小麦SSR标记辅助遗传背景选择技术研究
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摘要
回交育种是重要的育种方法之一,通过分子标记辅助选择技术提高回交育种效率具有重要意义。本研究以转大豆抗旱基因GmDREB1的小麦品系MG349为供体亲本、济麦22为轮回亲本的360个BC_1F_2单株为材料,用GmDREB1基因本身的功能基因标记对该基因进行跟踪检测,选用两个亲本间多态性好并覆盖小麦21个连锁群的46个SSR标记进行轮回亲本遗传背景分析,通过遗传背景回复率的比较,探索适宜的小麦回交育种群体以及利用分子标记进行遗传背景选择时所需要的适宜的标记数、标记的合理分布和经济有效的选择方式,旨在探索加快小麦回交育种速度、提高回交育种效率的途径。主要研究结果如下:
1、对360株BC_1F_2个体遗传回复率的分析表明,轮回亲本遗传回复率的平均值为0.762,与理论值0.75接近,其中48.1%的个体遗传回复率在0.70到0.80之间,32.3%的个体遗传回复率在0.80以上,1.7%的个体遗传回复率大于0.90。因此,就加快回交育种速度而言,适当加大回交群体,从回交群体中选择既具有目的基因,同时又具有较高的轮回亲本遗传回复率的个体是可行的。换句话说,可以通过扩大回交分离群体,减少必要的回交次数,用较少的回交次数达到较多回交次数的效果。
2、比较了标记在基因组中均匀分布和随机选取标记两种情况下不同标记数目对遗传背景选择效果的影响。在7个同源群中每个同源群只取1个(共7个标记)、2个(共14个标记)和3个标记(共21个标记)时,轮回亲本遗传背景回复率与采用46个标记得出的遗传背景回复率的相关系数分别为0.506、0.645和0.773,均达显著水平(P﹤0.01);在利用46个标记选出的36个遗传背景回复率最高个体中,分别有34%、43%和51%的个体与采用每个同源群只取1个、2个和3个标记时选出的36个遗传背景回复率最高个体相同。而在随机选取7个、14个和21个标记时,轮回亲本遗传背景回复率与采用46个标记得出的遗传背景回复率的相关系数则分别为0.431、0.556和0.652,均达显著水平(P<0.01);在利用46个标记选出的36个遗传背景回复率最高个体中,分别有32 %、41 %和47 %的个体与随机选取7个、14个和21个标记时选出的36个遗传背景回复率最高个体相同。因此,在进行小麦分子标记辅助背景选择时,可先在每个同源群上选择2~3个标记对初始群体进行扫描,选出遗传背景回复率高的个体,然后根据育种实际需要,决定是否再增加标记对选出的个体进行检测,以达到经济有效的选择效果。
3、研究了小麦A、B、D三个染色体组和7个同源群的选择效果。结果显示三个染色体组的遗传背景回复率与总回复率相关性差异较小,但是B染色体组背景回复率显著低于A组、D组和总回复率(P<0.01),且B染色体组与A组、D组及总回复率均表现显著正相关(P<0.01),而A、D两个染色体组回复率不相关。由于遗传背景的回复率是由A、B、D三个基因组的回复率组成,因此,为了提高选择效率,最好增加在B基因组上的标记。当然,这可能与两个亲本的遗传背景有关,是否具有普遍性值得进一步研究。比较7个同源群遗传背景回复率时发现,7个同源群之间不存在相关性,与总回复率的相关系数从大到小依次为第六同源群(0.545) >第五同源群(0.478)>第四同源群(0.427)>第七同源群(0.421)>第三同源群(0.406)>第一同源群(0.379)>第二同源群(0.271)。这种排序与每个连锁群上的标记数目多少排序基本相同。但在比较均具有六个标记位点的第二、第三、第七同源群时,发现第七同源群和第三同源群与遗传背景总回复率的相关系数几乎是第二同源群的2倍,而且只有第七同源群的回复率显著低于总回复率,因此,推测在不增加总的标记数量时,增加第七同源群的标记数量,可能更有利于提高总的遗传背景回复率。与染色体组间背景回复率差异比较一样,不同同源群间背景回复率差异也可能与两个亲本的遗传背景有关,值得进一步研究。
Backcrossing is widely regarded as one of important methods in plant breeding. And marker assisted selection may play a considerable role in backcrossing breeding. In this study, a transgenic line MG349 with GmDREB1 gene, which has been cloned in soybean and proved to improve drought tolerance, was chosen as the donor parent. Jimai 22, the dominant wheat cultivar in Shandong province at present, was chosen as the recurrent parent. 360 BC_1F_2 plants from the cross between the two parents were detected for GmDREB1 gene by using the gene itself function maker, and the recovery of the genetic background of Jimai22 in the offspring was analyzed by 46 SSR markers, which have polymorphism between the parents and cover 21 linkage groups of wheat, aiming to explore the suitable number of markers and their distribution in the genome, cost-efficient selection methods in SSR marker assisted backcrossing (MAB). The main results are as follows:
1. The analysis of recovery ratio of genetic background (RRGB) of the 360 BC_1F_2 plants showed that means of RRGB was 0.762, close to the theoretical value of 0.75, in which 48.1% plants ranged 0.70~0.80, 32.3% plants over 0.80 and 1.7% plants over 0.90 in RRGB respectively. So it indicated that, in terms of speeding-up wheat backcrossing, selecting the plants having both the targeted gene or genes and increased RRGB by increasing the population size is possible. In other words, the required times of backcrossing could be reduced by increasing the backcrossing population.
