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1.  A Simple Genetic Architecture Underlies Morphological Variation in Dogs 
PLoS Biology  2010;8(8):e1000451.
The largest genetic study to date of morphology in domestic dogs identifies genes controlling nearly 100 morphological traits and identifies important trends in phenotypic variation within this species.
Domestic dogs exhibit tremendous phenotypic diversity, including a greater variation in body size than any other terrestrial mammal. Here, we generate a high density map of canine genetic variation by genotyping 915 dogs from 80 domestic dog breeds, 83 wild canids, and 10 outbred African shelter dogs across 60,968 single-nucleotide polymorphisms (SNPs). Coupling this genomic resource with external measurements from breed standards and individuals as well as skeletal measurements from museum specimens, we identify 51 regions of the dog genome associated with phenotypic variation among breeds in 57 traits. The complex traits include average breed body size and external body dimensions and cranial, dental, and long bone shape and size with and without allometric scaling. In contrast to the results from association mapping of quantitative traits in humans and domesticated plants, we find that across dog breeds, a small number of quantitative trait loci (≤3) explain the majority of phenotypic variation for most of the traits we studied. In addition, many genomic regions show signatures of recent selection, with most of the highly differentiated regions being associated with breed-defining traits such as body size, coat characteristics, and ear floppiness. Our results demonstrate the efficacy of mapping multiple traits in the domestic dog using a database of genotyped individuals and highlight the important role human-directed selection has played in altering the genetic architecture of key traits in this important species.
Author Summary
Dogs offer a unique system for the study of genes controlling morphology. DNA from 915 dogs from 80 domestic breeds, as well as a set of feral dogs, was tested at over 60,000 points of variation and the dataset analyzed using novel methods to find loci regulating body size, head shape, leg length, ear position, and a host of other traits. Because each dog breed has undergone strong selection by breeders to have a particular appearance, there is a strong footprint of selection in regions of the genome that are important for controlling traits that define each breed. These analyses identified new regions of the genome, or loci, that are important in controlling body size and shape. Our results, which feature the largest number of domestic dogs studied at such a high level of genetic detail, demonstrate the power of the dog as a model for finding genes that control the body plan of mammals. Further, we show that the remarkable diversity of form in the dog, in contrast to some other species studied to date, appears to have a simple genetic basis dominated by genes of major effect.
doi:10.1371/journal.pbio.1000451
PMCID: PMC2919785  PMID: 20711490
2.  Genome-wide association studies for agronomical traits in a world wide spring barley collection 
BMC Plant Biology  2012;12:16.
Background
Genome-wide association studies (GWAS) based on linkage disequilibrium (LD) provide a promising tool for the detection and fine mapping of quantitative trait loci (QTL) underlying complex agronomic traits. In this study we explored the genetic basis of variation for the traits heading date, plant height, thousand grain weight, starch content and crude protein content in a diverse collection of 224 spring barleys of worldwide origin. The whole panel was genotyped with a customized oligonucleotide pool assay containing 1536 SNPs using Illumina's GoldenGate technology resulting in 957 successful SNPs covering all chromosomes. The morphological trait "row type" (two-rowed spike vs. six-rowed spike) was used to confirm the high level of selectivity and sensitivity of the approach. This study describes the detection of QTL for the above mentioned agronomic traits by GWAS.
Results
Population structure in the panel was investigated by various methods and six subgroups that are mainly based on their spike morphology and region of origin. We explored the patterns of linkage disequilibrium (LD) among the whole panel for all seven barley chromosomes. Average LD was observed to decay below a critical level (r2-value 0.2) within a map distance of 5-10 cM. Phenotypic variation within the panel was reasonably large for all the traits. The heritabilities calculated for each trait over multi-environment experiments ranged between 0.90-0.95. Different statistical models were tested to control spurious LD caused by population structure and to calculate the P-value of marker-trait associations. Using a mixed linear model with kinship for controlling spurious LD effects, we found a total of 171 significant marker trait associations, which delineate into 107 QTL regions. Across all traits these can be grouped into 57 novel QTL and 50 QTL that are congruent with previously mapped QTL positions.
Conclusions
Our results demonstrate that the described diverse barley panel can be efficiently used for GWAS of various quantitative traits, provided that population structure is appropriately taken into account. The observed significant marker trait associations provide a refined insight into the genetic architecture of important agronomic traits in barley. However, individual QTL account only for a small portion of phenotypic variation, which may be due to insufficient marker coverage and/or the elimination of rare alleles prior to analysis. The fact that the combined SNP effects fall short of explaining the complete phenotypic variance may support the hypothesis that the expression of a quantitative trait is caused by a large number of very small effects that escape detection. Notwithstanding these limitations, the integration of GWAS with biparental linkage mapping and an ever increasing body of genomic sequence information will facilitate the systematic isolation of agronomically important genes and subsequent analysis of their allelic diversity.
doi:10.1186/1471-2229-12-16
PMCID: PMC3349577  PMID: 22284310
3.  Integrating genome annotation and QTL position to identify candidate genes for productivity, architecture and water-use efficiency in Populus spp 
BMC Plant Biology  2012;12:173.
Background
Hybrid poplars species are candidates for biomass production but breeding efforts are needed to combine productivity and water use efficiency in improved cultivars. The understanding of the genetic architecture of growth in poplar by a Quantitative Trait Loci (QTL) approach can help us to elucidate the molecular basis of such integrative traits but identifying candidate genes underlying these QTLs remains difficult. Nevertheless, the increase of genomic information together with the accessibility to a reference genome sequence (Populus trichocarpa Nisqually-1) allow to bridge QTL information on genetic maps and physical location of candidate genes on the genome. The objective of the study is to identify QTLs controlling productivity, architecture and leaf traits in a P. deltoides x P. trichocarpa F1 progeny and to identify candidate genes underlying QTLs based on the anchoring of genetic maps on the genome and the gene ontology information linked to genome annotation. The strategy to explore genome annotation was to use Gene Ontology enrichment tools to test if some functional categories are statistically over-represented in QTL regions.
Results
Four leaf traits and 7 growth traits were measured on 330 F1 P. deltoides x P. trichocarpa progeny. A total of 77 QTLs controlling 11 traits were identified explaining from 1.8 to 17.2% of the variation of traits. For 58 QTLs, confidence intervals could be projected on the genome. An extended functional annotation was built based on data retrieved from the plant genome database Phytozome and from an inference of function using homology between Populus and the model plant Arabidopsis. Genes located within QTL confidence intervals were retrieved and enrichments in gene ontology (GO) terms were determined using different methods. Significant enrichments were found for all traits. Particularly relevant biological processes GO terms were identified for QTLs controlling number of sylleptic branches: intervals were enriched in GO terms of biological process like ‘ripening’ and ‘adventitious roots development’.
