Genomic approaches to analyzing natural variation in Arabidopsis thaliana

The genomics tools available for studying Arabidopsis thaliana are a great resource for researchers trying to characterize and understand the genetic basis of natural variation. Abundant polymorphic markers aid quantitative trait locus (QTL) mapping, the fully sequenced genome provides rapid identification of candidate loci, and extensive knockout collections allow those candidate loci to be tested. Combining QTL mapping of classic phenotypic traits with biochemical or expression analysis is providing mechanistic insight into the traits of interest. Conversely, natural variation studies are now being done on genomic traits such as methylation or chiasma frequency.     

solQTL: a tool for QTL analysis,visualization and linking to genomes at SGN database

Background  

A common approach to understanding the genetic basis of complex traits is through identification of associated quantitative trait loci (QTL). Fine mapping QTLs requires several generations of backcrosses and analysis of large populations, which is time-consuming and costly effort. Furthermore, as entire genomes are being sequenced and an increasing amount of genetic and expression data are being generated, a challenge remains: linking phenotypic variation to the underlying genomic variation. To identify candidate genes and understand the molecular basis underlying the phenotypic variation of traits, bioinformatic approaches are needed to exploit information such as genetic map, expression and whole genome sequence data of organisms in biological databases.     

The genetic architecture of the behavioral ontogeny of foraging in honeybee workers

The initiation of foraging during the life course of honeybee workers is of central interest to understanding the division of labor in social insects, a central theme in sociobiology and behavioral research. It also provides one of the most complex phenotypic traits in biological systems because of the interaction of various external, social, and individual factors. This study reports on a comprehensive investigation of the genetic architecture of the age of foraging initiation in honeybees. It comprises an estimation of genetic variation, the study of candidate loci, and two complementary quantitative trait loci (QTL) maps using two selected, continually bred lines of honeybees. We conclude that considerable genetic variation exists between the selected lines for this central life history component. The study reveals direct pleiotropic and epistatic effects of candidate loci (including previously identified QTL for foraging behavior). Furthermore, two maps of the honeybee genome were constructed from over 400 AFLP markers. Both maps confirm the extraordinary recombinational size of the honeybee genome. On the basis of these maps, we report four new significant QTL and two more suggestive QTL that influence the initiation of foraging.     

Genetic linkage mapping in fungi: current state, applications, and future trends

Genetic mapping is a basic tool for eukaryotic genomic research. Linkage maps provide insights into genome organization and can be used for genetic studies of traits of interest. A genetic linkage map is a suitable support for the anchoring of whole genome sequences. It allows the localization of genes of interest or quantitative trait loci (QTL) and map-based cloning. While genetic mapping has been extensively used in plant or animal models, this discipline is more recent in fungi. The present article reviews the current status of genetic linkage map research in fungal species. The process of linkage mapping is detailed, from the development of mapping populations to the construction of the final linkage map, and illustrated based on practical examples. The range of specific applications in fungi is browsed, such as the mapping of virulence genes in pathogenic species or the mapping of agronomically relevant QTL in cultivated edible mushrooms. Future prospects are finally discussed in the context of the most recent advances in molecular techniques and the release of numerous fungal genome sequences.     

Genomic dissection of variation in clutch size and egg mass in a wild great tit (Parus major) population

Clutch size and egg mass are life history traits that have been extensively studied in wild bird populations, as life history theory predicts a negative trade‐off between them, either at the phenotypic or at the genetic level. Here, we analyse the genomic architecture of these heritable traits in a wild great tit (Parus major) population, using three marker‐based approaches – chromosome partitioning, quantitative trait locus (QTL) mapping and a genome‐wide association study (GWAS). The variance explained by each great tit chromosome scales with predicted chromosome size, no location in the genome contains genome‐wide significant QTL, and no individual SNPs are associated with a large proportion of phenotypic variation, all of which may suggest that variation in both traits is due to many loci of small effect, located across the genome. There is no evidence that any regions of the genome contribute significantly to both traits, which combined with a small, nonsignificant negative genetic covariance between the traits, suggests the absence of genetic constraints on the independent evolution of these traits. Our findings support the hypothesis that variation in life history traits in natural populations is likely to be determined by many loci of small effect spread throughout the genome, which are subject to continued input of variation by mutation and migration, although we cannot exclude the possibility of an additional input of major effect genes influencing either trait.     

