**Part 1**

**Olive Oil Composition, Analysis and Quality**

**1**

**Volatile and Non-Volatile**

Cinzia Benincasa, Kaouther Ben Hassine, Naziha Grati Kammoun and Enzo Perri

*2Institut de l'Olivier, Sfax*

*1Italy 2Tunisia*

**Compounds of Single Cultivar**

**Systems and Storage Conditions**

**Virgin Olive Oils Produced in Italy and**

*1CRA-OLI Olive Growing and Olive Oil Industry Research Center Rende,*

Virgin olive oil has a fundamental role in the markets of alimentary oils because of its unique aroma, its stability and its healthy benefits. In this chapter the attention will be

The oils under investigation were produced by different extraction systems and characterised for their volatile and non-volatile compounds (Benincasa et al., 2003; Cerretani et al., 2005; Garcia et al., 1996). It is well known that volatile and non-volatile components of products of plant origin are dependent on genetic, agronomic and environmental factors. There are few reports (Angerosa et al., 1996, 1998a, 1998b, 1999; Morales et al., 1995; Solinas et al., 1998) on the evaluation of the relationships between the aroma components of virgin olive oil with the metabolic pathways and varietal factors. Olive ripening process and, to some extent, the fruit growing environment, affect also the composition of the volatile compounds of the oil (Aparicio & Morales, 1998; De Nino et al., 2000; Guth & Grosh, 1993; Montedoro & Garofalo, 1984; Morales et al., 1996). Volatile and non-volatile compounds are retained by virgin olive oils during their mechanical extraction process from olive fruits (*Olea europaea* L.). Non-volatile compounds such as phenolic compounds stimulate the tasting receptors such as the bitterness perception, the pungency, astringency and metallic attributes. Instead volatile compounds, stimulating the olfactive receptors, are responsible for the whole aroma of the virgin olive oil. The chromatograms of volatile compounds of olive oils were obtained by solid phase micro extraction-gas chromatography/mass spectrometry (SPME-GC/MS) (Hatanaka, 1993; Kataoka et al., 2000; Steffen & Pawliszyn, 1996). The method is based on the assay of the terminal species of the "lipoxygenase pathway" which are present in the volatile fraction of the sampled compounds (Hatanaka,

focused on Tunisian and Italian single cultivar olive oils.

**1. Introduction**

1993).

**Tunisia with Regard to Different Extraction**


**3. Conclusions**

With millions of device patients around the world, it is important to develop good guidance in terms of how to manage postoperative pain in these patients. Most postoperative pain is moderate to severe but has a predictable trajectory in which the pain is most intense immediately after surgery and diminished day over day over the course of several days. A good strategy for pain control for CIED patients is to consider managing pain perioperatively and then offer the patient postoperative counseling for pain management at home along with appropriate analgesics. For appropriate patients, a short course of opioid analgesics may be appropriate but other nonopioid agents may be considered as well. Subcutaneous ICD implantation is likely associated with more severe or longer-duration postoperative pain although there are no specific head-to-head comparative pain studies. Barring complications, device patients recover over the course of days and weeks and should need analgesia only for a short duration of time.

### **Conflict of interest**

Peter Magnusson has received speaker's fees or grants from Abbott, Alnylam, Amicus Therapeutics, AstraZeneca, Bayer, BMS, Boehringer-Ingelheim, Coala Life, Internetmedicin, Lilly, MSD, Novo Nordisk, Octopus Medical, Orion Pharma, Pfizer, Sanofi, Vifor Pharma, and Zoll.

Jo Ann LeQuang has no disclosures.

*Postoperative Pain Control Following Cardiac Implantable Electronic Device Implantation DOI: http://dx.doi.org/10.5772/intechopen.101517*

Joseph Pergolizzi is a consultant/speaker, owner, or researcher for Spirify, US World Meds, Salix, Enalare, Scilex, Pfizer, Lilly, Teva, Taketa, Regeneron, Grünenthal, Neumentum, NativeCardio, BDSI, and Bridge Therapeutics.


**1. Introduction**

The plant kingdom has provided a wide variety of natural products with diverse chemical structures and a vast array of biological activities, many of which found applications in health sciences. Over 80% of the approximately 30,000 known natural products are of plant origin. In 1985, 3500 new chemical structures were identified out of which 2600 were derived from higher plants and 121 clinically useful drugs were derived from plants [1]. Plants will continue to provide novel products as well as chemical models for new drugs in the near future [2].

Many of the plant species that produce medicinal herbs have been scientifically evaluated for their possible medical applications. The economic importance of phytopharmaceuticals in plants has led to their inevitable collection from their natural habitats and thus creating environmental and geopolitical instabilities posing a threat to their survival. The reckless collection of plants has put several of them under the categories of endangered or at the verge of extinction. This has prompted industries and scientists to find the alternative technologies for the production of phytopharmaceuticals so that the natural habitat of plants can be preserved.

Plant cell cultures have served as potential renewable resources for the production of valuable medicinal compounds, flavors, fragrances, pigments, dyes, cosmetics and fine chemicals. All these compounds belong to a group collectively known as secondary metabolites. The commercial importance of secondary metabolites and the possibilities of their production by means of cell culture technologies have gained great interest in the recent years. The current review is a survey and analysis of current status of various plant cell culture technologies used for the production of medicinally important metabolites. The future prospects of cell culture technologies in light of successful case studies have been reviewed and possible improvements are suggested.


**4. Treatment modalities**

Medical treatment with antithrombotic drugs (aspirin [56], cilostazol [57], clopidogrel [58, 59], low molecular-weight heparin, argatroban) [60] or calcium channel blockers [61] is used for mild asymptomatic cases, although their effectiveness has never been proven convincingly [15, 62]. Cilostazol is preferred over aspirin and clopidogrel because of its lower hemorrhagic risk [60], and clopidogrel recommended when aspirin is not tolerated [60]. These antithrombotic agents are advised for the ischemic-type moyamoya disease [15]. In any case, they are not very useful [63] as some researchers have reported an almost 3-fold chance of future neurological deterioration in the patients treated conservatively compared to those treated surgically [54, 64].

Direct, indirect or combined revascularization provides much better long-term results than conservative treatment, minimising the risk of ischemic and haemorrhagic strokes [54, 65, 66]. Early surgical intervention, particularly in paediatric patients, reduces cerebrovascular events and should be considered in asymptomatic children [44, 67]. Specific hospital perioperative morbidity and mortality have to be considered when recommending surgical treatment to these patients [12]. Thus, surgical treatment should be advised a soon as there is any sign of clinical or subclinical deterioration.


**Geological Sciences**

Change in the environment poses certain hazards in certain places. The capacity to quickly and accurate assess the results of an extreme natural event is an ever-present desire. The coastal zone is one of the most dynamic environments on Earth as it is in constant flux due to the interactions between the land, the atmosphere, and the ocean. Evaluation of the impacts of storms on barrier islands presents certain challenges, but our fourth chapter, "Monitoring Storm Impacts on Sandy Coastlines with UAVs" by Alex Smith, Brianna Lunardi, Elizabeth George, and Chris Houser presents an effort to use unmanned aerial vehicles to do just that, with speed and precision as its goal. The authors describe the methods, challenges, and limitations of their new approach in the context of Prince Edward Island, Canada. Their study reveals the effectiveness of linear, aerial, and volumetric monitoring of the spatial changes of dunes after a storm using drone technology. They demonstrate an approach to discerning the spatial variability of coastal geomorphological processes.

Similarly focused on the spatial variability of coastal geomorphology, our fifth chapter "Recent Advances in Coastal Survey Techniques: From GNSS to LiDAR and Digital Photogrammetry: Examples on the Northern Coast of France" examines the effectiveness of the technologies used to gather spatial data. Olivier Cohen and Arnaud Héquette discuss the benefits and limitations of several sensor technologies to gather land surface measurements from which digital terrain models (DTMs) can be derived. The authors employ these methods in case studies along France's northern coast. They demonstrate the implications of the selection of technologies on analysis of the spatial variability of coastal geomorphology.


**3.1.4 Montane formation**

The forest communities distributed over the intermediate slopes of the mountain ranges at elevations above the Submontane limits are classified as Montane formations. According to Roderjan et al. (2002) and Blum (2006), in the state of Paraná these communities are situated between 600 and 1200 m a.s.l..

It is noteworthy that the upper limit is also variable depending on specific soil and climate and, in many cases, the Upper Montane formation can already occur below 1200 m a.s.l. (Pires et al., 2005). Floristic differences are observable in relation to the lower level, but structurally and physiognomic, the Montane and Submontane formations are similar (Roderjan et al., 2002; Pires et al., 2005). Cambisols (with no textural gradient) and Entisols are very common in the Montane belt (Schorn, 1992; Roderjan, 1994, Blum, 2006).

The main environmental factors that affect the differentiation between the Montane and Submontane formations (Lower Montane Forests) are the climate, the topography and the soils. In the Montane Atlantic Forest it can occur occasional frosts, which are extremely limiting for many typical species of Submontane formation, that are subject to milder climate. It should be noted that the geomorphological differences result in distinct pedologies. The Montane terrains are steep and dissected while the Submontane sites are usually understated (Roderjan et al., 2002; Pires et al., 2005; Blum, 2006; 2010).

The canopy of the Montane Dense Rainforest is regular, varying about 20-25 m height. Several species of Lauraceae dominate in the upper strata, especially *Ocotea catharinensis*, *Ocotea odorifera, Ocotea bicolor* and *Cryptocarya aschersoniana. Aspidosperma pyricollum, Pouteria torta*, *Cabralea canjerana, Sloanea lasiocoma, Guapira opositta, Ilex paraguariensis* and *Guatteria australis* are also relevant. The lower strata are characterized by Myrtaceae, Rubiaceae and Monimiaceae families. Ferns (tree ferns) such as *Cyathea phalerata* and another species of Cyatheaceae are common in the understory (IBGE, 1992; Roderjan, 1994; Blum et al., 2001; Roderjan et al., 2002; Pires et al., 2005; Blum, 2006).


**6. Pattern dystrophies**

This paragraph is focused on pattern dystrophies in an attempt to offer a complete overview of OCT findings in IRD. However, in this case, the available literature is poor because of the rarity of these conditions, and in many cases, OCTA studies are completely absent.

Pattern dystrophies are a group of very rare autosomal dominant macular diseases characterized by very heterogeneous patterns of retinal pigment depositions and outer retinal alterations. Although a definite classification is still absent, pattern dystrophies are usually divided into five different categories: (I) Adult-onset foveomacular vitelliform dystrophy (AFVD); (II) Butterfly-shaped pigment dystrophy (BPD); (III) Reticular dystrophy; (IV) Multifocal Pattern Dystrophy Simulating Stargardt Disease (MPDSSD); and (V) Fundus pulverulentus [87]. However, especially looking at *PRPH2* gene variants, the clinical manifestations of pattern dystrophies may be extremely variable. In many cases, pattern dystrophies are caused by pathogenic variants of *PRPH2* gene, although other genes have been described as involved in the pathogenesis of these rare conditions. A complete noninvasive multimodal retinal imaging case diagnosed as pattern dystrophy associated with *PRPH2* gene variants is shown in **Figure 5**.

