大花序桉生长和材性遗传变异及SSR关联分析

发布时间：2018-05-13 13:26

本文选题：大花序桉 + 近红外光谱　；参考：《中国林业科学研究院》2017年博士论文

【摘要】：大花序桉(Eucalyptus cloeziana)是优良的实木用材树种,其生长和材性的遗传变异和关联基因组位点的研究对理解重要性状的遗传规律和挖掘分子育种的基因组资源均有重要作用。本研究利用大花序桉种源/家系试验林为材料,建立了基于近红外(near infrared,NIR)光谱的材性预测模型,开展了生长和材性的遗传参数分析与种源/家系选择,挖掘了与生长和材性关联的简单重复序列(simple sequence repeats,SSR)标记,主要结论如下。(1)建立的大花序桉木材物理力学性质的NIR预测模型具有较高的可靠性。利用75~109株样木的标准测定值和NIR光谱数据,建立了木材基本密度(basic density,BD)、生材密度(green density,GD)、抗弯弹性模量(modulus of elasticity in static bending,MOE)、抗弯强度(modulus of rupture,MOR)和顺纹抗压强度(compressive strength parallel to grain,σc)的NIR预测模型。其中,利用85%(64~93株)的样木,建立的各性状NIR交互验证模型的参数分别为:BD的交互验证均方根误差(root-mean-squares error of cross-validation,RMSECV)为0.03、决定系数(coefficient of determination,R~2)为0.78、预测残差(residual predictive deviation,RPD)为2.16,GD的RMSECV为0.02、R~2为0.77、RPD为2.06,MOE的RMSECV为1.09、R~2为0.81、RPD为2.26,MOR的RMSECV为6.39、R~2为0.78、RPD为2.13,σc的RMSECV为2.30、R~2为0.80、RPD为2.26。利用另外15%(11~16株)的样木进行了NIR交互验证模型的验证,BD、GD、MOE、MOR和σc的预测相关因子(coefficient of determination of prediction,R_p~2)分别为0.74、0.69、0.81、0.63和0.65,预测值均方根误差(root mean squares error of prediction,RMSEP)分别为0.060、0.041、1.100、12.500和4.790,表明NIR模型具有较高的预测可靠性。(2)估算了大花序桉生长和材性遗传参数,了解了生长性状遗传参数随林龄变化趋势,分析了生长和材性的性状相关。生长性状包括胸径(diameter at breast height,D)、树高(height,H)和单株材积(individual volume,V),材性性状包括BD、GD、MOE、MOR和σc。以大花序桉种源/家系试验林为材料,利用SAS软件相关模型,估算生长和材性的种源方差分量、种源内家系方差分量、狭义遗传力、种源重复力,分析生长性状早晚相关和生长与材性的性状相关。结果表明,胸径、单株材积和材性在种源间和种源内家系间差异显著(P0.05)。林龄0.5~9.5年生生长的狭义遗传力范围为0.04~0.35,9.5年生材性的狭义遗传力范围为0.06~0.24,表明生长和材性受到低至中等强度的遗传控制。生长性状间的表型相关和遗传相关均显著正相关,材性间的表型相关和遗传相关也显著正相关,生长和材性间的表型相关和遗传相关呈显著或不显著的负相关,这对生长和材性的同时改良是一个挑战。此外,早晚期生长的表型相关和遗传相关均显著正相关(除H2.5),随着林龄的增加相关关系越强,表明存在早期选择的可能性。(3)利用多性状综合指数法选择了一批生长和材性优良的种源和家系。利用多性状综合指数法对9.5年生大花序桉H_(9.5)、D_(9.5)、V_(9.5)、BD、GD、σ_c、MOE和MOR 8个性状进行综合选择,根据指数选择遗传力、综合育种值遗传进展值和指数选择效率判断,认为指数方程I3和I7作为多性状综合指数选择方程较理想。根据指数值大小进行种源和家系选择,种源前三名为南部近沿海种源20725、20724和20722,家系前三名为家系65、56和51。(4)开发了与大花序桉生长和材性关联的SSR位点共26个。利用839个分布于巨桉(E.grandis)全基因组的SSR标记,基于D9.5、BD、MOE、MOR和σ_c五个性状高值和低值基因池(至少8株/池/性状,共32株)筛选的47个标记,对348株大花序桉样品(来自115个自由授粉的半同胞家系)进行了5个性状的关联分析。利用一般线性模型(general linear model,GLM)和混合线性模型(mixed linear model,MLM),发现与D_(9.5)、BD、MOE、MOR和σ_c显著关联的SSR标记分别为8、7、6、4和9个(P0.05),标记对表型变异解释率为5.7~44.1%,有6个标记与2个性状同时关联;鉴定了各标记的显著性等位片段,最大的平均增效和减效效应分别达6.3%和3.5%。
[Abstract]:Eucalyptus cloeziana (Eucalyptus grandis) is a good tree species for solid wood. The genetic variation of growth and timber and the study of the associated genome site play an important role in understanding the genetic rules of important traits and mining the genomic resources of molecular breeding. The genetic parameter analysis of growth and wood properties and selection of source / family lines were carried out for the genetic parameter analysis of the near infrared (near infrared, NIR) spectra, and a simple repeat sequence (simple sequence repeats, SSR) associated with growth and wood properties was excavated. The main conclusions are as follows. (1) a NIR prediction model for the physical and mechanical properties of Eucalyptus grandis wood The basic density (basic density, BD), the density of raw material (green density, GD), the flexural modulus of elasticity (modulus of elasticity in), the flexural strength and the CIS compression strength were established by using the standard measurement values and NIR spectra data of 75~109 wood like wood. In the NIR prediction model of GTH parallel to grain, sigma C, the parameters of each character NIR interaction validation model established by 85% (64~93 strain) are as follows: BD's interactive verifying root mean square error (root-mean-squares error of) is 0.03, and the decision coefficient is 0.78. Esidual predictive deviation, RPD) is 2.16, RMSECV for GD is 0.02, R~2 is 0.77, RPD is 2.06, MOE RMSECV is 1.09, R~2 is 0.81, RPD is 2.26, 2.13, 2.13, 2.13, 0.80 The prediction related