The present invention relates to methods and compositions for identifying, selecting and/or producing a plant or germplasm having root increased drought tolerance and/or increased yield under non-drought conditions as compared to a control plant. A maize plant, part thereof and/or germplasm, including any progeny and/or seeds derived from a maize plant or germplasm identified, selected and/or produced by any of the methods of the present invention is also provided.

The present invention relates to methods and compositions for identifying, selecting and/or producing a soybean plant or germplasm having iron deficiency chlorosis tolerance. A soybean plant, part thereof and/or germplasm, including any progeny and/or seeds derived from a soybean plant or germplasm identified, selected and/or produced by any of the methods of the present invention is also provided.

The present invention relates to methods and compositions for identifying, selecting and/or producing a maize plant or plant part having increased fertility. A maize plant or plant part that has been identified, selected and/or produced by any of the methods of the present invention is also provided.

Natural lodging due to severe weather conditions is a coomon occurance- with annual estimated yield losses of 5-20% . We have evaluated natural lodging that has occurred in a population of 1,723 diverse inbred maize genotypes that were crossed to a common tester and evaluated in five environments over multiple years. Several QTL identified in this study for lodging overlapped with loci previously mapped for stalk strength in related maize inbred lines. QTL intervals are enriched for candidate genes implicated in the regulation of lignin and cellulose synthesis.

Population structure is an important factor that is often treated as a nuisance parameter in genetic mapping and genetic association studies- and is often acocunted for and discarded. In this study we show that depening on traits and species, population structure can explain a large fraction of heritable genetic variation for rice and maize, that can be leveraged to improve genetic prediction accuracies for phenotypes for genomic selection experiments when used in concert with other within sub-population genetic variance components.

This study evaluated the accuracy improvements of across-environment predictions by using genetic and residual covariance across correlated environments as variables in a predictive model. Provided that sufficient marker density and significant correlation between environmnets are present, significant improvements in prediction accuracies can be achieved through inclusion of correlation between environments in the prediction model with little negative effects due to residual correlation.

We generated and sequenced expressed sequence tag (EST) libraries from thirteen diverse switchgrass cultivars representing both upland and lowland ecotypes, as well as tetraploid and octoploid genomes. We followed this with reduced genomic library preparation and massively parallel sequencing of the same samples using the Illumina Genome Analyzer technology platform. EST libraries were used to generate unigene clusters and establish a gene-space reference sequence, thus providing a framework for assembly of the short sequence reads. SNPs were identified utilizing these scaffolds. We used a custom software program for alignment and SNP detection and identified over 149,000 SNPs across the 13 short-read sequencing libraries (SRSLs). Approximately 25,000 additional SNPs were identified from the entire EST collection available for the species. This sequencing effort generated data that are suitable for marker development and for estimation of population genetic parameters, such as nucleotide diversity and linkage disequilibrium.

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