AFVD probably represents the most common pattern dystrophy, closely resembling BVMD, however is characterized by later onset, usually at 40–60 years of age. The clinical presentation is characterized by round, bilateral, symmetrical, grayishyellow, lesions within the macular area. Visual acuity is usually preserved, and electrophysiology is within normal limits. Pathogenic variants are associated with *BEST1*, *PRPH2,* or *IMPG1* genes [88, 89]. FAF is characterized by hyperautofluorescent macular lesion in the vitelliform stage; changes in FAF signals follow the progressive degeneration of the outer retina. Structural OCT shows the presence of a subretinal

*New OCT and OCTA Insights in Inherited Retinal Dystrophies DOI: http://dx.doi.org/10.5772/intechopen.109953*

#### **Figure 5.**

*Multimodal retinal imaging in PRPH2 gene-related pattern dystrophy. Confocal multicolor image (A) shows almost complete central retina atrophy. FAF image (B) confirms this finding, detecting a central mainly hypoautofluorescent signal. Structural OCT (C) shows disappeared or markedly attenuated outer retinal bands and inner retinal layers thinning. OCTA shows partially altered SCP with some segmentation and motion artifacts (D), strongly altered DCP with many projection artifacts (E), and absent central CC with exposure of choroidal vessels (F).*

vitelliform lesion very similar to the vitelliform stage of BVMD. Vitelliruptive, as well as atrophic stages, can be detected also in AFVD. The clinical course of AFVD is usually more benign than BVMD. OCTA detects flow deficits especially interesting the DCP; neovascular complications can be well-reconstructed [90].

BPD has an onset around 20–40 years of age and is characterized by bilateral accumulation of yellowish or pigmented material in a butterfly wings-like pattern at the level of the RPE; visual acuity is usually good [91]. These butterfly-like pigment changes, corresponding both to hyper- and hypoautofluorescent signals on FAF, are well-detected on structural OCT, with heterogeneous patterns of outer retinal band alterations [92]. To the best of our knowledge, no OCTA studies are available for BPD.

Reticular dystrophy is characterized by a RPE network of hyperpigmentation, resembling a fishing net, extending from the macula in all directions, with sparing of the retinal periphery in early stages. In advanced stages, this network is complicated by atrophy [92, 93].

MPDSSD shows flecks-like alterations sparse within the entire posterior pole, with atrophic changes detected in the later stages of the disease. Flecks are often localized around the vascular arcades. Fundus appearance and OCT are very similar to STGD, and the differential diagnosis is mainly based on the detection of the autosomal dominant mechanism of inheritance [94].

Fundus pulverulentus is the rarest of all pattern dystrophies and is characterized by a granular appearance with coarse and punctiform mottling of the macular RPE. The clinical presentation is quite similar to age-related macular degeneration, leading to possible diagnostic delays [95]. Choroidal neovascularization has been described as a possible complication [96].

### **7. Final remarks**

In this chapter, the most relevant OCT and OCTA findings in IRD have been described. Although several steps forward have been performed, especially looking at the most frequent IRD types, further prospective studies are needed to provide a definite categorization of each IRD and to improve the knowledge regarding the pathogenesis and the patterns of progression. Moreover, it should always be taken into account that OCT and OCTA are prone to imaging artifacts. These can be categorized as patient-related, technology-related, and disease-related artifacts [1]. Patient-related artifacts are mainly related to patients' fixation and collaboration, leading to many artifacts, including motion and blinking artifacts. Technology-related artifacts are related to the current limitation of the post-processing algorithm in carefully segmenting images and detecting signals, thus leading to biases, such as projection and segmentation artifacts. Disease-related artifacts are secondary to the disease-related alterations affecting the retinal structures, potentially masking or altering the detection of the signals. In brief, the higher are both fixation instability and pathological modifications of retinal structures, the higher will be the artifacts affecting the quality and the interpretation of OCT and OCTA images. This is particularly true for IRD, since in most of the cases, the retinal structures are remarkably impaired, and the visual function of the patients is strongly compromised. For these reasons, from one side both expert and nonexpert ophthalmologists should be familiar with imaging artifacts, to avoid misinterpretation of the findings. On the other end, further technological improvements are warranted to make noninvasive retinal imaging even more reliable for performing the diagnostic workup of IRD patients.


**6. Conclusions**

In conclusion, there are several pharmacological and nonpharmacological treatments that have been tested in preclinical and clinical phases. However, so far have not yielded fully satisfactory results; even using combined therapies. Further studies are needed in order to identify novel therapeutic targets and strategies that provide a better medical care avoiding complications.


**1. Introduction**

Cotton (*Gossypium* spp.) is the most important natural textile fiber source globally. The worldwide economic impact of the cotton industry is estimated at approximately \$500 billion per year with an annual utilization of about 27 million metric tons of cotton fiber [1].The tetraploid species *Gossypium hirsutum* L. (n=26, AD genome), commonly referred to as Upland cotton, accounts for 95% of the world's cotton production [2]. Most of the objectives in cotton breeding, such as yield, fiber quality, biotic-and abiotic-stress tolerance, are all complex traits, which are controlled by a large number of quantitative trait loci (QTLs). It is becoming progressively more difficult to improve these traits using conventional breeding methods due to their complex architecture and inheritance [3]. Fortunately, the development in applied genomics research has provided alternative tools to improve efficiency in plant breeding programs. Molecular markers tightly linked to the target genes or QTLs can be used for markerassisted selection (MAS) and/or genomic selection (GS) [4-5]. In the past two decades, the availability of abundant molecular markers has made tagging QTL harboring functional genes through family-based linkage mapping a routine process, and a large number of QTLs for fiber quality properties [6-40], yield and its components [6, 8, 10-11, 14, 18-19, 27, 30-32, 36, 40-45], nematode resistance [46-49], Verticillium wilt resistance [50-53] as well as Fusarium wilt resistance [54-60] have been identified in cotton.

However, approximately 80% of the previously reported QTLs could not be confirmed in subsequent studies, and few have actually been applied in breeding programs [61-62]. This may be because most QTLs were population-specific, and the genetic variations detected

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

in a unique bi-parental population were not shared with other genetic populations, or shared but fixed in the parental lines. In addition, limited genetic recombination events in most populations used for linkage mapping make it difficult to map QTL with a high resolution, which severely limits their application in breeding programs. With the poten‐ tial to exploit all recombination events that occurred in the evolutionary history of natural populations, linkage disequilibrium (LD) based association mapping (AM) has become a powerful approach for the dissection of complex traits and identification of causal varia‐ tion with modest effects for target traits in many plant species [3, 63] including cotton [64-73]. Furthermore, although AM has been successfully used to detect the QTLs underly‐ ing quantitative traits in some crops, from a breeding standpoint, detecting associated loci is just the first step; analyzing the genetic effects of alleles and identifying favorable alleles will be more beneficial for target trait improvement. For example, Breseghello and Sorrells identified several potentially beneficial alleles for kernel size and milling quality by comparing the average phenotypic value with specific alleles and null alleles in a soft winter wheat population [74]; Jia et al. identified some putative resistant alleles for Sheath Blight resistance in a rice panel composed of 217 accessions from the USDA core collection, and found that the number of putative resistant alleles presented in an entry was highly and significantly correlated with the decrease of ShB rating [75].

assembled to construct an AM panel. The population consisted of 348 cultivars developed in China, seven introduced from the U.S. including the genetic standard line TM-1, and one introduced from Uganda. According to their release year, the 348 Chinese cultivars could be divided into the following six groups: I (1930–1960, 26 lines); II (1961–1970, 26 lines); III (1971– 1980, 39 lines), IV (1981–1990, 83 lines); V (1991–2000, 125 lines); and VI (2000–2005, 49 lines). The cultivars introduced from abroad, DPL 15, DPL 16, STV 2B, King, Foster 6 and Uganda 3, were used as a check group for genetic diversity and allele transmission evaluation, because they had been used as the main founder parents in China's Upland cotton breeding programs [71]. Yield and its components of the accessions were evaluated in three diverse environments, including lint yield per plant (LP), seed cotton yield per plant (SY), bolls per plant (BN), boll weight (BW), lint percentage (LP), lint index (LI) and seed index (SI). Three hundred and eightone pairs of SSR primers that amplify loci evenly covering the tetraploid cotton genome [78]

Exploitation of Chinese Upland Cotton Cultivar Germplasm Resources to Mine Favorable QTL Alleles…

http://dx.doi.org/10.5772/58587

57

The Bayesian model-based program STRUCTURE 2.3 was used to infer the population structure using 66 unlinked or weakly linked SSR markers as described in reference [79]. The length of the burn-in period and the number of Markov Chain Monte Carlo replications after burn-in were all assigned at 100,000 with an admixture and allele frequencies correlated model. Five independent run iterations were performed with the hypothetical number of subpopu‐ lations (*k*) ranging from 1 to 10. However, the result showed that the Ln*P*(D) value corre‐ sponding to each hypothetical *k* kept increasing with *k* value and did not show any peak (Figure 1). Thus an ad hoc statistic Δ*k* [80] was joined to correctly estimate the population structure. The Δ*k* value showed a much higher likelihood at *k*=2 than at *k*=3-10 (Figure 1), suggesting that the total panel could be divided into 2 major subpopulations. Based on the correct *k*, all accessions were assigned to each of the two subpopulations (P1 and P2), for which the membership value (Q value) was >0.5, and the population structure matrix (Q) was generated for further association mapping. The P1 group contained 115 accessions including 63 cultivars from Yellow River cotton growing region, 46 lines from North and Northwest China regions, and 6 cultivars from Yangtze River region. The P2 group consisted of 241 accessions including 116 lines from Yellow River region, 107 lines from Yangtze River region, 10 lines from the North and Northwest China regions, and 8 lines introduced from abroad (see reference [71] for details). The software SPAGeDi was used to calculate the pair-wise relatedness [81]. For the kinship coefficient values, 86.85% was less than 0.05, 8.56% had a range of 0.05–0.10, and the remaining 4.59% showed various degrees of genetic relatedness. Based on the results of

the relatedness analysis, a K matrix was constructed for further association mapping.

Marker-trait association analysis were performed with the MLM model, considering both kinship (K) and population structure (Q), implemented in TASSEL software [82]. At the *α*=0.01 (-log*P*=2) level, a total of 195 significant associations were detected between 82 SSR markers and 7 lint yield traits. Among these, most of the associations (125 of 195) were detected in only one environment, and the proportion of phenotypic variation explained by markers ranged from 1.52% to 9.40%, with an average of 3.70% (see reference [71] for details). After Bonferroni correction [83], 55 associations were found to be significant (*P*≤0.05/145,-log*P*≥3.46) between 26 SSR markers and 7 lint yield traits (Table 1). Most of the associations could be detected in

were selected to genotype the 356 accessions.

China is the world's largest cotton-growing nation, but not a cotton domestication region. Most Upland cotton cultivars developed in China were derived from a few germplasm resources such as Deltapine (DPL), Stoneville (STV), Foster, King and Uganda, all of which were introduced from abroad [76]. More than 8800 accessions have been collected, developed, and maintained in Chinese cotton germplasm collection [77]. Among these collections, *G. hirsu‐ tum* varieties and breeding lines comprise about 83% of the total. Current and obsolete cultivars have been and continue to be the main resources for cotton breeding programs in China. Dissecting the genetic basis of main breeding objective traits will be of great benefits to germplasm evaluation and future molecular breeding. In this chapter, we reviewed QTLs underlying yield and its components, fiber quality properties and Fusarium wilt resistance detected by association mapping and their favorable alleles identified in 2 Chinese Upland cotton AM panels [71-73]. We hope that these results should provide useful information for further understanding the genetic basis of these traits, and would facilitate future breeding process by MAS in Upland cotton.

