核质互作QTL分析方法及其在强优势玉米杂交种苏玉16号遗传解析中的应用
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摘要
通过建立各种新的研究方法和手段,开展杂种优势以及复杂性状基因的功能研究已经成为当今遗传学领域的主要研究内容。随着生物学技术的进步,特别是近十年来人类以及其他模式生物基因组计划的开展,对于复杂性状的遗传研究越来越深入。然而,要彻底了解杂种优势以及复杂性状表型变异的遗传基础,仍具有相当大的挑战,需要从控制表型建成的各个遗传系统全局考虑。各个遗传系统,包括核基因组、线粒体基因组以及叶绿体基因组在复杂性状的遗传表达以及杂种优势形成中均可能发挥重要的作用。细胞质提供了基因表达的环境,细胞质中的线粒体更是生物进行生化活动的重要能量来源,基因功能的实现是核内基因与细胞质遗传物质共同作用的结果。细胞质效应在动植物中广泛存在,在模式生物果蝇、小鼠和酵母上的研究表明,细胞质与细胞核间的上位性互作效应对后代有明显的影响。遗传物质间上位性是物种分化和适应的重要遗传学动力,也是复杂性状和杂种优势形成的重要遗传组分。然而,由于技术发展的限制和相关统计分析方法的缺乏,上位性,特别是核质互作上位性在复杂性状遗传中的重要性常常被研究人员忽视。因此,发展出合适的统计分析方法在分子层次上剖析细胞质与细胞核基因间的上位性作用是揭开核质互作遗传机理的核心问题。本研究结合遗传交配设计,在常规QTL分析方法的基础上,提出一种剖析核质互作QTL的统计模型和分析方法,即核质互作QTL分析方法(Cytonuclear interacting QTL mapping,CNQM),以定位存在核质上位性的QTL。
玉米是我国最重要的三大粮食作物之一,同时也是重要的饲料和经济作物,在国民经济中占有重要地位。玉米杂种优势的利用是玉米生产发展的重要动力,广泛开展玉米复杂性状以及杂种优势遗传基础的研究和探讨具有十分重要的理论价值和现实意义。本研究在提出核质互作QTL分析方法的基础上,同时采用强优势玉米杂交种苏玉16(JB×Y53)的两个亲本自交系,配置具有不同细胞质背景的混合作图群体,构建分子标记连锁图谱,并采用本研究提出的核质互作QTL分析方法(CNQM)定位玉米重要农艺和产量性状相关的核质互作QTL。研究结果概述如下:
(1)建立了一套剖分核质互作遗传机制的统计遗传模型和分析方法
本文提出利用遗传设计获得具有不同细胞质背景的混合分离群体,并以此为基础建立了一种剖分核质互作遗传机制的统计分析方法。该方法既可无偏估计核质互作QTL在核基因组中的位置与效应,又可对有关QTL与细胞质遗传物质间的重要互作效应作出测验和估计。方法的有效性和可行性通过染色体水平和基因组水平两套模拟方案得到验证。
模拟结果表明:低遗传力和小样本容量下,QTL的统计功效低,参数估计值有偏,随着遗传力的提高,QTL的统计功效不断提高,各参数的估计值也接近真值,这与一般期望相符。在遗传力达到15%以上时,正反交各100的样本容量即可获得100%的统计功效。染色体水平的模拟试验表明,无论对于主效QTL,还是对具有不同核质互作方式的QTL,本文方法均具有很好的分析能力。
(2)基于核质互作QTL分析方法解析强优势玉米杂交种苏玉16号的遗传组成
本研究在上述遗传交配设计的基础上,利用玉米强优势杂交组合的两个亲本材料,即自交系JB和Y53组配正反交F2和F2:3群体,利用105个SSR标记构建连锁图谱,覆盖全基因组1214.6cM,各标记间平均图距为11.7cM。同时考察F2和F2:3群体的株高,穗位高等12个重要农艺性状和产量性状。采用本文提出的核质互作QTL定位方法定位有关的QTL,分析其相对贡献的大小,深入解析具有强优势玉米杂交种苏玉16号重要农艺性状的各遗传组分,包括细胞质效应,核内QTL效应,核内QTL与细胞质的互作效应以及它们对表型的贡献。为了便于比较,本文同时采用QTL Network 2.0和Windows QTL Cartographer 2.5软件在不考虑细胞质背景的情况下进行有关QTL的重演性验证。
分析结果显示:采用CNQM方法共检测到99个QTL,其中53个能够被重复检测到。抽穗期、株高、雄穗一级分支数、雄穗长、穗位高和茎粗检测到的QTL达到了10个以上,且平均一半以上的QTL能够被重复检测到,被重复检测到的QTL的平均贡献率在10%以上。在第7染色体上检测到的QTL最多,达到24个,核质互作QTL有8个,24个QTL中11个能够被重复检测到。在第10染色体上仅检测到1个QTL。99个QTL中34个具有显著的加性×细胞质或显性×细胞质互作效应,可被认为是具有核质互作效应的QTL。平均每个性状有3个核质互作QTL。这些结果表明,本研究调查的12个农艺性状不同程度地受到核质上位性的影响。此外,许多QTL定位在同一标记区间内,这可能是控制不同性状基因间存在紧密连锁,也可能是同一QTL影响不同的性状。
Functional research of the genes underlying heterosis and complex traits is the main objective of modern genetics, through new technology and method. With the development of biological technology, especially the development of genome project of human and other model organism, the research on the genetic basis of complex traits is more intensive. However, understanding the genetic basis of complex traits and heterosis is still a great challenge, which should be considered from the overall genetic systems. Each genetic system, including nuclear genome, mitochondrial genome and chloroplast genome, plays an important role in the genetic expression of complex traits and heterosis. Cytoplasm is the environment of gene expression, and mitochondrion provides the energy to support the biochemical reaction. It can be said that the realization of gene function is the result of cooperation between the nuclear gene and maternal cytoplasmic environment. Cytoplasmic effect exists widely in plants and animals. Researches on drosophila, mice and yeast also suggested that epistasis between nuclear and mitochondria affected significantly the phenotype of offspring. Furthermore, it was indicated that epistasis was the main genetic dynamics of speciation and adaption and the primary genetic component of heterosis and complex traits. However, because of the limitation of technology and the lack of flexible statistical method, epistasis, especially the cytonuclear epistasis was neglected too often in complex trait studies. Therefore, developing the proper methodology for dissecting the interaction between nuclear and cytoplasmic background was the core to understand the genetic basis of the cytonuclear epistasis. In this paper, we proposed a special genetic design to dissect cytonuclear epistasis. We called it cytonuclear interacting QTL mapping method, abbreviated to CNQM.
Maize is one of the most important food crops in China. It plays a vital role in agricultural economy of China. The utilization of heterosis promotes the maize production greatly. So, understanding the genetic basis of heterosis and complex traits highlights the theoretical value and practical significance in maize. Based on the proposed genetic design, the maize inbreed lines JB and Y53, which are the parental lines of maize hybrid suyu 16 with strong heterosis, were used to create the mixed mapping population with two different cytoplasm backgrounds. The proposed method for mapping cytonuclear epistatic QTL were used to identify the cytonuclear QTL underlying maize agricultural and yield traits. The results are as follows.
(1) Statictical model and method for dissecting cytonuclear epistasis
The mixed segregating population with the different cytoplasm backgrounds was created by using the reciprocal mating design. And the corresponding statistical method was proposed for dissecting cytonuclear epistatic interaction. The method can unbiasedly estimate positions and effects of cytonuclear epistatic QTL as well as simultaneously detect the important epistatic interaction between QTL and inherited cytoplasmic genomes. The validation of the statistical procedure was verified through two sets of simulation studies which are implemented via chromosome level and genome level, respectively.
Simulation results showed that, higher QTL heritability and larger sample sizes tend to produce more accurate and precise estimates and higher statistical power, whereas lower heritability, especially with smaller sample sizes, produce less accurate estimates with large estimation errors and lower statistical power, which is in accordance with our general expectations. When the QTL heritability was above 15%, the statistical power of QTL can reach almost 100% when only 200 individuals were collected. Genome level simulation results suggested that, no matter which interaction model was adopted, the proposed method would perform very well.
(2) The genetic dissection of maize hybrid Suyu 16 with strong heterosis
Based on the proposed genetic design, JB and Y53, the parental lines of maize hybrid Suyu 16 with strong heterosis, were used to create reciprocal F2 and F2:3 populations. The genetic linkage map was constructed containing 105 SSR markers, which covered 1214.6cM of maize genome. The average distance of the flanking markers was 11.7cM. The phenotypic value of 12 traits were investigated in reciprocal F2 and F2:3 populations. The proposed method was used to identify the QTL with cytonuclear epistatic effect and to evaluate the contribution of QTL to the phenotypic variation. This will help us to understand the genetic components of maize hybrid Suyu 16, including cytoplasmic effect, nuclear gene effect, cytonuclear epistatic effect. For comparison, two QTL mapping softwares, QTL Network 2.0 and Windows QTL Cartographer 2.5, were used to verify the QTL mapping result without considering the cytoplasmic effect and cytonuclear epistasis.
The results showed that 99 QTL controlling 12 traits were detected by CNQM method, and 53 of them were verified by different segregating populations or by different mapping methods. More than 10 QTL were detected in heading date, plant height, tassel branch number, tassel length, ear height and stem diameter, respectively. On average, half of them can be verified. The contribution of these QTL was over 10% of the phenotypic variation. Twenty four QTL were detected on Chromosome 7, in which eight of them had cytonuclear epistatic effects and eleven of them were verified by different mapping methods. However, only one QTL was detected on Chromosome 10. Thirty four of total 99 QTL showed significant cytonuclear epistatic effects. The size of cytonuclear epistasis had different altitudes in different traits. In addition, many QTL were found locating at the same marker interval. This implied the existence of pleiotropic QTL or tightly linked QTL.
玉米是我国最重要的三大粮食作物之一,同时也是重要的饲料和经济作物,在国民经济中占有重要地位。玉米杂种优势的利用是玉米生产发展的重要动力,广泛开展玉米复杂性状以及杂种优势遗传基础的研究和探讨具有十分重要的理论价值和现实意义。本研究在提出核质互作QTL分析方法的基础上,同时采用强优势玉米杂交种苏玉16(JB×Y53)的两个亲本自交系,配置具有不同细胞质背景的混合作图群体,构建分子标记连锁图谱,并采用本研究提出的核质互作QTL分析方法(CNQM)定位玉米重要农艺和产量性状相关的核质互作QTL。研究结果概述如下:
(1)建立了一套剖分核质互作遗传机制的统计遗传模型和分析方法
本文提出利用遗传设计获得具有不同细胞质背景的混合分离群体,并以此为基础建立了一种剖分核质互作遗传机制的统计分析方法。该方法既可无偏估计核质互作QTL在核基因组中的位置与效应,又可对有关QTL与细胞质遗传物质间的重要互作效应作出测验和估计。方法的有效性和可行性通过染色体水平和基因组水平两套模拟方案得到验证。
模拟结果表明:低遗传力和小样本容量下,QTL的统计功效低,参数估计值有偏,随着遗传力的提高,QTL的统计功效不断提高,各参数的估计值也接近真值,这与一般期望相符。在遗传力达到15%以上时,正反交各100的样本容量即可获得100%的统计功效。染色体水平的模拟试验表明,无论对于主效QTL,还是对具有不同核质互作方式的QTL,本文方法均具有很好的分析能力。
(2)基于核质互作QTL分析方法解析强优势玉米杂交种苏玉16号的遗传组成
本研究在上述遗传交配设计的基础上,利用玉米强优势杂交组合的两个亲本材料,即自交系JB和Y53组配正反交F2和F2:3群体,利用105个SSR标记构建连锁图谱,覆盖全基因组1214.6cM,各标记间平均图距为11.7cM。同时考察F2和F2:3群体的株高,穗位高等12个重要农艺性状和产量性状。采用本文提出的核质互作QTL定位方法定位有关的QTL,分析其相对贡献的大小,深入解析具有强优势玉米杂交种苏玉16号重要农艺性状的各遗传组分,包括细胞质效应,核内QTL效应,核内QTL与细胞质的互作效应以及它们对表型的贡献。为了便于比较,本文同时采用QTL Network 2.0和Windows QTL Cartographer 2.5软件在不考虑细胞质背景的情况下进行有关QTL的重演性验证。
分析结果显示:采用CNQM方法共检测到99个QTL,其中53个能够被重复检测到。抽穗期、株高、雄穗一级分支数、雄穗长、穗位高和茎粗检测到的QTL达到了10个以上,且平均一半以上的QTL能够被重复检测到,被重复检测到的QTL的平均贡献率在10%以上。在第7染色体上检测到的QTL最多,达到24个,核质互作QTL有8个,24个QTL中11个能够被重复检测到。在第10染色体上仅检测到1个QTL。99个QTL中34个具有显著的加性×细胞质或显性×细胞质互作效应,可被认为是具有核质互作效应的QTL。平均每个性状有3个核质互作QTL。这些结果表明,本研究调查的12个农艺性状不同程度地受到核质上位性的影响。此外,许多QTL定位在同一标记区间内,这可能是控制不同性状基因间存在紧密连锁,也可能是同一QTL影响不同的性状。
Functional research of the genes underlying heterosis and complex traits is the main objective of modern genetics, through new technology and method. With the development of biological technology, especially the development of genome project of human and other model organism, the research on the genetic basis of complex traits is more intensive. However, understanding the genetic basis of complex traits and heterosis is still a great challenge, which should be considered from the overall genetic systems. Each genetic system, including nuclear genome, mitochondrial genome and chloroplast genome, plays an important role in the genetic expression of complex traits and heterosis. Cytoplasm is the environment of gene expression, and mitochondrion provides the energy to support the biochemical reaction. It can be said that the realization of gene function is the result of cooperation between the nuclear gene and maternal cytoplasmic environment. Cytoplasmic effect exists widely in plants and animals. Researches on drosophila, mice and yeast also suggested that epistasis between nuclear and mitochondria affected significantly the phenotype of offspring. Furthermore, it was indicated that epistasis was the main genetic dynamics of speciation and adaption and the primary genetic component of heterosis and complex traits. However, because of the limitation of technology and the lack of flexible statistical method, epistasis, especially the cytonuclear epistasis was neglected too often in complex trait studies. Therefore, developing the proper methodology for dissecting the interaction between nuclear and cytoplasmic background was the core to understand the genetic basis of the cytonuclear epistasis. In this paper, we proposed a special genetic design to dissect cytonuclear epistasis. We called it cytonuclear interacting QTL mapping method, abbreviated to CNQM.
Maize is one of the most important food crops in China. It plays a vital role in agricultural economy of China. The utilization of heterosis promotes the maize production greatly. So, understanding the genetic basis of heterosis and complex traits highlights the theoretical value and practical significance in maize. Based on the proposed genetic design, the maize inbreed lines JB and Y53, which are the parental lines of maize hybrid suyu 16 with strong heterosis, were used to create the mixed mapping population with two different cytoplasm backgrounds. The proposed method for mapping cytonuclear epistatic QTL were used to identify the cytonuclear QTL underlying maize agricultural and yield traits. The results are as follows.
