introgression genetics and breeding between up

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渐渗系在染色体上的大概位置图

L. pennellii Introgression lines (ILs)Congenic lines that differ in a single defined chromosome segment are useful for the study of complex phenotypes, as they allow isolation of the effect of a particular quantitative trait locus (QTL) from those of the entire genome. We developed a set of Lycopersicon pennellii-derived introgression lines (ILs) that together cover the entire genome in the background of L. esculentum Var. M82. This resource is very powerful for the study of genes affecting complex phenotypes.The second generation IL population is composed of 76 ILs (the 50 original lines and 26 new ILs), each containing a single introgression from L. pennellii (LA 716) in the genetic background of the processing tomato variety M82. The IL map was connected to thehigh-resolution F2 map composed of 1500 markers. This was achieved by probing all of the specific chromosome lines with the RFLP markers from the framework F2 map. A total of 614 markers were probed and the ends of the introgressions were mapped with the resolution of the F2 map.The L. pennellii introgressed segments appear as solid bars in which the boundary edge of each segment is indicated by inclusive (+) and exclusive (-) RFLP markers. All ILs are homozygous for the introgressed segment except for part of IL8-1 (dashed line). Bins are designated by the chromosome number followed by a capital letter and indicate a unique area of IL overlap and singularity; it is important to note that some of the bin designations might change as more probing is done. Molecular and genetic markers are indicated to the right of each chromosome and the genetic distances (in cM) according to Tanksley et al. (1992) are indicated to the left.Seed of the second generation ILs is presently being increased by The C.M. Rick Tomato Genetics Resource Center, University of California Davis and the ILs were assigned accession numbers LA4028 - LA4103.∙Chromosome 1∙Chromosome 2∙Chromosome 3∙Chromosome 4∙Chromosome 5∙Chromosome 6∙Chromosome 7∙Chromosome 8∙Chromosome 9∙Chromosome 10∙Chromosome 11Chromosome 12IL ReferencesEshed Y, M Abu-Abied, Y Saranga, D Zamir (1992) Lycopersicon esculentum lines containing small overlapping introgressions from L. pennellii. Theor Appl Genet 83:1027-1034Eshed Y and D. Zamir (1994) Introgressions from Lycopersicon pennellii can improve the soluble-solids yield of tomato hybrids. Theor Appl Genet 88:891-897.Eshed Y and D. Zamir (1994) A genomic library of Lycopersicon pennellii in L. esculentum: A tool for fine mapping of genes. Euphytica 79:175-179.Eshed Y and D Zamir (1995) An introgression line population of Lycopersicon pennellii in the cultivated tomato enables the identification and fine mapping of yield associated QTL. Genetics 141:1147-1162.Eshed Y and D Zamir (1996) Less than additive epistatic interactions of QTL in tomato. Genetics 143:1807-1817.Eshed Y, G Gera and D Zamir (1996) A genome-wide search for wild-species alleles that increase horticultural yield of processing tomatoes Theor Appl Genet 93: 877-886.Zamir D and Y Eshed (1998) Tomato genetics and breeding using nearly isogenic introgression lines derived from wild species. in: Molecular Dissection of Complex Traits. ed. AH Paterson. CRC Press Inc. Fl. 207-217.Qilin P, Yong-Sheng L, Budai-Hadrian O, Sela M, Carmel-Goren L, Zamir D and R Fluhr (2000) Comparative genetics of NBS-LRR resistance gene homologues in the genomes of two dicotyledons: tomato and Arabidopsis. Genetics 155: 309-322.Fridman E, Pleban T and D Zamir (2000) A recombination hotspot delimits a wild species QTL for tomato sugar content to 484-bp within an invertase gene. Proc Natl Acad Sci USA 97: 4718-4723.。

如何保护野生动物英语作文

Protecting wildlife is a critical aspect of preserving the balance of our ecosystem and ensuring the survival of various species.Here are some key points that can be included in an English essay on how to protect wildlife:1.Raising Awareness:Educating the public about the importance of wildlife conservation is the first step.Awareness campaigns can be conducted through schools,community centers,and social media platforms to inform people about the threats faced by wildlife and the role they can play in conservation efforts.2.Legislation and Enforcement:Strong laws need to be in place to protect wildlife from poaching,habitat destruction,and illegal trade.Enforcement agencies must be wellequipped and trained to monitor and penalize violators effectively.3.Habitat Preservation:Protecting and restoring natural habitats is essential for the survival of wildlife.This includes preventing deforestation,promoting reforestation,and establishing protected areas such as national parks and wildlife reserves.4.Sustainable Land Use:Encouraging sustainable agricultural practices and urban planning that minimize the impact on wildlife habitats can help reduce humanwildlife conflicts and preserve biodiversity.5.Conservation Programs:Supporting and participating in conservation programs that focus on the breeding and reintroduction of endangered species into their natural habitats can help increase their population and genetic diversity.munity Involvement:Engaging local communities in conservation efforts is crucial,especially in areas where their livelihoods depend on the land.Providing alternative sources of income and education can help reduce their reliance on activities that harm wildlife.7.Research and Monitoring:Continuous research is necessary to understand wildlife behavior,population dynamics,and the impact of human activities on their survival. Monitoring programs help track changes and inform conservation strategies.8.International Cooperation:Many wildlife species migrate across borders,making international cooperation essential for their protection.Collaborative efforts,such as the Convention on International Trade in Endangered Species of Wild Fauna and Flora CITES,play a vital role in regulating trade and protecting species.9.Adopting Ecofriendly Practices:Individuals can contribute to wildlife protection byadopting ecofriendly practices such as reducing,reusing,and recycling waste using renewable energy sources and supporting businesses that prioritize sustainability.10.Supporting Conservation Organizations:Donating to or volunteering with organizations dedicated to wildlife conservation can help fund important research,rescue efforts,and habitat restoration projects.By incorporating these points into an essay,students can provide a comprehensive overview of the various ways in which wildlife protection can be achieved and the importance of collective action in ensuring a sustainable future for all species.。

高一生物必修二第一章第一节笔记

高一生物必修二第一章第一节笔记Biology is the study of life and living organisms. 生物是研究生命和生物体的科学。

It is an incredibly diverse and broad field that encompasses everything from the molecular workings of cells to the complex interactions of ecosystems. 生物学是一个非常多样化和广泛的领域，涵盖了从细胞的分子作用到生态系统复杂相互作用的一切。

By studying biology, we gain a deeper understanding of the world around us and our place within it. 通过学习生物学，我们可以更深入地了解我们周围的世界以及我们在其中的位置。

One of the fundamental concepts in biology is the idea of evolution. 生物学中的一个基本概念是进化的概念。

This concept, first proposed by Charles Darwin, explains how species change over time in response to their environment, leading to the diversity of life on Earth. 这个概念是由查尔斯·达尔文首次提出的，它解释了物种如何随着时间的推移而在环境中变化，从而导致地球上生命的多样性。

Evolution is driven by the process of natural selection, where organisms with beneficial traits are more likely to survive and reproduce, passing on those traits to future generations. 进化是由自然选择的过程驱动的，具有有益特征的生物更有可能存活和繁殖，将这些特征传递给后代。

基因比环境更重要英语作文

基因比环境更重要英语作文Paragraph 1:In the ongoing debate about nature versus nurture, it is often argued that both genes and environment play pivotal roles in shaping an individual's traits and behaviors. However, increasingly compelling evidence from genetic research suggests that genes might hold more sway than environmental factors. "在关于先天与后天的持续辩论中，基因和环境都被认为在塑造个体特征和行为方面起着至关重要的作用。

然而，来自遗传学研究的愈发令人信服的证据表明，基因可能较环境因素更具影响力。

”Paragraph 2:Genetic makeup determines the basic biological blueprint for human development. Each person inherits a unique set of genes from their parents, which dictate physical attributes like eye color, height, and susceptibility to certain diseases. This fundamental principle underlines the dominance of genes over environmental influences. "基因构成决定了人类发展的基本生物学蓝图。

每个人从父母那里继承了一套独特的基因，这些基因决定了眼睛颜色、身高以及对某些疾病的易感性等身体属性。

Detection of QTLs with additive effects and additive-by-environment interaction effects on panicl

