花生巢式群體的脂肪含量遺傳分析

黃冰艷 孫子淇 劉 華 房元瑾 石 磊 苗利娟 張毛寧 張忠信 徐 靜 張夢圓 董文召 張新友,*

花生巢式群體的脂肪含量遺傳分析

黃冰艷1,**孫子淇1,**劉 華1房元瑾1石 磊1苗利娟1張毛寧1張忠信1徐 靜1張夢圓2董文召1張新友1,*

1河南省農(nóng)業(yè)科學(xué)院河南省作物分子育種研究院/ 鄭州大學(xué)研究生研究培訓(xùn)基地 / 農(nóng)業(yè)農(nóng)村部黃淮海油料作物重點(diǎn)實驗室 / 河南省油料作物遺傳改良重點(diǎn)實驗室, 河南鄭州 450002;2河南科技大學(xué)農(nóng)學(xué)院, 河南洛陽 471023

巢式群體可以利用多個親本解析復(fù)雜性狀的遺傳機(jī)制。本研究利用1個共同親本與6個基礎(chǔ)親本所配置巢式組合F2:3家系的種子脂肪含量數(shù)據(jù), 分析了花生脂肪含量的遺傳模型, 旨在探明不同的基礎(chǔ)親本組合中脂肪含量性狀的遺傳差異, 為制定脂肪含量遺傳改良的親本選配和后代選擇策略提供依據(jù)。6個組合的共同親本為高脂肪含量的普通型大果品種豫花15號, 其他6個基礎(chǔ)親本為不同脂肪含量和不同植物學(xué)類型的品種。結(jié)果表明, 在不同雜交組合中脂肪含量的遺傳模式有所不同, 6個組合分別符合無主基因模型、1對主基因加性顯性模型和2對主基因等顯性模型3種遺傳模式。各種遺傳效應(yīng)的估計值也各不相同, 主基因遺傳力從32%到80%, 說明不同雜交組合中, 控制脂肪含量的基因位點(diǎn)差異及其重組和分離方式不同。高脂肪含量雙親雜交后代的高脂肪含量個體較多, 但主基因遺傳力較低, 不宜在早代實施表型選擇; 雙親脂肪含量差異較大的后代脂肪含量變異幅度更大, 能夠選擇到不同脂肪含量的類型。本研究也表明, 巢式組合具有較豐富的脂肪含量變異類型, 揭示出脂肪含量性狀遺傳的復(fù)雜性和多基因調(diào)控的特點(diǎn), 為較全面地了解脂肪含量的遺傳提供了基礎(chǔ)。該巢式群體也將有助于進(jìn)一步開展脂肪含量的QTL定位研究。

花生(L.); 巢式群體; 脂肪含量; F2:3家系; 遺傳模型

花生廣泛種植于非洲、亞洲和美洲的100多個國家, 2018年世界花生種植面積達(dá)到2800萬公頃, 總產(chǎn)4500余萬噸[1]。花生是中國、印度、阿根廷、塞內(nèi)加爾等國家植物油、蛋白營養(yǎng)和農(nóng)民收入的主要來源, 也是美國、澳大利亞等國家的重要食品加工原料。我國是世界第一花生生產(chǎn)和消費(fèi)大國, 花生油是深受城鄉(xiāng)居民喜愛的優(yōu)質(zhì)烹調(diào)用油, 我國的花生50%以上用于榨油, 花生生產(chǎn)對保障我國食用油安全具有重要意義。

不同的花生品種其種子脂肪含量具有較大差異, 在我國的花生種質(zhì)資源中, 花生籽仁的含油量變異幅度為45%~60%?；ㄉ贩N按照植物學(xué)特征分為普通型、龍生型、珍珠豆型、多粒型和中間型5種類型, 花生的脂肪含量與品種類型相關(guān), 也受種植環(huán)境和氣候的影響, 提高花生品種的脂肪含量及單位面積的脂肪產(chǎn)量, 是我國花生育種的重要目標(biāo)。

我國目前推廣的主要花生品種脂肪含量平均為51.4%, 略高于種質(zhì)資源的平均數(shù)[2]。脂肪含量高于55%的高油花生品種多為中粒型品種, 主要通過常規(guī)的雜交育種技術(shù)選育而成, 存在育種周期長、性狀預(yù)見性差、選擇效率低等突出問題。因此, 深入闡明花生脂肪含量遺傳的分子機(jī)制, 能夠為脂肪含量的品質(zhì)育種提供親本選配和后代選擇的依據(jù), 并且對于進(jìn)一步開發(fā)與脂肪含量關(guān)聯(lián)的分子標(biāo)記或者調(diào)控基因位點(diǎn), 構(gòu)建分子設(shè)計育種體系具有重要意義。