2. The effects of number of SSR markers on selection efficiency of RRGB under the both conditions of considering the distribution of the markers in the genome and randomly selection of the markers were analyzed. Under the condition of considering the marker distribution, 7 markers (one in each homoeologous group), 14 markers (two in each homoeologous group) and 21 markers (three in each homoeologous group) were chosen. Correlation coefficients between the different number of markers and total of 46 markers in RRGB were 0.506、0.645 and 0.773 respectively, and all reached the significant level (P<0.01) . Among the 36 plants with the highest RRGB value obtained by 46 markers, 34%, 43% and 51% of the plants were the same as those obtained by 7, 14 and 21 markers respectively. When only considering the number of markers, without considering their distribution in the genome, correlation coefficients between the different number of markers and total of 46 markers in RRGB were 0.432, 0.556 and 0.652 respectively, and all reached the significant level (P<0.01) . Among the 36 plants with the highest RRGB value obtained by 46 markers, 32%, 41% and 47% of the plants were the same as those obtained by 7, 14 and 21 markers respectively. So in considering the cost-efficient way in the background selection, we recommended that, 2~3 markers in every homoeologous group were selected at the beginning to scan the initial population, and then select plants with higher RRGB by using more markers if the result is not satisfied.
3. The effects of SSR markers on selection efficiency of RRGB from genome A, B, D and the 7 homoeologous groups were also studied. The result showed that there were slight differences among the correlation coefficients between each genome and total RRGB obtained by 46 markers. The RRGB of B genome was significant lower than A, D genome and total RRGB, and there was significant positive correlation between B and A(P<0.01), as well as B and D. Thinking that the total RRGB is determined by the three genomes, we speculate that it’s a good choice to increase the number of markers in the B genome for a better selection efficiency. Certainly, the difference between A, B and D genome may be induced by special genetic background of the two parents, so its validation is needed to be verified further. We also found that there’s no correlation among the 7 homoeologous group across the marker data. But they all have positive correlation with the total RRGB and the sequence of correlation coefficients were: homoeologous group 6 (0.545) > homoeologous group 5 (0.478) > homoeologous group 4 (0.427) > homoeologous group 7 (0.421) > homoeologous group 3 (0.406) > homoeologous group 1(0.379)> homoeologous group 2(0.271).The order is consistent with the number of markers in the homoeologous group. But homoeologous group 2, homoeologous group 3 and homoeologous group 7 with the same number of markers have different correlation coefficients with total RRGB. The correlation coefficients with total RRGB of homoeologous group 3 and homoeologous group 7 were almost as twice as that of homoeologous group 2. And RRGB of homoeologous group 2 was the only one which had significant positive correlation with total RRGB. So it indicated that with the same number of markers, increasing the number of markers in the homoeologous group 2 may increase the selection efficiency in RRGB. As the same as what mentioned in comparing the differences in correlation with total RRGB between genomes, the differences in RRGB between homoeologous groups may be induced by specific background of the two parents.
