Abstract

A pool of 200 traditional, landraces and modern elite and old cultivars of rice, mainly japonica varieties adapted to temperate regions, have been used to perform a genome wide association study to detect chromosome regions associated to low temperature germination (LTG) regulation using a panel of 1672 SNP markers. Phenotyping was performed by determining growth rates when seeds were germinated at 25° and 15°C in order to separate the germination vigorousness from cold tolerance effects. As expected, the ability to produce viable seedlings varied widely among rice cultivars and also depended greatly on temperature. Furthermore, we observed a differential response during seed germination and in coleoptile elongation. Faster development at 15°C was observed in seeds from varieties traditionally used as cold tolerant parents by breeders, along with other potentially useful cultivars, mainly of Italian origin. When phenotypic data were combined with the panel of SNPs for japonica rice cultivars, significant associations were detected for 31 markers: 7 were related to growth rate at 25°C and 24 to growth rates at 15°. Among the latter, some chromosome regions were associated to LTG while others were related to coleoptile elongation. Individual effects of the associated markers were low, but by combining favourable alleles in a linear regression model we estimated that 27 loci significantly explained the observed phenotypic variation. From these, a core panel of 13 markers was selected and, furthermore, two wide regions of chromosomes 3 and 6 were consistently associated to rice LTG. Varieties with higher numbers of favourable alleles for the panels of associated markers significantly correlated with increased phenotypic values at both temperatures, thus corroborating the utility of the tagged markers for marker assisted selection (MAS) when breeding japonica rice for LTG.

Highlights

  • Quantitative trait loci (QTL) mapping and genome wide association studies (GWAS) have been used with genomic data, mostly single nucleotide polymorphisms (SNP), to dissect the genetic architecture of complex traits

  • We observed a differential performance of the 180 japonica cultivars under different temperatures, as revealed by the significant correlation coefficient estimated between V25 and V15 phenotypes (0.200, p = 0.007), and a differential response during seed germination and coleoptile elongation, since the Spearman correlation coefficient between V1514d and V153w was significant (0.257, p = 0.001)

  • A GWAS of seed transcriptome at normal and low temperature revealed that the delayed development of rice seed in low temperature conditions is mainly caused by the inhibition of the development-related genes and that the transcriptional response of seed and seedling to low temperature is different [45]]

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Summary

Introduction

Quantitative trait loci (QTL) mapping and genome wide association studies (GWAS) have been used with genomic data, mostly single nucleotide polymorphisms (SNP), to dissect the genetic architecture of complex traits. The aims of GWAS and GS are different, both approaches share common features, since they are based on a population for which phenotypic and genotypic data are available, and its predictions about associated markers and genotypes must be validated in advanced populations to test their applicability in plant breeding programs. To exploit GWAS results in GS, Zhang et al [2] proposed, using data for 11 traits in rice, the adoption of a Best Linear Unbiased Prediction (BLUP) model including prior knowledge about the genetic regulation of the character or QTL regions detected in previous GWAS, which could improve the accuracy of GS in traits with low heritability. The accuracy of prediction based on GWAS/GS models has been recently corroborated by Spindel et al [10]

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