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IntroductionA combination of factors including migration, climate change and the encroachment of growing cities are associated with the extinction of species and a reduction in biodiversity. In Mexico maize landraces are an important source of germplasm that could be invaluable for future breeding programs in response a changing environment. Efforts to develop conservation strategies are hampered by the costs of housing and maintaining large germplasm collections. Effective criteria are therefore needed to identify rare genotypes that are in danger of being lost and to define minimal cost-effective core collections.MethodsA large-scale genotyping analysis of Mexican maize landraces was carried out using 14 SSR markers spread across the 10 maize chromosomes. DNA was extracted from 30 individual plants of each accession and 3 bulks containing 220ng of DNA from each of 10 individual plants were prepared. Amplification reactions were carried out individually on each bulk sample and SSRs were detected using an automatic sequencer. Binary scores were used to produce phylogenies and a rareness index Ri was developed to identify a minimal collection of accessions.ResultsA total of 40,140 individual plants from accessions obtained in 14 different Mexican states and covering 47 maize races were analyzed. Accessions from Northern and Central-Southern states could be distinguished based on genotype and accessions from Michoacán state were found to be distinct to all other accessions. A minimal collection of 56 accessions that covered all identified alleles and included rare allele combinations was determined.DiscussionThe strategy employed successfully distinguished accessions from different regions of Mexico and identified rare germplasm and offers a cost effective method to determine minimal germplasm collections for conservation in situ or ex situ. The strategy could easily be adapted for use in other native crop species such as common bean, chili pepper and tomato among others.

Gene co-expression networks are powerful tools to understand functional interactions between genes. However, large co-expression networks are difficult to interpret and do not guarantee that the relations found will be true for different genotypes. Statistically verified time expression profiles give information about significant changes in expressions through time, and genes with highly correlated time expression profiles, which are annotated in the same biological process, are likely to be functionally connected. A method to obtain robust networks of functionally related genes will be useful to understand the complexity of the transcriptome, leading to biologically relevant insights. We present an algorithm to construct gene functional networks for genes annotated in a given biological process or other aspects of interest. We assume that there are genome-wide time expression profiles for a set of representative genotypes of the species of interest. The method is based on the correlation of time expression profiles, bound by a set of thresholds that assure both, a given false discovery rate, and the discard of correlation outliers. The novelty of the method consists in that a gene expression relation must be repeatedly found in a given set of independent genotypes to be considered valid. This automatically discards relations particular to specific genotypes, assuring a network robustness, which can be set a priori. Additionally, we present an algorithm to find transcription factors candidates for regulating hub genes within a network. The algorithms are demonstrated with data from a large experiment studying gene expression during the development of the fruit in a diverse set of chili pepper genotypes. The algorithm is implemented and demonstrated in a new version of the publicly available R package “Salsa” (version 1.0).