(1) Statictical model and method for dissecting cytonuclear epistasis
The mixed segregating population with the different cytoplasm backgrounds was created by using the reciprocal mating design. And the corresponding statistical method was proposed for dissecting cytonuclear epistatic interaction. The method can unbiasedly estimate positions and effects of cytonuclear epistatic QTL as well as simultaneously detect the important epistatic interaction between QTL and inherited cytoplasmic genomes. The validation of the statistical procedure was verified through two sets of simulation studies which are implemented via chromosome level and genome level, respectively.
Simulation results showed that, higher QTL heritability and larger sample sizes tend to produce more accurate and precise estimates and higher statistical power, whereas lower heritability, especially with smaller sample sizes, produce less accurate estimates with large estimation errors and lower statistical power, which is in accordance with our general expectations. When the QTL heritability was above 15%, the statistical power of QTL can reach almost 100% when only 200 individuals were collected. Genome level simulation results suggested that, no matter which interaction model was adopted, the proposed method would perform very well.
(2) The genetic dissection of maize hybrid Suyu 16 with strong heterosis
Based on the proposed genetic design, JB and Y53, the parental lines of maize hybrid Suyu 16 with strong heterosis, were used to create reciprocal F2 and F2:3 populations. The genetic linkage map was constructed containing 105 SSR markers, which covered 1214.6cM of maize genome. The average distance of the flanking markers was 11.7cM. The phenotypic value of 12 traits were investigated in reciprocal F2 and F2:3 populations. The proposed method was used to identify the QTL with cytonuclear epistatic effect and to evaluate the contribution of QTL to the phenotypic variation. This will help us to understand the genetic components of maize hybrid Suyu 16, including cytoplasmic effect, nuclear gene effect, cytonuclear epistatic effect. For comparison, two QTL mapping softwares, QTL Network 2.0 and Windows QTL Cartographer 2.5, were used to verify the QTL mapping result without considering the cytoplasmic effect and cytonuclear epistasis.
The results showed that 99 QTL controlling 12 traits were detected by CNQM method, and 53 of them were verified by different segregating populations or by different mapping methods. More than 10 QTL were detected in heading date, plant height, tassel branch number, tassel length, ear height and stem diameter, respectively. On average, half of them can be verified. The contribution of these QTL was over 10% of the phenotypic variation. Twenty four QTL were detected on Chromosome 7, in which eight of them had cytonuclear epistatic effects and eleven of them were verified by different mapping methods. However, only one QTL was detected on Chromosome 10. Thirty four of total 99 QTL showed significant cytonuclear epistatic effects. The size of cytonuclear epistasis had different altitudes in different traits. In addition, many QTL were found locating at the same marker interval. This implied the existence of pleiotropic QTL or tightly linked QTL.
引文
[1] Rieseberg L H, Sinervo B, Linder C R, Ungerer M C, Arias D M. Role of gene interactions in hybrid speciation: evidence from ancient and experimental hybrids. Science. 1996, 272(5262): 741-745
[2] Allard R W. Genetic basis of the evolution of adaptedness in plants. Euphytica. 1996, 92(1-2): 1-11
[3] Hua J, Xing Y, Wu W, Xu C, Sun X, Yu S, Zhang Q. Single-locus heterotic effects and dominance by dominance interactions can adequately explain the genetic basis of heterosis in an elite rice hybrid. Proceedings of the National Academy of Sciences. 2003, 100(5): 2574-2579
[4] Malmberg R L, Held S, Waits A, Mauricio R. Epistasis for fitness-related quantitative traits inArabidopsis thaliana grown in the field and in the greenhouse. Genetics. 2005, 171(4): 2013-2027
[5] Zeyl C, Andreson B, Weninck E. Nuclear-mitochondrial epistasis for fitness in Saccharomyces cerevisiae. International Journal of Organic Evolution. 2005, 59(4): 910-914
[6] Rand D M, Fry A, Sheldahl L. Nuclear-mitochondrial epistasis and drosophila aging: introgression of Drosophila simulans mtDNA modifies longevity in D. melanogaster nuclear backgrounds. Genetics. 2006, 172(1): 329-341
[7] Li Z K, Luo L J, Mei H W, Wang D L, Shu Q Y, Tabien R, Zhong D B, Ying C S, Stansel J W, Khush G S, Paterson A H. Overdominant epistatic loci are the primary genetic basis of inbreeding depression and heterosis in rice. I. Biomass and grain yield. Genetics. 2001, 158(4): 1737-1753
[8] Jasienski M, Ayala F J, Bazzaz F A. Phenotypic plasticity and similarity of DNA among genotypes of an annual plant. Heredity. 1997, 78 (2): 176-181
[9] Churchill G A, Airey D C, Allayee H, Angel J M, Attie A D, Beatty J, Beavis W D, Belknap J K, Bennett B, Berrettini W, Bleich A, Bogue M, Broman K W, Buck K J, Buckler E, Burmeister M, Chesler E J, Cheverud J M, Clapcote S, Cook M N, Cox R D, Crabbe J C, Crusio W E, Darvasi A, Deschepper C F, Doerge R W, Farber C R, Forejt J, Gaile D, Garlow S J, Geiger H, Gershenfeld H, Gordon T, Gu J, Gu W, de Haan G, Hayes N L, Heller C, Himmelbauer H, Hitzemann R, Hunter K, Hsu H C, Iraqi F A, Ivandic B, Jacob H J, Jansen R C, Jepsen K J, Johnson D K, Johnson T E, Kempermann G, Kendziorski C, Kotb M, Kooy R F, Llamas B, Lammert F, Lassalle J M, Lowenstein P R, Lu L, Lusis A, Manly K F, Marcucio R, Matthews D, Medrano J F, Miller D R, Mittleman G, Mock B A, Mogil J S, Montagutelli X, Morahan G, Morris D G, Mott R, Nadeau J H, Nagase H, Nowakowski R S, O'Hara B F, Osadchuk A V, Page G P, Paigen B, Paigen K, Palmer A A, Pan H J, Peltonen-Palotie L, Peirce J, Pomp D, Pravenec M, Prows D R, Qi Z, Reeves R H, Roder J, Rosen G D, Schadt E E, Schalkwyk L C, Seltzer Z, Shimomura K, Shou S, Sillanpaa M J, Siracusa L D, Snoeck H W, Spearow J L, Svenson K, Tarantino L M, Threadgill D, Toth L A, Valdar W, de Villena F P, Warden C, Whatley S, Williams R W, Wiltshire T, Yi N, Zhang D, Zhang M, Zou F. The Collaborative Cross, a community resource for the genetic analysis of complex traits. Nature Genetics. 2004, 36(11): 1133-1147
[10] Carlson C S, Eberle M A, Kruglyak L, Nickerson D A. Mapping complex disease loci in whole-genome association studies. Nature. 2004, 429(6990): 446-452
[11] Bamshad M, Wooding S, Salisbury B A, Stephens J C. Deconstructing the relationship between genetics and race. Nature Review Genetics. 2004, 5(8): 598-609
[12] Funalot B, Varenne O, Mas J L. A call for accurate phenotype definition in the study of complex disorders. Nature Genetics. 2004, 36(1): 3
[13] Darvasi A. Closing in on complex traits. Nature Genetics. 2006, 38(8): 861-862
[14] Glazier A M, Nadeau J H, Aitman T J. Finding genes that underlie complex traits. science. 2002, 298(5602): 2345-2349
[15] Weiss K M, Terwilliger J D. How many diseases does it take to map a gene with SNPs? Nature Genetics. 2000, 26(2): 151-157
[16] Yang Q, Khoury M J, Friedman J, Little J, Flanders W D. How many genes underlie theoccurrence of common complex diseases in the population? International Journal of Epidemiology. 2005, 34(5): 1129-1137
[17] Flint J, Valdar W, Shifman S, Mott R. Strategies for mapping and cloning quantitative trait genes in rodents. Nature Review Genetics. 2005, 6(4): 271-286
[18] Sax K. The association of size differences with seed-coat pattern and pigmentation in Phaseolus Vulgaris. Genetics. 1923, 8(6): 552-560
[19] Thoday J M. Location of Polygenes. Nature. 1961, 191: 368-370
[20] Spickett S G, Thoday J M. Regular responses to selection 3. Interaction between located polygenes. Genetical Research. 7: 96-121
[21] Lander E S, Botstein D. Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics. 1989, 121(1): 185-199
[22]惠大丰,姜长鉴,莫惠栋.数量性状基因图谱构建方法的比较.作物学报. 1997, 23(2): 129-136
[23] Weller J I. Maximum likelihood techniques for the mapping and analysis of quantitative trait loci with the aid of genetic markers. Biometrics. 1986, 42(3): 627-640
[24] Luo Z W, Kearsey M J. Maximum likelihood estimation of linkage between a marker gene and a quantitative locus. Heredity. 1989, 63(3): 401-408
[25] Luo Z W, Kearsey M J. Maximum likelihood estimation of linkage between a marker gene and a quantitative trait locus. II. Application to backcross and doubled haploid populations. Heredity. 1991 66(1): 117-124.
[26] Darvasi A, Weller J I. On the use of the moments method of estimation to obtain approximate maximum likelihood estimates of linkage between a genetic marker and a quantitative locus. Heredity. 1992, 68(1): 43-46
[27] Luo Z W, Woolliams J A. Estimation of genetic parameters using linkage between a marker gene and a locus underlying a quantitative character in F2 populations. Heredity. 1993, 70: 245-253
[28]周明全,章志宏,胡中立.植物QTL分析的理论研究进展.武汉植物学研究. 2001, 19(5): 428-436
[29] Kearsey M J, Hyne V. QTL analysis: a simple‘marker-regression’approach. Theoretical and Applied Genetics. 1994, 89(6): 698-702
[30] Hyne V, Kearsey M J. QTL analysis: further uses of‘marker regression’. Theoretical and Applied Genetics. 1995, 91(3): 471-476
[31] Charmet G, Cadalen T, Sourdille P, Bernard M. An extension of the‘marker regression’method to interactive QTL. Molecular Breeding. 1998, 4(1): 67-72
[32] Wu W R, Li W M. A new approach for mapping quantitative trait loci using complete genetic marker linkage maps. Theoretical and Applied Genetics. 1994, 89(5): 535-539
[33] Stuber C W, Edwards M D, Wendel J F. Molecular marker-facilitated investigations of quantitative trait loci in maize. II: Factors influencing yield and its component traits. Crop science. 1987, 27(4): 639-648
[34] Lebowitz R J, Soller M, Beckmann J S. Trait-based analyses for the detection of linkage between marker loci and quantitative trait loci in crosses between inbred lines. Theoretical and Applied Genetics. 1987, 73(4): 556-562
[35] Jensen J. Estimation of recombination parameters between a quantitative trait locus (QTL) and two marker gene loci. Theoretical and Applied Genetics. 1989, 78(5): 613-618
[36] Knapp S J, Bridges W C, Birkes D. Mapping quantitative trait loci using molecular marker linkage maps. Theoretical and Applied Genetics. 1990, 79(5): 583-592
[37] Paterson A H, DeVerna J W, Lanini B, Tanksley S D. Fine mapping of quantitative trait loci using selected overlapping recombinant chromosomes, in an interspecies cross of tomato. Genetics. 1990, 124(3): 735-742
[38] Ooijen J W. Accuracy of mapping quantitative trait loci in autogamous species. Theoretical and Applied Genetics. 1992, 84(7): 803-811
[39] Luo Z W, Kearsey M J. Interval mapping of quantitative trait loci in an F2 population. Heredity. 1992, 69: 236-242
[40] Haley C S, Knott S A. A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity. 1992, 69(4): 315-324
[41] Martínez O, Curnow R N. Estimating the locations and the sizes of the effects of quantitative trait loci using flanking markers. Theoretical and Applied Genetics. 1992, 85(4): 480-488
[42] Xu S. A Comment on the simple regression method for interval mapping. Genetics. 1995, 141(4): 1657-1659
[43] Xu S. Further investigation on the regression method of mapping quantitative trait loci. Heredity. 1998, 80(3): 364-373
[44]胡中立,章志宏,张修富. DH群体中区间分子标记定位QTL的相关方法.生物数学学报. 1998, 13(3): 365-371
[45]李宏.基于三点测交的双标记-QTL基因定位的相关方法.生物数学学报. 2000, 15 (1): 93-98
[46] Zeng Z B. Theoretical basis for separation of multiple linked gene effects in mapping quantitative trait loci. Proceedings of the National Academy of Sciences. 1993, 90(23): 10972-10976
[47] Rodolphe F, Lefort M. A Multi-marker model for detecting chromosomal segments displaying QTL activity. Genetics. 1993, 134(4): 1277-1288
[48] Jansen R C. Interval mapping of multiple quantitative trait loci. Genetics. 1993, 135(1): 205-211
[49] Jansen R C. Controlling the type I and type II errors in mapping quantitative trait loci. Genetics. 1994, 138(3): 871-881
[50] Jansen R C, Stam P. High resolution of quantitative traits into multiple loci via interval mapping. Genetics. 1994, 136(4): 1447-1455
[51] Zeng Z B. Precision mapping of quantitative trait loci. Genetics. 1994, 136(4): 1457-1468
[52] Jiang C, Zeng Z B. Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics. 1995, 140(3): 1111-1127
[53] Zhu J, Weir B S. Mixed model approaches for genetic analysis of quantitative traits. Advanced topics in biomathematics: Proceedings of the International Conference on Mathematical Biology Singapore: World Scientific Publ. Co., 1998, 321–330.
