Partial Dominance, Overdominance and Epistasis as the Genetic Basis of Heterosis in Upland Cotton (Gossypium hirsutum L.)

Qingzhi Liang; Lianguang Shang; Yumei Wang; Jinping Hua

doi:10.1371/journal.pone.0143548

Abstract

Determination of genetic basis of heterosis may promote hybrid production in Upland cotton (Gossypium hirsutum L.). This study was designed to explore the genetic mechanism of heterosis for yield and yield components in F_{2: 3} and F_{2: 4} populations derived from a hybrid ‘Xinza No. 1’. Replicated yield field trials of the progenies were conducted in 2008 and 2009. Phenotypic data analyses indicated overdominance in F₁ for yield and yield components. Additive and dominance effects at single-locus level and digenic epistatic interactions at two-locus level were analyzed by 421 marker loci spanning 3814 cM of the genome. A total of 38 and 49 QTLs controlling yield and yield components were identified in F_{2: 3} and F_{2: 4} populations, respectively. Analyses of these QTLs indicated that the effects of partial dominance and overdominance contributed to heterosis in Upland cotton simultaneously. Most of the QTLs showed partial dominance whereas 13 QTLs showing overdominance in F_2:3 population, and 19 QTLs showed overdominance in F_2:4. Among them, 21 QTLs were common in both F_{2: 3} and F_{2: 4} populations. A large number of two-locus interactions for yield and yield components were detected in both generations. AA (additive × additive) epistasis accounted for majority portion of epistatic effects. Thirty three complementary two-locus homozygotes (11/22 and 22/11) were the best genotypes for AA interactions in terms of bolls per plant. Genotypes of double homozygotes, 11/22, 22/11 and 22/22, performed best for AD/DA interactions, while genotype of 11/12 performed best for DD interactions. These results indicated that (1) partial dominance and overdominance effects at single-locus level and (2) epistasis at two-locus level elucidated the genetic basis of heterosis in Upland cotton.

Citation: Liang Q, Shang L, Wang Y, Hua J (2015) Partial Dominance, Overdominance and Epistasis as the Genetic Basis of Heterosis in Upland Cotton (Gossypium hirsutum L.). PLoS ONE 10(11): e0143548. https://doi.org/10.1371/journal.pone.0143548

Editor: David D. Fang, USDA-ARS-SRRC, UNITED STATES

Received: August 16, 2015; Accepted: November 5, 2015; Published: November 30, 2015

Copyright: © 2015 Liang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: All relevant data are within the paper and its Supporting Information files. Readers may direct questions to the corresponding author (jinping_hua@cau.edu.cn).

Funding: This research was supported in part by a grant from the National Natural Science Foundation of China (www.nsfc.gov.cn) (31171591), grants from the National High Technology Research and Development Program (2011AA10A102), the New Century Excellent Talents of the Ministry of Education (NCET-06-0106), and the Key Project of Ministry of Education (10712) to J HUA.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: cM, centi-Morgan; QTL, quantitative trait loci; RIL, recombinant inbred line; SSR, simple sequence repeat; AA, additive × additive; AD or DA, additive × dominance or dominance ×additive; DD, dominance × dominance

Introduction

Cotton (Gossypium spp.) is the most important fiber crop in the world. A number of experiments showed that significant heterosis existed in Upland cotton for yield and yield components [1–3]. Hybrids with acceptable heterosis have been released for cotton production in both India, and China,.