近年來, 我國學(xué)者利用主基因加多基因遺傳模型對花生的脂肪含量性狀開展了遺傳分析。禹山林等[3]研究結(jié)果顯示, 該性狀受2對加性-顯性-上位性主基因+加性-顯性多基因控制, 多基因遺傳力為72.55%。陳四龍等[4]研究結(jié)果表明, 有些組合符合1對加性-顯性主基因+加性-顯性-上位性基因控制, 主基因遺傳力為45%~47%, 多基因遺傳力為18%~22%; 有些組合多基因遺傳力為66%。Zhang等[5]和劉華等[6-7]的研究也得出多基因效應(yīng)比較大的結(jié)論。這些研究初步探明了花生脂肪含量的主要遺傳模式, 但是, 由于所用親本材料及試驗環(huán)境不同, 難以相互比較分析。本研究利用高脂肪含量品種為共同親本, 構(gòu)建了包括6個不同雜交組合的巢式群體, 通過對巢式群體F2:3世代種子脂肪含量的分析, 揭示花生脂肪含量的遺傳模式, 旨在為較全面理解花生的脂肪含量性狀遺傳和進(jìn)一步的分子機(jī)制解析提供基礎(chǔ)。

1 材料與方法

1.1 群體材料

以豫花15號為共同親本的巢式群體包括6個親本組合(表1)。豫花15號是我國大面積推廣的高產(chǎn)高油大果花生品種, 由河南省農(nóng)業(yè)科學(xué)院選育, 相繼通過了河南省、安徽省、北京市和國家品種審定委員會審定, 品種類型為中間型, 脂肪含量高達(dá)56%以上。另外6個基礎(chǔ)親本分別為遠(yuǎn)雜9102、中花6號、粵油20、四粒紅、伏花生和NC94022, 分別來自我國的黃淮海地區(qū)、長江流域、華南、東北、華東等主要花生產(chǎn)區(qū)和美國。其中, 遠(yuǎn)雜9102、中花6號、粵油20為珍珠豆型高產(chǎn)品種, 分別為河南、湖北和廣東選育, 脂肪含量55%左右; NC94022為美國普通型小果(蘭娜型)品種, 脂肪含量約為54%; 四粒紅為多粒型品種, 脂肪含量約為53%。

1.2 田間種植

2018年春季, 經(jīng)過分子鑒定為真雜種的F1種子及其親本按照組合種植于河南省農(nóng)業(yè)科學(xué)院原陽試驗基地; 2019年春季, 種植F2種子及其親本, 田間按照常規(guī)管理, 秋季正常成熟后按照單株收獲、晾曬莢果。

表1 巢式雜交群體信息

A: 遠(yuǎn)雜9102; B: 中花6號; C: 豫花15號; E: 粵油20; F: 四粒紅; G: 伏花生; H: NC94022。

A: Yuanza 9102;.B: Zhonghua 6; C: Yuhua 15; E: Yueyou 20; F: Silihong; G: Fuhuasheng; H: NC94022.

1.3 脂肪含量測定

從F2單株上挑選成熟莢果, 剝?nèi)★枬M籽粒, 利用DA7200型近紅外分析儀(瑞典, Perten)測定脂肪含量, 樣本容量為10粒, 取3次重復(fù)平均數(shù)。

1.4 模型分析

采用王建康等[8-9]提出的植物數(shù)量性狀遺傳模型主基因加多基因F2世代分析方法, 以AIC (Akaike’s Information Criterion)值較小的遺傳模型為備選模型; 對備選模型進(jìn)行適合性測驗, 包括均勻性檢驗(12、22、32)、Smirnov檢驗(2)和Kolmogorov檢驗(D), 統(tǒng)計量均不顯著或者顯著個數(shù)最少的模型為最適模型。按入選的遺傳模型估計相應(yīng)的遺傳參數(shù), 包括基因的各種效應(yīng)值、方差及遺傳力等。