[54]胡中立.对QTL复合区间作图法的一点改进.武汉大学学报:理学版. 2000, 46(6): 766-768
[55] Wu W R, Li W M, Lu H R. Composite interval mapping of quantitative trait loci based on least squares estimation. Journal of Fujian Agricultural University. 1996, 25(4): 394-399
[56] Wu W R, Li W M, Tang D Z, Lu H R, Worland A J. Time-related mapping of quantitative trait loci underlying tiller number in rice. Genetics. 1999, 151(1): 297-303
[57]吴为人,李维明.基于性状-标记回归的QTL区间测验方法.遗传. 2001, 23(2): 143-146
[58]徐辰武,顾世梁.构建数量性状基因图的双侧标记基因型均值回归法.扬州大学学报:自然科学版. 1999, 2(1): 42-47
[59] Li H, Ye G, Wang J. A modified algorithm for the improvement of composite interval mapping. Genetics. 2007, 175(1): 361-374
[60] Li H, Ribaut J M, Li Z, Wang J. Inclusive composite interval mapping (ICIM) for digenic epistasis of quantitative traits in biparental populations. Theoretical and Applied Genetics. 2008, 116(2): 243-260
[61] Kao C H, Zeng Z B, Teasdale R D. Multiple interval mapping for quantitative trait loci. Genetics. 1999, 152(3): 1203-1216
[62] Kao C H, Zeng Z B. General formulas for obtaining the mles and the asymptotic variance-covariance matrix in mapping quantitative trait loci when using the EM algorithm. Biometrics. 1997, 53(2): 653-665
[63] Zeng Z B, Kao C H, Basten C J. Estimating the genetic architecture of quantitative traits. Genetical Research. 1999, 74(3): 279-289
[64] Wang H, Zhang Y, Li X, Masinde G L, Mohan S, Baylink D J, Xu S. Bayesian Shrinkage Estimation of Quantitative Trait Loci Parameters. Genetics. 2005, 170(1): 465-480
[65] Smith A, Roberts G. Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods. Journal of the Royal Statistical Society(B). 1993, 55(1): 23-24
[66] Chib S, Greenberg E. Understanding the Metropolis-Hastings algorithm. The American Statistician. 1995, 49(4): 327-335
[67] Hoeschele I. Mapping quantitative trait loci in outbred pedigrees. Handbook of Statistical Genetics. Balding D, Bishop M, Cannings C(eds), Wiley. 2001: 599-644
[68] Carlin B P, Louis T A. Bayes and Empirical Bayes methods for data analysis. 2nd edition. Chapman & Hall. London, UK. 2000
[69] Hoeschele I, VanRaden P M. Bayesian analysis of linkage between genetic markers and quantitative trait loci. I. Prior knowledge. Theoretical and Applied Genetics. 1993, 85(8): 953-960
[70] Hoeschele I, VanRaden P M. Bayesian analysis of linkage between genetic markers and quantitative trait loci. II. Combining prior knowledge with experimental evidence. Theoretical and Applied Genetics. 1993, 85(8): 946-952
[71] Thaller G, Hoeschele I. A Monte Carlo method for Bayesian analysis of linkage between single markers and quantitative trait loci. I. Methodology. Theoretical and Applied Genetics. 1996, 93(7): 1161-1166
[72] Thaller G, Hoeschele I. A Monte Carlo method for Bayesian analysis of linkage between single markers and quantitative trait loci. II. A simulation study. Theoretical and Applied Genetics. 1996, 93(7): 1167-1174
[73] Uimari P, Thaller G, Hoeschele I. The use of multiple markers in a Bayesian method formapping quantitative trait loci. Genetics. 1996, 143(4): 1831-1842
[74] Uimari P, Hoeschele I. Mapping-linked quantitative trait loci using bayesian analysis and Markov chain Monte Carlo algorithms. Genetics. 1997, 146(2): 735-743
[75] Satagopan J M, Yandell B S. Estimating the number of quantitative trait loci via bayesian model determination. Chicago. 1996:
[76] Stephens D A, Smith A F. Bayesian inference in multipoint gene mapping. Annals of Human Genetics. 1993, 57(1): 65-82
[77] Green P J. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika. 1995, 82(4): 711-732
[78] Hurme P, Sillanp?? M J, Arjas E, Repo T, Savolainen O. Genetic basis of climatic adaptation in scots pine by bayesian quantitative trait locus analysis. Genetics. 2000, 156(3): 1309-1322
[79] Bink M, Uimari P, Sillanp?? M, Janss L, Jansen R. Multiple QTL mapping in related plant populations via a pedigree-analysis approach. Theoretical and Applied Genetics. 2002, 104(5): 751-762
[80] Sen S, Churchill G A. A statistical framework for quantitative trait mapping. Genetics. 2001, 159(1): 371-387
[81] Yi N, Xu S. Mapping quantitative trait loci with epistatic effects. Genetical Research. 2002, 79: 185-198
[82] Satagopan J M, Yandell B S, Newton M A, Osborn T C. A Bayesian approach to detect quantitative trait loci using Markov chain Monte Carlo. Genetics. 1996, 144(2): 805-816
[83] Yi N, Diament A, Chiu S, Kim K, Allison D B, Fisler J S, Warden C H. Characterization of epistasis influencing complex spontaneous obesity in the BSB Model. Genetics. 2004, 167(1): 399-409
[84] Yi N, Yandell B S, Churchill G A, Allison D B, Eisen E J, Pomp D. Bayesian model selection for genome-wide epistatic quantitative trait loci analysis. Genetics. 2005, 170(3): 1333-1344
[85] Yi N, Chiu S, Allison D B, Fisler J S, Warden C H. Epistatic interaction between two nonstructural loci on chromosomes 7 and 3 influences hepatic lipase activity in BSB mice. Journal of Lipid Research. 2004, 45(11): 2063-2070
[86] Xu S. Estimating polygenic effects using markers of the entire genome. Genetics. 2003, 163(2): 789-801
[87] Meuwissen T H E, Hayes B J, Goddard M E. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001, 157(4): 1819-1829
[88] Zhang Y-M, Xu S. Mapping quantitative trait loci in F2 incorporating phenotypes of F3 progeny. Genetics. 2004, 166(4): 1981-1993
[89] Zhang Y M, Xu S. A penalized maximum likelihood method for estimating epistatic effects of QTL. Heredity. 2005, 95: 96-104
[90] Xu, S. An empirical Bayes method for estimating epistatic effects of quantitative trait loci. Biometrics. 2007, 63: 513–521
[91] Xu S, Jia Z. Genome-wide analysis of epistatic effects for quantitative traits in barley. Genetics. 2007, 175(4): 1955-1963
[92] Yi N, Zinniel D K, Kim K, Eisen E J, Bartolucci A, Allison D B, Pomp D. Bayesian analyses of multiple epistatic QTL models for body weight and body composition in mice. GeneticResearch. 2006, 87(1): 45-60
[93] Jennings H S. The numerical results of diverse systems of breeding, with respect to two pairs of characters, linked or independent, with special relation to the effects of linkage. Genetics 1917, 2(2): 97-154
[94] Raymond J P, David G, Jeffrey C L. Effects of worldwide population subdivision on ALDH2 linkage disequilibrium. Genome Research. 1999, 9(9): 844-852
[95] Flint-Garcia S A, Thornsberry J M, Buckler E S t. Structure of linkage disequilibrium in plants. Annual Review of Plant Biology, 2003, 54: 357-374
[96] Gupta P K, Rustgi S, Kulwal P L. Linkage disequilibrium and association studies in higher plants: present status and future prospects. Plant Molecular Biology. 2005, 57(4): 461-485
[97] Thornsberry J M, Goodman M M, Doebley J, Kresovich S, Nielsen D, Buckler E S t. Dwarf8 polymorphisms associate with variation in flowering time. Nature Genetics. 2001, 28(3): 286-289
[98] Olsen K M, Halldorsdottir S S, Stinchcombe J R, Weinig C, Schmitt J, Purugganan M D. Linkage disequilibrium mapping of Arabidopsis CRY2 flowering time alleles. Genetics. 2004, 167(3): 1361-1369
[99] Luo Z W. Detecting linkage disequilibrium between a polymorphic marker locus and a trait locus in natural population. Heredity. 1998, 80(2): 198-208
[100] Luo Z W, Suhai S. Estimating linkage disequilibrium between a polymorphic marker locus and a trait locus in natural populations. Genetics. 1999, 151(1): 359-371
[101] Schena M, Shalon D, Davis R W, Brown P O. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science. 1995, 270(5235): 467-470
[102] Jansen R C, Nap J P. Genetical genomics: the added value from segregation. Trends in Genetics. 2001, 17(7): 388–391
[103] Kendziorski C M, Chen M, Yuan M, Lan H, Attie A D. Statistical methods for expression quantitative trait loci (eQTL) mapping. Biometrics. 2006, 62(1): 19-27
[104] Dupuis J, Siegmund D. Statistical methods for mapping quantitative trait loci from a dense set of markers. Genetics. 1999, 151(1): 373-386
[105] Schadt E E, Monks S A, Drake T A, Lusis A J, Che N, Colinayo V, Ruff T G, Milligan S B, Lamb J R, Cavet G. Genetics of gene expression surveyed in maize, mouse and man. Nature. 2003, 422(6929): 297-302
[106] Storey J D, Taylor J E, Siegmund D. Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach. Journal of the Royal Statistical Society Series B(Statistical Methodology). 2004, 66(1): 187-205
[107] Qu Y, Xu S. Quantitative trait associated microarray gene expression data analysis. Molecular Biology and Evolution. 2006, 23(8): 1558-1573
[108] Jansen R C, Ooijen J W V, Stam P, Lister C, Dean C. Genotype-by-Environment interaction in genetic mapping of multiple quantitative trait loci. Theoretical and Applied Genetics. 1995, 91: 33-37
[109] Tinker N A, Mather D E. Methods for QTL analysis with progeny replicated in multiple environments. Journal of Agricultural Genomics. 1995, 1: 1
[110] Sari-Gorla M, Calinski T, Kaczmarek Z, Krajewski P. Detection of QTL×environmentinteraction in maize by a least squares interval mapping method. Heredity. 1997, 78: 146-157
[111] Wang D L, Zhu J, Li Z K, Paterson A H. Mapping QTLs with epistatic effects and QTL environment interactions by mixed linear model approaches. Theoretical and Applied Genetics. 1999, 99: 1255-1264
[112] Stratton D A, Haley C S, Knott S A. Reaction normfunctions and QTL2environment interactions for flowering time in Arabidopsis thaliana. Heredity. 1998, 81(2): 144-155
[113] Korol A B, Ronin Y I, Nevo E. Approximate analysis of QTL-environment interaction with no limits on the number of environments. Genetics. 1998 148: 2015-2028
[114] Crossa J, Burgueno J, Cornelius P L, McLaren G, Trethowan R, Krishnamachari A. Modeling genotype×environment interaction using additive genetic covariances of relatives for predicting breeding values of wheat genotypes. Crop Science. 2006, 46(4): 1722-1733
[115] Xu S. Mapping quantitative trait loci using multiple families of line crosses. Genetics. 1998, 148(1): 517-524
[116] Xu S. Mapping quantitative trait loci using four-way crosses. Genetical Research. 1996, 68: 175-181
[117] Broman K W. The genomes of recombinant inbred lines. Genetics. 2005, 169(2): 1133-1146
[118] Li R, Lyons M A, Wittenburg H, Paigen B, Churchill G A. Combining data from multiple inbred line crosses improves the power and resolution of quantitative trait loci mapping. Genetics. 2005, 169(3): 1699-1709
[119] Verhoeven K J, Jannink J L, McIntyre L M. Using mating designs to uncover QTL and the genetic architecture of complex traits. Heredity. 2006, 96(2): 139-149
[120] Xu S, Atchley W R. Mapping quantitative trait loci for complex binary diseases using line crosses. Genetics. 1996, 143(3): 1417-1424
[121] Wright S. An analysis of variability in number of digits in an inbred strain of guinea pigs. Genetics. 1934, 19(6): 506-536
[122] Reba A. Comparison of methods for regression interval mapping in QTL analysis with non-normal traits. Genetical Research. 2000, 69(01): 69-74
[123] Yi N, Xu S. Bayesian mapping of quantitative trait loci for complex binary traits. Genetics. 2000, 155(3): 1391-1403
[124] Rao S, Xu S. Mapping quantitative trait loci for ordered categorical traits in four-way crosses. Heredity. 1998, 81(2): 214-224
[125] Xu C, Li Z, Xu S. Joint mapping of quantitative trait loci for multiple binary characters. Genetics. 2005, 169(2): 1045-1059
[126] Luo L, Zhang Y M, Xu S. A quantitative genetics model for viability selection. Heredity. 2005, 94: 347-355
[127] Nichols R A. Quantitative genetics focus issue. Heredity. 2005, 94(3): 273-274
[128] Wu R, Lin M, Box P. Functional mapping-how to map and study the genetic architecture of dynamic complex traits. Nature Reviews Genetics. 2006, 7: 229-237
[129] Yang R, Tian Q, Xu S. Mapping quantitative trait loci for longitudinal traits in line crosses. Genetics. 2006, 173(4): 2339-2356
[130] Xi Z Y, He F H, Zeng R Z, Zhang Z M, Ding X H, Li W T, Zhang G Q. Development of a wide population of chromosome single-segment substitution lines in the genetic backgroundof an elite cultivar of rice (Oryza sativa L.). Genome Biology. 2006, 49(5): 476-484
[131] Ebitani T, Takeuchi Y, Nonoue Y, Yamamoto T, Takeuchi K, Yano M. Construction and evaluation of chromosome segment substitution lines carrying overlapping chromosome segments of indica rice cultivar Kasalath in a genetic background of japonica elite cultivar Koshihikari. Breeding Science. 2005, 55(1): 65-73
[132] Wan X Y, Wan J M, Su C C, Wang C M, Shen W B, Li J M, Wang H L, Jiang L, Liu S J, Chen L M, Yasui H, Yoshimura A. QTL detection for eating quality of cooked rice in a population of chromosome segment substitution lines. Theoretical and Applied Genetics. 2004, 110(1): 71-79
[133] Singer J B, Hill A E, Burrage L C, Olszens K R, Song J, Justice M, O'Brien W E, Conti D V, Witte J S, Lander E S, Nadeau J H. Genetic dissection of complex traits with chromosome substitution strains of mice. Science. 2004, 304(5669): 445-448
[134] Tanksley S D, Nelson J C. Advanced backcross QTL analysis: A method for the simultaneous discovery and transfer of valuable QTLs from unadapted germplasm into elite breeding lines. Theoretical and Applied Genetics. 1996, 92(2): 191-203
[135] Salvi S, Tuberosa R. To clone or not to clone plant QTLs: present and future challenges. Trends in Plant Science. 2005, 10(6): 297-304
