Unraveling the genetic structure of Brazilian commercial sugarcane cultivars through microsatellite markers.

The Brazilian sugarcane industry plays an important role in the worldwide supply of sugar and ethanol. Investigation into the genetic structure of current commercial cultivars and comparisons to the main ancestor species allow sugarcane breeding programs to better manage crosses and germplasm banks as well as to promote its rational use. In the present study, the genetic structure of a group of Brazilian cultivars currently grown by commercial producers was assessed through microsatellite markers and contrasted with a group of basic germplasm mainly composed of Saccharum officinarum and S. spontaneum accessions. A total of 285 alleles was obtained by a set of 12 SSRs primer pairs that taken together were able to efficiently distinguish and capture the genetic variability of sugarcane commercial cultivars and basic germplasm accessions allowing its application in a fast and cost-effective way for routine cultivar identification and management of sugarcane germplasm banks. Allelic distribution revealed that 97.6% of the cultivar alleles were found in the basic germplasm while 42% of the basic germplasm alleles were absent in cultivars. Of the absent alleles, 3% was exclusive to S. officinarum, 33% to S. spontaneum and 19% to other species/exotic hybrids. We found strong genetic differentiation between the Brazilian commercial cultivars and the two main species (S. officinarum: [Formula: see text] = 0.211 and S. spontaneum: [Formula: see text] = 0.216, P<0.001), and significant contribution of the latter in the genetic variability of commercial cultivars. Average dissimilarity within cultivars was 1.2 and 1.4 times lower than that within S. officinarum and S. spontaneum. Genetic divergence found between cultivars and S. spontaneum accessions has practical applications for energy cane breeding programs as the choice of more divergent parents will maximize the frequency of transgressive individuals in the progeny.

Mapping epistatic quantitative trait loci.

BACKGROUND: How to map quantitative trait loci (QTL) with epistasis efficiently and reliably has been a persistent problem for QTL mapping analysis. There are a number of difficulties for studying epistatic QTL. Linkage can impose a significant challenge for finding epistatic QTL reliably. If multiple QTL are in linkage and have interactions, searching for QTL can become a very delicate issue. A commonly used strategy that performs a two-dimensional genome scan to search for a pair of QTL with epistasis can suffer from low statistical power and also may lead to false identification due to complex linkage disequilibrium and interaction patterns. RESULTS: To tackle the problem of complex interaction of multiple QTL with linkage, we developed a three-stage search strategy. In the first stage, main effect QTL are searched and mapped. In the second stage, epistatic QTL that interact significantly with other identified QTL are searched. In the third stage, new epistatic QTL are searched in pairs. This strategy is based on the consideration that most genetic variance is due to the main effects of QTL. Thus by first mapping those main-effect QTL, the statistical power for the second and third stages of analysis for mapping epistatic QTL can be maximized. The search for main effect QTL is robust and does not bias the search for epistatic QTL due to a genetic property associated with the orthogonal genetic model that the additive and additive by additive variances are independent despite of linkage. The model search criterion is empirically and dynamically evaluated by using a score-statistic based resampling procedure. We demonstrate through simulations that the method has good power and low false positive in the identification of QTL and epistasis. CONCLUSION: This method provides an effective and powerful solution to map multiple QTL with complex epistatic pattern. The method has been implemented in the user-friendly computer software Windows QTL Cartographer. This will greatly facilitate the application of the method for QTL mapping data analysis.

Factorial analysis of bootstrap variances of population genetic parameter estimates

We presented an alternative way to verify the relative contribution to the total variance, of the sources of variation due to populations (P), individuals within populations (I), the (P*I) interaction, and the standard error of the following parameter estimates: total (F) and intrapopulation (f) fixation indices, and divergence among populations (q). The knowledge of this relative contribution is important to establish sampling strategies of natural populations. To attain these objectives, the bootstrap method was used to resample simultaneously populations and individuals, considering different combinations of P and I. This procedure was repeated five times for a given combination of each analyzed data set. For each data set, five estimates of these variances were obtained for each combination of P and I, and a given parameter estimate. These variance estimates were submitted to an analysis of variance, considering a factorial structure. The sources of variation considered in this analysis were P, I and P*I. The coefficient of determination (R²) was calculated for each source of variation. Sources of variation with greater R² are responsible for bigger errors of the estimates. The method applied was efficient for answering the questions initially proposed, and the results indicated that there are no ideal sample sizes for a species, but rather for a specific data set, because each data set has its own particularities. However, for investigations on the genetic structure of natural populations using population parameters, the number of populations to be sampled is a critical factor. Thus, more efforts should be made to increase the number of sampled populations, rather than the number of individuals within populations. A sampling strategy is given as a guide for investigations of this kind, when there is no previous knowledge about the genetic structure and the mating system of the populations.