Action Of Quantitative Trait Loci Research Articles

Inferring trait-specific similarity among individuals from molecular markers and phenotypes with Bayesian regression

Modeling covariance structure based on genetic similarity between pairs of relatives plays an important role in evolutionary, quantitative and statistical genetics. Historically, genetic similarity between individuals has been quantified from pedigrees via the probability that randomly chosen homologous alleles between individuals are identical by descent (IBD). At present, however, many genetic analyses rely on molecular markers, with realized measures of genomic similarity replacing IBD-based expected similarities. Animal and plant breeders, for example, now employ marker-based genomic relationship matrices between individuals in prediction models and in estimation of genome-based heritability coefficients. Phenotypes convey information about genetic similarity as well. For instance, if phenotypic values are at least partially the result of the action of quantitative trait loci, one would expect the former to inform about the latter, as in genome-wide association studies. Statistically, a non-trivial conditional distribution of unknown genetic similarities, given phenotypes, is to be expected. A Bayesian formalism is presented here that applies to whole-genome regression methods where some genetic similarity matrix, e.g., a genomic relationship matrix, can be defined. Our Bayesian approach, based on phenotypes and markers, converts prior (markers only) expected similarity into trait-specific posterior similarity. A simulation illustrates situations under which effective Bayesian learning from phenotypes occurs. Pinus and wheat data sets were used to demonstrate applicability of the concept in practice. The methodology applies to a wide class of Bayesian linear regression models, it extends to the multiple-trait domain, and can also be used to develop phenotype-guided similarity kernels in prediction problems.

The evolution of sex ratio differences and inflorescence architectures in Begonia (Begoniaceae)

A major benefit conferred by monoecy is the ability to alter floral sex ratio in response to selection. In monoecious species that produce flowers of a given sex at set positions on the inflorescence, floral sex ratio may be related to inflorescence architecture. We studied the loci underlying differences in inflorescence architecture between two monoecious Begonia species and related this to floral sex ratios. We performed trait comparisons and quantitative trait locus (QTL) mapping in a segregating backcross population between Central American Begonia plebeja and B. conchifolia. We focused on traits related to inflorescence architecture, sex ratios, and other reproductive traits. The inflorescence branching pattern of B. conchifolia was more asymmetric than B. plebeja, which in turn affects the floral sex ratio. Colocalizing QTLs of moderate effect influenced both the number of male flowers and the fate decisions of axillary meristems, demonstrating the close link between inflorescence architecture and sex ratio. Additional QTLs were found for stamen number (30% variance explained, VE) and pollen sterility (12.3% VE). One way in which Begonia species develop different floral sex ratios is through modifications of their inflorescence architecture. The potential pleiotropic action of QTL on inflorescence branching and floral sex ratios may have major implications for trait evolution and responses to selection. The presence of a single QTL of large effect on stamen number may allow rapid divergence for this key floral trait. We propose candidate loci for stamen number and inflorescence branching for future characterization.

Co-localisation of host plant resistance QTLs affecting the performance and feeding behaviour of the aphid Myzus persicae in the peach tree

The architecture and action of quantitative trait loci (QTL) contributing to plant resistance mechanisms against aphids, the largest group of phloem-feeding insects, are not well understood. Comparative mapping of several components of resistance to the green peach aphid (Myzus persicae) was undertaken in Prunus davidiana, a wild species related to peach. An interspecific F(1) population of Prunus persica var. Summergrand × P. davidiana clone P1908 was scored for resistance (aphid colony development and foliar damage) and 17 aphid feeding behaviour traits monitored by means of the electrical penetration graph technique. Seven resistance QTLs were detected, individually explaining 6.1-43.1% of the phenotypic variation. Consistency was shown over several trials. Nine QTLs affecting aphid feeding behaviour were identified. All resistance QTLs except one co-located with QTLs underlying aphid feeding behaviour. A P. davidiana resistance allele at the major QTL was associated with drastic reductions in phloem sap ingestion by aphids, suggesting a phloem-based resistance mechanism. Resistance was also positively correlated with aphid salivation into sieve elements, suggesting an insect response to restore the appropriate conditions for ingestion after phloem occlusion. No significant QTL was found for traits characterising aphid mouthpart activity in plant tissues other than phloem vessels. Two QTLs with effects on aphid feeding behaviour but without effect on resistance were identified. SSR markers linked to the main QTLs involved in resistance are of potential use in marker-assisted selection for aphid resistance. Linking our results with the recent sequencing of the peach genome may help clarify the physiological resistance mechanisms.