[136] Lin H X, T Y, Sasaki T, Yano M. Characterization and detection of epistatic interactions of 3 QTLs, Hd1, Hd2, and Hd3, controlling heading date in rice using nearly isogenic lines. Theoretical and Applied Genetics. 2000, 101(7): 1021-1028
[137] Darvasi A, Soller M. Advanced intercross lines, an experimental population for fine genetic mapping. Genetics. 1995, 141(3): 1199-1207
[138] Iraqi F, Clapcott S J, Kumari P, Haley C S, Kemp S J, Teale A J. Fine mapping of trypanosomiasis resistance loci in murine advanced intercross lines. Mammalian Genome. 2000, 11(8): 645-648
[139] Wang X, Le Roy I, Nicodeme E, Li R, Wagner R, Petros C, Churchill G A, Harris S, Darvasi A, Kirilovsky J, Roubertoux P L, Paigen B. Using advanced intercross lines for high-resolution mapping of HDL cholesterol quantitative trait loci. Genome Research. 2003, 13(7): 1654-1664
[140] Vogel G. Scientists dream of 1001 complex mice. Science. 2003, 301(5632): 456-457
[141] Valdar W, Flint J, Mott R. Simulating the collaborative cross: power of quantitative trait loci detection and mapping resolution in large sets of recombinant inbred strains of mice. Genetics. 2006, 172(3): 1783-1797
[142] Valdar W, Solberg L C, Gauguier D, Burnett S, Klenerman P, Cookson W O, Taylor M S, Rawlins J N, Mott R, Flint J. Genome-wide genetic association of complex traits in heterogeneous stock mice. Nature Genetics. 2006, 38(8): 879-887
[143] Krakowsky M D, Lee M, Coors J G. Quantitative trait loci for cell wall components in recombinant inbred lines of maize (Zea mays L.) II: leaf sheath tissue. Theoretical and Applied Genetics. 2006, 112(4): 717-726
[144] You A, Lu X, Jin H, Ren X, Liu K, Yang G, Yang H, Zhu L, He G. Identification of quantitative trait loci across recombinant inbred lines and testcross populations for traits of agronomic importance in rice. Genetics. 2006, 172(2): 1287-1300
[145] el-Lithy M E, Bentsink L, Hanhart C J, Ruys G J, Rovito D, Broekhof J L, van der Poel H J, van Eijk M J, Vreugdenhil D, Koornneef M. New arabidopsis recombinant inbred line populations genotyped using SNPWave and their use for mapping flowering-time quantitative trait loci. Genetics. 2006, 172(3): 1867-1876
[146] Reiter R S, Williams J G, Feldmann K A, Rafalski J A, Tingey S V, Scolnik P A. Global and local genome mapping in Arabidopsis thaliana by using recombinant inbred lines and random amplified polymorphic DNAs. Proceedings of the National Academy of Sciences . 1992, 89(4): 1477-1481
[147] Darvasi A. Interval-specific congenic strains (ISCS): an experimental design for mapping a QTL into a 1-centimorgan interval. Mammalian Genome. 1997, 8(3): 163-167
[148] Valdar W S, Flint J, Mott R. QTL fine-mapping with recombinant-inbred heterogeneous stocks and in vitro heterogeneous stocks. Mammalian Genome. 2003, 14(12): 830-838
[149] Shifman S, Darvasi A. Mouse inbred strain sequence information and yin-yang crosses for quantitative trait locus fine mapping. Genetics. 2005, 169(2): 849-854
[150] Tuinstra M R, Ejeta G, Goldsbrough P B. Heterogeneous inbred family (HIF) analysis: a method for developing near-isogenic lines that differ at quantitative trait loci. Theoretical and Applied Genetics. 1997, 95(5-6): 1005-1011
[151] Darvasi A. Experimental strategies for the genetic dissection of complex traits in animal models. Nature Genetics. 1998, 18(1): 19-24
[152] Xu S. Mapping quantitative trait loci using four-way crosses. Genetical Research. 1996, 68(2): 175-181
[153] Frary A, Nesbitt T C, Grandillo S, Knaap E, Cong B, Liu J, Meller J, Elber R, Alpert K B, Tanksley S D. fw2.2: a quantitative trait locus key to the evolution of tomato fruit size. Science. 2000, 289(5476): 85-88
[154] Yano M, Katayose Y, Ashikari M, Yamanouchi U, Monna L, Fuse T, Baba T, Yamamoto K, Umehara Y, Nagamura Y, Sasaki T. Hd1, a major photoperiod sensitivity quantitative trait locus in rice, is closely related to the arabidopsis flowering time gene CONSTANS. Plant Cell. 2000, 12(12): 2473-2484
[155] Takahashi Y, Shomura A, Sasaki T, Yano M. Hd6, a rice quantitative trait locus involved in photoperiod sensitivity, encodes the alpha subunit of protein kinase CK2. Proceedings of the National Academy of Sciences . 2001, 98(14): 7922-7927
[156] Kojima S, Takahashi Y, Kobayashi Y, Monna L, Sasaki T, Araki T, Yano M. Hd3a, a rice ortholog of the Arabidopsis FT gene, promotes transition to flowering downstream of Hd1 under short-day conditions. Plant Cell and Physiology. 2002, 43(10): 1096-1105
[157] Doi K, Izawa T, Fuse T, Yamanouchi U, Kubo T, Shimatani Z, Yano M, Yoshimura A. Ehd1, a B-type response regulator in rice, confers short-day promotion of flowering and controls FT-like gene expression independently of Hd1. Genes and Development. 2004, 18(8): 926-936
[158] Ashikari M, Sakakibara H, Lin S, Yamamoto T, Takashi T, Nishimura A, Angeles E R, Qian Q, Kitano H, Matsuoka M. Cytokinin oxidase regulates rice grain production. Science. 2005, 309(5735): 741-745
[159] Ishimaru K, Ono K, Kashiwagi T. Identification of a new gene controlling plant height in rice using the candidate-gene strategy. Planta. 2004, 218(3): 388-395
[160] Nishimura A, Ashikari M, Lin S, Takashi T, Angeles E R, Yamamoto T, Matsuoka M. Isolation of a rice regeneration quantitative trait loci gene and its application to transformation systems. Proceedings of the National Academy of Sciences . 2005, 102(33): 11940-11944
[161] Ren Z H, Gao J P, Li L G, Cai X L, Huang W, Chao D Y, Zhu M Z, Wang Z Y, Luan S, Lin H X. A rice quantitative trait locus for salt tolerance encodes a sodium transporter. Nature Genetics. 2005, 37(10): 1141-1146
[162] Doebley J, Stec A, Gustus C. Teosinte branched1 and the origin of maize: evidence for epistasis and the evolution of dominance. Genetics. 1995, 141(1): 333-346
[163] Fridman E, Carrari F, Liu Y S, Fernie A R, Zamir D. Zooming in on a quantitative trait for tomato yield using interspecific introgressions. Science. 2004, 305(5691): 1786-1789
[164] Fridman E, Pleban T, Zamir D. A recombination hotspot delimits a wild-species quantitative trait locus for tomato sugar content to 484 bp within an invertase gene. Proceedings of the National Academy of Sciences . 2000, 97(9): 4718-4723
[165] Cong B, Liu J, Tanksley S D. Natural alleles at a tomato fruit size quantitative trait locus differ by heterochronic regulatory mutations. Proceedings of the National Academy of Sciences . 2002, 99(21): 13606-13611
[166] Liu J, Van Eck J, Cong B, Tanksley S D. A new class of regulatory genes underlying the cause of pear-shaped tomato fruit. Proceedings of the National Academy of Sciences. 2002, 99(20): 13302-13306
[167] El-Din El-Assal S, Alonso-Blanco C, Peeters A J, Raz V, Koornneef M. A QTL for flowering time in Arabidopsis reveals a novel allele of CRY2. Nature Genetics. 2001, 29(4): 435-540
[168] Werner J D, Borevitz J O, Warthmann N, Trainer G T, Ecker J R, Chory J, Weigel D. Quantitative trait locus mapping and DNA array hybridization identify an FLM deletion as a cause for natural flowering-time variation. Proceedings of the National Academy of Sciences. 2005, 102(7): 2460-2465
[169] Maloof J N, Borevitz J O, Dabi T, Lutes J, Nehring R B, Redfern J L, Trainer G T, Wilson J M, Asami T, Berry C C, Weigel D, Chory J. Natural variation in light sensitivity of Arabidopsis. Nature Genetics. 2001, 29(4): 441-446
[170] Kroymann J, Donnerhacke S, Schnabelrauch D, Mitchell-Olds T. Evolutionary dynamics of an Arabidopsis insect resistance quantitative trait locus. Proceedings of the National Academy of Sciences. 2003, 100(2): 14587-14592
[171] Doebley J, Stec A, Hubbard L. The evolution of apical dominance in maize. Nature. 1997, 386(6624): 485-488
[172] Montgomery M K, Fire A. Double-stranded RNA as a mediator in sequence-specific genetic silencing and co-suppression. Trends in Genetics. 1998, 14(7): 255-258
[173] Chuang C, Meyerowitz E M. Specific and heritable genetic interference by double-stranded RNA in Arabidopsis thaliana. Proceedings of the National Academy of Sciences. 2000, 97(9): 4985-4990
[174] Price A H. Believe it or not, QTLs are accurate! Trends in Plant Science. 2006, 11(5): 213-216
[175] Nadeau J H, Frankel W N. The roads from phenotypic variation to gene discovery: mutagenesis versus QTLs. Nature Genetics. 2000, 25(4): 381-384
[176] Morton N E. Linkage disequilibrium maps and association mapping. Journal of ClinicalInvestigation. 2005, 115(6): 1425-1430
[177] Damerval C, Maurice A, Josse J M, de Vienne D. Quantitative trait loci underlying gene product variation: a novel perspective for analyzing regulation of genome expression. Genetics. 1994, 137(1): 289-301
[178] Schadt E E, Monks S A, Drake T A, Lusis A J, Che N, Colinayo V, Ruff T G, Milligan S B, Lamb J R, Cavet G, Linsley P S, Mao M, Stoughton R B, Friend S H. Genetics of gene expression surveyed in maize, mouse and man. Nature. 2003, 422(6929): 297-302
[179]朱军.遗传学(第三版).中国农业出版社. 2001
[180] Shinozaki K, Ohme M, Tanaka M, Wakasugi T, Hayashida N, Matsubayashi T, Zaita N, Chunwongse J, Obokata J, Yamaguchi-Shinozaki K, Ohto C, Torazawa K, Meng B Y, Sugita M, Deno H, Kamogashira T, Yamada K, Kusuda J, Takaiwa F, Kato A, Tohdoh N, Shimada H, Sugiura M. The complete nucleotide sequence of the tobacco chloroplast genome: its gene organization and expression. Embo Journal. 1986, 5(9): 2043-2049
[181] Oda K, Yamato K, Ohta E, Nakamura Y, Takemura M, Nozato N, Akashi K, Kanegae T, Ogura Y, Kohchi T, Ohyama K. Gene organization deduced from the complete sequence of liverwort Marchantia polymorpha mitochondrial DNA. A primitive form of plant mitochondrial genome. Journal of Molecular Biology. 1992, 223(1): 1-7
[182] Hiratsuka J, Shimada H, Whittier R, Ishibashi T, Sakamoto M, Mori M, Kondo C, Honji Y, Sun C R, Meng B Y, Li Y, Kanno A, Nishizawa Y, Hirai A, Shinozaki K and Sugiura M. The complete sequence of the rice (Oryza sativa) chloroplast genome: intermolecular recombination between distinct tRNA genes accounts for a major plastid DNA inversion during the evolution of the cereals. Molecular Genetics and Genomics. 1989, 217(2-3): 185-194
[183] Cui L, Veeraraghavan N, Richter A, Wall K, Jansen R K, Leebens-Mack J, Makalowska I, dePamphilis C W. ChloroplastDB: the chloroplast genome database. Nucleic Acids Research. 2006, 34: D692-696
[184] Shahid Masood M, Nishikawa T, Fukuoka S, Njenga P K, Tsudzuki T, Kadowaki K. The complete nucleotide sequence of wild rice (Oryza nivara) chloroplast genome: first genome wide comparative sequence analysis of wild and cultivated rice. Gene. 2004, 340(1): 133-139
[185] Tang J, Xia H, Cao M, Zhang X, Zeng W, Hu S, Tong W, Wang J, Wang J, Yu J, Yang H, Zhu L. A comparison of rice chloroplast genomes. Plant Physiology. 2004, 135(1): 412-420
[186]邢少辰, Clarke J.叶绿体基因组研究进展.生物化学与生物物理进展. 2008, 35(1): 21-28
[187] Raghavendra A S, Reumann S, Heldt H W. Participation of mitochondrial metabolism in photorespiration. Reconstituted system of peroxisomes and mitochondria from spinach leaves. Plant Physiology. 1998, 116(4): 1333-1337
[188] Raubeson L A, Jansen R K. Chloroplast genomes of plants. In:Henry R J ed. Plant Diversity and Evolution: Genotypic and Phenotypic Variation in Higher Plants. UK:CABI publishing. 2005: 45-68
[189] Bedbrook J R, Kolodner R, Bogorad L. Zea mays chloroplast ribosomal RNA genes are part of a 22,000 base pair inverted repeat. Cell. 1977, 11(4): 739-749
[190] Relle M, Furst P, Wild A. Short duplication in a cDNA clone of the rbcL gene from Picea abies. Plant Physiology. 1995, 107(3): 1037-1038
[191] Yohn C B, Cohen A, Rosch C, Kuchka M R, Mayfield S P. Translation of the chloroplast psbA mRNA requires the nuclear-encoded poly(A)-binding protein, RB47. Journal of Cell Biology. 1998, 142(2): 435-442
[192] Efimov V A, Andreeva A V, Reverdatto S V, Chakhmakhcheva O G. Nucleotide sequence of the barley chloroplast psbD gene for the D2 protein of photosystem II. Nucleic Acids Research. 1988, 16(12): 5686
[193] Anderson S, Bankier A T, Barrell B G, de Bruijn M H L, Coulson A R D, J, Eperson I C, Nierlich D C, Roe B A, Sanger F, Schreier P H, Smith A J H, Staden R, Young I G. Sequence and organization of the human mitochondrial genome. Nature. 1981, (457-464):
[194] Oda K, Yamato K, Ohta E, Nakamura Y, Takemura M, Nozato N, Akashi K, Kanegae T, Ogura Y, Kohchi T, Ohyama K. Gene organization deduced from the complete sequence of liverwort Marchantia polymorpha mitochondrial DNA. A primitive form of plant mitochondrial genome. Journal of Molecular Biology 1992, 223(1): 1-7
[195] Warden C H, Yi N J, Fisler J. Epistasis among genes is a universal phenomenon in obesity: Evidence from rodent models. Nutrition. 2004, 20(1): 74-77
[196] Moore J H. The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Human Heredity. 2003, 56(1-3): 73-82
[197] Kao C H, Zeng Z B. Modeling epistasis of quantitative trait loci using Cockerham's model. Genetics. 2002, 160(3): 1243-1261
[198] Gauderman W J, Thomas D C. The role of interacting determinants in the localization of genes. Advances in Genetics Incorporating Molecular Genetic Medicine. 2001, 42: 393-412
[199] Clark A G. Limits to prediction of phenotype from knowledge of genotypes. In: Clegg M T, eds. Limits to knowledge in evolutionary genetics. New York: Kluwer Academic/Penum Publishers. 2000
[200] Albuquerque L G, Keown J F, Van Vleck L D. Variances of direct genetic effects, maternal genetic effects, and cytoplasmic inheritance effects for milk yield, fat yield, and fat percentage. Journal of Dairy Science. 1998, 81(2): 544-549