1、对360株BC_1F_2个体遗传回复率的分析表明,轮回亲本遗传回复率的平均值为0.762,与理论值0.75接近,其中48.1%的个体遗传回复率在0.70到0.80之间,32.3%的个体遗传回复率在0.80以上,1.7%的个体遗传回复率大于0.90。因此,就加快回交育种速度而言,适当加大回交群体,从回交群体中选择既具有目的基因,同时又具有较高的轮回亲本遗传回复率的个体是可行的。换句话说,可以通过扩大回交分离群体,减少必要的回交次数,用较少的回交次数达到较多回交次数的效果。
2、比较了标记在基因组中均匀分布和随机选取标记两种情况下不同标记数目对遗传背景选择效果的影响。在7个同源群中每个同源群只取1个(共7个标记)、2个(共14个标记)和3个标记(共21个标记)时,轮回亲本遗传背景回复率与采用46个标记得出的遗传背景回复率的相关系数分别为0.506、0.645和0.773,均达显著水平(P﹤0.01);在利用46个标记选出的36个遗传背景回复率最高个体中,分别有34%、43%和51%的个体与采用每个同源群只取1个、2个和3个标记时选出的36个遗传背景回复率最高个体相同。而在随机选取7个、14个和21个标记时,轮回亲本遗传背景回复率与采用46个标记得出的遗传背景回复率的相关系数则分别为0.431、0.556和0.652,均达显著水平(P<0.01);在利用46个标记选出的36个遗传背景回复率最高个体中,分别有32 %、41 %和47 %的个体与随机选取7个、14个和21个标记时选出的36个遗传背景回复率最高个体相同。因此,在进行小麦分子标记辅助背景选择时,可先在每个同源群上选择2~3个标记对初始群体进行扫描,选出遗传背景回复率高的个体,然后根据育种实际需要,决定是否再增加标记对选出的个体进行检测,以达到经济有效的选择效果。
3、研究了小麦A、B、D三个染色体组和7个同源群的选择效果。结果显示三个染色体组的遗传背景回复率与总回复率相关性差异较小,但是B染色体组背景回复率显著低于A组、D组和总回复率(P<0.01),且B染色体组与A组、D组及总回复率均表现显著正相关(P<0.01),而A、D两个染色体组回复率不相关。由于遗传背景的回复率是由A、B、D三个基因组的回复率组成,因此,为了提高选择效率,最好增加在B基因组上的标记。当然,这可能与两个亲本的遗传背景有关,是否具有普遍性值得进一步研究。比较7个同源群遗传背景回复率时发现,7个同源群之间不存在相关性,与总回复率的相关系数从大到小依次为第六同源群(0.545) >第五同源群(0.478)>第四同源群(0.427)>第七同源群(0.421)>第三同源群(0.406)>第一同源群(0.379)>第二同源群(0.271)。这种排序与每个连锁群上的标记数目多少排序基本相同。但在比较均具有六个标记位点的第二、第三、第七同源群时,发现第七同源群和第三同源群与遗传背景总回复率的相关系数几乎是第二同源群的2倍,而且只有第七同源群的回复率显著低于总回复率,因此,推测在不增加总的标记数量时,增加第七同源群的标记数量,可能更有利于提高总的遗传背景回复率。与染色体组间背景回复率差异比较一样,不同同源群间背景回复率差异也可能与两个亲本的遗传背景有关,值得进一步研究。
Backcrossing is widely regarded as one of important methods in plant breeding. And marker assisted selection may play a considerable role in backcrossing breeding. In this study, a transgenic line MG349 with GmDREB1 gene, which has been cloned in soybean and proved to improve drought tolerance, was chosen as the donor parent. Jimai 22, the dominant wheat cultivar in Shandong province at present, was chosen as the recurrent parent. 360 BC_1F_2 plants from the cross between the two parents were detected for GmDREB1 gene by using the gene itself function maker, and the recovery of the genetic background of Jimai22 in the offspring was analyzed by 46 SSR markers, which have polymorphism between the parents and cover 21 linkage groups of wheat, aiming to explore the suitable number of markers and their distribution in the genome, cost-efficient selection methods in SSR marker assisted backcrossing (MAB). The main results are as follows:
1. The analysis of recovery ratio of genetic background (RRGB) of the 360 BC_1F_2 plants showed that means of RRGB was 0.762, close to the theoretical value of 0.75, in which 48.1% plants ranged 0.70~0.80, 32.3% plants over 0.80 and 1.7% plants over 0.90 in RRGB respectively. So it indicated that, in terms of speeding-up wheat backcrossing, selecting the plants having both the targeted gene or genes and increased RRGB by increasing the population size is possible. In other words, the required times of backcrossing could be reduced by increasing the backcrossing population.
