Showing papers on "Quantitative trait locus published in 2005"

An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: The basic concepts

Abstract: Recognizing the enormous potential of DNA markers in plant breeding, many agricultural research centers and plant breeding institutes have adopted the capacity for marker development and marker-assisted selection (MAS). However, due to rapid developments in marker technology, statistical methodology for identifying quantitative trait loci (QTLs) and the jargon used by molecular biologists, the utility of DNA markers in plant breeding may not be clearly understood by non-molecular biologists. This review provides an introduction to DNA markers and the concept of polymorphism, linkage analysis and map construction, the principles of QTL analysis and how markers may be applied in breeding programs using MAS. This review has been specifically written for readers who have only a basic knowledge of molecular biology and/or plant genetics. Its format is therefore ideal for conventional plant breeders, physiologists, pathologists, other plant scientists and students.

Cytokinin Oxidase Regulates Rice Grain Production

Abstract: Most agriculturally important traits are regulated by genes known as quantitative trait loci (QTLs) derived from natural allelic variations. We here show that a QTL that increases grain productivity in rice, Gn1a, is a gene for cytokinin oxidase/dehydrogenase (OsCKX2), an enzyme that degrades the phytohormone cytokinin. Reduced expression of OsCKX2 causes cytokinin accumulation in inflorescence meristems and increases the number of reproductive organs, resulting in enhanced grain yield. QTL pyramiding to combine loci for grain number and plant height in the same genetic background generated lines exhibiting both beneficial traits. These results provide a strategy for tailormade crop improvement.

A rice quantitative trait locus for salt tolerance encodes a sodium transporter.

Abstract: Many important agronomic traits in crop plants, including stress tolerance, are complex traits controlled by quantitative trait loci (QTLs). Isolation of these QTLs holds great promise to improve world agriculture but is a challenging task. We previously mapped a rice QTL, SKC1, that maintained K(+) homeostasis in the salt-tolerant variety under salt stress, consistent with the earlier finding that K(+) homeostasis is important in salt tolerance. To understand the molecular basis of this QTL, we isolated the SKC1 gene by map-based cloning and found that it encoded a member of HKT-type transporters. SKC1 is preferentially expressed in the parenchyma cells surrounding the xylem vessels. Voltage-clamp analysis showed that SKC1 protein functions as a Na(+)-selective transporter. Physiological analysis suggested that SKC1 is involved in regulating K(+)/Na(+) homeostasis under salt stress, providing a potential tool for improving salt tolerance in crops.

An integrative genomics approach to infer causal associations between gene expression and disease

Abstract: A key goal of biomedical research is to elucidate the complex network of gene interactions underlying complex traits such as common human diseases. Here we detail a multistep procedure for identifying potential key drivers of complex traits that integrates DNA-variation and gene-expression data with other complex trait data in segregating mouse populations. Ordering gene expression traits relative to one another and relative to other complex traits is achieved by systematically testing whether variations in DNA that lead to variations in relative transcript abundances statistically support an independent, causative or reactive function relative to the complex traits under consideration. We show that this approach can predict transcriptional responses to single gene–perturbation experiments using gene-expression data in the context of a segregating mouse population. We also demonstrate the utility of this approach by identifying and experimentally validating the involvement of three new genes in susceptibility to obesity.

The human obesity gene map: the 2004 update.