### **2. Favorable QTL alleles for yield and its components**

In recent years, demand for cotton fiber in the world market has dramatically increased. While cotton acreage has declined worldwide in the past few years, mainly it was due to strong competition from other crops as well as production costs [1]. To improve lint yield of Upland cotton cultivars will remain critical for meeting worldwide demand and maintaining profita‐ bility for cotton growers. A total of 356 representative Upland cotton cultivars and breeding lines were selected from the cotton germplasm collection in Cotton Research Institute, Chinese Academy of Agricultural Sciences (CRI-CAAS), and from the collection in our laboratory, and assembled to construct an AM panel. The population consisted of 348 cultivars developed in China, seven introduced from the U.S. including the genetic standard line TM-1, and one introduced from Uganda. According to their release year, the 348 Chinese cultivars could be divided into the following six groups: I (1930–1960, 26 lines); II (1961–1970, 26 lines); III (1971– 1980, 39 lines), IV (1981–1990, 83 lines); V (1991–2000, 125 lines); and VI (2000–2005, 49 lines). The cultivars introduced from abroad, DPL 15, DPL 16, STV 2B, King, Foster 6 and Uganda 3, were used as a check group for genetic diversity and allele transmission evaluation, because they had been used as the main founder parents in China's Upland cotton breeding programs [71]. Yield and its components of the accessions were evaluated in three diverse environments, including lint yield per plant (LP), seed cotton yield per plant (SY), bolls per plant (BN), boll weight (BW), lint percentage (LP), lint index (LI) and seed index (SI). Three hundred and eightone pairs of SSR primers that amplify loci evenly covering the tetraploid cotton genome [78] were selected to genotype the 356 accessions.

in a unique bi-parental population were not shared with other genetic populations, or shared but fixed in the parental lines. In addition, limited genetic recombination events in most populations used for linkage mapping make it difficult to map QTL with a high resolution, which severely limits their application in breeding programs. With the poten‐ tial to exploit all recombination events that occurred in the evolutionary history of natural populations, linkage disequilibrium (LD) based association mapping (AM) has become a powerful approach for the dissection of complex traits and identification of causal varia‐ tion with modest effects for target traits in many plant species [3, 63] including cotton [64-73]. Furthermore, although AM has been successfully used to detect the QTLs underly‐ ing quantitative traits in some crops, from a breeding standpoint, detecting associated loci is just the first step; analyzing the genetic effects of alleles and identifying favorable alleles will be more beneficial for target trait improvement. For example, Breseghello and Sorrells identified several potentially beneficial alleles for kernel size and milling quality by comparing the average phenotypic value with specific alleles and null alleles in a soft winter wheat population [74]; Jia et al. identified some putative resistant alleles for Sheath Blight resistance in a rice panel composed of 217 accessions from the USDA core collection, and found that the number of putative resistant alleles presented in an entry was highly and

China is the world's largest cotton-growing nation, but not a cotton domestication region. Most Upland cotton cultivars developed in China were derived from a few germplasm resources such as Deltapine (DPL), Stoneville (STV), Foster, King and Uganda, all of which were introduced from abroad [76]. More than 8800 accessions have been collected, developed, and maintained in Chinese cotton germplasm collection [77]. Among these collections, *G. hirsu‐ tum* varieties and breeding lines comprise about 83% of the total. Current and obsolete cultivars have been and continue to be the main resources for cotton breeding programs in China. Dissecting the genetic basis of main breeding objective traits will be of great benefits to germplasm evaluation and future molecular breeding. In this chapter, we reviewed QTLs underlying yield and its components, fiber quality properties and Fusarium wilt resistance detected by association mapping and their favorable alleles identified in 2 Chinese Upland cotton AM panels [71-73]. We hope that these results should provide useful information for further understanding the genetic basis of these traits, and would facilitate future breeding

In recent years, demand for cotton fiber in the world market has dramatically increased. While cotton acreage has declined worldwide in the past few years, mainly it was due to strong competition from other crops as well as production costs [1]. To improve lint yield of Upland cotton cultivars will remain critical for meeting worldwide demand and maintaining profita‐ bility for cotton growers. A total of 356 representative Upland cotton cultivars and breeding lines were selected from the cotton germplasm collection in Cotton Research Institute, Chinese Academy of Agricultural Sciences (CRI-CAAS), and from the collection in our laboratory, and

significantly correlated with the decrease of ShB rating [75].

**2. Favorable QTL alleles for yield and its components**

process by MAS in Upland cotton.

56 World Cotton Germplasm Resources

The Bayesian model-based program STRUCTURE 2.3 was used to infer the population structure using 66 unlinked or weakly linked SSR markers as described in reference [79]. The length of the burn-in period and the number of Markov Chain Monte Carlo replications after burn-in were all assigned at 100,000 with an admixture and allele frequencies correlated model. Five independent run iterations were performed with the hypothetical number of subpopu‐ lations (*k*) ranging from 1 to 10. However, the result showed that the Ln*P*(D) value corre‐ sponding to each hypothetical *k* kept increasing with *k* value and did not show any peak (Figure 1). Thus an ad hoc statistic Δ*k* [80] was joined to correctly estimate the population structure. The Δ*k* value showed a much higher likelihood at *k*=2 than at *k*=3-10 (Figure 1), suggesting that the total panel could be divided into 2 major subpopulations. Based on the correct *k*, all accessions were assigned to each of the two subpopulations (P1 and P2), for which the membership value (Q value) was >0.5, and the population structure matrix (Q) was generated for further association mapping. The P1 group contained 115 accessions including 63 cultivars from Yellow River cotton growing region, 46 lines from North and Northwest China regions, and 6 cultivars from Yangtze River region. The P2 group consisted of 241 accessions including 116 lines from Yellow River region, 107 lines from Yangtze River region, 10 lines from the North and Northwest China regions, and 8 lines introduced from abroad (see reference [71] for details). The software SPAGeDi was used to calculate the pair-wise relatedness [81]. For the kinship coefficient values, 86.85% was less than 0.05, 8.56% had a range of 0.05–0.10, and the remaining 4.59% showed various degrees of genetic relatedness. Based on the results of the relatedness analysis, a K matrix was constructed for further association mapping.

Marker-trait association analysis were performed with the MLM model, considering both kinship (K) and population structure (Q), implemented in TASSEL software [82]. At the *α*=0.01 (-log*P*=2) level, a total of 195 significant associations were detected between 82 SSR markers and 7 lint yield traits. Among these, most of the associations (125 of 195) were detected in only one environment, and the proportion of phenotypic variation explained by markers ranged from 1.52% to 9.40%, with an average of 3.70% (see reference [71] for details). After Bonferroni correction [83], 55 associations were found to be significant (*P*≤0.05/145,-log*P*≥3.46) between 26 SSR markers and 7 lint yield traits (Table 1). Most of the associations could be detected in

**Traits Marker loci Chr. Position**

**-LogP** *R2(%)*

NAU3100 D09(Chr.23) 25.262 3.67 5.38 4.31 4.08 7.08 5.41 NAU2776 D10(Chr.20) 9.452 ns 3.81 2.54 4.41 2.76

Exploitation of Chinese Upland Cotton Cultivar Germplasm Resources to Mine Favorable QTL Alleles…

 Estimated Ln*P*(D) and ∆*k* over five repeats of STRUCTURE analysis (a) LnP(D) for *k* from 1 to 10 for 356 acces‐ sions. Note that Ln*P*(D) value of each hypothetical *k* kept increasing with *k* value. (b) ∆*k* for *k* from 2 to 9 for 356 acces‐ sions. The Δ*k* value showed a much higher likelihood at *k*=2 than at *k*=3–10, suggesting that the total panel should be divided into two major subpopulations

more than one environment, and the proportion of phenotypic variation explained by markers ranged from 1.63% to 9.40%, with an average of 4.51%. The number of SSR markers associated with LY, SY, BN, BW, LP, LI and SI were 9, 4, 6, 4, 14, 17 and 1, respectively. Seventeen loci were co-associated with two or more different traits (Table 1), for example, NAU3269 (Chr. 5) and NAU3100 (Chr. 23) were simultaneously associated with FY, SY, BN, LP, and LI, and most of the lint yield-associated loci were associated with at least one of its components. These associations coincided with phenotypic correlations among these traits [71]. This might result from pleiotropy of a single causal gene or tight linkage of multiple causal genes. We found that 10 of 14 markers associated with LP were detected in all three environments, which was consistent with the results in phenotypic evaluation that LP possessed the highest broad-sense heritability (*h*B 2 =75.77%) [71]. The phenotype of complex traits often result from the combined actions of multiple genes and environmental factors [3]; only those traits with high heritability can be stably detected. The resulting stably associated markers should be useful for cotton breeding with broad adaptability to different environments.

Exploitation of Chinese Upland Cotton Cultivar Germplasm Resources to Mine Favorable QTL Alleles… http://dx.doi.org/10.5772/58587 59

more than one environment, and the proportion of phenotypic variation explained by markers ranged from 1.63% to 9.40%, with an average of 4.51%. The number of SSR markers associated with LY, SY, BN, BW, LP, LI and SI were 9, 4, 6, 4, 14, 17 and 1, respectively. Seventeen loci were co-associated with two or more different traits (Table 1), for example, NAU3269 (Chr. 5) and NAU3100 (Chr. 23) were simultaneously associated with FY, SY, BN, LP, and LI, and most of the lint yield-associated loci were associated with at least one of its components. These associations coincided with phenotypic correlations among these traits [71]. This might result from pleiotropy of a single causal gene or tight linkage of multiple causal genes. We found that 10 of 14 markers associated with LP were detected in all three environments, which was consistent with the results in phenotypic evaluation that LP possessed the highest broad-sense

**Figure 1.** Estimated Ln*P*(D) and ∆*k* over five repeats of STRUCTURE analysis (a) LnP(D) for *k* from 1 to 10 for 356 acces‐ sions. Note that Ln*P*(D) value of each hypothetical *k* kept increasing with *k* value. (b) ∆*k* for *k* from 2 to 9 for 356 acces‐ sions. The Δ*k* value showed a much higher likelihood at *k*=2 than at *k*=3–10, suggesting that the total panel should be

actions of multiple genes and environmental factors [3]; only those traits with high heritability can be stably detected. The resulting stably associated markers should be useful for cotton

LY NAU3269 A05(Chr.05) 182.215 3.43 4.42 3.33 2.83 4.60 3.05 NAU5166 A10(Chr.10) 23.423 ns 5.49 ns 5.40 NAU2935 A10(Chr.10) 51.592 ns 4.00 ns 4.72 JESPR204 A13(Chr.13) 59.714 4.12 3.00 ns 4.60 4.08

CIR246 D02(Chr.14) 112.473 ns ns 3.55 4.44

TMK19 D06(Chr.25) 70.38 4.30 2.77 3.97 4.21 3.21 4.28

BNL3594 D06(Chr.25) 7.66 ns 4.51 ns 7.65

=75.77%) [71]. The phenotype of complex traits often result from the combined

**-LogP** *R2(%)*

**E1 E2 E3 E1 E2 E3**

heritability (*h*B

2

divided into two major subpopulations

58 World Cotton Germplasm Resources

breeding with broad adaptability to different environments.

**Traits Marker loci Chr. Position**

LI by 0.89, 0.42 g, 4.93% and 0.94 g, respectively; while NAU493-1 deceased SI by 0.17 g. Lint yield of cotton is the result of series components and their interactions, such as boll number, boll weight, lint percentage, lint index, and seed index. Developing potentially high-yielding cultivars thus relies to some extent on selecting the appropriate yield components. As some of the QTLs were associated with more than one yield component, favorable alleles must be treated with caution. Positively co-associated genetic loci could simultaneously improve

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61

multiple target traits, while negative linkages must be broken.