2. The effects of number of SSR markers on selection efficiency of RRGB under the both conditions of considering the distribution of the markers in the genome and randomly selection of the markers were analyzed. Under the condition of considering the marker distribution, 7 markers (one in each homoeologous group), 14 markers (two in each homoeologous group) and 21 markers (three in each homoeologous group) were chosen. Correlation coefficients between the different number of markers and total of 46 markers in RRGB were 0.506、0.645 and 0.773 respectively, and all reached the significant level (P<0.01) . Among the 36 plants with the highest RRGB value obtained by 46 markers, 34%, 43% and 51% of the plants were the same as those obtained by 7, 14 and 21 markers respectively. When only considering the number of markers, without considering their distribution in the genome, correlation coefficients between the different number of markers and total of 46 markers in RRGB were 0.432, 0.556 and 0.652 respectively, and all reached the significant level (P<0.01) . Among the 36 plants with the highest RRGB value obtained by 46 markers, 32%, 41% and 47% of the plants were the same as those obtained by 7, 14 and 21 markers respectively. So in considering the cost-efficient way in the background selection, we recommended that, 2~3 markers in every homoeologous group were selected at the beginning to scan the initial population, and then select plants with higher RRGB by using more markers if the result is not satisfied.
3. The effects of SSR markers on selection efficiency of RRGB from genome A, B, D and the 7 homoeologous groups were also studied. The result showed that there were slight differences among the correlation coefficients between each genome and total RRGB obtained by 46 markers. The RRGB of B genome was significant lower than A, D genome and total RRGB, and there was significant positive correlation between B and A(P<0.01), as well as B and D. Thinking that the total RRGB is determined by the three genomes, we speculate that it’s a good choice to increase the number of markers in the B genome for a better selection efficiency. Certainly, the difference between A, B and D genome may be induced by special genetic background of the two parents, so its validation is needed to be verified further. We also found that there’s no correlation among the 7 homoeologous group across the marker data. But they all have positive correlation with the total RRGB and the sequence of correlation coefficients were: homoeologous group 6 (0.545) > homoeologous group 5 (0.478) > homoeologous group 4 (0.427) > homoeologous group 7 (0.421) > homoeologous group 3 (0.406) > homoeologous group 1(0.379)> homoeologous group 2(0.271).The order is consistent with the number of markers in the homoeologous group. But homoeologous group 2, homoeologous group 3 and homoeologous group 7 with the same number of markers have different correlation coefficients with total RRGB. The correlation coefficients with total RRGB of homoeologous group 3 and homoeologous group 7 were almost as twice as that of homoeologous group 2. And RRGB of homoeologous group 2 was the only one which had significant positive correlation with total RRGB. So it indicated that with the same number of markers, increasing the number of markers in the homoeologous group 2 may increase the selection efficiency in RRGB. As the same as what mentioned in comparing the differences in correlation with total RRGB between genomes, the differences in RRGB between homoeologous groups may be induced by specific background of the two parents.
引文
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37. John Davies, William A., Berzonsky, Gene D. Leach. A comparison of marker-assisted and phenotypic selection for high grain protein content in spring wheat. Euphytica, 2006, 152:117–134.
38. Lecomte L, Duffe′P, Buret M, Servin B. et al. Marker-assisted introgression of five QTLs controlling fruit quality traits into three tomato lines revealed interactions between QTLs and genetic backgrounds. Theor. Appl Genet. 2004, 109, 658–668.
39. Lee M . DNA markers and plant breeding programs. Adv Agron, 1995, 55:265–344.
40. Liu Q., Kasuga M., SakumaY et al. Two transcription factors, DREB1 and DREB2,with an EREBP/AP2 DNA binding domain, separate two cellular signal transduction pathways in drought- and low temperature– resp onsive gene expression, respectively, in Arabidopsis. Plant Cell, 1998,10: 1391–1406.