Whole genome mapping in a wheat doubled haploid population using SSRs and TRAPs and the identification of QTL for agronomic traits

Genetic maps are useful for detecting quantitative trait loci (QTL) associated with quantitative traits and for marker-assisted selection (MAS) in breeding. In this research, we used the wheat × maize method to develop a doubled haploid (DH) population derived from the synthetic hexaploid wheat (SHW) line TA4152-60 and the North Dakota hard red spring wheat line ND495. The population consisted of 213 lines, of which a subset of 120 lines was randomly selected and used to construct linkage maps of all 21 chromosomes and for QTL detection. The whole genome maps consisted of 632 markers including 410 SSRs, 218 TRAPs, 1 RFLP, and 3 phenotypic markers, and spanned 3,811.5 cM with an average density of one marker per 6.03 cM. Telomere sequence-based TRAPs allowed us to define the ends of seven linkage groups. Analysis revealed major QTLs associated with the traits of days to heading on chromosomes 5A and 5B, plant height on chromosomes 4D and 5A, and spike characteristics on chromosomes 3D, 4A, 4D, 5A and 5B. The DH population and genetic map will be a useful tool for the identification of disease resistance QTL and agronomically important loci, and will aid in the identification and development of markers for MAS. Mention of trade names or commercial products in this article is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture.     

Genetic mapping of quantitative phenotypic traits in Saccharomyces cerevisiae

Saccharomyces cerevisiae has become a favorite production organism in industrial biotechnology presenting new challenges to yeast engineers in terms of introducing advantageous traits such as stress tolerances. Exploring subspecies diversity of S. cerevisiae has identified strains that bear industrially relevant phenotypic traits. Provided that the genetic basis of such phenotypic traits can be identified inverse engineering allows the targeted modification of production strains. Most phenotypic traits of interest in S. cerevisiae strains are quantitative, meaning that they are controlled by multiple genetic loci referred to as quantitative trait loci (QTL). A straightforward approach to identify the genetic basis of quantitative traits is QTL mapping which aims at the allocation of the genetic determinants to regions in the genome. The application of high-density oligonucleotide arrays and whole-genome re-sequencing to detect genetic variations between strains has facilitated the detection of large numbers of molecular markers thus allowing high-resolution QTL mapping over the entire genome. This review focuses on the basic principle and state of the art of QTL mapping in S. cerevisiae. Furthermore we discuss several approaches developed during the last decade that allow down-scaling of the regions identified by QTL mapping to the gene level. We also emphasize the particular challenges of QTL mapping in nonlaboratory strains of S. cerevisiae.     

Advanced technologies for genomic analysis in farm animals and its application for QTL mapping

Rapid progress in farm animal breeding has been made in the last few decades. Advanced technologies for genomic analysis in molecular genetics have led to the identification of genes or markers associated with genes that affect economic traits. Molecular markers, large-insert libraries and RH panels have been used to build the genetic linkage maps, physical maps and comparative maps in different farm animals. Moreover, EST sequencing, genome sequencing and SNPs maps are helping us to understand how genomes function in various organisms and further areas will be studied by DNA microarray technologies and proteomics methods. Because most economically important traits in farm animals are controlled by multiple genes and the environment, the main goal of genome research in farm animals is to map and characterize genes determining QTL. There are two main strategies to identify trait loci, candidate gene association tests and genome scan approaches. In recent years, some new concepts, such as RNAi, miRNA and eQTL, have been introduced into farm animal research, especially for QTL mapping and finding QTN. Several genes that influence important traits have already been identified or are close to being identified, and some of them have been applied in farm animal breeding programs by marker-assisted selection.     

Genetic architecture of the dog: sexual size dimorphism and functional morphology

Purebred dogs are a valuable resource for genetic analysis of quantitative traits. Quantitative traits are complex, controlled by many genes that are contained within regions of the genome known as quantitative trait loci (QTL). The genetic architecture of quantitative traits is defined by the characteristics of these genes: their number, the magnitude of their effects, their positions in the genome and their interactions with each other. QTL analysis is a valuable tool for exploring genetic architecture, and highlighting regions of the genome that contribute to the variation of a trait within a population.     

