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There is a recent unparalleled increase in demand for rice in sub-Saharan Africa, yet its production is affected by blast disease. Characterization of blast resistance in adapted African rice cultivars can provide important information to guide growers and rice breeders. We used molecular markers for known blast resistance genes (Pi genes; n=21) to group African rice genotypes (n=240) into similarity clusters. We then used greenhouse-based assays to challenge representative rice genotypes (n=56) with African isolates (n=8) of Magnaporthe oryzae which varied in virulence and genetic lineage. The markers grouped rice cultivars into five blast resistance clusters (BRC) which differed in foliar disease severity. Using stepwise regression, we found that the Pi genes associated with reduced blast severity were Pi50 and Pi65, while Pik-p, Piz-t, and Pik were associated with increased susceptibility. All rice genotypes in the most resistant cluster, BRC 4, possessed Pi50 and Pi65, the only genes that were significantly associated with reduced foliar blast severity. Cultivar IRAT109, which contains Piz-t was resistant against seven African M. oryzae isolates, while ARICA 17 was susceptible to eight isolates. The popular Basmati 217 and Basmati 370 were among the most susceptible genotypes.These findings indicate that most tested genes were not effective against African blast pathogen collections. Pyramiding genes in the Pi2/9 multifamily blast resistance cluster on chromosome 6 and Pi65 on chromosome 11 could confer broad-spectrum resistance capabilities. To gain further insights into genomic regions associated with blast resistance, gene mapping could be conducted with resident blast pathogen collections.

BackgroundSesame is an important oil crop widely cultivated in Africa and Asia continent. Characterization of genetic diversity and population structure of sesame genotypes in these continents can be used to designing breeding methods. In the present study, 300 sesame genotypes comprising 209 local, and 75 exotic collection, and 16 released varieties provided from the Ethiopian Biodiversity Institute and research centers were used in the present study.ResultsThe panel was genotyped using two ultra-high-throughput diversity array technology (DArT) markers (silicoDArT and SNP). Both markers were used to identify the genetic diversity and population structure of sesame germplasm. A total of 6115 silicoDArT and 6474 SNP markers were reported, of which 5002 silicoDArT and 4638 SNP markers were screening with quality control parameters. The average polymorphic information content values of silicoDArT and SNP markers were 0.07 and 0.08, respectively. For further analysis, the allele frequency for each SNP site was calculated and purified with MAF < 0.01 and left 2997 high-quality SNPs evenly distributed across the whole genome that could be used for subsequent analysis. All genotypes used in this study were descended from eight 8 geographical origins. The genetic diversity analysis showed that the average nucleotide diversity of the panel was 0.14. Considering the genotypes based on their geographical origin, Africa collections (0.21) as a whole without Ethiopian collection was more diverse than Asia and when further portioned Africa, North Africa (0.23) collection was more diverse than others, but at the continent level, Asia (0.17) was more diverse than Africa (0.14). The genetic distance among the sesame populations was ranged from 0.015 to 0.394, with an average of 0.165. The sesame populations was clustered into four groups. The structure analysis divided the panel into four subgroups and 21 genotypes were clustered as an admixture. These indicates genotypes from the same origin didn’t classify properly on the premise of the country of origin. ConclusionsThe genetic diversity and population structure revealed in this study should guide the future research work to design association studies and the systematic utilization of the genetic variation characterizing the sesame panel.

Coffee Berry Disease (CBD) caused by the fungal pathogen Colletotrichum kahawae destroys about 80% of the crop on susceptible Coffea arabica cultivars if no control is applied. Resistance to CBD is governed by three genes: T, R that are dominant, and the recessive k in varieties Hibrido de Timor, Rume Sudan (RS), and K7 respectively. The marker for the T-gene had earlier been identified. The study aimed to identify the DNA marker for the R-gene from RS. The mapping population comprised of 106 F2 genotypes from varieties RS (resistant) and SL28 (susceptible). The genotypes were assessed for their reaction to CBD using hypocotyl inoculation test on a scale of 1–12. The Single Nucleotide Polymorphism (SNP) markers were obtained through Genotyping by Sequencing. The population structure was assessed by both principal component analysis and marker-based kinship while pairwise linkage disequilibrium was estimated using squared allele frequency correlations (r2). Genome-Wide Association Study was carried out using a compressed mixed linear model in Genomic Association and Prediction Integrated Tool. Two SNP markers were significantly associated with CBD resistance in coffee Chromosomes (Chr) 1 and 2 accounting for 12.5% and 11% respectively of the total phenotypic variation explained by the model. The sequence search for the markers was associated with proteins for disease resistance within the NCBI database. The two SNP markers 100025973|F|0-59:T > C-59:T > C and 100034991|F|0-44:C > T-44:C > T are identified as DNA markers for the genetic loci conferring resistance to CBD in RS, christened as Ck-2 and Ck-3 and are recommended for marker assisted selection.

