0878-B1

Quantitative Trait Loci (QTL) Mapping of Growth and Wood Property Traits in Populus

Zhang Deqiang^[1], Zhang Zhiyi, Yang Kai and Li Bailian

Abstract

The genetic control of wood volume growth and wood chemical contents traits was studied in an interspecific backcross progeny derived from the cross between clone “LM50” (P. tomentosa) and its hybrid clone “TB01” (P. tomentosa, P. bolleana). Genetic maps were constructed for each parent with AFLP markers according to Pseudo-test-cross mapping strategy, based on 120 offspring. A total of 247 and 146 markers distributed among 19 linkage groups covered 3265.1 cM and 1992 cM for the clone “LM50” and clone “TB01” parents, respectively. Using genetic linkage maps constructed in this research, 32 putative QTLs were detected that are associated with the five main economic traits by internal mapping approach. They were dispersed on 16 linkage groups for clone “LM50” maps and 10 for clone “TB01” maps. The phenotypic variance explained by each QTL ranged from 7.0% to 14.6%. QTLs controlling correlated traits between sylleptic branch numbers and stem volume were co-localized in two genomic regions on linkage groups TLG6 and TLG8, respectively. The favorable alleles were derived from the parent P. tomentosa, which was phenotypically superior over parent P. bolleana for sylleptic branch angle, stem volume and wood chemical content traits, conversely for the sylleptic branch number trait. These preliminary results should be verified in larger population, different environmental backgrounds and years, and eventually the stable QTLs were detected for future development of marker-assisted selection for growth and wood properties in P. tomentosa.

Introduction

Populus tomentosa is a native species of main economic importance among the forest trees cultivated in China. It has played a key role in timber production and ecological environment protection along the Yellow River. Over past several years, genetic improvements in P. tomentosa by hybridization, chromosome doubling and clone breeding have been developed, and great progress has been achieved. Owing to poor research of genetic control system for this indigenous species, however, the existed problems for propagated rooting, yields of wood and quality have not been resolved in essence to date. These commercially economic traits are of a quantitative nature, and controlled by its corresponding quantitative trait loci (QTLs). The use of molecular markers enables one to identify and map QTLs that are involved in the variation of such traits in the segregated population. Knowledge of the number, genomic regions and individual effects of genes controlling these traits will provide a better understanding of the genetic architecture of quantitative traits and expedite breeding progress through opportunities for developing marker-assisted breeding strategies in forest trees.

For the last decade, QTLs mapping with molecular markers has been received extensive attention in forest trees. QTLs were detected in poplar for many important traits such as growth, form, phenology and disease resistance, based on F₂ generation population of P. trichocarpa?P. deltoides (Bradshaw and Stettler 1995; Frewen et al. 2000). Tree growth, wood quality and vegetative propagation in Eucalyptus (Grattapaglia et al. 1996; Marques et al. 1999), growth, adaptation, wood specific gravity, wood physical and chemical properties in Pinus (Groover et al. 1994; Sewell et al. 2000, 2002) and many more quantitative traits have already been studied in other forest tree species. However, the QTLs controlling growth and wood chemical content traits remain unknown in P. tomentosa. To detect the QTLs affecting stem growth and wood chemical contents traits, the parent-specific moderate resolution maps with AFLP markers were constructed for two cultivars clone “LM50” (P. tomentosa) and clone “TB01” (P. tomentosa´P. bolleana) based on 120 progeny. In the present study, the separated AFLP genetic maps were used with objectives: (1) identify QTLs associated with five economic importance traits for sylleptic branch numbers, sylleptic branch angle, stem volume, wood cellulose content and wood lignin content; (2) estimate the number, genomic location and individual effect of gene affecting these traits, using the interval mapping approach. To our knowledge, this is the first report on QTL analysis based on an interspecific BC₁ population in P. tomentosa.

Materials and methods

Mapping population

A subset of 120 progeny, chosen randomly from inbred population of 696 lines derived from a cross between two elite poplar species, “TB01” clone and “LM50”clone, was used in this research.

Linkage map construction

Separated genetic linkage maps have been constructed with AFLP markers according to Pseudo-test-cross mapping strategy (Grattapaglia and sedroff 1994) using the Kosambi’s mapping function.

QTL analysis

QTL detection was performed with software package MAPMAKER-QTL (Lander and Botstein 1989) underling the backcross model, based on the individual linkage maps for clone “TB01” and clone “LM50”.

Results

QTL analysis

Thirty-two putative QTLs associated with the 5 main economic traits (sylleptic angle, sylleptic number, stem volume, cellulose content and lignin content) were identified. These putative QTLs were dispersed on 16 linkage groups for clone “LM50” maps and 10 for clone “TB01” maps, respectively. The number of QTLs detected for each trait ranged from four to eight. The proportion of phenotypic variation explained by each of these QTLs varied from 7.0% (TBLG17) to 14.6% (TBLG11). More than 50.0% of total phenotypic variation was explained by the QTLs for each trait except for stem volume trait. Table 1 summarized the genomic location and associated additive effect of magnitude for all traits, together with their corresponding confidence interval and the percentage of the phenotypic variation explained by each QTL.

Sylleptic angle

Six QTLs detected were fell into 6 genomic regions corresponding, respectively, to linkage groups TLG2, TLG5, TLG7, TLG10, TLG16 and TBLG14 (Table 1). Among which 3 QTLs increased phenotypic values and another 3 have negative effects on the Sylleptic angles. The percentage of phenotypic variance explained by each QTLs ranged from 7.4% (TLG16) to 12.8% (TLG10). Total phenotypic variation explained by the six putative QTL was 57.7%.

