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1.  A Hierarchical Bayesian Approach to Multi-Trait Clinical Quantitative Trait Locus Modeling 
Recent advances in high-throughput genotyping and transcript profiling technologies have enabled the inexpensive production of genome-wide dense marker maps in tandem with huge amounts of expression profiles. These large-scale data encompass valuable information about the genetic architecture of important phenotypic traits. Comprehensive models that combine molecular markers and gene transcript levels are increasingly advocated as an effective approach to dissecting the genetic architecture of complex phenotypic traits. The simultaneous utilization of marker and gene expression data to explain the variation in clinical quantitative trait, known as clinical quantitative trait locus (cQTL) mapping, poses challenges that are both conceptual and computational. Nonetheless, the hierarchical Bayesian (HB) modeling approach, in combination with modern computational tools such as Markov chain Monte Carlo (MCMC) simulation techniques, provides much versatility for cQTL analysis. Sillanpää and Noykova (2008) developed a HB model for single-trait cQTL analysis in inbred line cross-data using molecular markers, gene expressions, and marker-gene expression pairs. However, clinical traits generally relate to one another through environmental correlations and/or pleiotropy. A multi-trait approach can improve on the power to detect genetic effects and on their estimation precision. A multi-trait model also provides a framework for examining a number of biologically interesting hypotheses. In this paper we extend the HB cQTL model for inbred line crosses proposed by Sillanpää and Noykova to a multi-trait setting. We illustrate the implementation of our new model with simulated data, and evaluate the multi-trait model performance with regard to its single-trait counterpart. The data simulation process was based on the multi-trait cQTL model, assuming three traits with uncorrelated and correlated cQTL residuals, with the simulated data under uncorrelated cQTL residuals serving as our test set for comparing the performances of the multi-trait and single-trait models. The simulated data under correlated cQTL residuals were essentially used to assess how well our new model can estimate the cQTL residual covariance structure. The model fitting to the data was carried out by MCMC simulation through OpenBUGS. The multi-trait model outperformed its single-trait counterpart in identifying cQTLs, with a consistently lower false discovery rate. Moreover, the covariance matrix of cQTL residuals was typically estimated to an appreciable degree of precision under the multi-trait cQTL model, making our new model a promising approach to addressing a wide range of issues facing the analysis of correlated clinical traits.
PMCID: PMC3368303  PMID: 22685451
Bayesian multilevel modeling; genetic architecture; linked marker-expression pairs; pleiotropy
2.  Whole-Genome Analysis of Multienvironment or Multitrait QTL in MAGIC 
G3: Genes|Genomes|Genetics  2014;4(9):1569-1584.
Multiparent Advanced Generation Inter-Cross (MAGIC) populations are now being utilized to more accurately identify the underlying genetic basis of quantitative traits through quantitative trait loci (QTL) analyses and subsequent gene discovery. The expanded genetic diversity present in such populations and the amplified number of recombination events mean that QTL can be identified at a higher resolution. Most QTL analyses are conducted separately for each trait within a single environment. Separate analysis does not take advantage of the underlying correlation structure found in multienvironment or multitrait data. By using this information in a joint analysis—be it multienvironment or multitrait — it is possible to gain a greater understanding of genotype- or QTL-by-environment interactions or of pleiotropic effects across traits. Furthermore, this can result in improvements in accuracy for a range of traits or in a specific target environment and can influence selection decisions. Data derived from MAGIC populations allow for founder probabilities of all founder alleles to be calculated for each individual within the population. This presents an additional layer of complexity and information that can be utilized to identify QTL. A whole-genome approach is proposed for multienvironment and multitrait QTL analysis in MAGIC. The whole-genome approach simultaneously incorporates all founder probabilities at each marker for all individuals in the analysis, rather than using a genome scan. A dimension reduction technique is implemented, which allows for high-dimensional genetic data. For each QTL identified, sizes of effects for each founder allele, the percentage of genetic variance explained, and a score to reflect the strength of the QTL are found. The approach was demonstrated to perform well in a small simulation study and for two experiments, using a wheat MAGIC population.
PMCID: PMC4169149  PMID: 25237109
mixed models; multienvironment; multitrait; QTL; WGAIM; Multiparent Advanced Generation Inter-Cross (MAGIC); multiparental populations; MPP
3.  Development of a next-generation NIL library in Arabidopsis thaliana for dissecting complex traits 
BMC Genomics  2013;14:655.
The identification of the loci and specific alleles underlying variation in quantitative traits is an important goal for evolutionary biologists and breeders. Despite major advancements in genomics technology, moving from QTL to causal alleles remains a major challenge in genetics research. Near-isogenic lines are the ideal raw material for QTL validation, refinement of QTL location and, ultimately, gene discovery.
In this study, a population of 75 Arabidopsis thaliana near-isogenic lines was developed from an existing recombinant inbred line (RIL) population derived from a cross between physiologically divergent accessions Kas-1 and Tsu-1. First, a novel algorithm was developed to utilize genome-wide marker data in selecting RILs fully isogenic to Kas-1 for a single chromosome. Seven such RILs were used in 2 generations of crossing to Tsu-1 to create BC1 seed. BC1 plants were genotyped with SSR markers so that lines could be selected that carried Kas-1 introgressions, resulting in a population carrying chromosomal introgressions spanning the genome. BC1 lines were genotyped with 48 genome-wide SSRs to identify lines with a targeted Kas-1 introgression and the fewest genomic introgressions elsewhere. 75 such lines were selected and genotyped at an additional 41 SNP loci and another 930 tags using 2b-RAD genotyping by sequencing. The final population carried an average of 1.35 homozygous and 2.49 heterozygous introgressions per line with average introgression sizes of 5.32 and 5.16 Mb, respectively. In a simple case study, we demonstrate the advantage of maintaining heterozygotes in our library whereby fine-mapping efforts are conducted simply by self-pollination. Crossovers in the heterozygous interval during this single selfing generation break the introgression into smaller, homozygous fragments (sub-NILs). Additionally, we utilize a homozygous NIL for validation of a QTL underlying stomatal conductance, a low heritability trait.
The present results introduce a new and valuable resource to the Brassicaceae research community that enables rapid fine-mapping of candidate loci in parallel with QTL validation. These attributes along with dense marker coverage and genome-wide chromosomal introgressions make this population an ideal starting point for discovery of genes underlying important complex traits of agricultural and ecological significance.
PMCID: PMC3849958  PMID: 24063355
2b-RAD; Fine-mapping; Quantitative trait loci; Stomatal conductance
4.  Genetic Networks Controlling Structural Outcome of Glucosinolate Activation across Development 
PLoS Genetics  2008;4(10):e1000234.
