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Development and utilization of nested association mapping population in rice

KITONY, Justine Kipruto 名古屋大学

2022.01.05

概要

高スループット DNA シーケンス技術の時代となり、量的形質遺伝子座(quantitative trait loci、QTL)マッピングにおける制限要因は、DNA マーカーではなく、遺伝解析 材料となった。農業形質や環境適応などの遺伝的構造を研究するための遺伝資源は、 作物の改良に必須である。Nested association mapping (NAM)は、複数の系統を親と する遺伝解析集団を利用する解析法で、従来の連鎖解析とアソシエーション解析の両 方の利点を併せ持ち、複雑形質の解析に有用である。NAM 集団は、複数の組換え自 殖系統(recombinant inbred lines、RILs)の集まりであり、各 RIL は共通親と多様 性供与親の交雑に由来する。NAM 集団を用いた遺伝解析では、連鎖解析をベースと する手法(joint QTL mapping)および genome-wide association study(GWAS)を ベースとする手法の両方を利用することができ、両者を照合してより良い精度を得る ことが期待される。また、固定系統であるため、遺伝子型-環境間相互作用の解析にも 利用可能である。一方、ゲノミックセレクション(genomic selection、GS)は、次 世代の育種技術として注目されている。植物育種における GS の実装では、たとえば、 DNA マーカー遺伝子型を説明変数とし、形質を従属変数とする予測モデルを作成する ための比較的小規模な集団と、DNA マーカー遺伝子型のみを得るための大規模集団を 組合せ、予測モデルと大規模集団の遺伝子型から、大規模集団からの選抜を DNA マ ーカーの情報のみで行う方法が想定されている。この予測モデルを評価するための集 団としても NAM 集団は有用である。本研究では、イネ aus 品種を多様性供与親とし、 japonica 品種である台中 65 号(T65)を共通親とする NAM 集団(aus-NAM)を作 出した。初期の材料として、7 組合せの RILs からなる集団(aus-NAM-I)を用いた 解析を行い、遺伝率の高い形質である出穂期により、遺伝的振る舞いを確認した。次 に、14 組合せからなる集団(aus-NAM-II)に集団を拡大してさらに GWAS および形 質予測モデルの作成と評価を行った。

まず、aus-NAM-I の作出においては、名古屋大学で育成された 5 組合せからなる集 団と、九州大学で育成された 2 組合せの計 7 組合せ(供与親は WRC2、WRC29、WRC31、 WRC35、WRC39、DV85 および ARC10313)、計 895 系統を得た。これらの系統の 遺伝子型決定は KpnI と MspI の 2 種類の制限酵素を用いた genotyping- bysequencing ( GBS )法により行った。得られた 1 塩基多型( single nucleotide polymorphism、SNP)マーカーは交配組合せにより異なり、2868 マーカーから 4285 マーカーが得られ、フィルタリングにより 887 系統を解析に使用した。集団構造 (population structure)を確認するために、確率的主成分分析を行った結果、WRC29 由来の集団のみ隔離された集団を形成したが、十分にマッピングに利用可能と考えら れた。形質として、2015 年の東郷フィールドの通常期栽培における出穂期を用い、joint QTL 解析および GWAS 解析を試みた。joint QTL 解析においては 7 組合せのそれぞ れの DNA マーカー連鎖地図において共通する SNP マーカーを抽出し、1786 マーカ ーからなる共通連鎖地図を作成して解析を行った。joint QTL 解析に先立って行った 組合せごとの QTL 解析(single family 解析)においては、染色体 5、6、7 および 10 に有意な QTL を検出した。joint QTL 解析においては染色体 6、7 および 10 に single family 解析と同じ QTL が、染色体 1、2 および 3 に新たな QTL が検出され、染色体 5 の QTL は検出されなかった。染色体 6 および 7 の QTL は複数のピークとして検出 された。また、GWAS による遺伝子マッピングでは、親系統のリシーケンスデータか ら 41561 個の SNP を抽出し、これを GBS によるマーカー遺伝子型にプロジェクショ ンすることで遺伝子型データを作成し、これに系譜情報を加えた上で、集団構造と近 縁度を考慮した混合線型モデル(MLM(Q+K))により解析を行った。その結果、 染色体 6、7 および 10 に有意な QTL を検出した。これらの QTL のうち、染色体 6、 7 および 10 のものは既知の遺伝子座(RFT1、Hd3a、Hd1、Ghd7 および Ehd1)で あると考えられたため、マッピング精度の評価を行った。Ehd1 と思われる QTL のピ ークは約 741kb の範囲に検出され、この範囲に Ehd1 が含まれていた。Hd1 と思われ る joint QTL 解析により検出された QTL は、single family 解析においては WRC39 由来の集団のみで検出されており、その位置は実際の Hd1 の位置とよく一致した。 WRC39 の塩基配列を確認したところ、WRC39 は、解析に用いた親系統(T65 および 7 系統の aus)の中で、唯一 Hd1 の機能型アレルを持っており、この QTL が single family 解析および joint QTL 解析により正しく検出されたと考えられた。しかしなが ら、GWAS 解析においては Hd1 だけでなく、Hd1 より 6MB 以上上流の RFT1/Hd3a を含む広い領域がピークとして検出され、single family 解析および joint QTL 解析の 情報が無ければ Hd1 を QTL をとして同定することは困難であった。Ghd7 は、joint QTL 解析における染色体 7 の 2 箇所のピークのうちの 1 つとして検出されたが、こち らも GWAS で得られた位置情報は不明確であった。これらのことから、NAM 集団に おける遺伝子マッピングでは、位置情報の精度に優れる single family 解析および joint QTL 解析と、狭い範囲でピークを検出できる GWAS 解析、および親系統のリシ ーケンスデータを総合して利用することで、遺伝子マッピングおよび原因遺伝子の絞り込みの迅速化が期待できることが明らかとなった。