Abstract: This paper presents the eleventh update of the human obesity gene map, which incorporates published results up to the end of October 2004. Evidence from single-gene mutation obesity cases, Mendelian disorders exhibiting obesity as a clinical feature, transgenic and knockout murine models relevant to obesity, quantitative trait loci (QTLs) from animal cross-breeding experiments, association studies with candidate genes, and linkages from genome scans is reviewed. As of October 2004, 173 human obesity cases due to single-gene mutations in 10 different genes have been reported, and 49 loci related to Mendelian syndromes relevant to human obesity have been mapped to a genomic region, and causal genes or strong candidates have been identified for most of these syndromes. There are 166 genes which, when mutated or expressed as transgenes in the mouse, result in phenotypes that affect body weight and adiposity. The number of QTLs reported from animal models currently reaches 221. The number of human obesity QTLs derived from genome scans continues to grow, and we have now 204 QTLs for obesity-related phenotypes from 50 genome-wide scans. A total of 38 genomic regions harbor QTLs replicated among two to four studies. The number of studies reporting associations between DNA sequence variation in specific genes and obesity phenotypes has also increased considerably with 358 findings of positive associations with 113 candidate genes. Among them, 18 genes are supported by at least five positive studies. The obesity gene map shows putative loci on all chromosomes except Y. Overall, >600 genes, markers, and chromosomal regions have been associated or linked with human obesity phenotypes. The electronic version of the map with links to useful publications and genomic and other relevant sites can be found at http://obesitygene.pbrc.edu.

Maize association population: a high-resolution platform for quantitative trait locus dissection.

Abstract: *Summary Crop improvement and the dissection of complex genetic traits require germplasm diversity. Although this necessary phenotypic variability exists in diverse maize, most research is conducted using a small subset of inbred lines. An association population of 302 lines is now available – a valuable research tool that captures a large proportion of the alleles in cultivated maize. Provided that appropriate statistical models correcting for population structure are included, this tool can be used in association analyses to provide high-resolution evaluation of multiple alleles. This study describes the population structure of the 302 lines, and investigates the relationship between population structure and various measures of phenotypic and breeding value. On average, our estimates of population structure account for 9.3% of phenotypic variation, roughly equivalent to a major quantitative trait locus (QTL), with a high of 35%. Inclusion of population structure in association models is critical to meaningful analyses. This new association population has the potential to identify QTL with small effects, which will aid in dissecting complex traits and in planning future projects to exploit the rich diversity present in maize.

The effects of artificial selection on the maize genome.

Abstract: Domestication promotes rapid phenotypic evolution through artificial selection. We investigated the genetic history by which the wild grass teosinte (Zea mays ssp. parviglumis) was domesticated into modern maize (Z. mays ssp. mays). Analysis of single-nucleotide polymorphisms in 774 genes indicates that 2 to 4% of these genes experienced artificial selection. The remaining genes retain evidence of a population bottleneck associated with domestication. Candidate selected genes with putative function in plant growth are clustered near quantitative trait loci that contribute to phenotypic differences between maize and teosinte. If we assume that our sample of genes is representative, ∼1200 genes throughout the maize genome have been affected by artificial selection.

Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function

Abstract: Patterns of gene expression in the central nervous system are highly variable and heritable. This genetic variation among normal individuals leads to considerable structural, functional and behavioral differences. We devised a general approach to dissect genetic networks systematically across biological scale, from base pairs to behavior, using a reference population of recombinant inbred strains. We profiled gene expression using Affymetrix oligonucleotide arrays in the BXD recombinant inbred strains, for which we have extensive SNP and haplotype data. We integrated a complementary database comprising 25 years of legacy phenotypic data on these strains. Covariance among gene expression and pharmacological and behavioral traits is often highly significant, corroborates known functional relations and is often generated by common quantitative trait loci. We found that a small number of major-effect quantitative trait loci jointly modulated large sets of transcripts and classical neural phenotypes in patterns specific to each tissue. We developed new analytic and graph theoretical approaches to study shared genetic modulation of networks of traits using gene sets involved in neural synapse function as an example. We built these tools into an open web resource called WebQTL that can be used to test a broad array of hypotheses.