Sylleptic number

A total of eight genomic regions were found to have effects on Sylleptic number (Table 1) and seven QTLs originated from both parents increased phenotypic value. The proportion of the total variance explained by a single QTLs varied between 7.0% (TBLG17) and 9.2% (TLG9). The total phenotypic variation explained by these seven putative QTLs was 61.7%, but individual QTL explained a small proportion of the phenotypic variance (less than 10.0%).

Stem volume

Four putative QTLs were identified that influenced stem volume (Table 1). The percentage of trait variation explained by each individual QTL ranged from 8.0% (TLG1) to 11.0% (TLG6) with most of them accounting for less than 10.0% of the total phenotypic variation. At these four loci, stem volume was increased by the alleles from or a closely linked QTL had additive effects between 0.936 and 1.198 m³ for the phenotypic value. QTLs controlling correlated traits between sylleptic branch numbers and stem volume were co-localized in two genomic regions on linkage groups TLG6 and TLG8, respectively.

Wood cellulose content

Eight unique quantitative trait loci affecting cellulose content were detected on linkage group TLG1, TLG5, TLG12, TLG13, TLG18, TBLG9, TBLG11 and TBLG14, respectively (Table 1). The respective putative QTLs accounted for between 8.7% (TLG13) and 14.6% (TBLG11) of phenotypic variation, with LOD scores ranging from 2.02 to 3.2. These eight QTLs could account for 83.7% of the total variation. One QTL of these was located in an identical region to the QTL for sylleptic number on TLG5 at interval of E35M52350r-E44M6097r.

Klason lignin content

For this trait, six putative QTLs were identified (Table 1). They were positioned on linkage group TLG2 and TLG15 in linkage map of “LM50” and on linkage group TBLG3, TBLG6, TBLG8 and TBLG5 in linkage map of “TB01”, respectively. Together these explained 54.9% of the additive genetic variation and all but one each explained less than 10.0% of the total phenotypic variation for the trait analyzed.

Discussion

QTL identification in inbred population

In order to understand the genetic control of growth and wood chemical components, backcrossing was used as a method to generate segregating population for QTL mapping. The use of genetic maps based on molecular markers enables one to identify and map QTLs that are involved in the variation of such traits in the segregated population. These could expedite efficiency of early selection of main economic traits through marker-assisted breeding strategies contrast to classical quantitative genetics. In this paper we identified 32 putative QTLs on 26 genomic regions controlling wood growth and chemical content traits. The number of QTLs identified ranged from 4 to 8 per trait and each QTL accounted only for a small proportion of the phenotypic variation (7.0%-14.6%). A previous QTL detection in the F₂ progeny of P. trichocarpa ´P. deltoids was achieved with RFLP and RAPD markers by Bradshaw et al.(1995). Their QTL mapping results suggest that 24.4-33.4% phenotypic variation for growth and development trait was explained by single QTL. There are many reasons why no more than 14.6% of the phenotypic variation explained by single QTL for any trait was found in our study. The reason for this may be due to types and the sample size of mapping population, types and density of marker used in genetic maps, and QTL mapping software. First, the efficiency of QTL detection obtained from backcross is less than that obtained from F₂ population because one, rather than two, recombinant gametes are sampled in plant. The small size of mapping population decreases the sensitivity of detecting QTLs, and only major QTLs could be detected, while smaller ones might remain unrevealed. Further, the dominant markers only used by us lead to lower LOD scores and also to an under-estimation of the magnitude of explained variation if multiple alleles are segregating at a locus in offspring of out-breeding species. Furthermore, our medium-density parentic maps constructed have several gaps and have not full Populus genome coverage. As a result, some QTLs of large effect have gone undetected. Therefore, There is much additive genetic variation unaccounted for which should require further field trials in order to resolve, or attribute to the QTLs in larger population.

Table 1 Putative QTLs detected for growth and wood chemical content traits by interval mapping via MapMaker/QTL.

^aInterval between the two flanking markers (cM) where a QTL is located
^b QTL position from the first marker (cM)
^c Phenotypic variation explained by each QTL
^dSylleptic brance angle
^eSylleptic brance number
^fStem volume
^gWood cellulose content
^hwood lignin content

Trait correlation and pleiotropic effects of QTLs

Conventional quantitative genetics suggest that trait correlations may be attributable to either pleiotropic effects of single genes or to tight linkage of several genes that individually influence specific traits. Thus, it is expected that correlated traits have QTLs mapping to the same genomic regions (Veldboom et al. 1994; Xiao et al. 1996). The same trend was observed in our study. A significant positive correlation was found between sylleptic branch number and stem volume, and two QTLs (on linkage group TLG6 and TLG8, respectively) affecting both traits were mapped to two same genomic regions. The presence of QTLs for both sylleptic number and stem volume are detected in the same genomic regions may be due to the presence of two genes tightly linked or to one gene with a pleiotrophic effect on these two traits. In a previous study by Bradshaw et al.(1995), pleiotropy was suggested for QTLs on linkage group E that were simultaneously associated with spring flush, stem height, basal area and sylleptic branch number. In fact, it is observed that the close trait correlations cannot be explained by QTLs mapped in the same genomic region, or non-significant correlations were found to locate in same position were been observed in some cases (Damerval et al. 1994; Wu et al. 1997). For example, in this study, the QTLs located on same position were found to associate with sylleptic angle and cellulose content although non-correlation both traits have been observed. The real reasons for this phenomenon have not well understood but are generally believed to include that some genomic regions controlling these phenotypic traits have not been detected due to not saturated linkage maps and small experimental population or genetic factors such as repulsion and epistasis, and statistical methods or sampling error.

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