Conclusion
Beyond the simple identification of QTLs, this study is the first to use a global approach of GO terms enrichment analysis to fully explore gene function under QTLs confidence intervals in plants. This global approach may lead to identification of new candidate genes for traits of interest.
doi:10.1186/1471-2229-12-173
PMCID: PMC3520807  PMID: 23013168
4.  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.
doi:10.1038/msb.2010.64
PMCID: PMC2964119  PMID: 20865005
gene expression; gene regulatory networks; genetic variation; quantitative trait loci; transcription factors
5.  QTL mapping for sexually dimorphic fitness-related traits in wild bighorn sheep 
Heredity  2011;108(3):256-263.
Dissecting the genetic architecture of fitness-related traits in wild populations is key to understanding evolution and the mechanisms maintaining adaptive genetic variation. We took advantage of a recently developed genetic linkage map and phenotypic information from wild pedigreed individuals from Ram Mountain, Alberta, Canada, to study the genetic architecture of ecologically important traits (horn volume, length, base circumference and body mass) in bighorn sheep. In addition to estimating sex-specific and cross-sex quantitative genetic parameters, we tested for the presence of quantitative trait loci (QTLs), colocalization of QTLs between bighorn sheep and domestic sheep, and sex × QTL interactions. All traits showed significant additive genetic variance and genetic correlations tended to be positive. Linkage analysis based on 241 microsatellite loci typed in 310 pedigreed animals resulted in no significant and five suggestive QTLs (four for horn dimension on chromosomes 1, 18 and 23, and one for body mass on chromosome 26) using genome-wide significance thresholds (Logarithm of odds (LOD) >3.31 and >1.88, respectively). We also confirmed the presence of a horn dimension QTL in bighorn sheep at the only position known to contain a similar QTL in domestic sheep (on chromosome 10 near the horns locus; nominal P<0.01) and highlighted a number of regions potentially containing weight-related QTLs in both species. As expected for sexually dimorphic traits involved in male–male combat, loci with sex-specific effects were detected. This study lays the foundation for future work on adaptive genetic variation and the evolutionary dynamics of sexually dimorphic traits in bighorn sheep.
doi:10.1038/hdy.2011.69
PMCID: PMC3282393  PMID: 21847139
adaptive variation; animal model; domestic sheep; Ovis aries; sexual dimorphism; sexual selection
6.  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.
doi:10.3389/fgene.2012.00097
PMCID: PMC3368303  PMID: 22685451
Bayesian multilevel modeling; genetic architecture; linked marker-expression pairs; pleiotropy
7.  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.
doi:10.1371/journal.pgen.1000234
PMCID: PMC2565841  PMID: 18949035
8.  Genetic Complexity and Quantitative Trait Loci Mapping of Yeast Morphological Traits  
PLoS Genetics  2007;3(2):e31.
Functional genomics relies on two essential parameters: the sensitivity of phenotypic measures and the power to detect genomic perturbations that cause phenotypic variations. In model organisms, two types of perturbations are widely used. Artificial mutations can be introduced in virtually any gene and allow the systematic analysis of gene function via mutants fitness. Alternatively, natural genetic variations can be associated to particular phenotypes via genetic mapping. However, the access to genome manipulation and breeding provided by model organisms is sometimes counterbalanced by phenotyping limitations. Here we investigated the natural genetic diversity of Saccharomyces cerevisiae cellular morphology using a very sensitive high-throughput imaging platform. We quantified 501 morphological parameters in over 50,000 yeast cells from a cross between two wild-type divergent backgrounds. Extensive morphological differences were found between these backgrounds. The genetic architecture of the traits was complex, with evidence of both epistasis and transgressive segregation. We mapped quantitative trait loci (QTL) for 67 traits and discovered 364 correlations between traits segregation and inheritance of gene expression levels. We validated one QTL by the replacement of a single base in the genome. This study illustrates the natural diversity and complexity of cellular traits among natural yeast strains and provides an ideal framework for a genetical genomics dissection of multiple traits. Our results did not overlap with results previously obtained from systematic deletion strains, showing that both approaches are necessary for the functional exploration of genomes.
Author Summary
A familiar face or a dog breed is easily recognized because morphology of individuals differs according to their genetic backgrounds. For single-cell organisms, morphology reduces to the shape and size of cellular features. Microbiologists noticed that the shape of S. cerevisiae cells (baker's yeast) differs from one strain to another, but these differences were usually described qualitatively. We used a high-throughput imaging platform to study the morphology of yeast cells when they divide. Cells were stained with three fluorescent dyes so that their periphery, their DNA, and their actin could be recognized, and their images were analysed by a specialized software program. Numerous morphological differences were found between two distant strains of S. cerevisiae. By crossing these two strains, we performed quantitative genetics: several loci controlling morphological variations were found on the genome, and correlations were made between gene expression and morphology changes. Using bioinformatics, we showed that the results obtained do not overlap with previous results obtained from yeast cells in which specific genes are deleted. The study, therefore, illustrates how mutagenesis and the use of natural genetic variations provide complementary knowledge.
doi:10.1371/journal.pgen.0030031
PMCID: PMC1802830  PMID: 17319748
9.  Four Linked Genes Participate in Controlling Sporulation Efficiency in Budding Yeast 
PLoS Genetics  2006;2(11):e195.
Quantitative traits are conditioned by several genetic determinants. Since such genes influence many important complex traits in various organisms, the identification of quantitative trait loci (QTLs) is of major interest, but still encounters serious difficulties. We detected four linked genes within one QTL, which participate in controlling sporulation efficiency in Saccharomyces cerevisiae. Following the identification of single nucleotide polymorphisms by comparing the sequences of 145 genes between the parental strains SK1 and S288c, we analyzed the segregating progeny of the cross between them. Through reciprocal hemizygosity analysis, four genes, RAS2, PMS1, SWS2, and FKH2, located in a region of 60 kilobases on Chromosome 14, were found to be associated with sporulation efficiency. Three of the four “high” sporulation alleles are derived from the “low” sporulating strain. Two of these sporulation-related genes were verified through allele replacements. For RAS2, the causative variation was suggested to be a single nucleotide difference in the upstream region of the gene. This quantitative trait nucleotide accounts for sporulation variability among a set of ten closely related winery yeast strains. Our results provide a detailed view of genetic complexity in one “QTL region” that controls a quantitative trait and reports a single nucleotide polymorphism-trait association in wild strains. Moreover, these findings have implications on QTL identification in higher eukaryotes.