The landscape of genetic complexity across 5,700 gene expression traits in yeast

Abstract: Many studies have identified quantitative trait loci (QTLs) that contribute to continuous variation in heritable traits of interest. However, general principles regarding the distribution of QTL numbers, effect sizes, and combined effects of multiple QTLs remain to be elucidated. Here, we characterize complex genetics underlying inheritance of thousands of transcript levels in a cross between two strains of Saccharomyces cerevisiae. Most detected QTLs have weak effects, with a median variance explained of 27% for highly heritable transcripts. Despite the high statistical power of the study, no QTLs were detected for 40% of highly heritable transcripts, indicating extensive genetic complexity. Modeling of QTL detection showed that only 3% of highly heritable transcripts are consistent with single-locus inheritance, 17–18% are consistent with control by one or two loci, and half require more than five loci under additive models. Strikingly, analysis of parent and progeny trait distributions showed that a majority of transcripts exhibit transgressive segregation. Sixteen percent of highly heritable transcripts exhibit evidence of interacting loci. Our results will aid design of future QTL mapping studies and may shed light on the evolution of quantitative traits.

Assumption-Free Estimation of Heritability from Genome-Wide Identity-by-Descent Sharing between Full Siblings

Abstract: The study of continuously varying, quantitative traits is important in evolutionary biology, agriculture, and medicine. Variation in such traits is attributable to many, possibly interacting, genes whose expression may be sensitive to the environment, which makes their dissection into underlying causative factors difficult. An important population parameter for quantitative traits is heritability, the proportion of total variance that is due to genetic factors. Response to artificial and natural selection and the degree of resemblance between relatives are all a function of this parameter. Following the classic paper by R. A. Fisher in 1918, the estimation of additive and dominance genetic variance and heritability in populations is based upon the expected proportion of genes shared between different types of relatives, and explicit, often controversial and untestable models of genetic and non-genetic causes of family resemblance. With genome-wide coverage of genetic markers it is now possible to estimate such parameters solely within families using the actual degree of identity-by-descent sharing between relatives. Using genome scans on 4,401 quasi-independent sib pairs of which 3,375 pairs had phenotypes, we estimated the heritability of height from empirical genome-wide identity-by-descent sharing, which varied from 0.374 to 0.617 (mean 0.498, standard deviation 0.036). The variance in identity-by-descent sharing per chromosome and per genome was consistent with theory. The maximum likelihood estimate of the heritability for height was 0.80 with no evidence for non-genetic causes of sib resemblance, consistent with results from independent twin and family studies but using an entirely separate source of information. Our application shows that it is feasible to estimate genetic variance solely from within-family segregation and provides an independent validation of previously untestable assumptions. Given sufficient data, our new paradigm will allow the estimation of genetic variation for disease susceptibility and quantitative traits that is free from confounding with non-genetic factors and will allow partitioning of genetic variation into additive and non-additive components.

Sucrose-Specific Induction of Anthocyanin Biosynthesis in Arabidopsis Requires the MYB75/PAP1 Gene

Abstract: Sugar-induced anthocyanin accumulation has been observed in many plant species. We observed that sucrose (Suc) is the most effective inducer of anthocyanin biosynthesis in Arabidopsis (Arabidopsis thaliana) seedlings. Other sugars and osmotic controls are either less effective or ineffective. Analysis of Suc-induced anthocyanin accumulation in 43 Arabidopsis accessions shows that considerable natural variation exists for this trait. The Cape Verde Islands (Cvi) accession essentially does not respond to Suc, whereas Landsberg erecta is an intermediate responder. The existing Landsberg erecta/Cvi recombinant inbred line population was used in a quantitative trait loci analysis for Suc-induced anthocyanin accumulation (SIAA). A total of four quantitative trait loci for SIAA were identified in this way. The locus with the largest contribution to the trait, SIAA1, was fine mapped and using a candidate gene approach, it was shown that the MYB75/PAP1 gene encodes SIAA1. Genetic complementation studies and analysis of a laboratory-generated knockout mutation in this gene confirmed this conclusion. Suc, in a concentration-dependent way, induces MYB75/PAP1 mRNA accumulation. Moreover, MYB75/PAP1 is essential for the Suc-mediated expression of the dihydroflavonol reductase gene. The SIAA1 locus in Cvi probably is a weak or loss-of-function MYB75/PAP1 allele. The C24 accession similarly shows a very weak response to Suc-induced anthocyanin accumulation encoded by the same locus. Sequence analysis showed that the Cvi and C24 accessions harbor mutations both inside and downstream of the DNA-binding domain of the MYB75/PAP1 protein, which most likely result in loss of activity.