Quantitative trait loci mapping of phenotypic plasticity and genotype-environment interactions in plant and insect performance

Community genetic studies generally ignore the plasticity of the functional traits through which the effect is passed from individuals to the associated community. However, the ability of organisms to be phenotypically plastic allows them to rapidly adapt to changing environments and plasticity is commonly observed across all taxa. Owing to the fitness benefits of phenotypic plasticity, evolutionary biologists are interested in its genetic basis, which could explain how phenotypic plasticity is involved in the evolution of species interactions. Two current ideas exist: (i) phenotypic plasticity is caused by environmentally sensitive loci associated with a phenotype; (ii) phenotypic plasticity is caused by regulatory genes that simply influence the plasticity of a phenotype. Here, we designed a quantitative trait loci (QTL) mapping experiment to locate QTL on the barley genome associated with barley performance when the environment varies in the presence of aphids, and the composition of the rhizosphere. We simultaneously mapped aphid performance across variable rhizosphere environments. We mapped main effects, QTL × environment interaction (QTL×E), and phenotypic plasticity (measured as the difference in mean trait values) for barley and aphid performance onto the barley genome using an interval mapping procedure. We found that QTL associated with phenotypic plasticity were co-located with main effect QTL and QTL×E. We also located phenotypic plasticity QTL that were located separately from main effect QTL. These results support both of the current ideas of how phenotypic plasticity is genetically based and provide an initial insight into the functional genetic basis of how phenotypically plastic traits may still be important sources of community genetic effects.     

Maternal effects as the cause of parent-of-origin effects that mimic genomic imprinting

Epigenetic effects are increasingly recognized as an important source of variation in complex traits and have emerged as the focus of a rapidly expanding area of research. Principle among these effects is genomic imprinting, which has generally been examined in analyses of complex traits by testing for parent-of-origin-dependent effects of alleles. However, in most of these analyses maternal effects are confounded with genomic imprinting because they can produce the same patterns of phenotypic variation expected for various forms of imprinting. Distinguishing between the two is critical for genetic and evolutionary studies because they have entirely different patterns of gene expression and evolutionary dynamics. Using a simple single-locus model, we show that maternal genetic effects can result in patterns that mimic those expected under genomic imprinting. We further demonstrate how maternal effects and imprinting effects can be distinguished using genomic data from parents and offspring. The model results are applied to a genome scan for quantitative trait loci (QTL) affecting growth- and weight-related traits in mice to illustrate how maternal effects can mimic imprinting. This genome scan revealed five separate maternal-effect loci that caused a diversity of patterns mimicking those expected under various modes of genomic imprinting. These results demonstrate that the appearance of parent-of-origin-dependent effects (POEs) of alleles at a locus cannot be taken as direct evidence that the locus is imprinted. Moreover, they show that, in gene mapping studies, genetic data from both parents and offspring are required to successfully differentiate between imprinting and maternal effects as the cause of apparent parent-of-origin effects of alleles.     

Do large effect QTL fractionate? A case study at the maize domestication QTL teosinte branched1

Quantitative trait loci (QTL) mapping is a valuable tool for studying the genetic architecture of trait variation. Despite the large number of QTL studies reported in the literature, the identified QTL are rarely mapped to the underlying genes and it is usually unclear whether a QTL corresponds to one or multiple linked genes. Similarly, when QTL for several traits colocalize, it is usually unclear whether this is due to the pleiotropic action of a single gene or multiple linked genes, each affecting one trait. The domestication gene teosinte branched1 (tb1) was previously identified as a major domestication QTL with large effects on the differences in plant and ear architecture between maize and teosinte. Here we present the results of two experiments that were performed to determine whether the single gene tb1 explains all trait variation for its genomic region or whether the domestication QTL at tb1 fractionates into multiple linked QTL. For traits measuring plant architecture, we detected only one QTL per trait and these QTL all mapped to tb1. These results indicate that tb1 is the sole gene for plant architecture traits that segregates in our QTL mapping populations. For most traits related to ear morphology, we detected multiple QTL per trait in the tb1 genomic region, including a large effect QTL at tb1 itself plus one or two additional linked QTL. tb1 is epistatic to two of these additional QTL for ear traits. Overall, these results provide examples for both a major QTL that maps to a single gene, as well as a case in which a QTL fractionates into multiple linked QTL.     