Synopsis
Genes controlling many medically and agriculturally important complex traits in various organisms and their organization as quantitative trait loci (QTLs) are of major interest. To identify QTLs responsible for such a quantitative trait, the authors employed a two-step strategy: First, single-nucleotide markers (called SNPs) distributed throughout the genome were screened for prevalence among progeny with extreme characteristics, thus identifying three candidate genomic regions. Next, in one of these regions, manipulation of individual genes revealed four tightly linked genes that affected the trait, sporulation efficiency. A fifth gene that affects sporulation was recently and independently identified in the same region. This 60-kilobase region has a complex and interesting architecture: One strain, which sporulates efficiently, has sporulation-promoting alleles (alternative forms) at two major genes and inhibiting alleles at the three less important ones, whereas another strain, with inefficient sporulation, has the opposite alleles at the five genes. Moreover, one causative SNP for this trait, in the promoter region of the gene RAS2, explains sporulation differences among a set of ten winery yeast strains. These results provide a detailed view of genetic complexity in one “QTL region” and an SNP-trait association example among wild strains.
doi:10.1371/journal.pgen.0020195
PMCID: PMC1636695  PMID: 17112318
10.  A gene-based high-resolution comparative radiation hybrid map as a framework for genome sequence assembly of a bovine chromosome 6 region associated with QTL for growth, body composition, and milk performance traits 
BMC Genomics  2006;7:53.
Background
A number of different quantitative trait loci (QTL) for various phenotypic traits, including milk production, functional, and conformation traits in dairy cattle as well as growth and body composition traits in meat cattle, have been mapped consistently in the middle region of bovine chromosome 6 (BTA6). Dense genetic and physical maps and, ultimately, a fully annotated genome sequence as well as their mutual connections are required to efficiently identify genes and gene variants responsible for genetic variation of phenotypic traits. A comprehensive high-resolution gene-rich map linking densely spaced bovine markers and genes to the annotated human genome sequence is required as a framework to facilitate this approach for the region on BTA6 carrying the QTL.
Results
Therefore, we constructed a high-resolution radiation hybrid (RH) map for the QTL containing chromosomal region of BTA6. This new RH map with a total of 234 loci including 115 genes and ESTs displays a substantial increase in loci density compared to existing physical BTA6 maps. Screening the available bovine genome sequence resources, a total of 73 loci could be assigned to sequence contigs, which were already identified as specific for BTA6. For 43 loci, corresponding sequence contigs, which were not yet placed on the bovine genome assembly, were identified. In addition, the improved potential of this high-resolution RH map for BTA6 with respect to comparative mapping was demonstrated. Mapping a large number of genes on BTA6 and cross-referencing them with map locations in corresponding syntenic multi-species chromosome segments (human, mouse, rat, dog, chicken) achieved a refined accurate alignment of conserved segments and evolutionary breakpoints across the species included.
Conclusion
The gene-anchored high-resolution RH map (1 locus/300 kb) for the targeted region of BTA6 presented here will provide a valuable platform to guide high-quality assembling and annotation of the currently existing bovine genome sequence draft to establish the final architecture of BTA6. Hence, a sequence-based map will provide a key resource to facilitate prospective continued efforts for the selection and validation of relevant positional and functional candidates underlying QTL for milk production and growth-related traits mapped on BTA6 and on similar chromosomal regions from evolutionary closely related species like sheep and goat. Furthermore, the high-resolution sequence-referenced BTA6 map will enable precise identification of multi-species conserved chromosome segments and evolutionary breakpoints in mammalian phylogenetic studies.
doi:10.1186/1471-2164-7-53
PMCID: PMC1475854  PMID: 16542434
11.  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.
Synopsis
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.
doi:10.1371/journal.pgen.0020172
PMCID: PMC1617131  PMID: 17054398
12.  Growth-related quantitative trait loci in domestic and wild rainbow trout (Oncorhynchus mykiss) 
BMC Genetics  2010;11:63.
Background
Somatic growth is a complex process that involves the action and interaction of genes and environment. A number of quantitative trait loci (QTL) previously identified for body weight and condition factor in rainbow trout (Oncorhynchus mykiss), and two other salmonid species, were used to further investigate the genetic architecture of growth-influencing genes in this species. Relationships among previously mapped candidate genes for growth and their co-localization to identified QTL regions are reported. Furthermore, using a comparative genomic analysis of syntenic rainbow trout linkage group clusters to their homologous regions within model teleost species such as zebrafish, stickleback and medaka, inferences were made regarding additional possible candidate genes underlying identified QTL regions.
Results
Body weight (BW) QTL were detected on the majority of rainbow trout linkage groups across 10 parents from 3 strains. However, only 10 linkage groups (i.e., RT-3, -6, -8, -9, -10, -12, -13, -22, -24, -27) possessed QTL regions with chromosome-wide or genome-wide effects across multiple parents. Fewer QTL for condition factor (K) were identified and only six instances of co-localization across families were detected (i.e. RT-9, -15, -16, -23, -27, -31 and RT-2/9 homeologs). Of note, both BW and K QTL co-localize on RT-9 and RT-27. The incidence of epistatic interaction across genomic regions within different female backgrounds was also examined, and although evidence for interaction effects within certain QTL regions were evident, these interactions were few in number and statistically weak. Of interest, however, was the fact that these predominantly occurred within K QTL regions. Currently mapped growth candidate genes are largely congruent with the identified QTL regions. More QTL were detected in male, compared to female parents, with the greatest number evident in an F1 male parent derived from an intercross between domesticated and wild strain of rainbow trout which differed strongly in growth rate.