2 結(jié)果與分析

2.1 不同雜交組合F2脂肪含量的群體分布特征

從6個組合的脂肪含量分布圖可以看出, F2脂肪含量呈現(xiàn)連續(xù)分布特征, 6個組合的脂肪含量變幅分別為52.31%~63.87%、51.11%~61.68%、49.14%~ 58.82%、47.55%~58.87%、47.04%~56.41%、48.88%~ 59.53%, 并且分別表現(xiàn)出低于低親和高于高親的超親現(xiàn)象(圖1)。

遠(yuǎn)雜9102×豫花15號、中花6號×豫花15號、豫花15號×粵油20三個組合的父母本脂肪含量差異不大, 但F2表現(xiàn)出2種不同的脂肪分布特點(diǎn), 遠(yuǎn)雜9102×豫花15號組合F2表現(xiàn)出高于高親的個體數(shù)及極端高的個體數(shù)均較多, 而且沒有脂肪含量低于52%的個體; 中花6號×豫花15號組合和豫花15號×粵油20組合的F2的脂肪分布則表現(xiàn)偏向低脂肪含量親本方向, 低于低親的個體數(shù)和變異范圍均多于高于高親的個體數(shù)。

豫花15號×四粒紅、豫花15號×伏花生、豫花15×NC94022三個組合的親本脂肪含量差異較大, 其F2脂肪含量分布均偏向低脂肪含量親本方向, 低于低親的個體數(shù)顯著多于高于高親的個體數(shù), 尤其是豫花15號×伏花生組合, 沒有個體的脂肪含量高于高親品種, 但豫花15號×NC94022組合有一定數(shù)量的F2個體脂肪含量高于高親品種。

2.2 脂肪含量遺傳模型的建立

根據(jù)主基因加多基因模型分別對各組合進(jìn)行最大似然值和AIC值估計, 根據(jù)組合將多個擬合模型的AIC值按照從小到大排列(表2), 分別為無主基因(0 Major Gene, 0MG)、1對主基因(1MG)、2對主基因(2MG)的加性(A)、加性顯性(AD)、完全顯性(CD)、負(fù)向完全顯性(NCD)、等加性(EA)、等顯性(EAD)等模型。

各個組合的各個遺傳模型的適合性檢驗見表3,12、22、32、2、D五個特征參數(shù)的值均大于0.05, 未達(dá)到顯著水平。說明模型的擬合度均具有統(tǒng)計學(xué)意義。

2.3 基于不同模型的遺傳參數(shù)估計

根據(jù)AIC值最小的原則選擇最適合的模型, 6個組合分別符合1對主基因加性顯性模型(1MG-AD), 無主基因(0MG)、2對主基因等顯性模型(2MG-EAD) 3類遺傳模式, 主基因遺傳力32%~80% (表4)。遠(yuǎn)雜9102和豫花15號的雜交組合表現(xiàn)1對主基因的加性顯性遺傳模式, 但主基因遺傳力僅為32%; 豫花15號與中花6號、粵油20、伏花生3個雜交組合均表現(xiàn)2對主基因的等顯性模型, 主基因遺傳力為47%~80%; 豫花15號與四粒紅、NC94022等2個雜交組合表現(xiàn)出無主基因的遺傳模式。表明不同遺傳背景的品種的脂肪含量調(diào)控基因的差異。

3 討論

我國油料作物脂肪含量的遺傳研究經(jīng)歷了雙列雜交配合力分析[10-14]、雙親群體的多世代聯(lián)合分析主基因加多基因遺傳模型[3,5-7,15-16]、雙親群體的QTL定位等多種數(shù)量遺傳學(xué)研究方法的發(fā)展[17-21]。但迄今為止, 定位到的脂肪含量QTL由于易受環(huán)境影響或者定位區(qū)間過大, 尚未成功應(yīng)用于育種實踐。借鑒人類利用全基因組關(guān)聯(lián)分析(genome-wide association study, GWAS)的成功經(jīng)驗, 自然群體的GWAS分析也在油料作物的脂肪含量關(guān)聯(lián)位點(diǎn)方面得到應(yīng)用[22-23]。GWAS具有等位基因多樣性豐富的優(yōu)點(diǎn), 分子標(biāo)記密度也大大提高, 但是存在難以定位稀有等位基因的缺陷, 而且自然群體的結(jié)構(gòu)和分層現(xiàn)象嚴(yán)重干擾性狀關(guān)聯(lián)位點(diǎn)的精確解析[24]。

表2 脂肪含量遺傳模式分析

A: 遠(yuǎn)雜9102; B: 中花6號; C: 豫花15號; E: 粵油20; F: 四粒紅; G: 伏花生; H: NC94022。

A: Yuanza 9102;.B: Zhonghua 6; C: Yuhua 15; E: Yueyou 20; F: Silihong; G: Fuhuasheng; H: NC94022. MG: major gene; AD: additive and dominant; NCD: negative complete dominant; EA: equal additive; EAD: equal dominant. AIC: Akaike’s Information Criterion.