Heritability of Cardiovascular and Personality Traits in 6,148 Sardinians

Abstract: In family studies, phenotypic similarities between relatives yield information on the overall contribution of genes to trait variation. Large samples are important for these family studies, especially when comparing heritability between subgroups such as young and old, or males and females. We recruited a cohort of 6,148 participants, aged 14–102 y, from four clustered towns in Sardinia. The cohort includes 34,469 relative pairs. To extract genetic information, we implemented software for variance components heritability analysis, designed to handle large pedigrees, analyze multiple traits simultaneously, and model heterogeneity. Here, we report heritability analyses for 98 quantitative traits, focusing on facets of personality and cardiovascular function. We also summarize results of bivariate analyses for all pairs of traits and of heterogeneity analyses for each trait. We found a significant genetic component for every trait. On average, genetic effects explained 40% of the variance for 38 blood tests, 51% for five anthropometric measures, 25% for 20 measures of cardiovascular function, and 19% for 35 personality traits. Four traits showed significant evidence for an X-linked component. Bivariate analyses suggested overlapping genetic determinants for many traits, including multiple personality facets and several traits related to the metabolic syndrome; but we found no evidence for shared genetic determinants that might underlie the reported association of some personality traits and cardiovascular risk factors. Models allowing for heterogeneity suggested that, in this cohort, the genetic variance was typically larger in females and in younger individuals, but interesting exceptions were observed. For example, narrow heritability of blood pressure was approximately 26% in individuals more than 42 y old, but only approximately 8% in younger individuals. Despite the heterogeneity in effect sizes, the same loci appear to contribute to variance in young and old, and in males and females. In summary, we find significant evidence for heritability of many medically important traits, including cardiovascular function and personality. Evidence for heterogeneity by age and sex suggests that models allowing for these differences will be important in mapping quantitative traits.

Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease

Abstract: Integration of genome-wide expression profiling with linkage analysis is a new approach to identifying genes underlying complex traits. We applied this approach to the regulation of gene expression in the BXH/HXB panel of rat recombinant inbred strains, one of the largest available rodent recombinant inbred panels and a leading resource for genetic analysis of the highly prevalent metabolic syndrome. In two tissues important to the pathogenesis of the metabolic syndrome, we mapped cis- and trans-regulatory control elements for expression of thousands of genes across the genome. Many of the most highly linked expression quantitative trait loci are regulated in cis, are inherited essentially as monogenic traits and are good candidate genes for previously mapped physiological quantitative trait loci in the rat. By comparative mapping we generated a data set of 73 candidate genes for hypertension that merit testing in human populations. Mining of this publicly available data set is expected to lead to new insights into the genes and regulatory pathways underlying the extensive range of metabolic and cardiovascular disease phenotypes that segregate in these recombinant inbred strains.

Strategies for mapping and cloning quantitative trait genes in rodents

Abstract: Over the past 15 years, more than 2,000 quantitative trait loci (QTLs) have been identified in crosses between inbred strains of mice and rats, but less than 1% have been characterized at a molecular level. However, new resources, such as chromosome substitution strains and the proposed Collaborative Cross, together with new analytical tools, including probabilistic ancestral haplotype reconstruction in outbred mice, Yin-Yang crosses and in silico analysis of sequence variants in many inbred strains, could make QTL cloning tractable. We review the potential of these strategies to identify genes that underlie QTLs in rodents.

