Genetical research最新文献

Agrostis species are mainly used in athletic fields and golf courses. Their integrity is maintained by fungicides, which makes the development of disease-resistance varieties a high priority. However, there is a lack of knowledge about resistance (R) genes and their use for genetic improvement in Agrostis species. The objective of this study was to identify and clone constitutively expressed cDNAs encoding R gene-like (RGL) sequences from three Agrostis species (colonial bentgrass (A. capillaris L.), creeping bentgrass (A. stolonifera L.) and velvet bentgrass (A. canina L.)) by PCR-based motif-directed RNA fingerprinting towards relatively conserved nucleotide binding site (NBS) domains. Sixty-one constitutively expressed cDNA sequences were identified and characterized. Sequence analysis of ESTs and probable translation products revealed that RGLs are highly conserved among these three Agrostis species. Fifteen of them were shown to share conserved motifs found in other plant disease resistance genes such as MLA13, Xa1, YR6, YR23 and RPP5. The molecular evolutionary forces, analysed using the Ka/Ks ratio, reflected purifying selection both on NBS and leucine-rich repeat (LRR) intervening regions of discovered RGL sequences in these species. This study presents, for the first time, isolation and characterization of constitutively expressed RGL sequences from Agrostis species revealing the presence of TNL (TIR-NBS-LRR) type R genes in monocot plants. The characterized RGLs will further enhance knowledge on the molecular evolution of the R gene family in grasses.

Indirect estimates of the genomic rate of deleterious mutations (lambda), their average homozygous effect (s) and their degree of dominance (h) can be obtained from genetic parameters of natural populations, assuming that the frequencies of the loci controlling a given fitness trait are at mutation-selection equilibrium. In 1996, H.-W. Deng and M. Lynch developed a general methodology for obtaining these estimates from inbreeding/outbreeding experiments. The prediction of the sign and magnitude of the biases incurred by these estimators is essential for a correct interpretation of the empirical results. However, the assessment of these biases has been tested so far under a rather limited model of the distribution of dominance effects. In this paper, the application of this method to outbred populations is evaluated, focusing on the level of variation in h values (sigma(h)(2) and the magnitude of the negative correlation (rs,h) between s and h. It is shown that the method produces upwardly biased estimates of lambda and downwardly biased estimates of the average s in the reference situation where rs,h=0, particularly for large values of sigma(h)(2), and biases of different sign depending on the magnitude of the correlation. A modification of the method, substituting the estimates of the average h for alternative ones, allows estimates to be obtained with little or no bias for the case of rs,h=0, but is otherwise biased. Information on rs,h and sigma(h)(2), gathered from mutation-accumulation experiments, suggests that sigma(h)(2) may be rather large and rs,h is usually negative but not higher than about -0.2, although the data are scarce and noisy, and should be used with caution.

Deng et al. have recently proposed that estimates of an upper limit to the rate of spontaneous mutations and their average heterozygous effect can be obtained from the mean and variance of a given fitness trait in naturally segregating populations, provided that allele frequencies are maintained at the balance between mutation and selection. Using simulations they show that this estimation method generally has little bias and is very robust to violations of the mutation-selection balance assumption. Here I show that the particular parameters and models used in these simulations generally reduce the amount of bias that can occur with this estimation method. In particular, the assumption of a large mutation rate in the simulations always implies a low bias of estimates. In addition, the specific model of overdominance used to check the violation of the mutation-selection balance assumption is such that there is not a dramatic decline in mean fitness from overdominant mutations, again implying a low bias of estimates. The assumption of lower mutation rates and/or other models of balancing selection may imply considerably larger biases of the estimates, making the reliability of the proposed method highly questionable.

This study is aimed at improving the analysis of data used in identifying marker-associated effects on quantitative traits, specifically to account for possible departures from a Gaussian distribution of the trait data and to allow for asymmetry of marker effects attributable to phenotypic divergence between parental lines. A Bayesian procedure for analysing marker effects at the whole-genome level is presented. The procedure adopts a skewed t-distribution as a prior distribution of marker effects. The model with the skewed t-process includes Gaussian prior distributions, skewed Gaussian prior distributions and symmetric t-distributions as special cases. A Markov Chain Monte Carlo algorithm for obtaining marginal posterior distributions of the unknowns is also presented. The method was applied to a dataset on three traits (live weight, carcass length and backfat depth) measured in an F2 cross between Iberian and Landrace pigs. The distribution of marker effects was clearly asymmetric for carcass length and backfat depth, whereas it was symmetric for live weight. The t-distribution seems more appropriate for describing the distribution of marker effects on backfat depth.

Selective sweeps of variation caused by fixation of major genes may have a dramatic impact on the genetic gain from background polygenic variation, particularly in the genome regions closely linked to the major gene. The response to selection can be restrained because of the reduced selection intensity and the reduced effective population size caused by the increase in frequency of the major gene. In the context of a selected population where fixation of a known major gene is desired, the question arises as to which is the optimal path of increase in frequency of the gene so that the selective sweep of variation resulting from its fixation is minimized. Using basic theoretical arguments we propose a frequency path that maximizes simultaneously the effective population size applicable to the selected background and the selection intensity on the polygenic variation by minimizing the average squared selection intensity on the major gene over generations up to a given fixation time. We also propose the use of mating between carriers and non-carriers of the major gene, in order to promote the effective recombination between the major gene and its linked polygenic background. Using a locus-based computer simulation assuming different degrees of linkage, we show that the path proposed is more effective than a similar path recently published, and that the combination of the selection and mating methods provides an efficient way to palliate the negative effects of a selective sweep.

An interval quantitative trait locus (QTL) mapping method for complex polygenic diseases (as binary traits) showing QTL by environment interactions (QEI) was developed for outbred populations on a within-family basis. The main objectives, within the above context, were to investigate selection of genetic models and to compare liability or generalized interval mapping (GIM) and linear regression interval mapping (RIM) methods. Two different genetic models were used: one with main QTL and QEI effects (QEI model) and the other with only a main QTL effect (QTL model). Over 30 types of binary disease data as well as six types of continuous data were simulated and analysed by RIM and GIM. Using table values for significance testing, results show that RIM had an increased false detection rate (FDR) for testing interactions which was attributable to scale effects on the binary scale. GIM did not suffer from a high FDR for testing interactions. The use of empirical thresholds, which effectively means higher thresholds for RIM for testing interactions, could repair this increased FDR for RIM, but such empirical thresholds would have to be derived for each case because the amount of FDR depends on the incidence on the binary scale. RIM still suffered from higher biases (15-100% over- or under-estimation of true values) and high standard errors in QTL variance and location estimates than GIM for QEI models. Hence GIM is recommended for disease QTL mapping with QEI. In the presence of QEI, the model including QEI has more power (20-80% increase) to detect the QTL when the average QTL effect is small (in a situation where the model with a main QTL only is not too powerful). Top-down model selection is proposed in which a full test for QEI is conducted first and then the model is subsequently simplified. Methods and results will be applicable to human, plant and animal QTL mapping experiments.