Use Of Quantitative Trait Loci Research Articles

Modeling the Effects of Drought Stress on Leaf Development in a Brassica oleracea Doubled Haploid Population Using Two-phase Linear Functions

The combination of quantitative trait loci (QTL) analysis and ecophysiological modeling has been suggested as an approach to reveal the genetic basis of complex traits since phenotypes change with time and environmental conditions and the variation within populations can be described by genotype-specific parameterization of response curves on time and influential environmental factors. The objectives of the present study are a genotype-specific parameterization of a model describing leaf area development under well-watered and drought stress conditions, the use of QTL for estimating model input parameters, an evaluation of the model, and a comparison of the genotype-specific and QTL-based model parameterization. We used a two-phase linear function to describe preflowering leaf area development in a Brassica oleracea L. doubled haploid population. To illustrate effects of drought on leaf growth, the function was combined with a plateau function, which estimates the soil water status at which stress effects begin to reduce leaf expansion, a genotype-specific slope of the response to soil water status, and the soil water status at which leaf expansion becomes zero. A total number of 14 QTL were detected on the parameters of the two-phase linear function describing preflowering leaf area development and the plateau function describing the effects of drought on leaf area development. Nine of these QTL colocalized to QTL detected on data of static leaf area measurements and osmotic adjustment. The entire model was able to distinguish between genotypes during later growth stages under well-watered and drought stress conditions. However, the predictability was largely reduced when drought stress became more severe at the final measurement dates. Independent evaluation trials showed that the accuracy of the model was on the same level or even higher when genotype specific input parameters were replaced by allele-specific QTL effects.

Opportunities and limitations of genetic analysis of hypertensive rat strains

Genetic influences contribute upto 30–50% to blood pressure variability in human essential hypertension [1]. Intense efforts have been dedicated to unraveling the role of genes in this disease, following two parallel and interacting tracks. Studies on human populations have mostly focused on the candidate gene linkage approach, associating single nucleotide polymorphic (SNP) markers with the hypertensive phenotype. In spite of some promising observations [2], most attempts to replicate association studies have not been encouraging [3]. There are calls for application of stricter quality criteria for genetic studies, including better definition of phenotype characteristics, increased attention to environmental factors, sharing and combining data resources and more emphasis on whole genome-linkage scans in the context of collaborative multidisciplinary approaches [4–17]. Recently, however, there has been impressive progress in designing studies, understanding the mechanisms of disease and genotyping technology [17]. The second parallel track is the development and study of rodent experimental models of disease. Genetically hypertensive rat strains have been developed over the past 60 years. The models were obtained from initial random rat populations by brother—sister mating selecting for a high blood pressure phenotype. The better characterized genetically hypertensive strains are the spontaneously hypertensive rat (SHR) [18], stroke-prone SHR (SPSHR) [19], Dahl saltsensitive [20], Milan hypertensive (MHS) [21], New Zealand genetically hypertensive [22], Sabra hypertensive (SBH) [23] and the Lyon hypertensive rats [24]. Comparisons are generally made with inbred normotensive strains derived from the original rat population and sharing most of the genetic background with the corresponding hypertensive strain. The most common normotensive strains are the Dahl salt-resistant, Wistar—Kyoto (WKY), Milan normotensive (MNS), genetically normotensive, Sabra normotensive (SBN) and Lyon normotensive and Lyon low blood pressure rats [18–24]. Physiological and pharmacological approaches to the study of these models have enhanced our understanding of many systems for blood pressure control. During the 1990s, technological advances allowed the use of quantitative trait locus (QTL) mapping to identify chromosomal regions with possible genetic variants affecting blood pressure. Many candidate genes for a role in human hypertension were identified as a consequence of findings in rodent animal models [25–30].

Simultaneous selection of major and minor genes: use of QTL to increase selection efficiency of coleoptile length of wheat (Triticum aestivum L.)

Plant breeders simultaneously select for qualitative traits controlled by one or a small number of major genes, as well as for polygenic traits controlled by multiple genes that may be detected as quantitative trait loci (QTL). In this study, we applied computer simulation to investigate simultaneous selection for alleles at both major and minor gene (as QTL) loci in breeding populations of two wheat parental lines, HM14BS and Sunstate. Loci targeted for selection included six major genes affecting plant height, disease resistance, and grain quality, plus 6 known and 11 "unidentified" QTL affecting coleoptile length (CL). Parental line HM14BS contributed the target alleles at two of the major gene loci, while parental line Sunstate contributed target alleles at four loci. The parents have similar plant height, but HM14BS has a longer coleoptile, a desirable attribute for deep sowing in rainfed environments. Including the wild-type allele at the major reduced-height locus Rht-D1, HM14BS was assumed to have 13 QTL for increased CL, and Sunstate four; these assumptions being derived from mapping studies and empirical data from an actual HM14BS/Sunstate population. Simulation indicated that compared to backcross populations, a single biparental F(1) cross produced the highest frequency of target genotypes (six desired alleles at major genes plus desired QTL alleles for long CL). From 1,000 simulation runs, an average of 2.4 individuals with the target genotype were present in unselected F(1)-derived doubled haploid (DH) or recombinant inbred line (RIL) populations of size 200. A selection scheme for the six major genes increased the number of target individuals to 19.1, and additional marker-assisted selection (MAS) for CL increased the number to 23.0. Phenotypic selection (PS) of CL outperformed MAS in this study due to the high heritability of CL, incompletely linked markers for known QTL, and the existence of unidentified QTL. However, a selection scheme combining MAS and PS was equally as efficient as PS and would result in net savings in production and time to delivery of long coleoptile wheats containing the six favorable alleles.