Most phenotypic variation present in natural populations is under polygenic control, largely determined by genetic variation at quantitative trait loci (QTLs). These genetic loci frequently interact with the environment, development, and each other, yet the importance of these interactions on the underlying genetic architecture of quantitative traits is not well characterized. To better study how epistasis and development may influence quantitative traits, we studied genetic variation in Arabidopsis glucosinolate activation using the moderately sized Bayreuth×Shahdara recombinant inbred population, in terms of number of lines. We identified QTLs for glucosinolate activation at three different developmental stages. Numerous QTLs showed developmental dependency, as well as a large epistatic network, centered on the previously cloned large-effect glucosinolate activation QTL, ESP. Analysis of Heterogeneous Inbred Families validated seven loci and all of the QTL×DPG (days post-germination) interactions tested, but was complicated by the extensive epistasis. A comparison of transcript accumulation data within 211 of these RILs showed an extensive overlap of gene expression QTLs for structural specifiers and their homologs with the identified glucosinolate activation loci. Finally, we were able to show that two of the QTLs are the result of whole-genome duplications of a glucosinolate activation gene cluster. These data reveal complex age-dependent regulation of structural outcomes and suggest that transcriptional regulation is associated with a significant portion of the underlying ontogenic variation and epistatic interactions in glucosinolate activation.
Author Summary
A principal interest in biology is to understand how natural genetic variation translates into phenotypic variation. A key component of this connection is how the genetic variation interacts with other sources of variation, such as environment (G×E), development (G×D), or other genetic loci (G×G or epistasis). To analyze the molecular underpinnings of these quantitative genetics interaction terms, we investigated the genetic architecture of an adaptive trait, glucosinolate activation, in Arabidopsis thaliana during the development of what is considered a static mature rosette. Variation in glucosinolate activation was principally controlled by epistatic and G×D interactions. Epistatic interactions identified both Mendelian epistasis, where regulatory loci controlled enzymatic loci, and quantitative interactions between regulatory loci. G×D appeared to involve master regulatory loci as determined by trans-eQTL hotspot analysis. Finally, two common glucosinolate activation QTLs appear to have evolved via gene loss and sub-functionalization following quadruplication of an ancestral genomic fragment, potentially by two whole-genome duplications. Thus, genomic duplication events may facilitate the formation of quantitative genetic variation. This study provides insights into the molecular basis of the link between genetic and phenotypic variation in a potentially adaptive trait.
PMCID: PMC2565841  PMID: 18949035
5.  Quantitative trait loci for seed yield and yield-related traits, and their responses to reduced phosphorus supply in Brassica napus 
Annals of Botany  2012;109(4):747-759.
Background and Aims
One of the key targets of breeding programmes in rapeseed (Brassica napus) is to develop high-yield varieties. However, the lack of available phosphorus (P) in soils seriously limits rapeseed production. The aim of this study was to dissect the genetic control of seed yield and yield-related traits in B. napus grown with contrasting P supplies.
Two-year field trials were conducted at one site with normal and low P treatments using a population of 124 recombinant inbred lines derived from a cross between ‘B104-2’ and ‘Eyou Changjia’. Seed yield, seed weight, seed number, pod number, plant height, branch number and P efficiency coefficient (PEC) were investigated. Quantitative trait locus (QTL) analysis was performed by composite interval mapping.
Key Results
The phenotypic values of most of the tested traits were reduced under the low P conditions. In total, 74 putative QTLs were identified, contributing 7·3–25·4 % of the phenotypic variation. Of these QTLs, 16 (21·6 %) were detected in two seasons and in the mean value of two seasons, and eight QTLs for two traits were conserved across P levels. Low-P-specific QTLs were clustered on chromosomes A1, A6 and A8. By comparative mapping between Arabidopsis and B. napus, 161 orthologues of 146 genes involved in Arabidopsis P homeostasis and/or yield-related trait control were associated with 45 QTLs corresponding to 23 chromosomal regions. Four gene-based markers developed from genes involved in Arabidopsis P homeostasis were mapped to QTL intervals.
Different genetic determinants were involved in controlling seed yield and yield-related traits in B. napus under normal and low P conditions. The QTLs detected under reduced P supply may provide useful information for improving the seed yield of B. napus in soils with low P availability in marker-assisted selection.
PMCID: PMC3286287  PMID: 22234558
Brassica napus; phosphorus deficiency; phosphorus use efficiency; recombinant inbred line; seed yield; quantitative trait locus; comparative mapping
6.  QTL analysis of novel genomic regions associated with yield and yield related traits in new plant type based recombinant inbred lines of rice (Oryza sativa L.) 
BMC Plant Biology  2012;12:137.
Rice is staple food for more than half of the world’s population including two billion Asians, who obtain 60-70% of their energy intake from rice and its derivatives. To meet the growing demand from human population, rice varieties with higher yield potential and greater yield stability need to be developed. The favourable alleles for yield and yield contributing traits are distributed among two subspecies i.e., indica and japonica of cultivated rice (Oryza sativa L.). Identification of novel favourable alleles in indica/japonica will pave way to marker-assisted mobilization of these alleles in to a genetic background to break genetic barriers to yield.
A new plant type (NPT) based mapping population of 310 recombinant inbred lines (RILs) was used to map novel genomic regions and QTL hotspots influencing yield and eleven yield component traits. We identified major quantitative trait loci (QTLs) for days to 50% flowering (R2 = 25%, LOD = 14.3), panicles per plant (R2 = 19%, LOD = 9.74), flag leaf length (R2 = 22%, LOD = 3.05), flag leaf width (R2 = 53%, LOD = 46.5), spikelets per panicle (R2 = 16%, LOD = 13.8), filled grains per panicle (R2 = 22%, LOD = 15.3), percent spikelet sterility (R2 = 18%, LOD = 14.24), thousand grain weight (R2 = 25%, LOD = 12.9) and spikelet setting density (R2 = 23%, LOD = 15) expressing over two or more locations by using composite interval mapping. The phenotypic variation (R2) ranged from 8 to 53% for eleven QTLs expressing across all three locations. 19 novel QTLs were contributed by the NPT parent, Pusa1266. 15 QTL hotpots on eight chromosomes were identified for the correlated traits. Six epistatic QTLs effecting five traits at two locations were identified. A marker interval (RM3276-RM5709) on chromosome 4 harboring major QTLs for four traits was identified.
The present study reveals that favourable alleles for yield and yield contributing traits were distributed among two subspecies of rice and QTLs were co-localized in different genomic regions. QTL hotspots will be useful for understanding the common genetic control mechanism of the co-localized traits and selection for beneficial allele at these loci will result in a cumulative increase in yield due to the integrative positive effect of various QTLs. The information generated in the present study will be useful to fine map and to identify the genes underlying major robust QTLs and to transfer all favourable QTLs to one genetic background to break genetic barriers to yield for sustained food security.
PMCID: PMC3438134  PMID: 22876968
7.  Detection and validation of stay-green QTL in post-rainy sorghum involving widely adapted cultivar, M35-1 and a popular stay-green genotype B35 
BMC Genomics  2014;15(1):909.
Sorghum [Sorghum bicolor (L.) Moench] is an important dry-land cereal of the world providing food, fodder, feed and fuel. Stay-green (delayed-leaf senescence) is a key attribute in sorghum determining its adaptation to terminal drought stress. The objective of this study was to validate sorghum stay-green quantitative trait loci (QTL) identified in the past, and to identify new QTL in the genetic background of a post-rainy adapted genotype M35-1.
