Combining Genome-Wide Association Mapping and Transcriptional Networks to Identify Novel Genes Controlling Glucosinolates in Arabidopsis thaliana
Biologists across fields possess a common need to identify the genetic variation causing natural phenotypic variation ., Genome-wide association ( GWA ) studies are a promising route to associate phenotypes with genotypes , at a genome-wide level , using “unrelated” individuals 1 ., In contrast to the traditional use of structured mapping populations derived from two parent genomes , GWA studies allow a wide sampling of the genotypes present within a species , potentially identifying a greater proportion of the variable loci contributing to polygenic traits ., However , the uneven distribution of this increased genotypic diversity across populations ( population structure ) , as well as the sheer number of statistical tests performed in a genome-wide scan , can cause detection of a high rate of “false-positive” genotype-phenotype associations that may make it difficult to distinguish loci that truly affect the tested phenotype 1–5 ., Epistasis and natural selection can also lead to a high false-negative rate , wherein loci with experimentally validated effects on the focal trait are not detected by GWA tests 4–5 ., Repeated detection of a genotype-phenotype association across populations or experiments has been proposed to increase support for the biological reality of that association , and has even been proposed as a requirement for validation of trait-phenotype associations 2 ., However , replication across populations or experiments is not solely dependent upon genotypes , but also differences in environment and development that significantly influence quantitative traits 5–8 ., Thus , validation of a significant association through replication , while at face value providing a stringent criterion for significance , may bias studies against detection of causal associations that show significant Genotype×Environment interactions 9 ., In this study we employed replicated genotypes to test the conditionality of GWA results upon the environment or development stage within which the phenotype was measured ., Integrating GWA mapping results with additional forms of genome-scale data , such as transcript profiling or proteomics datasets , has also been proposed to strengthen support for detected gene-trait associations and reduce the incidence of false-positive associations 10 ., To date , network approaches have largely focused upon comparing GWA results with natural variation in gene expression across genotypes in transcriptomic datasets ( i . e . , expression quantitative trait loci ( eQTLs ) ) 11–13 ., This requires that candidate genes show natural variation in transcript accumulation , which is not always the functional level at which biologically relevant variation occurs 14 ., Another network approach maps GWA results onto previously generated interaction networks within a single genotype , such as a protein-protein interaction network , enhancing support for associations that cluster within the network 15 ., This network filtering approach has yet to be tested with GWA data where the environment or tissue is varied ., To evaluate the influence of environmental or developmentally conditional genetics on GWA mapping and the utility of network filtering in identifying candidate causal genes , we focused on defense metabolism within the plant Arabidopsis thaliana ., A . thaliana has become a key model for advancing genetic technologies and analytical approaches for studying complex quantitative genetics in wild species 16 ., These advances include experiments testing the ability of genome resequencing and transcript profiling to elucidate the genetics of complex expression traits 17–19 and querying the complexity of genetic epistasis in laboratory and natural populations 20–26 ., Additionally , A . thaliana has long provided a model system for applying concepts surrounding GWA mapping 3–5 , 27–30 ., As a model set of phenotypes , we used the products of two related A . thaliana secondary metabolite pathways , responsible for aliphatic and indolic glucosinolate ( GSL ) biosynthesis ., These pathways have become useful models for quantitative genetics and ecology ( Figure 1 ) 31 ., Aliphatic , or methionine-derived , GSL are critical determinants of fitness for A . thaliana and related cruciferous species via their ability to defend against insect herbivory and non-host pathogens 32–35 ., Indolic GSL , derived from tryptophan , play important roles in resistance to pathogens and aphids 36–40 ., A . thaliana accessions display significant natural genetic variation controlling the production of type and amount of both classes of GSL , with direct impacts on plant fitness in the field 33 , 41–47 ., Additionally , GSL display conditional genetic variation dependent upon both the environment and developmental stage of measurement 48–51 ., GSL thus provide an excellent model to explore the impact of conditional genetics upon GWA analysis ., While the evolutionary and ecological importance of GSL is firmly established , the nearly complete description of GSL biosynthetic pathways provides an additional