ORIGINAL PAPERDetection of QTLs with additive effects and additive-by-environment interaction effects on panicle number in rice (Oryza sativa L.)with single-segment substitution linesGuifu Liu ÆZemin Zhang ÆHaitao Zhu ÆFangming Zhao ÆXiaohua Ding ÆRuizhen Zeng ÆWentao Li ÆGuiquan ZhangReceived:20July 2007/Accepted:27January 2008ÓSpringer-Verlag 2008Abstract A novel population consisting of 35single-segment substitution lines (SSSLs)originating from crosses between the recipient parent,Hua-jing-xian 74(HJX74),and 17donor parents was evaluated in six cropping season environments to reveal the genetic basis of genetic main effect (G)and genotype-by-environment interaction effect (GE)for panicle number (PN)in rice.Subsets of lines were grown in up to six environments.An indirect analysis method was applied,in which the total genetic effect was ﬁrst partitioned into G and GE by using the mixed linear-model approach,and then QTL (quantitative trait locus)analyses on these effects were conducted separately.At least 18QTLs for PN in rice were detected and identiﬁed on 9of 12rice chromosomes.A single QTL effect (a +ae )ranging from -1.5to 1.2was divided into two components,additive effect (a )and additive 9environment interaction effect (ae ).A total number of 9and 16QTLs were identiﬁed with a ranging from -0.4to 0.6and ae ranging from -1.0to 0.6,respectively,the former being stable but the latter unstable across environments.Three types of QTLs were suggested according to their effects expressed.Two QTLs (Pn-1b and Pn-6d )expressed stably across environments due to the association with only a ,nine QTLs (Pn-1a,Pn-3c,Pn-3d,Pn-4,Pn-6a,Pn-6b,Pn-8,Pn-9and Pn-12)with only ae were unstable,and the remaining seven ofQTLs were identiﬁed with both a and ae ,which also were unstable across environments.This is the ﬁrst report on the detection of QE (QTL-by-environment interaction effect)of QTLs with SSSLs.Our results illustrate the efﬁciency of characterizing QTLs and analyzing action of QTLs through SSSLs,and further demonstrate that QE is an important property of many rmation provided in this paper could be used in the application of marker-assisted selection to manipulate PN in rice.IntroductionRice (Oryza sativa L.)is one of the most important crops in the world.It has been estimated that more than 50%of the human population depends on rice as its main source of nutrition (Brar and Khush 2002).It is unique among cereals by having a storage protein,which is primarily made of glutelin,and has a more balanced amino acid proﬁle than the prolamine-rich storage proteins found in most cereals (Juliano 1985).On the other hand,the rice genome is more than a resource for understanding the biology of a single species.It is a window into the structure and function of genes in other crop grasses as well.It has also become a useful plant for studying biology,as a model plant for monocots due to its small genome relative to those of other species of the Gramineae,synteny with other grasses such as wheat,barley,and maize,efﬁcient transformation,dense molecular genetic maps,large sequence libraries and abundance of genetic resources (Motoyuki and Makoto 2002).For these reasons,rice has been the subject of numerous genetic and breeding studies over the past 100years,and has provided much useful information for plant biology and plant breeding.Communicated by D.Mather.Guifu Liu and Zemin Zhang contributed equally to this work.G.Liu ÁZ.Zhang ÁH.Zhu ÁF.Zhao ÁX.Ding ÁR.Zeng ÁW.Li ÁG.Zhang (&)Guangdong Key Laboratory of Plant Molecular Breeding,South China Agricultural University,Guangzhou 510642,People’s Republic of China e-mail:gqzhang@Theor Appl GenetDOI 10.1007/s00122-008-0724-4Panicle number(PN)in rice,an important agronomic character for grain production,is normally one of the main determinants of grain yield,even at adequate plant popu-lations(Counce et al.1992).The development of PN is affected by various environmental factors including plant nutrients,planting density,and climatic circumstances such as light,temperature and water supply.Scientists have paid increasing attention to PN in rice due to reduced tillering capacity being one of the main target traits for the super-rice ideotype(Khush2000).Research using mutant mate-rials conﬁrmed that the PN in rice could be controlled by one single gene(Li et al.2003a,b).Most studies by using traditional and molecular genetic analysis reported that rice PN was inﬂuenced by multiple quantitative trait loci (QTLs)(Ahmad et al.1986;Li et al.1997).QTLs for PN in rice have been identiﬁed on10of the12chromosomes of rice(Yan et al.1998;Liao et al.2001;Hittalmani et al. 2003;Jiang et al.2004)using populations of recombinant inbred lines(RILs)or doubled haploid lines(DHLs).With such populations,it is difﬁcult to differentiate quantitative trait locus(QTL)effects from background noise,particu-larly for QTLs with small and/or interacting effects(Eshed and Zamir1995).To overcome these limitations and achieve high-reso-lution mapping of QTLs,Eshed and Zamir(1995)proposed the application of introgression line(IL)populations.In rice,several permanent mapping populations,such as chromosome segment substitution lines(CSSLs)and backcross inbred lines(BILs)have been developed and have been used to detect many QTLs affecting heading date(Yano et al.19972000,2001;Yamamoto et al.1998, 2000;Takahashi et al.2001).Wan et al.(2003)used a mapping population of66japonica chromosome segment substitution lines in an indica genetic background to detect QTLs for leaf bronzing index,stem dry weight,plant height,root length and root dry weight under F e2+stress. Tian et al.(2006)constructed introgression lines carrying wild rice(Oryza ruﬁpogon Griff.)segments in a cultivated rice(Oryza sativa L.)background and characterized int-rogressed segments associated with yield-related traits.We have constructed a library of1,123single-segment substi-tution lines(SSSLs)in rice(Zhang et al.2004;He et al. 2005a;Xi et al.2006),and have used it to detect QTLs affecting many agronomic traits in rice by using the library (He et al.2005b,c;Xi et al.2006).As each of these studies was conducted in only one environment,it was not possible to estimate QTL-by-environment interaction effects(QE) (Zhu1999;Wang et al.1999).Most plant traits are quantitative in nature,and are thought to be controlled by polygenes that have small effects and are easily affected by the environment.Thus genotype-by-environment interaction effect(GE)is a common phenomenon for quantitative traits(Falconer 1960).GE occurs when the deviations between two geno-types perform differently in different environments,and is thus described as differential genotypic sensitivities to environments(Falconer1981).GE is also of great impor-tance in plant evolution and breeding.In plant evolution, high level of GE allows plants better adaptation to their changing environments and the maintenance of genetic variation in populations(Jain and Marshall1967).In plant breeding,GE has received considerable attention as it is closely related to the stability of varieties.Because of its importance,GE of quantitative traits has been the subject of extensive investigation(Baker1988;Cooper and Ham-mer1996).QTL analysis has made it possible to track the performance of individual QTL across environments, allowing GE to be dissected into its component of QE(Zhu 1999;Wang et al.1999).Despite technical difﬁculties,QE has been revealed in many crops(Paterson et al.1991; Zhuang et al.1997;Jiang et al.1999).Most of the previous studies inferred QTL-by-environment interaction by com-paring QTLs detected in different environments,leading to results that may mix GE with G and that do not provide unbiased estimates of QE(Yan et al.1999;Hittalmani et al. 2003;Li et al.2003a,b).Zhu(1998)proposed an indirect analysis methodology for QE,in which the total genetic effect isﬁrst partitioned into G and GE,and then QTLs are mapped for these effects separately.QTLs mapped for the G led to estimation of the genetic main effect of QTLs,independent of change in environmental conditions,while those mapped for GE led to the identiﬁcation of QE that are signiﬁcantly affected by variation in environmental e of this approach in rice has provided valuable information about QE in a population of DHLs(Yan et al.1999;Hittalmani et al. 2003;Li et al.2003a,b).In the present study,each of35 rice single-segment substitution lines selected from our library was evaluated in up to six environments.QTL analyses on PN were conductedﬁrst on total genetic effect (G+GE)estimated in data from individual environment, and then on G and GE separately.QTLs identiﬁed according to G+GE were expected to contain mixed effects of additive effect(a)and additive-by-environment interaction effect(ae),while QTLs obtained on G and GE were with a and ae,respectively.The aims of the study were to detect a and ae of QTLs,and to evaluate stability of the QTLs for PN in rice.Materials and methodsPlant materialsThe SSSLs in the library were developed by using of Hua-jing-xian74(HJX74),an elite indica variety from SouthTheor Appl GenetChina,as recipient,and24varieties including14indica and10japonica varieties collected worldwide as donors (Zhang et al.2004).Development of the SSSLs,through backcrossing and SSR marker selection,was described by He et al.(2005a)and Xi et al.(2006).For this study,35 SSSLs were selected(Table1),each containing only one chromosomal segment from a donor substituted in the HJX74genetic background.The substituted segments dis-tribute on10chromosomes and range in length from2.6to 96.2cM with an average of26.86cM,and a total length of 940.35cM(Fig.1).Field trialsPhenotypic experiments were conducted at the experi-mental farm of South China Agricultural University, Guangzhou(at*113°east longitude and*23°northTable1Thirty-ﬁve single-segment substitution lines(SSSLs)and their codes,donors and experimental environmentsSSSL Code Donor Experimental environmentE1(2003F)E2(2004S)E3(2004F)E4(2005F)E5(2006S)E6(2006F)W07-14-10-04S1Suoyunuo++++++W02-17-08-14S2Amol3++++++W15-05-07-15S3American jasmine++++++W11-15-08-10-05S4Basmati370++++++W08-15-06-04-04S5IR64+++++W02-17-06-15S6Amol3++++W11-15-09-03S7Basmati370++++W18-06-02-02S8IRAT261++++W07-14-08-04S9Suoyunuo+++++W09-38-54-07-06-01S10Basmati385+++++W14-18-06-06-02S11Lianjian33++++++W17-10-06-01-08-07S12Ganxiangnuo+++++W20-20-05-19-07S13Chenglongshuijingmi+++++W23-07-06-01-01-08S14Lemont+++++W14-18-06-10-01S15Lianjian33++++W17-10-07-05-12S16Ganxiangnuo++++W17-46-40-10-07-04S17Ganxiangnuo++++W20-20-05-05-11S18Chenglongshuijingmi++++W27-14-01-09-18S19IAPAR9+++W20-12-02-01-04S20Chenglongshuijingmi+++D21(W15-05-07-15-03-S)S21American jasmine+++W08-09-05-03S22IR64++W08-16-03-59S23IR64++W20-20-05-06S24Chenglongshuijingmi++W27-14-06-20S25IAPAR9++W23-07-06-10-06S26Lemont++W04-45-50-04-05-01S27BG367++W13-11-29-06-04-08-10S28Jiangxi-Si-Miao++W08-18-09-09-06-02S29IR64+++W19-18-09-06S37Kyeema++W15-05-09-02-01S38American jasmine++W15-05-09-06-04-02S39American jasmine++W07-07-02-07-03S40Suoyunuo++W06-26-21-04-03-02S41Katy++W10-31-35-06-06-06S42Nangyangzhan++E1–E6represent the six experimental environments.The numbers and letters in parentheses indicate the growing year and season(S for spring from March to July,or F for fall from July to November).