Composition and Flowering Quality of Cacahuacintle Maize Populations from the High Valleys of Mexico.

Cacahuacintle is one of the maize types with great demand for pozole preparation; however, little is known about the variation in chemical composition and flowered grain quality among populations. Physicochemical characteristics, flowered grain quality, pasting properties, and starch microstructure were evaluated in 33 populations of Cacahuacintle maize collected in Valles Altos, Mexico. The seeds samples of corn were obtained in 2017 from local farmers in the states of Mexico, Puebla, and Tlaxcala. Results were analyzed under a completely randomized design, and the ANOVA, Tukey test, and principal components were obtained. The ANOVA showed significance (p ≤ 0.05) in 18 of the 22 variables evaluated. The TE-6, AM-7, and CA-6 populations were outstanding for the good quality of their protein, pasting viscosity, and flowered grain quality. Nine populations collected in Calimaya, estate of Mexico, and Serdan Valley, state of Puebla, presented excellent physical, pasting, and flowery grain characteristics, with reduced protein content and lysine and tryptophan values typical of maize with normal endosperm. The softness of the endosperm grain, starch microstructural, and pasting characteristics of Cacahuacintle maize populations have a fundamental role in reducing the time and increasing the flowered grain volume, properties that were different from those observed in the Chalqueño, included as dent maize check. Variations in grain quality among Cacahuacintle maize populations is an important genetic resource for the improvement of the nutritional and flowering quality of Cacahuacintle maize.

A molecular selection index method based on eigenanalysis.

The traditional molecular selection index (MSI) employed in marker-assisted selection maximizes the selection response by combining information on molecular markers linked to quantitative trait loci (QTL) and phenotypic values of the traits of the individuals of interest. This study proposes an MSI based on an eigenanalysis method (molecular eigen selection index method, MESIM), where the first eigenvector is used as a selection index criterion, and its elements determine the proportion of the trait's contribution to the selection index. This article develops the theoretical framework of MESIM. Simulation results show that the genotypic means and the expected selection response from MESIM for each trait are equal to or greater than those from the traditional MSI. When several traits are simultaneously selected, MESIM performs well for traits with relatively low heritability. The main advantages of MESIM over the traditional molecular selection index are that its statistical sampling properties are known and that it does not require economic weights and thus can be used in practical applications when all or some of the traits need to be improved simultaneously.