Empirical patterns of linkage disequilibrium (LD) can be used to increase the statistical power of genetic mapping. This study was carried out with the objective of verifying the efficacy of factor analysis (AF) applied to data sets of molecular markers of the SNP type, in order to identify linkage groups and haplotypes blocks. The SNPs data set used was derived from a simulation process of an F2 population, containing 2000 marks with information of 500 individuals. The estimation of the factorial loadings of FA was made in two ways, considering the matrix of distances between the markers (A) and considering the correlation matrix (R). The number of factors (k) to be used was established based on the graph scree-plot and based on the proportion of the total variance explained. Results indicated that matrices A and R lead to similar results. Based on the scree-plot we considered k equal to 10 and the factors interpreted as being representative of the bonding groups. The second criterion led to a number of factors equal to 50, and the factors interpreted as being representative of the haplotypes blocks. This showed the potential of the technique, making it possible to obtain results applicable to any type of population, helping or corroborating the interpretation of genomic studies. The study demonstrated that AF was able to identify patterns of association between markers, identifying subgroups of markers that reflect factor binding groups and also linkage disequilibrium groups.

This paper studies monthly precipitation time series in Viçosa MG, Brazil. We aimed to detect serial patterns in precipitation like trend and seasonality and make predictions for the 2019 year. The idea was to understand water shortage events that occur in Viçosa as well as the challenges regarding water supply for human consumption and agricultural production faced by urban and rural citizens. We used time series and SARIMA (1,0,0) x (0,1,1) model approaches selected based on Bayesian and Akaike Information criteria values (BIC and AIC, respectively). In addition, the ARCH (2) model, selected through AIC, was used to fit SARIMA (1,0,0) x (0,1,1) residues with heteroscedasticity. Our results reveal no changes in precipitation for Viçosa, MG, Brazil, with large variations observed only for specific periods.