Conclusions
Strain background influences the degree to which QTL effects are evident for growth-related genes. The process of domestication (which primarily selects faster growing fish) may largely reduce the genetic influences on growth-specific phenotypic variation. Although heritabilities have been reported to be relatively high for both BW and K growth traits, the genetic architecture of K phenotypic variation appears less defined (i.e., fewer major contributing QTL regions were identified compared with BW QTL regions).
doi:10.1186/1471-2156-11-63
PMCID: PMC2914766  PMID: 20609225
13.  Different sets of QTLs influence fitness variation in yeast 
We have carried out a combination of in-lab-evolution (ILE) and congenic crosses to identify the gene sets that contribute to the ability of yeast cells to survive under alkali stress.Each selected line acquired a different set of mutations, all resulting in the same phenotype. We identified a total of 15 genes in ILE and 17 candidates in the congenic approach, and studied their individual contribution to the phenotype.The total additive effect of the QTLs was much larger than the difference between the ancestor and the evolved strains, suggesting epistatic interactions between the QTLs.None of the genes identified encode structural components of the pH machinery. Instead, most encode regulatory functions, such as ubiquitin ligases, chromatin remodelers, GPI anchoring and copper/iron sensing transcription factors.
The majority of phenotypes in nature are complex traits affected by multiple genes [usually called quantitative trait loci (QTLs)], as well as by environmental factors. Many traits with practical importance such as crop yield in plants and susceptibility to various diseases in humans fall under this category. Understanding the architecture of complex traits has become the new frontier of genetic research, and many studies have greatly contributed to this field. However, to date, the genetic basis of only a few of these traits has been identified, and many questions regarding the architecture of complex traits and the accumulation of QTLs during evolution still remain unanswered. Among them are: How many QTLs affect complex phenotypes? What is the effect of each QTL? How do complex traits change during evolution? Is the adaptation process repeatable?, etc. In order to identify the QTLs that affect one of the important components of fitness variability in yeast, and to answer some of the questions above, we combined in-lab evolution (ILE) with the construction of congenic lines to isolate and map several gene sets that contribute to the ability of yeast cells to survive under alkali stress.
We carried out an ILE experiment, in which we grew yeast populations under increasing alkali stress to enrich for beneficial mutations. This process was followed by hybridizations to tiling arrays to identify the mutations acquired during the laboratory selective process. The ILE procedure revealed mutations in 15 genes, thus defining the QTLs and mechanisms that affect, in a quantitative fashion, the ability to cope with alkali stress. Our results indicate that during ILE several populations acquired different sets of QTLs that conferred the same phenotype. We identified each individual mutation in these strains, and validated and estimated their contribution to the phenotype. The total additive effect of the QTLs was much larger than the difference between the ancestor and the evolved strains, suggesting epistatic interactions between the QTLs.
In addition to the ILE, we have studied the mechanisms regulating fitness under alkali stress at natural habitats. We used a clinically isolated strain able to grow at high pH and a standard laboratory strain with a limited ability to sustain high pH as the parents of series of backcrosses to construct congenic lines up to the 8th generation. Seventeen genomic intervals that are candidates to contain QTLs were thus identified. In order to detect the contributing QTL in each interval, a predictive algorithm was applied, which scored the candidate genes in each genomic interval based on their interactions and similarity to the ILE genes. The algorithm was validated by testing the effect of the predicted candidate gene's deletions on the phenotype. Twelve out of 29 deletions were found to affect the trait (P-value 0.023).
Interestingly, our results show that almost all beneficial mutations affected regulatory genes, and not structural components of the pH homeostasis machinery (such as proton pumps, which control the cell's pH). The genes identified affect global regulators, such as ubiquitin ligases, proteins involved in GPI anchoring, copper sensing and chromatin remodelers. Thus, we show that adaptive changes tend to occur in genes with wide influence, rather than in genes narrowly affecting the phenotype selected for.
One example of genes identified both in the ILE and in the congenic lines is the copper-sensing transcription factor MAC1, and its downstream targets CTR1 and CTR3, which encode copper transporters. Different mutations at the same residue (Cys 271) were found in four out of five independent ILE lines. These mutations inactivate a copper-sensing region of Mac1 and cause up-regulation of its target genes. The CTR1 and CTR3 genes were identified in the congenic lines. Moreover, we found that a Ty transposable element is responsible for the decreased expression of CTR3 in some strains, and its excision caused transcriptional activation, affecting the ability to thrive at high pH.
This work provides insights on both evolutionary and genetic issues (such as the appearance of adaptive mutations and the architecture of complex traits), while at the same time providing information about the mechanisms that contribute to growth at high pH, a subject with ramifications for cell physiology, pathogenicity, and stress response.
Most of the phenotypes in nature are complex and are determined by many quantitative trait loci (QTLs). In this study we identify gene sets that contribute to one important complex trait: the ability of yeast cells to survive under alkali stress. We carried out an in-lab evolution (ILE) experiment, in which we grew yeast populations under increasing alkali stress to enrich for beneficial mutations. The populations acquired different sets of affecting alleles, showing that evolution can provide alternative solutions to the same challenge. We measured the contribution of each allele to the phenotype. The sum of the effects of the QTLs was larger than the difference between the ancestor phenotype and the evolved strains, suggesting epistatic interactions between the QTLs. In parallel, a clinical isolated strain was used to map natural QTLs affecting growth at high pH. In all, 17 candidate regions were found. Using a predictive algorithm based on the distances in protein-interaction networks, candidate genes were defined and validated by gene disruption. Many of the QTLs found by both methods are not directly implied in pH homeostasis but have more general, and often regulatory, roles.
doi:10.1038/msb.2010.1
PMCID: PMC2835564  PMID: 20160707
congenic lines; growth on alkali; in-lab evolution; QTL mapping; Saccharomyces cerevisiae
14.  A Missense Mutation in PPARD Causes a Major QTL Effect on Ear Size in Pigs 
PLoS Genetics  2011;7(5):e1002043.
Chinese Erhualian is the most prolific pig breed in the world. The breed exhibits exceptionally large and floppy ears. To identify genes underlying this typical feature, we previously performed a genome scan in a large scale White Duroc × Erhualian cross and mapped a major QTL for ear size to a 2-cM region on chromosome 7. We herein performed an identical-by-descent analysis that defined the QTL within a 750-kb region. Historically, the large-ear feature has been selected for the ancient sacrificial culture in Erhualian pigs. By using a selective sweep analysis, we then refined the critical region to a 630-kb interval containing 9 annotated genes. Four of the 9 genes are expressed in ear tissues of piglets. Of the 4 genes, PPARD stood out as the strongest candidate gene for its established role in skin homeostasis, cartilage development, and fat metabolism. No differential expression of PPARD was found in ear tissues at different growth stages between large-eared Erhualian and small-eared Duroc pigs. We further screened coding sequence variants in the PPARD gene and identified only one missense mutation (G32E) in a conserved functionally important domain. The protein-altering mutation showed perfect concordance (100%) with the QTL genotypes of all 19 founder animals segregating in the White Duroc × Erhualian cross and occurred at high frequencies exclusively in Chinese large-eared breeds. Moreover, the mutation is of functional significance; it mediates down-regulation of β-catenin and its target gene expression that is crucial for fat deposition in skin. Furthermore, the mutation was significantly associated with ear size across the experimental cross and diverse outbred populations. A worldwide survey of haplotype diversity revealed that the mutation event is of Chinese origin, likely after domestication. Taken together, we provide evidence that PPARD G32E is the variation underlying this major QTL.