practical advantage to studying these compounds 52–54 ., A large number of QTL and genes controlling GSL natural variation have been cloned from A . thaliana using a variety of network biology approaches similar to network filtering in GWA studies ( Figure 1 ) 55–59 ., These provide a set of positive control genes of known natural variability and importance to GSL phenotypes , enabling empirical assessment of the level of false-positive and false-negative associations ., Within this study , we measure GSL phenotypes in two developmental stages and stress conditions/treatments using a collection of wild A . thaliana accessions to test the relative influence of these components upon GWA ., In agreement with previous analyses from structured mapping populations , we found that differences in development have more impact on conditioning genetic variation in A . thaliana GSL accumulation ., This is further supported by our observation that GWA-identified candidate genes show a non-random distribution across the three datasets with the GWA candidates from the two developmental stages analyzed overlapping less than expected ., The large list of candidate genes identified via GWA was refined with a network co-expression approach , identifying a number of potential networks ., A subset of loci from these networks was validated for effects on GSL phenotypes ., Even for adaptive traits like GSL accumulation , these analyses suggest the influence of numerous small effect loci affecting the phenotype at levels that are potentially exposed to natural selection ., We measured GSL from leaves of 96 A . thaliana accessions at 35 d post-germination 27–28 using either untreated leaves or leaves treated with AgNO3 ( silver ) to mimic pathogen attack ., In addition , we measured seedling glucosinolates from the same accessions to provide a tissue comparison as well as a treatment comparison ., Seedlings were measured at 2 d post-germination at a stage where the GSL are largely representative of the GSL present within the mature seed 48 , 60 ., GSL from both foliar and seedling tissue grown under these conditions have been measured in multiple independent QTL experiments that used recombinant inbred line ( RIL ) populations generated from subsets of these 96 accessions , thus providing independent corroboration of observed GSL phenotypes 41 , 51 , 61 ., For the untreated leaves , this analysis detected 18 aliphatic GSL compounds and four indolic GSL compounds ., These combined with an additional 21 synthetic variables that describe discrete components of the biochemical pathway to total 43 GSLtraits for analysis 4 , 61–62 ., For the AgNO3-treated samples , we detected only 16 aliphatic GSL and four indolic GSL , but also were able to measure camalexin , which is related to indolic GSL ( Table S3 ) , which in combination with derived measures provided us with 42 AgNO3 treated GSL traits 61 ., For the seedling GSL samples , we detected 19 aliphatic GSLs , two indolic , and three seedling specific phenylalanine GSLs ( Table S4 ) , which in combination with derived descriptive variables gave us a total of 46 total GSL traits 61 ., Population stratification has previously been noted in this set of A . thaliana accessions , where eight subpopulations were proposed to describe the accessions genetic differences 27–28 ., Less explored is the joint effect of population structure and environmental factors , both external ( exogenous treatment ) and internal ( tissue comparison ) on GSL ., We used our three glucosinolate datasets to test for potential confounding effects of environmental variation , population structure , and their various interaction terms upon the GSL phenotypes ( Figure 2 ) ., On average , 36% ( silver versus control ) and 23% ( seedling versus control ) of phenotypic variance in GSL traits was solely attributable to accession ., An additional 7% ( silver versus control ) and 14% ( seedling versus control ) of phenotypic variance was attributable to an interaction between accession and treatment or tissue ., This suggests that , on average and given the statistical power of the experiments , 30%–50% of the detectable genetically controlled variance is stable across conditions , while at least 20% of the variance is conditional on treatment and/or tissue ., In contrast , population structure by itself accounted for 10%–15% of total variance in GSL ( Figure 2 ) ., Interestingly , significantly less variance ( <5% ) could be attributed to interaction of treatment or tissue with population structure ., This suggests that for GSL , large-effect polymorphisms that may be linked with population structure are stable across treatment and tissue while the polymorphisms with conditional effects are less related to the species demographic structure ( Figure 2 ) ., This is consistent with QTL studies using RIL that find greater repeatability of large-effect QTL across populations and conditions than of treatment-dependent loci 41 , 51 , 61 , 63 ., This is further supported by the fact that we utilized replication of defined genotypes across all conditions and tissues and as such have better power to detect these effects than in systems where it is not possible to replicate