41. Frisch Matthias and Melchinger Albrecht E. Selection Theory for Marker-Assisted Backcrossing. Genetics, 2005, 170: 909–917.
42. Neeraja C, Maghirang-Rodriguez R, Pamplona A et al. A marker-assisted backcross approach for developing submergence-tolerant rice cultivars. Theor Appl Genet, 2007, 115:767–776.
43. Nei M,Li W H.Probability of identical monomorphism in related species.Genet Res,1975,26(1):31-43.
44. Paterson A H. 1996. Genome mapping in plants.Current Biology, 1993, 4: 142-147.
45. Pellegrineschi A, Reynolds M, Yamaguchi-Shinozaki K, et al. Stress-induced expression in wheat of the Arabidopsis thaliana DREB1A gene delays water stress symptoms under greenhouse conditions. Genome, 2004, 47: 493~500.
46. Pestsova E, Ganal MW, R?der MS. Isolation and mapping of microsatellite markers specific for the D genome of bread wheat. Genome, 2000, 43:689–697.
47. Peter M, Viicher, Chris S, Haley, Robin Thompson. Marker-Assisted Introgression in Backcross Breeding Programs. Genetics Society of America,1996, 144: 1923-1932
48. Phil Bregitzer, Dennis Tonks. Inheritance and Expression of Transgenes in Barley. Crop Sci.2003, 43:4–12.
49. Phillip N. Miklas. Marker-Assisted Backcrossing QTL for Partial Resistance to Sclerotinia White Mold in Dry Bean. Crop Sci, 2007, 47:935–942.
50. Powell W, Morgante M, Ander C et al. The comparison of RFLP, RAPD, AFLP and SSR (microsatellite) markers for germplasm analysis. Mol Breed, 1996, 2: 225~238
51. Prem P. Jauhar, Modern Biotechnology as an Integral Supplement to Conventional Plant breeding: The Prospects and Challenges. Crop Sci, 2006, 46:1841–1859.
52. Qin F, Sakuma Y, Li J et al. Cloning and functional analysis of a novel DREB1/CBF transcription factor involved in cold-responsive gene expression in Zea mays. Plant Cell Physiol, 2004, 45 (8):1042-1052.
53. Ramsey S. Lewis, Kernodle S. P. A method for accelerated trait conversion in plant breeding.Theor Appl Genet, 2009, 118:1499–1508.
54. R?der MS, Korzun V, Wandehake K et al. A microsatellite map of wheat. Genetics, 1998, 149:2007–2023.
55. R?der MS, Plaschke J, K?nig SU et al. Abundance, variability and chromosomal location of microsatellites in wheat. Mol Gen Genet, 1995, 246:327-333.
56. Ribaut J M, Jiang C, Hoisington D. Simulation Experiments on Efficiencies of Gene Introgression by Backcrossing. Crop Sci, 2002, 42:557–565.
57. Semagn K, Bj?rnstad ?, Ndjiondjop M. N. Progress and prospects of marker assisted backcrossing as a tool in crop breeding programs. African Journal of Biotechnology, 2006, 5 (25): 2588-2603.
58. Shen L, Courtois B, McNally KL et al. Evaluation of near-isogenic lines of rice introgressed with QTLs for root depth through marker-aided selection. Theor. Appl. Genet, 2001, 103: 75-83.
59. Singh S, Sidhu JS, Huang N et al. Pyramiding three bacterial blight resistance genes (xa5, xa13 and Xa21) using marker-assisted selection into indica rice cultivar PR106. Theor. Appl. Genet, 2001, 102: 1011-1015.
60. Somers Daryl J., Isaac Peter, Edwards Keith. A high-density microsatellite consensus map for bread wheat (Triticum aestivum L.). Theor Appl Genet, 2004, 109: 1105–1114.
61. Song QJ, Fickus EW, Cregan PB. Characterization of trinucleotide SSR motifs in wheat. Theor Appl Genet, 2002, 104:286–293.
62. Song Q J, Shi J R, Singh S et al. Development and mapping of microsatellite (SSR) markers in wheat. Theor Appl Genet, 2005, 110: 550–560.