Author Summary
A central but challenging objective in current biology is to dissect the genetic basis of quantitative traits. Numerous quantitative trait loci (QTL) have been uncovered in model and farm animals, providing unexpected insights into the biology of complex traits. However, only a few causal variants underlying the QTL have been explicitly identified. By using a battery of genetic and functional assays, we herein show that a major QTL effect on pig ear size is most likely caused by a single base substitution in an evolutionary conserved region of the PPARD gene. The protein-altered mutation is of functional significance and explains a proportion of variation in ear size across diverse pig breeds. A worldwide survey showed that the mutant allele for increased ear size was derived from a common ancestor in Chinese pigs, likely after domestication. These findings establish, for the first time, an essential role of PPARD in ear development and highlight the great potential of naturally occurring mutations in farm animals to gain insights into mammalian biology. Moreover, the knowledge of the PPARD causal mutation adds to the limited list of quantitative trait genes and quantitative trait nucleotides characterized in domesticated animals.
doi:10.1371/journal.pgen.1002043
PMCID: PMC3088719  PMID: 21573137
15.  Lung eQTLs to Help Reveal the Molecular Underpinnings of Asthma 
PLoS Genetics  2012;8(11):e1003029.
Genome-wide association studies (GWAS) have identified loci reproducibly associated with pulmonary diseases; however, the molecular mechanism underlying these associations are largely unknown. The objectives of this study were to discover genetic variants affecting gene expression in human lung tissue, to refine susceptibility loci for asthma identified in GWAS studies, and to use the genetics of gene expression and network analyses to find key molecular drivers of asthma. We performed a genome-wide search for expression quantitative trait loci (eQTL) in 1,111 human lung samples. The lung eQTL dataset was then used to inform asthma genetic studies reported in the literature. The top ranked lung eQTLs were integrated with the GWAS on asthma reported by the GABRIEL consortium to generate a Bayesian gene expression network for discovery of novel molecular pathways underpinning asthma. We detected 17,178 cis- and 593 trans- lung eQTLs, which can be used to explore the functional consequences of loci associated with lung diseases and traits. Some strong eQTLs are also asthma susceptibility loci. For example, rs3859192 on chr17q21 is robustly associated with the mRNA levels of GSDMA (P = 3.55×10−151). The genetic-gene expression network identified the SOCS3 pathway as one of the key drivers of asthma. The eQTLs and gene networks identified in this study are powerful tools for elucidating the causal mechanisms underlying pulmonary disease. This data resource offers much-needed support to pinpoint the causal genes and characterize the molecular function of gene variants associated with lung diseases.
Author Summary
Recent genome-wide association studies (GWAS) have identified genetic variants associated with lung diseases. The challenge now is to find the causal genes in GWAS–nominated chromosomal regions and to characterize the molecular function of disease-associated genetic variants. In this paper, we describe an international effort to systematically capture the genetic architecture of gene expression regulation in human lung. By studying lung specimens from 1,111 individuals of European ancestry, we found a large number of genetic variants affecting gene expression in the lung, or lung expression quantitative trait loci (eQTL). These lung eQTLs will serve as an important resource to aid in the understanding of the molecular underpinnings of lung biology and its disruption in disease. To demonstrate the utility of this lung eQTL dataset, we integrated our data with previous genetic studies on asthma. Through integrative techniques, we identified causal variants and genes in GWAS–nominated loci and found key molecular drivers for asthma. We feel that sharing our lung eQTLs dataset with the scientific community will leverage the impact of previous large-scale GWAS on lung diseases and function by providing much needed functional information to understand the molecular changes introduced by the susceptibility genetic variants.
doi:10.1371/journal.pgen.1003029
PMCID: PMC3510026  PMID: 23209423
16.  Genetic and Genomic Analysis of a Fat Mass Trait with Complex Inheritance Reveals Marked Sex Specificity 
PLoS Genetics  2006;2(2):e15.
The integration of expression profiling with linkage analysis has increasingly been used to identify genes underlying complex phenotypes. The effects of gender on the regulation of many physiological traits are well documented; however, “genetical genomic” analyses have not yet addressed the degree to which their conclusions are affected by sex. We constructed and densely genotyped a large F2 intercross derived from the inbred mouse strains C57BL/6J and C3H/HeJ on an apolipoprotein E null (ApoE−/−) background. This BXH.ApoE−/− population recapitulates several “metabolic syndrome” phenotypes. The cross consists of 334 animals of both sexes, allowing us to specifically test for the dependence of linkage on sex. We detected several thousand liver gene expression quantitative trait loci, a significant proportion of which are sex-biased. We used these analyses to dissect the genetics of gonadal fat mass, a complex trait with sex-specific regulation. We present evidence for a remarkably high degree of sex-dependence on both the cis and trans regulation of gene expression. We demonstrate how these analyses can be applied to the study of the genetics underlying gonadal fat mass, a complex trait showing significantly female-biased heritability. These data have implications on the potential effects of sex on the genetic regulation of other complex traits.
Synopsis
Although their genomes are nearly identical, the males and females of a species exhibit striking differences in many traits, including complex traits such as obesity. This study combines genetic and genomic tools to identify in parallel quantitative trait loci (QTLs) for a measure of gonadal fat mass and for expression of transcripts in the liver. The results are used to explore the relationship between genetic variation, sexual differentiation, and obesity in the mouse model. Using over 300 intercross progeny of two inbred mouse strains, five loci in the genome were found to be highly correlated with abdominal fat mass. Four of the five loci exhibited opposite effects on obesity in the two sexes, a phenomenon known as sexual antagonism. To identify candidate genes that may be involved in obesity through their expression in the liver, global gene expression analysis was employed using microarrays. Many of these expression QTLs also show sex-specific effects on transcription. A hotspot for trans-acting QTLs regulating the expression of transcripts whose abundance is correlated with gonadal fat mass was identified on Chromosome 19. This region of the genome colocalizes with a clinical QTL for gonadal fat mass, suggesting that it harbors a good candidate gene for obesity.
doi:10.1371/journal.pgen.0020015
PMCID: PMC1359071  PMID: 16462940
17.  Genetic dissection of growth, wood basic density and gene expression in interspecific backcrosses of Eucalyptus grandis and E. urophylla 
BMC Genetics  2012;13:60.