Pleiotropic scaling of gene effects and the ‘cost of complexity’

As perceived by Darwin, evolutionary adaptation by the processes of mutation and selection is difficult to understand for complex features that are the product of numerous traits acting in concert, for example the eye or the apparatus of flight. Typically, mutations simultaneously affect multiple phenotypic characters. This phenomenon is known as pleiotropy. The impact of pleiotropy on evolution has for decades been the subject of formal analysis. Some authors have suggested that pleiotropy can impede evolutionary progress (a so-called 'cost of complexity'). The plausibility of various phenomena attributed to pleiotropy depends on how many traits are affected by each mutation and on our understanding of the correlation between the number of traits affected by each gene substitution and the size of mutational effects on individual traits. Here we show, by studying pleiotropy in mice with the use of quantitative trait loci (QTLs) affecting skeletal characters, that most QTLs affect a relatively small subset of traits and that a substitution at a QTL has an effect on each trait that increases with the total number of traits affected. This suggests that evolution of higher organisms does not suffer a 'cost of complexity' because most mutations affect few traits and the size of the effects does not decrease with pleiotropy.

A systematic strategy for the discovery of candidate genes responsible for phenotypic variation

IntroductionThe use of Quantitative Trait Loci (QTL) data is increasingly used to aid in the discovery of candidate genes involved in phenotypic variation. Tens to hundreds of genes, however, may lie within even well defined QTL. It is therefore vital that the identification, selection and functional testing of candidate Quantitative Trait genes (QTg) are carried out systematically, and without bias [1]. With the advent of microarrays, researchers are able to directly examine the expression of all genes on a genome wide scale, including those underlying QTL regions.The scale of data being generated by such high-throughput experiments has led some investigators to follow a hypothesis-driven approach [2]. Although these techniques for candidate gene identification are valid, they run the risk of overlooking genes that have less obvious associations with the phenotype. By making selections based on prior assumptions of what processes may be involved, the genes that may actually be involved in the phenotype can be overlooked. A further complication is that the use of ad hoc methods for candidate gene identification are inherently difficult to replicate and are compounded by poor documentation of the methods used to generate and capture the data from such investigations in published literature.With an ever increasing number of institutes offering programmatic access to their resources in the form of web services, however, experiments previously conducted manually can now be replaced by automated experiments, capable of processing a far greater volume of data. By reconstructing the original investigation methods in the form of workflows, we are now able to pass data directly from one service to the next. This enables us to process the data in a much more systematic, un-biased, and explicit manner.MethodsWe propose a data-driven methodology that identifies the known pathways that intersect a QTL and those derived from a set of differentially expressed genes from a microarray study. This methodology is implemented systematically through the use of web services and workflows. For the purpose of implementing this systematic pathway-driven approach, we have chosen to use the Taverna workbench [3].Results and DiscussionPreliminary studies into the modes of resistance to African Trypanosomiasis were carried out for the mouse model organism. These studies illustrated how the large-scale analysis of microarray gene expression and QTL data, investigated at the level of biological pathways, enables links between genotype and phenotype to be successfully established [4]. This approach was implemented systematically through the use of explicitly defined workflows.

Putative Quantitative Trait Loci for Physical and Chemical Components of Common Bean

In common bean (Phaseolus vulgaris L.), some nutritional traits such as proteins and mineral content are lower in the cultivated form as compared with the wild counterpart. To assess the feasibility of wild bean as the putative source of desirable traits such as minerals or tannins, this study was performed to identify quantitative trait loci (QTL) associated with seed mass, Ca, Fe, Zn, and tannin content in bean seed. Two‐hundred‐ninety‐one amplified fragment length polymorphism (AFLP) markers were scored in 120 F2:3 segregating individuals derived from a cross between cultivated ‘Bayo Baranda’ and wild common bean accession G‐22837. Seed weight and minerals and tannin contents were quantified on the seed harvested from the 120 individual plants. Significant transgressive segregation was observed among the F2:3 individuals for some characteristics. A total of 57 AFLP markers were distributed among five linkage groups with a coverage of 497 centiMorgans (cM). Five putative QTL were significantly associated with seed mass, two with Ca, two with Fe, one with Zn, and four with tannin content in the seed. These QTL explained ≈42, 25, 25, 15, and 42% of the phenotypic variance, respectively. Due to known environmental effect on most nutritional traits, the use of QTL with larger effects could be used to screen segregating populations that include wild genotypes, wild populations, and ancestral landraces from the region where outstanding wild populations are identified.