Rapid advances in high density molecular genetic linkage maps have enabled a fine dissection of quantitative traits into genetic effects of individual Mendelian loci [4]. To understand the genome of cotton and detect quantitative trait loci (QTLs) for lint yield and fiber quality, the first genetic linkage map was constructed in cotton [5]. Since then, a variety of genetic linkage maps with improved high density were constructed in different mapping populations derived from either interspecific tetraploids or cultivated allotetraploid cotton [6–12]. Because genetic polymorphism in intraspecific Upland cotton was lower than that in interspecific tetraploids, the genome coverage of genetic linkage maps in intraspecific tetraploids was relatively low [13]. An intraspecific genetic linkage map comprising 604 loci and covering 3141 cM was recently constructed in Upland cotton [14]. Some other Upland cotton intraspecific linkage maps were also reported such as a linkage map with 471 loci and coverage of 3070 cM [15], and another linkage map with 978 loci and coverage of 4,184 cM [16]. More recently, a genetic map was constructed containing 421 SSR loci with a span of 3814.3 cM using an F₂ population in Upland cotton [17]. Moreover, the most marker-rich intraspecific linkage map, which contained 1540 loci and spanned 2,842.06 cM, was constructed using recombinant inbred line (RIL) population derived from a cross between Upland cotton cultivar/line ‘Yumian 1’ and ‘7235’ [18]. QTL mapping of yield and fiber quality traits based on high-density genetic linkage maps of intraspecific tetraploids can be very useful in revealing the genetic basis of lint yield and fiber properties. Recently, Liu et al. (2012) identified a total of 225 QTLs controlling yield and its components in both the XZM2-derived RILs and IF₂ populations, and from a total of 111 non-redundant QTLs, of these, 23 were detected in both two populations simultaneously [19]. Shang et al. (2015) mapped a total of 20 QTLs for fiber quality traits using 177 RILs derived from ‘Xinza No. 1’, a cross between ‘GX11359’ and ‘GX100-2’ [20]. Sequences of the D5-genome of G. raimondii, A2-genome of the G. arboreum and genome sequence of cultivated Upland cotton (Gossypium hirsutum TM-1) were publically available recently [21–25]. These physical maps provide new opportunities to develop rich SSR and functional markers for construction of high-density genetic maps and further dissect the genetic basis of complex quantitative traits as well as heterosis in Upland cotton.

Utilizations of heterosis have made significant economic benefits in crops during the last century. However, the mechanism of heterosis remains enigmatic [26]. Different hypotheses including dominance hypothesis [27–29], overdominance hypothesis [30–31] and epistasis hypothesis [32–34] were proposed to explore the mechanisms of heterosis. Epistasis is a phenomenon which the effect of one gene is modified by one or several other genes. Epistasis can be contrasted to dominance, which is an interaction between alleles at the same gene locus. The dominance hypothesis attributes the superiority of hybrids to the suppression of undesirable (deleterious) recessive alleles from one parent by dominant alleles from another parent. The overdominance hypothesis states that some combinations of alleles are especially advantageous when paired in a heterozygous individual. Recent research using quantitative molecular tools provided new evidence for different hypotheses in dissecting heterosis [26, 35–38]. In two rice BC₁F₇ populations between recombinant inbred lines (RIL) and their parents, the heterozygotes were superior to the respective homozygotes in most QTLs, which supported the hypothesis that dominance was the genetic basis of heterosis in hybrid rice [39]. Stuber et al. [40] studied heterosis in the elite maize hybrid ‘B73 × Mo17’ and concluded that overdominance (or pseudo-overdominance) (i.e., a higher yield in the heterozygote than that in either homozygote) was the major cause of heterosis for grain yield. The hypothesis for epistasis as the genetic basis of heterosis was supported by a series of researches too [41–43]. The fact that a large number of digenic interactions for yield and yield components were detected in an F_{2: 3} population provided evidence for epistasis as the primary genetic basis of heterosis in rice [43]. Hua et al. [41–42] and Zhou et al. [44] further verified that heterozygotes were not necessarily advantageous for phenotypic traits and epistasis was the important genetic basis of heterosis in elite rice hybrid ‘Shanyou 63’. Multiple genetic mechanisms were identified to play roles in heterosis. In rapeseed, epistasis as well as all levels of dominance from partial to overdominance were responsible for the expression of heterosis [45]. Another study in hybrid maize ‘Yuyu No. 22’ suggested that genetic basis of grain yield heterosis relied on the cumulative effects of dominance, overdominance, and epistasis by genetic dissection using an “immortalized F₂” population [46].

