional file 3: Table S3). In such cases, the causal gene underlying the literature trait might be different from the causal gene for the trait included in this analysis. Second, even when the trait is identical, the populations in the dataset and in the experimental study in which the candidate gene was fine-mapped do not need to be the same. The causal gene that was fine-mapped may therefore not be the causal gene in the QTL region we used. Third, available fine-mapping results do not always exclude that a neighboring gene is the actual causal gene. The resolution of fine-mapping is limited and often the causal gene is chosen from a small number of fine-mapped candidates based on e.g. molecular function. One example of both the first and third source of uncertainty is given by the gene LOC_Os06g04820 fine mapped for the trait ‘small panicle and dwarfness’ . This trait did not match exactly to a trait in our input set, but we used ‘plant height’ and ‘grain yield per plant’ as substitute traits, because some of the input QTL regions for those traits overlapped with the region analyzed in this reference. Our prioritization approach did not return LOC_Os06g04820. In addition to the potential mismatch between the traits, this could also be due to the fact that the fine-mapping by  did not identify LOC_Os06g04820 unambiguously, but identified a group of four genes (LOC_Os06g04810, LOC_Os06g04820, LOC_Os06g04830 and LOC_Os06g04840) among which LOC_Os06g04820 was chosen as the most likely candidate. Although neither of those other three genes was identified by our prioritization approach, a gene immediately neighbouring these genes, LOC_Os06g04800, was prioritized for both the traits ‘plant height’ and ‘grain yield per plant’ by our approach.
Comparison with large scale experimental datasets
Validated causal genes a
Overrepresented biological processes involved
intracellular protein kinase cascade; pattern specification process; xylem and phloem pattern formation; signal transduction by phosphorylation
cellular response to ethylene stimulus
positive regulation of macromolecule biosynthetic process/nitrogen compound metabolic process/gene expression
cellular response to ethylene stimulus
relative water content
two-component signal transduction system (phosphorelay); ethylene mediated signaling pathway; cellular response to ethylene stimulus
NADPH regeneration; nicotinamide nucleotide metabolic process
positive regulation of macromolecule biosynthetic process/nitrogen compound metabolic process/gene expression
Second, we screened the prioritization with the results of two rice GWAS studies ,. For 14 traits in the Gramene QTL compendium, an equivalent trait was present in the GWAS data (Additional file 3: Table S4). For 12 of these traits, genes in QTL regions were prioritized. For these genes we assessed whether they were found in the neighborhood of significant SNPs identified by GWAS (neighborhood was defined as the three genes nearest to the GWAS SNP). Note that, similar as for the above presented comparison with gene-trait combinations, we do not expect perfect agreement between our QTL-based prioritization and the results of these GWAS studies. Nevertheless, 37 of the prioritized candidate genes were in the neighborhood of significant SNPs identified by GWAS; these involved 6 of the 12 traits. Comparison with randomized sets of genes selected from the QTL regions for those traits indicates that the number of 37 genes was significant (p ~ 0.03). Taken together, these results demonstrate that our prioritization strategy results in lists of prioritized candidate genes that are significantly enriched for trait-relevant genes.
Importance of transcription factors among prioritized genes
An important question with respect to the prioritized candidate genes is whether these have any special properties which make them a priori more likely to be causal genes. In particular, we analyzed the role of transcription factors (TFs) among the prioritized candidate genes. In the rice genome, 3.1% of the genes are transcription factors , and in the set of all genes in the QTL regions (i.e. all candidate genes) it is 3.8%. However, in the set of prioritized candidate genes, the percentage of TFs is 11.0%. When distinguishing prioritized candidate genes associated to only one trait (2,758 in total) and those associated with more than one trait (3,417 in total), the percentage of TFs is higher in the latter: 13% for genes linked to at least two traits, and 15% for genes linked to at least four traits. The preference for TFs to be associated with traits is in line with the fact that in our input set of gene function predictions for rice, TFs obtain approximately twofold higher number of associated biological processes compared to other genes (not shown). This important role of TFs could explain the fact that QTLs associate preferentially with large-effect mutations .