‘+’indicates the environments in which each line was evaluatedTheor Appl Genetlatitude),China,in spring (from March to July)2004and 2006and autumn (from July to November)2003,2004,2005and 2006.HJX74was grown in all six environments,and each of the SSSLs was grown in at least two of the environments (Table 1).In each experiment,the germi-nated seeds were sown in a seedling bed and seedlings were transplanted to a paddy ﬁeld 20days later,with two plants per hill spaced at 16.7cm 920.0cm.Each plot consisted of thirteen 6.2m long rows with 32hills,and all plots were arranged in a randomized complete block design with three replications.The management of the ﬁeld experiments was in accordance with local standard prac-tices.At maturity,the number of panicles (PN)was counted for each of 20hills from the middle of each plot,and the average PN value of the 20hills was used as raw data in the analysis.Mixed linear models for estimating G effects and GE interaction effectsFor a genetic experiment conducted only in one environ-ment,the phenotypic performance of the j th genetic entry in the k th block can be expressed by y jk ¼l þG j þB k þe jkð1Þwhere,l =population mean,ﬁxed;G j =genetic main effect of j th genotype,G j *N (0,r G 2);B k =block effect of k th block,B k *N (0,r B 2);and e hjk =residual effect,e hjk *N (0,r e 2).For a genetic experiment conducted within multiple environments,the phenotypic performance of the j th genetic entry in the k th block within the h th environment can be expressedbyFig.1The distribution and the lengths of substituted chromosome segments in 35single-segment substitution lines (SSSLs).The substituted segments are represented by vertical lines .The number at the top of each vertical line is the number of the SSSL carrying thatsegment.The number with ‘*’indicates the SSSL carrying a QTL on its substituted segment.Codes on the right of each chromosome designate molecular marker lociTheor Appl Genety hjk¼lþE hþG jþGE hjþB kþe hjkð2Þwhere,l=population mean,ﬁxed;E h=environment effect of h th environment,E h*N(0,r E2);G j=genetic main effect of j th genotype,G j*N(0,r G2);GE hj=geno-type9environment interaction effect between j th genotype and h th environment,GE hj*N(0,r GE2);B k=block effect of k th block,B k*N(0,r B2);and e hjk=residual effect, e hjk*N(0,r e2).The minimum norm quadratic unbiased estimation(MINQUE)method with all prior values set at1 (Zhu and Weir1996)was used to estimate variance compo-nents for the trait.Values of G and GE were predicted by the Best Linear Unbiased Prediction(BLUP)method(Zhu and Weir1996).All estimations were performed using the QGAStation software package(Chen and Zhu2003).QTL analysesAn indirect approach was conducted to analyze QTL effects(Zhu1998).First,values of G and GE for HJX74 and all individual SSSLs on PN within each environment were estimated according to model(1)and model(2) mentioned above,respectively.Next,QTLs were mapped using these estimated values as input data separately.QTLs identiﬁed according to G in model(1)are expected to contain mixed effects of a and ae,and will be referred to here as with a+ae.QTLs obtained using G and GE from model(2)have a and ae,respectively.The estimates obtained for each SSSL were compared to those for HJX74 with one-tailed Duncan’s multiple range tests(Chen and Zhu2003)conducted at a signiﬁcance level of0.05.It was assumed that each SSSL affecting the trait carries only one QTL,and a signiﬁcant QTL affecting PN was declared only if one type of the effect of SSSL is signiﬁcantly dif-ferent from the corresponding effect of HJX74(Eshed and Zamir1995).QTL effect values(a+ae,a and ae)were calculated as the differences of genetic effects between each SSSL and HJX74.ResultsPhenotypic variation for PNThe PN of the parent HJX74ranged from7.3in environment E4to8.1in environments E1and E2,with standard devia-tions ranging from0.08to0.56.The average PN of HJX74 was8.0in spring,7.7in fall,and7.7across all six envi-ronments.The average PN of the SSSLs was similar to that of HJX74in all environments except for E1,where the average PN of the SSSLs was7.7compared to8.1for HJX74 (Table2).Analysis of variance on phenotypic values of PN from all experimental environments indicated that variance components of the genotype(including HJX74and SSSLs) and the GE were signiﬁcant(data not shown),with relative contributions to the total phenotypic variation of9.35and 18.42%,respectively.QTLs with a+ae effects on PNQTL mapping based on the data estimated in individual environments according to model(1)led to the identiﬁ-cation of18QTLs with mixed effects of a+ae in the SSSLs for PN in rice(Table3,Fig.1).Four QTLs were detected on each of chromosomes3and6,two on each of chromosomes1,2and7and one on each of chromosomes 4,8,9and12(Fig.1).Of18QTLs detected,7(QTLs Pn-1a,Pn-1b,Pn-2a,Pn-2b,Pn-3a,Pn-3b and Pn-6a) were detectable in three environments(out of4,5or6 environments in which the corresponding SSSLs were evaluated),4(QTLs Pn-6c,Pn-6d,Pn-7a and Pn-7b)in two environments(out of2or4),and the remainder in only one environment(out of2,4or5)(Table3).Some QTLs that were detected in multiple environments expressed different effects across environments,with dif-ferences observed in both the magnitudes and directions of effects(Table3).QTL Pn-6a showed the most variation among environments,with effects ranging from-1.3in E1to0.8in E3.Three QTLs,Pn-1b,Pn-6d and Pn-7a, had quite consistent expression across environments (Table3).QTLs for PN with a effectsQuantitative trait locus mapping based on the G values estimated according to model(2)identiﬁed9QTLs with a Table2Mean and standard deviations(SD)for the number of panicles per hill in the rice line HJX74grown in six environments(E1 to E6)and means,standard deviations,maxima(Max)and minima (Min)for varying numbers(N)of single-segment substitution lines (SSSLs)evaluated in those environmentsEnvironment HJX74SSSLsMean SD N Mean SD Max Min E18.10.22157.70.399.1 6.4 E28.10.09268.10.428.87.3 E37.70.08247.90.539.2 6.8 E47.30.56257.10.468.2 6.3 E58.00.26207.90.428.97.2 E67.40.29167.50.228.07.1 All7.70.431267.70.599.2 6.3Theor Appl Genetthat are stable across environments(Table4).These QTLs were located onﬁve rice chromosomes:one on chromo-some1and two on each of chromosomes2,3,6and7.At four QTLs(Pn-1b,Pn-2a,Pn-6d and Pn-7b)the alleles derived from the donor parents reduced PN(with effects ranging from-0.2to-0.4).At the remainingﬁve QTLs,the donor alleles increased PN(by between0.3and0.6) (Table4).QTLs for PN with ae interaction effectsThere were16QTLs with signiﬁcant ae for PN(Table4): four on chromosome3and one to three on each of eight other chromosomes.QTL Pn-1a had no a,but showed signiﬁcant interactions,with positive ae values in E1and E6and negative ae values in E3and E5.Other environ-ment-sensitive QTLs with signiﬁcant ae values in fewer environments:QTLs Pn-2a and Pn-6a in three environ-ments,QTL Pn-3b in two environments,and the remaining 12QTLs in only one environment.All estimated ae values ranged from-1.0of QTL Pn-6a to0.6of QTL Pn-1a in E1. QTL Pn-6a showed the largest variation among environ-ments,with ae values of-1.0,0.5and0.4in E1,E3and E4, respectively.DiscussionQTL detection through SSSLsIn the present study,we used SSSLs as experimental materials and an indirect method to analyze data from six environments to map QTLs with additive and/or additive-by-environment interaction effects on PN in rice.For comparison,QTL mapping was also performed using data from each individual environment.Since each of35SSSLs used contained only one substituted segment from a donor in HJX74genetic background,all the genetic variation between one of SSSLs and HJX74can be associated with the substituted segment.In the development of the SSSLs, 574SSR markers,distributed throughout the genome with an average interval of2.7cM were surveyed in the BC2F1 generation(Zhang et al.2004),and polymorphic markers were re-examined in the BC4F1and BC4F2generations to ensure the uniformity of genetic background of the SSSLs (Xi et al.2006).This should minimize the background genetic effects,providing more reliable QTL detection and estimation of QTL effects.QTLs affecting PN were detected in10distinct regions of the rice genome.This is more than in most previous QTL mapping studies in rice. For example,Yan et al.(1998)detected three QTLs for tiller number at maturity in a DHL population.Liao et al. (2001)detected six QTLs in a DHL population and nine QTLs in a RIL population in bothﬁeld and pot experi-ments.And Hittalmani et al.(2003)detected a total of twenty signiﬁcant QTLs for PN in a DHL population evaluated in nine locations across four countries in Asia, but the number of QTLs at any location varied from zero to three.QTLs in three regions on chromosomes2,4and12 detected here are likely in common with QTLs detected in previous studies,but other QTLs detected here have not previously been detected(Yan et al.1998;Liao et al.2001; Hittalmani et al.2003).Given that the substituted chro-mosome segments used here did not cover the whole genome and that some segments were quite long,there remains scope for further work of this type,aimed at detecting QTLs elsewhere in the genome and/or deter-mining whether any of the segments contain more than one QTL.Table3Effects of QTLs on panicle number per hill in rice,asestimated by evaluating single-segment substitution lines(SSSLs)invarious environments(E1to E6)QTL SSSL code a+aeE1E2E3E4E5E6Pn-1a S40.9***-0.7**-0.3*Pn-1b S11-0.3*-0.3*-0.2*Pn-2a S7-1.5****0.3*-0.3*––Pn-2b S190.4*0.7***0.6**––Pn-3a S13–0.4*0.8****0.5*Pn-3b S18–– 1.2****0.4*0.2*Pn-3c S23-0.4*––––Pn-3d S24-0.3*––––Pn-4S15-0.8***––Pn-6a S3-1.3****0.8***0.5*Pn-6b S14–0.3*Pn-6c S16–0.3*0.7***–Pn-6d S22–––-0.6**-0.4*–Pn-7a S27––0.3*0.4*––Pn-7b S37-0.2*-0.4*––––Pn-8S17––-0.3*Pn-9S6-0.9***––Pn-12S5-0.4*–QTLs are designated by codes beginning with‘Pn-’(for the traitpanicle number)followed by the chromosome number and in somecases a letter to distinguish between two or more possible QTLs onthe same chromosome.a+ae is the confounded effect of the QTLestimated at a given environmentThe sign indicates the direction of the effect of the donor allele.