Background
F1 hybrid clones of Eucalyptus grandis and E. urophylla are widely grown for pulp and paper production in tropical and subtropical regions. Volume growth and wood quality are priority objectives in Eucalyptus tree improvement. The molecular basis of quantitative variation and trait expression in eucalypt hybrids, however, remains largely unknown. The recent availability of a draft genome sequence (http://www.phytozome.net) and genome-wide genotyping platforms, combined with high levels of genetic variation and high linkage disequilibrium in hybrid crosses, greatly facilitate the detection of quantitative trait loci (QTLs) as well as underlying candidate genes for growth and wood property traits. In this study, we used Diversity Arrays Technology markers to assess the genetic architecture of volume growth (diameter at breast height, DBH) and wood basic density in four-year-old progeny of an interspecific backcross pedigree of E. grandis and E. urophylla. In addition, we used Illumina RNA-Seq expression profiling in the E. urophylla backcross family to identify cis- and trans-acting polymorphisms (eQTLs) affecting transcript abundance of genes underlying QTLs for wood basic density.
Results
A total of five QTLs for DBH and 12 for wood basic density were identified in the two backcross families. Individual QTLs for DBH and wood basic density explained 3.1 to 12.2% of phenotypic variation. Candidate genes underlying QTLs for wood basic density on linkage groups 8 and 9 were found to share trans-acting eQTLs located on linkage groups 4 and 10, which in turn coincided with QTLs for wood basic density suggesting that these QTLs represent segregating components of an underlying transcriptional network.
Conclusion
This is the first demonstration of the use of next-generation expression profiling to quantify transcript abundance in a segregating tree population and identify candidate genes potentially affecting wood property variation. The QTLs identified in this study provide a resource for identifying candidate genes and developing molecular markers for marker-assisted breeding of volume growth and wood basic density. Our results suggest that integrated analysis of transcript and trait variation in eucalypt hybrids can be used to dissect the molecular basis of quantitative variation in wood property traits.
doi:10.1186/1471-2156-13-60
PMCID: PMC3416674  PMID: 22817272
18.  Genetic Effects at Pleiotropic Loci Are Context-Dependent with Consequences for the Maintenance of Genetic Variation in Populations 
PLoS Genetics  2011;7(9):e1002256.
Context-dependent genetic effects, including genotype-by-environment and genotype-by-sex interactions, are a potential mechanism by which genetic variation of complex traits is maintained in populations. Pleiotropic genetic effects are also thought to play an important role in evolution, reflecting functional and developmental relationships among traits. We examine context-dependent genetic effects at pleiotropic loci associated with normal variation in multiple metabolic syndrome (MetS) components (obesity, dyslipidemia, and diabetes-related traits). MetS prevalence is increasing in Western societies and, while environmental in origin, presents substantial variation in individual response. We identify 23 pleiotropic MetS quantitative trait loci (QTL) in an F16 advanced intercross between the LG/J and SM/J inbred mouse strains (Wustl:LG,SM-G16; n = 1002). Half of each family was fed a high-fat diet and half fed a low-fat diet; and additive, dominance, and parent-of-origin imprinting genotypic effects were examined in animals partitioned into sex, diet, and sex-by-diet cohorts. We examine the context-dependency of the underlying additive, dominance, and imprinting genetic effects of the traits associated with these pleiotropic QTL. Further, we examine sequence polymorphisms (SNPs) between LG/J and SM/J as well as differential expression of positional candidate genes in these regions. We show that genetic associations are different in different sex, diet, and sex-by-diet settings. We also show that over- or underdominance and ecological cross-over interactions for single phenotypes may not be common, however multidimensional synthetic phenotypes at loci with pleiotropic effects can produce situations that favor the maintenance of genetic variation in populations. Our findings have important implications for evolution and the notion of personalized medicine.
Author Summary
We look at gene-by-diet and gene-by-sex interactions underlying natural variation in multiple metabolic traits mapping to the same regions of the genome in a mouse model. We find that the underlying genetic architecture of these traits is different in different sex and diet contexts. We further use expression data and whole-genome polymorphism data to identify compelling candidates for experimental follow-up. We use these results to examine theoretical evolutionary predictions about how variation in populations can be maintained. There has been much discussion of late on how to use evolutionary theory to inform medical genomics. Mouse models may be especially appropriate for bridging the divide between evolutionary and biomedical research, because they allow the study of the effects of natural alleles on normal variation and because human-mouse homology is well defined. Our study is unique in examining quantitative trait loci from both evolutionary and biomedical perspectives, and we highlight the complex connections of the traits comprising the metabolic syndrome and the evolutionary implications of their underlying genetic architecture. This is important for understanding disease etiology and is relevant to personalized medicine.
doi:10.1371/journal.pgen.1002256
PMCID: PMC3169520  PMID: 21931559
19.  Integrative Modeling of eQTLs and Cis-Regulatory Elements Suggests Mechanisms Underlying Cell Type Specificity of eQTLs 
PLoS Genetics  2013;9(8):e1003649.
Genetic variants in cis-regulatory elements or trans-acting regulators frequently influence the quantity and spatiotemporal distribution of gene transcription. Recent interest in expression quantitative trait locus (eQTL) mapping has paralleled the adoption of genome-wide association studies (GWAS) for the analysis of complex traits and disease in humans. Under the hypothesis that many GWAS associations tag non-coding SNPs with small effects, and that these SNPs exert phenotypic control by modifying gene expression, it has become common to interpret GWAS associations using eQTL data. To fully exploit the mechanistic interpretability of eQTL-GWAS comparisons, an improved understanding of the genetic architecture and causal mechanisms of cell type specificity of eQTLs is required. We address this need by performing an eQTL analysis in three parts: first we identified eQTLs from eleven studies on seven cell types; then we integrated eQTL data with cis-regulatory element (CRE) data from the ENCODE project; finally we built a set of classifiers to predict the cell type specificity of eQTLs. The cell type specificity of eQTLs is associated with eQTL SNP overlap with hundreds of cell type specific CRE classes, including enhancer, promoter, and repressive chromatin marks, regions of open chromatin, and many classes of DNA binding proteins. These associations provide insight into the molecular mechanisms generating the cell type specificity of eQTLs and the mode of regulation of corresponding eQTLs. Using a random forest classifier with cell specific CRE-SNP overlap as features, we demonstrate the feasibility of predicting the cell type specificity of eQTLs. We then demonstrate that CREs from a trait-associated cell type can be used to annotate GWAS associations in the absence of eQTL data for that cell type. We anticipate that such integrative, predictive modeling of cell specificity will improve our ability to understand the mechanistic basis of human complex phenotypic variation.
