杨树杂交子代耐旱性MGIDI及FAI-BLUP指数多性状评价

doi:10.11707/j.1001-7488.LYKX20250784

摘要/Abstract

摘要：

目的: 以小黑杨×欧洲黑杨杂交子代为材料，通过苗期干旱胁迫试验，评价其耐旱性表现，筛选优良耐旱基因型，为杨树耐旱育种提供材料和依据。方法: 以小黑杨无性系‘ZL-3’为母本、欧洲黑杨无性系‘N188’为父本杂交获得的145个子代为材料，在温室盆栽条件下设置正常供水（NW）与干旱胁迫（LW）处理，测定生长、光合、叶片形态、气孔及根系等相关性状，并计算耐旱系数（LWindex）。利用混合效应模型，通过限制最大似然法/最佳线性无偏预测（REML/BLUP）估算各性状遗传参数与育种值，并采用基于因子分析与基因型?理想型距离指数（FAI-BLUP）、基于BLUP和LWindex的多性状基因型?理想型距离指数（MGIDI_BLUP、MGIDI_LWindex）3种多性状选择策略（FAI-BLUP、MGIDI_BLUP和MGIDI_LWindex），对杂交子代进行耐旱性综合评价与排序。结果: 所有测定性状均受干旱胁迫显著影响。遗传参数分析显示，生长与根系性状具有较高遗传力，而叶片形态、光合参数和气孔性状遗传力普遍较低。基于这些结果，最终确定3个生长性状（D₂、H₂、DS）和4个根系性状（RDW、TRL、RSA、AD）以及2个光合参数（TR、WUE）共9个性状用于多性状综合评价。在选择强度（SI）25%时，基于BLUP值和LWindex的3种多性状指数（FAI-BLUP、MGIDI_BLUP和MGIDI_LWindex），各筛选出了36个优良基因型，其生长性状的选择增益（SG）分别为7.916%~12.172%、8.907%~13.672%和4.331%~22.058%，其中4个基因型（E4-148、E4-410、E4-79和E4-371）被共同筛选出。与生物量积累和耐旱指数相关性状在FAI-BLUP与MGIDI分析中表现出较高的选择差（SD）和选择增益（SG），表明这些性状可作为杨树耐旱性筛选的评价指标。结论: 筛选出的杂交子代耐旱基因型为杨树耐旱性选择育种提供了新的候选材料。FAI-BLUP与MGIDI多性状指数法在耐旱性综合评价中具有较好的选择效果，可为杨树耐旱基因型筛选提供方法支持。

关键词: 小黑杨, 耐旱系数, BLUP, 多性状指数选择, FAI-BLUP, MGIDI

Abstract:

Objective: Drought is a major abiotic stress that severely limits the growth and yield of poplar plantations. In this study, the progenies derived from the cross between Populus simonigra and P. nigra were used as the experimental materials to evaluate drought tolerance performance with their seedling-stage drought stress trials, and screen superior genotypes, so as to provide candidate materials and a theoretical basis for drought-tolerance breeding in poplar. Method: A total of 145 hybrid progenies derived from the female parent (P. simonigra cl. ‘ZL-3’) and the male parent (P. nigra cl. ‘N188’) were subjected to two water regimes: normal-watering (NW) and drought-stress (LW) in greenhouse with a pot experiment. Growth, photosynthesis, leaf morphology, stomatal characteristics, and root system architecture were measured, and the drought-tolerance index (LWindex) was calculated. A mixed linear model was employed to estimate genetic parameters and breeding values for each trait via restricted maximum likelihood/best linear unbiased prediction (REML/BLUP). Furthermore, the three advanced multi-trait selection strategies of FAI (factor analysis and genotype-ideotype distance index)-BLUP, MGIDI (multi-trait genotype-ideotype distance index)_BLUP, and MGIDI_LWindex were implemented to comprehensively evaluate and rank the drought tolerance of the hybrid progeny. Result: All measured traits were significantly affected by drought stress. Genetic parameter analysis revealed that growth and root traits exhibited high heritability, while leaf morphology, photosynthetic, and stomatal traits showed relatively low heritability. Based on these results, nine core traits were selected for multi-trait evaluation: three growth traits (D₂, H₂, DS), four root traits (RDW, TRL, RSA, AD), and two photosynthetic parameters (TR, WUE). Under a selection intensity (SI) of 25%, 36 superior genotypes were identified and selected from the progeny based on the three multi trait indices (FAI-BLUP, MGIDI_BLUP, and MGIDI_LWindex) using BLUP value and LWindex. The selection gain (SG) for their growth traits ranged 7.916%–12.172%, 8.907%–13.672%, and 4.331%–22.058% with the three strategies, respectively. Among them, four genotypes (E4-148, E4-410, E4-79, and E4-371) were consistently selected by all three methods. Notably, the traits related to biomass accumulation and the drought tolerance index exhibited higher selection differentials (SD) and SG, indicating that these traits can be used as key evaluation indicators for drought tolerance screening in poplar. Conclusion: The drought-tolerant genotypes of the hybrid progeny screened in this study provide new candidate materials for selection and breeding drought-tolerant poplar. The multi-trait index methods of FAI-BLUP and MGIDI have demonstrated high selection efficiency in the comprehensive evaluation of drought tolerance. These approaches offer robust methodological support for screening drought-tolerant genotypes and lay a foundation for further dissecting the genetic mechanisms underlying drought tolerance in poplar.