Detection of QTLs with additive effects and additive-by-environment interaction effects on panicle number in rice (Oryza sativa L.) with single-segment substitution lines

A novel population consisting of 35 single-segment substitution lines (SSSLs) originating from crosses between the recipient parent, Hua-jing-xian 74 (HJX74), and 17 donor parents was evaluated in six cropping season environments to reveal the genetic basis of genetic main effect (G) and genotype-by-environment interaction effect (GE) for panicle number (PN) in rice. Subsets of lines were grown in up to six environments. An indirect analysis method was applied, in which the total genetic effect was first partitioned into G and GE by using the mixed linear-model approach, and then QTL (quantitative trait locus) analyses on these effects were conducted separately. At least 18 QTLs for PN in rice were detected and identified on 9 of 12 rice chromosomes. A single QTL effect (a + ae) ranging from -1.5 to 1.2 was divided into two components, additive effect (a) and additive x environment interaction effect (ae). A total number of 9 and 16 QTLs were identified with a ranging from -0.4 to 0.6 and ae ranging from -1.0 to 0.6, respectively, the former being stable but the latter unstable across environments. Three types of QTLs were suggested according to their effects expressed. Two QTLs (Pn-1b and Pn-6d) expressed stably across environments due to the association with only a, nine QTLs (Pn-1a, Pn-3c, Pn-3d, Pn-4, Pn-6a, Pn-6b, Pn-8, Pn-9 and Pn-12) with only ae were unstable, and the remaining seven of QTLs were identified with both a and ae, which also were unstable across environments. This is the first report on the detection of QE (QTL-by-environment interaction effect) of QTLs with SSSLs. Our results illustrate the efficiency of characterizing QTLs and analyzing action of QTLs through SSSLs, and further demonstrate that QE is an important property of many QTLs. Information provided in this paper could be used in the application of marker-assisted selection to manipulate PN in rice.

QTL analysis for rice grain length and fine mapping of an identified QTL with stable and major effects

Grain length in rice plays an important role in determining rice appearance, milling, cooking and eating quality. In this study, the genetic basis of grain length was dissected into six main-effect quantitative trait loci (QTLs) and twelve pairs of epistatic QTLs. The stability of these QTLs was evaluated in four environments using an F7 recombinant inbred line (RIL) population derived from the cross between a Japonica variety, Asominori, and an Indica variety, IR24. Moreover, chromosome segment substitution lines (CSSLs) harboring each of the six main-effect QTLs were used to evaluate gene action of QTLs across eight environments. A major QTL denoted as qGL-3a, was found to express stably not only in the isogenic background of Asominori but also in the recombinant background of Asominori and IR24 under multiple environments. The IR24 allele at qGL-3a has a positive effect on grain length. Based on the test of advanced backcross progenies, qGL-3a was dissected as a single Mendelian factor, i.e., long rice grain was controlled by a recessive gene gl-3. High-resolution genetic and physical maps were further constructed for fine mapping gl-3 by using 11 simple sequence repeat (SSR) markers designed using sequence information from seven BAC/PAC clones and a BC4F2 population consisting of 2,068 individuals. Consequently, the gl-3 gene was narrowed down to a candidate genomic region of 87.5 kb long defined by SSR markers RMw357 and RMw353 on chromosome 3, which provides a basis for map-based cloning of this gene and for marker-aided QTL pyramiding in rice quality breeding.

Identification and mapping of yield and yield related QTLs from an Indian accession of Oryza rufipogon

BackgroundCultivated rice (Oryza sativa L.) is endowed with a rich genetic variability. In spite of such a great diversity, the modern rice cultivars have narrow genetic base for most of the agronomically important traits. To sustain the demand of an ever increasing population, new avenues have to be explored to increase the yield of rice. Wild progenitor species present potential donor sources for complex traits such as yield and would help to realize the dream of sustained food security.ResultsAdvanced backcross method was used to introgress and map new quantitative trait loci (QTLs) relating to yield and its components from an Indian accession of Oryza rufipogon. An interspecific BC2 testcross progeny (IR58025A/O. rufipogon//IR580325B///IR58025B////KMR3) was evaluated for 13 agronomic traits pertaining to yield and its components. Transgressive segregants were obtained for all the traits. Thirty nine QTLs were identified using interval mapping and composite interval mapping. In spite of it's inferiority for most of the traits studied, O. rufipogon alleles contributed positively to 74% of the QTLs. Thirty QTLs had corresponding occurrences with the QTLs reported earlier, indicating that these QTLs are stable across genetic backgrounds. Nine QTLs are novel and reported for the first time.ConclusionThe study confirms that the progenitor species constitute a prominent source of still unfolded variability for traits of complex inheritance like yield. With the availability of the complete genome sequence of rice and the developments in the field of genomics, it is now possible to identify the genes underlying the QTLs. The identification of the genes constituting QTLs would help us to understand the molecular mechanisms behind the action of QTLs.

