Small-effect QTL Research Articles

Variation in Salamander Tail Regeneration Is Associated with Genetic Factors That Determine Tail Morphology

Very little is known about the factors that cause variation in regenerative potential within and between species. Here, we used a genetic approach to identify heritable genetic factors that explain variation in tail regenerative outgrowth. A hybrid ambystomatid salamander (Ambystoma mexicanum x A. andersoni) was crossed to an A. mexicanum and 217 offspring were induced to undergo metamorphosis and attain terrestrial adult morphology using thyroid hormone. Following metamorphosis, each salamander’s tail tip was amputated and allowed to regenerate, and then amputated a second time and allowed to regenerate. Also, DNA was isolated from all individuals and genotypes were determined for 187 molecular markers distributed throughout the genome. The area of tissue that regenerated after the first and second amputations was highly positively correlated across males and females. Males presented wider tails and regenerated more tail tissue during both episodes of regeneration. Approximately 66–68% of the variation in regenerative outgrowth was explained by tail width, while tail length and genetic sex did not explain a significant amount of variation. A small effect QTL was identified as having a sex-independent effect on tail regeneration, but this QTL was only identified for the first episode of regeneration. Several molecular markers significantly affected regenerative outgrowth during both episodes of regeneration, but the effect sizes were small (<4%) and correlated with tail width. The results show that ambysex and minor effect QTL explain variation in adult tail morphology and importantly, tail width. In turn, tail width at the amputation plane largely determines the rate of regenerative outgrowth. Because amputations in this study were made at approximately the same position of the tail, our results resolve an outstanding question in regenerative biology: regenerative outgrowth positively co-varies as a function of tail width at the amputation site.

Small- and Large-Effect Quantitative Trait Locus Interactions Underlie Variation in Yeast Sporulation Efficiency

Quantitative trait loci (QTL) with small effects on phenotypic variation can be difficult to detect and analyze. Because of this a large fraction of the genetic architecture of many complex traits is not well understood. Here we use sporulation efficiency in Saccharomyces cerevisiae as a model complex trait to identify and study small-effect QTL. In crosses where the large-effect quantitative trait nucleotides (QTN) have been genetically fixed we identify small-effect QTL that explain approximately half of the remaining variation not explained by the major effects. We find that small-effect QTL are often physically linked to large-effect QTL and that there are extensive genetic interactions between small- and large-effect QTL. A more complete understanding of quantitative traits will require a better understanding of the numbers, effect sizes, and genetic interactions of small-effect QTL.

Detection of QTL for flowering time in multiple families of elite maize

Flowering time is a fundamental quantitative trait in maize that has played a key role in the postdomestication process and the adaptation to a wide range of climatic conditions. Flowering time has been intensively studied and recent QTL mapping results based on diverse founders suggest that the genetic architecture underlying this trait is mainly based on numerous small-effect QTL. Here, we used a population of 684 progenies from five connected families to investigate the genetic architecture of flowering time in elite maize. We used a joint analysis and identified nine main effect QTL explaining approximately 50% of the genotypic variation of the trait. The QTL effects were small compared with the observed phenotypic variation and showed strong differences between families. We detected no epistasis with the genetic background but four digenic epistatic interactions in a full 2-dimensional genome scan. Our results suggest that flowering time in elite maize is mainly controlled by main effect QTL with rather small effects but that epistasis may also contribute to the genetic architecture of the trait.

Marker-Assisted Selection to Pyramid Nematode Resistance and the High Oleic Trait in Peanut

Despite important strides in marker technologies, the use of marker-assisted selection has stagnated for the improvement of quantitative traits. Biparental mating designs for the detection of loci affecting these traits (quantitative trait loci [QTL]) impede their application, and the statistical methods used are ill-suited to the traits' polygenic nature. Genomic selection (GS) has been proposed to address these deficiencies. Genomic selection predicts the breeding values of lines in a population by analyzing their phenotypes and high-density marker scores. A key to the success of GS is that it incorporates all marker information in the prediction model, thereby avoiding biased marker effect estimates and capturing more of the variation due to small-effect QTL. In simulations, the correlation between true breeding value and the genomic estimated breeding value has reached levels of 0.85 even for polygenic low heritability traits. This level of accuracy is sufficient to consider selecting for agronomic performance using marker information alone. Such selection would substantially accelerate the breeding cycle, enhancing gains per unit time. It would dramatically change the role of phenotyping, which would then serve to update prediction models and no longer to select lines. While research to date shows the exceptional promise of GS, work remains to be done to validate it empirically and to incorporate it into breeding schemes.