Example: analysis of QTL regions for the trait days to heading
Genes predicted as causal genes for days to heading a
Available existing annotation
signal peptide peptidase-like 2B
MYB family transcription factor
OsMADS34 - MADS-box family gene with MIKCc type-box
Core histone H2A/H2B/H3/H4 domain containing protein
OsMADS18 - MADS-box family gene with MIKCc type-box
PWWP domain containing protein
ZOS9-20 - C2H2 zinc finger protein
GATA zinc finger domain containing protein
Among the biological processes associated with the trait days to heading, the related processes ‘ribonucleoprotein complex biogenesis’ and ‘ribosome biogenesis’ had only low similarity to other biological processes associated with this trait; this is indicated by their position relative to other terms in Figure 4B. In total, 72 genes involved in these two biological processes are prioritized as potentially causal genes for days to heading (Additional file 3: Table S6). Although a role of the ribosome in flowering time has not been described in great detail, circumstantial evidence in the literature suggests that the ribosome might indeed be important. In particular, TOR kinase which mediates ribosomal biogenesis, regulates flowering and senescence in Arabidopsis . In maize, a protein involved in translation initiation has been confirmed as underlying a flowering time QTL , and in Solanum chacoense, a protein involved in ribosome biogenesis influenced flowering .
These examples show how the approach taken to link traits with biological processes and subsequently to genes can generate relevant leads for future laboratory experimentation.
In order to exploit the information hidden in plant genomics data for breeding, better understanding of genotype-to-phenotype relationships is essential. The biological and molecular basis of most quantitative trait variation is poorly understood and QTL mapping approaches generally result in too large numbers of candidate genes to be able to identify causal genes easily. The prioritization of candidate genes is not only of fundamental interest, but also of high practical value, because causal genes for any trait-of-interest make perfect markers for breeding. Our results demonstrate that associations between overrepresented biological processes and traits help to prioritize candidate genes and zoom in on the potentially causal genes for the trait-of-interest. Our integrated analysis is the first large-scale application assessing explicitly the performance of overrepresentation of predicted gene functions for the identification of potentially causal genes for plant traits in genomic regions obtained by QTL mapping.
Our approach resulted in a reduction in total number of genes of more than ten-fold compared to the number of genes in the input QTL regions. Based on comparison with different experimental datasets, the predicted causal genes are clearly statistically significant. Although we could only compare the prioritized genes with a limited number of fine-mapped genes available in literature, our predictions enable to test potentially causal genes underlying QTLs at a larger scale. This paves the way towards obtaining more detailed insight into the role of specific genes underlying QTLs which in turn should enable further validation of our predictions in the future. As demonstrated by the example of genes prioritized for days to heading, included in the set of prioritized genes are genes with so far completely unknown function. Such genes will be particularly interesting targets for experimental verification.
Out of 179 traits, for 26 no predictions were obtained. It could be that for some of these 26 traits, causal genes underlying different QTL regions are not involved in the same biological process. If indeed for each QTL region for a trait-of-interest a different biological process would be underlying, our enrichment analysis would not be able to predict these biological processes. However, for ~30% (8 of 26) of these traits only one QTL region was available, two times the percentage of traits with only one QTL region observed overall (~15%). This indicates that traits with multiple QTL regions are more likely to indeed contain overrepresented BPs. In other words, the analysis of overrepresented BPs profits from the availability of multiple QTL regions. This is in line with the above mentioned assumption underlying our prioritization method, that multiple QTL regions for a trait reflect variation in genes involved in the same biological process. Taken together, our results clearly indicate that this assumption is often correct.
We found that transcription factors are prominently present among the prioritized candidate genes. This points towards an explanation for the fact that QTL studies preferably find large effect mutations . It may also emphasize the important role of transcription factors in domestication. Half to two-third of genes known to be involved in domestication consist of transcription factors , and many of the traits important for breeding are relevant in the context of domestication .
The input needed for prioritization as here developed consists of QTL regions and predicted gene functions. Incorporating the significance level of the association of genome regions with a trait using QTL Logarithm Of the Odds (LOD) scores could improve the analysis as could better assessment of the overrepresentation of biological process terms using e.g. gene set enrichment analysis , iterative group analysis , or approaches that take the hierarchy of the Gene Ontology into account . Yet, in such enrichment analysis the importance of the source of the gene function annotations is often underestimated. Especially in case of agricultural crops, knowledge of what all the genes predicted to be present in the genome are actually doing, is scarce . For example, existing databases describing rice gene functions only contain relatively small number of cases ,. Having a large set of high-quality gene function predictions  results in much higher numbers of significant associations between traits and biological processes compared to using existing annotations.
The set of potentially causal genes that results from the prioritization approach here demonstrated could be an important dataset for future applications in rice breeding. Other crops as well as relevant animal species could be addressed in a similar way. It may motivate research communities to generate the data necessary for such analyses. QTL data are available for various plant species and we generated sets of high-quality biological process predictions for different plant species, including major crops . In the future it should be possible to analyze data from various species simultaneously to find overrepresented biological processes among QTL regions linked to the same trait in different species. Such comparative approach will help to extract more useful information from available data in order to elucidate and exploit the link between genotype and phenotype.