Key words: Populus simonigra, drought tolerance coefficient, BLUP, Multi-trait index selection, FAI-BLUP, MGIDI

中图分类号:

S722.3

曹明嵘,周振渊,贾东旭,刘成功,黄秦军,李金花. 杨树杂交子代耐旱性MGIDI及FAI-BLUP指数多性状评价[J]. 林业科学, 2026, 62(8): 144-157.

Mingrong Cao,Zhenyuan Zhou,Dongxu Jia,Chenggong Liu,Qinjun Huang,Jinhua Li. Multi-trait Comprehensive Evaluation of Drought Tolerance in Poplar Hybrid Progeny Using MGIDI and FAI-BLUP Index[J]. Scientia Silvae Sinicae, 2026, 62(8): 144-157.

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性状 Traits	表型方差 Phenotypic variance	广义遗传力 h²_g	均值遗传力 h²_gm	基因型?环境互作效应的决定系数R²_GEI	基因型?环境相关系数r_ge	选择准确性 A_c	遗传变异系数 CV_g
H₂	112.007	0.281	0.537	0.367	0.510	0.733	15.835
HS	45.241	1.087E-10	4.447E-10	0.233	0.233	2.109E-05	8.358E-04
D₂	1.109	0.380	0.719	0.136	0.219	0.848	13.592
DS	0.664	0.130	0.443	0.057 1	0.065 7	0.666	25.186
LA	26.169	0	0	0.085 6	0.085 6	0	0
FW	0.021 5	0.014 6	0.079 8	0.014 1	0.014 3	0.282	2.901
TW	0.030 8	0.012 9	0.068 1	0.034 3	0.034 8	0.267	2.797
LDW	0.010 8	0.011 2	0.063 4	0.001 2	0.001 2	0.252	5.490
RWC	0.001 1	7.293E-09	3.997E-08	0.047 5	0.047 5	1.999 E-04	2.520E-04
PN	8.449	0.038 5	0.182	0.037 7	0.039 3	0.426	5.815
GS	2.727	0	0	0	0	0	0
CI	1.059	0	0	0.002 5	0.002 5	0	0
TR	1.662	0.052 9	0.215	0.105	0.111	0.464	7.760
WUE	1.610	0.053 8	0.240	0.037 4	0.039 6	0.490	9.936
LS	6.565E-03	0	0	2.747E-15	2.747E-15	0	0
SD	787.402	0.043 3	0.214	0	0	0.462	4.642
SL	6.501	0.043 5	0.214	0	0	0.463	3.164
SW	1.840	0	0	0	0	0	0
SA	481.133	0.013	0.071	0	0	0.267	3.323
RDW	0.371	0.187	0.426	0.352	0.433	0.652	15.798
TRL	0.696	0.198	0.596	2.625E-16	3.271E-16	0.772	17.103
RSA	0.080	0.261	0.679	0	0	0.824	20.082
AD	0.104	0.189	0.583	0	0	0.763	15.335

性状 Traits	因子1 FA1	因子2 FA2	因子3 FA3	因子4 FA4	共同度 Communality	变量 VAR	因子 Factor	总体均值X_o	中选均值X_s	选择差 SD	选择增益SG%	方向 Sense	目标 Goal
H₂	–0.757	–0.084	0.135	0.175	0.628	H₂	FA1	35.409	38.212	2.803	7.916	上升Increase	100
D₂	–0.931	–0.062	–0.010	0.106	0.882	D₂	FA1	4.775	5.207	0.432	9.045	上升Increase	100
DS	–0.809	–0.065	–0.015	–0.027	0.659	DS	FA1	1.168	1.310	0.142	12.172	上升Increase	100
RDW	–0.739	0.141	–0.138	–0.081	0.592	RDW	FA1	1.668	1.847	0.179	10.711	上升Increase	100
TRL	–0.159	–0.103	–0.684	–0.361	0.634	TR	FA2	3.822	3.727	–0.096	–2.506	下降Decrease	100
RSA	–0.125	–0.058	0.745	–0.359	0.703	WUE	FA2	2.962	3.056	0.0937	3.162	上升Increase	100
AD	–0.128	0.041	–0.025	0.853	0.746	TRL	FA3	2.169	2.201	0.0321	1.481	上升Increase	100
TR	0.017	–0.874	–0.023	–0.105	0.776	RSA	FA3	0.721	0.731	0.100	1.372	上升Increase	100
WUE	0.060	0.883	0.007	–0.037	0.785	AD	FA4	0.913	0.924	0.011	1.157	上升Increase	100
特征值 Eigenvalues	2.729	1.613	1.061	1.002
方差 Variance （%）	30.317	17.918	11.793	11.138
累计方差 Cum.variance（%）	30.317	48.235	60.028	71.166

