BMC Plant Biology · 2021 · 22 citations · 63 references
Life SciencesAgronomyGeneticsGenomicsPlant GeneticsTranscriptomicsAgronomic Performance EvaluationAgronomic Trait EvaluationCereal GeneticsExpression Quantitative Trait Loci AnalysisGenome-wide Association StudiesQuantitative Trait LociAgronomic CharacterizationAgronomic Trait DiscoveryCereal Crop GeneticsGenome-wide Association MappingMarker-trait Association AnalysisMarker-trait Genetic CorrelationPopulation Structure AnalysisWheat Breeding
BACKGROUND: Bread wheat (Triticum aestivum L.) is one of the most widely consumed cereal crops, but its complex genome makes it difficult to investigate the genetic effect on important agronomic traits. Genome-wide association (GWA) analysis is a useful method to identify genetic loci controlling complex phenotypic traits. With the RNA-sequencing based gene expression analysis, putative candidate genes governing important agronomic trait can be suggested and also molecular markers can be developed. RESULTS: We observed major quantitative agronomic traits of wheat; the winter survival rate (WSR), days to heading (DTH), days to maturity (DTM), stem length (SL), spike length (SPL), awn length (AL), liter weight (LW), thousand kernel weight (TKW), and the number of seeds per spike (SPS), of 287 wheat accessions from diverse country origins. A significant correlation was observed between the observed traits, and the wheat genotypes were divided into three subpopulations according to the population structure analysis. The best linear unbiased prediction (BLUP) values of the genotypic effect for each trait under different environments were predicted, and these were used for GWA analysis based on a mixed linear model (MLM). A total of 254 highly significant marker-trait associations (MTAs) were identified, and 28 candidate genes closely located to the significant markers were predicted by searching the wheat reference genome and RNAseq data. Further, it was shown that the phenotypic traits were significantly affected by the accumulation of favorable or unfavorable alleles. CONCLUSIONS: From this study, newly identified MTA and putative agronomically useful genes will help to study molecular mechanism of each phenotypic trait. Further, the agronomically favorable alleles found in this study can be used to develop wheats with superior agronomic traits.
63
Fitting Linear Mixed-Effects Models Using <b>lme4</b>
Douglas M. Bates, Martin Mächler, Benjamin M. Bolker et al. · Journal of Statistical Software · 2015 · 85.6K citations · Full text
TASSEL: software for association mapping of complex traits in diverse samples
Peter J. Bradbury, Zhiwu Zhang, Dallas E. Kroon et al. · Bioinformatics · 2007 · 8.3K citations · Full text