表3 遺傳模式的適合性測驗

(續(xù)表3)

A: 遠(yuǎn)雜9102; B: 中花6號; C: 豫花15號; E: 粵油20; F: 四粒紅; G: 伏花生; H: NC94022。12、22、32、2、D分別為均勻性檢驗、Smirnov-Kolmogorov檢驗的特征參數(shù)。

A: Yuanza 9102; B: Zhonghua 6; C: Yuhua 15; E: Yueyou 20; F: Silihong; G: Fuhuasheng; H: NC94022.12,22,32,2,Dare parameters of Utility test, Smirnov test and Kolmogorov test. MG: major gene; AD: additive and dominant; NCD: negative complete dominant; EA: equal additive; EAD: equal dominant.

表4 脂肪含量遺傳參數(shù)估計

(續(xù)表4)

A: 遠(yuǎn)雜9102; B: 中花6號; C: 豫花15號; E: 粵油20; F: 四粒紅; G: 伏花生; H: NC94022。: 群體平均數(shù);a: 第1對主基因加性效應(yīng);b: 第2對主基因加性效應(yīng);a: 第1對主基因顯性效應(yīng);b: 第2對主基因顯性效應(yīng); Var.mg: 主基因方差;mg: 主基因遺傳力。

A: Yuanza 9102; B: Zhonghua 6; C: Yuhua 15; E: Yueyou 20; F: Silihong; G: Fuhuasheng; H: NC94022. m:mean of population;a: additive effect of the first major gene;b: additive effect of the second major gene;a: dominant effect of the first major gene;b: dominant effect of the second major gene; Var.mg: major gene variance;mg: heritability of major gene. MG: major gene; AD: additive and dominant; NCD: negative complete dominant; EA: equal additive; EAD: equal dominant.

巢式關(guān)聯(lián)作圖群體(nested association mapping, NAM)彌補(bǔ)了上述2種群體缺點(diǎn), 近年來逐漸成為農(nóng)作物數(shù)量性狀基因定位的研究熱點(diǎn)。NAM群體綜合考慮了稀有基因頻率和復(fù)等位基因等問題, 圖譜分辨率得到顯著提高[25-26]。Bouchet等[27]利用高粱的NAM群體進(jìn)行開花期和株高的QTL定位表明, QTL定位效率比關(guān)聯(lián)分析提高了3倍。其他作物利用NAM群體成功解析了復(fù)雜性狀的關(guān)聯(lián)基因, 例如大麥[28]、大豆[29-32]、玉米[33-34]、油菜[35]、小麥[36-37]。Marla等[38]利用NAM群體解析獲得了5~10個高粱耐冷QTL, 表型解釋率達(dá)到20%~41%?；诨ㄉ蚪M研究的快速進(jìn)展, Gangurde等[39]利用2個NAM群體解析了花生百果重和百仁重的QTL, 分別獲得8個和13個相關(guān)QTL。

本研究構(gòu)建了6個具有共同親本的巢式群體并利用F2:3開展了花生脂肪含量的遺傳模式分析, 結(jié)果表明, 6個組合的脂肪含量遺傳模型表現(xiàn)出多樣性,分別屬于無主基因、1對主基因加性顯性模型、2對主基因等顯性模型3種模式, 各種效應(yīng)值也各不相同, 主基因遺傳力從32%到80%。這些結(jié)果能夠為脂肪含量遺傳改良工作中親本選配和后代選擇提供參考, 高脂肪含量雙親雜交后代中高脂肪含量個體較多, 但主基因遺傳力較低, 不宜在早代實施表型選擇; 雙親脂肪含量差異較大的雜交后代中脂肪含量變異幅度更大, 能夠選擇到不同脂肪含量的類型, 但選擇出脂肪含量超高親后代的概率很低, 根據(jù)雜交組合的不同, 遺傳力變幅也較大。另外, 本研究顯示了巢式群體在揭示花生脂肪含量遺傳復(fù)雜性和創(chuàng)制豐富變異類型方面的應(yīng)用潛力, 同時也為進(jìn)一步利用該群體的高世代NAM開展聯(lián)合QTL定位和脂肪含量的遺傳解析等奠定了材料基礎(chǔ)。