Author Summary
When interpreting genome-wide association studies showing that specific genetic variants are associated with disease risk, scientists look for a link between the genetic variant and a biological mechanism behind that disease. One functional mechanism is that the genetic variant may influence gene transcription via a co-localized genomic regulatory element, such as a transcription factor binding site within an open chromatin region. Often this type of regulation occurs in some cell types but not others. In this study, we look across eleven gene expression studies with seven cell types and consider how genetic transcription regulators, or eQTLs, replicate within and between cell types. We identify pervasive allelic heterogeneity, or transcriptional control of a single gene by multiple, independent eQTLs. We integrate extensive data on cell type specific regulatory elements from ENCODE to identify general methods of transcription regulation through enrichment of eQTLs within regulatory elements. We also build a classifier to predict eQTL replication across cell types. The results in this paper present a path to an integrative, predictive approach to improve our ability to understand the mechanistic basis of human phenotypic variation.
doi:10.1371/journal.pgen.1003649
PMCID: PMC3731231  PMID: 23935528
20.  Genetic Architecture of Aluminum Tolerance in Rice (Oryza sativa) Determined through Genome-Wide Association Analysis and QTL Mapping 
PLoS Genetics  2011;7(8):e1002221.
Aluminum (Al) toxicity is a primary limitation to crop productivity on acid soils, and rice has been demonstrated to be significantly more Al tolerant than other cereal crops. However, the mechanisms of rice Al tolerance are largely unknown, and no genes underlying natural variation have been reported. We screened 383 diverse rice accessions, conducted a genome-wide association (GWA) study, and conducted QTL mapping in two bi-parental populations using three estimates of Al tolerance based on root growth. Subpopulation structure explained 57% of the phenotypic variation, and the mean Al tolerance in Japonica was twice that of Indica. Forty-eight regions associated with Al tolerance were identified by GWA analysis, most of which were subpopulation-specific. Four of these regions co-localized with a priori candidate genes, and two highly significant regions co-localized with previously identified QTLs. Three regions corresponding to induced Al-sensitive rice mutants (ART1, STAR2, Nrat1) were identified through bi-parental QTL mapping or GWA to be involved in natural variation for Al tolerance. Haplotype analysis around the Nrat1 gene identified susceptible and tolerant haplotypes explaining 40% of the Al tolerance variation within the aus subpopulation, and sequence analysis of Nrat1 identified a trio of non-synonymous mutations predictive of Al sensitivity in our diversity panel. GWA analysis discovered more phenotype–genotype associations and provided higher resolution, but QTL mapping identified critical rare and/or subpopulation-specific alleles not detected by GWA analysis. Mapping using Indica/Japonica populations identified QTLs associated with transgressive variation where alleles from a susceptible aus or indica parent enhanced Al tolerance in a tolerant Japonica background. This work supports the hypothesis that selectively introgressing alleles across subpopulations is an efficient approach for trait enhancement in plant breeding programs and demonstrates the fundamental importance of subpopulation in interpreting and manipulating the genetics of complex traits in rice.
Author Summary
While rice (Oryza sativa) is significantly more Al tolerant than other cereals, no genes underlying Al tolerance in rice have been reported. Using genome-wide association (GWA) and bi-parental QTL mapping, we investigated the genetic architecture of Al tolerance in rice. Japonica varieties were twice as Al tolerant as indica and aus varieties. Overall, 57% of the phenotypic variation was correlated with subpopulation, consistent with observations that different genes and genomic regions were associated with Al tolerance in different subpopulations. Four regions identified by GWA co-localized with a priori candidate genes, and two highly significant regions co-localized with previously identified quantitative trait loci (QTL). Haplotype and sequence analysis around the candidate gene, Nrat1, identified a susceptible haplotype explaining 40% of the Al tolerance variation within the aus subpopulation and three non-synonymous mutations within Nrat1 that were predictive of Al sensitivity. Using Indica × Japonica mapping populations, we identified QTLs associated with transgressive variation where alleles from a susceptible indica or aus parent enhanced Al tolerance in a tolerant japonica background. This work demonstrates the importance of subpopulation in interpreting and manipulating complex traits in rice and provides a roadmap for breeders aiming to capture genetic value from phenotypically inferior lines.
doi:10.1371/journal.pgen.1002221
PMCID: PMC3150440  PMID: 21829395
21.  Murine Gut Microbiota Is Defined by Host Genetics and Modulates Variation of Metabolic Traits 
PLoS ONE  2012;7(6):e39191.
The gastrointestinal tract harbors a complex and diverse microbiota that has an important role in host metabolism. Microbial diversity is influenced by a combination of environmental and host genetic factors and is associated with several polygenic diseases. In this study we combined next-generation sequencing, genetic mapping, and a set of physiological traits of the BXD mouse population to explore genetic factors that explain differences in gut microbiota and its impact on metabolic traits. Molecular profiling of the gut microbiota revealed important quantitative differences in microbial composition among BXD strains. These differences in gut microbial composition are influenced by host-genetics, which is complex and involves many loci. Linkage analysis defined Quantitative Trait Loci (QTLs) restricted to a particular taxon, branch or that influenced the variation of taxa across phyla. Gene expression within the gastrointestinal tract and sequence analysis of the parental genomes in the QTL regions uncovered candidate genes with potential to alter gut immunological profiles and impact the balance between gut microbial communities. A QTL region on Chr 4 that overlaps several interferon genes modulates the population of Bacteroides, and potentially Bacteroidetes and Firmicutes–the predominant BXD gut phyla. Irak4, a signaling molecule in the Toll-like receptor pathways is a candidate for the QTL on Chr15 that modulates Rikenellaceae, whereas Tgfb3, a cytokine modulating the barrier function of the intestine and tolerance to commensal bacteria, overlaps a QTL on Chr 12 that influence Prevotellaceae. Relationships between gut microflora, morphological and metabolic traits were uncovered, some potentially a result of common genetic sources of variation.
doi:10.1371/journal.pone.0039191
PMCID: PMC3377628  PMID: 22723961
22.  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.