[201] Allen J O. Effect of teosinte cytoplasmic genomes on maize phenotype. Genetics. 2005, 169(2): 863-880
[202] Meng F R, Ni Z F, Wu L M, Xie X D, Wang Z G, Sun Q X. Differential gene expression between reciprocal cross fertilized gernels and their parents during the early stages of seed development in wheat. Acta Agronomica Sinica. 2005, 31(1): 119-123
[203] Roubertoux P L, Sluyter F, Carlier M, Marcet B, Maarouf-Veray F, Cherif C, Marican C, Arrechi P, Godin F, Jamon M, Verrier B, Cohen-Salmon C. Mitochondrial DNA modifies cognition in interaction with the nuclear genome and age in mice. Nature Genetics. 2003, 35(1): 65-69
[204] Rand D M, Haney R A, Fry A J. Cytonuclear coevolution: the genomics of cooperation. Trends Ecol Evol. 2004, 19(12): 645-653
[205] Tang Z, Wang X, Hu Z, Yang Z, Xu C. Genetic dissection of cytonuclear epistasis in line crosses. Genetics. 2007, 177(1): 669-672
[206] Tibshirani R. Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society B. 1996, 58: 267-288
[207] Fan J, Li R. Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association 2001, 96: 1348-1360
[208] Omholt S W, Plahte E, Oyehaug L, Xiang K. Gene regulatory networks generating the phenomena of additivity, dominance and epistasis. Genetics. 2000, 155(2): 969-980
[209] Eta-Ndu J T, Openshaw S J. Epistasis for grain yield in two F2 populations of maize. Crop Science. 1999, 39(2): 346-352
[210] Carlborg O, Haley C S. Epistasis: too often neglected in complex trait studies? Nature Review Genetics. 2004, 5(8): 618-625
[211] Wyrick J J, Young R A. Deciphering gene expression regulatory networks. Current Opinion in Genetics and Development. 2002, 12(2): 130-136
[212] Tsai H K, Lu H H, Li W H. Statistical methods for identifying yeast cell cycle transcription factors. Proceedings of the National Academy of Sciences. 2005, 102(38): 13532-13537
[213] Drees B L, Thorsson V, Carter G W, Rives A W, Raymond M Z, Avila-Campillo I, Shannon P, Galitski T. Derivation of genetic interaction networks from quantitative phenotype data. Genome Biology 2005, 6(4): R38
[214] Johan D Peleman, Jeroen Rouppe van der Voort. Breeding by design. Trends in Plant Science. 2003, 8(7): 330-334
[1] Jones D F. Dominance of linked factors as a means of accounting for heterosis. Genetics. 1917, 2(5): 466-479
[2] East E M. Heterosis. Genetics. 1936, 4(21): 375-397
[3] Birchler J A, Auger D L, Riddle N C. In search of the molecular basis of heterosis. Plant Cell. 2003, 15(10): 2236-2239
[4] Hua J, Xing Y, Wu W, Xu C, Sun X, Yu S, Zhang Q F. Single-locus heterotic effects and dominance by dominance interactions can adequately explain the genetic basis of heterosis in an elite rice hybrid. Proceedings of the National Academy of Sciences. 2003, 100(5): 2574-2579
[5] Lu H, Romero-Severson J, Bernardo R. Genetic basis of heterosis explored by simple sequence repeat markers in a random-mated maize population. Theoretical and Applied Genetics. 2003, 107(3): 494-502
[6] Omholt S W, Plahte E, Oyehaug L, Xiang K. Gene regulatory networks generating the phenomena of additivity, dominance and epistasis. Genetics. 2000, 155(2): 969-980
[7] Yu S B, Li J X, Xu C G, Tan Y F, Gao Y J, Li X H, Zhang Q, Maroof M A. Importance of epistasis as the genetic basis of heterosis in an elite rice hybrid. Proceedings of the National Academy of Sciences. 1997, 94(17): 9226-9231
[8] Li Z K, Luo L J, Mei H W, Wang D L, Shu Q Y, Tabien R, Zhong D B, Ying C S, Stansel J W, Khush G S, Paterson A H. Overdominant epistatic loci are the primary genetic basis ofinbreeding depression and heterosis in rice. I. Biomass and grain yield. Genetics. 2001, 158(4): 1737-1753
[9] Malmberg R L, Held S, Waits A, Mauricio R. Epistasis for fitness-related quantitative traits in Arabidopsis thaliana grown in the field and in the greenhouse. Genetics. 2005, 171(4): 2013-2027
[10] Eta-Ndu J T, Openshaw S J. Epistasis for grain yield in two F2 populations of maize. Crop Science. 1999, 39(2): 346-352
[11] Allen J O. Effect of teosinte cytoplasmic genomes on maize phenotype. Genetics. 2005, 169(2): 863-880
[12] Albuquerque L G, Keown J F, Van Vleck L D. Variances of direct genetic effects, maternal genetic effects, and cytoplasmic inheritance effects for milk yield, fat yield, and fat percentage. Journal of Dairy Science. 1998, 81(2): 544-549
[13] Meng F R, Ni Z F, Wu L M, Xie X D, Wang Z G, Sun Q X. Differential gene expression between reciprocal cross fertilized gernels and their parents during the early stages of seed development in wheat. Acta Agronomica Sinica. 2005, 31(1): 119-123
[14] Rand D M, Fry A, Sheldahl L. Nuclear-mitochondrial epistasis and drosophila aging: introgression of Drosophila simulans mtDNA modifies longevity in D. melanogaster nuclear backgrounds. Genetics. 2006, 172(1): 329-341
[15] Zeyl C, Andreson B, Weninck E. Nuclear-mitochondrial epistasis for fitness in Saccharomyces cerevisiae. Evolution Int J Org Evolution. 2005, 59(4): 910-914
[16] Roubertoux P L, Sluyter F, Carlier M, Marcet B, Maarouf-Veray F, Cherif C, Marican C, Arrechi P, Godin F, Jamon M, Verrier B, Cohen-Salmon C. Mitochondrial DNA modifies cognition in interaction with the nuclear genome and age in mice. Nature Genetics. 2003, 35(1): 65-69
[17] Rand D M, Haney R A, Fry A J. Cytonuclear coevolution: the genomics of cooperation. Trends in Ecology & Evolution. 2004, 19(12): 645-653
[18] Carlborg O, Haley C S. Epistasis: too often neglected in complex trait studies? Nature Reviews Genetics. 2004, 5(8): 618-625
[19] Xu C, He X, Xu S. Mapping quantitative trait loci underlying triploid endosperm traits. Heredity. 2003, 90(3): 228-235
[20] Piepho H P. A quick method for computing approximate thresholds for quantitative trait loci detection. Genetics. 2001, 157(1): 425-432
[21] Allard R W. Genetic basis of the evolution of adaptedness in plants. Euphytica. 1996, 92(1-2): 1-11
[22] Rieseberg L H, Sinervo B, Linder C R, Ungerer M C, Arias D M. Role of gene interactions in hybrid speciation: evidence from ancient and experimental hybrids. Science. 1996, 272(5262): 741-745
[23] Carelli V, Giordano C, d'Amati G. Pathogenic expression of homoplasmic mtDNA mutations needs a complex nuclear-mitochondrial interaction. Trends in Genetics. 2003, 19(5): 257-262
[24] Chinnery P F. Searching for nuclear-mitochondrial genes. Trends in Genetics. 2003, 19(2): 60-62
[25] Reik W, Walter J. Genomic imprinting: parental influence on the genome. Nature ReviewsGenetics. 2001, 2(1): 21-32
[26] Wilkins J F. Genomic imprinting and methylation: epigenetic canalization and conflict. Trends in Genetics. 2005, 21(6): 356-365
[1] Rogers S, Bendich, A J. Extraction of DNA from plant tissues. Plant Molecular Biology Manual. 1988, A6: 1-10
[2] Van Ooijen J W, Vorrips R E. JoinMap? Version 3.0, Software for the calculation of genetic linkage map. Plant Research International. Wageningen, Netherlands, 2001
[3] SAS Institute. SAS/STAT? User's Guide Version 9.1. Cary, NC: SAS Institute, Inc.1999
[4] Tang Z, Wang X, Hu Z, Yang Z, Xu C. Genetic dissection of cytonuclear epistasis in line crosses. Genetics. 2007, 177(1): 669-672
[5] Yang J, Hu C, Hu H, Yu R, Xia Z, Ye X, Zhu J. QTLNetwork: mapping and visualizing genetic architecture of complex traits in experimental populations. Bioinformatics. 2008, 24(5): 721-723
[6] Zeng Z B. Precision mapping of quantitative trait loci. Genetics. 1994, 136(4): 1457-1468
[7] Wang S, Basten C J, Zeng Z-B. Windows QTL Cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh, NC. 2007
[8] McCouch S R, Cho Y G, Yano P E, Blinstrub M, Morishima H, Kinoshita T. Report on QTL nomenclature. Rice Genetics Newsletter. 1997, 14: 11-13
[9] Moore J H. The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Human Heredity. 2003, 56(1-3): 73-82
[10] Kao C H, Zeng Z B. Modeling epistasis of quantitative trait loci using Cockerham's model. Genetics. 2002, 160(3): 1243-1261
[11] Gauderman W J, Thomas D C. The role of interacting determinants in the localization of genes. Advances in Genetics Incorporating Molecular Genetic Medicine. 2001, 42: 393-412
[1] Tang Zaixiang, Wang Xuefeng, Hu Zhiqiu, Yang Zefeng, Xu Chenwu. Genetic dissection of cytonuclear epistasis in line crosses. Genetics, 2007, 177: 669-672
[2] Allard R W. Genetic basis of the evolution of adaptedness in plants. Euphytica. 1996, 92(1-2): 1-11
[3] Hua J, Xing Y, Wu W, Xu C, Sun X, Yu S, Zhang Q. Single-locus heterotic effects and dominance by dominance interactions can adequately explain the genetic basis of heterosis in an elite rice hybrid. Proceedings of the National Academy of Sciences. 2003, 100(5): 2574-2579
[4] Malmberg R L, Held S, Waits A, Mauricio R. Epistasis for fitness-related quantitative traits inArabidopsis thaliana grown in the field and in the greenhouse. Genetics. 2005, 171(4): 2013-2027
[5] Zeyl C, Andreson B, Weninck E. Nuclear-mitochondrial epistasis for fitness in Saccharomyces cerevisiae. International Journal of Organic Evolution. 2005, 59(4): 910-914
[6] Rand D M, Fry A, Sheldahl L. Nuclear-mitochondrial epistasis and drosophila aging: introgression of Drosophila simulans mtDNA modifies longevity in D. melanogaster nuclear backgrounds. Genetics. 2006, 172(1): 329-341
[7] Li Z K, Luo L J, Mei H W, Wang D L, Shu Q Y, Tabien R, Zhong D B, Ying C S, Stansel J W, Khush G S, Paterson A H. Overdominant epistatic loci are the primary genetic basis of inbreeding depression and heterosis in rice. I. Biomass and grain yield. Genetics. 2001, 158(4): 1737-1753
[8] Jasienski M, Ayala F J, Bazzaz F A. Phenotypic plasticity and similarity of DNA among genotypes of an annual plant. Heredity. 1997, 78 (2): 176-181
[9] Churchill G A, Airey D C, Allayee H, Angel J M, Attie A D, Beatty J, Beavis W D, Belknap J K, Bennett B, Berrettini W, Bleich A, Bogue M, Broman K W, Buck K J, Buckler E, Burmeister M, Chesler E J, Cheverud J M, Clapcote S, Cook M N, Cox R D, Crabbe J C, Crusio W E, Darvasi A, Deschepper C F, Doerge R W, Farber C R, Forejt J, Gaile D, Garlow S J, Geiger H, Gershenfeld H, Gordon T, Gu J, Gu W, de Haan G, Hayes N L, Heller C, Himmelbauer H, Hitzemann R, Hunter K, Hsu H C, Iraqi F A, Ivandic B, Jacob H J, Jansen R C, Jepsen K J, Johnson D K, Johnson T E, Kempermann G, Kendziorski C, Kotb M, Kooy R F, Llamas B, Lammert F, Lassalle J M, Lowenstein P R, Lu L, Lusis A, Manly K F, Marcucio R, Matthews D, Medrano J F, Miller D R, Mittleman G, Mock B A, Mogil J S, Montagutelli X, Morahan G, Morris D G, Mott R, Nadeau J H, Nagase H, Nowakowski R S, O'Hara B F, Osadchuk A V, Page G P, Paigen B, Paigen K, Palmer A A, Pan H J, Peltonen-Palotie L, Peirce J, Pomp D, Pravenec M, Prows D R, Qi Z, Reeves R H, Roder J, Rosen G D, Schadt E E, Schalkwyk L C, Seltzer Z, Shimomura K, Shou S, Sillanpaa M J, Siracusa L D, Snoeck H W, Spearow J L, Svenson K, Tarantino L M, Threadgill D, Toth L A, Valdar W, de Villena F P, Warden C, Whatley S, Williams R W, Wiltshire T, Yi N, Zhang D, Zhang M, Zou F. The Collaborative Cross, a community resource for the genetic analysis of complex traits. Nature Genetics. 2004, 36(11): 1133-1147
[10] Carlson C S, Eberle M A, Kruglyak L, Nickerson D A. Mapping complex disease loci in whole-genome association studies. Nature. 2004, 429(6990): 446-452
[11] Bamshad M, Wooding S, Salisbury B A, Stephens J C. Deconstructing the relationship between genetics and race. Nature Review Genetics. 2004, 5(8): 598-609
[12] Funalot B, Varenne O, Mas J L. A call for accurate phenotype definition in the study of complex disorders. Nature Genetics. 2004, 36(1): 3
[13] Darvasi A. Closing in on complex traits. Nature Genetics. 2006, 38(8): 861-862
[14] Glazier A M, Nadeau J H, Aitman T J. Finding genes that underlie complex traits. science. 2002, 298(5602): 2345-2349
[15] Weiss K M, Terwilliger J D. How many diseases does it take to map a gene with SNPs? Nature Genetics. 2000, 26(2): 151-157
[16] Yang Q, Khoury M J, Friedman J, Little J, Flanders W D. How many genes underlie theoccurrence of common complex diseases in the population? International Journal of Epidemiology. 2005, 34(5): 1129-1137
[17] Flint J, Valdar W, Shifman S, Mott R. Strategies for mapping and cloning quantitative trait genes in rodents. Nature Review Genetics. 2005, 6(4): 271-286
[18] Sax K. The association of size differences with seed-coat pattern and pigmentation in Phaseolus Vulgaris. Genetics. 1923, 8(6): 552-560
[19] Thoday J M. Location of Polygenes. Nature. 1961, 191: 368-370
[20] Spickett S G, Thoday J M. Regular responses to selection 3. Interaction between located polygenes. Genetical Research. 7: 96-121
[21] Lander E S, Botstein D. Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics. 1989, 121(1): 185-199
[22]惠大丰,姜长鉴,莫惠栋.数量性状基因图谱构建方法的比较.作物学报. 1997, 23(2): 129-136
[23] Weller J I. Maximum likelihood techniques for the mapping and analysis of quantitative trait loci with the aid of genetic markers. Biometrics. 1986, 42(3): 627-640
[24] Luo Z W, Kearsey M J. Maximum likelihood estimation of linkage between a marker gene and a quantitative locus. Heredity. 1989, 63(3): 401-408
[25] Luo Z W, Kearsey M J. Maximum likelihood estimation of linkage between a marker gene and a quantitative trait locus. II. Application to backcross and doubled haploid populations. Heredity. 1991 66(1): 117-124.