genotypes ., As such , controlling for population structure will reduce the number of false-positives detected but lead to an elevated false-negative rate , given this significant association between the measured phenotypes and population structure ., Interestingly , developmental effects ( average of 15% ) accounted for 3 times more of the variation in GSL than environmental effects ( average 5% ) ., In particular , only three GSL ( two indolic GSL , I3M and 4MOI3M , and total indolic GSL ) were affected more strongly by AgNO3-treatment than by accession ( Table S1 and Figure S1 ) , whereas 11 GSL traits were found to be influenced more by tissue type than accession ( Table S2 ) ., This agrees with these indolic GSL being regulated by defense response 36 , 64 ., Similarly , twice as much GSL variation could be attributed to the interaction between accession and tissue type compared to the interaction between accession and AgNO3 treatment ., Thus , it appears that intraspecific genetic variation has greater impact on GSL in relation to development than in response to simulated pathogen attack ., Using 229 , 940 SNP available for this collection of 96 accessions , we conducted GWA-mapping for GLS traits in both the Seedling and Silver datasets using a maximum likelihood approach that accounts for genetic similarity ( EMMA ) 65 ., This identified a large number of significant SNPs and genes for both datasets ( Table 1 ) ., We tested the previously published criteria used to assess significance of candidate genes to ensure that different treatments or tissues did not bias the results produced under these criteria 4 ., These criteria required ≥1 SNP , ≥2 SNPs , or ≥20% of SNPs within a gene to show significant association with a specific GSL trait ., This test was independently repeated for all GSL traits in both datasets ( Tables S5 and S6 ) ., As previously found using the control leaf GSL data , the more stringent ≥2 SNPs/gene criterion greatly decreased the overall number of significant genes identified while not overtly influencing the false-negative rate when using a set of GSL genes known to be naturally variable and causal within the 96 accessions ( Tables 2 and 3 ) ., Interestingly , including multiple treatments and tissues did not allow us to decrease the high empirical false-negative rate ( ∼75% ) in identifying validated causal candidate genes ( Table, 3 ) 4 , 31 ., Using the ≥2 SNPs/gene criterion identified 898 genes for GSL accumulation in silver-treated leaves and 909 genes for the seedling GSL data ., As previously found , the majority of these candidate genes were specific to a subset of GSL phenotypes and no gene was linked to all GSL traits within any dataset ( Figure S2 ) 4 ., We estimated the variance explained by the candidate GWA genes identified in this study using a mixed polygenic model of inheritance for each phenotype within each dataset using the GenABEL package in R 66–67 ., This showed that , on average , the candidate genes explained 37% of the phenotypic variation with a range of 1% to 99% ( Table S10 ) ., Interestingly , if the phenotypes are separated into their rough biosynthetic classes of indolic , long-chain , or short-chain aliphatic 68 , there is evidence for different levels of explained phenotypic variation where indolic has the highest percent variance at 45% while short-chain has the lowest at 25% ( p\u200a=\u200a0 . 001 ) ., This is not explainable by differential heritability as the short-chain aliphatic GSLs have the highest heritability in numerous studies including this one ( Tables S1 and S2 ) 4 , 41 , 61 ., This is instead likely due to the fact that short-chain aliphatic GLS show higher levels of multi-locus epistasis that complicates the ability to estimate the explained variance within GWA studies 31 , 41 , 61 ., Previous work with untreated GSL leaf samples showed that candidate genes clustered in hotspots , with the two predominant hotspots surrounding the previously cloned AOP and MAM loci 4 , where multiple polymorphisms surrounding the region of these two causal genes significantly associate with multiple GLS phenotypes ., We plotted GWA-identified candidate genes for GSL accumulation from the silver and seedling datasets to see if treatment or tissue altered this pattern ( Figure 3 ) ., Both datasets showed statistically significant ( p<0 . 05; Figure, 3 ) hotspots of candidate genes that clustered predominantly around the AOP and MAM loci with some minor treatment- or tissue-specific hotspots containing fewer genes ., This phenomenon is observed across multiple GLS traits ( Figure 3 ) ., The AOP and MAM hotspots are known to be generated by local blocks of linkage disequilibrium ( LD ) wherein a large set of non-causal genes are physically linked with the causal AOP2/3 and MAM1/3 genes 4 ., Interestingly , while the silver and control leaf GWA datasets showed similar levels of clustering around the AOP and MAM loci , the hotspot at the MAM locus was much more pronounced than the AOP locus in the seedling GWA dataset ( Figure 3 ) , suggesting more seedling GLS traits are associated with the MAM locus ., This agrees with QTL-mapping results in structured RIL populations of A . thaliana