This work was supported by the FP7 “Infrastructures” project transPLANT Award 283496 and by the BioRange program of the Netherlands Bioinformatics Centre (NBIC) which is supported by a BSIK grant through the Netherlands Genomics Initiative (NGI).
- Li ZK, Zhang F: Rice breeding in the post-genomics era: from concept to practice. Curr Opin Plant Biol. 2013, 16 (2): 261-269. 10.1016/j.pbi.2013.03.008.View ArticlePubMedGoogle Scholar
- Varshney RK, Terauchi R, McCouch SR: Harvesting the promising fruits of genomics: applying genome sequencing technologies to crop breeding. PLoS Biol. 2014, 12 (6): e1001883-10.1371/journal.pbio.1001883.PubMed CentralView ArticlePubMedGoogle Scholar
- Egan AN, Schlueter J, Spooner DM: Applications of next-generation sequencing in plant biology. Am J Bot. 2012, 99 (2): 175-185. 10.3732/ajb.1200020.View ArticlePubMedGoogle Scholar
- Cobb JN, DeClerck G, Greenberg A, Clark R, McCouch S: Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement. Theor Appl Genet. 2013, 126 (4): 867-887. 10.1007/s00122-013-2066-0.PubMed CentralView ArticlePubMedGoogle Scholar
- Han B, Huang X: Sequencing-based genome-wide association study in rice. Curr Opin Plant Biol. 2013, 16 (2): 133-138. 10.1016/j.pbi.2013.03.006.View ArticlePubMedGoogle Scholar
- Huang X, Zhao Y, Wei X, Li C, Wang A, Zhao Q, Li W, Guo Y, Deng L, Zhu C, Fan D, Lu Y, Weng Q, Liu K, Zhou T, Jing Y, Si L, Dong G, Huang T, Lu T, Feng Q, Qian Q, Li J, Han B: Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat Genet. 2012, 44 (1): 32-39. 10.1038/ng.1018.View ArticleGoogle Scholar
- Tian F, Bradbury PJ, Brown PJ, Hung H, Sun Q, Flint-Garcia S, Rocheford TR, McMullen MD, Holland JB, Buckler ES: Genome-wide association study of leaf architecture in the maize nested association mapping population. Nat Genet. 2011, 43 (2): 159-162. 10.1038/ng.746.View ArticlePubMedGoogle Scholar
- Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, Li C, Zhu C, Lu T, Zhang Z, Li M, Fan D, Guo Y, Wang A, Wang L, Deng L, Li W, Lu Y, Weng Q, Liu K, Huang T, Zhou T, Jing Y, Li W, Lin Z, Buckler ES, Qian Q, Zhang QF, Li J, Han B: Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet. 2010, 42 (11): 961-967. 10.1038/ng.695.View ArticlePubMedGoogle Scholar
- Fridman E, Carrari F, Liu YS, Fernie AR, Zamir D: Zooming in on a quantitative trait for tomato yield using interspecific introgressions. Science. 2004, 305 (5691): 1786-1789. 10.1126/science.1101666.View ArticlePubMedGoogle Scholar
- Bai X, Wu B, Xing Y: Yield-related QTLs and their applications in rice genetic improvement. J Integr Plant Biol. 2012, 54 (5): 300-311. 10.1111/j.1744-7909.2012.01117.x.View ArticlePubMedGoogle Scholar
- Sifrim A, Popovic D, Tranchevent LC, Ardeshirdavani A, Sakai R, Konings P, Vermeesch JR, Aerts J, De Moor B, Moreau Y: eXtasy: variant prioritization by genomic data fusion. Nat Methods. 2013, 10 (11): 1083-1084. 10.1038/nmeth.2656.View ArticlePubMedGoogle Scholar
- Bornigen D, Tranchevent LC, Bonachela-Capdevila F, Devriendt K, De Moor B, De Causmaecker P, Moreau Y: An unbiased evaluation of gene prioritization tools. Bioinformatics. 2012, 28 (23): 3081-3088. 10.1093/bioinformatics/bts581.View ArticlePubMedGoogle Scholar
- Liu Y, Maxwell S, Feng T, Zhu X, Elston RC, Koyuturk M, Chance MR: Gene, pathway and network frameworks to identify epistatic interactions of single nucleotide polymorphisms derived from GWAS data. BMC Syst Biol. 2012, 6 (Suppl 3): S15-10.1186/1752-0509-6-S3-S15.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang K, Li M, Hakonarson H: Analysing biological pathways in genome-wide association studies. Nat Rev Genet. 2010, 11 (12): 843-854. 10.1038/nrg2884.View ArticlePubMedGoogle Scholar