次に、aus-NAM-I から親のリシーケンスデータがない 2 組合せ(DV85、ARC10313) を除き、新たに 9 組合せを育成して 14 組合せからなる aus-NAM-II 集団を確立した。 aus-NAM-II の遺伝子型決定においては、aus-NAM-I で用いた手法に、イルミナの index を加えることで、従来のバーコード方式よりも多くの組合せ・多くの検体をひ とまとめにして解析できるようになった。遺伝子型決定の結果、1818 系統を解析に使 用した。形質評価は 2015 年および 2018 年の東郷フィールド・普通期栽培で行い、到 穂日数、稈長、穂長、穂軸長、穂数、穂重、茎葉重、主茎の 1 次枝梗数、主茎の 1 穂 粒数、バイオマス(穂重+茎葉重)および種子稔性を調査した。到穂日数に関する GWAS 解析により、既知の QTL(Ehd1、Hd1、Hd3a/RFT1、Hd9、DTH2、Hd7、Se14 な ど)が正しく検出された。その他の形質に関しても、既知の QTL が検出されたほか、 新規の QTL を検出した。到穂日数以外の形質では検出されるピークは少なく、また -logP 値も低い傾向で、これらの形質の遺伝率の低さが反映されていると考えられた。 ゲノム予測(genomic prediction)モデルの作成には、Bayesian B(BayesB)、Bayesian least absolute shrinkage regression( BL) 、 reproducing kernel Hilbert space regression(RKHS)、ridge regression best linear unbiased prediction(rrBLUP) の 4 つの手法を試みた。NAM 集団の GP においては RKHS が最も良い性能を示す事 が明らかとなった。また、マーカー数は GBS によるマーカー数(2006 マーカー)で 十分であり、親系統のリシーケンスデータを用いた projection は不要であることが示 された。これらの条件検討を行った結果、実測値と予測値の相関係数は最も高い形質 で 0.87(到穂日数、稈長)、最も低い形質で 0.56(バイオマス)となった。実際の育 種現場において到穂日数は優先順位が高いことから、イネにおける GS 育種の実装に おいては、到穂日数に着目すると、選抜を精度良く、かつ効率的に行なうことができ ることが示された。

以上、本研究においては、イネにおける NAM 集団の作出および高密度 DNA マー カーによる遺伝子型決定を行った。得られた材料は固定系統であり、複数回・複数箇 所における形質評価が可能な優れた遺伝子マッピングのリソースである。また、遺伝 子マッピングの精度および形質予測の精度は期待通りであることが示された。今後は さらなる新規遺伝子の探索や GP モデルの開発、遺伝子型-環境間相互作用のモデル化 など幅広い分野での利用が期待される。

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