factors of sigma C (coefficient of determination of prediction, R_p~2) are 0.74,0.69,0.81,0.63 and 0.65 respectively, and the predicted mean square root error (root mean squares error) is respectively and 4.790, indicating that the model has higher predictive reliability. (2) the growth of Eucalyptus grandis growth is estimated. And the genetic parameters of wood properties, we understand the trend of genetic parameters of growth traits with forest age, and analyze the correlation between growth and wood properties. Growth traits include diameter at breast height (D), tree height (height, H) and individual volume (individual volume, V). SAS software related models were used to estimate the source variance components of growth and wood properties, family variance components, narrow sense heritability and provenance repeat force in the provenance. The correlation between early and late growth traits and growth and wood properties was analyzed. The results showed that there was significant difference in diameter, volume and timber between the sources and the provenances (P0.05). The narrow sense heritability of the growth of 0.5~9.5 years of forest age is that the narrow sense heritability of 0.04~0.35,9.5 annual timber is 0.06~0.24, which indicates that growth and timber are controlled by low to medium intensity. The phenotype correlation and genetic correlation between the growth traits are significantly positively correlated, and the phenotypic correlation and genetic correlation between wood properties are also positively correlated. The phenotypic correlation and genetic correlation between growth and timber has a significant or not significant negative correlation, which is a challenge for the simultaneous improvement of growth and timber. In addition, there is a significant positive correlation between phenotypic and genetic correlations in early and late growth (except H2.5), the stronger the correlation of forest age, the possibility of early selection. (3) (3) A batch of seed and family lines with good growth and wood properties were selected by the multi character comprehensive index method. The multiple character synthesis index method was used to select the 9.5 year old Eucalyptus H_ (9.5), D_ (9.5), V_ (9.5), BD, GD, sigma _c, MOE and MOR 8, and select the heritability according to the index, and identify the genetic progress value and index selection efficiency of the comprehensive breeding value. The index equation I3 and I7 are ideal for multiple character index selection equations. According to the index number, the source and family selection are selected. The top three are 2072520724 and 20722 of the southern coastal provenances, and three family 65,56 and 51. (4) in the first family of the family have developed a total of 26 SSR loci associated with the growth and timber of Eucalyptus grandis. Use 839 points. The SSR markers of the whole genome of Eucalyptus grandis (E.grandis), based on 47 markers, based on the five traits of D9.5, BD, MOE, MOR and sigma _c in high and low value gene pools (at least 8 strains / pools / traits, 32 strains), were used to analyze 5 characters of 5 characters of Eucalyptus grandis samples (from 115 free pollination half sib families). The general linear model (Gen) was used. Eral linear model, GLM) and the mixed linear model (mixed linear model, MLM). It is found that the markers that are significantly associated with D_ (9.5), BD, MOE, MOR and sigma are respectively, and 6 markers are associated with 2 personality traits, and the significant alleles of the markers are identified and the maximum average is identified. The effects of synergism and reduction were 6.3% and 3.5%., respectively.

【学位授予单位】：中国林业科学研究院
【学位级别】：博士
【学位授予年份】：2017
【分类号】：S792.39

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