 SSR marker loci significantly associated with lint yield traits and their explained proportion of phenotypic variation in 3 different environments. LY: lint yield (g/plant); SY: seed cotton yield (g/plant); BN: bolls per plant; BW: boll weight (g); LP: lint percentage (%); LI: lint index (g/100 seeds); SI: seed index (g/100 seeds); E1: Jiangpu in 2009; E2: Dafeng in 2010; E3: Zhengzhou in 2010

To identify favorable alleles, the phenotypic allele effect was estimated through comparison between the average phenotypic value over accessions with the specific allele and that of all accessions:

$$a\_i = \sum \mathbf{x}\_{ij} \left/ m\_i - \sum \mathbf{N}\_k \right/ m\_k$$

where *ai* is the phenotypic effect of the *ith* allele; *xij* is the phenotypic value over the *jth* accession with the *ith* allele; *ni* is the number of accessions with the *ith* allele; *Nk* is the phenotypic value over all accessions; *nk* is the number of accessions. If the value of *ai* > 0, the allele is considered to have a positive effect, if it is < 0, it corresponds to a negative allele. The favorable alleles were then identified according to the breeding objective of each target trait [69, 71]. Phenotypic effects of each QTL allele for the 41 associated loci detected in more than one environment were measured, and 5, 2, 3, 4, 12, 14 and 1 favorable alleles for FY, SY, BN, BW, LP, LI and SI were identified, respectively. Phenotypic effects and representative accessions for each favorable allele are shown in Table 2. Among the favorable alleles, NAU3100-2 had the most positive phenotypic effect for FY and SY, and increased FY and SY by 3.61 g and 7.27 g, respectively; NAU6584-2, NAU3398-2, NAU5166-2 and NAU3917-2 increased BN, BW, LP and LI by 0.89, 0.42 g, 4.93% and 0.94 g, respectively; while NAU493-1 deceased SI by 0.17 g. Lint yield of cotton is the result of series components and their interactions, such as boll number, boll weight, lint percentage, lint index, and seed index. Developing potentially high-yielding cultivars thus relies to some extent on selecting the appropriate yield components. As some of the QTLs were associated with more than one yield component, favorable alleles must be treated with caution. Positively co-associated genetic loci could simultaneously improve multiple target traits, while negative linkages must be broken.

**Traits Marker loci Chr. Position**

60 World Cotton Germplasm Resources

E2: Dafeng in 2010; E3: Zhengzhou in 2010

accessions:

where *ai*

*ai* =∑ *xij* / *ni* −∑ *Nk* / *nk*

**-LogP** *R2(%)*

JESPR135 A11(Chr.11) 55.787 ns 5.43 2.34 5.20 2.09

BNL3590 D03(Chr.17) 39.284 3.30 2.70 3.47 3.67 3.13 4.26

NAU3100 D09(Chr.23) 25.262 5.90 2.74 3.47 7.19 3.84 4.93 NAU2776 D10(Chr.20) 9.452 ns ns 3.57 4.30 NAU3917 D10(Chr.20) 31.125 ns 3.66 2.03 4.13 2.45 BNL1404 D11(Chr.21) 33.571 ns 5.85 2.72 5.64 2.50 Gh508 D11(Chr.21) 54.48 ns 3.67 3.41 3.38 3.27 NAU2361 D11(Chr.21) 101.215 6.27 5.16 4.58 9.40 8.45 8.12

Gh369 A11(Chr.11) 84.701 3.77 2.09 ns 4.79 3.00

NAU3398 A13(Chr.13) 3.311 6.01 2.49 ns 8.19 4.15 JESPR204 A13(Chr.13) 59.714 ns 3.68 ns 4.81 CIR246 D02(Chr.14) 112.473 4.75 3.14 ns 5.77 4.17

NAU2233 D05(Chr.19) 171.278 4.86 2.15 ns 5.96 3.04 TMK19 D06(Chr.25) 70.38 4.83 4.02 ns 5.19 4.55

SI NAU493 D07(Chr.16) 113.413 3.70 2.85 5.24 3.85 2.90 5.75

**Table 1.** SSR marker loci significantly associated with lint yield traits and their explained proportion of phenotypic variation in 3 different environments. LY: lint yield (g/plant); SY: seed cotton yield (g/plant); BN: bolls per plant; BW: boll weight (g); LP: lint percentage (%); LI: lint index (g/100 seeds); SI: seed index (g/100 seeds); E1: Jiangpu in 2009;

To identify favorable alleles, the phenotypic allele effect was estimated through comparison between the average phenotypic value over accessions with the specific allele and that of all

with the *ith* allele; *ni* is the number of accessions with the *ith* allele; *Nk* is the phenotypic value over all accessions; *nk* is the number of accessions. If the value of *ai* > 0, the allele is considered to have a positive effect, if it is < 0, it corresponds to a negative allele. The favorable alleles were then identified according to the breeding objective of each target trait [69, 71]. Phenotypic effects of each QTL allele for the 41 associated loci detected in more than one environment were measured, and 5, 2, 3, 4, 12, 14 and 1 favorable alleles for FY, SY, BN, BW, LP, LI and SI were identified, respectively. Phenotypic effects and representative accessions for each favorable allele are shown in Table 2. Among the favorable alleles, NAU3100-2 had the most positive phenotypic effect for FY and SY, and increased FY and SY by 3.61 g and 7.27 g, respectively; NAU6584-2, NAU3398-2, NAU5166-2 and NAU3917-2 increased BN, BW, LP and

is the phenotypic effect of the *ith* allele; *xij* is the phenotypic value over the *jth* accession

NAU1151 A12(Chr.12) 97.965 6.08 ns ns 5.63

**E1 E2 E3 E1 E2 E3**

present at moderate to low frequencies in most cultivar groups have been underutilized in modern breeding programs, and should be regarded as essential elements for increasing lint

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**Favorable alleles CK I II III IV V VI Total** NAU6584-2 0.3333 0.5000 0.6923 0.7692 0.6747 0.4720 0.7755 0.6096 NAU3269-2 0.6667 0.5769 0.5385 0.6154 0.3976 0.3040 0.1020 0.3736 BNL1414-2 0.5000 0.3077 0.1923 0.4872 0.2410 0.2320 0.1633 0.2612 NAU5166-2 0.0000 0.0000 0.0000 0.0500 0.0000 0.0320 0.0417 0.0225 NAU2508-2 0.4000 0.1200 0.2400 0.2250 0.3012 0.3952 0.4694 0.3333 NAU980-3 0.0000 0.0200 0.0000 0.0256 0.0244 0.0369 0.0000 0.0233 JESPR135-1 1.0000 0.8462 1.0000 1.0000 0.9759 0.9840 1.0000 0.9775 Gh369-3 0.0000 0.0000 0.0000 0.0000 0.0241 0.0480 0.0000 0.0225 NAU4047-2 0.6667 0.5000 0.5769 0.6154 0.5663 0.6400 0.7551 0.6208 NAU3398-2 0.0000 0.0385 0.0000 0.1026 0.0843 0.0720 0.0204 0.0618 JESPR204-1 1.0000 0.8462 0.8077 0.7692 0.8675 0.9200 0.9388 0.8820 CIR246-3 0.0000 0.0000 0.0000 0.0256 0.0241 0.0800 0.0612 0.0449 BNL3590-1 0.5000 0.8077 0.8462 0.8462 0.9036 0.9440 0.8776 0.8904 NAU2233-1 0.6667 0.4231 0.5000 0.3590 0.5542 0.6560 0.8163 0.5955 TMK19-2 0.6667 0.5000 0.5769 0.6667 0.6024 0.7280 0.8163 0.6742 NAU493-1 0.6667 0.5000 0.7308 0.6923 0.6265 0.6800 0.5714 0.6461 NAU3100-2 0.3333 0.2308 0.0769 0.2308 0.2289 0.2400 0.3673 0.2444 JESPR208-2 0.5000 0.3077 0.1154 0.4359 0.2289 0.2240 0.1633 0.2444 NAU2776-1 0.1667 0.4231 0.3462 0.5385 0.4458 0.4480 0.3061 0.4242 NAU3917-2 0.0000 0.0000 0.0000 0.0513 0.0000 0.0240 0.0204 0.0169 BNL1404-1 1.0000 0.8462 1.0000 1.0000 0.9759 0.9840 1.0000 0.9775 Gh508-1 1.0000 0.9231 1.0000 0.9750 1.0000 0.9920 1.0000 0.9888 NAU2361-3 0.2143 0.0769 0.0417 0.1538 0.1125 0.2810 0.3673 0.2066

**Table 3.** Allele frequency for each favorable QTL allele in historically released Chinese Upland cotton cultivar groups. CK, I, II, III, IV, V and VI indicates the founder parent group (CK), and the Chinese cultivars released in 1930–1960,

With the acceleration of spinning speed, the demand for cotton fiber quality is increasing rapidly [9, 17]. It is important to elucidate the molecular genetics of Upland cotton fiber

1961–1970, 1971–1980, 1981–1990, 1991–2000 and 2000–2005, respectively

**3. Favorable QTL alleles for fiber quality traits**

yield potential.

**Table 2.** Favorable QTL alleles, their phenotypic effects (*ai )* and representative accessions. LY: lint yield (g/plant); SY: seed cotton yield (g/plant); BN: bolls per plant; BW: boll weight (g); LP: lint percentage (%); LI: lint index (g/100 seeds); SI: seed index (g/100 seeds); Representative accessions are the top-3 entries for the target trait value of accessions with the corresponding favorable allele

Allele frequencies of the 23 favorable alleles in the CK group and the six Chinese historically released cultivar groups are summarized in Table 3. Based on allele frequencies across the different groups, these favorable alleles could be categorized into three classes. The alleles in the first class, such as JESPR135-1, BNL1404-1 and Gh508-1, presented in the founder cultivars and with high frequency in all populations, might have been passed down stably from the original parents and were almost fixed in modern cultivars by selection. Alleles in the second class, such as BNL3269-2, BNL1414-2, NAU3100-2 and JESPR208-2, presented in the founder cultivars and with moderate to low frequency in most populations, should have been underu‐ tilized in modern breeding programs. Those in the third class, such as NAU5166-2, NAU980-3, Gh369-3 and CIR246-3, not presented in the founder cultivars and presented at low frequency in modern cultivars, might be from other original parents or could have been generated by mutations and/or recombinations. Favorable alleles, especially of the latter two classes, should have a great potential in future Upland cotton genetic improvement. We suggest that a multiparent population should be constructed using cultivars that possess most of the favorable alleles, and in the meantime, a ranking system for MAS or genomic selection should be developed based on the results of AM. Favorable alleles that were passed down from the founder parents and have been almost fixed in modern cultivars formed the basis of lint yield of Chinese Upland cotton, and should be treated as fundamental elements in order to reject deleterious alleles at the corresponding loci. Alleles either absent in the founder cultivars or present at moderate to low frequencies in most cultivar groups have been underutilized in modern breeding programs, and should be regarded as essential elements for increasing lint yield potential.