From genotype to phenotype in bovine functional genomics

In past 5 years, the promise that came with genome sequencing has revolutionized the functional genomics research field at unprecedented manner. It would soon know what all known genes do, particularly genes involved in genetic improvement of animal health and increase food animal production. With the availability of full bovine genomic sequence, yet we still have a lot of daunting tasks on 'genotype-to-phenotype problem' particularly about the phenotypic variations and trying to predict what genes are likely to be involved, and improved integrated interactive database. This article outlined and discussed about the current status of bovine functional genomics, recent development in bovine genome databases particularly in annotation of bovine genome, bovine quantitative trait loci database and its potential impact to unveil the from genotype-to-phenotype problem.     

Genetic complexity and quantitative trait loci mapping of yeast morphological traits

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.     

ezQTL: A Web Platform for Interactive Visualization and Colocalization of QTLs and GWAS Loci

Genome-wide association studies (GWAS) have identified thousands of genomic loci associated with complex diseases and traits, including cancer. The vast majority of common trait-associated variants identified via GWAS fall in non-coding regions of the genome, posing a challenge in elucidating the causal variants, genes, and mechanisms involved. Expression quantitative trait locus (eQTL) and other molecular QTL studies have been valuable resources in identifying candidate causal genes from GWAS loci through statistical colocalization methods. While QTL colocalization is becoming a standard analysis in post-GWAS investigation, an easy web tool for users to perform formal colocalization analyses with either user-provided or public GWAS and eQTL datasets has been lacking. Here, we present ezQTL, a web-based bioinformatic application to interactively visualize and analyze genetic association data such as GWAS loci and molecular QTLs under different linkage disequilibrium (LD) patterns (1000 Genomes Project, UK Biobank, or user-provided data). This application allows users to perform data quality control for variants matched between different datasets, LD visualization, and two-trait colocalization analyses using two state-of-the-art methodologies (eCAVIAR and HyPrColoc), including batch processing. ezQTL is a free and publicly available cross-platform web tool, which can be accessed online at https://analysistools.cancer.gov/ezqtl.     

Identification of quantitative trait loci for agronomically important traits and their association with genic-microsatellite markers in sorghum

The identification of quantitative trait loci (QTLs) affecting agronomically important traits enable to understand their underlying genetic mechanisms and genetic basis of their complex interactions. The aim of the present study was to detect QTLs for 12 agronomic traits related to staygreen, plant early development, grain yield and its components, and some growth characters by analyzing replicated phenotypic datasets from three crop seasons, using the population of 168 F₇ RILs of the cross 296B × IS18551. In addition, we report mapping of a subset of genic-microsatellite markers. A linkage map was constructed with 152 marker loci comprising 149 microsatellites (100 genomic- and 49 genic-microsatellites) and three morphological markers. QTL analysis was performed by using MQM approach. Forty-nine QTLs were detected, across environments or in individual environments, with 1–9 QTLs for each trait. Individual QTL accounted for 5.2–50.4% of phenotypic variance. Several genomic regions affected multiple traits, suggesting the phenomenon of pleiotropy or tight linkage. Stable QTLs were identified for studied traits across different environments, and genetic backgrounds by comparing the QTLs in the study with previously reported QTLs in sorghum. Of the 49 mapped genic-markers, 18 were detected associating either closely or exactly as the QTL positions of agronomic traits. EST marker Dsenhsbm19, coding for a key regulator (EIL-1) of ethylene biosynthesis, was identified co-located with the QTLs for plant early development and staygreen trait, a probable candidate gene for these traits. Similarly, such exact co-locations between EST markers and QTLs were observed in four other instances. Collectively, the QTLs/markers identified in the study are likely candidates for improving the sorghum performance through MAS and map-based gene isolations.     