4 結(jié)論

本研究利用巢式群體揭示出花生脂肪含量的遺傳表現(xiàn)出多種模式, 受到多對主基因調(diào)控, 分別具有加性和顯性等遺傳效應(yīng)。巢式群體包括了共同的雜交親本和更多樣性的基礎(chǔ)親本, 遺傳變異類型豐富, 具有更加全面地解析重要農(nóng)藝性狀遺傳調(diào)控機(jī)制的潛力。

[1] FAOSTAT, 2020. FAO. http://www.fao.org/faostat/en/#home.

[2] 廖伯壽, 雷永, 王圣玉, 李棟, 黃家權(quán), 姜慧芳, 任小平. 花生重組近交系群體的遺傳變異與高油種質(zhì)的創(chuàng)新. 作物學(xué)報, 2008, 34: 999–1004. Liao B S, Lei Y, Wang S Y, Li D, Huang J Q, Jiang H F, Ren X P. Genetic diversity of peanut RILs and enhancement for high oil genotypes., 2008, 34: 999–1004 (in Chinese with English abstract).

[3] 禹山林, 楊慶利, 潘麗娟, 薄文娜. 花生種子含油量的遺傳分析. 植物遺傳資源學(xué)報, 2009, 10: 453–456. Yu S L, Yang Q L, Pan L J, Bo W N. Genetic analysis for oil content of peanut seeds., 2009, 10: 453–456 (in Chinese with English abstract).

[4] 陳四龍, 李玉榮, 程增書, 廖伯壽, 雷永, 劉吉生. 花生含油量雜種優(yōu)勢表現(xiàn)及主基因+多基因遺傳效應(yīng)分析. 中國農(nóng)業(yè)科學(xué), 2009, 42: 3048–3057. Chen S L, Li Y R, Cheng Z S, Liao B S, Lei Y, Liu J S. Heterosis and genetic analysis of oil content in peanut using mixed model of major gene and polygene., 2009, 42: 3048–3057 (in Chinese with English abstract).

[5] Zhang X, Zhu S, Han S, Xu J, Liu H, Tang F, Dong W, Zang X, Zhang Z X. Inheritance of fat and fatty acid compositions in peanut (L.)., 2011, 12: 943–946.

[6] 劉華, 張新友, 崔黨群, 湯豐收, 董文召, 韓鎖義, 徐靜, 臧秀旺, 張忠信. 花生蛋白質(zhì)和脂肪含量的主基因+多基因遺傳分析. 江蘇農(nóng)業(yè)科學(xué), 2011, 39(2): 127–130. Liu H, Zhang X Y, Cui D X, Tang F S, Dong W Z, Han S Y, Xu J, Zang X W, Zhang Z X. Genetic analysis of protein and fat content using major gene plus polygene methods in peanut (L.)., 2011, 39(2): 127–130 (in Chinese with English abstract).

[7] 劉華, 秦利, 張新友, 韓鎖義, 杜培, 張忠信, 孫子淇, 齊飛艷, 董文召, 黃冰艷. 基于花生種間雜交遺傳群體的脂肪及脂肪酸含量的遺傳模型分析. 中國油料作物學(xué)報, 2016, 38: 172–178. Liu H, Qin L, Zhang X Y, Han S Y, Du P, Zhang Z X, Sun Z Q, Qi F Y, Dong W Z, Huang B Y. Genetic models of peanut fat and fatty acid compositions based on interspecific RIL population., 2016, 38: 172–178 (in Chinese with English abstract).

[8] 王建康, 蓋鈞鎰. 利用雜種F2世代鑒定數(shù)量性狀主基因-多基因混合遺傳模型并估計其遺傳效應(yīng). 遺傳學(xué)報, 1997, 24: 432–440. Wang J K, Gai J Y. Identification of major gene and polygene mixed inheritance model and estimation of genetic parameters of a quantitative trait from F2progeny., 1997, 24: 432–440 (in Chinese with English abstract).

[9] Meng L, Li H, Zhang L, Wang J. QTL IciMapping: integrated software for genetic linkage map construction and quantitative trait locus mapping in bi-parental populations., 2015, 3: 269–283.