A high-density genetic map of hexaploid wheat (Triticum aestivum L.) from the cross Chinese Spring × SQ1 and its use to compare QTLs for grain yield across a range of environments

Abstract: A population of 96 doubled haploid lines (DHLs) was prepared from F1 plants of the hexaploid wheat cross Chinese Spring × SQ1 (a high abscisic acid-expressing breeding line) and was mapped with 567 RFLP, AFLP, SSR, morphological and biochemical markers covering all 21 chromosomes, with a total map length of 3,522 cM. Although the map lengths for each genome were very similar, the D genome had only half the markers of the other two genomes. The map was used to identify quantitative trait loci (QTLs) for yield and yield components from a combination of 24 site × treatment × year combinations, including nutrient stress, drought stress and salt stress treatments. Although yield QTLs were widely distributed around the genome, 17 clusters of yield QTLs from five or more trials were identified: two on group 1 chromosomes, one each on group 2 and group 3, five on group 4, four on group 5, one on group 6 and three on group 7. The strongest yield QTL effects were on chromosomes 7AL and 7BL, due mainly to variation in grain numbers per ear. Three of the yield QTL clusters were largely site-specific, while four clusters were largely associated with one or other of the stress treatments. Three of the yield QTL clusters were coincident with the dwarfing gene Rht-B1 on 4BS and with the vernalisation genes Vrn-A1 on 5AL and Vrn-D1 on 5DL. Yields of each DHL were calculated for trial mean yields of 6 g plant−1 and 2 g plant−1 (equivalent to about 8 t ha−1 and 2.5 t ha−1, respectively), representing optimum and moderately stressed conditions. Analyses of these yield estimates using interval mapping confirmed the group-7 effects on yield and, at 2 g plant−1, identified two additional major yield QTLs on chromosomes 1D and 5A. Many of the yield QTL clusters corresponded with QTLs already reported in wheat and, on the basis of comparative genetics, also in rice. The implications of these results for improving wheat yield stability are discussed.

Comparative Transcriptional Profiling of Two Contrasting Rice Genotypes under Salinity Stress during the Vegetative Growth Stage

Abstract: Rice (Oryza sativa), a salt-sensitive species, has considerable genetic variation for salt tolerance within the cultivated gene pool. Two indica rice genotypes, FL478, a recombinant inbred line derived from a population developed for salinity tolerance studies, and IR29, the sensitive parent of the population, were selected for this study. We used the Affymetrix rice genome array containing 55,515 probe sets to explore the transcriptome of the salt-tolerant and salt-sensitive genotypes under control and salinity-stressed conditions during vegetative growth. Response of the sensitive genotype IR29 is characterized by induction of a relatively large number of probe sets compared to tolerant FL478. Salinity stress induced a number of genes involved in the flavonoid biosynthesis pathway in IR29 but not in FL478. Cell wall-related genes were responsive in both genotypes, suggesting cell wall restructuring is a general adaptive mechanism during salinity stress, although the two genotypes also had some differences. Additionally, the expression of genes mapping to the Saltol region of chromosome 1 were examined in both genotypes. Single-feature polymorphism analysis of expression data revealed that IR29 was the source of the Saltol region in FL478, contrary to expectation. This study provides a genome-wide transcriptional analysis of two well-characterized, genetically related rice genotypes differing in salinity tolerance during a gradually imposed salinity stress under greenhouse conditions.

Integrating Genetic and Network Analysis to Characterize Genes Related to Mouse Weight

Abstract: Systems biology approaches that are based on the genetics of gene expression have been fruitful in identifying genetic regulatory loci related to complex traits. We use microarray and genetic marker data from an F2 mouse intercross to examine the large-scale organization of the gene co-expression network in liver, and annotate several gene modules in terms of 22 physiological traits. We identify chromosomal loci (referred to as module quantitative trait loci, mQTL) that perturb the modules and describe a novel approach that integrates network properties with genetic marker information to model gene/trait relationships. Specifically, using the mQTL and the intramodular connectivity of a body weight–related module, we describe which factors determine the relationship between gene expression profiles and weight. Our approach results in the identification of genetic targets that influence gene modules (pathways) that are related to the clinical phenotypes of interest.

Uncovering regulatory pathways that affect hematopoietic stem cell function using 'genetical genomics'.