A genetic linkage map based on 245 F9 Recombinant Inbred Lines (RILs) derived from a cross between M35-1 (more senescent) and B35 (less senescent) with 237 markers consisting of 174 genomic, 60 genic and 3 morphological markers was used. The phenotypic data collected for three consecutive post-rainy crop seasons on the RIL population (M35-1 × B35) was used for QTL analysis. Sixty-one QTL were identified for various measures of stay-green trait and each trait was controlled by one to ten QTL. The phenotypic variation explained by each QTL ranged from 3.8 to 18.7%. Co-localization of QTL for more than five traits was observed on two linkage groups i.e. on SBI-09-3 flanked by S18 and Xgap206 markers and, on SBI-03 flanked by XnhsbSFCILP67 and Xtxp31. QTL identified in this study were stable across environments and corresponded to sorghum stay-green and grain yield QTL reported previously. Of the 60 genic SSRs mapped, 14 were closely linked with QTL for ten traits. A genic marker, XnhsbSFCILP67 (Sb03g028240) encoding Indole-3-acetic acid-amido synthetase GH3.5, was co-located with QTL for GLB, GLM, PGLM and GLAM on SBI-03. Genes underlying key enzymes of chlorophyll metabolism were also found in the stay-green QTL regions.
We validated important stay-green QTL reported in the past in sorghum and detected new QTL influencing the stay-green related traits consistently. Stg2, Stg3 and StgB were prominent in their expression. Collectively, the QTL/markers identified are likely candidates for subsequent verification for their involvement in stay-green phenotype using NILs and to develop drought tolerant sorghum varieties through marker-assisted breeding for terminal drought tolerance in sorghum.
Electronic supplementary material
The online version of this article (doi:10.1186/1471-2164-15-909) contains supplementary material, which is available to authorized users.
PMCID: PMC4219115  PMID: 25326366
Stay-green; Sorghum; Post-flowering drought tolerance; Quantitative trait loci; Marker assisted breeding
8.  The Flowering Repressor SVP Underlies a Novel Arabidopsis thaliana QTL Interacting with the Genetic Background 
PLoS Genetics  2013;9(1):e1003289.
The timing of flowering initiation is a fundamental trait for the adaptation of annual plants to different environments. Large amounts of intraspecific quantitative variation have been described for it among natural accessions of many species, but the molecular and evolutionary mechanisms underlying this genetic variation are mainly being determined in the model plant Arabidopsis thaliana. To find novel A. thaliana flowering QTL, we developed introgression lines from the Japanese accession Fuk, which was selected based on the substantial transgression observed in an F2 population with the reference strain Ler. Analysis of an early flowering line carrying a single Fuk introgression identified Flowering Arabidopsis QTL1 (FAQ1). We fine-mapped FAQ1 in an 11 kb genomic region containing the MADS transcription factor gene SHORT VEGETATIVE PHASE (SVP). Complementation of the early flowering phenotype of FAQ1-Fuk with a SVP-Ler transgen demonstrated that FAQ1 is SVP. We further proved by directed mutagenesis and transgenesis that a single amino acid substitution in SVP causes the loss-of-function and early flowering of Fuk allele. Analysis of a worldwide collection of accessions detected FAQ1/SVP-Fuk allele only in Asia, with the highest frequency appearing in Japan, where we could also detect a potential ancestral genotype of FAQ1/SVP-Fuk. In addition, we evaluated allelic and epistatic interactions of SVP natural alleles by analysing more than one hundred transgenic lines carrying Ler or Fuk SVP alleles in five genetic backgrounds. Quantitative analyses of these lines showed that FAQ1/SVP effects vary from large to small depending on the genetic background. These results support that the flowering repressor SVP has been recently selected in A. thaliana as a target for early flowering, and evidence the relevance of genetic interactions for the intraspecific evolution of FAQ1/SVP and flowering time.
Author Summary
In many plant species, the timing of flowering initiation shows abundant quantitative variation among natural varieties, which reflects the importance of this trait for adaptation to different environments. Currently, a major goal in plant biology is to determine the molecular and evolutionary bases of this natural genetic variation. In this study we demonstrate that the central flowering regulator SHORT VEGETATIVE PHASE (SVP), encoding a MADS transcription factor, is involved in the flowering natural variation of the model organism Arabidopsis thaliana. In particular, we prove that a structural change caused by a single amino acid substitution generates a SVP early flowering allele that is distributed only in Asia. Furthermore, genetic interactions have been shown to be a component of the natural variation for many important adaptive traits. However, very few studies, either in animals or plants, have systematically addressed the extent of genetic interactions among specific alleles responsible for the natural variation of complex traits. Our study shows that the flowering effects of SVP natural alleles depend significantly on the genetic background; and, subsequently, we demonstrate the relevance of epistasis for the evolution of this crucial transcription factor and flowering time.
PMCID: PMC3561112  PMID: 23382706
9.  Linkage and Association Mapping of Arabidopsis thaliana Flowering Time in Nature 
PLoS Genetics  2010;6(5):e1000940.
Flowering time is a key life-history trait in the plant life cycle. Most studies to unravel the genetics of flowering time in Arabidopsis thaliana have been performed under greenhouse conditions. Here, we describe a study about the genetics of flowering time that differs from previous studies in two important ways: first, we measure flowering time in a more complex and ecologically realistic environment; and, second, we combine the advantages of genome-wide association (GWA) and traditional linkage (QTL) mapping. Our experiments involved phenotyping nearly 20,000 plants over 2 winters under field conditions, including 184 worldwide natural accessions genotyped for 216,509 SNPs and 4,366 RILs derived from 13 independent crosses chosen to maximize genetic and phenotypic diversity. Based on a photothermal time model, the flowering time variation scored in our field experiment was poorly correlated with the flowering time variation previously obtained under greenhouse conditions, reinforcing previous demonstrations of the importance of genotype by environment interactions in A. thaliana and the need to study adaptive variation under natural conditions. The use of 4,366 RILs provides great power for dissecting the genetic architecture of flowering time in A. thaliana under our specific field conditions. We describe more than 60 additive QTLs, all with relatively small to medium effects and organized in 5 major clusters. We show that QTL mapping increases our power to distinguish true from false associations in GWA mapping. QTL mapping also permits the identification of false negatives, that is, causative SNPs that are lost when applying GWA methods that control for population structure. Major genes underpinning flowering time in the greenhouse were not associated with flowering time in this study. Instead, we found a prevalence of genes involved in the regulation of the plant circadian clock. Furthermore, we identified new genomic regions lacking obvious candidate genes.
Author Summary
Dissecting the genetic bases of adaptive traits is of primary importance in evolutionary biology. In this study, we combined a genome-wide association (GWA) study with traditional linkage mapping in order to detect the genetic bases underlying natural variation in flowering time in ecologically realistic conditions in the plant Arabidopsis thaliana. Our study involved phenotyping nearly 20,000 plants over 2 winters under field conditions in a temperate climate. We show that combined linkage and association mapping clearly outperforms each method alone when it comes to identifying true associations. This highlights the utility of combining different methods to localize genes involved in complex trait natural variation. Most candidate genes found in this study are involved in the regulation of the plant circadian clock and, surprisingly, were not associated with flowering time scored under greenhouse conditions. While rapid advances have been made in high-throughput genotyping and sequencing, high-throughput phenotyping of complex traits under natural conditions will be the next challenge for dissecting the genetic bases of adaptive variation in “laboratory” model organisms.