63. Sood BC, Sidiq EA. A rapid technique for scent determination in rice. Indian J. Genet. Plant Breed, 1978, 38:268–271.
64. Stephen P. Moose and Rita H. Mumm. Molecular Plant Breeding as the Foundation for 21st Century Crop Improvement. Plant Physiology, 2008, 147: 969–977.
65. Tanksley SD, Ganal MW and Martin GB. Chromosome landing: a paradigm for map-based gene cloning in plants with large genomes. Trends Genet, 1995, 11: 63-68.
66. Tanksley SD, Young ND, Patterson AH, Bonierbale MW (. RFLP mapping in plant breeding: New tools for an old science.Bio/Technology, 1989, 7: 257-263.
67. Thabuis A, Palloix A, Servin B et al. Marker assisted introgression of 4 Phytophthora capsici resistance QTL alleles into a bell pepper line: validation of additive and epistatic effects. Mol. Breed. 2004, 14:9–20.
68. Prigge Vanessa, Hans Peter Maurer, David J. Mackill et al. Comparison of the observed with the simulated distributions of the parental genome contribution in two marker-assisted backcross programs in rice. Theor Appl Genet, 2008, 116:739–744.
69. Prigge Vanessa, Melchinger Albrecht E, Dhillon Baldev S, Frisch Matthias. Efficiency gain of marker-assisted backcrossing by sequentially increasing marker densities over generations.TAG, 2009, 119:23-32.
70. Visarada K B R S, Kanti Meena, Srujana S et al. Transgenic Breeding: Perspectives and Prospects. Crop Sci. 2009, 49:1555–1563.
71. Wang L, Zhang Z, Wei L et al.The residual background genome from a donor within an improved line selected by marker-assisted selection: impact on phenotype and combining ability.2009, Plant Breeding 128, 429-435.
72. Wang XY, Chen PD, Zhang SZ. Pyramiding and marker-assisted selection for powdery mildew resistance genes in common wheat. Acta Genet, 2001, Sinica 28: 640-646.
73. Weeden NR, Muehlbauer FJ, Ladizinsky G. Extensive conservation of linkage relationships between pea and lentil genetic maps. J. Hered, 1992, 83: 123-129.
74. Willcox M C, Khairallah M M, Bergvinson D. et al. Selection for Resistance to Southwestern Corn Borer Using Marker-Assisted and Conventional Backcrossing. Crop Sci, 2002, 42:1516–1528.
75. William F. Tracy, Breeding: The Backcross Method. Encyclopedia of Plant and Crop Science, 2004, 1:1,237 -240.
76. Xu Yunbi, Henry Beachell, and Susan R. McCouch. A Marker-Based Approach to Broadening the Genetic Base of Rice in the USA. Crop Sci, 2004,44:1947–1959 .
77. Yamaguchi-Shinozaki, K. and Shinozaki, K. A novel cis-acting element in an Arabidopsis gene is involved in responsiveness to drought, lowtemperature, or high-salt stress. Plant Cell, 1994, 6: 251–264.
78. You G X, Zhang X Y, Wang L F. An estimation of the minimum number of SSR loci needed to reveal genetic relationships in wheat varieties: Information from 96 random accessions with maximized genetic diversity. Molecular Breeding, 2004, 14: 397–406, 2004.
79. Young ND, Tanksley SD. Restriction fragment length polymorphism maps and the concept of graphical geno-types. Theor Appl Genet, 1989, 77(1): 95―101.
80. Yu K, Park SJ, Poysa V. Marker assisted selection of common beans for resistance to common bacterial blight: efficacy and economics. Plant Breeding, 2000, 119: 411-415.
81. Zhang XY, Li CW, Wang LF et al. An estimation of the minimum number of SSR alleles needed to reveal genetic relationships in wheat varieties.I. Information from large-scale planted varieties and cornerstone breeding parents in Chinese wheat improvement and production. Theor Appl Genet , 2002,106:112–117.
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37. John Davies, William A., Berzonsky, Gene D. Leach. A comparison of marker-assisted and phenotypic selection for high grain protein content in spring wheat. Euphytica, 2006, 152:117–134.
38. Lecomte L, Duffe′P, Buret M, Servin B. et al. Marker-assisted introgression of five QTLs controlling fruit quality traits into three tomato lines revealed interactions between QTLs and genetic backgrounds. Theor. Appl Genet. 2004, 109, 658–668.