**Traits Favorable allele** *ai* **Accessions Representative accessions**

**Table 2.** Favorable QTL alleles, their phenotypic effects (*ai*

accessions with the corresponding favorable allele

62 World Cotton Germplasm Resources

NAU980-3 0.84 9 I40005, Zhongmiansuo5, Hua101 JESPR135-1 0.02 343 Huakangmian1, Emian23, I40005 Gh369-3 0.11 8 Emian16, Ekangmian8, Yumian20

NAU3398-2 0.80 22 Huakangmian1, I40005, Zhongmiansuo5 CIR246-3 0.63 16 Hua101, Zhongmiansuo41, Yumian9 BNL3590-1 0.06 327 Huakangmian1, Emian23, I40005 NAU2233-1 0.01 212 Huakangmian1, Emian23, I40005 TMK19-2 0.10 235 Huakangmian1, Emian23, I40005 NAU3100-2 0.38 86 I40005, Zhongmiansuo5, Hua101 NAU3917-2 0.94 6 Huakangmian1, Simian4, Sumian9 BNL1404-1 0.03 343 Huakangmian1, Emian23, I40005 Gh508-1 0.01 347 Huakangmian1, Emian23, I40005 NAU2361-3 0.28 73 Emian23, I40005, Zhongmiansuo5 SI NAU493-1 -0.17 230 Chaoyangmian1, Xuzhou1818, XiangSC-24

seed cotton yield (g/plant); BN: bolls per plant; BW: boll weight (g); LP: lint percentage (%); LI: lint index (g/100 seeds); SI: seed index (g/100 seeds); Representative accessions are the top-3 entries for the target trait value of

Allele frequencies of the 23 favorable alleles in the CK group and the six Chinese historically released cultivar groups are summarized in Table 3. Based on allele frequencies across the different groups, these favorable alleles could be categorized into three classes. The alleles in the first class, such as JESPR135-1, BNL1404-1 and Gh508-1, presented in the founder cultivars and with high frequency in all populations, might have been passed down stably from the original parents and were almost fixed in modern cultivars by selection. Alleles in the second class, such as BNL3269-2, BNL1414-2, NAU3100-2 and JESPR208-2, presented in the founder cultivars and with moderate to low frequency in most populations, should have been underu‐ tilized in modern breeding programs. Those in the third class, such as NAU5166-2, NAU980-3, Gh369-3 and CIR246-3, not presented in the founder cultivars and presented at low frequency in modern cultivars, might be from other original parents or could have been generated by mutations and/or recombinations. Favorable alleles, especially of the latter two classes, should have a great potential in future Upland cotton genetic improvement. We suggest that a multiparent population should be constructed using cultivars that possess most of the favorable alleles, and in the meantime, a ranking system for MAS or genomic selection should be developed based on the results of AM. Favorable alleles that were passed down from the founder parents and have been almost fixed in modern cultivars formed the basis of lint yield of Chinese Upland cotton, and should be treated as fundamental elements in order to reject deleterious alleles at the corresponding loci. Alleles either absent in the founder cultivars or

*)* and representative accessions. LY: lint yield (g/plant); SY:

**Table 3.** Allele frequency for each favorable QTL allele in historically released Chinese Upland cotton cultivar groups. CK, I, II, III, IV, V and VI indicates the founder parent group (CK), and the Chinese cultivars released in 1930–1960, 1961–1970, 1971–1980, 1981–1990, 1991–2000 and 2000–2005, respectively

## **3. Favorable QTL alleles for fiber quality traits**

With the acceleration of spinning speed, the demand for cotton fiber quality is increasing rapidly [9, 17]. It is important to elucidate the molecular genetics of Upland cotton fiber qualities, and such information would enable the subsequent improvement of cotton cultivars by pyramiding favorable alleles of fiber quality traits. We performed a marker-trait association mapping for fiber quality traits using MLM model implemented in TASSEL [82] with the panel mentioned above [71]. At the *α*=0.01 (-log*P*=2) level, a total of 59 significant associations were detected between 41 SSR markers and 5 fiber quality traits (Table 4). Among these, almost all of the associations were detected in only one environment, and the proportion of phenotypic variation explained by markers ranged from 1.84% to 7.12%, with an average of 3.53% (Mei et al. unpublished data). If a more stringent threshold with Bonferroni correction [83] was adopted, only 9 associations were found to be significant (Table 4). The result might be caused by low diversity of fiber quality properties existed in the 356-accession panel. Although the yield potential of Chinese cultivars are equal to or a bit higher than those developed in the United States or Australia, the fiber quality traits of Chinese cultivars are not as good as those of American or Australian cultivars [73, 84]. Narrow variation in fiber qualities of Chinese cultivars had severely limited the marker-trait association detection power in association mapping.

**E1 E2 E3 E1 E2 E3**

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65

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qualities, and such information would enable the subsequent improvement of cotton cultivars by pyramiding favorable alleles of fiber quality traits. We performed a marker-trait association mapping for fiber quality traits using MLM model implemented in TASSEL [82] with the panel mentioned above [71]. At the *α*=0.01 (-log*P*=2) level, a total of 59 significant associations were detected between 41 SSR markers and 5 fiber quality traits (Table 4). Among these, almost all of the associations were detected in only one environment, and the proportion of phenotypic variation explained by markers ranged from 1.84% to 7.12%, with an average of 3.53% (Mei et al. unpublished data). If a more stringent threshold with Bonferroni correction [83] was adopted, only 9 associations were found to be significant (Table 4). The result might be caused by low diversity of fiber quality properties existed in the 356-accession panel. Although the yield potential of Chinese cultivars are equal to or a bit higher than those developed in the United States or Australia, the fiber quality traits of Chinese cultivars are not as good as those of American or Australian cultivars [73, 84]. Narrow variation in fiber qualities of Chinese cultivars had severely limited the marker-trait association detection power in association

**-LogP** *R2 (%)*

**E1 E2 E3 E1 E2 E3**

mapping.

64 World Cotton Germplasm Resources

**Traits Marker loci Chr. Position**

FL NAU845 A07(Chr.07) 54.589 4.16 ns ns 5.91

FS NAU6584 A03(Chr.03) 74.975 2.3 ns ns 3.16

NAU2561 A05(Chr.05) 56.73 2.96 ns ns 3.08

NAU3269 A05(Chr.05) 182.215 3.53 ns ns 3.88

NAU3427 A06(Chr.06) 5.598 2.25 ns ns 2.91

NAU5166 A10(Chr.10) 23.423 2.09 ns ns 1.95

NAU3654 A07(Chr.07) 74.363 ns 3.02 ns 4.36

NAU3398 A13(Chr.13) 3.311 ns 3.15 ns 5.64

JESPR297 D07(Chr.16) 43.109 ns 2.18 ns 2.08

NAU5418 D11(Chr.21) 75.156 ns 2.08 ns 4.47

BNL3537 D12(Chr.26) 39.496 ns ns 2.06 2.77

NAU2980 D13(Chr.18) 0 ns ns 2.02 2.64

BNL1707 A13(Chr.13) 104.694 2.39 ns ns 2.27

JESPR152 D01(Chr.15) 110.252 2.59 ns ns 3.25

BNL3359 D06(Chr.25) 140.397 4.3 ns ns 5.67

SSR markers, including the markers linked to fiber properties reported in previous studies [17-19], were used to screen the 99 accessions, and 97 of which showed polymorphisms [73]. Marker-trait association analysis was performed with the MLM model implemented in TASSEL [82]. At the *α*=0.01 (-log*P*=2) level, a total of 51 significant associations were detected between 33 SSR markers and 3 lint yield traits, and 7 of the these associations could be detected in both 2 years. The proportion of phenotypic variation explained by markers ranged from 7.76% to 23.99%, with an average of 14.07%. After Bonferroni correction [83], 17 associations were still significant (*P*≤0.05/97,-log*P*≥3.29). Compared with the results from our previous 356 accession panel, both the number of significant associations and the proportion of phenotypic

Exploitation of Chinese Upland Cotton Cultivar Germplasm Resources to Mine Favorable QTL Alleles…

**-logP** *R2*

**2004 2007 2004 2007**

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67

variation explained by markers were improved in the current panel.

 SSR loci significantly (-log*P*≥2.0) associated with fiber quality traits detected in the 356 Chinese Upland cotton cultivars. FL: fiber length; FS: fiber strength; FF: fiber fineness measured by micronaire value; FU: fiber uniformity ratio; FSCI: fiber spinning consistency index; E1: Jiangpu in 2009; E2: Dafeng in 2010; E3: Zhengzhou in 2010

To embrace the genetic diversity of fiber qualities as large as possible, a previous collection comprising 99 Upland cotton cultivars and breeding lines was used for further association mapping. The collection included 63 cultivars and 36 breeding lines with elite fiber quality traits (13 of which were PD lines introduced from the United States), and the fiber properties including fiber length, strength, and fineness were evaluated in 2004 and 2007. A total of 260 SSR markers, including the markers linked to fiber properties reported in previous studies [17-19], were used to screen the 99 accessions, and 97 of which showed polymorphisms [73]. Marker-trait association analysis was performed with the MLM model implemented in TASSEL [82]. At the *α*=0.01 (-log*P*=2) level, a total of 51 significant associations were detected between 33 SSR markers and 3 lint yield traits, and 7 of the these associations could be detected in both 2 years. The proportion of phenotypic variation explained by markers ranged from 7.76% to 23.99%, with an average of 14.07%. After Bonferroni correction [83], 17 associations were still significant (*P*≤0.05/97,-log*P*≥3.29). Compared with the results from our previous 356 accession panel, both the number of significant associations and the proportion of phenotypic variation explained by markers were improved in the current panel.

 SSR loci significantly (-log*P*≥2.0) associated with fiber quality traits detected in the 356 Chinese Upland cotton cultivars. FL: fiber length; FS: fiber strength; FF: fiber fineness measured by micronaire value; FU: fiber uniformity ratio; FSCI: fiber spinning consistency index; E1: Jiangpu in 2009; E2: Dafeng in 2010; E3: Zhengzhou in

To embrace the genetic diversity of fiber qualities as large as possible, a previous collection comprising 99 Upland cotton cultivars and breeding lines was used for further association mapping. The collection included 63 cultivars and 36 breeding lines with elite fiber quality traits (13 of which were PD lines introduced from the United States), and the fiber properties including fiber length, strength, and fineness were evaluated in 2004 and 2007. A total of 260

NAU1070 D02(Chr.14) 13.885 3.19 ns ns 4.15 1.99

FSCI NAU2561 A05(Chr.05) 56.73 2.33 ns ns 2.31

BNL3261 A12(Chr.12) 40.276 ns 2.08 ns 4.45

BNL1606 D03(Chr.17) 50.883 ns 2.48 ns 3.2

**E1 E2 E3 E1 E2 E3**

**Traits Marker loci Chr. Position**

phenotypic variation in two years

the future breeding programs.