性状 Traits	因子1 FA1	因子2 FA2	因子3 FA3	因子4 FA4	共同度 Communality	唯一性 Uniquenesses	变量 VAR	因子 Factor	总体均值X_o	中选均值X_s	选择差SD	选择增益SG%	方向 Sense	目标 Goal
H₂	–0.757	–0.084	0.135	–0.175	0.628	0.372	H₂	FA1	35.4	38.563	3.154	8.907	上升Increase	100
D₂	–0.931	–0.062	–0.010	–0.106	0.882	0.118	D₂	FA1	4.78	5.298	0.523	10.948	上升Increase	100
DS	–0.809	–0.065	–0.015	0.027	0.659	0.341	DS	FA1	1.168	1.328	0.156	13.672	上升Increase	100
RDW	–0.739	0.141	–0.138	0.081	0.592	0.408	RDW	FA1	1.668	1.857	0.189	11.319	上升Increase	100
TRL	–0.159	–0.103	–0.684	0.361	0.634	0.366	TR	FA2	3.822	3.762	–0.061	–1.583	下降Decrease	100
RSA	–0.125	–0.057	0.745	0.359	0.703	0.297	WUE	FA2	2.962	2.984	0.022	0.755	上升Increase	100
AD	–0.128	0.041	–0.025	–0.853	0.746	0.254	TRL	FA3	2.169	2.195	0.026	1.197	上升Increase	100
TR	–0.017	0.874	0.023	–0.105	0.776	0.224	RSA	FA3	0.721	0.741	0.020	2.833	上升Increase	100
WUE	0.060	0.883	0.007	0.037	0.785	0.215	AD	FA4	0.913	0.949	0.0363	3.925	上升Increase	100
特征值Eigenvalues	2.729	1.613	1.061	1.002
方差 Variance （%）	30.317	17.917	11.793	11.138
累计方差 Cum.variance（%）	30.317	48.235	60.028	71.166

变量 VAR	因子1 FA1	因子2 FA2	因子3 FA3	因子4 FA4	共同度 Communality	唯一性 Uniquenesses	变量 VAR	因子 Factor	总体均值X_o	中选均值X_s	选择差 SD	选择增益SG%	方向 Sense	目标 Goal
H₂	–0.623	0.089	0.416	–0.101	0.582	0.418	H₂	FA1	0.7901	0.965	0.174	22.058	上升Increase	100
D₂	–0.895	–0.00468	0.0427	–0.0839	0.811	0.189	D₂	FA1	0.882	0.957	0.0749	8.489	上升Increase	100
DS	–0.679	–0.00849	–0.335	0.311	0.671	0.329	DS	FA1	1.019	1.0628	0.0441	4.331	上升Increase	100
RDW	–0.136	0.183	0.464	0.0383	0.269	0.731	TR	FA2	0.620	0.607	–0.0132	–2.130	下降Decrease	100
TRL	0.192	0.131	0.372	–0.304	0.285	0.715	WUE	FA2	1.236	1.239	0.00294	0.237	上升Increase	100
RSA	0.0361	–0.149	0.750	0.0962	0.595	0.405	RDW	FA3	0.871	0.947	0.0758	8.702	上升Increase	100
AD	–0.061	–0.0828	–0.120	–0.927	0.885	0.115	TRL	FA3	1.034	1.035	0.00134	0.129	上升Increase	100
TR	–0.010	0.892	0.0784	–0.0217	0.802	0.198	RSA	FA3	0.993	0.994	0.000213	0.0215	上升Increase	100
WUE	–0.0216	0.871	0.0192	0.0576	0.763	0.237	AD	FA4	0.733	0.740	0.00714	0.974	上升Increase	100
特征值 Eigenvalues	1.792	1.627	1.211	1.031
方差 Variance （%）	19.914	18.075	13.456	10.270
累计方差 Cum.variance（%）	19.914	37.988	51.447	62.903

选择指数1 Selection index 1	选择指数2 Selection index 2	中选基因型数量 Number of selected genotypes	3种选择策略中选基因型 Selected genotypes of three selection strategies	共选基因型 Co-selected genotypes
FAI-BLUP	MGIDI_BLUP	27	E4-70、E4-85、E4-191、E4-59、E4-109、E4-115、E4-63、 E4-148、E4-410、E4-145、E4-278、E4-79、E4-371、E4-170、 E4-208、E4-309、E4-152、E4-162、E4-122、E4-46、E4-294、 E4-292、E4-230、E4-214、E4-139、E4-291、E4-290	E4-148、E4-410、 E4-79、E4-371
MGIDI_BLUP	MGIDI_LWindex	7	E4-148、E4-410、E4-79、E4-371、E4-281、E4-205、E4-270
MGIDI_LWindex	FAI-BLUP	6	E4-410、E4-436、E4-79、E4-371、E4-478、E4-148