PMCID: PMC2865524  PMID: 20463887
10.  Heritability and Tissue Specificity of Expression Quantitative Trait Loci 
PLoS Genetics  2006;2(10):e172.
Variation in gene expression is heritable and has been mapped to the genome in humans and model organisms as expression quantitative trait loci (eQTLs). We applied integrated genome-wide expression profiling and linkage analysis to the regulation of gene expression in fat, kidney, adrenal, and heart tissues using the BXH/HXB panel of rat recombinant inbred strains. Here, we report the influence of heritability and allelic effect of the quantitative trait locus on detection of cis- and trans-acting eQTLs and discuss how these factors operate in a tissue-specific context. We identified several hundred major eQTLs in each tissue and found that cis-acting eQTLs are highly heritable and easier to detect than trans-eQTLs. The proportion of heritable expression traits was similar in all tissues; however, heritability alone was not a reliable predictor of whether an eQTL will be detected. We empirically show how the use of heritability as a filter reduces the ability to discover trans-eQTLs, particularly for eQTLs with small effects. Only 3% of cis- and trans-eQTLs exhibited large allelic effects, explaining more than 40% of the phenotypic variance, suggestive of a highly polygenic control of gene expression. Power calculations indicated that, across tissues, minor differences in genetic effects are expected to have a significant impact on detection of trans-eQTLs. Trans-eQTLs generally show smaller effects than cis-eQTLs and have a higher false discovery rate, particularly in more heterogeneous tissues, suggesting that small biological variability, likely relating to tissue composition, may influence detection of trans-eQTLs in this system. We delineate the effects of genetic architecture on variation in gene expression and show the sensitivity of this experimental design to tissue sampling variability in large-scale eQTL studies.
The combined application of genome-wide expression profiling from microarray experiments with genetic linkage analysis enables the mapping of expression quantitative trait loci (eQTLs), which are primary control points for gene expression across the genome. This approach has been called “genetical genomics”, and recent technological and methodological advances have made its large-scale application feasible in humans and model organisms. Using this approach, the authors have carried out an extensive analysis of the genetic architecture underlying variation in gene expression using a panel of 30 rat recombinant inbred strains. The results are used to explore the relationship between heritability of gene expression, cis- and trans-acting genetic effects, tissue heterogeneity, and statistical cut-offs of significance, which are important factors for large-scale eQTL studies. By examining large eQTL data from four tissues, the authors provide a detailed picture of cis- and trans-eQTL features that may help understanding of the genetic regulation of transcription on a genomic scale. The results also show the sensitivity of this approach to discriminate between cis and trans regulation and the value of the rat system in studying large eQTL datasets from multiple tissues.
PMCID: PMC1617131  PMID: 17054398
11.  Gene Transposition Causing Natural Variation for Growth in Arabidopsis thaliana 
PLoS Genetics  2010;6(5):e1000945.
A major challenge in biology is to identify molecular polymorphisms responsible for variation in complex traits of evolutionary and agricultural interest. Using the advantages of Arabidopsis thaliana as a model species, we sought to identify new genes and genetic mechanisms underlying natural variation for shoot growth using quantitative genetic strategies. More quantitative trait loci (QTL) still need be resolved to draw a general picture as to how and where in the pathways adaptation is shaping natural variation and the type of molecular variation involved. Phenotypic variation for shoot growth in the Bur-0 × Col-0 recombinant inbred line set was decomposed into several QTLs. Nearly-isogenic lines generated from the residual heterozygosity segregating among lines revealed an even more complex picture, with major variation controlled by opposite linked loci and masked by the segregation bias due to the defective phenotype of SG3 (Shoot Growth-3), as well as epistasis with SG3i (SG3-interactor). Using principally a fine-mapping strategy, we have identified the underlying gene causing phenotypic variation at SG3: At4g30720 codes for a new chloroplast-located protein essential to ensure a correct electron flow through the photosynthetic chain and, hence, photosynthesis efficiency and normal growth. The SG3/SG3i interaction is the result of a structural polymorphism originating from the duplication of the gene followed by divergent paralogue's loss between parental accessions. Species-wide, our results illustrate the very dynamic rate of duplication/transposition, even over short periods of time, resulting in several divergent—but still functional—combinations of alleles fixed in different backgrounds. In predominantly selfing species like Arabidopsis, this variation remains hidden in wild populations but is potentially revealed when divergent individuals outcross. This work highlights the need for improved tools and algorithms to resolve structural variation polymorphisms using high-throughput sequencing, because it remains challenging to distinguish allelic from paralogous variation at this scale.
Author Summary
Plant growth is a very complex character impacted by almost any aspect of plant biology and showing continuous variation among natural populations of a single species like Arabidopsis thaliana. Although difficult, it is important to reveal the precise genetic architecture of such a trait's variation to improve our understanding of the mechanisms and evolutionary significance of phenotypic variation. By using recombinant inbred lines derived from a cross between the reference strain ‘Col-0’ and the Irish strain ‘Bur-0’, we have localized several regions of the genome impacting plant growth. When attempting to confirm one of this region's effect, we revealed an even more complex genetic architecture where a first locus (which had remained undetected initially) has a major effect on growth only when a specific genotype was present at a second locus. We have shown here that the reason for this epistatic interaction between the two loci is that the functional allele for a gene important for photosynthesis efficiency and, consequently, growth, had been transposed from one locus to the other in Bur-0 compared to Col-0. This type of structural polymorphism seems to be frequent among strains and, although more difficult to detect, is likely to be of significant evolutionary importance.
PMCID: PMC2869320  PMID: 20485571
12.  Identifying the genetic determinants of transcription factor activity 
Genome-wide messenger RNA expression levels are highly heritable. However, the molecular mechanisms underlying this heritability are poorly understood.The influence of trans-acting polymorphisms is often mediated by changes in the regulatory activity of one or more sequence-specific transcription factors (TFs). We use a method that exploits prior information about the DNA-binding specificity of each TF to estimate its genotype-specific regulatory activity. To this end, we perform linear regression of genotype-specific differential mRNA expression on TF-specific promoter-binding affinity.Treating inferred TF activity as a quantitative trait and mapping it across a panel of segregants from an experimental genetic cross allows us to identify trans-acting loci (‘aQTLs') whose allelic variation modulates the TF. A few of these aQTL regions contain the gene encoding the TF itself; several others contain a gene whose protein product is known to interact with the TF.Our method is strictly causal, as it only uses sequence-based features as predictors. Application to budding yeast demonstrates a dramatic increase in statistical power, compared with existing methods, to detect locus-TF associations and trans-acting loci. Our aQTL mapping strategy also succeeds in mouse.