[10] 甘信民, 曹玉良, 魏家祥, 崔務(wù)峰, 顧淑媛, 劉法生. 花生數(shù)量性狀配合力的研究. 中國油料作物學(xué)報, 1981, (3): 33–45. Gan S M, Cao Y L, Wei J X, Cui W F, Gu S Y, Liu F S. Combining ability analysis of quatitative traits in peanut., 1981, (3): 33–45 (in Chinese with English abstract).

[11] Isleib T G, Pattee H E, Giesbrecht F G. Oil, sugar, and starch characteristics in peanut breeding lines selected for low and high oil content and their combining ability.2004, 52: 3165?3168.

[12] 徐宜民, 甘信民, 曹玉良, 顧淑媛, 劉法生. 花生主要營養(yǎng)品質(zhì)性狀和農(nóng)藝性狀配合力的研究. 中國農(nóng)業(yè)科學(xué), 1995, 28(2): 15–23. Xu Y M, Gan X M, Cao Y L, Gu S Y, Liu F S. studies on combining ability of major nutritional quality characters and agronomic characters in peanut., 1995, 28(2): 15–23 (in Chinese with English abstract) .

[13] 梁炫強(qiáng), 鄭廣柔, 向榮英, 黎秀英. 珍珠豆型花生產(chǎn)量和含油率性狀配合力分析. 花生科技, 1991, (3): 11–14. Liang X Q, Zheng G R, Xiang R Y, Li X Y. Combining ability analysis of yield and oil content of Spanish type peanut., 1991, (3): 11–14 (in Chinese with English abstract).

[14] 梁炫強(qiáng), 鄭廣柔, 向榮英, 黎秀英. 珍珠豆型花生產(chǎn)量和含油率遺傳特性的雙列分析. 中國油料, 1992, (1): 19–23. Liang X Q, Zheng G R, Xiang R Y, Li X Y. A diallel analysis of yield and oil content of spanish type peanut., 1992, (1): 19–23 (in Chinese with English abstract).

[15] 付三雄, 戚存扣. 甘藍(lán)型油菜含油量的主基因+多基因遺傳分析. 江蘇農(nóng)業(yè)學(xué)報, 2009, 25: 731–736. Fu S X, Qi C K. Major gene plus polygene inheritance of oil content inL., 2009, 25: 731–736 (in Chinese with English abstract).

[16] Zhang S F, Ma C Z, Zhu J C, Wang J P, Wen Y C, Fu T D. Genetic analysis of oil content inL. using mixed model of major gene and polygene., 2006, 2: 171–180.

[17] 金夢陽, 李加納, 付福友, 張正圣, 張學(xué)昆, 劉列釗. 甘藍(lán)型油菜含油量及皮殼率的QTL分析. 中國農(nóng)業(yè)科學(xué), 2007, 40: 677–684. Jin M Y, Li J N, Fu F Y, Zhang Z S, Zhang X K, Liu L Z. QTL analysis of oil and hull content inL., 2007, 40: 677–684 (in Chinese with English abstract).

[18] 張潔夫, 戚存扣, 浦惠明, 陳松, 陳鋒, 高建芹, 陳新軍, 顧慧, 傅壽仲. 甘藍(lán)型油菜含油量的遺傳與QTL定位. 作物學(xué)報, 2007, 33: 1495–1501. Zhang J F, Qi C K, Pu H M, Chen S, Chen F , Gao J Q, Chen X J, Gu H, Fu S Z. Inheritance and QTL identification of oil content in rapeseed (L.)., 2007, 33: 1495–1501 (in Chinese with English abstract).

[19] 張新友, 韓鎖義, 徐靜, 嚴(yán)玫, 劉華, 湯豐收, 董文召, 黃冰艷. 花生主要品質(zhì)性狀的QTLs定位分析. 中國油料作物學(xué)報, 2012, 34: 311–315. Zhang X Y, Han S Y, Xu J, Yan M, Liu H, Tang F S, Dong W Z, Huang B Y. Identification of QTLs for important quality traits in cultivated peanut (L.)., 2012, 34: 311–315 (in Chinese with English abstract).

[20] 黃莉, 趙新燕, 張文華, 樊志明, 任小平, 廖伯壽, 姜慧芳, 陳玉寧. 利用RIL群體和自然群體檢測與花生含油量相關(guān)的SSR標(biāo)記. 作物學(xué)報, 2011, 37: 1967–1974. Huang L, Zhao X Y, Zhang W H, Fan Z M, Ren X P, Liao B S, Jiang H F, Chen Y N. Identification of SSR markers linked to oil content in peanut (L.) through RIL population and natural population., 2011, 37: 1967–1974 (in Chinese with English abstract).

