Genetic Analysis of Tertiary Rice Panicle Branches

Panicle branching trait of rice is one of the key factors in detemining grain yield. This study was aimed to elucidate the genetic inheritance pattern of the tertiary rice panicle branches trait. Six rice populations i.e F1, F1R, F2, F2R, BC1P1, and BC1P2 generation including parental lines were generated as materials. The experiment was conducted at IPB University, Bogor, Indonesia from April 2017 to February 2019. Several parameters of genetic inheritance was observed. The result revealed that the inheritance of the tertiary panicle branching was controlled by many genes with the high broad-sense heritability and the moderate narrow-sense ones. Based on the scaling test, it was observed that additive dominant model did not fit to the number of tertiary branches and the number of grains of tertiary panicle branches. These phenomenons suspected may due probably to the effect of epistasis. In the advanced analysis employed by using the joint scaling test revealed that the gene action of the number of tertiary branches and number of grains on the tertiary branches were additive and additive×additive.

Genomic prediction with the additive-dominant model by dimensionality reduction methods

The objective of this work was to evaluate the application of different dimensionality reduction methods in the additive-dominant model and to compare them with the genomic best linear unbiased prediction (G-BLUP) method. The dimensionality reduction methods evaluated were: principal components regression (PCR), partial least squares (PLS), and independent components regression (ICR). A simulated data set composed of 1,000 individuals and 2,000 single-nucleotide polymorphisms was used, being analyzed in four scenarios: two heritability levels × two genetic architectures. To help choose the number of components, the results were evaluated as to additive, dominant, and total genomic information. In general, PCR showed higher accuracy values than the other methods. However, none of the methodologies are able to recover true genomic heritabilities and all of them present biased estimates, under- or overestimating the genomic genetic values. For the simultaneous estimation of the additive and dominance marker effects, the best alternative is to choose the number of components that leads the dominance genomic value to a higher accuracy.