39. Lee M . DNA markers and plant breeding programs. Adv Agron, 1995, 55:265–344.
40. Liu Q., Kasuga M., SakumaY et al. Two transcription factors, DREB1 and DREB2,with an EREBP/AP2 DNA binding domain, separate two cellular signal transduction pathways in drought- and low temperature– resp onsive gene expression, respectively, in Arabidopsis. Plant Cell, 1998,10: 1391–1406.
41. Frisch Matthias and Melchinger Albrecht E. Selection Theory for Marker-Assisted Backcrossing. Genetics, 2005, 170: 909–917.
42. Neeraja C, Maghirang-Rodriguez R, Pamplona A et al. A marker-assisted backcross approach for developing submergence-tolerant rice cultivars. Theor Appl Genet, 2007, 115:767–776.
43. Nei M,Li W H.Probability of identical monomorphism in related species.Genet Res,1975,26(1):31-43.
44. Paterson A H. 1996. Genome mapping in plants.Current Biology, 1993, 4: 142-147.
45. Pellegrineschi A, Reynolds M, Yamaguchi-Shinozaki K, et al. Stress-induced expression in wheat of the Arabidopsis thaliana DREB1A gene delays water stress symptoms under greenhouse conditions. Genome, 2004, 47: 493~500.
46. Pestsova E, Ganal MW, R?der MS. Isolation and mapping of microsatellite markers specific for the D genome of bread wheat. Genome, 2000, 43:689–697.
47. Peter M, Viicher, Chris S, Haley, Robin Thompson. Marker-Assisted Introgression in Backcross Breeding Programs. Genetics Society of America,1996, 144: 1923-1932
48. Phil Bregitzer, Dennis Tonks. Inheritance and Expression of Transgenes in Barley. Crop Sci.2003, 43:4–12.
49. Phillip N. Miklas. Marker-Assisted Backcrossing QTL for Partial Resistance to Sclerotinia White Mold in Dry Bean. Crop Sci, 2007, 47:935–942.
50. Powell W, Morgante M, Ander C et al. The comparison of RFLP, RAPD, AFLP and SSR (microsatellite) markers for germplasm analysis. Mol Breed, 1996, 2: 225~238
51. Prem P. Jauhar, Modern Biotechnology as an Integral Supplement to Conventional Plant breeding: The Prospects and Challenges. Crop Sci, 2006, 46:1841–1859.
52. Qin F, Sakuma Y, Li J et al. Cloning and functional analysis of a novel DREB1/CBF transcription factor involved in cold-responsive gene expression in Zea mays. Plant Cell Physiol, 2004, 45 (8):1042-1052.
53. Ramsey S. Lewis, Kernodle S. P. A method for accelerated trait conversion in plant breeding.Theor Appl Genet, 2009, 118:1499–1508.
54. R?der MS, Korzun V, Wandehake K et al. A microsatellite map of wheat. Genetics, 1998, 149:2007–2023.
55. R?der MS, Plaschke J, K?nig SU et al. Abundance, variability and chromosomal location of microsatellites in wheat. Mol Gen Genet, 1995, 246:327-333.
56. Ribaut J M, Jiang C, Hoisington D. Simulation Experiments on Efficiencies of Gene Introgression by Backcrossing. Crop Sci, 2002, 42:557–565.
57. Semagn K, Bj?rnstad ?, Ndjiondjop M. N. Progress and prospects of marker assisted backcrossing as a tool in crop breeding programs. African Journal of Biotechnology, 2006, 5 (25): 2588-2603.
58. Shen L, Courtois B, McNally KL et al. Evaluation of near-isogenic lines of rice introgressed with QTLs for root depth through marker-aided selection. Theor. Appl. Genet, 2001, 103: 75-83.
59. Singh S, Sidhu JS, Huang N et al. Pyramiding three bacterial blight resistance genes (xa5, xa13 and Xa21) using marker-assisted selection into indica rice cultivar PR106. Theor. Appl. Genet, 2001, 102: 1011-1015.
60. Somers Daryl J., Isaac Peter, Edwards Keith. A high-density microsatellite consensus map for bread wheat (Triticum aestivum L.). Theor Appl Genet, 2004, 109: 1105–1114.