- Holmans P, Green EK, Pahwa JS, Ferreira MA, Purcell SM, Sklar P, Owen MJ, O’Donovan MC, Craddock N, Wellcome Trust Case-Control C: Gene ontology analysis of GWA study data sets provides insights into the biology of bipolar disorder. Am J Hum Genet. 2009, 85 (1): 13-24. 10.1016/j.ajhg.2009.05.011.PubMed CentralView ArticlePubMedGoogle Scholar
- Herold C, Mattheisen M, Lacour A, Vaitsiakhovich T, Angisch M, Drichel D, Becker T: Integrated genome-wide pathway association analysis with INTERSNP. Hum Hered. 2012, 73 (2): 63-72. 10.1159/000336196.View ArticlePubMedGoogle Scholar
- Schaid DJ, Sinnwell JP, Jenkins GD, McDonnell SK, Ingle JN, Kubo M, Goss PE, Costantino JP, Wickerham DL, Weinshilboum RM: Using the gene ontology to scan multilevel gene sets for associations in genome wide association studies. Genet Epidemiol. 2012, 36 (1): 3-16. 10.1002/gepi.20632.PubMed CentralView ArticlePubMedGoogle Scholar
- Atias N, Istrail S, Sharan R: Pathway-based analysis of genomic variation data. Curr Opin Genet Dev. 2013, 23 (6): 622-626. 10.1016/j.gde.2013.09.002.View ArticlePubMedGoogle Scholar
- Hou L, Chen M, Zhang CK, Cho J, Zhao H: Guilt by rewiring: gene prioritization through network rewiring in Genome Wide Association Studies. Hum Mol Genet. 2014, 23 (10): 2780-2790. 10.1093/hmg/ddt668.PubMed CentralView ArticlePubMedGoogle Scholar
- Chen J, Bardes EE, Aronow BJ, Jegga AG: ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 2009, 37 (Web Server issue): W305-W311. 10.1093/nar/gkp427.PubMed CentralView ArticlePubMedGoogle Scholar
- Lee I, Blom UM, Wang PI, Shim JE, Marcotte EM: Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res. 2011, 21 (7): 1109-1121. 10.1101/gr.118992.110.PubMed CentralView ArticlePubMedGoogle Scholar
- Moreau Y, Tranchevent LC: Computational tools for prioritizing candidate genes: boosting disease gene discovery. Nat Rev Genet. 2012, 13 (8): 523-536. 10.1038/nrg3253.View ArticlePubMedGoogle Scholar
- Shriner D, Baye TM, Padilla MA, Zhang S, Vaughan LK, Loraine AE: Commonality of functional annotation: a method for prioritization of candidate genes from genome-wide linkage studies. Nucleic Acids Res. 2008, 36 (4): e26-10.1093/nar/gkn007.PubMed CentralView ArticlePubMedGoogle Scholar
- Atwell S, Huang YS, Vilhjalmsson BJ, Willems G, Horton M, Li Y, Meng D, Platt A, Tarone AM, Hu TT, Jiang R, Muliyati NW, Zhang X, Amer MA, Baxter I, Brachi B, Chory J, Dean C, Debieu M, de Meaux J, Ecker JR, Faure N, Kniskern JM, Jones JD, Michael T, Nemri A, Roux F, Salt DE, Tang C, Todesco M, et al: Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature. 2010, 465 (7298): 627-631. 10.1038/nature08800.PubMed CentralView ArticlePubMedGoogle Scholar
- Chen C, DeClerck G, Tian F, Spooner W, McCouch S, Buckler E: PICARA, an analytical pipeline providing probabilistic inference about a priori candidates genes underlying genome-wide association QTL in plants. PLoS ONE. 2012, 7 (11): e46596-10.1371/journal.pone.0046596.PubMed CentralView ArticlePubMedGoogle Scholar
- Makita Y, Kobayashi N, Mochizuki Y, Yoshida Y, Asano S, Heida N, Deshpande M, Bhatia R, Matsushima A, Ishii M, Kawaguchi S, Iida K, Hanada K, Kuromori T, Seki M, Shinozaki K, Toyoda T: PosMed-plus: an intelligent search engine that inferentially integrates cross-species information resources for molecular breeding of plants. Plant Cell Physiol. 2009, 50 (7): 1249-1259. 10.1093/pcp/pcp086.PubMed CentralView ArticlePubMedGoogle Scholar
- Makita Y, Kobayashi N, Yoshida Y, Doi K, Mochizuki Y, Nishikata K, Matsushima A, Takahashi S, Ishii M, Takatsuki T, Bhatia R, Khadbaatar Z, Watabe H, Masuya H, Toyoda T: PosMed: ranking genes and bioresources based on Semantic Web Association Study. Nucleic Acids Res. 2013, 41 (Web Server issue): W109-W114. 10.1093/nar/gkt474.PubMed CentralView ArticlePubMedGoogle Scholar