Abstract: We combined large-scale mRNA expression analysis and gene mapping to identify genes and loci that control hematopoietic stem cell (HSC) function. We measured mRNA expression levels in purified HSCs isolated from a panel of densely genotyped recombinant inbred mouse strains. We mapped quantitative trait loci (QTLs) associated with variation in expression of thousands of transcripts. By comparing the physical transcript position with the location of the controlling QTL, we identified polymorphic cis-acting stem cell genes. We also identified multiple trans-acting control loci that modify expression of large numbers of genes. These groups of coregulated transcripts identify pathways that specify variation in stem cells. We illustrate this concept with the identification of candidate genes involved with HSC turnover. We compared expression QTLs in HSCs and brain from the same mice and identified both shared and tissue-specific QTLs. Our data are accessible through WebQTL, a web-based interface that allows custom genetic linkage analysis and identification of coregulated transcripts.

Identification of a missense mutation in the bovine ABCG2 gene with a major effect on the QTL on chromosome 6 affecting milk yield and composition in Holstein cattle

Abstract: We previously localized a quantitative trait locus (QTL) on chromosome 6 affecting milk fat and protein concentration to a 4-cM confidence interval, centered on the microsatellite BM143. We characterized the genes and sequence variation in this region and identified common haplotypes spanning five polymorphic sites in the genes IBSP, SPP1, PKD2, and ABCG2 for two sires heterozygous for this QTL. Expression of SPP1 and ABCG2 in the bovine mammary gland increased from parturition through lactation. SPP1 and all the coding exons of ABCG2 and PKD2 were sequenced for these two sires. The single nucleotide change capable of encoding a substitution of tyrosine-581 to serine (Y581S) in the ABCG2 transporter was the only polymorphism corresponding to the segregation status of all 3 heterozygous and 15 homozygous sires for the QTL in the Israeli and U.S. Holstein populations. The allele substitution fixed effects on the genetic evaluations of 335 Israeli sires were -341 kg milk, +0.16% fat, and +0.13% protein (F-value = 200). No other polymorphism gave significant effect for fat and protein concentration in models that also included Y581S. The allele substitution effects on the genetic evaluations of 670 cows, daughters of two heterozygous sires, were -226 kg milk, 0.09% fat, and 0.08% protein (F-value = 394), with partial dominance towards the 581S homozygotes. We therefore propose that Y581S in ABCG2 is the causative site for this QTL.

Genetic interactions between polymorphisms that affect gene expression in yeast

Abstract: Interactions between polymorphisms at different quantitative trait loci (QTLs) are thought to contribute to the genetics of many traits, and can markedly affect the power of genetic studies to detect QTLs1. Interacting loci have been identified in many organisms1,2,3,4,5. However, the prevalence of interactions6,7,8, and the nucleotide changes underlying them9,10, are largely unknown. Here we search for naturally occurring genetic interactions in a large set of quantitative phenotypes—the levels of all transcripts in a cross between two strains of Saccharomyces cerevisiae7. For each transcript, we searched for secondary loci interacting with primary QTLs detected by their individual effects. Such locus pairs were estimated to be involved in the inheritance of 57% of transcripts; statistically significant pairs were identified for 225 transcripts. Among these, 67% of secondary loci had individual effects too small to be significant in a genome-wide scan. Engineered polymorphisms in isogenic strains confirmed an interaction between the mating-type locus MAT and the pheromone response gene GPA1. Our results indicate that genetic interactions are widespread in the genetics of transcript levels, and that many QTLs will be missed by single-locus tests but can be detected by two-stage tests that allow for interactions.