[26] Darvasi A, Weller J I. On the use of the moments method of estimation to obtain approximate maximum likelihood estimates of linkage between a genetic marker and a quantitative locus. Heredity. 1992, 68(1): 43-46
[27] Luo Z W, Woolliams J A. Estimation of genetic parameters using linkage between a marker gene and a locus underlying a quantitative character in F2 populations. Heredity. 1993, 70: 245-253
[28]周明全,章志宏,胡中立.植物QTL分析的理论研究进展.武汉植物学研究. 2001, 19(5): 428-436
[29] Kearsey M J, Hyne V. QTL analysis: a simple‘marker-regression’approach. Theoretical and Applied Genetics. 1994, 89(6): 698-702
[30] Hyne V, Kearsey M J. QTL analysis: further uses of‘marker regression’. Theoretical and Applied Genetics. 1995, 91(3): 471-476
[31] Charmet G, Cadalen T, Sourdille P, Bernard M. An extension of the‘marker regression’method to interactive QTL. Molecular Breeding. 1998, 4(1): 67-72
[32] Wu W R, Li W M. A new approach for mapping quantitative trait loci using complete genetic marker linkage maps. Theoretical and Applied Genetics. 1994, 89(5): 535-539
[33] Stuber C W, Edwards M D, Wendel J F. Molecular marker-facilitated investigations of quantitative trait loci in maize. II: Factors influencing yield and its component traits. Crop science. 1987, 27(4): 639-648
[34] Lebowitz R J, Soller M, Beckmann J S. Trait-based analyses for the detection of linkage between marker loci and quantitative trait loci in crosses between inbred lines. Theoretical and Applied Genetics. 1987, 73(4): 556-562
[35] Jensen J. Estimation of recombination parameters between a quantitative trait locus (QTL) and two marker gene loci. Theoretical and Applied Genetics. 1989, 78(5): 613-618
[36] Knapp S J, Bridges W C, Birkes D. Mapping quantitative trait loci using molecular marker linkage maps. Theoretical and Applied Genetics. 1990, 79(5): 583-592
[37] Paterson A H, DeVerna J W, Lanini B, Tanksley S D. Fine mapping of quantitative trait loci using selected overlapping recombinant chromosomes, in an interspecies cross of tomato. Genetics. 1990, 124(3): 735-742
[38] Ooijen J W. Accuracy of mapping quantitative trait loci in autogamous species. Theoretical and Applied Genetics. 1992, 84(7): 803-811
[39] Luo Z W, Kearsey M J. Interval mapping of quantitative trait loci in an F2 population. Heredity. 1992, 69: 236-242
[40] Haley C S, Knott S A. A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity. 1992, 69(4): 315-324
[41] Martínez O, Curnow R N. Estimating the locations and the sizes of the effects of quantitative trait loci using flanking markers. Theoretical and Applied Genetics. 1992, 85(4): 480-488
[42] Xu S. A Comment on the simple regression method for interval mapping. Genetics. 1995, 141(4): 1657-1659
[43] Xu S. Further investigation on the regression method of mapping quantitative trait loci. Heredity. 1998, 80(3): 364-373
[44]胡中立,章志宏,张修富. DH群体中区间分子标记定位QTL的相关方法.生物数学学报. 1998, 13(3): 365-371
[45]李宏.基于三点测交的双标记-QTL基因定位的相关方法.生物数学学报. 2000, 15 (1): 93-98
[46] Zeng Z B. Theoretical basis for separation of multiple linked gene effects in mapping quantitative trait loci. Proceedings of the National Academy of Sciences. 1993, 90(23): 10972-10976
[47] Rodolphe F, Lefort M. A Multi-marker model for detecting chromosomal segments displaying QTL activity. Genetics. 1993, 134(4): 1277-1288
[48] Jansen R C. Interval mapping of multiple quantitative trait loci. Genetics. 1993, 135(1): 205-211
[49] Jansen R C. Controlling the type I and type II errors in mapping quantitative trait loci. Genetics. 1994, 138(3): 871-881
[50] Jansen R C, Stam P. High resolution of quantitative traits into multiple loci via interval mapping. Genetics. 1994, 136(4): 1447-1455
[51] Zeng Z B. Precision mapping of quantitative trait loci. Genetics. 1994, 136(4): 1457-1468
[52] Jiang C, Zeng Z B. Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics. 1995, 140(3): 1111-1127
[53] Zhu J, Weir B S. Mixed model approaches for genetic analysis of quantitative traits. Advanced topics in biomathematics: Proceedings of the International Conference on Mathematical Biology Singapore: World Scientific Publ. Co., 1998, 321–330.
[54]胡中立.对QTL复合区间作图法的一点改进.武汉大学学报:理学版. 2000, 46(6): 766-768
[55] Wu W R, Li W M, Lu H R. Composite interval mapping of quantitative trait loci based on least squares estimation. Journal of Fujian Agricultural University. 1996, 25(4): 394-399
[56] Wu W R, Li W M, Tang D Z, Lu H R, Worland A J. Time-related mapping of quantitative trait loci underlying tiller number in rice. Genetics. 1999, 151(1): 297-303
[57]吴为人,李维明.基于性状-标记回归的QTL区间测验方法.遗传. 2001, 23(2): 143-146
[58]徐辰武,顾世梁.构建数量性状基因图的双侧标记基因型均值回归法.扬州大学学报:自然科学版. 1999, 2(1): 42-47
[59] Li H, Ye G, Wang J. A modified algorithm for the improvement of composite interval mapping. Genetics. 2007, 175(1): 361-374
[60] Li H, Ribaut J M, Li Z, Wang J. Inclusive composite interval mapping (ICIM) for digenic epistasis of quantitative traits in biparental populations. Theoretical and Applied Genetics. 2008, 116(2): 243-260
[61] Kao C H, Zeng Z B, Teasdale R D. Multiple interval mapping for quantitative trait loci. Genetics. 1999, 152(3): 1203-1216
[62] Kao C H, Zeng Z B. General formulas for obtaining the mles and the asymptotic variance-covariance matrix in mapping quantitative trait loci when using the EM algorithm. Biometrics. 1997, 53(2): 653-665
[63] Zeng Z B, Kao C H, Basten C J. Estimating the genetic architecture of quantitative traits. Genetical Research. 1999, 74(3): 279-289
[64] Wang H, Zhang Y, Li X, Masinde G L, Mohan S, Baylink D J, Xu S. Bayesian Shrinkage Estimation of Quantitative Trait Loci Parameters. Genetics. 2005, 170(1): 465-480
[65] Smith A, Roberts G. Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods. Journal of the Royal Statistical Society(B). 1993, 55(1): 23-24
[66] Chib S, Greenberg E. Understanding the Metropolis-Hastings algorithm. The American Statistician. 1995, 49(4): 327-335
[67] Hoeschele I. Mapping quantitative trait loci in outbred pedigrees. Handbook of Statistical Genetics. Balding D, Bishop M, Cannings C(eds), Wiley. 2001: 599-644
[68] Carlin B P, Louis T A. Bayes and Empirical Bayes methods for data analysis. 2nd edition. Chapman & Hall. London, UK. 2000
[69] Hoeschele I, VanRaden P M. Bayesian analysis of linkage between genetic markers and quantitative trait loci. I. Prior knowledge. Theoretical and Applied Genetics. 1993, 85(8): 953-960
[70] Hoeschele I, VanRaden P M. Bayesian analysis of linkage between genetic markers and quantitative trait loci. II. Combining prior knowledge with experimental evidence. Theoretical and Applied Genetics. 1993, 85(8): 946-952
[71] Thaller G, Hoeschele I. A Monte Carlo method for Bayesian analysis of linkage between single markers and quantitative trait loci. I. Methodology. Theoretical and Applied Genetics. 1996, 93(7): 1161-1166
[72] Thaller G, Hoeschele I. A Monte Carlo method for Bayesian analysis of linkage between single markers and quantitative trait loci. II. A simulation study. Theoretical and Applied Genetics. 1996, 93(7): 1167-1174
[73] Uimari P, Thaller G, Hoeschele I. The use of multiple markers in a Bayesian method formapping quantitative trait loci. Genetics. 1996, 143(4): 1831-1842
[74] Uimari P, Hoeschele I. Mapping-linked quantitative trait loci using bayesian analysis and Markov chain Monte Carlo algorithms. Genetics. 1997, 146(2): 735-743
[75] Satagopan J M, Yandell B S. Estimating the number of quantitative trait loci via bayesian model determination. Chicago. 1996:
[76] Stephens D A, Smith A F. Bayesian inference in multipoint gene mapping. Annals of Human Genetics. 1993, 57(1): 65-82
[77] Green P J. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika. 1995, 82(4): 711-732
[78] Hurme P, Sillanp?? M J, Arjas E, Repo T, Savolainen O. Genetic basis of climatic adaptation in scots pine by bayesian quantitative trait locus analysis. Genetics. 2000, 156(3): 1309-1322
[79] Bink M, Uimari P, Sillanp?? M, Janss L, Jansen R. Multiple QTL mapping in related plant populations via a pedigree-analysis approach. Theoretical and Applied Genetics. 2002, 104(5): 751-762
[80] Sen S, Churchill G A. A statistical framework for quantitative trait mapping. Genetics. 2001, 159(1): 371-387
[81] Yi N, Xu S. Mapping quantitative trait loci with epistatic effects. Genetical Research. 2002, 79: 185-198
[82] Satagopan J M, Yandell B S, Newton M A, Osborn T C. A Bayesian approach to detect quantitative trait loci using Markov chain Monte Carlo. Genetics. 1996, 144(2): 805-816
[83] Yi N, Diament A, Chiu S, Kim K, Allison D B, Fisler J S, Warden C H. Characterization of epistasis influencing complex spontaneous obesity in the BSB Model. Genetics. 2004, 167(1): 399-409
[84] Yi N, Yandell B S, Churchill G A, Allison D B, Eisen E J, Pomp D. Bayesian model selection for genome-wide epistatic quantitative trait loci analysis. Genetics. 2005, 170(3): 1333-1344
[85] Yi N, Chiu S, Allison D B, Fisler J S, Warden C H. Epistatic interaction between two nonstructural loci on chromosomes 7 and 3 influences hepatic lipase activity in BSB mice. Journal of Lipid Research. 2004, 45(11): 2063-2070
[86] Xu S. Estimating polygenic effects using markers of the entire genome. Genetics. 2003, 163(2): 789-801
[87] Meuwissen T H E, Hayes B J, Goddard M E. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001, 157(4): 1819-1829
[88] Zhang Y-M, Xu S. Mapping quantitative trait loci in F2 incorporating phenotypes of F3 progeny. Genetics. 2004, 166(4): 1981-1993
[89] Zhang Y M, Xu S. A penalized maximum likelihood method for estimating epistatic effects of QTL. Heredity. 2005, 95: 96-104
[90] Xu, S. An empirical Bayes method for estimating epistatic effects of quantitative trait loci. Biometrics. 2007, 63: 513–521
[91] Xu S, Jia Z. Genome-wide analysis of epistatic effects for quantitative traits in barley. Genetics. 2007, 175(4): 1955-1963
[92] Yi N, Zinniel D K, Kim K, Eisen E J, Bartolucci A, Allison D B, Pomp D. Bayesian analyses of multiple epistatic QTL models for body weight and body composition in mice. GeneticResearch. 2006, 87(1): 45-60
[93] Jennings H S. The numerical results of diverse systems of breeding, with respect to two pairs of characters, linked or independent, with special relation to the effects of linkage. Genetics 1917, 2(2): 97-154
[94] Raymond J P, David G, Jeffrey C L. Effects of worldwide population subdivision on ALDH2 linkage disequilibrium. Genome Research. 1999, 9(9): 844-852
[95] Flint-Garcia S A, Thornsberry J M, Buckler E S t. Structure of linkage disequilibrium in plants. Annual Review of Plant Biology, 2003, 54: 357-374
[96] Gupta P K, Rustgi S, Kulwal P L. Linkage disequilibrium and association studies in higher plants: present status and future prospects. Plant Molecular Biology. 2005, 57(4): 461-485
[97] Thornsberry J M, Goodman M M, Doebley J, Kresovich S, Nielsen D, Buckler E S t. Dwarf8 polymorphisms associate with variation in flowering time. Nature Genetics. 2001, 28(3): 286-289
[98] Olsen K M, Halldorsdottir S S, Stinchcombe J R, Weinig C, Schmitt J, Purugganan M D. Linkage disequilibrium mapping of Arabidopsis CRY2 flowering time alleles. Genetics. 2004, 167(3): 1361-1369
[99] Luo Z W. Detecting linkage disequilibrium between a polymorphic marker locus and a trait locus in natural population. Heredity. 1998, 80(2): 198-208
[100] Luo Z W, Suhai S. Estimating linkage disequilibrium between a polymorphic marker locus and a trait locus in natural populations. Genetics. 1999, 151(1): 359-371
[101] Schena M, Shalon D, Davis R W, Brown P O. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science. 1995, 270(5235): 467-470
[102] Jansen R C, Nap J P. Genetical genomics: the added value from segregation. Trends in Genetics. 2001, 17(7): 388–391
[103] Kendziorski C M, Chen M, Yuan M, Lan H, Attie A D. Statistical methods for expression quantitative trait loci (eQTL) mapping. Biometrics. 2006, 62(1): 19-27
[104] Dupuis J, Siegmund D. Statistical methods for mapping quantitative trait loci from a dense set of markers. Genetics. 1999, 151(1): 373-386
[105] Schadt E E, Monks S A, Drake T A, Lusis A J, Che N, Colinayo V, Ruff T G, Milligan S B, Lamb J R, Cavet G. Genetics of gene expression surveyed in maize, mouse and man. Nature. 2003, 422(6929): 297-302
[106] Storey J D, Taylor J E, Siegmund D. Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach. Journal of the Royal Statistical Society Series B(Statistical Methodology). 2004, 66(1): 187-205
[107] Qu Y, Xu S. Quantitative trait associated microarray gene expression data analysis. Molecular Biology and Evolution. 2006, 23(8): 1558-1573
[108] Jansen R C, Ooijen J W V, Stam P, Lister C, Dean C. Genotype-by-Environment interaction in genetic mapping of multiple quantitative trait loci. Theoretical and Applied Genetics. 1995, 91: 33-37
[109] Tinker N A, Mather D E. Methods for QTL analysis with progeny replicated in multiple environments. Journal of Agricultural Genomics. 1995, 1: 1
[110] Sari-Gorla M, Calinski T, Kaczmarek Z, Krajewski P. Detection of QTL×environmentinteraction in maize by a least squares interval mapping method. Heredity. 1997, 78: 146-157