*,**,***and****show the signiﬁcances at0.05,0.01,0.005and0.001ofprobability level,respectively.‘–’indicates that a particular SSSLwas not evaluated in a particular environmentTheor Appl GenetDetection of QTLs with QE interaction effectsQTL-by-environment interaction is clearly important in affecting quantitative traits,and signiﬁcant QE interactions have been reported(e.g.Paterson et al.1991;Zhuang et al. 1997).In most previous mapping studies,the existence of QE interaction was inferred by comparing QTLs and their effects in multiple environments(e.g.,Paterson et al.1991; Bubeck et al.1993).In this study,we applied a direct method of analysis conducted separately for each envi-ronment,and an indirect method(Zhu1998)in which QTLs with QE were mapped using predicted total geno-type9environment interaction effects.The two methods identiﬁed the same SSSLs as carrying QTLs affecting PN (Tables3,4).The direct method provided estimates of the effects of QTLs within a single environment,but effects can be mixed by both a and ae of QTLs.The indirect method allowed for separation of a and ae.A total of16 QTLs were detected with signiﬁcant ae values ranging from-1.0to0.6on PN in rice(Table4).Interactions of QTLs with environments included cases in which:(1)a QTL expresses in one environment but not in another;(2)a QTL expresses very differently and has opposite effects in different environments;and(3)a QTL expresses strongly in one environment but weakly in another.The total effect of each QTL at a speciﬁc environment(Table3),as esti-mated by the direct method,tended to be approximately the sum of the a value and the ae value of the QTL at that environment(Table4),as estimated by the indirect method.Patterns of QE interaction for PN in riceIn any speciﬁc environment,the total effect of a QTL includes the main effect of the QTL and QE interaction effects for that environment.The QTL main effect is expressed in the same way across different environments and free from environmental inﬂuence,while the QE interaction effect is speciﬁc to a particular set of environ-mental conditions(Zhu1998;Yan et al.1999).Of the18 QTLs detected in this study,two QTLs,Pn-1b and Pn-6d had signiﬁcant a values but no signiﬁcant ae values (Table4).Because the expression of such QTLs is free from environmental interactions,their use in selection should improve trait performances across all environments similar to those two QTLs included in this study.As both of these QTLs had negative a values,the SSSLs in which they were detected(S11for Pn-1b and S22for Pn-6d) could be useful in breeding to reduce PN in rice(Table4).A second type of QTLs is associated with ae only.This category includes nine of the QTLs(Pn-1a,Pn-3c,Pn-3d, Pn-4,Pn-6a,Pn-6b,Pn-8,Pn-9and Pn-12)detected in this study(Table4).Since the expression of such QTLs is speciﬁc to a particular set of environmental conditions, they will be suitable for selection only for thoseTable4QTL effect components on panicle number in rice,as estimated by evaluating single-segment substitution lines(SSSLs)in various experimental environments(E1to E6) QTLs are designated by codes beginning with‘Pn-’(for the trait panicle number)followed by the chromosome number and in some cases a letter to distinguish between two or more possible QTLs on the same chromosome.The sign indicates the direction of the effect of the donor allele.‘*,**,***and****’show the signiﬁcances at0.05,0.01,0.005and0.001of probability level,respectively.‘–’indicates that a particular SSSL was not evaluated in a particular environment QTL SSSL code a aeE1E2E3E4E5E6Pn-1a S40.6****-0.5***-0.3*0.3* Pn-1b S11-0.2*Pn-2a S7-0.3*-0.9****0.296*0.4**––Pn-2b S190.6****0.2*––Pn-3a S130.4****–0.4*Pn-3b S180.5****––0.6****-0.3*Pn-3c S23-0.341*––––Pn-3d S24-0.305*––––Pn-4S15-0.5***––Pn-6a S3-1.0****0.5***0.4*Pn-6b S14–0.3*Pn-6c S160.3*–0.387*–Pn-6d S22-0.4***––––Pn-7a S270.3*––0.3*––Pn-7c S37-0.2*-0.321*––––Pn-8S17––-0.3*Pn-9S6-0.5***––Pn-12S5-0.3*–Theor Appl Genetenvironmental conditions.The QTL Pn-1a provides an example of the complexity of QTL interactions,with positive ae values in E1and E6,negative ae values in E3 and E5,and no signiﬁcant ae values in E2or E4(Table4). The third type of QTLs identiﬁed was associated with both a and ae.Seven QTLs(Pn-2a,Pn-2b,Pn-3a,Pn-3b,Pn-6c, Pn-7a and Pn-7b)identiﬁed in this study fell in this cate-gory.The expression of such QTLs is affected by environmental conditions.If the a value of such a QTL is in the desired direction(i.e.,negative,for PN),and if there are no large ae values in the opposite direction,then selection for the donor allele could still contribute to improvement in performance across environments,albeit with variable responses in particular environments.For example,SSSL S37might be a useful source of an allele at QTL Pn-7c.However,at QTLs with large ae values in opposing directions,selection for an allele with favorable effects in certain environments could lead to undesired results in other environments.For example,selection for the Pn-2a allele from SSSL S7could reduce PN in envi-ronments similar to E1,but might increase PN in environments similar to E3.Agricultural researchers have long recognized the implications of genotype-by-environ-ment interactions in breeding programs.Understanding QE would help breeders in deciding which QTL to use in their breeding programs while tailoring crop cultivars for spe-ciﬁc or more diverse environments.This is theﬁrst report on detection of QE values of QTLs with SSSLs.It illus-trates the value of analyzing action of QTLs through SSSLs with an indirect method that allows estimation of additive effects and QTL by environment interactions. Acknowledgments This research was supported by the National Basic Research Program of China(2006CB101700)and the National Natural Science Foundation of China(30330370).ReferenceAhmad L,Zakri AH,Jalani BS,Omar D(1986)Detection of additive and non-additive variation in rice.In:Rice genetics.IRRI, Manila,pp555–564Baker RJ(1988)Differential response to environmental stress.In:Weir BS,Eisen EJ,Goodman MM,Namkoong G(eds) Proceedings of the2nd international conference on quantitative genetics.Sinauer,Massachusetts,pp492–504Brar DS,Khush GS(2002)Transferring genes from wild species into rice.In:Kang MS(ed)Quantitative genetics,genomics and plant breeding.CABI,Oxford,pp197–217Bubeck DM,Goodman MM,Beavis WD,Grant D(1993)Quanti-tative trait loci controlling resistance to gray leaf spot in maize.Crop Sci33:838–847Chen GB,Zhu J(2003)Department of Agronomy,Zhejiang University,Hangzhou,China./software/qga/ index.htmCooper M,Hammer GL(1996)Plant adaptation and crop improve-ment.CAB International,Oxon Counce PA,Wells BR,Gravois KA(1992)Yield and harvest index responses to preﬂood nigtrogen ferﬁlization at low rice plant populations.J Prod Agric5:492–497Eshed Y,Zamir D(1995)An introgression line population of Lycopersicon pennellii in the cultivated tomato enables the identiﬁcation andﬁne mapping of yield-associated QTL.Genetics141:1147–l162Falconer DS(1960)Introduction to quantitative genetics.Ronald Press,New YorkFalconer DS(1981)Introduction to quantitative genetics,2nd edn.Longman Press,New YorkHe FH,Xi ZY,Zeng RZ,Zhang GQ(2005a)Developing single segment substitution lines(SSSLs)in rice(Oryza sativa L.) using advanced backcrosses and MAS(in Chinese).Acta Genet Sin32(8):825–831He FH,Xi ZY,Zeng RZ,Zhang GQ(2005b)Mapping heading date QTL in rice(Oryza sativa L.)using single segment substitution lines(SSSLs)(in Chinese).Acta Agric Sin38(8):1505–1513 He FH,Zeng RZ,Xi ZY,Talukdar A,Zhang GQ(2005c) Identiﬁcation of QTLs for plant height and its components by using single segment substitution lines in rice(Oryza sativa L.).Rice Sci12(3):151–156Hittalmani S,Huang N,Courtois B,Venuprasad R,Shashidhar HE, Zhuang JY,ÁZheng KL,Liu GF,Wang GC,Sidhu JS, Srivantaneeyakul S,ÁSingh VP,Bagali PG,Prasanna HC, McLaren G,Khush GS(2003)Identiﬁcation of QTL for growth-and grain yield-related traits in rice across nine locations of Asia.Theor Appl Genet107:679–690Jain SK,Marshall DR(1967)Population studies in predominantly self-pollinated species.X:Variation in natural populations of A vena fatua and A.barbata.Am Nat101:19–33Jiang C,Edmeades GO,Armstead I,Lafﬁte HR,Hayward MD(1999) Genetic analysis of adaptation difference between highland and lowland tropical maize using molecular markers.Theor Appl Genet99:1106–1119Jiang GH,Xu CG,Li XH,He YQ(2004)Characterization of the genetic basis for yield and its component traits of rice revealed by doubled haploid population.Acta Genet Sin31(1):63–72 Juliano BO(1985)Criteria and test for rice grain quality.In:Juliano BO(ed)Rice chemistry and technology.American Association of Cereal Chemists,Inc.St.Paul,pp443–513Khush GS(2000)Chairs introduction.In:Rice biotechnology.Improving yield,stress tolerance and grain quality.Willey, EnglandLi ZK,Pinson SRM,Park WD,Paterson AH,Stansel JW(1997) Epistasis for three grain yield components in rice(Oryza sativa L.).Genetics145:453–465Li X,Qian Q,Fu Z,Wang Y,Xiong G,Zeng D,Wang X,Liu X,Teng S,Fujimoto H,Yuan M,Luo D,Han B,Li J(2003a)Control of tillering in rice.Nature422:618–621Li ZK,Yu SB,Laﬁtte HR,Huang N,Courtois B,Hittalmani S, Vijayakumar CHM,Liu GF,Wang GC,Shashidhar HE,Zhuang JY,Zheng KL,Singh VP,Sidhu JS,Srivantaneeyakul S,Khush GS(2003b)QTL9environment interactions in rice.I:Heading date and plant height.Theor Appl Genet108:141–153Liao CY,Wu P,Hu B,Yi KK(2001)Effects of genetic background and environment on QTLs and epistasis for rice(Oryza sativa L.) panicle number.Theor Appl Genet103:104–111Motoyuki A,Makoto M(2002)Application of rice genomics to plant biology and breeding.Bull Acad Sin43:1–11Paterson AH,Deverna JW,Lanini B,Tanksley SD(1991)Mendelian factors underlying quantitative traits in tomato:comparison across species,generations,and environments.Genetics 127:181–197Takahashi Y,Shumura A,Sasaki T,Yano M(2001)Hd6,a rice quantitative trait locus involved in photoperiod sensitivity,Theor Appl Genet。