Mapping Determinants of Gene Expression Plasticity by Genetical Genomics in C. elegans

Abstract: Recent genetical genomics studies have provided intimate views on gene regulatory networks. Gene expression variations between genetically different individuals have been mapped to the causal regulatory regions, termed expression quantitative trait loci. Whether the environment-induced plastic response of gene expression also shows heritable difference has not yet been studied. Here we show that differential expression induced by temperatures of 16 °C and 24 °C has a strong genetic component in Caenorhabditis elegans recombinant inbred strains derived from a cross between strains CB4856 (Hawaii) and N2 (Bristol). No less than 59% of 308 trans-acting genes showed a significant eQTL-by-environment interaction, here termed plasticity quantitative trait loci. In contrast, only 8% of an estimated 188 cis-acting genes showed such interaction. This indicates that heritable differences in plastic responses of gene expression are largely regulated in trans. This regulation is spread over many different regulators. However, for one group of trans-genes we found prominent evidence for a common master regulator: a transband of 66 coregulated genes appeared at 24 °C. Our results suggest widespread genetic variation of differential expression responses to environmental impacts and demonstrate the potential of genetical genomics for mapping the molecular determinants of phenotypic plasticity.

High-dimensional and large-scale phenotyping of yeast mutants

Abstract: One of the most powerful techniques for attributing functions to genes in uni- and multicellular organisms is comprehensive analysis of mutant traits. In this study, systematic and quantitative analyses of mutant traits are achieved in the budding yeast Saccharomyces cerevisiae by investigating morphological phenotypes. Analysis of fluorescent microscopic images of triple-stained cells makes it possible to treat morphological variations as quantitative traits. Deletion of nearly half of the yeast genes not essential for growth affects these morphological traits. Similar morphological phenotypes are caused by deletions of functionally related genes, enabling a functional assignment of a locus to a specific cellular pathway. The high-dimensional phenotypic analysis of defined yeast mutant strains provides another step toward attributing gene function to all of the genes in the yeast genome.

Challenges for identifying functionally important genetic variation: the promise of combining complementary research strategies

Abstract: Strategies for the identification of functional genetic variation underlying phenotypic traits of ecological and evolutionary importance have received considerable attention in the literature recently. This paper aims to bring together and compare the relative strengths and limitations of various potentially useful research strategies for dissecting functionally important genetic variation in a wide range of organisms. We briefly explore the relative strengths and limitations of traditional and emerging approaches and evaluate their potential use in free-living populations. While it is likely that much of the progress in functional genetic analyses will rely on progress in traditional model species, it is clear that with prudent choices of methods and appropriate sampling designs, much headway can be also made in a diverse range of species. We suggest that combining research approaches targeting different functional and biological levels can potentially increase understanding the genetic basis of ecological and evolutionary processes both in model and non-model organisms.

Cis-acting expression quantitative trait loci in mice

Abstract: We previously reported the analysis of genome-wide expression profiles and various diabetes-related traits in a segregating cross between inbred mouse strains C57BL/6J (B6) and DBA/2J (DBA). By considering transcript levels as quantitative traits, we identified several thousand expression quantitative trait loci (eQTL) with LOD score >4.3. We now experimentally address the problem of multiple comparisons by estimating the fraction of false-positive eQTL that are under cis-acting regulation. For this, we have utilized a classic cis–trans test with (B6 × DBA)F1 mice to determine the relative levels of transcripts from the B6 and DBA alleles. The results suggest that at least 64% of cis-acting eQTL with LOD >4.3 are true positives, while the remaining 36% could not be confirmed as truly cis-acting. Moreover, we find that >96% of apparent cis-acting eQTL occur in regions that do not share SNP haplotypes. Cis-acting eQTL serve as an important new resource for the identification of positional candidates in QTL studies in mice. Also, we use the analysis of the correlation structures between genotypes, gene expression traits, and phenotypic traits to further characterize genes expressed in liver that are under cis-acting control, and highlight the advantages and disadvantages of integrating genetics and gene expression data in segregating populations.