61. Song QJ, Fickus EW, Cregan PB. Characterization of trinucleotide SSR motifs in wheat. Theor Appl Genet, 2002, 104:286–293.
62. Song Q J, Shi J R, Singh S et al. Development and mapping of microsatellite (SSR) markers in wheat. Theor Appl Genet, 2005, 110: 550–560.
63. Sood BC, Sidiq EA. A rapid technique for scent determination in rice. Indian J. Genet. Plant Breed, 1978, 38:268–271.
64. Stephen P. Moose and Rita H. Mumm. Molecular Plant Breeding as the Foundation for 21st Century Crop Improvement. Plant Physiology, 2008, 147: 969–977.
65. Tanksley SD, Ganal MW and Martin GB. Chromosome landing: a paradigm for map-based gene cloning in plants with large genomes. Trends Genet, 1995, 11: 63-68.
66. Tanksley SD, Young ND, Patterson AH, Bonierbale MW (. RFLP mapping in plant breeding: New tools for an old science.Bio/Technology, 1989, 7: 257-263.
67. Thabuis A, Palloix A, Servin B et al. Marker assisted introgression of 4 Phytophthora capsici resistance QTL alleles into a bell pepper line: validation of additive and epistatic effects. Mol. Breed. 2004, 14:9–20.
68. Prigge Vanessa, Hans Peter Maurer, David J. Mackill et al. Comparison of the observed with the simulated distributions of the parental genome contribution in two marker-assisted backcross programs in rice. Theor Appl Genet, 2008, 116:739–744.
69. Prigge Vanessa, Melchinger Albrecht E, Dhillon Baldev S, Frisch Matthias. Efficiency gain of marker-assisted backcrossing by sequentially increasing marker densities over generations.TAG, 2009, 119:23-32.
70. Visarada K B R S, Kanti Meena, Srujana S et al. Transgenic Breeding: Perspectives and Prospects. Crop Sci. 2009, 49:1555–1563.
71. Wang L, Zhang Z, Wei L et al.The residual background genome from a donor within an improved line selected by marker-assisted selection: impact on phenotype and combining ability.2009, Plant Breeding 128, 429-435.
72. Wang XY, Chen PD, Zhang SZ. Pyramiding and marker-assisted selection for powdery mildew resistance genes in common wheat. Acta Genet, 2001, Sinica 28: 640-646.
73. Weeden NR, Muehlbauer FJ, Ladizinsky G. Extensive conservation of linkage relationships between pea and lentil genetic maps. J. Hered, 1992, 83: 123-129.
74. Willcox M C, Khairallah M M, Bergvinson D. et al. Selection for Resistance to Southwestern Corn Borer Using Marker-Assisted and Conventional Backcrossing. Crop Sci, 2002, 42:1516–1528.
75. William F. Tracy, Breeding: The Backcross Method. Encyclopedia of Plant and Crop Science, 2004, 1:1,237 -240.
76. Xu Yunbi, Henry Beachell, and Susan R. McCouch. A Marker-Based Approach to Broadening the Genetic Base of Rice in the USA. Crop Sci, 2004,44:1947–1959 .
77. Yamaguchi-Shinozaki, K. and Shinozaki, K. A novel cis-acting element in an Arabidopsis gene is involved in responsiveness to drought, lowtemperature, or high-salt stress. Plant Cell, 1994, 6: 251–264.
78. You G X, Zhang X Y, Wang L F. An estimation of the minimum number of SSR loci needed to reveal genetic relationships in wheat varieties: Information from 96 random accessions with maximized genetic diversity. Molecular Breeding, 2004, 14: 397–406, 2004.
79. Young ND, Tanksley SD. Restriction fragment length polymorphism maps and the concept of graphical geno-types. Theor Appl Genet, 1989, 77(1): 95―101.
80. Yu K, Park SJ, Poysa V. Marker assisted selection of common beans for resistance to common bacterial blight: efficacy and economics. Plant Breeding, 2000, 119: 411-415.
81. Zhang XY, Li CW, Wang LF et al. An estimation of the minimum number of SSR alleles needed to reveal genetic relationships in wheat varieties.I. Information from large-scale planted varieties and cornerstone breeding parents in Chinese wheat improvement and production. Theor Appl Genet , 2002,106:112–117.