Genetic sequence variation naturally perturbs mRNA expression levels in the cell. In recent years, analysis of parallel genotyping and expression profiling data for segregants from genetic crosses between parental strains has revealed that mRNA expression levels are highly heritable. Expression quantitative trait loci (eQTLs), whose allelic variation regulates the expression level of individual genes, have successfully been identified (Brem et al, 2002; Schadt et al, 2003). The molecular mechanisms underlying the heritability of mRNA expression are poorly understood. However, they are likely to involve mediation by transcription factors (TFs). We present a new transcription-factor-centric method that greatly increases our ability to understand what drives the genetic variation in mRNA expression (Figure 1). Our method identifies genomic loci (‘aQTLs') whose allelic variation modulates the protein-level activity of specific TFs. To map aQTLs, we integrate genotyping and expression profiling data with quantitative prior information about DNA-binding specificity of transcription factors in the form of position-specific affinity matrices (Bussemaker et al, 2007). We applied our method in two different organisms: budding yeast and mouse.
In our approach, the inferred TF activity is explicitly treated as a quantitative trait, and genetically mapped. The decrease of ‘phenotype space' from that of all genes (in the eQTL approach) to that of all TFs (in our aQTL approach) increases the statistical power to detect trans-acting loci in two distinct ways. First, as each inferred TF activity is derived from a large number of genes, it is far less noisy than mRNA levels of individual genes. Second, the number of trait/marker combinations that needs to be tested for statistical significance in parallel is roughly two orders of magnitude smaller than for eQTLs. We identified a total of 103 locus-TF associations, a more than six-fold improvement over the 17 locus-TF associations identified by several existing methods (Brem et al, 2002; Yvert et al, 2003; Lee et al, 2006; Smith and Kruglyak, 2008; Zhu et al, 2008). The total number of distinct genomic loci identified as an aQTL equals 31, which includes 11 of the 13 previously identified eQTL hotspots (Smith and Kruglyak, 2008).
To better understand the mechanisms underlying the identified genetic linkages, we examined the genes within each aQTL region. First, we found four ‘local' aQTLs, which encompass the gene encoding the TF itself. This includes the known polymorphism in the HAP1 gene (Brem et al, 2002), but also novel predictions of trans-acting polymorphisms in RFX1, STB5, and HAP4. Second, using high-throughput protein–protein interaction data, we identified putative causal genes for several aQTLs. For example, we predict that a polymorphism in the cyclin-dependent kinase CDC28 antagonistically modulates the functionally distinct cell cycle regulators Fkh1 and Fkh2. In this and other cases, our approach naturally accounts for post-translational modulation of TF activity at the protein level.
We validated our ability to predict locus-TF associations in yeast using gene expression profiles of allele replacement strains from a previous study (Smith and Kruglyak, 2008). Chromosome 15 contains an aQTL whose allelic status influences the activity of no fewer than 30 distinct TFs. This locus includes IRA2, which controls intracellular cAMP levels. We used the gene expression profile of IRA2 replacement strains to confirm that the polymorphism within IRA2 indeed modulates a subset of the TFs whose activity was predicted to link to this locus, and no other TFs.
Application of our approach to mouse data identified an aQTL modulating the activity of a specific TF in liver cells. We identified an aQTL on mouse chromosome 7 for Zscan4, a transcription factor containing four zinc finger domains and a SCAN domain. Even though we could not detect a candidate causal gene for Zscan4p because of lack of information about the mouse genome, our result demonstrates that our method also works in higher eukaryotes.
In summary, aQTL mapping has a greatly improved sensitivity to detect molecular mechanisms underlying the heritability of gene expression. The successful application of our approach to yeast and mouse data underscores the value of explicitly treating the inferred TF activity as a quantitative trait for increasing statistical power of detecting trans-acting loci. Furthermore, our method is computationally efficient, and easily applicable to any other organism whenever prior information about the DNA-binding specificity of TFs is available.
Analysis of parallel genotyping and expression profiling data has shown that mRNA expression levels are highly heritable. Currently, only a tiny fraction of this genetic variance can be mechanistically accounted for. The influence of trans-acting polymorphisms on gene expression traits is often mediated by transcription factors (TFs). We present a method that exploits prior knowledge about the in vitro DNA-binding specificity of a TF in order to map the loci (‘aQTLs') whose inheritance modulates its protein-level regulatory activity. Genome-wide regression of differential mRNA expression on predicted promoter affinity is used to estimate segregant-specific TF activity, which is subsequently mapped as a quantitative phenotype. In budding yeast, our method identifies six times as many locus-TF associations and more than twice as many trans-acting loci as all existing methods combined. Application to mouse data from an F2 intercross identified an aQTL on chromosome VII modulating the activity of Zscan4 in liver cells. Our method has greatly improved statistical power over existing methods, is mechanism based, strictly causal, computationally efficient, and generally applicable.
PMCID: PMC2964119  PMID: 20865005
gene expression; gene regulatory networks; genetic variation; quantitative trait loci; transcription factors
13.  QTLs for Seed Vigor-Related Traits Identified in Maize Seeds Germinated under Artificial Aging Conditions 
PLoS ONE  2014;9(3):e92535.
High seed vigor is important for agricultural production due to the associated potential for increased growth and productivity. However, a better understanding of the underlying molecular mechanisms is required because the genetic basis for seed vigor remains unknown. We used single-nucleotide polymorphism (SNP) markers to map quantitative trait loci (QTLs) for four seed vigor traits in two connected recombinant inbred line (RIL) maize populations under four treatment conditions during seed germination. Sixty-five QTLs distributed between the two populations were identified and a meta-analysis was used to integrate genetic maps. Sixty-one initially identified QTLs were integrated into 18 meta-QTLs (mQTLs). Initial QTLs with contribution to phenotypic variation values of R2>10% were integrated into mQTLs. Twenty-three candidate genes for association with seed vigor traits coincided with 13 mQTLs. The candidate genes had functions in the glycolytic pathway and in protein metabolism. QTLs with major effects (R2>10%) were identified under at least one treatment condition for mQTL2, mQTL3-2, and mQTL3-4. Candidate genes included a calcium-dependent protein kinase gene (302810918) involved in signal transduction that mapped in the mQTL3-2 interval associated with germination energy (GE) and germination percentage (GP), and an hsp20/alpha crystallin family protein gene (At5g51440) that mapped in the mQTL3-4 interval associated with GE and GP. Two initial QTLs with a major effect under at least two treatment conditions were identified for mQTL5-2. A cucumisin-like Ser protease gene (At5g67360) mapped in the mQTL5-2 interval associated with GP. The chromosome regions for mQTL2, mQTL3-2, mQTL3-4, and mQTL5-2 may be hot spots for QTLs related to seed vigor traits. The mQTLs and candidate genes identified in this study provide valuable information for the identification of additional quantitative trait genes.
PMCID: PMC3961396  PMID: 24651614
14.  An Eight-Parent Multiparent Advanced Generation Inter-Cross Population for Winter-Sown Wheat: Creation, Properties, and Validation 
G3: Genes|Genomes|Genetics  2014;4(9):1603-1610.