that have shown that the MAM/Elong locus has stronger effects upon seedling GSL phenotypes in comparison to leaves , whereas the effect of the AOP locus is stronger in leaves than seedlings 41 , 62–63 ., In addition , the relationship of GSL phenotypes across accessions is highly similar in the two leaf datasets , while the phenotypic relationships across accessions are shifted when comparing the seedling to the leaf ( Figure 4 ) ., Together , this suggests greater similarity in the genetic variation affecting GSL phenotypic variation between the two leaf datasets than between leaf and seedling datasets , suggesting that GSL variation is impacted more by development than simulated pathogen attack ., This is further supported by the analysis of variance ( Figure 2 ) ., To further test if measuring the same phenotypes in different tissues or treatments will identify similar GWA mapping candidates , we investigated the overlap of GWA candidate genes identified across the three datasets ., For this analysis we excluded genes within the known AOP and MAM LD blocks as previous research has shown that all of these genes except the AOP and MAM genes are likely false-positives and would bias our overlap analysis 4 , 69–71 ., The remaining GWA mapping candidate genes showed more overlap between the two leaf datasets than between leaf and seedling datasets ( Figure 5 ) ., Interestingly , the overlap between GWA-identified candidate gene sets from seedling and leaf data was smaller than would be expected by chance ( χ2 p<0 . 001 for all three sectors ) ( Figure 5 ) ., This suggests that outside of the AOP and MAM loci , distinct sets of genetic variants may contribute to the observed phenotypic diversity in GSL across these tissues , which agrees with QTL-mapping studies identifying distinct GSL QTL for seedling and leaf 41 , 62–63 ., As such , focusing simply on GWA mapping candidates independently identified in multiple treatments or tissues to call true significant associations will overlook genes whose genotype-to-phenotype association is conditional upon differences in the experiments ., Similarly , the amount of phenotypic variance explained by the candidates differed between the datasets , with control and treated having the highest average explained variance , 39% and 41% , respectively ., In contrast , the seedling dataset had the lowest explained variance at 32% , similarly suggesting that altering the conditions of the experiments will change commonly reported summary variables such as explained variance ., GWA studies generally produce large lists of candidate genes , presumed to contain a significant fraction of false-positive associations ., One proposed strategy refines these results by searching for enrichment of candidate genes within pre-defined proteomic or transcriptomic networks 15 ., To test the applicability of this approach to our GWA study , we overlaid our list of 2 , 436 candidate genes ( excluding genes showing proximal LD to the causal AOP2/3 and MAM1/2/3 genes 4 ) that associated with at least one GSL phenotype in at least one of the three datasets ( Figure, 5 ) onto a previously published co-expression network 72 ., If the network filtering approach is valid and there are true causal genes within the candidate gene lists , then the candidate genes should show tighter network linkages to previously validated causal genes than the average gene ., Measuring the distances between all candidate genes to all known GSL causal genes within the co-expression network showed that , for all datasets , the GWA candidate genes were on average closer to known causal genes than non-candidates ( Figure S4 ) ., Interestingly , the GWA mapping candidate genes actually showed closer linkages to the cysteine , homocysteine , and glutathione biosynthetic pathways than to the core GSL biosynthetic pathways , suggesting that natural variation in these pathways may impact A . thaliana secondary metabolism ( Figure S4 and Dataset S1 ) ., The network proximity of GWA mapping candidates to known causal genes supports the utility of the network filtering approach in identifying true causal genes among the long list of GWA mapping candidate genes ., To determine if this network filtering approach finds whole co-expression networks or isolated genes , we extended the co-expression network to include known and predicted GSL causal genes ( Table S7 ) ., The largest network obtained from this analysis centered on the core-biosynthetic genes for the aliphatic and tryptophan derived GSL as well as sulfur metabolism genes ( Figures 6 and S3 ) ., Interestingly , this large network linked to a defense signaling network represented by CAD1 , PEN2 , and EDS1 ( Figure, 6 ) 73 ., The defense signaling pathway associated with PEN2 and , more recently , CAD2 and EDS1 had previously been linked to altered GSL accumulation via both signaling and biosynthetic roles 36 , 39 , 74–75 ., However , the current network analysis has identified new candidate participants in this network altering GSL accumulation ., To test these predicted linkages , we obtained a mutant line possessing a T-DNA insertional disruption of the previously undescribed locus At4g38550 , which is