Opposite to genetic mechanisms of heterosis, reports on the genetic basis of inbreeding depression were inconsistent due to diverse materials in different experiments [47–54]. Wright [51] suggested that recessive or partially recessive deleterious effects of alleles were the most important cause of inbreeding depression. However, overdominance and epistasis were suggested as the primary genetic basis of inbreeding depression in rice [48–49]. Dominance effect was clearly a cause of inbreeding depression for yield while dominance and epistasis effects were the genetic basis of heterosis in soybean [53]. To understand the genetic basis of inbreeding depression in rice, Li and Luo et al. [48–49] reported that hybrid breakdown (part of inbreeding depression) in an intersubspecific F₄ population and recombinant inbred lines and it’s two BC and two testcross hybrid populations derived from crosses between the RILs and their parents, and these results implied epistasis as a genetic basis of inbreeding depression. Li et al. [54] reported that hybrid breakdown (part of inbreeding depression) in an intersubspecific F₄ population was largely due to additive epistatic loci, which implies epistasis as a genetic basis of heterosis.

In the present research, the main objective was to explore the genetic basis of heterosis using the populations of F_{2: 3} and F_{2: 4} generations in Upland cotton by dissecting QTLs effects at both single- and two-locus levels. These results are expected to improve our understanding of the genetic basis of heterosis in cotton which will promote cotton hybrid production for lint yield.

Materials and Methods

Ethics Statement

No permits were required to conduct the field research or genotyping analyses. The field studies did not involve endangered or protected species.

Plant Materials

As described previously [17, 20, 55–56], the hybrid of Upland cotton ‘Xinza No. 1’ from the cross of ‘GX1135’ (P₁) and ‘GX100-2’ (P₂) was bred by the Guoxin Seed Company (Hebei Province, China), and the hybrid was released as a cultivar in Anhui Province in 2006. In the present study, F₁ seeds were developed from a manual emasculated cross between P₁ and P₂ grown in Hainan Province in winter 2006, and the F₁ seeds were then grown at the Xinzhou Cotton Breeding Station (Wuhan, 30°34'N, 114°16'E) in April 2007. The genotype of the F₁ individuals was distinguished by a codominant molecular marker, and one F₁ individual with the confirmed genotype was self-pollinated to produce F₂ seeds. A total of 256 randomly selected F₂ seeds were cultured in nutrient solution [57] in green house at China Agricultural University (Beijing) in October 2007. All the F₂ seedlings were transported by air and transplanted at the Hainan South Propagation Station (Sanya, Hainan Province) to produce F₃ seeds by self pollination. The F₃ family lines were bulk self-pollinated to produce F₄ seeds. A population of 173 F_2:3 family lines were planted with their parents and the only one F₁ individual as, and a population of 173 F_2:4 family lines were planted with their parents and F₂ plants as controls in 2009.

Field Planting and Traits Examination

Field trials of the F_{2: 3} and F_{2: 4} populations were conducted at two locations, Quzhou Experimental Station of China Agricultural University at Handan City, Hebei Province (36°78'N, 114°92'E) and Hejian Guoxin Cotton Breeding Experiment Station at Cangzhou City, Hebei Province (38°43'N, 116°09'E) during 2008 and 2009. The two parents GX1135 (P₁) and GX100-2 (P₂) and their F₁ hybrid, ‘Xinza No. 1’, were also included as controls in the field tests. Field experiments followed a randomized complete block design with two replications at each location. Plants were planted in two row plots with 14 plants each. Plots were 4 m in length with 80 cm row spacing for the experiment at Handan and 4 m in length and 60 cm row spacing for the experiment at Cangzhou. Plants were spaced 30 cm in rows. Field management followed conventional standard field practices. Seedlings of approximate 25 days old were transplanted to fields in 2008. The seeds of F_{2: 4} were planted by sowing directly in 2009. Bolls from seven and five consecutive plants in the middle of each plot were sampled at Handan and Cangzhou, respectively. Boll samples were ginned for seed-cotton yield (SY, t/ha), lint yield (LY, t/ha), bolls/plant (BNP), boll weight (BW, g), lint percentage (LP, %). Traits examined included yield per plant measured as the seed cotton weight of all bolls of the individual. The remaining bolls in each plot were collected for yield estimation. Mid-parent heterosis (MPH) was calculated as MPH = (F₁-M)/M × 100, M = (P₁ + P₂)/2. Where M is the mean yield of both parents and P₁ and P₂ are maternal and paternal yield, respectively. Hybrid breakdown (part of inbreeding depression) in an intersubspecific F₄ population was analyzed by the method provided previously by Li et al. [54].