介绍基因多样性的英文开场白表达

介绍基因多样性的英文开场白表达1．Gene diversity,also known as heterogeneity index in genetics, refers to the variation of genetic structure within and between biological populations,and is the probability of non-uniformity among randomly selected genes.Each species includes several populations composed of several individuals.Due to mutation,natural selection or other reasons, each population is often genetically different.Gene diversity provides breeding materials for cultivated plants and domestic animals,enabling people to select individuals and populations with traits that meet people's requirements.2．Gene diversity represents the variation of genetic structure within and between biological populations.Each species includes several populations composed of several individuals.Due to mutation,natural selection or other reasons,each population is often genetically different.3．Some populations have gene mutations that are not found in other populations,or alleles that are rare in one population may appear in many in another population.These genetic differences enable all organisms to reproduce and adapt more successfully under specific conditions in the local environment.Not only do different populations of the same species have different genetic characteristics,that is,there is genetic diversity among populations;There is also gene diversity within the same population-some individuals often have gene mutations in apopulation.The genetic diversity within this population is the evolutionary material.In a population with high genetic diversity,some individuals may be able to endure adverse changes in the environment and pass on their genes to their offspring.With the rapid change of environment,the protection of gene diversity plays a very important role in biodiversity conservation.Gene diversity provides breeding materials for cultivated plants and domestic animals,enabling people to select individuals and populations with traits that meet people's requirements. Academician Yuan made use of genetic diversity by crossing wild rice with common rice.4．Gene diversity provides breeding materials for cultivated plants and domestic animals,enabling people to select individuals and populations with traits that meet people's requirements.。

植物体细胞无性系变异及其育种上的应用

植物体细胞无性系变异及其育种上的应用在Schleiden和Schwann的细胞学基础上，1902年德国Haberlandt提出植物细胞具有全能性（totipotent）的理论，直到二十世纪四十年代，组织培养得以建立。

经众多科学家和学者的不断努力，植物组织培养技术得以完善，被应用于植物生产的众多领域。

植物组织培养（plant tissue culture）是指植物的一个细胞、器官或组织，在无菌条件下，经人工培养，使其最终形成完整的新植株的过程。

虽然植物细胞、器官或组织具有分化成完整的植株的能力已广为人知，但是在未来的几十年里，这仍然被视为科学界的重大问题之一[1]。

1980年，Shepard等[2]发现利用可无性繁殖的植物——马铃薯（Solanumtuberosum）栽培品种“Russet Burbank”的叶肉原生质体培养，可获得突变频率较高的突变体。

随后，Larkin和Scowcroft[3]将这种现象命名为体细胞无性系变异（somaclonal variation）来描述植物细胞组织培养过程中的再生细胞存在的大量变异现象，为体细胞无性系的筛选和新变异来源做了铺垫。

目前，对于体细胞无性系变异的研究已有很多，但仍有许多没有研究清楚的地方，有待后人在这一方面做出更多贡献，并大规模推广应用。

1.体细胞无性系变异的遗传基础体细胞无性系变异是具有遗传基础的，具体表现在染色体变异、基因突变以及转座子激活等方面[4]。

在水稻[5]、小麦[6]和大蒜[7]等植物愈伤组织培养过程中，均发现了染色体数目倍性变异的现象；组培大蒜愈伤组织[8]，再生黑麦根尖细胞[9]，均发现其中发生了不同类型的染色体结构变异。

袁云香等[10]用含Ac/Ds转座元件的愈伤组织组培，结果6%的再生植株仅含Ac，而Ds因切离而丢失，表明组织培养可获得突变体。

此外，组织培养还会造成植物DNA甲基化的变异，经组织培养的香蕉[11]和豌豆[12]等，研究表明其DNA甲基化水平上升；而在大豆[13]、大麦[14]和草莓[15]上发现，DNA甲基化水平降低。

基因编辑育种新基因

基因编辑育种新基因英文回答：Genetic editing in breeding has revolutionized thefield of agriculture and animal husbandry. By manipulating the genetic makeup of organisms, scientists are able to create new genes or modify existing ones to enhance desirable traits in plants and animals. This technology has the potential to significantly improve crop yields, increase resistance to diseases, and produce animals with higher meat quality.One example of genetic editing in breeding is the development of disease-resistant crops. By introducing specific genes into plants, scientists can enhance their ability to fight off diseases and pests. For instance, a gene from a naturally resistant plant can be inserted into a susceptible crop, making it more resilient to pathogens. This not only reduces the need for chemical pesticides but also ensures a more sustainable and environmentallyfriendly approach to farming.Another application of genetic editing in breeding is the creation of animals with improved meat quality. For example, scientists can edit the genes responsible for muscle growth in livestock, resulting in animals with leaner and more tender meat. This not only benefits consumers who prefer healthier meat options but also reduces the environmental impact of livestock farming, as less feed is required to produce the same amount of meat.Furthermore, genetic editing in breeding can also be used to enhance the nutritional content of crops. By modifying the genes responsible for nutrient production, scientists can create plants that are more nutritious and better suited to meet the dietary needs of the population. This is particularly important in regions where malnutrition is prevalent, as it provides a sustainable solution to address nutritional deficiencies.It is important to note that genetic editing in breeding is not without controversy. Critics argue that itraises ethical concerns and poses potential risks to the environment and human health. However, with properregulation and oversight, these risks can be minimized, and the benefits of genetic editing can be maximized.In conclusion, genetic editing in breeding has the potential to revolutionize agriculture and animal husbandry. By manipulating the genetic makeup of organisms, scientists can create new genes or modify existing ones to enhance desirable traits. This technology has the ability toimprove crop yields, increase disease resistance, and produce animals with better meat quality. While there are ethical and safety concerns, with proper regulation,genetic editing can bring significant benefits to the field of breeding.中文回答：基因编辑育种在农业和畜牧业领域带来了革命性的变化。