MAGIC populations represent one of a new generation of crop genetic mapping resources combining high genetic recombination and diversity. We describe the creation and validation of an eight-parent MAGIC population consisting of 1091 F7 lines of winter-sown wheat (Triticum aestivum L.). Analyses based on genotypes from a 90,000-single nucleotide polymorphism (SNP) array find the population to be well-suited as a platform for fine-mapping quantitative trait loci (QTL) and gene isolation. Patterns of linkage disequilibrium (LD) show the population to be highly recombined; genetic marker diversity among the founders was 74% of that captured in a larger set of 64 wheat varieties, and 54% of SNPs segregating among the 64 lines also segregated among the eight founder lines. In contrast, a commonly used reference bi-parental population had only 54% of the diversity of the 64 varieties with 27% of SNPs segregating. We demonstrate the potential of this MAGIC resource by identifying a highly diagnostic marker for the morphological character "awn presence/absence" and independently validate it in an association-mapping panel. These analyses show this large, diverse, and highly recombined MAGIC population to be a powerful resource for the genetic dissection of target traits in wheat, and it is well-placed to efficiently exploit ongoing advances in phenomics and genomics. Genetic marker and trait data, together with instructions for access to seed, are available at
PMCID: PMC4169152  PMID: 25237112
quantitative genetics; genome-wide association scan; single nucleotide polymorphism; QTL mapping; recombinant inbred line (RIL); high-density genotyping; Multiparent Advanced Generation Inter-Cross (MAGIC); multiparental populations; MPP
15.  Fine mapping and replication of QTL in outbred chicken advanced intercross lines 
Linkage mapping is used to identify genomic regions affecting the expression of complex traits. However, when experimental crosses such as F2 populations or backcrosses are used to map regions containing a Quantitative Trait Locus (QTL), the size of the regions identified remains quite large, i.e. 10 or more Mb. Thus, other experimental strategies are needed to refine the QTL locations. Advanced Intercross Lines (AIL) are produced by repeated intercrossing of F2 animals and successive generations, which decrease linkage disequilibrium in a controlled manner. Although this approach is seen as promising, both to replicate QTL analyses and fine-map QTL, only a few AIL datasets, all originating from inbred founders, have been reported in the literature.
We have produced a nine-generation AIL pedigree (n = 1529) from two outbred chicken lines divergently selected for body weight at eight weeks of age. All animals were weighed at eight weeks of age and genotyped for SNP located in nine genomic regions where significant or suggestive QTL had previously been detected in the F2 population. In parallel, we have developed a novel strategy to analyse the data that uses both genotype and pedigree information of all AIL individuals to replicate the detection of and fine-map QTL affecting juvenile body weight.
Five of the nine QTL detected with the original F2 population were confirmed and fine-mapped with the AIL, while for the remaining four, only suggestive evidence of their existence was obtained. All original QTL were confirmed as a single locus, except for one, which split into two linked QTL.
Our results indicate that many of the QTL, which are genome-wide significant or suggestive in the analyses of large intercross populations, are true effects that can be replicated and fine-mapped using AIL. Key factors for success are the use of large populations and powerful statistical tools. Moreover, we believe that the statistical methods we have developed to efficiently study outbred AIL populations will increase the number of organisms for which in-depth complex traits can be analyzed.
PMCID: PMC3034666  PMID: 21241486
16.  solQTL: a tool for QTL analysis, visualization and linking to genomes at SGN database 
BMC Bioinformatics  2010;11:525.
A common approach to understanding the genetic basis of complex traits is through identification of associated quantitative trait loci (QTL). Fine mapping QTLs requires several generations of backcrosses and analysis of large populations, which is time-consuming and costly effort. Furthermore, as entire genomes are being sequenced and an increasing amount of genetic and expression data are being generated, a challenge remains: linking phenotypic variation to the underlying genomic variation. To identify candidate genes and understand the molecular basis underlying the phenotypic variation of traits, bioinformatic approaches are needed to exploit information such as genetic map, expression and whole genome sequence data of organisms in biological databases.
The Sol Genomics Network (SGN, is a primary repository for phenotypic, genetic, genomic, expression and metabolic data for the Solanaceae family and other related Asterids species and houses a variety of bioinformatics tools. SGN has implemented a new approach to QTL data organization, storage, analysis, and cross-links with other relevant data in internal and external databases. The new QTL module, solQTL,, employs a user-friendly web interface for uploading raw phenotype and genotype data to the database, R/QTL mapping software for on-the-fly QTL analysis and algorithms for online visualization and cross-referencing of QTLs to relevant datasets and tools such as the SGN Comparative Map Viewer and Genome Browser. Here, we describe the development of the solQTL module and demonstrate its application.
solQTL allows Solanaceae researchers to upload raw genotype and phenotype data to SGN, perform QTL analysis and dynamically cross-link to relevant genetic, expression and genome annotations. Exploration and synthesis of the relevant data is expected to help facilitate identification of candidate genes underlying phenotypic variation and markers more closely linked to QTLs. solQTL is freely available on SGN and can be used in private or public mode.
PMCID: PMC2984588  PMID: 20964836
17.  Impact of Natural Genetic Variation on Gene Expression Dynamics 
PLoS Genetics  2013;9(6):e1003514.
DNA sequence variation causes changes in gene expression, which in turn has profound effects on cellular states. These variations affect tissue development and may ultimately lead to pathological phenotypes. A genetic locus containing a sequence variation that affects gene expression is called an “expression quantitative trait locus” (eQTL). Whereas the impact of cellular context on expression levels in general is well established, a lot less is known about the cell-state specificity of eQTL. Previous studies differed with respect to how “dynamic eQTL” were defined. Here, we propose a unified framework distinguishing static, conditional and dynamic eQTL and suggest strategies for mapping these eQTL classes. Further, we introduce a new approach to simultaneously infer eQTL from different cell types. By using murine mRNA expression data from four stages of hematopoiesis and 14 related cellular traits, we demonstrate that static, conditional and dynamic eQTL, although derived from the same expression data, represent functionally distinct types of eQTL. While static eQTL affect generic cellular processes, non-static eQTL are more often involved in hematopoiesis and immune response. Our analysis revealed substantial effects of individual genetic variation on cell type-specific expression regulation. Among a total number of 3,941 eQTL we detected 2,729 static eQTL, 1,187 eQTL were conditionally active in one or several cell types, and 70 eQTL affected expression changes during cell type transitions. We also found evidence for feedback control mechanisms reverting the effect of an eQTL specifically in certain cell types. Loci correlated with hematological traits were enriched for conditional eQTL, thus, demonstrating the importance of conditional eQTL for understanding molecular mechanisms underlying physiological trait variation. The classification proposed here has the potential to streamline and unify future analysis of conditional and dynamic eQTL as well as many other kinds of QTL data.