[21] Pandey M K, Wang M L, Qiao L X, Feng S P, Khera P, Wang H, Tonnis B, Barkley N A, Wang J P, Holbrook C C, Culbreath A K, Varshney R K, Guo B Z. Identification of QTLs associated with oil content and mapping FAD2 genes and their relative contribution to oil quality in peanut (L.)., 2014, 15: 133.

[22] 魏大勇, 崔藝馨, 梅家琴, 湯青林, 李加納, 錢偉. 油菜種子含油量GWAS分析及位點(diǎn)整合系統(tǒng)構(gòu)建. 作物學(xué)報, 2018, 44: 1311–1319. Wei D Y, Cui Y X, Mei J Q, Tang Q L, Li J N, Qian W. Genome-wide association study on seed oil content in rapeseed and construction of integration system for oil content loci., 2018, 44: 1311–1319 (in Chinese with English abstract).

[23] Sun F M, Liu J, Hua W, Sun X C, Wang X F, Wang H Z. Identification of stable QTLs for seed oil content by combined linkage and association mapping in, 2016, 252: 388–399.

[24] Yu J, Holland J B, McMullen M D, Buckler E S. Genetic design and statistical power of nested association mapping in maize., 2008, 178: 539–551.

[25] Li H, Bradbury P, Ersoz E, Buckler E S, Wang J. Joint QTL linkage mapping for multiple-cross mating design sharing one common parent., 2011, 6: e17573.

[26] Xavier A, Xu S, Muir W M, Rainey K M. NAM: association studies in multiple populations., 2015, 31: 3862–3864.

[27] Bouchet S, Olatoye M O, Marla SR, Perumal R, Tesso T, Yu J M, Tuinstra M, Morris G P. Increased power to dissect adaptive traits in global sorghum diversity using a nested association mapping population., 2017, 206: 573–585.

[28] Schnaithmann F, Kopahnke D, Pillen K. A first step toward the development of a barley NAM population and its utilization to detect QTLs conferring leaf rust seedling resistance., 2014, 127: 1513–1525.

[29] Xavier A, Jarquin D, Howard R, Ramasubramanian V, Specht J E, Graef G L, Beavis W D, Diers B W, Song Q, Cregan P B, Nelson R, Mian R, Shannon J G, McHale L, Wang D, Schapaugh W, Lorenz A J, Xu S, Muir W M., Rainey K M. Genome-wide analysis of grain yield stability and environmental interactions in a multiparental soybean population.:, 2018, 8: 519–529.

[30] Khan M A, Tong F, Wang W, He J, Zhao T, Gai J. Analysis of QTL-allele system conferring drought tolerance at seedling stage in a nested association mapping population of soybean [(L.) Merr.] using a novel GWAS procedure., 2018, 248: 947–962.

[31] Chen Q, Yang C J, York A M, Xue W, Daskalska L L, DeValk C A, Krueger K W, Lawton S B, Spiegelberg B G, Schnell J M, Neumeyer M A, Perry J S, Peterson A C, Kim B, Bergstrom L, Yang L, Barber I C, Tian F, Doebley J F. TeoNAM: a nested association mapping population for domestication and agronomic trait analysis in maize., 2019,213:1065–1078.

[32] Beche E, Gillman J D, Song Q, Nelson R, Beissinger T, Decker J, Shannon G, Scaboo A M. Nested association mapping of important agronomic traits in three interspecific soybean populations., 2020, 133: 1039–1054.

[33] Garin V, Wimmer V, Mezmouk S, Malosetti M, Eeuwijk F. How do the type of QTL effect and the form of the residual term influence QTL detection in multi-parent populations? A case study in the maize EU-NAM population., 2017, 130: 1753–1764.

[34] Monir M M, Zhu J. Dominance and epistasis interactions revealed as important variants for leaf traits of maize NAM population., 2018, 9: 627.

[35] Hu J, Guo C, Wang B, Ye J, Liu M, Wu Z, Xiao Y, Zhang Q, Li H, King G J, Liu K. Genetic properties of a nested association mapping population constructed with semi-winter and spring oilseed rapes.,2018, 9: 1740.