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REVIEWIntrogression genetics and breeding between Upland and Pima cotton:a reviewJinfa Zhang •Richard G.Percy •Jack C.McCarty Jr.Received:13January 2014/Accepted:1March 2014/Published online:18March 2014ÓSpringer Science+Business Media Dordrecht 2014Abstract The narrow genetic base of elite Upland cotton (Gossypium hirsutum L.)germplasm has been a signiﬁcant impediment to sustained progress in the development of cotton cultivars to meet the needs of growers and industry in recent years.The prospect of widening the genetic base of Upland cotton by accessing the genetic diversity and ﬁber quality of Pima cotton (Gossypium barbadense L.)has encour-aged interspeciﬁc hybridization and introgression efforts for the past century.However,success is limited due mainly to genetic barriers between the two species in the forms of divergent gene regulatory systems,accumulated gene mutations,gene order rearrangements and cryptic chromosomal structure differences that have resulted in hybrid breakdown,hybrid sterility and selective elimination of genes.The objective of this paper is to provide a mini-review in interspeciﬁc hybridization between Upland and Pima cotton relevant to breeding under the followingsections:(1)qualitative genetics;(2)cytogenetic stocks;(3)quantitative genetics;(4)heterosis,and (5)introgression breeding.Case studies of successful examples are provided.Keywords Upland cotton ÁPima cotton ÁIntrogression breeding ÁGeneticsIntroductionCotton (Gossypium spp.),as the world’s leading ﬁber crop,is grown on 33.45million hectares in more than 80countries and supplies approximately 35%of the total ﬁber used (USDA-ERS 2013;USDA-FAS 2013).China is the largest raw cotton producer and consumer,followed by India and the United States.Together,these three countries produce two-thirds of the world’s cotton (USDA-ERS 2009).In the US cotton is grown on more than 12million acres across 17southern states from Virginia to California with 17million bales (3.26billion kg)of cotton harvested annually (National Cotton Council,US).More than half the crop (64%)goes into apparel,28%into home furnishings and 8%into industrial products.The US supplies 7million bales or more to the world’s cotton exports,accounting for about 25%of the total world cotton export market.Annual values of US cotton sold overseas recently have averaged [$3billion andJ.Zhang (&)Department of Plant and Environmental Sciences,New Mexico State University,Box 30003,Las Cruces,NM 88003,USAe-mail:jinzhang@R.G.PercyUSDA-ARS,Crop Germplasm Research Unit,College Station,TX 77845,USAJ.C.McCarty Jr.USDA-ARS,Crop Science Research Laboratory,P.O.Box 5367,Mississippi State,MS 39762,USAEuphytica (2014)198:1–12DOI 10.1007/s10681-014-1094-4revenue stimulation by the crop in the US economy is estimated approximately$120billion.Stagnation in cottonﬁber yield improvement has been experienced(Meredith2000).Thus,improving cotton yield andﬁber quality is increasingly recog-nized as pivotal to the survival of the US cotton industry in the internationally competitive market-place.The narrow genetic base of Upland cotton(G. hirsutum L.)germplasm used in breeding has been considered one of the major contributing factors to the lack of steady progress in the development of cotton cultivars to meet the needs of cotton growers and industry in the US in the recent15years(Meredith 2000;Lewis2001).May et al.(1995)predicated that a narrow gene base may result in a decline in long-term genetic gains in lint yield andﬁber quality.A series of studies on pedigrees,coefﬁcients of parentage,and genetic diversity for260cotton cultivars released in the US between1970and1990indicated a fairly narrow genetic base for commercial Upland cotton (Bowman et al.1996;May et al.1995;Van Esbroeck et al.1998;Bowman and Gutierrez2003).Investiga-tions utilizing various molecular marker techniques agree with the pedigree analyses and coefﬁcient of parentage estimates,conﬁrming a low level of genetic diversity in cultivated cotton germplasm(Wendel et al.1992;Tatineni et al.1996;Abdalla et al.2001; Iqbal et al.2001;Gutierrez et al.2002;Lu and Myers 2002).Studies in Australia,China,and Pakistan have also obtained similar results(Multani and Lyon1995; Zuo et al.2000;Rahman et al.2002).Increasing lint yield and enhancingﬁber quality has been an important breeding goal for cotton improve-ment.Upland cotton is known for its high yield potential,wide adaptation,fuzzed seed,and high lint percentage and produces95%of the world cotton. Long extra staple cotton,known as Pima or Egyptian cotton(G.barbadense L.),has superiorﬁber quality, naked seed,lower lint percentage,and lower yield potential,but is only grown in semi-arid and arid areas in the world.Even though both are cultivated tetraploid species that originated from the same ancestor1–2million years ago and are cross compat-ible,tremendous genetic variation has accumulated during evolution and artiﬁcial selection since domes-tication.This has resulted in reproductive barrier, indicated by hybrid breakdown in interspeciﬁc F2and advanced generations.Transferring desirable traits from G.barbadense(hereafter Pima)to Upland cotton has been a long lasting goal for many cotton genet-icists and breeders since the re-discovery of the Mendelian laws at the turn of the20th century. However,success in developing commercial cotton cultivars from interspeciﬁc breeding is limited.In the mid-1980s,the senior author started to employ interspeciﬁc crossing between Upland and Pima to introduce desirable genes for resistance to spider mites and Verticillium wilt from Pima to Upland cotton.Then,breeding objectives were expanded to includeﬁber quality,sub-okra leaf shape and heterosis(Zhang1993,2011).This resulted in the development of high-yielding breeding lines with sub-okra leaf or cleistogamousﬂowers,and lines with high ﬁber quality in the mid-1990s.Since the early2000s, molecular markers and gene expression proﬁling have been used to identify quantitative trait loci(QTLs)or differentially expressed genes in interspeciﬁc popula-tions or introgression genotypes(Pang et al.2012a,b; Fang et al.2013a,b;Yu et al.2012,2013).Meanwhile, new introgression lines with higher yield potential and/or betterﬁber quality than Upland cotton parents have been developed,indicating a simultaneous introduction of desirable genes for yield andﬁber quality from Pima into Upland cotton(Zhang et al. unpublished).Tolerance to drought and salt stresses, and Verticillium wilt has also been evaluated in these introgression lines(Adams et al.2011;Abdelraheem 2012;Zhang et al.2012;Tiwari et al.2013a,b).Drawn on our more than25years of experience and knowl-edge,the objective of this review is to provide an update on introgression genetics and breeding from interspeciﬁc hybridization between Upland and Pima cotton based on a conference presentation(Zhang and Percy2007).Qualitative geneticsBoth species differ in many morphological traits (Table1)and segregating analyses of interspeciﬁc crosses have identiﬁed many major or Mendelian genes.Genetic studies using interspeciﬁc hybrids between Upland cotton and Pima cotton were initiated when the Mendelian laws were rediscovered in1900. After several generations of efforts,about200genes have been identiﬁed in tetraploid cotton.More than 1/3of the genes or alleles([70with gene symbols) controlling morphological traits in the tetraploidcottons were discovered in Pima or Upland9Pima interspeciﬁc crosses,which have facilitated the under-standing of the genetic basis of the diversity between the two species.These genes or alleles are categorized under the following groups(see Percy and Kohel 1999),•Plant pigments:sunred R1dar,petal spot R2,petal spot,red stems and veins R2v,chlorophyll deﬁ-ciency chl1chl2,virescent v7and v21,yellow green yg1yg2,albivirescent av1av2,and light green ltg;•Plant trichomes:glabrous T2b,adaxial and long trichomes t4and t5,hairy boll hb1,hair anther t2a and I t(Zhang et al.2000a);•Leaf and bract shape:sub-okra leaf L2e,ovate leaf ov1ov2,crinkled leaf cr,wrinkled leaf wr1wr2, rugate leaf Ru,round leaf Rl3,fringeless nectarines fng-a and fng-b,and golden veins Gv;•Flower traits:yellow petals Y1and Y2,yellow pollen P1,P2and p3,cleistogamy cg1cg2,ﬂower-ing F,rudimentary stigma rs,and open buds ob1ob2;•Male sterility:Ms11,Ms12,ms13,msc2,Msc5,Msc6, msc7,and fertility enhancer E;•Female sterility:asynapsis as1as2;•Lethality:Le1Le2•Corky:ck o and ck x ck y;•Fiber,fuzz color and distribution:brown lint Lc2, and naked seed n2;•Gossypol glands:glandless stem and bolls gl1, glandless Gl2e,glandless modiﬁers gl4and gl5, glandless gl6,rugate boll and high gossypol content Gl3r,pitted bolls b p,and leaky glands L k;•Semigamy:haploid producing Se(Zhang and Stewart2004);•Fruiting patterns:short branch cl1cl2;•Seed shape:kidney seed k;•Disease resistance:(1)bacterial blight resistant B5;(2)two dominant genes with additive effects toFusarium wilt,one dominant resistance gene in Pima S-7(Fov1;Wang and Roberts2006),or one recessive gene susceptible to Fusarium wilt, depending on genotypes;and(3)one dominant gene resistant to Verticillium wilt(Wilhelm et al.1972,1974;Ma et al.2000;Zhang et al.2000b);•Insect resistance:one dominant gene S1resistant to spider mites(Zhang et al.1992),and one dominant gene Thr for thrips resistance(Zhang et al.2013).Many of these are new traits and do not exist within either species,but were identiﬁed only from the interspeciﬁc segregating populations,such as chloro-phyll deﬁciency(chl1chl2),yellow green leaves(yg1-chromosome c16and yg2-c7),clustering(cl1-also c16 and cl2-also c7),open buds(ob1-c18and ob2-c13), cleistogamousﬂowers(cg1cg2),and desynapsis/asyn-apsis(as1as2).Plants with chlorophyll deﬁciency or desynapsis/asynapsis are lethal.Some other gene combinations may also lead to hybrid breakdown or reducedﬁtness in interspeciﬁc segregants. Cytogenetic stocksA number of individual chromosomes or chromosome arms from Pima cotton have been transferred into Upland cotton through monosomic or telosomic cytogenetic stocks,resulting in the development of 17chromosome substitution lines(CSL)in Upland cotton TM-1background(Kohel et