linked to both CAD1 and PEN2 ( Figure 6 , Table S9 ) ., This mutant had elevated levels of all aliphatic GSL within the rosette leaves as well as 4-methoxyindol-3-ylmethyl GSL , shown to mediate non-host resistance ( Table S9 ) 36 , 39 ., These results suggest a role for At4g38550 in either defense responses or GSL accumulation ., Network analysis also identified several previously described ( RML1 ) and novel candidate ( ATSFGH , At1g06640 , and At1g04770 ) genes that were associated with the core-biosynthetic part of the network ., RML1 ( synonymous with PAD2 , CAD2 ) , a biosynthetic enzyme for glutathione , has previously been shown to control GSL accumulation either via a signaling role or actual biosynthesis of glutathione 74–75 ., To test if ATSFGH ( S-formylglutathione hydrolase , At2g41530 ) , At1g06640 ( unknown 2-oxoacid dependent dioxygenase – 2-ODD ) , or At1g04770 ( tetratricopeptide containing protein ) may play a role in GSL accumulation , we obtained insertional mutants ., This showed that the disruption of At1g06640 led to significantly increased accumulation of the short-chain methylsulfinyl GSL but not the corresponding methylthio or long-chain GSL ( Table S9 ) ., In contrast , the AtSFGH mutant had elevated levels of all short-chain GSL along with a decreased accumulation of the long-chain 8-MTO GSL ( Table S9 ) ., The At1g04770 mutant showed no altered GSL levels other than a significantly decreased accumulation of 8-MTO GSL ( Table S9 ) ., This suggests that these genes alter GSL accumulation , although the specific molecular mechanism remains to be identified ., Interestingly , network membership is not sufficient to predict a GSL impact , as T-DNA disruption of homoserine kinase ( At2g17265 ) , a gene co-expressed with the GSL core but not a candidate from the GWA analysis , had no detectable impact upon GSL accumulation ( Table S9 ) ., Thus , the network filtering approach identified genes closely linked to the GSL biosynthetic network that can control GSL accumulation and are GWA-identified candidate genes ., The above analysis shows that GWA candidate genes which co-express with known GSL genes are likely to influence GSL accumulation ., However , networks might influence GSL accumulation independent of co-expression with known GSL genes ., To test this , we investigated several co-expression networks that involved solely GWA-identified candidate genes and genes not previously implicated in influencing GSL accumulation ( Figure 7 ) ., Three of these networks included genes that affect natural variation in non-GSL phenotypes within A . thaliana , namely PHOTOTROPIN 2 ( PHOT2 ) , Erecta ( ER ) 76 , and ELF3/GI ( Figure 7 ) 77 , 78 ., The fourth network did not involve any genes previously linked to natural variation ( Figure 7 ) ., We obtained A . thaliana seed stocks with mutations in a subset of genes for each of these three networks to test whether loss of function at these loci affects GSL accumulation ., The largest network containing no previously known GSL-related genes that we examined is a blue light/giberellin signaling pathway represented by PHOT2 ( Figure 7A ) ., This pathway had not been previously ascribed any role in GSL accumulation in A . thaliana ., We tested this GWA-identified association by measuring GSL in the single and double PHOT1/PHOT2 mutants 79 ., PHOT1 was included as it has been shown to function either redundantly or epistatically with PHOT2 79 ., The single phot1 or phot2 mutation had no significant effect upon GSL accumulation ( Table S9 ) ., The double phot1/phot2 knockout plants showed a significant increase in the production of detected methylthio GSL as well as a decrease in the accumulation of 3-carbon GSL compared to control plants ., Thus , it appears that GSL are influenced by the PHOT1/PHOT2 signaling pathway , possibly in response to blue light signaling ( Table S9 ) ., This agrees with previous reports from Raphanus sativa that blue light controls GSL 80 , 81 ., The second non-GSL network we examined contains the ER gene ( Figure 7B ) ., The ER ( Erecta ) network and specifically the ER locus had previously been queried for the ability to alter GSL accumulation using two Arabidopsis RIL populations ( Ler×Col-0 and Ler×Cvi ) that segregate for a loss-of-function allele at the ER locus 41 , 51 , 63 , 82–86 ., In these analyses , the ER locus was linked to seed/seedling GSL accumulation in only one of the two populations and not linked to mature leaf GSL accumulation 41 , 86 ., Analysis of the ER mutant within the Col-0 genotype showed that the Erecta gene does influence GSL content within leaves as suggested by the GWA results ( Table S9 , Figure 7A ) ., Plants with loss of function at Erecta showed increased levels of methylthio GSL , long-chain GSL , and 4-substituted indole GSL ( Table S9 ) ., Interestingly , the ER network contains a number of chromatin remodeling genes ., We obtained A . thaliana lines with loss-of-function mutations in three of these genes ( Table S9 ) to test if the extended network also alters GSL accumulation ., Mutation of two of the three genes ( At5g18620 – CHR17 and At4g02060 – PRL ) was associated with increased levels of short-chain aliphatic GSL