Genetic architecture of a selection response in Arabidopsis thaliana

Quantitative trait locus (QTL) mapping has become an established and effective method for studying the genetic architecture of complex traits. In this report, we use a QTL mapping approach in combination with data from a large selection experiment in Arabidopsis thaliana to explore a response to selection of experimental populations with differentiated genetic backgrounds. Experimental populations with genetic backgrounds derived from ecotypes Landsberg and Niederzenz were exposed to multiple generations of fertility and viability selection. This selection resulted in phenotypic shifts in a number of life-history and fitness-related characters including early development time, flowering time, dry biomass, longevity, and fruit production. Quantitative trait loci were mapped for these traits and their positions were compared to previously characterized allele frequency changes in the experimental populations (Ungerer et al. 2003). Quantitative trait locus positions largely colocalized with genomic regions under strong and consistent selection in populations with differentiated genetic backgrounds, suggesting that alleles for these traits were selected similarly in differentiated genetic backgrounds. However, one QTL region exhibited a more variable response; being positively selected on one genetic background but apparently neutral in another. This study demonstrates how QTL mapping approaches can be combined with map-based population genetic data to study how selection acts on standing genetic variation in populations.     

Population-based resequencing analysis of improved wheat germplasm at wheat leaf rust resistance locus Lr21

Little is known about the genetic control of heterosis in the complex polyploid crop species oilseed rape (Brassica napus L.). In this study, two large doubled-haploid (DH) mapping populations and two corresponding sets of backcrossed test hybrids (THs) were analysed in controlled greenhouse experiments and extensive field trials for seedling biomass and yield performance traits, respectively. Genetic maps from the two populations, aligned with the help of common simple sequence repeat markers, were used to localise and compare quantitative trait loci (QTL) related to the expression of heterosis for seedling developmental traits, plant height at flowering, thousand seed mass, seeds per silique, siliques per unit area and seed yield. QTL were mapped using data from the respective DH populations, their corresponding TH populations and from mid-parent heterosis (MPH) data, allowing additive and dominance effects along with digenic epistatic interactions to be estimated. A number of genome regions containing numerous heterosis-related QTL involved in different traits and at different developmental stages were identified at corresponding map positions in the two populations. The co-localisation of per se QTL from the DH population datasets with heterosis-related QTL from the MPH data could indicate regulatory loci that may also contribute to fixed heterosis in the highly duplicated B. napus genome. Given the key role of epistatic interactions in the expression of heterosis in oilseed rape, these QTL hotspots might harbour genes involved in regulation of heterosis (including fixed heterosis) for different traits throughout the plant life cycle, including a significant overall influence on heterosis for seed yield.     

Molecular-Marker-Facilitated Investigations of Quantitative-Trait Loci in Maize. I. Numbers, Genomic Distribution and Types of Gene Action

Individual genetic factors which underlie variation in quantitative traits of maize were investigated in each of two F2 populations by examining the mean trait expressions of genotypic classes at each of 17-20 segregating marker loci. It was demonstrated that the trait expression of marker locus classes could be interpreted in terms of genetic behavior at linked quantitative trait loci (QTLs). For each of 82 traits evaluated, QTLs were detected and located to genomic sites. The numbers of detected factors varied according to trait, with the average trait significantly influenced by almost two-thirds of the marked genomic sites. Most of the detected associations between marker loci and quantitative traits were highly significant, and could have been detected with fewer than the 1800-1900 plants evaluated in each population. The cumulative, simple effects of marker-linked regions of the genome explained between 8 and 40% of the phenotypic variation for a subset of 25 traits evaluated. Single marker loci accounted for between 0.3% and 16% of the phenotypic variation of traits. Individual plant heterozygosity, as measured by marker loci, was significantly associated with variation in many traits. The apparent types of gene action at the QTLs varied both among traits and between loci for given traits, although overdominance appeared frequently, especially for yield-related traits. The prevalence of apparent overdominance may reflect the effects of multiple QTLs within individual marker-linked regions, a situation which would tend to result in overestimation of dominance. Digenic epistasis did not appear to be important in determining the expression of the quantitative traits evaluated. Examination of the effects of marked regions on the expression of pairs of traits suggests that genomic regions vary in the direction and magnitudes of their effects on trait correlations, perhaps providing a means of selecting to dissociate some correlated traits. Marker-facilitated investigations appear to provide a powerful means of examining aspects of the genetic control of quantitative traits. Modifications of the methods employed herein will allow examination of the stability of individual gene effects in varying genetic backgrounds and environments.