al.1977;Stelly et al.2005).Genetic effects of six Pima cotton chromosome c1(A1),c2(A2),c4(A4),c6(A6),c17 (D3)and c18(D13)were reported earlier,while13 chromosome substitution lines(B-c2,B-c4,B-c5sh, B-c6,B-c07,B-c14sh,B-c15sh,B-c16,B-c17,B-c18, B-c22Lo,B-c22sh,and B-c25)and their hybrids withTable1Phenotypic comparisons between Upland and Pima cottonTrait Upland Pima Type ofinheritanceLeaf shape Normal Sub-okraQualitative Flower color Cream Yellow Qualitative Pollen color Cream Yellow QualitativePetal spot No Yes QualitativeSeed fuzz Fuzz Little Qualitative VerticilliumwiltSusceptible Tolerant BothSpider mite Susceptible Tolerant QualitativePlant height Short Tall QuantitativeLint yield High Low QuantitativeBoll size Large Small QuantitativeLint percentage High Low Quantitative Fiber length Shorter Long Quantitative Fiber strength Not strong Strong Quantitative Fiberﬁneness Coarse Fine Quantitativeseveral Upland cotton cultivars were tested for their effects on yield,yield components,ﬁber quality,and ﬂower production(Saha et al.2004,2006;Jenkins et al.2007a,b;McCarty et al.2006).Compared with their recurrent parent(recipient), i.e.,TM-1,7CSL(B-c2,B-c4,B-c5sh,B-c6,B-c7, B-c15sh,and B-c22Lo)had similar yield;5(B-c16, B-c18-,B-c5sh,B-c22sh,and B-c22Lo)had higher lint percentage;2(B-c17and B-c25)had lower micronaire;3(B-c25,B-c14sh,and B-c15sh)had longerﬁbers;4(B-c2,B-c25,B-c14sh,and B-c15sh) had strongerﬁbers(Saha et al.2004,2006;Jenkins et al.2007a,b);and CSL B-c5sh signiﬁcantly increasedﬂower production(McCarty et al.2006). The maximum positive effects of the individual Pima cotton chromosomes on theﬁber quality traits were only1/4–1/3of the Pima cotton genotypic effects, indicating the multiple genic control(accumulated effects from individual QTL and their interactions from different chromosomes)ofﬁber quality.Repul-sion linkage between QTL with opposite effects may also play a role.Some other Pima cotton chromosomes had deleterious effects on yield andﬁber quality traits. As expected,several CSL(B-c16,B-c17,B-c18, B-c14sh,and perhaps B-c22sh and B-c25)have signiﬁcant lower yield,indicating negative effects of the Pima cotton chromosomes on yield in the Upland background.This demonstrates the complexity of the genetic basis in yield andﬁber quality from Upland9Pima crosses.The genetic effects of4 CSL(B-c1,B-c11sh,B-c12sh and B-c26lo)were subsequently reported by Saha et al.(2008).The genetic association of lint yield with its components was studied using14CSL(Wu et al.2008).Wu et al. (2009a)also reported the genetic effects of the CSL on seed quality traits in a single Upland background,TM-1.Wu et al.(2009a,b,2010a,b)and Jenkins et al. (2012)further analyzed the genetic effects of9or13 CSL on plant growth,seed quality and agronomic traits when topcrossed withﬁve elite cotton genotypes. Based on a half diallel mating design involving6CSL (CS-B14sh,CS-B16,CS-B17,CS-B22sh,CS-B22Lo, and CS-B25)and another involving7CSL(CS-B05sh, CS-B06,CS-B09,CS-B10,CS-B12,CS-B17and CS-B18),Saha et al.(2010,2011,2013)reported the genetic effects from a single and two substituted chromosomes on lint percentage,boll weight,seed-cotton yield,and lint yield.Quantitative geneticsTo study the genetic basis of quantitative traits,one of the simplest strategies has been to create F1hybrids in a diallel scheme or North Carolina Design II.This allows the determination of hybrid performance, combining abilities and other quantitative genetic parameters such as heritability,genic effects,and minimum number of genes.Another often used genetic design is generation mean analysis where two parents(P1and P2),F1,F2,and BC1P1and BC1P2 are tested on an individual plant basis.Then,genetic parameters such as genetic variance,broad-sense and narrow-sense heritabilities,genetic effects,and min-imum number of genes are estimated for the traits of interests.Cottonﬁber quality traits are typically quantita-tively inherited and affected by genetics and environ-mental factors.Meredith(1984)and May(1999) comprehensively reviewed the genetic basis ofﬁber properties and concluded that even though environ-mental effects(such as locations,years of testing) affectﬁber length,strength,andﬁneness,the magni-tude of genetic variation generally is greater than that of non-genetic factors.Among the genetic factors, additive variation and effects generally play a more important role than environmental effects in control-ling expression of theﬁber properties,thereby ren-dering moderate to high heritability.Effective numbers of genes controlling the traits were also estimated.For example,estimates of gene number affectingﬁber strength ranged from5to14.However, only a few major genes were involved in the devel-opment of several segregating populations(May 1999).Several morphological mutations,such asﬁber color and pilose,also affectﬁber quality.However,the traditional quantitative genetic approach cannot sep-arate individual gene effects and localize them on the cotton genome.Prior to the1950s,genetic variation of quantitative traits in interspeciﬁc hybrids between Pima and Upland cotton were studied(Knight1950).However, no deﬁnitive conclusions were drawn due partly to quantitative genetics being in its infancy at the time. Another reason was that the interspeciﬁc hybrids were mainly used in Mendelian genetic studies.For exam-ple,as referenced by Knight(1950),Harland reported no apparent Mendelian segregation in bract,boll,calyx,andﬁber traits in interspeciﬁc hybrids in a series of papers published between1915and1929.Kearney and Peebles in1926–1927reported that fruit and boll abscission rate between F2and F3was correlated in a cross of Pima9Acala(Knight1950).Ware (1929–1931)found that high lint percentage(LP) was partially dominant in F1from a cross between low LP Pima and high LP Upland and that the segregation results in F2and backcross between F1and the low LP parent indicated that LP was controlled by one pair of genes(Knight1950).In the late1950s,eight graduate students from Louisiana State University used the interspeciﬁc hybrid of Deltapine159Sea Island to study the performance and segregation of F1,F2,and F3in comparison with their parents with regard toﬁber quality traits,lint percentage,lint index,seed index, and mineral element contents(Anonymous,1968). Heritabilities of the above traits were estimated according to correlation analysis between F2and F2.3 families.The relationships betweenﬁber quality traits and other traits were also estimated.Since the late1950s and early1960s when diallel analysis,North Carolina Design II(NCII),and gen-eration mean analysis were proposed for analyzing quantitative traits,a number of quantitative genetic studies have been reported using interspeciﬁc hybrids between Upland and Pima cotton.Marani was theﬁrst to use the above quantitative genetic designs in interspeciﬁc hybrids in Israel(Marani1963,1964, 1967,1968a,b;Marani and Avieli1973).Quantitative genetics in Upland9Pima hybrids were also con-ducted in other major cotton growing countries such as US and China(Percy and Turcotte1988;Zhang et al. 1994a,b).Results from diallel matings and NCII designs indicated that for most of the traits studied including earliness,yield,yield components,ﬁber quality,and plant height,much of the variation was due to the general combining abilities(gca)i.e., additive genes,and in many cases variation due to speciﬁc combining abilities(sca)from non-additive genes(e.g.,dominant genes)was also detected.For earliness,gca was more predominant than sca.Based on generation mean analysis,additive effects,domi-nant effects,and epistatic effects usually existed for yield and yield components;forﬁber quality traits, both additive and dominant effects were important, while there existed epistatic effects(e.g.,addi-tive9additive)forﬁber length;for plant height and early maturity traits includingﬂowering date andﬁrst harvest percentage,both additive and dominant effects were detected.For example,Marani(1968a,b) reported that,there existed additive and dominant effects for seedcotton yield,lint yield,lint percentage, boll size,boll number,seed index,lint index,seed number per boll,ﬂowering number,boll setting percentage,maturity date,plant height,ﬁber length, strength,andﬁneness;except for seed index,ﬁber strength,andﬁneness,all the above traits had addi-tive9additive effects;except for boll size,seed index,seed number per boll,boll setting percentage, plant height,andﬁber quality traits,all the other traits had dominant9dominant effects;additive9domi-nant effects were only detected for seed index,lint index,and seed number per boll.HeterosisThere have been numerous reports on the performance of interspeciﬁc F1hybrids between Upland and Pima cotton(Davis1978,1979;Weaver et al.1984;Zhang et al.1994c;Saranga et al.1998).A general consensus is that heterosis in the interspeciﬁc hybrids is different from intraspeciﬁc hybrids in the following,(1)Inter-speciﬁc hybrids have strong seed and seedling vigor, and high early growth rate,resulting in strong vegetative growth,large leaf areas,tall plants,and heavy biomass.(2)Interspeciﬁc hybrids also usually have early squares andﬂowers and more fruiting sites with strong boll setting,high seed index,and are more cold and drought tolerant.(3)However,interspeciﬁc hybrids have very late cutout,resulting in a large number of bolls that cannot mature before frost,small boll size,low lint percentage,and high embryo abortion and motes,therefore less seed per boll.As a result,yield in F1hybrids is usually lower than Upland cotton but higher than Pima cotton.However,selec-tion of parental lines for short status and early maturity may increase hybrid productivity.