[111] Wang D L, Zhu J, Li Z K, Paterson A H. Mapping QTLs with epistatic effects and QTL environment interactions by mixed linear model approaches. Theoretical and Applied Genetics. 1999, 99: 1255-1264
[112] Stratton D A, Haley C S, Knott S A. Reaction normfunctions and QTL2environment interactions for flowering time in Arabidopsis thaliana. Heredity. 1998, 81(2): 144-155
[113] Korol A B, Ronin Y I, Nevo E. Approximate analysis of QTL-environment interaction with no limits on the number of environments. Genetics. 1998 148: 2015-2028
[114] Crossa J, Burgueno J, Cornelius P L, McLaren G, Trethowan R, Krishnamachari A. Modeling genotype×environment interaction using additive genetic covariances of relatives for predicting breeding values of wheat genotypes. Crop Science. 2006, 46(4): 1722-1733
[115] Xu S. Mapping quantitative trait loci using multiple families of line crosses. Genetics. 1998, 148(1): 517-524
[116] Xu S. Mapping quantitative trait loci using four-way crosses. Genetical Research. 1996, 68: 175-181
[117] Broman K W. The genomes of recombinant inbred lines. Genetics. 2005, 169(2): 1133-1146
[118] Li R, Lyons M A, Wittenburg H, Paigen B, Churchill G A. Combining data from multiple inbred line crosses improves the power and resolution of quantitative trait loci mapping. Genetics. 2005, 169(3): 1699-1709
[119] Verhoeven K J, Jannink J L, McIntyre L M. Using mating designs to uncover QTL and the genetic architecture of complex traits. Heredity. 2006, 96(2): 139-149
[120] Xu S, Atchley W R. Mapping quantitative trait loci for complex binary diseases using line crosses. Genetics. 1996, 143(3): 1417-1424
[121] Wright S. An analysis of variability in number of digits in an inbred strain of guinea pigs. Genetics. 1934, 19(6): 506-536
[122] Reba A. Comparison of methods for regression interval mapping in QTL analysis with non-normal traits. Genetical Research. 2000, 69(01): 69-74
[123] Yi N, Xu S. Bayesian mapping of quantitative trait loci for complex binary traits. Genetics. 2000, 155(3): 1391-1403
[124] Rao S, Xu S. Mapping quantitative trait loci for ordered categorical traits in four-way crosses. Heredity. 1998, 81(2): 214-224
[125] Xu C, Li Z, Xu S. Joint mapping of quantitative trait loci for multiple binary characters. Genetics. 2005, 169(2): 1045-1059
[126] Luo L, Zhang Y M, Xu S. A quantitative genetics model for viability selection. Heredity. 2005, 94: 347-355
[127] Nichols R A. Quantitative genetics focus issue. Heredity. 2005, 94(3): 273-274
[128] Wu R, Lin M, Box P. Functional mapping-how to map and study the genetic architecture of dynamic complex traits. Nature Reviews Genetics. 2006, 7: 229-237
[129] Yang R, Tian Q, Xu S. Mapping quantitative trait loci for longitudinal traits in line crosses. Genetics. 2006, 173(4): 2339-2356
[130] Xi Z Y, He F H, Zeng R Z, Zhang Z M, Ding X H, Li W T, Zhang G Q. Development of a wide population of chromosome single-segment substitution lines in the genetic backgroundof an elite cultivar of rice (Oryza sativa L.). Genome Biology. 2006, 49(5): 476-484
[131] Ebitani T, Takeuchi Y, Nonoue Y, Yamamoto T, Takeuchi K, Yano M. Construction and evaluation of chromosome segment substitution lines carrying overlapping chromosome segments of indica rice cultivar Kasalath in a genetic background of japonica elite cultivar Koshihikari. Breeding Science. 2005, 55(1): 65-73
[132] Wan X Y, Wan J M, Su C C, Wang C M, Shen W B, Li J M, Wang H L, Jiang L, Liu S J, Chen L M, Yasui H, Yoshimura A. QTL detection for eating quality of cooked rice in a population of chromosome segment substitution lines. Theoretical and Applied Genetics. 2004, 110(1): 71-79
[133] Singer J B, Hill A E, Burrage L C, Olszens K R, Song J, Justice M, O'Brien W E, Conti D V, Witte J S, Lander E S, Nadeau J H. Genetic dissection of complex traits with chromosome substitution strains of mice. Science. 2004, 304(5669): 445-448
[134] Tanksley S D, Nelson J C. Advanced backcross QTL analysis: A method for the simultaneous discovery and transfer of valuable QTLs from unadapted germplasm into elite breeding lines. Theoretical and Applied Genetics. 1996, 92(2): 191-203
[135] Salvi S, Tuberosa R. To clone or not to clone plant QTLs: present and future challenges. Trends in Plant Science. 2005, 10(6): 297-304
[136] Lin H X, T Y, Sasaki T, Yano M. Characterization and detection of epistatic interactions of 3 QTLs, Hd1, Hd2, and Hd3, controlling heading date in rice using nearly isogenic lines. Theoretical and Applied Genetics. 2000, 101(7): 1021-1028
[137] Darvasi A, Soller M. Advanced intercross lines, an experimental population for fine genetic mapping. Genetics. 1995, 141(3): 1199-1207
[138] Iraqi F, Clapcott S J, Kumari P, Haley C S, Kemp S J, Teale A J. Fine mapping of trypanosomiasis resistance loci in murine advanced intercross lines. Mammalian Genome. 2000, 11(8): 645-648
[139] Wang X, Le Roy I, Nicodeme E, Li R, Wagner R, Petros C, Churchill G A, Harris S, Darvasi A, Kirilovsky J, Roubertoux P L, Paigen B. Using advanced intercross lines for high-resolution mapping of HDL cholesterol quantitative trait loci. Genome Research. 2003, 13(7): 1654-1664
[140] Vogel G. Scientists dream of 1001 complex mice. Science. 2003, 301(5632): 456-457
[141] Valdar W, Flint J, Mott R. Simulating the collaborative cross: power of quantitative trait loci detection and mapping resolution in large sets of recombinant inbred strains of mice. Genetics. 2006, 172(3): 1783-1797
[142] Valdar W, Solberg L C, Gauguier D, Burnett S, Klenerman P, Cookson W O, Taylor M S, Rawlins J N, Mott R, Flint J. Genome-wide genetic association of complex traits in heterogeneous stock mice. Nature Genetics. 2006, 38(8): 879-887
[143] Krakowsky M D, Lee M, Coors J G. Quantitative trait loci for cell wall components in recombinant inbred lines of maize (Zea mays L.) II: leaf sheath tissue. Theoretical and Applied Genetics. 2006, 112(4): 717-726
[144] You A, Lu X, Jin H, Ren X, Liu K, Yang G, Yang H, Zhu L, He G. Identification of quantitative trait loci across recombinant inbred lines and testcross populations for traits of agronomic importance in rice. Genetics. 2006, 172(2): 1287-1300
[145] el-Lithy M E, Bentsink L, Hanhart C J, Ruys G J, Rovito D, Broekhof J L, van der Poel H J, van Eijk M J, Vreugdenhil D, Koornneef M. New arabidopsis recombinant inbred line populations genotyped using SNPWave and their use for mapping flowering-time quantitative trait loci. Genetics. 2006, 172(3): 1867-1876
[146] Reiter R S, Williams J G, Feldmann K A, Rafalski J A, Tingey S V, Scolnik P A. Global and local genome mapping in Arabidopsis thaliana by using recombinant inbred lines and random amplified polymorphic DNAs. Proceedings of the National Academy of Sciences . 1992, 89(4): 1477-1481
[147] Darvasi A. Interval-specific congenic strains (ISCS): an experimental design for mapping a QTL into a 1-centimorgan interval. Mammalian Genome. 1997, 8(3): 163-167
[148] Valdar W S, Flint J, Mott R. QTL fine-mapping with recombinant-inbred heterogeneous stocks and in vitro heterogeneous stocks. Mammalian Genome. 2003, 14(12): 830-838
[149] Shifman S, Darvasi A. Mouse inbred strain sequence information and yin-yang crosses for quantitative trait locus fine mapping. Genetics. 2005, 169(2): 849-854
[150] Tuinstra M R, Ejeta G, Goldsbrough P B. Heterogeneous inbred family (HIF) analysis: a method for developing near-isogenic lines that differ at quantitative trait loci. Theoretical and Applied Genetics. 1997, 95(5-6): 1005-1011
[151] Darvasi A. Experimental strategies for the genetic dissection of complex traits in animal models. Nature Genetics. 1998, 18(1): 19-24
[152] Xu S. Mapping quantitative trait loci using four-way crosses. Genetical Research. 1996, 68(2): 175-181
[153] Frary A, Nesbitt T C, Grandillo S, Knaap E, Cong B, Liu J, Meller J, Elber R, Alpert K B, Tanksley S D. fw2.2: a quantitative trait locus key to the evolution of tomato fruit size. Science. 2000, 289(5476): 85-88
[154] Yano M, Katayose Y, Ashikari M, Yamanouchi U, Monna L, Fuse T, Baba T, Yamamoto K, Umehara Y, Nagamura Y, Sasaki T. Hd1, a major photoperiod sensitivity quantitative trait locus in rice, is closely related to the arabidopsis flowering time gene CONSTANS. Plant Cell. 2000, 12(12): 2473-2484
[155] Takahashi Y, Shomura A, Sasaki T, Yano M. Hd6, a rice quantitative trait locus involved in photoperiod sensitivity, encodes the alpha subunit of protein kinase CK2. Proceedings of the National Academy of Sciences . 2001, 98(14): 7922-7927
[156] Kojima S, Takahashi Y, Kobayashi Y, Monna L, Sasaki T, Araki T, Yano M. Hd3a, a rice ortholog of the Arabidopsis FT gene, promotes transition to flowering downstream of Hd1 under short-day conditions. Plant Cell and Physiology. 2002, 43(10): 1096-1105
[157] Doi K, Izawa T, Fuse T, Yamanouchi U, Kubo T, Shimatani Z, Yano M, Yoshimura A. Ehd1, a B-type response regulator in rice, confers short-day promotion of flowering and controls FT-like gene expression independently of Hd1. Genes and Development. 2004, 18(8): 926-936
[158] Ashikari M, Sakakibara H, Lin S, Yamamoto T, Takashi T, Nishimura A, Angeles E R, Qian Q, Kitano H, Matsuoka M. Cytokinin oxidase regulates rice grain production. Science. 2005, 309(5735): 741-745
[159] Ishimaru K, Ono K, Kashiwagi T. Identification of a new gene controlling plant height in rice using the candidate-gene strategy. Planta. 2004, 218(3): 388-395
[160] Nishimura A, Ashikari M, Lin S, Takashi T, Angeles E R, Yamamoto T, Matsuoka M. Isolation of a rice regeneration quantitative trait loci gene and its application to transformation systems. Proceedings of the National Academy of Sciences . 2005, 102(33): 11940-11944
[161] Ren Z H, Gao J P, Li L G, Cai X L, Huang W, Chao D Y, Zhu M Z, Wang Z Y, Luan S, Lin H X. A rice quantitative trait locus for salt tolerance encodes a sodium transporter. Nature Genetics. 2005, 37(10): 1141-1146
[162] Doebley J, Stec A, Gustus C. Teosinte branched1 and the origin of maize: evidence for epistasis and the evolution of dominance. Genetics. 1995, 141(1): 333-346
[163] Fridman E, Carrari F, Liu Y S, Fernie A R, Zamir D. Zooming in on a quantitative trait for tomato yield using interspecific introgressions. Science. 2004, 305(5691): 1786-1789
[164] Fridman E, Pleban T, Zamir D. A recombination hotspot delimits a wild-species quantitative trait locus for tomato sugar content to 484 bp within an invertase gene. Proceedings of the National Academy of Sciences . 2000, 97(9): 4718-4723
[165] Cong B, Liu J, Tanksley S D. Natural alleles at a tomato fruit size quantitative trait locus differ by heterochronic regulatory mutations. Proceedings of the National Academy of Sciences . 2002, 99(21): 13606-13611
[166] Liu J, Van Eck J, Cong B, Tanksley S D. A new class of regulatory genes underlying the cause of pear-shaped tomato fruit. Proceedings of the National Academy of Sciences. 2002, 99(20): 13302-13306
[167] El-Din El-Assal S, Alonso-Blanco C, Peeters A J, Raz V, Koornneef M. A QTL for flowering time in Arabidopsis reveals a novel allele of CRY2. Nature Genetics. 2001, 29(4): 435-540
[168] Werner J D, Borevitz J O, Warthmann N, Trainer G T, Ecker J R, Chory J, Weigel D. Quantitative trait locus mapping and DNA array hybridization identify an FLM deletion as a cause for natural flowering-time variation. Proceedings of the National Academy of Sciences. 2005, 102(7): 2460-2465
[169] Maloof J N, Borevitz J O, Dabi T, Lutes J, Nehring R B, Redfern J L, Trainer G T, Wilson J M, Asami T, Berry C C, Weigel D, Chory J. Natural variation in light sensitivity of Arabidopsis. Nature Genetics. 2001, 29(4): 441-446
[170] Kroymann J, Donnerhacke S, Schnabelrauch D, Mitchell-Olds T. Evolutionary dynamics of an Arabidopsis insect resistance quantitative trait locus. Proceedings of the National Academy of Sciences. 2003, 100(2): 14587-14592
[171] Doebley J, Stec A, Hubbard L. The evolution of apical dominance in maize. Nature. 1997, 386(6624): 485-488
[172] Montgomery M K, Fire A. Double-stranded RNA as a mediator in sequence-specific genetic silencing and co-suppression. Trends in Genetics. 1998, 14(7): 255-258
[173] Chuang C, Meyerowitz E M. Specific and heritable genetic interference by double-stranded RNA in Arabidopsis thaliana. Proceedings of the National Academy of Sciences. 2000, 97(9): 4985-4990
[174] Price A H. Believe it or not, QTLs are accurate! Trends in Plant Science. 2006, 11(5): 213-216
[175] Nadeau J H, Frankel W N. The roads from phenotypic variation to gene discovery: mutagenesis versus QTLs. Nature Genetics. 2000, 25(4): 381-384
[176] Morton N E. Linkage disequilibrium maps and association mapping. Journal of ClinicalInvestigation. 2005, 115(6): 1425-1430
[177] Damerval C, Maurice A, Josse J M, de Vienne D. Quantitative trait loci underlying gene product variation: a novel perspective for analyzing regulation of genome expression. Genetics. 1994, 137(1): 289-301