- Chibon PY, Schoof H, Visser RG, Finkers R: Marker2sequence, mine your QTL regions for candidate genes. Bioinformatics. 2012, 28 (14): 1921-1922. 10.1093/bioinformatics/bts298.View ArticlePubMedGoogle Scholar
- Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA: Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A. 2009, 106 (23): 9362-9367. 10.1073/pnas.0903103106.PubMed CentralView ArticlePubMedGoogle Scholar
- Ikeda M, Miura K, Aya K, Kitano H, Matsuoka M: Genes offering the potential for designing yield-related traits in rice. Curr Opin Plant Biol. 2013, 16 (2): 213-220. 10.1016/j.pbi.2013.02.002.View ArticlePubMedGoogle Scholar
- Monclus R, Leple JC, Bastien C, Bert PF, Villar M, Marron N, Brignolas F, Jorge V: Integrating genome annotation and QTL position to identify candidate genes for productivity, architecture and water-use efficiency in Populus spp. BMC Plant Biol. 2012, 12: 173-10.1186/1471-2229-12-173.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhang X, Cal AJ, Borevitz JO: Genetic architecture of regulatory variation in Arabidopsis thaliana. Genome Res. 2011, 21 (5): 725-733. 10.1101/gr.115337.110.PubMed CentralView ArticlePubMedGoogle Scholar
- Hancock AM, Brachi B, Faure N, Horton MW, Jarymowycz LB, Sperone FG, Toomajian C, Roux F, Bergelson J: Adaptation to climate across the Arabidopsis thaliana genome. Science. 2011, 334 (6052): 83-86. 10.1126/science.1209244.View ArticlePubMedGoogle Scholar
- Bargsten JW, Severing EI, Nap JP, Sanchez-Perez GF, van Dijk AD: Biological process annotation of proteins across the plant kingdom. Current Plant Biology 2014(in press).Google Scholar
- Ni J, Pujar A, Youens-Clark K, Yap I, Jaiswal P, Tecle I, Tung CW, Ren L, Spooner W, Wei X, Avraham S, Ware D, Stein L, McCouch S: Gramene QTL database: development, content and applications. Database. 2009, 2009: bap005-10.1093/database/bap005.PubMed CentralView ArticlePubMedGoogle Scholar
- Youens-Clark K, Buckler E, Casstevens T, Chen C, Declerck G, Derwent P, Dharmawardhana P, Jaiswal P, Kersey P, Karthikeyan AS, Lu J, McCouch SR, Ren L, Spooner W, Stein JC, Thomason J, Wei S, Ware D: Gramene database in 2010: updates and extensions. Nucleic Acids Res. 2011, 39 (Database issue): D1085-D1094. 10.1093/nar/gkq1148.PubMed CentralView ArticlePubMedGoogle Scholar
- Kourmpetis YA, van Dijk AD, Bink MC, van Ham RC, ter Braak CJ: Bayesian Markov Random Field analysis for protein function prediction based on network data. PLoS ONE. 2010, 5 (2): e9293-10.1371/journal.pone.0009293.PubMed CentralView ArticlePubMedGoogle Scholar
- Kourmpetis YA, van Dijk AD, van Ham RC, ter Braak CJ: Genome-wide computational function prediction of Arabidopsis proteins by integration of multiple data sources. Plant Physiol. 2011, 155 (1): 271-281. 10.1104/pp.110.162164.PubMed CentralView ArticlePubMedGoogle Scholar
- Radivojac P, Clark WT, Oron TR, Schnoes AM, Wittkop T, Sokolov A, Graim K, Funk C, Verspoor K, Ben-Hur A, Pandey G, Yunes JM, Talwalkar AS, Repo S, Souza ML, Piovesan D, Casadio R, Wang Z, Cheng J, Fang H, Gough J, Koskinen P, Toronen P, Nokso-Koivisto J, Holm L, Cozzetto D, Buchan DW, Bryson K, Jones DT, Limaye B, et al: A large-scale evaluation of computational protein function prediction. Nat Methods. 2013, 10: 221-227. 10.1038/nmeth.2340.PubMed CentralView ArticlePubMedGoogle Scholar
- Mutwil M, Klie S, Tohge T, Giorgi FM, Wilkins O, Campbell MM, Fernie AR, Usadel B, Nikoloski Z, Persson S: PlaNet: combined sequence and expression comparisons across plant networks derived from seven species. Plant Cell. 2011, 23 (3): 895-910. 10.1105/tpc.111.083667.PubMed CentralView ArticlePubMedGoogle Scholar