Background
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.
Conclusions/Significance
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.
doi:10.1371/journal.pbio.1001125
PMCID: PMC3156686  PMID: 21857804
23.  Linkage Relationships Among Multiple QTL for Horticultural Traits and Late Blight (P. infestans) Resistance on Chromosome 5 Introgressed from Wild Tomato Solanum habrochaites 
G3: Genes|Genomes|Genetics  2013;3(12):2131-2146.
When the allele of a wild species at a quantitative trait locus (QTL) conferring a desirable trait is introduced into cultivated species, undesirable effects on other traits may occur. These negative phenotypic effects may result from the presence of wild alleles at other closely linked loci that are transferred along with the desired QTL allele (i.e., linkage drag) and/or from pleiotropic effects of the desired allele. Previously, a QTL for resistance to Phytophthora infestans on chromosome 5 of Solanum habrochaites was mapped and introgressed into cultivated tomato (S. lycopersicum). Near-isogenic lines (NILs) were generated and used for fine-mapping of this resistance QTL, which revealed coincident or linked QTL with undesirable effects on yield, maturity, fruit size, and plant architecture traits. Subsequent higher-resolution mapping with chromosome 5 sub-NILs revealed the presence of multiple P. infestans resistance QTL within this 12.3 cM region. In our present study, these sub-NILs were also evaluated for 17 horticultural traits, including yield, maturity, fruit size and shape, fruit quality, and plant architecture traits in replicated field experiments over the course of two years. Each previously detected single horticultural trait QTL fractionated into two or more QTL. A total of 41 QTL were detected across all traits, with ∼30% exhibiting significant QTL × environment interactions. Colocation of QTL for multiple traits suggests either pleiotropy or tightly linked genes control these traits. The complex genetic architecture of horticultural and P. infestans resistance trait QTL within this S. habrochaites region of chromosome 5 presents challenges and opportunities for breeding efforts in cultivated tomato.
doi:10.1534/g3.113.007195
PMCID: PMC3852376  PMID: 24122052
tomato; Solanum lycopersicum; introgression; QTL mapping; linkage drag
24.  Genome wide association studies for body conformation traits in the Chinese Holstein cattle population 
BMC Genomics  2013;14:897.
Background
Genome-wide association study (GWAS) is a powerful tool for revealing the genetic basis of quantitative traits. However, studies using GWAS for conformation traits of cattle is comparatively less. This study aims to use GWAS to find the candidates genes for body conformation traits.
Results
The Illumina BovineSNP50 BeadChip was used to identify single nucleotide polymorphisms (SNPs) that are associated with body conformation traits. A least absolute shrinkage and selection operator (LASSO) was applied to detect multiple SNPs simultaneously for 29 body conformation traits with 1,314 Chinese Holstein cattle and 52,166 SNPs. Totally, 59 genome-wide significant SNPs associated with 26 conformation traits were detected by genome-wide association analysis; five SNPs were within previously reported QTL regions (Animal Quantitative Trait Loci (QTL) database) and 11 were very close to the reported SNPs. Twenty-two SNPs were located within annotated gene regions, while the remainder were 0.6–826 kb away from known genes. Some of the genes had clear biological functions related to conformation traits. By combining information about the previously reported QTL regions and the biological functions of the genes, we identified DARC, GAS1, MTPN, HTR2A, ZNF521, PDIA6, and TMEM130 as the most promising candidate genes for capacity and body depth, chest width, foot angle, angularity, rear leg side view, teat length, and animal size traits, respectively. We also found four SNPs that affected four pairs of traits, and the genetic correlation between each pair of traits ranged from 0.35 to 0.86, suggesting that these SNPs may have a pleiotropic effect on each pair of traits.
Conclusions
A total of 59 significant SNPs associated with 26 conformation traits were identified in the Chinese Holstein population. Six promising candidate genes were suggested, and four SNPs showed genetic correlation for four pairs of traits.
doi:10.1186/1471-2164-14-897
PMCID: PMC3879203  PMID: 24341352
Dairy cattle; GWAS; Body conformation traits; SNP; Holstein; QTL
25.  A high-resolution linkage map for comparative genome analysis and QTL fine mapping in Asian seabass, Lates calcarifer 
BMC Genomics  2011;12:174.
Background
High density linkage maps are essential for comparative analysis of synteny, fine mapping of quantitative trait loci (QTL), searching for candidate genes and facilitating genome sequence assembly. However, in most foodfish species, marker density is still low. We previously reported a first generation linkage map with 240 DNA markers and its application to preliminarily map QTL for growth traits in Asian seabass (Lates calcarifer). Here, we report a high-resolution linkage map with 790 microsatellites and SNPs, comparative analysis of synteny, fine-mapping of QTL and the identification of potential candidate genes for growth traits.
Results
A second generation linkage map of Asian seabass was developed with 790 microsatellite and SNP markers. The map spanned a genetic length of 2411.5 cM, with an average intermarker distance of 3.4 cM or 1.1 Mb. This high density map allowed for comparison of the map with Tetraodon nigroviridis genome, which revealed 16 synteny regions between the two species. Moreover, by employing this map we refined QTL to regions of 1.4 and 0.2 cM (or 400 and 50 kb) in linkage groups 2 and 3 in a population containing 380 progeny; potential candidate genes for growth traits in QTL regions were further identified using comparative genome analysis, whose effects on growth traits were investigated. Interestingly, a QTL cluster at Lca371 underlying growth traits of Asian seabass showed similarity to the cathepsin D gene of human, which is related to cancer and Alzheimer's disease.
Conclusions
We constructed a high resolution linkage map, carried out comparative mapping, refined the positions of QTL, identified candidate genes for growth traits and analyzed their effects on growth. Our study developed a framework that will be indispensable for further identification of genes and analysis of molecular variation within the refined QTL to enhance understanding of the molecular basis of growth and speed up genetic improvement of growth performance, and it also provides critical resource for future genome sequence assembly and comparative genomics studies on the evolution of fish genomes.
doi:10.1186/1471-2164-12-174
PMCID: PMC3088568  PMID: 21457569

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