DNA Isolation and Genotype Analysis

Young leaves were collected from labeled F₂, P₁, P₂, and F₁ individuals, frozen in liquid nitrogen, and stored at -80°C. DNA was individually extracted as described by Paterson et al. [58]. A total of 16,405 SSR primer pairs were used to screen for polymorphic markers between parents. Among these primers, the sequences of 13,468 pairs including BNL, NAU, TM, JESPER, CIR, HAU, CM, MUSS, MUSB and MUCS primers were downloaded from Cotton Microsatellite Database (CMD, http://www.cottonmarker.org). The detailed information of these primers was described in literature [6–9, 14, 59]. The remaining 2,937 pairs of primers were designed and developed from DNA sequence library [60]. The polymorphic markers identified were used to genotype individuals of F₂ population.

Map Construction and QTL Analysis

MAPMAKER 3.0 [61] was employed to construct a genetic linkage map. Assignment of linkage groups to chromosomes was made based on previously chromosome-anchored SSR markers [6, 11–12, 18, 62]. When no chromosome reference was available, the linkage group was described as “un××” with “××” referring to its serial number. QTLs were detected by composite interval mapping as described by Zeng [63] using software of WinQTL Cartographer 2.5 [64]. A stringent LOD threshold of 3.0 was used to declare suggestive QTL [65], whereas the same QTL in another environment with LOD of at least 2.0 was considered to be a common QTL, as described by Shen et al. [66]. The graphic representation of the linkage group and QTL marked were created by Map Chart 2.2 as described by Voorrips [67].

Analysis, Confirmation of Digenic Interaction and Components Partition

Analysis of digenic interactions and further confirmation followed the methods described by Yu et al. [43] and Hua et al. [42]. The detected epistasis was confirmed by a randomization test conducted to identify those interactions more likely to be ‘really’ significant. In doing so, the entry order of the trait data in the analysis was randomly permutated and the F-statistic values were recalculated for the digenic interactions using the same marker data. This procedure was repeated 1000 times, and the resulting 1000 F-values were compared with the F-statistic values from the original data. If no more than one F-value from the random permutations was larger than the F-statistic value from the original data, the digenic interaction was regarded to be significant [41]. Significant interactions were further partitioned into three components, each specified by a single degree of freedom: AA (additive × additive), AD/DA (additive × dominance or dominance × additive), and DD (dominance × dominance). The significance for each term was assessed in an orthogonal contrast test using the statistical package STATISTICA [68].

Results

Measurements of Yield and Yield Components in F_{2: 3} and F_{2: 4} Populations and Mid-Parent Heterosis of Hybrid ‘Xinza No. 1’

The measurements of yield and yield components including boll number per plant (BNP), boll weight (BW), and lint percentage (LP) were made for hybrids and their parents in F_{2: 3} and F_{2: 4} populations (S1 File). Data are listed in Table 1 and illustrated in S1 Fig. The yield traits varied widely in the populations during the two-year experiments (S1 Fig). Transgressive segregation of both directions was observed for all yield traits in F_{2: 3} and F_{2: 4} populations. Seed cotton yield (SY) and lint yield (LY) of the F_{2: 4} population grown at Handan were much lower than that of the same population grown at Cangzhou, probably due to drought conditions at Handan which caused early aging during experiments. Lint yield was significantly correlated with all other yield traits and seed-cotton yield was significantly correlated with all the traits except lint percent. The correlation coefficients between different traits varied greatly from -0.23 to 0.97. Highest correlations were detected for bolls per plant vs. lint yield, 0.55 and 0.56 at Handan and Cangzhou respectively, in F_{2: 3} populations (Table 2). Significant heterosis for yield was detected in hybrid ‘Xinza No. 1’ with 56% and 62% mid-parent heterosis (MPH) for seed cotton yield and lint yield, respectively. In the F₂ population, MPH values were 38.1% and 43.3% for seed yield and lint yield, respectively.