Author Summary
Complex physiological traits are affected through subtle changes of molecular traits like gene expression in the relevant tissues, which in turn are caused by genetic variation. A genetic locus containing a sequence variation affecting gene expression is called an expression quantitative trait locus (eQTL). Understanding the tissue and cell type specificity of eQTL effects is essential for revealing the molecular mechanisms underlying disease phenotypes. However, so far the cell-state dependence of eQTL is poorly understood. In order to systematically assess the importance of cell state-specific eQTL, we propose to distinguish static, conditional and dynamic eQTL and suggest strategies for mapping these eQTL classes. We applied our framework to mouse gene expression data from four hematopoietic stages and related cellular traits. The different eQTL classes, although derived from the same expression data, represent functionally distinct types of eQTL. Importantly, conditional eQTL are well correlated with relevant hematological traits. These findings emphasize the condition specificity of many regulatory relationships, even if the conditions under study are related. This calls for due caution when transferring conclusions about regulatory mechanisms across cell types or tissues. The proposed classification will also help to unravel dynamic behaviors in many other kinds of QTL data.
PMCID: PMC3674999  PMID: 23754949
18.  Molecular mapping of QTLs for plant type and earliness traits in pigeonpea (Cajanus cajan L. Millsp.) 
BMC Genetics  2012;13:84.
Pigeonpea is an important grain legume of the semi-arid tropics and sub-tropical regions where it plays a crucial role in the food and nutritional security of the people. The average productivity of pigeonpea has remained very low and stagnant for over five decades due to lack of genomic information and intensive breeding efforts. Previous SSR-based linkage maps of pigeonpea used inter-specific crosses due to low inter-varietal polymorphism. Here our aim was to construct a high density intra-specific linkage map using genic-SNP markers for mapping of major quantitative trait loci (QTLs) for key agronomic traits, including plant height, number of primary and secondary branches, number of pods, days to flowering and days to maturity in pigeonpea.
A population of 186 F2:3 lines derived from an intra-specific cross between inbred lines ‘Pusa Dwarf’ and ‘HDM04-1’ was used to construct a dense molecular linkage map of 296 genic SNP and SSR markers covering a total adjusted map length of 1520.22 cM for the 11 chromosomes of the pigeonpea genome. This is the first dense intra-specific linkage map of pigeonpea with the highest genome length coverage. Phenotypic data from the F2:3 families were used to identify thirteen QTLs for the six agronomic traits. The proportion of phenotypic variance explained by the individual QTLs ranged from 3.18% to 51.4%. Ten of these QTLs were clustered in just two genomic regions, indicating pleiotropic effects or close genetic linkage. In addition to the main effects, significant epistatic interaction effects were detected between the QTLs for number of pods per plant.
A large amount of information on transcript sequences, SSR markers and draft genome sequence is now available for pigeonpea. However, there is need to develop high density linkage maps and identify genes/QTLs for important agronomic traits for practical breeding applications. This is the first report on identification of QTLs for plant type and maturity traits in pigeonpea. The QTLs identified in this study provide a strong foundation for further validation and fine mapping for utilization in the pigeonpea improvement.
PMCID: PMC3504571  PMID: 23043321
Earliness; Pigeonpea; Plant height; QTL mapping; SSR marker; SNP marker
19.  Identification of QTLs affecting scopolin and scopoletin biosynthesis in Arabidopsis thaliana 
BMC Plant Biology  2014;14(1):280.
Scopoletin and its glucoside scopolin are important secondary metabolites synthesized in plants as a defense mechanism against various environmental stresses. They belong to coumarins, a class of phytochemicals with significant biological activities that is widely used in medical application and cosmetics industry. Although numerous studies showed that a variety of coumarins occurs naturally in several plant species, the details of coumarins biosynthesis and its regulation is not well understood. It was shown previously that coumarins (predominantly scopolin and scopoletin) occur in Arabidopsis thaliana (Arabidopsis) roots, but until now nothing is known about natural variation of their accumulation in this model plant. Therefore, the genetic architecture of coumarins biosynthesis in Arabidopsis has not been studied before.
Here, the variation in scopolin and scopoletin content was assessed by comparing seven Arabidopsis accessions. Subsequently, a quantitative trait locus (QTL) mapping was performed with an Advanced Intercross Recombinant Inbred Lines (AI-RILs) mapping population EstC (Est-1 × Col). In order to reveal the genetic basis of both scopolin and scopoletin biosynthesis, two sets of methanol extracts were made from Arabidopsis roots and one set was additionally subjected to enzymatic hydrolysis prior to quantification done by high-performance liquid chromatography (HPLC). We identified one QTL for scopolin and five QTLs for scopoletin accumulation. The identified QTLs explained 13.86% and 37.60% of the observed phenotypic variation in scopolin and scopoletin content, respectively. In silico analysis of genes located in the associated QTL intervals identified a number of possible candidate genes involved in coumarins biosynthesis.
Together, our results demonstrate for the first time that Arabidopsis is an excellent model for studying the genetic and molecular basis of natural variation in coumarins biosynthesis in plants. It additionally provides a basis for fine mapping and cloning of the genes involved in scopolin and scopoletin biosynthesis. Importantly, we have identified new loci for this biosynthetic process.
Electronic supplementary material
The online version of this article (doi:10.1186/s12870-014-0280-9) contains supplementary material, which is available to authorized users.
PMCID: PMC4252993  PMID: 25326030
Coumarins; Natural variation; Plant-environment interaction; Scopoletin; Scopolin; Secondary metabolism; QTL mapping
20.  Metabolite profiling and quantitative genetics of natural variation for flavonoids in Arabidopsis  
Journal of Experimental Botany  2012;63(10):3749-3764.
Little is known about the range and the genetic bases of naturally occurring variation for flavonoids. Using Arabidopsis thaliana seed as a model, the flavonoid content of 41 accessions and two recombinant inbred line (RIL) sets derived from divergent accessions (Cvi-0×Col-0 and Bay-0×Shahdara) were analysed. These accessions and RILs showed mainly quantitative rather than qualitative changes. To dissect the genetic architecture underlying these differences, a quantitative trait locus (QTL) analysis was performed on the two segregating populations. Twenty-two flavonoid QTLs were detected that accounted for 11–64% of the observed trait variations, only one QTL being common to both RIL sets. Sixteen of these QTLs were confirmed and coarsely mapped using heterogeneous inbred families (HIFs). Three genes, namely TRANSPARENT TESTA (TT)7, TT15, and MYB12, were proposed to underlie their variations since the corresponding mutants and QTLs displayed similar specific flavonoid changes. Interestingly, most loci did not co-localize with any gene known to be involved in flavonoid metabolism. This latter result shows that novel functions have yet to be characterized and paves the way for their isolation.
PMCID: PMC3388840  PMID: 22442426
Arabidopsis; flavonoids; metabolite profiling; natural variation; quantitative trait loci
21.  Genetic dissection of rice grain shape using a recombinant inbred line population derived from two contrasting parents and fine mapping a pleiotropic quantitative trait locus qGL7 
BMC Genetics  2010;11:16.
The three-dimensional shape of grain, measured as grain length, width, and thickness (GL, GW, and GT), is one of the most important components of grain appearance in rice. Determining the genetic basis of variations in grain shape could facilitate efficient improvements in grain appearance. In this study, an F7:8 recombinant inbred line population (RIL) derived from a cross between indica and japonica cultivars (Nanyangzhan and Chuan7) contrasting in grain size was used for quantitative trait locus (QTL) mapping. A genetic linkage map was constructed with 164 simple sequence repeat (SSR) markers. The major aim of this study was to detect a QTL for grain shape and to fine map a minor QTL, qGL7.