Plants obtain soil-resident elements that support growth and metabolism via water-mediated flow facilitated by transpiration and active transport processes. The availability of elements in the environment interact with the genetic capacity of organisms to modulate element uptake through plastic adaptive responses, such as homeostasis. These interactions should cause the elemental contents of plants to vary such that the effects of genetic polymorphisms influencing elemental accumulation will be dramatically dependent on the environment in which the plant is grown. To investigate genotype by environment interactions underlying elemental accumulation, we analyzed levels of elements in maize kernels of the Intermated B73 x Mo17 (IBM) recombinant inbred population grown in 10 different environments spanning a total of six locations and five different years. In analyses conducted separately for each environment, we identified a total of 79 quantitative trait loci controlling seed elemental accumulation. While a set of these QTL were found in multiple environments, the majority were specific to a single environment, suggesting the presence of genetic by environment interactions. To specifically identify and quantify QTL by environment interactions (QEIs), we implemented two methods: linear modeling with environmental covariates and QTL analysis on trait differences between growouts. With these approaches, we found several instances of QEI, indicating that elemental profiles are highly heritable, interrelated, and responsive to the environment.Author SummaryPlants take up elements from the soil, a process that is highly regulated by the plant’s genome. To investigate how maize alters its elemental uptake in response to different environments, we analyzed the kernel elemental content of a population derived from a cross grown 10 different times in six locations. We found that environment had a profound effect on which genetic loci were important for elemental accumulation in the kernel. Our results suggest that to have a full understanding of elemental accumulation in maize kernels and other food crops, we will need to understand the interactions identified here at the level of the genes and the environmental variables that contribute to loading essential nutrients into seeds.

Elemental accumulation in seeds is the product of a combination of environment and a wide variety of genetically controlled physiological processes. We measured the kernel elemental composition of the Nested Association Mapping (NAM) of maize (Zea mays L.) grown in 4 different environments. Analysis of variance revealed strong effects of genotype, environment and genotype by environment interactions. Using Joint-linkage mapping on a set of 7000 markers we identified 354 quantitative trait loci (QTL) across 20 elements, four environments and a combination of the environments. Leveraging 20 M SNPs derived from genome resequencing on the parents of the population, genome-wide association mapping studies (GWAS) detected 8573 loci. While most of the GWAS SNPs were located near genes not previously implicated in elemental regulation, several SNPs were located next to orthologs of well-characterized elemental regulation genes.

phenotyping maize (Zea mays L.) for drought tolerance is costly and time consuming. our objectives were to determine (i) the heritability, genetic variance, and genetic correlations for grain yield and secondary traits in maize under drought and (ii) the efficiency of indirect selection through secondary traits versus genomewide selection. Testcrosses of 238 recombinant inbreds from the intermated B73 × Mo17 population were evaluated in multilocation trials under managed drought and nondrought (control) conditions in Minnesota in 2009 and 2010. Mean grain yield under drought was 52% of the mean grain yield in the control experiments. Heritability for grain yield was 0.37 ± 0.08 under drought and 0.60 ± 0.04 in the control experiments. Indirect selection based on anthesis-silking interval, leaf senescence, leaf chlorophyll content, or grain yield in control conditions was not predicted to be more efficient than direct selection for grain yield under drought. Genomewide selection (with 998 markers) for grain yield under drought had a predicted relative efficiency of 1.24. Genetic correlations estimated from genomewide marker effects agreed well with correlations estimated from genetic covariances. Given that multiple cycles of marker-based selection can be done per year in maize and that genotyping is cheaper than phenotyping for drought tolerance, our results suggest that genomewide selection could increase genetic gains per unit time for grain yield under drought. Dep. of Agronomy and Plant Genetics, Univ. of Minnesota, 411 Borlaug Hall, 1991 Upper Buford Circle, Saint Paul, MN 55108. Received 26 Nov. 2012. *Corresponding author (bernardo@umn.edu). Abbreviations: ASI, anthesis-silking interval; h2, heritability on an entry-mean basis; IBM, intermated B73 × Mo17; QTL, quantitative trait loci; REML, restricted maximum likelihood; rMP, correlation between predicted genotypic values and observed phenotypic values; RR-BLUP, ridge regression-best linear unbiased prediction; SPAD, soil plant analysis development; VG, genetic variance. Published in Crop Sci. 52:1269–1275 (2012). doi: 10.2135/cropsci2012.11.0651 Freely available online through the author-supported open-access option. © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher. Published May 17, 2013April 19, 2013