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Table 1. Yield and yield components in F_{2: 3}, F_{2: 4} populations and mid-parent heterosis in F₁ and F₂ generations.

https://doi.org/10.1371/journal.pone.0143548.t001

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Table 2. Correlation between yield and yield components in F_{2: 3} and F_{2: 4} populations.

https://doi.org/10.1371/journal.pone.0143548.t002

Linkage Map and QTL Mapping for Yield and Yield Components

A total of 450 polymorphic markers, screened from 16,405 pairs of SSR primer between parents, were used to construct a linkage group (S2 File). The linkage map was obtained with 421 loci linked into 49 groups leaving 29 loci unlinked. The map spanned 3814 cM with an average distance of 8.9 cM between adjacent markers, accounting for 73% of the entire tetraploid genome. Forty-eight of the 49 linkage groups were assigned to 26 chromosomes. The remaining one linkage group could not be associated with any chromosome, and the one group was tentatively named as ‘Un 1’.

A total of 67 QTLs controlling yield and yield components were identified in F_{2: 3} and F_{2: 4} generations using composite interval mapping. The data are given in Table 3 and S2 Fig. There were 38 QTLs identified in F_{2: 3} generation, among which seven were major QTLs detected in both environments. There were 49 QTLs identified in F_{2: 4} generation, among which, six were major QTLs detected in both environments.

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Table 3. QTLs for yield and yield-components in F_{2: 3} and F_{2: 4} populations identified using composite interval mapping.

https://doi.org/10.1371/journal.pone.0143548.t003

For seed cotton yield, a total of 12 QTLs were identified in two generations, of those three QTLs were detected simultaneously in the two generations. Seven QTLs were identified in F_{2: 3} generation, accounting for 5.2% to 12.7% of total variance, and eight QTLs were identified in F_{2: 4} generations, accounting for 3.6% to 23.1% of total variance, respectively. Among them, two QTLs, qSY-chr26-1 and qSY-chr14-1, were identified at both Handan and Cangzhou locations in F_{2: 3} generation. Three QTLs, qSY-chr11-1, qSY-chr12-1 and qSY-chr14-1, were detected in both F_{2: 3} and F_{2: 4} generations. Six QTLs identified in F_{2: 3} generation showed negative additive effects (Table 3) and the alleles from female parent GX1135 increased phenotypic variation (S2 Fig). Three QTLs in F_{2: 3} generation showed positive additive effects and the alleles from GX100-2 increased phenotypic variation. In F_{2: 3} and F_{2: 4} generations, eight QTLs showed negative dominance effects and the remaining nine QTLs showed positive dominance effects.

For lint yield, a total of 10 QTLs were identified in two generations, among which three QTLs were detected simultaneously in the two generations. Five and eight QTLs were identified in F_{2: 3} and F_{2: 4} generations, respectively, accounting for 4.8% to 12.6% and 4.7% to 16.2% of total phenotypic variance in the two generations, respectively. Four QTLs among these were identified simultaneously in more than two environments. Two QTLs, qLY-chr26-1 for lint yield and qSY-chr26-1 for seed cotton yield, were mapped to the same interval on chromosome 26 in the same environments (S2 Fig).

