Coffee leaf rust (CLR) caused by Hemileia vastatrix Berk. et Br. is one of the major Coffea arabica diseases worldwide. CLR resistance has been attributed to at least nine dominant genes, as single or in combination. We present an inheritance study and mapping loci involved in the Híbrido de Timor (HDT) UFV 443-03 resistance to race I, race II, and pathotype 001 of H. vastatrix. Molecular markers were used to ascertain the phenotypic results and to map the putative resistance loci. For all tree isolates, the inheritance study indicated that the resistance of HDT UFV 443-03 is conditioned by two independent dominant loci or by three independent loci (two dominant and one recessive). Molecular marker analyses ascertained that the resistance of HDT UFV 443-03 to race II is conditioned by at least two independent dominant loci, while the resistance to race I and pathotype 001 is conditioned by at least four independent dominant loci. Gene pyramiding might result in a cultivar with durable resistance; however, it is difficult to distinguish between plants with one or more resistance genes due to epistatic effects. Molecular markers linked to these genes were indicated for marker-assisted selection, as it is an efficient breeding alternative for CLR resistance with no such epistatic effects.

The aim of this work was to evaluate the carotenoid content and genetic variability of banana accessions from the Musa germplasm collection held at Embrapa Cassava and Tropical Fruits, Brazil. Forty-two samples were analyzed, including 21 diploids, 19 triploids and two tetraploids. The carotenoid content was analyzed spectrophotometrically and genetic variability was estimated using 653 DArT markers. The average carotenoid content was 4.73 μg.g -1 , and ranged from 1.06 μg.g -1 for the triploid Nanica (Cavendish group) to 19.24 μg.g -1 for the triploid Saney. The diploids Modok Gier and NBA-14 and the triploid Saney had a carotenoid content that was, respectively, 7-fold, 6-fold and 9-fold greater than that of cultivars from the Cavendish group (2.19 μg.g -1). The mean similarity among the 42 accessions was 0.63 (range: 0.24 to 1.00). DArT analysis revealed extensive genetic variability in accessions from the Embrapa Musa germplasm bank.

Genomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to use DT and its refinements for predicting resistance to orange rust in Arabica coffee. Additionally, DT and its refinements were used to identify the importance of markers related to the characteristic of interest. The results were compared with those from GBLASSO and ANN. Data on coffee rust resistance of 245 Arabica coffee plants genotyped for 137 markers were used. The DT refinements presented equal or inferior values of ApparentError Rate compared to those obtained by DT, GBLASSO, and ANN. Moreover, DT refinements were able to identify important markers for the characteristic of interest. Out of 14 of the most important markers analyzed in each methodology, 9.3 markers on average were in regions of quantitative trait loci (QTLs) related to resistance to disease listed in the literature.