[178] Schadt E E, Monks S A, Drake T A, Lusis A J, Che N, Colinayo V, Ruff T G, Milligan S B, Lamb J R, Cavet G, Linsley P S, Mao M, Stoughton R B, Friend S H. Genetics of gene expression surveyed in maize, mouse and man. Nature. 2003, 422(6929): 297-302
[179]朱军.遗传学(第三版).中国农业出版社. 2001
[180] Shinozaki K, Ohme M, Tanaka M, Wakasugi T, Hayashida N, Matsubayashi T, Zaita N, Chunwongse J, Obokata J, Yamaguchi-Shinozaki K, Ohto C, Torazawa K, Meng B Y, Sugita M, Deno H, Kamogashira T, Yamada K, Kusuda J, Takaiwa F, Kato A, Tohdoh N, Shimada H, Sugiura M. The complete nucleotide sequence of the tobacco chloroplast genome: its gene organization and expression. Embo Journal. 1986, 5(9): 2043-2049
[181] Oda K, Yamato K, Ohta E, Nakamura Y, Takemura M, Nozato N, Akashi K, Kanegae T, Ogura Y, Kohchi T, Ohyama K. Gene organization deduced from the complete sequence of liverwort Marchantia polymorpha mitochondrial DNA. A primitive form of plant mitochondrial genome. Journal of Molecular Biology. 1992, 223(1): 1-7
[182] Hiratsuka J, Shimada H, Whittier R, Ishibashi T, Sakamoto M, Mori M, Kondo C, Honji Y, Sun C R, Meng B Y, Li Y, Kanno A, Nishizawa Y, Hirai A, Shinozaki K and Sugiura M. The complete sequence of the rice (Oryza sativa) chloroplast genome: intermolecular recombination between distinct tRNA genes accounts for a major plastid DNA inversion during the evolution of the cereals. Molecular Genetics and Genomics. 1989, 217(2-3): 185-194
[183] Cui L, Veeraraghavan N, Richter A, Wall K, Jansen R K, Leebens-Mack J, Makalowska I, dePamphilis C W. ChloroplastDB: the chloroplast genome database. Nucleic Acids Research. 2006, 34: D692-696
[184] Shahid Masood M, Nishikawa T, Fukuoka S, Njenga P K, Tsudzuki T, Kadowaki K. The complete nucleotide sequence of wild rice (Oryza nivara) chloroplast genome: first genome wide comparative sequence analysis of wild and cultivated rice. Gene. 2004, 340(1): 133-139
[185] Tang J, Xia H, Cao M, Zhang X, Zeng W, Hu S, Tong W, Wang J, Wang J, Yu J, Yang H, Zhu L. A comparison of rice chloroplast genomes. Plant Physiology. 2004, 135(1): 412-420
[186]邢少辰, Clarke J.叶绿体基因组研究进展.生物化学与生物物理进展. 2008, 35(1): 21-28
[187] Raghavendra A S, Reumann S, Heldt H W. Participation of mitochondrial metabolism in photorespiration. Reconstituted system of peroxisomes and mitochondria from spinach leaves. Plant Physiology. 1998, 116(4): 1333-1337
[188] Raubeson L A, Jansen R K. Chloroplast genomes of plants. In:Henry R J ed. Plant Diversity and Evolution: Genotypic and Phenotypic Variation in Higher Plants. UK:CABI publishing. 2005: 45-68
[189] Bedbrook J R, Kolodner R, Bogorad L. Zea mays chloroplast ribosomal RNA genes are part of a 22,000 base pair inverted repeat. Cell. 1977, 11(4): 739-749
[190] Relle M, Furst P, Wild A. Short duplication in a cDNA clone of the rbcL gene from Picea abies. Plant Physiology. 1995, 107(3): 1037-1038
[191] Yohn C B, Cohen A, Rosch C, Kuchka M R, Mayfield S P. Translation of the chloroplast psbA mRNA requires the nuclear-encoded poly(A)-binding protein, RB47. Journal of Cell Biology. 1998, 142(2): 435-442
[192] Efimov V A, Andreeva A V, Reverdatto S V, Chakhmakhcheva O G. Nucleotide sequence of the barley chloroplast psbD gene for the D2 protein of photosystem II. Nucleic Acids Research. 1988, 16(12): 5686
[193] Anderson S, Bankier A T, Barrell B G, de Bruijn M H L, Coulson A R D, J, Eperson I C, Nierlich D C, Roe B A, Sanger F, Schreier P H, Smith A J H, Staden R, Young I G. Sequence and organization of the human mitochondrial genome. Nature. 1981, (457-464):
[194] Oda K, Yamato K, Ohta E, Nakamura Y, Takemura M, Nozato N, Akashi K, Kanegae T, Ogura Y, Kohchi T, Ohyama K. Gene organization deduced from the complete sequence of liverwort Marchantia polymorpha mitochondrial DNA. A primitive form of plant mitochondrial genome. Journal of Molecular Biology 1992, 223(1): 1-7
[195] Warden C H, Yi N J, Fisler J. Epistasis among genes is a universal phenomenon in obesity: Evidence from rodent models. Nutrition. 2004, 20(1): 74-77
[196] Moore J H. The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Human Heredity. 2003, 56(1-3): 73-82
[197] Kao C H, Zeng Z B. Modeling epistasis of quantitative trait loci using Cockerham's model. Genetics. 2002, 160(3): 1243-1261
[198] Gauderman W J, Thomas D C. The role of interacting determinants in the localization of genes. Advances in Genetics Incorporating Molecular Genetic Medicine. 2001, 42: 393-412
[199] Clark A G. Limits to prediction of phenotype from knowledge of genotypes. In: Clegg M T, eds. Limits to knowledge in evolutionary genetics. New York: Kluwer Academic/Penum Publishers. 2000
[200] Albuquerque L G, Keown J F, Van Vleck L D. Variances of direct genetic effects, maternal genetic effects, and cytoplasmic inheritance effects for milk yield, fat yield, and fat percentage. Journal of Dairy Science. 1998, 81(2): 544-549
[201] Allen J O. Effect of teosinte cytoplasmic genomes on maize phenotype. Genetics. 2005, 169(2): 863-880
[202] Meng F R, Ni Z F, Wu L M, Xie X D, Wang Z G, Sun Q X. Differential gene expression between reciprocal cross fertilized gernels and their parents during the early stages of seed development in wheat. Acta Agronomica Sinica. 2005, 31(1): 119-123
[203] Roubertoux P L, Sluyter F, Carlier M, Marcet B, Maarouf-Veray F, Cherif C, Marican C, Arrechi P, Godin F, Jamon M, Verrier B, Cohen-Salmon C. Mitochondrial DNA modifies cognition in interaction with the nuclear genome and age in mice. Nature Genetics. 2003, 35(1): 65-69
[204] Rand D M, Haney R A, Fry A J. Cytonuclear coevolution: the genomics of cooperation. Trends Ecol Evol. 2004, 19(12): 645-653
[205] Tang Z, Wang X, Hu Z, Yang Z, Xu C. Genetic dissection of cytonuclear epistasis in line crosses. Genetics. 2007, 177(1): 669-672
[206] Tibshirani R. Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society B. 1996, 58: 267-288
[207] Fan J, Li R. Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association 2001, 96: 1348-1360
[208] Omholt S W, Plahte E, Oyehaug L, Xiang K. Gene regulatory networks generating the phenomena of additivity, dominance and epistasis. Genetics. 2000, 155(2): 969-980
[209] Eta-Ndu J T, Openshaw S J. Epistasis for grain yield in two F2 populations of maize. Crop Science. 1999, 39(2): 346-352
[210] Carlborg O, Haley C S. Epistasis: too often neglected in complex trait studies? Nature Review Genetics. 2004, 5(8): 618-625
[211] Wyrick J J, Young R A. Deciphering gene expression regulatory networks. Current Opinion in Genetics and Development. 2002, 12(2): 130-136
[212] Tsai H K, Lu H H, Li W H. Statistical methods for identifying yeast cell cycle transcription factors. Proceedings of the National Academy of Sciences. 2005, 102(38): 13532-13537
[213] Drees B L, Thorsson V, Carter G W, Rives A W, Raymond M Z, Avila-Campillo I, Shannon P, Galitski T. Derivation of genetic interaction networks from quantitative phenotype data. Genome Biology 2005, 6(4): R38
[214] Johan D Peleman, Jeroen Rouppe van der Voort. Breeding by design. Trends in Plant Science. 2003, 8(7): 330-334
[1] Jones D F. Dominance of linked factors as a means of accounting for heterosis. Genetics. 1917, 2(5): 466-479
[2] East E M. Heterosis. Genetics. 1936, 4(21): 375-397
[3] Birchler J A, Auger D L, Riddle N C. In search of the molecular basis of heterosis. Plant Cell. 2003, 15(10): 2236-2239
[4] Hua J, Xing Y, Wu W, Xu C, Sun X, Yu S, Zhang Q F. Single-locus heterotic effects and dominance by dominance interactions can adequately explain the genetic basis of heterosis in an elite rice hybrid. Proceedings of the National Academy of Sciences. 2003, 100(5): 2574-2579
[5] Lu H, Romero-Severson J, Bernardo R. Genetic basis of heterosis explored by simple sequence repeat markers in a random-mated maize population. Theoretical and Applied Genetics. 2003, 107(3): 494-502
[6] Omholt S W, Plahte E, Oyehaug L, Xiang K. Gene regulatory networks generating the phenomena of additivity, dominance and epistasis. Genetics. 2000, 155(2): 969-980
[7] Yu S B, Li J X, Xu C G, Tan Y F, Gao Y J, Li X H, Zhang Q, Maroof M A. Importance of epistasis as the genetic basis of heterosis in an elite rice hybrid. Proceedings of the National Academy of Sciences. 1997, 94(17): 9226-9231
[8] Li Z K, Luo L J, Mei H W, Wang D L, Shu Q Y, Tabien R, Zhong D B, Ying C S, Stansel J W, Khush G S, Paterson A H. Overdominant epistatic loci are the primary genetic basis ofinbreeding depression and heterosis in rice. I. Biomass and grain yield. Genetics. 2001, 158(4): 1737-1753
[9] Malmberg R L, Held S, Waits A, Mauricio R. Epistasis for fitness-related quantitative traits in Arabidopsis thaliana grown in the field and in the greenhouse. Genetics. 2005, 171(4): 2013-2027
[10] Eta-Ndu J T, Openshaw S J. Epistasis for grain yield in two F2 populations of maize. Crop Science. 1999, 39(2): 346-352
[11] Allen J O. Effect of teosinte cytoplasmic genomes on maize phenotype. Genetics. 2005, 169(2): 863-880
[12] Albuquerque L G, Keown J F, Van Vleck L D. Variances of direct genetic effects, maternal genetic effects, and cytoplasmic inheritance effects for milk yield, fat yield, and fat percentage. Journal of Dairy Science. 1998, 81(2): 544-549
[13] Meng F R, Ni Z F, Wu L M, Xie X D, Wang Z G, Sun Q X. Differential gene expression between reciprocal cross fertilized gernels and their parents during the early stages of seed development in wheat. Acta Agronomica Sinica. 2005, 31(1): 119-123
[14] Rand D M, Fry A, Sheldahl L. Nuclear-mitochondrial epistasis and drosophila aging: introgression of Drosophila simulans mtDNA modifies longevity in D. melanogaster nuclear backgrounds. Genetics. 2006, 172(1): 329-341
[15] Zeyl C, Andreson B, Weninck E. Nuclear-mitochondrial epistasis for fitness in Saccharomyces cerevisiae. Evolution Int J Org Evolution. 2005, 59(4): 910-914
[16] Roubertoux P L, Sluyter F, Carlier M, Marcet B, Maarouf-Veray F, Cherif C, Marican C, Arrechi P, Godin F, Jamon M, Verrier B, Cohen-Salmon C. Mitochondrial DNA modifies cognition in interaction with the nuclear genome and age in mice. Nature Genetics. 2003, 35(1): 65-69
[17] Rand D M, Haney R A, Fry A J. Cytonuclear coevolution: the genomics of cooperation. Trends in Ecology & Evolution. 2004, 19(12): 645-653
[18] Carlborg O, Haley C S. Epistasis: too often neglected in complex trait studies? Nature Reviews Genetics. 2004, 5(8): 618-625
[19] Xu C, He X, Xu S. Mapping quantitative trait loci underlying triploid endosperm traits. Heredity. 2003, 90(3): 228-235
[20] Piepho H P. A quick method for computing approximate thresholds for quantitative trait loci detection. Genetics. 2001, 157(1): 425-432
[21] Allard R W. Genetic basis of the evolution of adaptedness in plants. Euphytica. 1996, 92(1-2): 1-11
[22] Rieseberg L H, Sinervo B, Linder C R, Ungerer M C, Arias D M. Role of gene interactions in hybrid speciation: evidence from ancient and experimental hybrids. Science. 1996, 272(5262): 741-745
[23] Carelli V, Giordano C, d'Amati G. Pathogenic expression of homoplasmic mtDNA mutations needs a complex nuclear-mitochondrial interaction. Trends in Genetics. 2003, 19(5): 257-262
[24] Chinnery P F. Searching for nuclear-mitochondrial genes. Trends in Genetics. 2003, 19(2): 60-62
[25] Reik W, Walter J. Genomic imprinting: parental influence on the genome. Nature ReviewsGenetics. 2001, 2(1): 21-32
[26] Wilkins J F. Genomic imprinting and methylation: epigenetic canalization and conflict. Trends in Genetics. 2005, 21(6): 356-365
[1] Rogers S, Bendich, A J. Extraction of DNA from plant tissues. Plant Molecular Biology Manual. 1988, A6: 1-10
[2] Van Ooijen J W, Vorrips R E. JoinMap? Version 3.0, Software for the calculation of genetic linkage map. Plant Research International. Wageningen, Netherlands, 2001
[3] SAS Institute. SAS/STAT? User's Guide Version 9.1. Cary, NC: SAS Institute, Inc.1999
[4] Tang Z, Wang X, Hu Z, Yang Z, Xu C. Genetic dissection of cytonuclear epistasis in line crosses. Genetics. 2007, 177(1): 669-672
[5] Yang J, Hu C, Hu H, Yu R, Xia Z, Ye X, Zhu J. QTLNetwork: mapping and visualizing genetic architecture of complex traits in experimental populations. Bioinformatics. 2008, 24(5): 721-723
[6] Zeng Z B. Precision mapping of quantitative trait loci. Genetics. 1994, 136(4): 1457-1468
[7] Wang S, Basten C J, Zeng Z-B. Windows QTL Cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh, NC. 2007
[8] McCouch S R, Cho Y G, Yano P E, Blinstrub M, Morishima H, Kinoshita T. Report on QTL nomenclature. Rice Genetics Newsletter. 1997, 14: 11-13
[9] Moore J H. The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Human Heredity. 2003, 56(1-3): 73-82
[10] Kao C H, Zeng Z B. Modeling epistasis of quantitative trait loci using Cockerham's model. Genetics. 2002, 160(3): 1243-1261
[11] Gauderman W J, Thomas D C. The role of interacting determinants in the localization of genes. Advances in Genetics Incorporating Molecular Genetic Medicine. 2001, 42: 393-412
[1] Tang Zaixiang, Wang Xuefeng, Hu Zhiqiu, Yang Zefeng, Xu Chenwu. Genetic dissection of cytonuclear epistasis in line crosses. Genetics, 2007, 177: 669-672