Four QTLs for GL were detected on chromosomes 3 and 7, and 10 QTLs for GW and 9 QTLs for GT were identified on chromosomes 2, 3, 5, 7, 9 and 10, respectively. A total of 28 QTLs were identified, of which several are reported for the first time; four major QTLs and six minor QTLs for grain shape were also commonly detected in both years. The minor QTL, qGL7, exhibited pleiotropic effects on GL, GW, GT, 1000-grain weight (TGW), and spikelets per panicle (SPP) and was further validated in a near isogenic F2 population (NIL-F2). Finally, qGL7 was narrowed down to an interval between InDel marker RID711 and SSR marker RM6389, covering a 258-kb region in the Nipponbare genome, and cosegregated with InDel markers RID710 and RID76.
Materials with very different phenotypes were used to develop mapping populations to detect QTLs because of their complex genetic background. Progeny tests proved that the minor QTL, qGL7, could display a single mendelian characteristic. Therefore, we suggested that minor QTLs for traits with high heritability could be isolated using a map-based cloning strategy in a large NIL-F2 population. In addition, combinations of different QTLs produced diverse grain shapes, which provide the ability to breed more varieties of rice to satisfy consumer preferences.
PMCID: PMC2846863  PMID: 20184774
22.  QTL meta-analysis in Arabidopsis reveals an interaction between leaf senescence and resource allocation to seeds 
Journal of Experimental Botany  2014;65(14):3949-3962.
Mapping of metaQTL controlling leaf senescence and seed resource allocation in Arabidopsis reveals that leaf senescence might disrupt the general negative correlation observed between yield and seed nitrogen concentration.
Sequential and monocarpic senescence are observed at vegetative and reproductive stages, respectively. Both facilitate nitrogen (N) remobilization and control the duration of carbon (C) fixation. Genetic and environmental factors control N and C resource allocation to seeds. Studies of natural variation in Arabidopsis thaliana revealed differences between accessions for leaf senescence phenotypes, seed N and C contents, and N remobilization efficiency-related traits. Here, a quantitative genetics approach was used to gain a better understanding of seed filling regulation in relation to leaf senescence and resource allocation. For that purpose, three Arabidopsis recombinant inbred line populations (Ct-1×Col-0, Cvi-0×Col-0, Bur-0×Col-0) were used to map QTL (quantitative trait loci) for ten traits related to senescence, resource allocation, and seed filling. The use of common markers across the three different maps allowed direct comparisons of the positions of the detected QTL in a single consensus map. QTL meta-analysis was then used to identify interesting regions (metaQTL) where QTL for several traits co-localized. MetaQTL were compared with positions of candidate genes known to be involved in senescence processes and flowering time. Finally, investigation of the correlation between yield and seed N concentration in the three populations suggests that leaf senescence disrupts the negative correlation generally observed between these two traits.
PMCID: PMC4106442  PMID: 24692652
Leaf senescence; nitrogen and carbon allocation; harvest index; yield; flowering time.
23.  Quantitative trait loci for energy balance traits in an advanced intercross line derived from mice divergently selected for heat loss 
PeerJ  2014;2:e392.
Obesity in human populations, currently a serious health concern, is considered to be the consequence of an energy imbalance in which more energy in calories is consumed than is expended. We used interval mapping techniques to investigate the genetic basis of a number of energy balance traits in an F11 advanced intercross population of mice created from an original intercross of lines selected for increased and decreased heat loss. We uncovered a total of 137 quantitative trait loci (QTLs) for these traits at 41 unique sites on 18 of the 20 chromosomes in the mouse genome, with X-linked QTLs being most prevalent. Two QTLs were found for the selection target of heat loss, one on distal chromosome 1 and another on proximal chromosome 2. The number of QTLs affecting the various traits generally was consistent with previous estimates of heritabilities in the same population, with the most found for two bone mineral traits and the least for feed intake and several body composition traits. QTLs were generally additive in their effects, and some, especially those affecting the body weight traits, were sex-specific. Pleiotropy was extensive within trait groups (body weights, adiposity and organ weight traits, bone traits) and especially between body composition traits adjusted and not adjusted for body weight at sacrifice. Nine QTLs were found for one or more of the adiposity traits, five of which appeared to be unique. The confidence intervals among all QTLs averaged 13.3 Mb, much smaller than usually observed in an F2 cross, and in some cases this allowed us to make reasonable inferences about candidate genes underlying these QTLs. This study combined QTL mapping with genetic parameter analysis in a large segregating population, and has advanced our understanding of the genetic architecture of complex traits related to obesity.
PMCID: PMC4045330  PMID: 24918027
QTL by sex interactions; Metabolic rate; Feed intake; Body weight and body composition
24.  The Genetic Architecture of Methotrexate Toxicity Is Similar in Drosophila melanogaster and Humans 
G3: Genes|Genomes|Genetics  2013;3(8):1301-1310.
The severity of the toxic side effects of chemotherapy varies among patients, and much of this variation is likely genetically based. Here, we use the model system Drosophila melanogaster to genetically dissect the toxicity of methotrexate (MTX), a drug used primarily to treat childhood acute lymphoblastic leukemia and rheumatoid arthritis. We use the Drosophila Synthetic Population Resource, a panel of recombinant inbred lines derived from a multiparent advanced intercross, and quantify MTX toxicity as a reduction in female fecundity. We identify three quantitative trait loci (QTL) affecting MTX toxicity; two colocalize with the fly orthologs of human genes believed to mediate MTX toxicity and one is a novel MTX toxicity gene with a human ortholog. A fourth suggestive QTL spans a centromere. Local single-marker association scans of candidate gene exons fail to implicate amino acid variants as the causative single-nucleotide polymorphisms, and we therefore hypothesize the causative variation is regulatory. In addition, the effects at our mapped QTL do not conform to a simple biallelic pattern, suggesting multiple causative factors underlie the QTL mapping results. Consistent with this observation, no single single-nucleotide polymorphism located in or near a candidate gene can explain the QTL mapping signal. Overall, our results validate D. melanogaster as a model for uncovering the genetic basis of chemotoxicity and suggest the genetic basis of MTX toxicity is due to a handful of genes each harboring multiple segregating regulatory factors.
PMCID: PMC3737169  PMID: 23733889
Drosophila synthetic population resource; pharmacogenomics; methotrexate; quantitative trait loci; chemotoxicity
25.  Combining Genome-Wide Association Mapping and Transcriptional Networks to Identify Novel Genes Controlling Glucosinolates in Arabidopsis thaliana 
PLoS Biology  2011;9(8):e1001125.
Genome-wide association mapping is highly sensitive to environmental changes, but network analysis allows rapid causal gene identification.
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.
Methodology/Principal Findings
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.
Author Summary
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.
PMCID: PMC3156686  PMID: 21857804

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