-Casssava (Manihot esculenta Crantz) is one of the main food and income sources of about 500 million people in the tropics. The crop is mainly cultivated by small farmers in tropical Africa, Asia and Latin America. Embrapa Mandioca e Fruticultura Tropical, based in Cruz das Almas, Bahia, maintains one of the largest cassava genebanks of Latin America. Among the accessions it contains, those with yellow-orange root color are particularly interesting. The objective of this study was to characterize 30 cassava accessions with yellow-orange root color by RAPD markers. The genetic distances of the 47 analyzed primers varied from 9.0 to 31.7 %, demonstrating the existing genetic variability to be exploited for the development of cassava varieties with higher beta-carotene contents.Molecular characterization of Cassava ( Molecular characterization of Cassava ( Molecular characterization of Cassava ( Molecular characterization of Cassava ( Molecular characterization of Cassava (Manihot esculenta Crantz Crantz Crantz Crantz Crantz) ) ) ) ) with yellow-orange roots for beta-carotene with yellow-orange roots for beta-carotene with yellow-orange roots for beta-carotene with yellow-orange roots for beta-carotene with yellow-orange roots for beta-carotene improvement improvement improvement improvement improvement In Brazil, 4132 accessions have been collected and are maintained in genebanks across the country (Fukuda 2000). Carotenes (-carotene, b-carotene, lycopene) represent the most multifaceted group of pigments in nature, with colors varying from yellow to red, found in photosynthetic and non-photosynthetic tissues, such as roots, seeds and fruits. Once ingested, b-carotene is transformed, in the liver, into Vitamin A. Vitamin A is a micro-nutrient with functions related to vision, cell differentiation, growth development, reproduction and the immune system. Vitamin A deficiency (VAD) can cause severe diseases, e.g., ocular syndrome, xerophthalmia, and advance to irreversible blindness (Underwood et al. 1999). Although the lack of vitamin A can be prevented, xerophthalmia is still a public health problem in many developing countries (Welch and Graham 2002). In general, staple foods are considered poor sources of micro-nutrients. Cassava genetic breeding may modify this situation, through the exploration of diversity encountered in yellow-orange root cassava accessions (Gregorio 2002, Welch 2002, Bedoya et al. 2003. Among the accessions of the Cassava Genebank of Embrapa Cassava and Tropical Fruits, those with yellow-orange roots, which have only recently become the subject of thorough studies, deserve special attention. The roots of these accessions possibly contain high b-carotene contents. Moreover, it is known that there is sufficient genetic diversity in the cassava genebanks that can be explored for this trait (Iglesias et al. 1997, Carvalho 2000. The inheritance for b-carotene concentration in cassava roots is controlled by few genes, i.e., the levels of b-carotene in cassava varieties can be improved thr...

-The objective of this work was to evaluate the use of artificial neural networks in comparison with Bayesian generalized linear regression to predict leaf rust resistance in Arabica coffee (Coffea arabica). This study used 245 individuals of a F 2 population derived from the self-fertilization of the F 1 H511-1 hybrid, resulting from a crossing between the susceptible cultivar Catuaí Amarelo IAC 64 (UFV 2148-57) and the resistant parent Híbrido de Timor (UFV 443-03). The 245 individuals were genotyped with 137 markers. Artificial neural networks and Bayesian generalized linear regression analyses were performed. The artificial neural networks were able to identify four important markers belonging to linkage groups that have been recently mapped, while the Bayesian generalized model identified only two markers belonging to these groups. Lower prediction error rates (1.60%) were observed for predicting leaf rust resistance in Arabica coffee when artificial neural networks were used instead of Bayesian generalized linear regression (2.4%). The results showed that artificial neural networks are a promising approach for predicting leaf rust resistance in Arabica coffee.Index terms: Coffea arabica, Hemileia vastatrix, artificial intelligence, molecular markers, prediction. Redes neurais artificiais comparadas com modelos lineares generalizados sob o enfoque bayesiano para predição de resistência à ferrugem em café arábicaResumo -O objetivo deste trabalho foi avaliar o uso de redes neurais artificiais em comparação à modelagem por meio de modelos lineares generalizados na predição de resistência à ferrugem em café arábica (Coffea arabica). Foram utilizados 245 indivíduos provenientes de uma população F 2 , oriundos da autofecundação do híbrido F 1 H511-1, resultante do cruzamento da cultivar suscetível Catuaí Amarelo IAC 64 (UFV 2148-57) e do genitor resistente Híbrido de Timor (UFV 443-03). Os 245 indivíduos foram genotipados com 137 marcadores. Realizaram-se análises com redes neurais artificiais e com modelos lineares generalizados sob o enfoque bayesiano. As redes neurais identificaram quatro marcadores importantes pertencentes a grupos de ligação que foram recentemente mapeados, enquanto o modelo generalizado bayesiano identificou somente dois marcadores pertencentes a esses grupos. Foram observadas taxas de erro de predição inferiores (1,60%) para predizer a resistência à ferrugem em café arábica, quando foram utilizadas as redes neurais artificiais em vez de modelos lineares generalizados sob o enfoque bayesiano (2,4%). Os resultados mostraram que as redes neurais artificiais são uma abordagem promissora para predizer a resistência à ferrugem em café arábica.Termos para indexação: Coffea arabica, Hemileia vastatrix, inteligência artificial, marcadores moleculares, predição.