Genome-wide identification and comparative analysis of drought-related microRNAs in two maize inbred lines with contrasting drought tolerance by deep sequencing

Drought is a severe threat to maize yield stability in Serbia and other temperate Southeast European countries occurring occasionally but with significant yield losses. The development of resilient genotypes that perform well under drought is one of the main focuses of maize breeding programmes. To test the tolerance of newly developed elite maize inbred lines to drought stress, field trials for grain yield performance and anthesis silk interval (ASI) were set in drought stressed environments in 2011 and 2012. Inbred lines performing well under drought, clustered into a group with short ASI and a smaller group with long ASI, were considered as a potential source for tolerance. The former contained inbreds from different heterotic groups and with a proportion of local germplasm. The latter consisted of genotypes with mixed exotic and Lancaster germplasm, which performed better in more drought-affected environments. Three inbreds were selected for their potential drought tolerance, showing an above-average yield and small ASI in all environments. Association analysis indicated significant correlations between ASI and grain yield and three microsatellites (bnlg1525, bnlg238 and umc1025). Eight alleles were selected for their favourable concurrent effect on yield increase and ASI decrease. The proportion of phenotypic variation explained by the markers varied across environments from 5.7% to 22.4% and from 4.6% to 8.1% for ASI and yield, respectively. The alleles with strongest effect on performance of particular genotypes and their interactions in specific environments were identified by the mean of partial least square interactions analysis indicating potential suitability of the makers for tolerant genotype selection.

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Molecular Predicting Drought Tolerance in Maize Inbred Lines by Machine Learning Approaches

10.1101/578880 ◽

2019 ◽

Keyword(s):

Machine Learning ◽

Drought Tolerance ◽

New Technologies ◽

Inbred Lines ◽

Attribute Selection ◽

Classification Algorithms ◽

Learning Approaches ◽

Maize Inbred Lines ◽

Ssr Loci ◽

Speed Up

Drought is one of the prime abiotic stresses in the world. Now, amongst the new technologies available for speed up the releasing of new drought tolerance genotypes, there is an emanate discipline called machine learning. The study presents Machine Learning for identification, classification and prediction of drought tolerance maize inbred lines based on SSR genetic markers datasets generated from PCR reactions. A total of 356 SSR reproducible fragment alleles were detected across the 71 polymorphic SSR loci. A dataset of 12 inbred lines with these fragments prepared as attributes and was imported into RapidMiner software. After removal of duplicates, useless and correlated features, 311 feature attributes were polymorphic, ranging in size from 1500 to 3500 bp. The most important attribute fragment alleles in different attribute weighting selected. Ten datasets created using attribute selection (weighting) algorithms. Different classification algorithms were applied on datasets. These can be used to identify groups of alleles with similar patterns of expression, and are able to create some models that have been applied successfully in the prediction, classification and pattern recognition in drought stress. Some unsupervised models were able to differentiate tolerant inbred lines from susceptible. Four unsupervised models were able to produce the different decision trees with root and leaves. The most important attribute alleles almost in all of models were phi033a3, bnlg1347a1 and bnlg172a2 respectively, that can help to identify tolerant maize inbred lines with high precision.

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