[36] Jordan K W, Wang S, He F, Chao S, Lun Y, Paux E, Sourdille P, Sherman J, Akhunova A, Blake N K, Pumphrey M O, Glover K, Dubcovsky J, Talbert L, Akhunov E D. The genetic architecture of genome-wide recombination rate variation in allopolyploid wheat revealed by nested association mapping., 2018, 95: 1039–1054.

[37] Kidane Y G, Gesesse C A. Hailemariam B N, Desta E A, Mengistu D K, Fadda C, Pè M E, Dell’Acqua M. A large nested association mapping population for breeding and quantitative trait locus mapping in Ethiopian durum wheat., 2019, 17: 1380–1393.

[38] Marla S R., Burow G, Chopra R, Hayes C, Olatoye M O, Felderhoff T, Hu Z, Raymundo R, Perumal R, and Morris G P. Genetic architecture of chilling tolerance in sorghum dissected with a nested association mapping population.:, 2019, 9: 4045–4057.

[39] Gangurde S S, Wang H, Yaduru S, Pandey M K, Fountain J C, Chu Y, Isleib T, Holbrook C C, Xavier A, Culbreath A K, Ozias-Akins P, Varshney R K, Guo B. Nested-association mapping (NAM)-based genetic dissection uncovers candidate genes for seed and pod weights in peanut ()., 2010, 18: 1457–1471.

Genetic analysis of fat content based on nested populations in peanut (L.)

HUANG Bing-Yan1,**, SUN Zi-Qi1,**, LIU Hua1, FANG Yuan-Jin1, SHI Lei1, MIAO Li-Juan1, ZHANG Mao-Ning1, ZHANG Zhong-Xin1, XU Jing1, ZHANG Meng-Yuan2, DONG Wen-Zhao1, and ZHANG Xin-You1,*

1Henan Academy of Crops Molecular Breeding, Henan Academy of Agricultural Sciences / Graduate R & T Base of Zhengzhou University / Key Laboratory of Oil Crops in Huang-Huai-Hai Plans, Ministry of Agriculture and Rural Affairs / Henan Provincial Key Laboratory for Oil Crops Improvement, Zhengzhou 450002, Henan, China;2College of Agriculture, Henan University of Science and Technology, Luoyang 471023, Henan, China

Nested populations can be used to dissect the heredity of complex traits. The genetic models of fat content of F2:3families in nested combinations with one common parent and six founder parents were analyzed, aiming to detect the genetic differences among the founders and to provide bases for breeding strategy for fat content improvement in peanut kernels. The common parent was Yuhua 15, an irregular-type variety with high fat content, and the other six founder parents were different botanical varieties with different fat contents. The results showed that the genetic model of fat content traits was different in different combinations. Six crosses were in accordance with three genetic patterns, including none major gene model, one major gene model with additive and dominant effect, and two major genes model with equal additive and dominant effect. The estimated values of various genetic effects were also different. The heritability of the main genes ranged from 32% to 80%, indicating that the gene loci controlling the fat content and their segregation patterns were different in different F2:3populations. There were more individuals with high fat content in the offspring from combinations with both parents of high fat content. However, the heritability was low and phenotypic selections for fat content were not suggested in the early generations in such combinations. The offspring from combinations with parents of significantly different fat content had a larger variation range in fat content, and phenotypes with variable fat content were available. In this study, the large variances in the nested populations demonstrated the genetic complexity of fat content and the characteristics of multi major gene regulation. These results provide a comprehensive base for understanding the genetics and regulation of fat content, and the nested populations will be helpful in further QTL detection of fat content in peanut.

peanut (L.); nested populations; fat content; F2:3family; inheritance model

10.3724/SP.J.1006.2021.04138

本研究由國家現(xiàn)代農(nóng)業(yè)產(chǎn)業(yè)技術(shù)體系建設(shè)專項(CARS-13), 河南省現(xiàn)代農(nóng)業(yè)產(chǎn)業(yè)技術(shù)體系建設(shè)專項(2016-05)和河南省重大科技專項(201300111000)資助。

This study was supported by the China Agricultural Research System (CARS-13), the Henan Agricultural Research System (2016-05), and the Key Scientific and Technological Project of Henan Province (201300111000).

張新友, E-mail: haasz@126.com

**同等貢獻(xiàn)(Contributed equally to this work)

E-mail: huangbingyan@aliyun.com

2020-06-24;

2020-12-01;

2020-12-30.

URL:https://kns.cnki.net/kcms/detail/11.1809.S.20201230.1540.004.html