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Time-series measurement of crop growth process and modeling of genetic and environmental effects

戸田, 悠介 東京大学 DOI:10.15083/0002006864

2023.03.24

概要





















戸田

悠介

収穫時に観察される収量や品質などの形質は、成長過程を通じて決定される
ため、成長過程の解析が育種学における重要な課題となっている。目的形質の
形成過程の詳細な解析は、遺伝機構の理解や遺伝的改良の効率化につながると
期待されるが、成長過程の計測には多大な労力が必要なため、多系統を用いた
遺伝解析を行うことが難しかった。近年のセンシング技術の発達は、無人航空
機(UAV)等を用いた多系統の経時的観察を可能とした。今後、こうした技術
を用いて成長過程の遺伝解析を行うには、適切なモデルの開発と応用が不可欠
となる。申請者は、このような視点から、作物の成長における遺伝と環境によ
る影響を解析するためのモデルを開発し、その有用性を検証した。
第 2 章では、研究背景となる各種手法(量的遺伝学の手法、成長過程のモデ
ル化手法、作物生育モデル)が紹介された。
第 3 章では、作物成長モデルとゲノミック予測モデルを融合するモデルが開
発された。同モデルは、標準的なゲノミック予測(GP)では扱えない遺伝子型
×環境交互作用(GxE)を考慮したモデルである。材料にはイネ組換え近交系
が用いられ、成長関連形質を介してバイオマスを予測する 2 段階モデルが開発
された。第 1 段階では、出穂日以外の成長関連形質が GP で予測され、出穂日
は出穂関連遺伝子に基づく発育速度モデルで予測された。第 2 段階では、予測
された成長関連形質をもとに作物成長モデル(CGM)や機械学習を用いてバイ
オマスが予測された。その結果、CGM や機械学習を用いたモデルは標準的な
GP よりも予測精度が優れていることが示された。
第 4 章では、UAV を用いたリモートセンシング(RS)と時系列成長モデル
を用いた手法が開発された。RS データに多形質を同時予測する GP(MGP)を
適用することで予測精度が向上することが知られていた。しかし、同方法は、
高次元の RS データへの適用が難しい。申請者は、UAV で収集された高次元 RS
データに時系列成長モデルを適用し、そのモデルパラメータに対して MGP を適
用する方法を提案した。同モデルをダイズ遺伝資源の栽培試験データに適用し
て収穫時バイオマスの予測を行った結果、成長モデルのパラメータをもとに成
長パターンの違いが表現できること、また、同パラメータの MGP により収穫時

バイオマスの予測精度を向上できる場合があることを示した。
第 5 章では、第 4 章と同様のアプローチであるが、成長と老化の両方を考慮
するモデルが開発された。申請者は、同モデルをダイズ遺伝資源の栽培試験デ
ータに適用し、RS で計測された群落植被面積の変化を予測するモデルを構築し
た。開発されたモデルは成長前期のデータから成長後期を予測する精度が高く、
栽培中に今後の成長を予測する場面で特に有用と考えられた。
第 6 章では、日々の成長に対する遺伝と環境による影響をモデル化し、成長
曲線を予測する手法が開発された。成長過程における GxE は、成長過程の計測
が困難なため、これまでほとんど研究が行われてこなかった。申請者は、UAV-RS
で得られる成長データから、日々の成長にみられる GxE をモデル化する手法を
開発した。なお、モデル化には植被面積と草丈の正確な計測値が必要となるが、
UAV-RS データは計測バイアスが大きいことが知られていた。申請者は、まず、
計測バイアスを推定し、計測値の補正を行うモデルを構築した。次に、統計モ
デルと機械学習モデルを用いて、日々の成長にみられる GxE をモデル化した。
機械学習モデルとして、環境データとマーカー遺伝子型データを入力とするブ
ラックボックスモデルが構築された。統計モデルとして、同じ入力をもとに、
環境応答を曲線として表現するモデルが構築された。その結果、統計モデルを
用いることで、土壌水分と成長の関係をモデル化できることが示された。一方、
日々の成長の予測精度では、機械学習モデルが統計モデルを上回った。
第 7 章では、成長過程の計測とそのモデル化手法という視点から、得られた
結果の総括と議論が行われた。
申請者が提案した手法により、CGM、GP、成長モデルを融合し、成長過程の
データ、ゲノムデータ、環境データ間の関係のモデル化が可能となる。今後、
RS が作物育種に広く利用されるようになれば、成長過程をモデル化する同手法
が、有用な遺伝的洞察を引き出し、作物育種に利益をもたらすと期待される。
これらの研究成果は、学術上応用上寄与するところが少なくない。よって、審
査委員一同は本論文が博士(農学)の学位論文として価値あるものと認めた。

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