- Falda M, Toppo S, Pescarolo A, Lavezzo E, Di Camillo B, Facchinetti A, Cilia E, Velasco R, Fontana P: Argot2: a large scale function prediction tool relying on semantic similarity of weighted Gene Ontology terms. BMC Bioinformatics. 2012, 13: S14-10.1186/1471-2105-13-S4-S14.PubMed CentralView ArticlePubMedGoogle Scholar
- Goodstein DM, Shu S, Howson R, Neupane R, Hayes RD, Fazo J, Mitros T, Dirks W, Hellsten U, Putnam N, Rokhsar DS: Phytozome: a comparative platform for green plant genomics. Nucleic Acids Res. 2012, 40 (Database issue): D1178-D1186. 10.1093/nar/gkr944.PubMed CentralView ArticlePubMedGoogle Scholar
- Team RDC: R: A Language and Environment for Statistical Computing. 2011Google Scholar
- Benjamini Y, Hochberg Y: Controlling the false discovery rate - a practical and powerful approach to multiple testing. J Roy Stat Soc B Met. 1995, 57 (1): 289-300.Google Scholar
- Jin J, Zhang H, Kong L, Gao G, Luo J: PlantTFDB 3.0: a portal for the functional and evolutionary study of plant transcription factors. Nucleic Acids Res. 2014, 42 (Database issue): D1182-D1187. 10.1093/nar/gkt1016.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhao K, Tung CW, Eizenga GC, Wright MH, Ali ML, Price AH, Norton GJ, Islam MR, Reynolds A, Mezey J, McClung AM, Bustamante CD, McCouch SR: Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun. 2011, 2: 467-10.1038/ncomms1467.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang P, Zhou G, Yu H, Yu S: Fine mapping a major QTL for flag leaf size and yield-related traits in rice. Theor Appl Genet. 2011, 123 (8): 1319-1330. 10.1007/s00122-011-1669-6.View ArticlePubMedGoogle Scholar
- Liu T, Mao D, Zhang S, Xu C, Xing Y: Fine mapping SPP1, a QTL controlling the number of spikelets per panicle, to a BAC clone in rice (Oryza sativa). Theor Appl Genet. 2009, 118 (8): 1509-1517. 10.1007/s00122-009-0999-0.View ArticlePubMedGoogle Scholar
- Su Y, Rao Y, Hu S, Yang Y, Gao Z, Zhang G, Liu J, Hu J, Yan M, Dong G, Zhu L, Guo L, Qian Q, Zeng D: Map-based cloning proves qGC-6, a major QTL for gel consistency of japonica/indica cross, responds by Waxy in rice (Oryza sativa L.). Theor Appl Genet. 2011, 123 (5): 859-867. 10.1007/s00122-011-1632-6.View ArticlePubMedGoogle Scholar
- Gao Z, Zeng D, Cheng F, Tian Z, Guo L, Su Y, Yan M, Jiang H, Dong G, Huang Y, Han B, Li J, Qian Q: ALK, the key gene for gelatinization temperature, is a modifier gene for gel consistency in rice. J Integr Plant Biol. 2011, 53 (9): 756-765.PubMedGoogle Scholar
- Dai X, Ding Y, Tan L, Fu Y, Liu F, Zhu Z, Sun X, Sun X, Gu P, Cai H, Sun C: LHD1, an allele of DTH8/Ghd8, controls late heading date in common wild rice (Oryza rufipogon). J Integr Plant Biol. 2012, 54 (10): 790-799. 10.1111/j.1744-7909.2012.01166.x.View ArticlePubMedGoogle Scholar
- Wei X, Xu J, Guo H, Jiang L, Chen S, Yu C, Zhou Z, Hu P, Zhai H, Wan J: DTH8 suppresses flowering in rice, influencing plant height and yield potential simultaneously. Plant Physiol. 2010, 153 (4): 1747-1758. 10.1104/pp.110.156943.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang S, Wu K, Yuan Q, Liu X, Liu Z, Lin X, Zeng R, Zhu H, Dong G, Qian Q, Zhang G, Fu X: Control of grain size, shape and quality by OsSPL16 in rice. Nat Genet. 2012, 44 (8): 950-954. 10.1038/ng.2327.View ArticlePubMedGoogle Scholar
- Li J, Zhang W, Wu H, Guo T, Liu X, Wan X, Jin J, Hanh TT, Thoa NT, Chen M, Liu S, Chen L, Liu X, Wang J, Zhai H, Wan J: Fine mapping of stable QTLs related to eating quality in rice (Oryza sativa L.) by CSSLs harboring small target chromosomal segments. Breed Sci. 2011, 61 (4): 338-346. 10.1270/jsbbs.61.338.PubMed CentralView ArticlePubMedGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000, 25 (1): 25-29. 10.1038/75556.PubMed CentralView ArticlePubMedGoogle Scholar