Genome-wide SNP discovery and linkage analysis in barley based on genes responsive to abiotic stress

Abstract: More than 2,000 genome-wide barley single nucleotide polymorphisms (SNPs) were developed by resequencing unigene fragments from eight diverse accessions. The average genome-wide SNP frequency observed in 877 unigenes was 1 SNP per 200 bp. However, SNP frequency was highly variable with the least number of SNP and SNP haplotypes observed within European cultivated germplasm reflecting effects of breeding history on genetic diversity. More than 300 SNP loci were mapped genetically in three experimental mapping populations which allowed the construction of an integrated SNP map incorporating a large number of RFLP, AFLP and SSR markers (1,237 loci in total). The genes used for SNP discovery were selected based on their transcriptional response to a variety of abiotic stresses. A set of known barley abiotic stress QTL was positioned on the linkage map, while the available sequence and gene expression information facilitated the identification of genes potentially associated with these traits. Comparison of the sequenced SNP loci to the rice genome sequence identified several regions of highly conserved gene order providing a framework for marker saturation in barley genomic regions of interest. The integration of genome-wide SNP and expression data with available genetic and phenotypic information will facilitate the identification of gene function in barley and other non-model organisms.

Epistasis and balanced polymorphism influencing complex trait variation

Abstract: Complex traits such as human disease, growth rate, or crop yield are polygenic, or determined by the contributions from numerous genes in a quantitative manner. Although progress has been made in identifying major quantitative trait loci (QTL), experimental constraints have limited our knowledge of small-effect QTL, which may be responsible for a large proportion of trait variation1,2,3. Here, we identified and dissected a one-centimorgan chromosome interval in Arabidopsis thaliana without regard to its effect on growth rate, and examined the signature of historical sequence polymorphism among Arabidopsis accessions. We found that the interval contained two growth rate QTL within 210 kilobases. Both QTL showed epistasis; that is, their phenotypic effects depended on the genetic background. This amount of complexity in such a small area suggests a highly polygenic architecture of quantitative variation, much more than previously documented4. One QTL was limited to a single gene. The gene in question displayed a nucleotide signature indicative of balancing selection, and its phenotypic effects are reversed depending on genetic background. If this region typifies many complex trait loci, then non-neutral epistatic polymorphism may be an important contributor to genetic variation in complex traits.

Positional identification of TNFSF4 , encoding OX40 ligand, as a gene that influences atherosclerosis susceptibility

Abstract: Ath1 is a quantitative trait locus on mouse chromosome 1 that renders C57BL/6 mice susceptible and C3H/He mice resistant to diet-induced atherosclerosis. The quantitative trait locus region encompasses 11 known genes, including Tnfsf4 (also called Ox40l or Cd134l), which encodes OX40 ligand. Here we report that mice with targeted mutations of Tnfsf4 had significantly (P

An Integrated Biplot Analysis System for Displaying, Interpreting, and Exploring Genotype × Environment Interaction

Abstract: Multienvironment trials (MET) generate two types of two-way data: genotype x environment data for a target trait and genotype x trait data in individual or across environments. These data can be visually analyzed by a GGE biplot and a genotype X trait biplot, respectively. This paper describes a third type of biplot, the covariate-effect biplot, and illustrates its tandem use with the other biplots to achieve a fuller understanding of MET data. The covariate-effect biplot is generated on the basis of an explanatory trait × environment two-way table consisting of correlation coefficients between the target trait (e.g., yield) and each of the other traits in each of the environments. This biplot displays the yield-trait relations in individual environments and addresses whether and how the genotype X environment interactions (GE) for yield can be explored by indirect selection for the other traits. These other traits are treated as genetic covariables and can be replaced by other genetic covariables such as genetic markers, QTL, or genes. The biplot methodology was demonstrated by MET data of barley (Hordeum vulgare L.) conducted across North America. Both the GGE biplot and the covariate-effect biplot showed that the environments fell into two (eastern vs. western) megaenvironments. The covariate-effect pattern explained 81% of the GGE pattern, suggesting that the GE pattern for yield can be effectively explored by indirect selection for these traits. Specifically, barley yield can be improved by selecting for larger kernel weight, earlier heading, and better lodging resistance in the eastern megaenvironment. In contrast, the yield-frait relationship in the western megaenvironment was highly variable, and yield improvement can be achieved only by selecting for yield per se across environments. We suggest that the GGE biplot, the genotype × trait biplot, and the covariate-effect biplot be used jointly to better understand and more fully explore MET data.