Retrotransposons as epigenetic mediators of phenotypic variation in mammals.

Phenotypic variation in mammals is frequently attributed to the action of quantitative trait loci (QTL) or the environment, but may also be epigenetic in origin. Here we consider a mechanism for phenotypic variation based on interference of transcription by somatically active retrotransposons. Transcriptionally competent retrotransposons may number in the tens of thousands in mammalian genomes. We propose that silencing of retrotransposons occurs by cosuppression during early embryogenesis, but that this process is imperfect and produces a mosaic pattern of retrotransposon expression in somatic cells. Transcriptional interference by active retrotransposons perturbs expression of neighboring genes in somatic cells, in a mosaic pattern corresponding to activity of each retrotransposon. The epigenotype of retrotransposon activity is reset in each generation, but incomplete resetting can lead to heritable epigenetic effects. The stochastic nature of retrotransposon activity, and the very large number of genes that may be affected, produce subtle phenotypic variations even between genetically identical individuals, which may affect disease risk and be heritable in a non-mendelian fashion.

Identification and mode of action of quantitative trait loci affecting seedling height and leaf area in Eucalyptus nitens

Regions of the genome influencing height and leaf area in seedlings of a three-generation outbred pedigree of Eucalyptus nitens have been identified. Three QTLs affecting height and two QTLs affecting leaf area were located using single-factor analysis of variance. The three QTLs affecting height each explained between 10.3 and 14.7% of the phenotypic variance, while the two QTLs for leaf area each explained between 9.8 and 11.6% of the phenotypic variation. Analysis of fully informative marker loci linked to the QTLs enabled the mode of action of the QTLs to be investigated. For three loci the QTL effect segregated from only one parent, while for two loci the QTL showed multiple alleles and the effect segregated from both parents in the pedigree. The two QTLs affecting leaf area were located in the same regions as two of the QTLs affecting height. Analysis of these regions with fully informative markers showed that both QTLs were linked to the same markers, but one had a similar size of effects and a similar mode of action for both height and leaf area, whilst the other showed a different mode of action for the two traits. These regions may contain two closely linked genes or may involve a single gene with a pleiotrophic effect on both height and leaf area. The QTL with the greatest effect showed multiple alleles and an intra-locus interaction that reduced the size of the effect. Assessment for two of the QTLs in a second related family did not show an effect associated with the marker loci; however, this was consistent with the mode of action of these QTLs and the pattern of inheritance in the second family.

Detection of additive and dominance effects of QTLs in interval mapping of F2 RFLP data

A model for interval mapping of quantitative trait loci (QTLs) in an F2 population using genetic markers such as restriction fragment length polymorphisms (RFLPs) is proposed. Based on this model the log-likelihood ratio statistic is divided into two useful statistics which can be used to detect additive and dominance effects of QTLs, separately. The properties of the two statistics were investigated for the theoretical construction of critical regions to test the gene action of QTLs. The model was applied to 1000 simulated-sets of 200 F2 individuals.

Mapping quantitative trait loci for behavioral traits in the mouse.

After many years of studying various behavioral characters in the mouse, it is clear that most are heritable and are specified by complexes of genes or quantitative trait loci (QTLs). In order to attain a more complete understanding of the genetic causes of individual differences in behavior, the mechanism of action of these QTLs must be elucidated. The most straightforward approach to determining the mechanism of action of a particular QTL is to identify and molecularly clone the gene; this can be done by positional cloning, which depends on precise knowledge of the genetic map position. As the genetic data base for the mouse genome continues to develop, such strategies will become increasingly easy to perform. Here we suggest a multistage strategy for QTL mapping using recombinant-inbred strains of mice: (1) characterize genomic DNA from parental strains originally used to generate the RI strains; (2) characterize the RI strains for a quantitative character and for DNA markers that differ in the parental strains; and (3) assess the quantitative character in F2 mice derived from crosses between the parental strains, then determine the genotypes of extreme F2 mice for markers that account for at least 5% of the additive genetic variance. Data from these F2 crosses can be used to test hypotheses from the analysis of RI strains, i.e., that a QTL maps to a particular region. Using data from the mouse genome data base, this strategy should allow the molecular identification of the gene based on a candidate-gene approach. We illustrate the approach with examples from our work in mapping QTLs specifying neural sensitivity to the anesthetic effects of ethanol.