- Armengaud P, Sulpice R, Miller AJ, Stitt M, Amtmann A, Gibon Y: Multilevel analysis of primary metabolism provides new insights into the role of potassium nutrition for glycolysis and nitrogen assimilation in Arabidopsis roots. Plant Physiol. 2009, 150 (2): 772-785. 10.1104/pp.108.133629.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhang C, Xu Y, Guo S, Zhu J, Huan Q, Liu H, Wang L, Luo G, Wang X, Chong K: Dynamics of brassinosteroid response modulated by negative regulator LIC in rice. PLoS Genet. 2012, 8 (4): e1002686-10.1371/journal.pgen.1002686.PubMed CentralView ArticlePubMedGoogle Scholar
- Shan JX, Zhu MZ, Shi M, Gao JP, Lin HX: Fine mapping and candidate gene analysis of spd6, responsible for small panicle and dwarfness in wild rice (Oryza rufipogon Griff.). Theor Appl Genet. 2009, 119 (5): 827-836. 10.1007/s00122-009-1092-4.View ArticlePubMedGoogle Scholar
- Gour P, Garg P, Jain R, Joseph SV, Tyagi AK, Raghuvanshi S: Manually curated database of rice proteins. Nucleic Acids Res. 2014, 42 (1): D1214-D1221. 10.1093/nar/gkt1072.PubMed CentralView ArticlePubMedGoogle Scholar
- Falke KC, Glander S, He F, Hu J, de Meaux J, Schmitz G: The spectrum of mutations controlling complex traits and the genetics of fitness in plants. Curr Opin Genet Dev. 2013, 23 (6): 665-671. 10.1016/j.gde.2013.10.006.View ArticlePubMedGoogle Scholar
- Fujisawa M, Nakano T, Shima Y, Ito Y: A large-scale identification of direct targets of the tomato MADS box transcription factor RIPENING INHIBITOR reveals the regulation of fruit ripening. Plant Cell. 2013, 25 (2): 371-386. 10.1105/tpc.112.108118.PubMed CentralView ArticlePubMedGoogle Scholar
- Sun L, Zhang H, Li D, Huang L, Hong Y, Ding XS, Nelson RS, Zhou X, Song F: Functions of rice NAC transcriptional factors, ONAC122 and ONAC131, in defense responses against Magnaporthe grisea. Plant Mol Biol. 2013, 81 (1–2): 41-56. 10.1007/s11103-012-9981-3.View ArticlePubMedGoogle Scholar
- Rauf M, Arif M, Fisahn J, Xue GP, Balazadeh S, Mueller-Roeber B: NAC transcription factor speedy hyponastic growth regulates flooding-induced leaf movement in arabidopsis. Plant Cell. 2013, 25 (12): 4941-4955. 10.1105/tpc.113.117861.PubMed CentralView ArticlePubMedGoogle Scholar
- Qi W, Sun F, Wang Q, Chen M, Huang Y, Feng YQ, Luo X, Yang J: Rice ethylene-response AP2/ERF factor OsEATB restricts internode elongation by down-regulating a gibberellin biosynthetic gene. Plant Physiol. 2011, 157 (1): 216-228. 10.1104/pp.111.179945.PubMed CentralView ArticlePubMedGoogle Scholar
- Jung C, Muller AE: Flowering time control and applications in plant breeding. Trends Plant Sci. 2009, 14 (10): 563-573. 10.1016/j.tplants.2009.07.005.View ArticlePubMedGoogle Scholar
- Milec Z, Valarik M, Bartos J, Safar J: Can a late bloomer become an early bird? Tools for flowering time adjustment. Biotechnol Adv. 2014, 32 (1): 200-214. 10.1016/j.biotechadv.2013.09.008.View ArticlePubMedGoogle Scholar
- Wu W, Zheng XM, Lu G, Zhong Z, Gao H, Chen L, Wu C, Wang HJ, Wang Q, Zhou K, Wang JL, Wu F, Zhang X, Guo X, Cheng Z, Lei C, Lin Q, Jiang L, Wang H, Ge S, Wan J: Association of functional nucleotide polymorphisms at DTH2 with the northward expansion of rice cultivation in Asia. Proc Natl Acad Sci U S A. 2013, 110 (8): 2775-2780. 10.1073/pnas.1213962110.PubMed CentralView ArticlePubMedGoogle Scholar
- Supek F, Bosnjak M, Skunca N, Smuc T: REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS ONE. 2011, 6 (7): e21800-10.1371/journal.pone.0021800.PubMed CentralView ArticlePubMedGoogle Scholar