**-logP** *R2*

BNL3436 D06(Chr.25) 105.073 2.24 ns 9.68 TMO06 D09(Chr.23) 109.242 2.85 ns 12.84 NAU1004 D09(Chr.23) 13.67 3.03 ns 13.67

Exploitation of Chinese Upland Cotton Cultivar Germplasm Resources to Mine Favorable QTL Alleles…

**Table 5.** SSR loci significantly (-log*P*≥2.0) associated with fiber quality traits detected in the and their explained

**Traits Favorable allele** *ai* **Accessions Representative materials**

NAU2354-2 0.3662 10 I-62478, I-62479, PD94045 BNL1317-3 0.8522 21 I-62434, 7235, I-62431 NAU445-3 0.7926 31 I-62434, 7235, I-62431 JESPR295-2 0.8233 31 I-62434, 7235, I-62478 JESPR153-3 0.8457 16 I-62434, I-62431, Yumian1 NAU1200-1 0.5373 49 I-62434, I-62478, I-62431

NAU1102-3 0.4564 22 I-62431, I-62479, I-62429

Fiber length NAU934-4 1.0498 17 I-62434, 7235, I-62478 NAU1037-1 4.5458 1 7235

NAU2272-5 5.8050 1 7235

Phenotypic effects of each QTL allele for the 33 significantly associated loci were measured, and 21, 24 and 7 favorable alleles for fiber length, strength and fineness were identified, respectively. Phenotypic effects and representative materials for each favorable allele are shown in Table 6. A wide variation of the favorable alleles processed in the 99 accessions was observed, with an average of 14.84 and a range of 7 to 21 (see reference [73] for details). Most of the representative materials were fiber-elite lines collected from China or introduced from the United States, and some of them, such as Yumian 1 and 7235, were introgression lines and contained more than half of the favorable alleles. For example, 7235 was developed from several hybridizations with multiple parents, such as a *G. anomalum* introgression line, Acala 3080 and PD4381 [85, 86]; Yumian 1 was an introgression line with *G. barbadense*, *G. arboreum*, and *G. raimondii* as putative donors, which is characterized by high lint yield and high fiber strength [24]. These fiber-elite materials had been used for tagging QTLs underlying fiber quality traits in previous family-based linkage mapping studies [13, 17-19, 24-25; 39]. With the genetic information provided by this and previous studies, MAS should be complemented in

**2004 2007 2004 2007**

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**Traits Marker loci Chr. Position**

68 World Cotton Germplasm Resources

Fiber strength NAU934 A05(Chr.05) 186.594 3.82 ns 14.08

NAU1200 A05(Chr.05) 2.68 ns 14.5

NAU445 A12(Chr.12) 123.764 2.60 ns 11.96 JESPR153 A13(Chr.13) 63.046 5.42 ns 23.3

BNL2634 D07(Chr.16) 2.72 ns 12.47 NAU1369 D08(Chr.24) 57.434 2.74 ns 12.13

NAU816 D08(Chr.24) 3.70 ns 13.6

Fiber fineness NAU934 A05(Chr.05) 186.594 ns 3.074 12.62

NAU1162 A11(Chr.11) 99.954 2.05 ns 11.43 JESPR295 A12(Chr.12) 32.732 2.87 ns 12.95

TMO06 D09(Chr.23) 109.242 5.51 3.80 23.66 19.23 NAU2443 D13(Chr.18) 62.214 6.13 3.16 22.85 13.02

BNL1317 A09(Chr.09) 88.456 4.31 2.31 18.74 9.71

NAU474 A07(Chr.07) 30.627 ns 2.43 17.44 NAU1043 A07(Chr.07) ns 2.14 15.86 BNL1317 A09(Chr.09) 88.456 ns 2.37 12.58 NAU2508 A10(Chr.10) 126.827 ns 3.29 23.99 JESPR295 A12(Chr.12) 32.732 2.28 2.72 10.56 14.19

BNL1395 D07(Chr.16) 78.652 ns 2.12 13.62 BNL1122 D07(Chr.16) 88.995 ns 2.23 14.19

BNL1521 D08(Chr.24) 73.477 ns 2.34 9.26 JESPR78 D08(Chr.24) 75.81 ns 2.34 9.26 NAU1197 D08(Chr.24) 77.384 ns 2.01 7.76 NAU1336 D08(Chr.24) 78.13 ns 2.34 9.26 NAU1262 D08(Chr.24) 88.599 ns 2.34 9.26 NAU1322 D08(Chr.24) 89.779 ns 2.04 10.82 JESPR127 D08(Chr.24) 97.939 ns 2.34 9.26 NAU780 D08(Chr.24) ns 3.60 18.14

**-logP** *R2*

**2004 2007 2004 2007**

**Table 5.** SSR loci significantly (-log*P*≥2.0) associated with fiber quality traits detected in the and their explained phenotypic variation in two years

Phenotypic effects of each QTL allele for the 33 significantly associated loci were measured, and 21, 24 and 7 favorable alleles for fiber length, strength and fineness were identified, respectively. Phenotypic effects and representative materials for each favorable allele are shown in Table 6. A wide variation of the favorable alleles processed in the 99 accessions was observed, with an average of 14.84 and a range of 7 to 21 (see reference [73] for details). Most of the representative materials were fiber-elite lines collected from China or introduced from the United States, and some of them, such as Yumian 1 and 7235, were introgression lines and contained more than half of the favorable alleles. For example, 7235 was developed from several hybridizations with multiple parents, such as a *G. anomalum* introgression line, Acala 3080 and PD4381 [85, 86]; Yumian 1 was an introgression line with *G. barbadense*, *G. arboreum*, and *G. raimondii* as putative donors, which is characterized by high lint yield and high fiber strength [24]. These fiber-elite materials had been used for tagging QTLs underlying fiber quality traits in previous family-based linkage mapping studies [13, 17-19, 24-25; 39]. With the genetic information provided by this and previous studies, MAS should be complemented in the future breeding programs.

**Traits Favorable allele** *ai* **Accessions Representative materials** JESPR127-2 8.9290 1 7235

Fiber fineness NAU934-4 -0.3064 17 Emian11, Emian6, CRI18

**Table 6.** Favorable QTL alleles for fiber qualities, their phenotypic effects (*ai*

**4. Favorable QTL alleles for FOV race 7 resistance**

favorable allele

TMO06-3 3.1629 19 I-62429, 7235, I-62433 NAU2443-2 3.0749 17 I-62429, 7235, Yumian1 BNL2634-3 0.8204 26 I-62429, I-62433, Yumian1 NAU780-1 0.4073 71 I-62429, I-62478, Yumian1 NAU816-3 1.6559 29 I-62429, PD94042, I-62478

Exploitation of Chinese Upland Cotton Cultivar Germplasm Resources to Mine Favorable QTL Alleles…

BNL1317-3 -0.3617 21 Jinmian2, Jinmian9, Jimian11

TMO06-3 -0.3587 19 Shiduan5, Tai8033-2, Sumian5

JESPR295-3 -0.2148 9 Qiannong465, Cangzhou7315-38, Jin185 BNL3436-3 -0.1132 60 Xiangmian11, Jinmian2, Qiannong465

NAU1004-2 -0.1530 14 Qiannong465, Wanmian73-10, Qinli514

*)* and representative materials.

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NAU1162-2 -0.2338 13 Sumian5, CRI16, Yu668

Representative accessions are the top-3 entries for the target trait value of accessions with the corresponding

Fusarium wilt (FW), caused by *Fusarium oxysporum* f. sp. *vasinfectum* (FOV), is a widespread disease that causes huge losses in cotton (*Gossypium* ssp.) production worldwide [87-90]. Once established in soil, FOV can survive in the field for several years as Chlamydia spores, even in the absence of a host, and is nearly impossible to eliminate. Although both chemical controls and cultural practices have been employed in protecting plants from damage, the most effective and efficient control should be provided through host resistance [87, 89]. In the past several years, eight races (race 1–8) of FOV had been indentified worldwide that use both cotton and non-cotton differential hosts [88]. Three FOV races (race 3, 7 and 8) had been found in China; race 7 possesses the highest virulence and is the most widely distributed race [91]. Recently, DNA-based techniques were employed in conjunction with pathogenicity tests to validate these races and to test new isolates, and highly virulent isolates of FOV were identified in Australia [92] and the United States [93-94]. Host resistance to FOV races has been widely evaluated in cotton germplasm under both field nursery and greenhouse conditions, and many highly resistant cotton cultivars and breeding lines have been developed through conventional breeding [89, 95-96]. However, little is known about the mechanism and genetic basis of FOV resistance. Some early classical genetic studies have suggested that the inheritance of FOV Exploitation of Chinese Upland Cotton Cultivar Germplasm Resources to Mine Favorable QTL Alleles… http://dx.doi.org/10.5772/58587 71

**Table 6.** Favorable QTL alleles for fiber qualities, their phenotypic effects (*ai )* and representative materials. Representative accessions are the top-3 entries for the target trait value of accessions with the corresponding favorable allele

## **4. Favorable QTL alleles for FOV race 7 resistance**

**Traits Favorable allele** *ai* **Accessions Representative materials** NAU1302-1 4.5458 1 7235 BNL1521-3 5.8050 1 7235 JESPR78-3 5.8050 1 7235 NAU1336-3 5.8050 1 7235 NAU1262-2 5.8050 1 7235 JESPR127-2 4.5458 1 7235

70 World Cotton Germplasm Resources

TMO06-3 1.3109 19 I-62434, 7235, Yumian1

NAU2443-2 0.8435 17 I-62434, 7235, I-62431

NAU1004-2 0.9346 14 I-62434, 7235, TM-1

NAU474-2 1.0231 31 PD94042, I-62478, PD93019

BNL1317-3 2.3136 21 I-62429, 7235, I-62433 NAU2508-1 3.7061 8 7235, PD6992, HS427 NAU445-3 0.8072 31 I-62429, 7235, I-62433 JESPR295-2 2.1342 31 I-62429, I-62478, 7235 JESPR153-3 2.8625 16 I-62429, I-62433, I-62434 NAU1200-1 0.3169 49 I-62429, PD94042, I-62478 NAU1043-2 0.9147 29 I-62478, PD93019, Yuwu19 BNL1395-1 2.2677 12 I-62478, 7235, Yumian1 BNL1122-1 2.2677 12 I-62478, 7235, Yumian1 NAU1369-2 1.4296 15 I-62429, I-62433, Yumian1

NAU904-3 2.3858 1 GA2001163

NAU780-4 4.5458 1 7235

Fiber strength NAU934-4 2.6759 17 I-62429, I-62478, PD93019

BNL1521-3 8.9290 1 7235 JESPR78-3 8.9290 1 7235

NAU1336-3 8.9290 1 7235 NAU1262-2 8.9290 1 7235

NAU1197-3 5.6730 2 Xiangmian13, 7235

NAU1322-2 1.7557 17 I-62429, 7235, I-62433

Fusarium wilt (FW), caused by *Fusarium oxysporum* f. sp. *vasinfectum* (FOV), is a widespread disease that causes huge losses in cotton (*Gossypium* ssp.) production worldwide [87-90]. Once established in soil, FOV can survive in the field for several years as Chlamydia spores, even in the absence of a host, and is nearly impossible to eliminate. Although both chemical controls and cultural practices have been employed in protecting plants from damage, the most effective and efficient control should be provided through host resistance [87, 89]. In the past several years, eight races (race 1–8) of FOV had been indentified worldwide that use both cotton and non-cotton differential hosts [88]. Three FOV races (race 3, 7 and 8) had been found in China; race 7 possesses the highest virulence and is the most widely distributed race [91]. Recently, DNA-based techniques were employed in conjunction with pathogenicity tests to validate these races and to test new isolates, and highly virulent isolates of FOV were identified in Australia [92] and the United States [93-94]. Host resistance to FOV races has been widely evaluated in cotton germplasm under both field nursery and greenhouse conditions, and many highly resistant cotton cultivars and breeding lines have been developed through conventional breeding [89, 95-96]. However, little is known about the mechanism and genetic basis of FOV resistance. Some early classical genetic studies have suggested that the inheritance of FOV resistance in cotton is determined by a single gene [97-99], while other studies have suggested that FOV resistance is controlled by multiple genes [100-102]. Large-scale resistance evalua‐ tions in breeding programs are time-consuming and labor-intensive, and it is not easy to obtain the ideal genotype simply through phenotypic selection. The cultivar development process has been slow in the face of the emergence of new, highly virulent FOV isolates [89-90].