and a corresponding decrease in long-chain aliphatic GSL ( Table S9 ) ., This shows that the Erecta network has the capacity to influence GSL accumulation ., Two smaller networks containing the ELF3 and GI genes were of interest as these two genes are associated with natural variation in the A . thaliana circadian clock ( Figure 7C ) 77 , 87 , 88 ., GSL analysis showed that both the elf3 and gi mutants had lower levels of aliphatic GSL than controls ( Table S9 ) ., Comparing multiple gi mutants from both the Col-0 and Ler genetic backgrounds showed that only gi mutants in the Col-0 background altered GSL accumulation ( Table S9 ) ., This suggests that gis link to glucosinolates is epistatic to other naturally variable loci within the genome , as previously noted for natural GI alleles in relation to other phenotypes ( Table S9 ) 78 ., An analysis of the elf4 mutant which has morphological similarities to elf3-1 but was not a GWA-identified candidate showed that this mutation did not alter GSL accumulation ., Thus , elf3/gi affects GSL via a more direct mechanism than altering plant morphology ., Given two genes in the circadian clock network directly affects GSL accumulation and given the expression of these two genes are correlated with other genes in the network , it is fair to hypothesize that circadian clock plays a role in GSL accumulation ., While the GSL phenotypes of the above laboratory-generated mutants suggest that variation in circadian clock plays a role in GSL accumulation , they do not prove that the natural alleles at these genes affect GSL accumulation ., To validate this , we leveraged germplasm developed in the course of previous research showing that natural variation at the ELF3 locus controls numerous phenotypes , including circadian clock periodicity and flowering time 77 ., We utilized quantitative complementation lines to test if natural variation at ELF3 also generates differences in GSL content 77 ., This showed that the ELF3 allele from the Bay-0 accession was associated with a higher level of short chain aliphatic GSL accumulation in comparison to plants containing the Sha allele ( Table S9 ) ., In contrast , both Bay-0 and Sha allele-bearing plants had elevated levels of 8-MTO GSL in comparison to Col-0 ( Tables S8 and S9 ) ., Thus , ELF3 is a polymorphic locus that contains multiple distinct alleles that influence GSL content within the plant and the ELF3/GI network causes natural variation in GSL content ., The final network examined here , represented by CLPX ( CLP protease ) , is likely involved in chlorophyll catabolism and possibly also chloroplast senescence 89 ., This network is uncharacterized and has not previously been associated with GSL accumulation or natural variation in any phenotype , but participation in chloroplast degradation is suggested by transcriptional correlation of CLPX with several catabolism genes ., Analysis of mutants deficient in function for two of these genes showed that they all possessed increased aliphatic GSL in comparison to wild-type controls ., These results suggest that natural variation in this putative network could influence GSL content in A . thaliana ., The majority ( 12 of 13 ) of genes in this network show significant variation in transcript abundance across A . thaliana accessions , a significantly greater proportion than expected by chance ( X2 p<0 . 001 ) 90–92 , further suggesting that this network may contribute to GSL variation across the accessions ., Finally , we tested a single two gene network found in the co-expression data wherein both genes had been annotated but not previously linked to GSL content ., This network involved AtPTR3 ( a putative peptide transporter , At5g46050 ) and DPL1 ( a dihydrosphingosine lyase , At1g27980 ) ., T-DNA mutants in both genes appeared to be lethal as we could not identify homozygous progeny ., However , comparison of the heterozygous progeny to wildtype homozygotes showed that mutants in both genes led to elevated levels of aliphatic GSL ( Table S9 ) ., Thus , there are likely more networks that are causal for GSL variation within this dataset that remain to be tested ., While GSL are considered “secondary” metabolites , these compounds are affected by many aspects of plant metabolism , thus GSL phenotyping is sensitive to any genetic perturbation that affects plant physiology ., As such , we identified six genes that were expressed in mature leaves but did not show any significant association of DNA sequence polymorphism with GSL phenotypes and were additionally not identified within any of the above co-expression networks ., Insertional mutants disrupted at these loci were designated as random mutant controls ( Table S9 ) ., Analyzing GSL within these six lines showed that on average 13%±4% of the GSL were affected in the random control mutant set even after correction for multiple testing ., While this suggests that GSL may be generally sensitive to mutations affecting genes expressed within the leaf , this incidence of significant GSL effects is much lower than observed for the T-DNA mutants selected to test GWA mapping-identified pathways ( CLPX - 78%±11% , PTR3 – 61%±6% , Erecta – 45%±10% , GSL – 46%±11% , ELF3/GI – 53%±17% ) ., In all cases the mutants deficient in GWA pathway-identified gene function showed significantly greater numbers of altered GSL phenotypes than the negative control T-DNA mutant set ( X2 , p<0 . 