Joint QTL Linkage Mapping for Multiple-Cross Mating Design Sharing One Common Parent

BackgroundNested association mapping (NAM) is a novel genetic mating design that combines the advantages of linkage analysis and association mapping. This design provides opportunities to study the inheritance of complex traits, but also requires more advanced statistical methods. In this paper, we present the detailed algorithm of a QTL linkage mapping method suitable for genetic populations derived from NAM designs. This method is called joint inclusive composite interval mapping (JICIM). Simulations were designed on the detected QTL in a maize NAM population and an Arabidopsis NAM population so as to evaluate the efficiency of the NAM design and the JICIM method.Principal FindingsFifty-two QTL were identified in the maize population, explaining 89% of the phenotypic variance of days to silking, and nine QTL were identified in the Arabidopsis population, explaining 83% of the phenotypic variance of flowering time. Simulations indicated that the detection power of these identified QTL was consistently high, especially for large-effect QTL. For rare QTL having significant effects in only one family, the power of correct detection within the 5 cM support interval was around 80% for 1-day effect QTL in the maize population, and for 3-day effect QTL in the Arabidopsis population. For smaller-effect QTL, the power diminished, e.g., it was around 50% for maize QTL with an effect of 0.5 day. When QTL were linked at a distance of 5 cM, the likelihood of mapping them as two distinct QTL was about 70% in the maize population. When the linkage distance was 1 cM, they were more likely mapped as one single QTL at an intermediary position.ConclusionsBecause it takes advantage of the large genetic variation among parental lines and the large population size, NAM is a powerful multiple-cross design for complex trait dissection. JICIM is an efficient and specialty method for the joint QTL linkage mapping of genetic populations derived from the NAM design.

The genetic characterisation of stripe rust resistance in the German wheat cultivar Alcedo.

Stripe rust resistance in the German winter wheat cv. Alcedo has been described as durable, the resistance having remained effective when grown extensively in Germany and Eastern Europe between 1975 and 1989. Genetic characterisation of field resistance in a cross between Alcedo and the stripe rust susceptible UK winter wheat cv. Brigadier identified two major QTL in Alcedo located on the long arms of chromosomes 2D (QPst.jic-2D) and 4B (QPst.jic-4B). Stripe rust resistance was evaluated by measuring the extent of fungal growth, percentage infection (Pi) and the necrotic/chlorotic response of the plant to infection, infection type (IT). Both QPst.jic-2D and QPst.jic-4B contributed significantly to the reduction in stripe rust infection (Pi), with QPst.jic-2D explaining up to 36.20% and QPst.jic-4B 28.90% of the phenotypic variation measured for Pi. Both QTL were identified by the IT phenotypic scores, with QPst.jic-2D in particular being associated with a strong necrotic phenotype (low IT), QPst.jic-2D explaining up to 53.10% of IT phenotypic variation and QPst.jic-4B 22.30%. In addition, two small effect QTL for field stripe rust resistance were identified in Brigadier, QPst.jic-1B on the long arm of chromosome 1B and QPst.jic-5A on the short arm of chromosome 5A. The influence of QPst.jic-1B was primarily seen with the Pi phenotype, contributing up to 13.10% of the explained phenotypic variation. QPst.jic-5A was only detected using an approximate multiple-QTL model and selecting markers linked to the major effect QTL, QPst.jic-2D and QPst.jic-4B as co-factors. Seedling stripe rust resistance was also mapped in the cross, which confirmed the location of Yr17 from Brigadier to the short arm of chromosome 2A. A seedling expressed QTL was also located in Alcedo that mapped to the same location as the field stripe rust resistance QPst.jic-2D.

Mapping QTL for dollar spot resistance in creeping bentgrass (Agrostis stolonifera L.)

Dollar spot caused by Sclerotinia homoeocarpa F. T. Bennett is the most economically important turf disease on golf courses in North America. Dollar spot resistance in a creeping bentgrass cultivar would greatly reduce the frequency, costs, and environmental impacts of fungicide application. Little work has been done to understand the genetics of resistance to dollar spot in creeping bentgrass. Therefore, QTL analysis was used to determine the location, number and effects of genomic regions associated with dollar spot resistance in the field. To meet this objective, field inoculations using a single isolate were performed over 2 years and multiple locations using progeny of a full sib mapping population '549 x 372'. Dollar spot resistance seems to be inherited quantitatively and broad sense heritability for resistance was estimated to be 0.88. We have detected one QTL with large effect on linkage group 7.1 with LOD values ranging from 3.4 to 8.6 and explaining 14-36% of the phenotypic variance. Several smaller effect QTL specific to rating dates, locations and years were also detected. The association of the tightly linked markers with the LG 7.1 QTL based on 106 progeny was further examined by single marker analysis on all 697 progeny. The high significance of the QTL on LG 7.1 at a sample size of 697 (P < 0.0001), along with its consistency across locations, years and ratings dates, indicated that it was stable over environments. Markers tightly linked to the QTL can be utilized for marker-assisted selection in future bentgrass breeding programs.

Chromosome-Wise Dissection of the Genome of the Extremely Big Mouse Line DU6i

The extreme high-body-weight-selected mouse line DU6i is a polygenic model for growth research, harboring many small-effect QTL. We dissected the genome of this line into 19 autosomes and the Y chromosome by the construction of a new panel of chromosome substitution strains (CSS). The DU6i chromosomes were transferred to a DBA/2 mice genetic background by marker-assisted recurrent backcrossing. Mitochondria and the X chromosome were of DBA/2 origin in the backcross. During the construction of these novel strains, >4000 animals were generated, phenotyped, and genotyped. Using these data, we studied the genetic control of variation in body weight and weight gain at 21, 42, and 63 days. The unique data set facilitated the analysis of chromosomal interaction with sex and parent-of-origin effects. All analyzed chromosomes affected body weight and weight gain either directly or in interaction with sex or parent of origin. The effects were age specific, with some chromosomes showing opposite effects at different stages of development.