- Wuriyanghan H, Zhang B, Cao WH, Ma BA, Lei G, Liu YF, Wei W, Wu HJ, Chen LJ, Chen HW, Cao YR, He SJ, Zhang WK, Wang XJ, Chen SY, Zhang JS: The ethylene receptor ETR2 delays floral transition and affects starch accumulation in rice. Plant Cell. 2009, 21 (5): 1473-1494. 10.1105/tpc.108.065391.PubMed CentralView ArticlePubMedGoogle Scholar
- Ouyang S, Zhu W, Hamilton J, Lin H, Campbell M, Childs K, Thibaud-Nissen F, Malek RL, Lee Y, Zheng L, Orvis J, Haas B, Wortman J, Buell CR: The TIGR rice genome annotation resource: improvements and new features. Nucleic Acids Res. 2007, 35 (Database issue): D883-D887. 10.1093/nar/gkl976.PubMed CentralView ArticlePubMedGoogle Scholar
- Gao X, Liang W, Yin C, Ji S, Wang H, Su X, Guo C, Kong H, Xue H, Zhang D: The SEPALLATA-like gene OsMADS34 is required for rice inflorescence and spikelet development. Plant Physiol. 2010, 153 (2): 728-740. 10.1104/pp.110.156711.PubMed CentralView ArticlePubMedGoogle Scholar
- Fornara F, Parenicova L, Falasca G, Pelucchi N, Masiero S, Ciannamea S, Lopez-Dee Z, Altamura MM, Colombo L, Kater MM: Functional characterization of OsMADS18, a member of the AP1/SQUA subfamily of MADS box genes. Plant Physiol. 2004, 135 (4): 2207-2219. 10.1104/pp.104.045039.PubMed CentralView ArticlePubMedGoogle Scholar
- Xiong Y, Sheen J: The role of target of rapamycin signaling networks in plant growth and metabolism. Plant Physiol. 2014, 164 (2): 499-512. 10.1104/pp.113.229948.PubMed CentralView ArticlePubMedGoogle Scholar
- Durand E, Bouchet S, Bertin P, Ressayre A, Jamin P, Charcosset A, Dillmann C, Tenaillon MI: Flowering time in maize: linkage and epistasis at a major effect locus. Genetics. 2012, 190 (4): 1547-+-10.1534/genetics.111.136903.PubMed CentralView ArticlePubMedGoogle Scholar
- Chantha SC, Matton DP: Underexpression of the plant NOTCHLESS gene, encoding a WD-repeat protein, causes pleitropic phenotype during plant development. Planta. 2007, 225 (5): 1107-1120. 10.1007/s00425-006-0420-z.View ArticlePubMedGoogle Scholar
- Lenser T, Theissen G: Molecular mechanisms involved in convergent crop domestication. Trends Plant Sci. 2013, 18 (12): 704-714. 10.1016/j.tplants.2013.08.007.View ArticlePubMedGoogle Scholar
- Meyer RS, Purugganan MD: Evolution of crop species: genetics of domestication and diversification. Nat Rev Genet. 2013, 14 (12): 840-852. 10.1038/nrg3605.View ArticlePubMedGoogle Scholar
- Fawcett JA, Kado T, Sasaki E, Takuno S, Yoshida K, Sugino RP, Kosugi S, Natsume S, Mitsuoka C, Uemura A, Takagi H, Abe A, Ishii T, Terauchi R, Innan H: QTL map meets population genomics: an application to rice. PLoS ONE. 2013, 8 (12): e83720-10.1371/journal.pone.0083720.PubMed CentralView ArticlePubMedGoogle Scholar
- Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005, 102 (43): 15545-15550. 10.1073/pnas.0506580102.PubMed CentralView ArticlePubMedGoogle Scholar
- Breitling R, Amtmann A, Herzyk P: Iterative Group Analysis (iGA): a simple tool to enhance sensitivity and facilitate interpretation of microarray experiments. BMC Bioinformatics. 2004, 5: 34-10.1186/1471-2105-5-34.PubMed CentralView ArticlePubMedGoogle Scholar
- Grossmann S, Bauer S, Robinson PN, Vingron M: Improved detection of overrepresentation of Gene-Ontology annotations with parent child analysis. Bioinformatics. 2007, 23 (22): 3024-3031. 10.1093/bioinformatics/btm440.View ArticlePubMedGoogle Scholar
- Rhee SY, Mutwil M: Towards revealing the functions of all genes in plants. Trends Plant Sci. 2013, 19 (4): 212-221. 10.1016/j.tplants.2013.10.006.View ArticlePubMedGoogle Scholar
- Yamamoto E, Yonemaru J, Yamamoto T, Yano M: OGRO: the overview of functionally characterized genes in rice online database. Rice 2012, 5:Google Scholar
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