Study on the genetic diversity of soybean roots under field conditions

概要

報告番号

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第

主

号

論

文

の

要

旨

Study on the genetic diversity of soybean
roots under field conditions
論文題目
氏

名

（圃場におけるダイズの根の遺伝的多様性に関する研
究）
BUI The Khuynh

論 文 内 容 の 要 旨
Abstract
The study was conducted to evaluate the genetic diversity of
soybean roots grown under field conditions in a diverse soybean
germplasm consisting of 200 accessions, of which mainly were from the
mini core-collections (the World mini core-collection and the Japan
mini-core collection). Soybean was grown under field conditions in a
sandy field in Tottori, Japan for three years under two irrigation
treatments: irrigated and non-irrigated treatment. The study revealed a
high diversity in root traits across the whole panel under both irrigated
and non-irrigated conditions. And of note, higher genetic diversity in
most root traits was seen among genotypes from the world mini -core
collection, compared with accessions from Japan mini -core collections.

Soybean accessions which showed large root improvement with
irrigation and those with the stable performance of root traits across
environments were identified. These accessions can be used as promising
materials for the genetic improvement of soybean root. In addition, a
genome-wide association study (GWAS) was applied to dissect the
genetic controls of root traits, and 7QTLs and candidate genes associated
with

root

traits

were

identified.

Among

the

candidate

genes,

Glyma.07G084300, associated with total root length under irrigated
conditions, known as the Arabidopsis CDK-activating kinase 1AT, was
reported for the roles in cell proliferation , cell expansion, and the
regulation of root cell initiations. Finally, attempts have been made to
predict the root traits of soybean using (i) genomic prediction approach,
and (ii) prediction of root traits from available data in the shoot
environment using machine learning methods (ML). The results from
these two approaches proved that it’s promising to predict the root traits
in the field conditions using either genomic data or easily assessable data
obtained in the field. The success of prediction models in our study was
also improved when the interaction of genotype and environment was
incorporated.