001 ) , suggesting that combining GWA-identified candidate genes with co-expression networks successfully identifies genes with the capacity to cause natural variation in GSL content ., Identifying the specific mechanisms involved will require significant future research ., A limiting factor for the utility of GWA studies has been the preponderance of false-positive and false-negative associations which makes the accurate prediction of biologically valid genotype-phenotype associations very difficult ., In this report , we describe the implementation and validation of a candidate gene co-expression filter that has given us a high success rate in candidate gene vali
Genome-wide association ( GWA ) is gaining popularity as a means to study the architecture of complex quantitative traits , partially due to the improvement of high-throughput low-cost genotyping and phenotyping technologies ., Glucosinolate ( GSL ) secondary metabolites within Arabidopsis spp ., can serve as a model system to understand the genomic architecture of adaptive quantitative traits ., GSL are key anti-herbivory defenses that impart adaptive advantages within field trials ., While little is known about how variation in the external or internal environment of an organism may influence the efficiency of GWA , GSL variation is known to be highly dependent upon the external stresses and developmental processes of the plant lending it to be an excellent model for studying conditional GWA ., To understand how development and environment can influence GWA , we conducted a study using 96 Arabidopsis thaliana accessions , >40 GSL phenotypes across three conditions ( one developmental comparison and one environmental comparison ) and ∼230 , 000 SNPs ., Developmental stage had dramatic effects on the outcome of GWA , with each stage identifying different loci associated with GSL traits ., Further , while the molecular bases of numerous quantitative trait loci ( QTL ) controlling GSL traits have been identified , there is currently no estimate of how many additional genes may control natural variation in these traits ., We developed a novel co-expression network approach to prioritize the thousands of GWA candidates and successfully validated a large number of these genes as influencing GSL accumulation within A . thaliana using single gene isogenic lines ., Together , these results suggest that complex traits imparting environmentally contingent adaptive advantages are likely influenced by up to thousands of loci that are sensitive to fluctuations in the environment or developmental state of the organism ., Additionally , while GWA is highly conditional upon genetics , the use of additional genomic information can rapidly identify causal loci en masse .
Understanding how genetic variation can control phenotypic variation is a fundamental goal of modern biology ., A major push has been made using genome-wide association mapping in all organisms to attempt and rapidly identify the genes contributing to phenotypes such as disease and nutritional disorders ., But a number of fundamental questions have not been answered about the use of genome-wide association: for example , how does the internal or external environment influence the genes found ?, Furthermore , the simple question of how many genes may influence a trait is unknown ., Finally , a number of studies have identified significant false-positive and -negative issues within genome-wide association studies that are not solvable by direct statistical approaches ., We have used genome-wide association mapping in the plant Arabidopsis thaliana to begin exploring these questions ., We show that both external and internal environments significantly alter the identified genes , such that using different tissues can lead to the identification of nearly completely different gene sets ., Given the large number of potential false-positives , we developed an orthogonal approach to filtering the possible genes , by identifying co-functioning networks using the nominal candidate gene list derived from genome-wide association studies ., This allowed us to rapidly identify and validate a large number of novel and unexpected genes that affect Arabidopsis thaliana defense metabolism within phenotypic ranges that have been shown to be selectable within the field ., These genes and the associated networks suggest that Arabidopsis thaliana defense metabolism is more readily similar to the infinite gene hypothesis , according to which there is a vast number of causative genes controlling natural variation in this phenotype ., It remains to be seen how frequently this is true for other organisms and other phenotypes .
genome-wide association studies, functional genomics, plant biology, population genetics, metabolic networks, plant science, genome complexity, genetic polymorphism, plant genetics, biology, systems biology, plant biochemistry, genetics, genomics, evolutionary biology, gene networks, computational biology, genetics and genomics