(4)Interspeciﬁc hybrids usually haveﬁber quality that is close or equal to Pima cotton.Due to high percentage of motes and late maturity,interspeciﬁc hybrids have not been broadly utilized in cotton production.There has been limited success in the US but more success in India in releasing and commercially growing interspeciﬁc hybrids.It is suggested that selecting intermediate type of Upland cotton breeding lines(e.g.,Acalatypes)with signiﬁcant introgression of Pima cotton germplasm may result in high heterotic interspeciﬁc hybrids with reduced motes that can be utilized as Pima cotton.For example,the interspeciﬁc hybrid cotton HA195developed by Hazera Genetics gained 0.36%of the Pima cotton acreage in California in 2006.Introgression breedingCotton breeders and geneticists have identiﬁed a signiﬁcant number of cotton germplasm lines with useful traits developed by interspeciﬁc crossing in the past century.Acala-typeﬁber quality and Delta-type ﬁber yield with good resistance to Verticillium wilt, Fusarium wilt,and nematodes would be a preferred combination for a new cotton cultivar.However,the unknown genes controlling these traits are scattered among different germplasm and different genome regions.Traditional breeding techniques have had little success in identifying and pyramiding the desirable genes to move cotton yield to a new level.For many quantitative traits,transgressive segregants from the interspeciﬁc segregating populations are very common in that extreme phenotypes can be frequently noticed and selected,for example,plant height from extremely short to tall;leaf,bract and boll size from very small to large;leaf color from yellow green to dark green;leaf retention from natural defoliation to stay-green(Fig.1);trichome from glabrous to pilose;pedicel length from very short to extremely long;maturity from very early to late(even with no squares);fruiting type from short/cluster branch to long branch;boll set from no bolls to proliﬁc;ﬁberﬁneness from very coarse to ﬁne;and shorter and weakerﬁbers than Upland cotton (Zhang1993).This indicates that the two species have different sets of dominant and recessive gene alleles for the same traits and the combinations of these gene alleles give rise to negative and positive transgressive segregations.However,segregants with signiﬁcantly longer or strongerﬁbers than Pima cotton appear to be rare.The genetic basis of transgressive segregation in interspeciﬁc populations is currently poorly understood.Great genetic diversity and many desirable or potentially desirable genes or traits from Pima cotton have encouraged cotton geneticists to work on inter-speciﬁc hybridization for the past century.However, Upland cotton breeding has not achieved much success in introducing desirable genes from Pima cotton,or vice versa,except for limited success inﬁber quality improvement in Acala cotton.The uniqueness of Acala cotton has been mostly due to its unique breeding history in which germplasm from G.barbadenseand Fig.1Natural defoliation(left)and stay-green(right)isolated from Gossypium hirsutum9G.barbadenseTriple Hybrid(ATH,G.arboreum9G.thur-beri9G.hirsutum)was introgressed into the Acala cotton(Smith and Cothren1999;Zhang et al.2005a, b).The interspeciﬁc introgression was also evident in the development of high quality Pee Dee germplasm lines(May2001).There have been attempts in introducingﬁber quality genes from Acala and/or Pee Dee lines into other cottons to develop high-yielding cultivars,but success has been limited(Bowman and Gutierrez2003),even though45%of US cotton cultivars developed from1970to1990contained New Mexico Acala germplasm(Bowman et al.1996).However,there were reports in successful transfer-ring of major genes including a dominant glandless gene allele,Gl2e(Yuan et al.2000),a sub-okra leaf allele,L2e (Zhang1993,2011),and pest resistance traits such as bacterial blight resistance gene,B5(Percy and Kohel 1999),Verticillium wilt(Wilhelm et al.1985;Zhang et al.2012),thrips(Zhang et al.2013),and spider mite resistance(Zhang et al.1992,1993)from Pima into Upland cotton.Recently,Zhang(2011)reported the development of an Upland breeding line with the sub-okra leaf shape,which had more than10%higher lint yield and5%higher lint percentage and similarﬁber quality,in comparison with its recurrent parent-a popular commercial cultivar.Furthermore,a breeding line with cleistogamousﬂowers(Fig.2)was developed. It had20–40%higher yield than its recurrent Upland parent which was also a commercial cultivar,with extremely high boll load and high lint percentage.But seed size and boll size were smaller.Since cotton ﬂowers are not open when blooming in the plants with cleistogamousﬂowers,it not only prevents out-crossing from insect pollinators,but also prevents water due to rains or sprinkle irrigation from getting inside the ﬂowers.So cotton plants can still set bolls when it rains during theﬂowering season,which is very common in many areas.After numerous generations of backcrossing,selﬁng, and continuous pedigree selection,many stable breed-ing lines with highﬁber quality traits have been released (Ma and Liu1982;Zhang1993;Cantrell and Davis 2000;Percy et al.2005;Gore et al.2012;Liu et al. 2005).Elite Upland breeding lines with Verticillium wilt resistance(Zhang et al.2012)and reniform nematode resistance(McCarty et al.2013)were recently developed or released.An extremely drought tolerant introgression line(Fig.3)from transgressive segregation was also recently reported(Zhang and Hughs2012).Tiwari et al.(2013a)recentlyﬁrst reported that Pima cotton is salt tolerant at seed germination and its tolerance was transferred to Upland cotton through backcrossing and repeated selﬁng.Some backcross inbred lines(BILs)also displayed better salt tolerance than both parents during seedling growth (Tiwari et al.2013b).However,up to now,no commercial cotton cultivars were claimed to be devel-oped directly from the interspeciﬁc introgression breeding.The major obstacle for successful introgression breeding has been hybrid breakdown,instability,and selective elimination of desirable genes duringselﬁng. Fig.2A plant with cleistogamousﬂowers isolated fromGossypium hirsutum9G.barbadenseFig.3An extremely drought tolerant line(indicated by thearrow)derived from Gossypium hirsutum9G.barbadenseunder drought stress conditionsEven though the genetic basis of hybrid breakdown in cotton is poorly understood,several possible causes are:(1)Duplicate recessive complementary genes causezygote selection such as chlorophyll deﬁciency,asynapsis,corky and open buds.(2)Chromosomal structural differences between Pimaand Upland may cause gamete selection.Theseinclude known chromosome translocationsbetween c2and c3,and between c4and c5,minorinversions and other cryptic structural differences.The genetic consequences of these events includepollen sterility,crossover/recombinationsuppression,selective eliminations of genes,andsegregation distortions.(3)Other genetic factors may include hybridweakness,seed unviability,sterile plants andlate maturity,among others.The interspeciﬁc introgression breeding is a long term process where conventional intra-speciﬁc breed-ing schemes proved to be unsuccessful and inefﬁcient.A breeder has to be extremely patient in that backcrossing to Upland cotton and then many gener-ations of selﬁng are a must to get rid of unwanted Pima cotton genetic material and stabilize the introgressed genetic backgrounds before and during selection. During the long process,many desirable genes from Pima cotton may have been lost due to selective elimination,lack of crossovers in homologous chro-mosome regions between Upland and Pima cotton, and small population size.To stabilize the interspeciﬁc genetic backgrounds, doubled haploid strategy via semigamy can be used (Mahill et al.1983).However,a large scale haploid production and chromosome doubling technique still remains to be established.Another strategy in interspeciﬁc introgression is to use Pima cotton chromosome substitution lines(CSL) on an Upland cotton background to cross with the same or other Upland cotton genotype.This will create chromosome substitution segment lines(CSSL)and the genome-wide hybrid breakdown will be mini-mized.A similar strategy is to apply marker-assisted selection in an advanced backcross process to select chromosome segment introgression lines(CSIL)on a recurrent parent background.Wang et al.(2012) reported the development of174CSIL containing 298introgressed G.barbadense segments,86of which (49.4%)carried single introgressed segments.The introgressed segments in the set of CSIL spanned 2,949cM with an average length of16.7cM and represented83%of the tetraploid cotton genome. However,based on the results published so far,only limited positive effects from a few Pima cotton chromosomes were detected.Their potential in applied cotton breeding remains to be seen.Interspeciﬁc backcrossing followed by repeated selﬁng to develop BIL may offer a solution in capturing desirable transgressive segregation.Based on Yu et al.(2013),in a BIL population,boll weight did not display transgressive segregations,while yield exhibited both positive and negative transgressive segregation and lint percentage displayed mostly negative transgressive segregation.Forﬁber quality traits,ﬁber length,strength,micronaire and uniformity displayed negative transgressive segregations,while ﬁber elongation exhibited only positive transgressive segregation.The results indicated that further yield improvement in Upland cotton is possible through interspeciﬁc backcross breeding,but not through the improvement of boll weight or lint percentage. Micronaire can also be reduced,thereby increasing ﬁberﬁneness in backcross progeny.Perhaps the most efﬁcient strategy in pyramiding ﬁber quality genes from Pima cotton is through population improvement using intermating or recur-rent selection and then developing homozygous inter-mediate Upland cotton lines with Pima typeﬁber quality,e.g.,Del Cerro(Smith and Cothren1999).The Upland cotton lines with Pimaﬁber quality can then be further utilized to cross with elite Upland cotton for further improvement.Most recently,Jenkins et al. (2013)have released a random mated barbadense Upland population(RMBUP-4).This population was derived from crosses between18Upland CSL each containing a Pima chromosome or arm and three Upland cotton cultivars,followed by random mating forﬁve cycles,providing a useful genetic resource for cotton breeding and genetic studies.Summary and perspectivesSince the turn of the last century,the prospect of combining Pima cottonﬁber quality with Upland cotton yield has encouraged tremendous efforts in interspeciﬁc breeding in major cotton-growing。