To accumulate useful information for understanding the genetic basis of FOV 7 resistance and identify favorable alleles for facilitating future molecular resistance breeding, we performed a marker-trait association mapping to detect QTLs underlying FOV 7 resistance in Upland cotton [72]. The 356-accession panel mentioned above [71] was used for association analysis and favorable allele mining. Meanwhile, a composite cross population (CP) with three parents was developed for linkage mapping. Three Upland cotton cultivars, Xuzhou 142, Yumian 21 and Shang 9901, were chosen as parents according to many years of FOV 7 resistance evalua‐ tion. Xuzhou 142, an obsolete cultivar selected from STV 2B in the 1970s with large boll size and high lint percentage that is severely infested by FOV 7, was selected as the susceptible parent. Yumian 21 was released in 1999 and is currently used as a resistant control in national cotton regional trials due to its extremely high resistance to FOV 7. The cultivar Shang 9901 is an anonymous breeding line with high yield potential and moderately high resistance to FOV 7. Thus serial QTLs for FOV 7 resistance were detected by joint population-based association mapping and family-based linkage mapping. Marker–trait association mapping was per‐ formed with the MLM model implemented in TASSEL software [82]. A total of 27 markers were significantly associated with FOV 7 resistance at the at the *α*=0.01 level (-log*P*=2.0), and were localized to 16 chromosomes (Table 7). Of these loci, 23 were detected under field nursery conditions, 10 were detected in the greenhouse and 6 were detected under both conditions. The proportion of phenotypic variation explained by the markers ranged from 1.48% to 12.99%, with an average of 4.21%. If a more stringent threshold by the Bonferroni correction (*P*≤0.05/145,-log*P*≥3.46) is adopted [83], only 10 associations were significant (Table 7). Five (*qFW-A3-1*, *qFW-A12-1*, *qFW-D3-1*, *qFW-D5-1* and *qFW-D8-1*) of the 7 QTLs identified by linkage mapping could be detected by association mapping at the *α*=0.05 level, while only 3 QTLs (*qFW-D3-1*, *qFW-D5-1* and *qFW-D8-1*) could be detected at the *α*=0.01 level (see reference [72] for details).

The phenotypic effects of each QTL allele of the 27 associated loci were estimated in both the greenhouse and field nursery evaluations according to the method mentioned above [71], and therefore, favorable alleles for FOV 7 resistance were identified (Table 8). The phenotypic effects of the 27 loci in the greenhouse and field nursery averaged-1.31 and-3.45, with ranges of-3.81to-0.01 and-14.88 to-0.20, respectively. Among the favorable alleles, NAU934-2 had the most negative phenotypic effect in the greenhouse assay and was able to decrease FW DI by 3.8, while NAU6966-3 had the most negative phenotypic effect in the field nursery and was able to decrease FW DI by 14.88. A wide variation of the favorable alleles processed in the 356 accessions was observed, with an average of 14.84 and a range of 7 to 21 (see [72] for details). Pearson correlation analysis between the number of favorable alleles and FW DI was carried out, and highly negative significance was found in both the greenhouse (*r*=-0.344, *P*<0.001) and

the field nursery (*r*=-0.488, *P*<0.001) evaluations. The top-3 resistant accessions for each

**Table 7.** SSR loci significantly (-log*P*≥2.0) associated with FOV 7 resistance and their explained phenotypic variation in greenhouse and field nursery evaluations. Gh and FN indicate the results evaluated in greenhouse and field nursery,

favorable allele were listed as representative materials in Table 8.

respectively; ns indicates non-significant at α=0.01 level

**Marker loci Chr. Position(cM)**

**-Log10P** *R2(%)*

Exploitation of Chinese Upland Cotton Cultivar Germplasm Resources to Mine Favorable QTL Alleles…

NAU2437 A01(Chr.01) 15.248 ns 2.19 5.21

JESPR304 A02(Chr.02) 22.517 ns 7.09 9.65 NAU934 A05(Chr.05) 19.421 ns 4.61 5.78 NAU3273 A05(Chr.05) 37.244 ns 2.85 2.81

BNL3255 A08(Chr.08) 81.913 ns 3.20 4.41 STV031 A10(Chr.10) 12.751 2.09 2.82 3.34 4.34 NAU440 A10(Chr.10) 42.017 2.80 2.57 2.81 2.51 Gh369 A11(Chr.11) 84.701 ns 2.29 3.61 BNL1066 A11(Chr.11) 134.486 ns 2.12 3.94

NAU1070 D02(Chr.14) 13.885 2.47 5.89 3.10 7.56 CIR246 D02(Chr.14) 112.473 2.55 3.14 3.65 4.56 BNL3590 D03(Chr.17) 39.284 ns 2.77 3.66 BNL834 D03(Chr.17) 81.098 ns 5.07 5.46 NAU6966 D04(Chr.22) 36.51 ns 2.47 4.16 NAU3095 D05(Chr.19) 10.634 4.20 4.04 5.12 4.99 NAU2816 D05(Chr.19) 160.89 ns 2.62 4.20 BNL3594 D06(Chr.25) 7.66 7.87 4.12 12.99 7.92 NAU3911 D07(Chr.16) 28.658 ns 3.52 3.93 NAU6752 D07(Chr.16) 74.671 ns 3.35 3.44

NAU4073 A01(Chr.01) 85.025 2.03 ns 1.48

NAU797 A05(Chr.05) 143.032 2.04 ns 3.22

BNL1707 A13(Chr.13) 104.694 2.29 ns 1.59

NAU478 D08(Chr.24) 67.677 2.08 ns 3.86

NAU3207 D08(Chr.24) 90.625 ns 5.21 5.70 NAU1350 D08(Chr.24) 108.529 ns 4.38 4.69 NAU5418 D11(Chr.21) 75.156 ns 3.71 6.98 NAU3084 D12(Chr.26) 0 ns 2.36 2.27

**Gh FN Gh FN**

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resistance in cotton is determined by a single gene [97-99], while other studies have suggested that FOV resistance is controlled by multiple genes [100-102]. Large-scale resistance evalua‐ tions in breeding programs are time-consuming and labor-intensive, and it is not easy to obtain the ideal genotype simply through phenotypic selection. The cultivar development process has been slow in the face of the emergence of new, highly virulent FOV isolates [89-90].

To accumulate useful information for understanding the genetic basis of FOV 7 resistance and identify favorable alleles for facilitating future molecular resistance breeding, we performed a marker-trait association mapping to detect QTLs underlying FOV 7 resistance in Upland cotton [72]. The 356-accession panel mentioned above [71] was used for association analysis and favorable allele mining. Meanwhile, a composite cross population (CP) with three parents was developed for linkage mapping. Three Upland cotton cultivars, Xuzhou 142, Yumian 21 and Shang 9901, were chosen as parents according to many years of FOV 7 resistance evalua‐ tion. Xuzhou 142, an obsolete cultivar selected from STV 2B in the 1970s with large boll size and high lint percentage that is severely infested by FOV 7, was selected as the susceptible parent. Yumian 21 was released in 1999 and is currently used as a resistant control in national cotton regional trials due to its extremely high resistance to FOV 7. The cultivar Shang 9901 is an anonymous breeding line with high yield potential and moderately high resistance to FOV 7. Thus serial QTLs for FOV 7 resistance were detected by joint population-based association mapping and family-based linkage mapping. Marker–trait association mapping was per‐ formed with the MLM model implemented in TASSEL software [82]. A total of 27 markers were significantly associated with FOV 7 resistance at the at the *α*=0.01 level (-log*P*=2.0), and were localized to 16 chromosomes (Table 7). Of these loci, 23 were detected under field nursery conditions, 10 were detected in the greenhouse and 6 were detected under both conditions. The proportion of phenotypic variation explained by the markers ranged from 1.48% to 12.99%, with an average of 4.21%. If a more stringent threshold by the Bonferroni correction (*P*≤0.05/145,-log*P*≥3.46) is adopted [83], only 10 associations were significant (Table 7). Five (*qFW-A3-1*, *qFW-A12-1*, *qFW-D3-1*, *qFW-D5-1* and *qFW-D8-1*) of the 7 QTLs identified by linkage mapping could be detected by association mapping at the *α*=0.05 level, while only 3 QTLs (*qFW-D3-1*, *qFW-D5-1* and *qFW-D8-1*) could be detected at the *α*=0.01 level (see reference

The phenotypic effects of each QTL allele of the 27 associated loci were estimated in both the greenhouse and field nursery evaluations according to the method mentioned above [71], and therefore, favorable alleles for FOV 7 resistance were identified (Table 8). The phenotypic effects of the 27 loci in the greenhouse and field nursery averaged-1.31 and-3.45, with ranges of-3.81to-0.01 and-14.88 to-0.20, respectively. Among the favorable alleles, NAU934-2 had the most negative phenotypic effect in the greenhouse assay and was able to decrease FW DI by 3.8, while NAU6966-3 had the most negative phenotypic effect in the field nursery and was able to decrease FW DI by 14.88. A wide variation of the favorable alleles processed in the 356 accessions was observed, with an average of 14.84 and a range of 7 to 21 (see [72] for details). Pearson correlation analysis between the number of favorable alleles and FW DI was carried out, and highly negative significance was found in both the greenhouse (*r*=-0.344, *P*<0.001) and

[72] for details).

72 World Cotton Germplasm Resources

**Table 7.** SSR loci significantly (-log*P*≥2.0) associated with FOV 7 resistance and their explained phenotypic variation in greenhouse and field nursery evaluations. Gh and FN indicate the results evaluated in greenhouse and field nursery, respectively; ns indicates non-significant at α=0.01 level

the field nursery (*r*=-0.488, *P*<0.001) evaluations. The top-3 resistant accessions for each favorable allele were listed as representative materials in Table 8.

recombinant inbred line (RIL; *G. hirsutum* TM-1 x *G. barbadense* Pima 3-79) population. The authors detected 6 QTLs (Fov1-C06, Fov1-C08, Fov1-C111, Fov1-C112, Fov1-C16 and Fov1-C19) conferring FOV 1 resistance in different genetic backgrounds [58]. Lopez-Lavalle et al. used an intraspecific cross between the Upland cotton MCU-5 and Siokra 1-4 to detected QTLs conferring resistance to Australian FOV races and found that MCU-5 resistance is complex, with 3 QTLs identified in F3 and 8 ones in F4. The QTLs were localized to chromosomes A6, D4 and D6 [60]. Very recently, Ulloa et al. investigated 3 intraspecific (*G. hirsutum* x *G. hirsutum* L. and *G. barbadense* x *G. barbadense* L.), 5 interspecific (*G. hirsutum* x *G. barbadense*) and one RIL population in 4 greenhouse and 2 field experiments and identified a set of 11 SSR markers across 6 linkage groups/chromosomes (3, 6, 8, 14, 17 and 25) associated with FOV 4 resistance [59]. Integrating information mentioned above with the QTL mapping results for FOV 7 in our study, it suggests that the inheritance of FOV resistance in cotton should be far more complex than has been elucidated to date. Interestingly, the map positions of some QTLs conferring resistance against different FOV races coincided, suggesting that these genes may play the same or similar roles in the process of different host-pathogen and/or environment interactions. While great differences in the resistance of cotton genotypes against FOV races have been demonstrated in many evaluations worldwide, a genotype with a high level of resistance against all FOV races has not been found. Given that FW is the result of interactions among the host, pathogen and environment, many genes must be involved in this process. The accumulation of resistance alleles, even with relatively minor individual effects, will result in higher levels of resistance. The Upland cotton cultivars from China and Africa have shown more resistance to Australian FOV races [90]. Therefore, the favorable alleles and their typical germplasm resources identified in this study should have great potential for developing highly

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resistant Upland cotton cultivars in future molecular breeding programs.

Compared to family-based linkage mapping, population-based association mapping offers several advantages such as: (1) the saving of time and money by using existing populations instead of creating cross-controlled populations, (2) analysis of more than two alleles per locus on average, and (3) high expected resolution owing to a short extent of linkage disequilibrium [103]. All these make association mapping an increasingly important tool for complex trait

A suitable association mapping panel should embrace as much phenotypic and genotypic diversity as possible. For example, because the 99-accession panel [73] comprised higher diversity in fiber properties than the 356-accession panel [71] and 81-accession panel [69], both the number of significant associations detected and the proportion of phenotypic variation explained by markers were improved. However, the presence of high diversity from different genetic origins may induce LD between unlinked loci, and consequently result in spurious marker-trait associations [105]. Although several statistical strategies have been developed to account for issues related to population structure and relatedness in order to decrease false positive [106-107], whether these approaches will induce new false negative remain unclear.

