Abstract
Grain-filling ability is one of the factors that controls grain yield in rice (Oryza sativa L.). We developed a method for describing grain weight distribution, which is the probability density function of single grain weight in a panicle, using 128 Japanese rice varieties. With this method, we quantitively analyzed genotypic differences in grain-filling ability and used the grain weight distribution parameters for genomic prediction subject to genetic improvement in grain yield in rice. The novel description method could represent the observed grain weight distribution with five genotype-specific parameters of a mixture of two gamma distributions. The estimated genotype-specific parameters representing the proportion of filled grains had applicability to explain the grain filling ability of genotypes comparable to that of sink-filling rate and the conventionally measured proportion of filled grains, which suggested the efficiency and flexibility of grain weight distribution parameters to handle several genotypes. We revealed that perfectly filled grains have to be prioritized over partially filled grains for the optimum allocation of the source of yield in a panicle, from the analysis for obtaining an ideal shape of grain weight distribution. We conducted genomic prediction of grain weight distribution considering five genotype-specific parameters of the distribution as phenotypes relating to grain filling ability. The proportion of filled grains, average weight of filled grains, and variance of filled grain weight, which were considered to control grain yield to a certain degree, were predicted with accuracies of 0.30, 0.28, and 0.53, respectively. The proposed description method of grain weight distribution facilitated not only the investigation of the optimum allocation of nutrients in a panicle for realizing high grain-filling ability, but also allowed genomic selection of grain weight distribution.
Highlights
The demand for an increase in food production has risen dramatically because of world population explosion and climate change [1,2]
To evaluate the relationship between the novel proposed parameters, which we describe later, and the conventional traits related to grain-filling characteristics, we measured the proportion of unfilled grains in other two different ways (p.23mg and p.90%) and the sink-filling rate. p.23mg was the probability of grains being lighter than 23 mg, which was determined based on conventional evaluation for cultivars for grain weight
The sudden convex and concave patterns could not be caught by the description method (e.g., Fig 1A and 1E), our description method is more reliable and balanced than conventional histograms because the sudden ruggedness in histograms owing to small sample sizes would disturb the subsequent analyses
Summary
The demand for an increase in food production has risen dramatically because of world population explosion and climate change [1,2]. In GS, a prediction model is built based on the information from a training population with phenotypic data (dependent variable) and genome-wide marker data (independent variables), and genotypic values of genotyped breeding materials (i.e. test population) are predicted using the prediction model [6]. Both simulation studies [7,8] and real breeding programs [9,10] demonstrated that GS could increase genetic gain in crop breeding. The efficient accumulation of the information on the secondary traits into the GS strategy will dramatically increase the genetic gain of complex target traits
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