**5. Discussion and future prospects**

dissection [63, 104].

**Table 8.** Favorable QTL alleles, phenotypic effects (*ai )* and their representative materials. *a*g and *a*f indicate phenotypic effects evaluated in greenhouse and field nursery conditions, respectively; Representative materials are the top-3 accessions with low disease index

Wang and Roberts identified a major resistance gene (*Fov1*) against FOV 1 in *G. barbadense* cv. Pima-S7 and indicated that one or more minor genes in Acala NemX could delay wilt symp‐ toms [54]. Ulloa et al. performed QTL mapping in an F2 (Pima-S7 x Acala NemX) and a recombinant inbred line (RIL; *G. hirsutum* TM-1 x *G. barbadense* Pima 3-79) population. The authors detected 6 QTLs (Fov1-C06, Fov1-C08, Fov1-C111, Fov1-C112, Fov1-C16 and Fov1-C19) conferring FOV 1 resistance in different genetic backgrounds [58]. Lopez-Lavalle et al. used an intraspecific cross between the Upland cotton MCU-5 and Siokra 1-4 to detected QTLs conferring resistance to Australian FOV races and found that MCU-5 resistance is complex, with 3 QTLs identified in F3 and 8 ones in F4. The QTLs were localized to chromosomes A6, D4 and D6 [60]. Very recently, Ulloa et al. investigated 3 intraspecific (*G. hirsutum* x *G. hirsutum* L. and *G. barbadense* x *G. barbadense* L.), 5 interspecific (*G. hirsutum* x *G. barbadense*) and one RIL population in 4 greenhouse and 2 field experiments and identified a set of 11 SSR markers across 6 linkage groups/chromosomes (3, 6, 8, 14, 17 and 25) associated with FOV 4 resistance [59]. Integrating information mentioned above with the QTL mapping results for FOV 7 in our study, it suggests that the inheritance of FOV resistance in cotton should be far more complex than has been elucidated to date. Interestingly, the map positions of some QTLs conferring resistance against different FOV races coincided, suggesting that these genes may play the same or similar roles in the process of different host-pathogen and/or environment interactions. While great differences in the resistance of cotton genotypes against FOV races have been demonstrated in many evaluations worldwide, a genotype with a high level of resistance against all FOV races has not been found. Given that FW is the result of interactions among the host, pathogen and environment, many genes must be involved in this process. The accumulation of resistance alleles, even with relatively minor individual effects, will result in higher levels of resistance. The Upland cotton cultivars from China and Africa have shown more resistance to Australian FOV races [90]. Therefore, the favorable alleles and their typical germplasm resources identified in this study should have great potential for developing highly resistant Upland cotton cultivars in future molecular breeding programs.

## **5. Discussion and future prospects**

**Favorable alleles** *ag af* **Accessions Representative materials**

NAU2437-4 -3.27 -8.49 26 Yumian21,Xingmian2,Shiyuan321 NAU4073-2 -1.46 -0.68 172 Xinluzao24,Xingmian2,Xinluzao28 JESPR304-1 -0.45 -2.69 316 Yumian21,Jimian14,Xinluzao24

NAU934-2 -3.81 -4.68 39 Xinluzao24,Zhongmiansuo3,Xinluzao2

STV031-1 -2.09 -4.91 137 Yumian21,Xingmian2,Zhongmiansuo3

NAU3273-1 -0.13 -0.79 341 Yumian21,Jimian14,Xinluzao24 NAU797-2 -1.51 -1.03 172 Yumian21,Jimian14,Xinluzao24 BNL3255-1 -0.01 -2.33 284 Yumian21,Jimian14,Xinluzao24

NAU440-1 -0.33 -0.60 347 Yumian21,Jimian14,Xinluzao24 Gh369-2 -1.02 -3.33 10 Xinluzao24,Xinluzao28,Emian13 BNL1066-4 -1.96 -4.14 15 Zhongmiansuo27,Jimian20,Emian13 BNL1707-2 -1.93 -4.79 112 Yumian21,Xinluzao24,Xingmian2 NAU1070-1 -1.73 -5.84 199 Jimian14,Xinluzao24,Xingmian2 CIR246-3 -2.77 -8.65 16 Ejing1,Ekangmian3,Zhongmiansuo49 BNL3590-1 -0.06 -1.04 315 Yumian21,Jimian14,Xinluzao24 BNL834-1 -0.24 -1.70 312 Yumian21,Jimian14,Xingmian2 NAU6966-3 -1.89 -14.88 37 Xinluzao24,Xinluzao28,GK44 NAU3095-1 -2.30 -4.55 192 Yumian21,Xinluzao24,Xingmian2 NAU2816-2 -1.70 -3.43 152 Xinluzao24,Jimian14,Zhongmiansuo3 BNL3594-1 -2.58 -5.23 201 Jimian14,Xingmian2,Zhongmiansuo9 NAU3911-1 -0.33 -1.91 282 Yumian21,Jimian14,Xingmian2 NAU6752-1 -0.14 -0.83 341 Yumian21,Jimian14,Xinluzao24 NAU478-1 -0.21 -0.20 239 Yumian21,Jimian14,Xingmian2 NAU3207-1 -0.17 -2.27 301 Yumian21,Xinluzao24,Xingmian2 NAU1350-1 -0.03 -0.62 348 Yumian21,Xinluzao24,Jimian14 NAU5418-1 -2.90 -2.44 47 Yumian21,Jimian14,Yumian20 NAU3084-1 -0.42 -1.13 326 Yumian21,Jimian14,Xinluzao24

effects evaluated in greenhouse and field nursery conditions, respectively; Representative materials are the top-3

Wang and Roberts identified a major resistance gene (*Fov1*) against FOV 1 in *G. barbadense* cv. Pima-S7 and indicated that one or more minor genes in Acala NemX could delay wilt symp‐ toms [54]. Ulloa et al. performed QTL mapping in an F2 (Pima-S7 x Acala NemX) and a

**Table 8.** Favorable QTL alleles, phenotypic effects (*ai*

accessions with low disease index

74 World Cotton Germplasm Resources

8

*)* and their representative materials. *a*g and *a*f indicate phenotypic

Compared to family-based linkage mapping, population-based association mapping offers several advantages such as: (1) the saving of time and money by using existing populations instead of creating cross-controlled populations, (2) analysis of more than two alleles per locus on average, and (3) high expected resolution owing to a short extent of linkage disequilibrium [103]. All these make association mapping an increasingly important tool for complex trait dissection [63, 104].

A suitable association mapping panel should embrace as much phenotypic and genotypic diversity as possible. For example, because the 99-accession panel [73] comprised higher diversity in fiber properties than the 356-accession panel [71] and 81-accession panel [69], both the number of significant associations detected and the proportion of phenotypic variation explained by markers were improved. However, the presence of high diversity from different genetic origins may induce LD between unlinked loci, and consequently result in spurious marker-trait associations [105]. Although several statistical strategies have been developed to account for issues related to population structure and relatedness in order to decrease false positive [106-107], whether these approaches will induce new false negative remain unclear. Synthetic association populations, such as the multi-parent advanced generation inter-cross (MAGIC) population [108] and the nested association mapping (NAM) population in maize [109], had been used for genome-wide association studies (GWAS); with which both high resolution and better population structure control can be achieved [110-111]. It is certain that such populations had been or are being developed in Upland cotton. Two artificially controlled multiple parent random-mating populations with each comprising more than 800 lines had been developed in our library, which should provide another theoretically ideal panel for association mapping.

**Author details**

, Xiefei Zhu2

\*Address all correspondence to<EMAIL_ADDRESS>
, Wangzhen Guo1

Plant Genomics 2008; ID 742304. doi: 10.1155/2008/742304.

prospects. Nat Rev Genet 2009; 10: 565–577.

practice. Brief Funct Genomics 2010; 9: 166–177.

practice. Crop Sci 2008; 48: 391–407.

1 National Key Laboratory of Crop Genetics & Germplasm Enhancement, PR China

[1] National Cotton Council, USA. http://www.cotton.org (accessed 10 October 2013).

[2] Zhang HB, Li YN, Wang BH, Chee PW. Recent advances in cotton genomics. Int J

[3] Mackay TFC, Stone EA, Ayroles JF. The genetics of quantitative traits: challenges and

[4] Xu Y, Crouch J. Marker-assisted selection in plant breeding: from publications to

[5] Jannink JL, Lorenz AJ, Iwata H. Genomic selection in plant breeding: from theory to

[6] Jiang CX, Writght RJ, EL-Zik KM, et al. Polyploid formation created unique avenues for response to selection in *Gossypium* (cotton). Proc Natl Acad Sci USA 1998; 95:

[7] Ulloa M, Meredith WR. Genetic linkage map and QTL analysis of agronomic and fi‐ ber quality traits in an intraspecific population. J Cotton Sci 2000; 4: 161–170.

[8] Ulloa M, Saha S, Jenkin N, et al. Chromosomal assignment of RFLP linkage groups harboring important QTL on an intraspecific cotton (*Gossypium hirsutum* L.) joinmap.

[9] Kohel RJ, Yu J, Park YH, et al. Molecular mapping and characterization of traits con‐

[10] Saranga Y, Menz M, Jiang CX, et al. Genomic dissection of genotype x environment interactions conferring adaptation of cotton to arid conditions. Genome Res 2001; 11:

[11] Ren LH, Guo WZ, Zhang TZ. Identification of quantitative trait loci (QTLs) affecting yield and fiber properties in chromosome 16 in cotton using substitution line. Acta

trolling fiber quality in cotton. Euphytica 2001; 121: 163–172.

2 Cotton Research Institute, Nanjing Agricultural University, Nanjing, PR China

, Caiping Cai1

Exploitation of Chinese Upland Cotton Cultivar Germplasm Resources to Mine Favorable QTL Alleles…

and Tianzhen Zhang1\*

http://dx.doi.org/10.5772/58587

77

Hongxian Mei1

**References**

4419–4424.

1988–1995.

J Hered 2005; 96: 132–144.

Botanica Sinica 2002; 44: 815–820.

The other inherent constraint limiting the successful use of association mapping is rare alleles exiting in natural populations. Given that the number of individuals with a specific genotype is quite small, the effect of rare alleles on mapping can go far beyond the effect of small population sizes [112]. Large population size had been considered as an impor‐ tant factor to improve the QTL detection power in association mapping studies [3, 112]. Many more marker-trait associations for fiber yield were detected in our 356-accession panel [71] than in the 81-accession panel [69] between the same markers and target traits at the same significance level. While family–based linkage mapping can make use of alleles that occur at low frequencies in natural populations by designing crosses to create artificial populations with inflated frequencies of those alleles. So specifically designed mapping populations such as recombinant inbred lines (RIL) and near isogenic lines (NIL) will remain important. Furthermore, joint linkage and association mapping was recommended as an alternative approach to overcome some of the inherent limitations of both linkage and association mapping [105, 112], and this approach has proven to be a powerful tool to detecting architecture of complex traits [72, 113-118].

Recently, two preliminary maps of the whole-genome scaffolds of *G. raimondii* (the putative diploid donor for tetraploid species) were separately released by two different groups [119-120], which will facilitate the tetraploid genome sequencing and assembly. Real genomewide association mapping will be realized in the near future through resequencing or other high-throughput genotyping technologies [121], which will dramatically accelerate genetic diversity exploitation and favorable allele mining in Upland cotton germplasm resources.