For bolls per plant, 14 QTLs were found in two generations, among which, three QTLs were detected simultaneously in the two generations. Eight and nine QTLs were identified in F_{2: 3} and F_{2: 4} generations, respectively, accounting for 4.8% to 12.2% and 4.5% to 20.2% of total phenotypic variance in the two respective generations. Four of them were detected simultaneously in more than two environments. Five and six QTLs with negative additive effects were detected in F_{2: 3} and F_{2: 4} generations, respectively. The alleles from female parent GX1135 increased phenotypic variation of lint yield. The remaining eight QTLs showed positive additive effects in the two generations. For the effects of these QTLs, the alleles from GX100-2 increased phenotypic variation. There were six and four QTLs identified with negative dominance effects in F_{2: 3} and F_{2: 4} generations, respectively. The results suggested that heterozygotes would have lower values than the two homozygotes for these QTLs. The remaining nine QTLs showed positive dominance effects indicating that heterozygotes would have higher phenotypic values than the two respective homozygotes for the effects of these nine QTLs.

A total of 15 QTLs referring boll weight were resolved in two generations, among which five QTLs were detected simultaneously in the two generations. Eight and 12 QTLs were identified in F_{2: 3} and F_{2: 4} generations, respectively, explaining 5.9% to 9.7% and 3.8% to 22.1% of phenotypic variance in the two respective generations. There were two QTLs (qBW-chr5-1 and qBW-chr5-2) simultaneously detected in F_{2: 3} and F_{2: 4} generations with negative additive effects. The alleles from the female parent GX1135 increased phenotypic variation for the effects of these QTLs. There were seven and eight QTLs showing negative dominance effects in F_{2: 3} and F_{2: 4} generations, respectively. There were three and eight QTLs in F_{2: 3} and F_{2: 4} generations, respectively, showing positive dominance effects.

For lint percent, a total of 15 QTLs were identified in two generations, among which seven QTLs were observed simultaneously in the two generations. Ten and 12 QTLs were identified in F_{2: 3} and F_{2: 4} generations, respectively, explaining 5.4% to 14.3% and 4.8% to 16.2% of phenotypic variance in the two respective generations. Among them, there were seven QTLs detected in more than two environments. There were four and five QTLs in F_{2: 3} and F_{2: 4} generations, respectively, with negative additive effects. For these QTLs, alleles from the female parent GX1135 increased phenotypic variation (Table 3). The remaining 15 QTLs in the two generations showed positive additive effects with the alleles from female parent GX100-2 increasing phenotypic variation. There were six and six QTLs detected in F_{2: 3} and F_{2: 4} generations, respectively, with negative dominance effects. The remaining 12 QTLs in the two generations showed positive dominance effects.

Dominance and Overdominance Effects of QTL

A locus is regarded as having overdominance if the ratio of the estimated dominance to the absolute value of additive effect is larger than one. Similarly, a locus is regarded as having partial dominance if the ratio is between 0 and 1 [34]. A total of 32 and 36 QTLs in F_{2: 3} and F_{2: 4} populations, respectively, showed partial dominance with their ratios ranging from 0 to 1. One QTL was detected for seed cotton yield showing dominance in F_{2: 4} generation. Thirteen and 19 QTLs in F_{2: 3} and F_{2: 4} populations, respectively, showed overdominance with ratio greater than 1. Partial dominance occurred more frequently than overdominance and dominance. More than half of the QTLs listed in Table 3 showed varying degrees of negative dominance indicating that heterozygosity does not necessarily favor over homozygosity for yield. Similar results were reported previously in a study in an elite rice hybrid [41].

Relationship between Marker Heterozygosity and Performance

Correlation coefficients were not significant between heterozygosity of the marker genotypes and the performance of these genotypes in terms of yield and yield components in four environments (Table 4). These results implied difficulty in prediction of agronomic performance based on heterozysity of marker genotypes. One possibility for the low correlation is that only a portion of the 362 marker loci is related to the performance of those traits. These results were consistent with previous reports [41, 69].

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Table 4. Correlation coefficients between genotypic heterozygosity and trait performance in F_{2: 3} and F_{2: 4} populations.

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Identification and Verification of Digenic Interactions

The number of digenic interactions identified by two-way ANOVA for lint yield and yield components are given in Table 5. There were a total of 65,341 tests using 362 co-dominant markers at the whole genome level. For individual tests at the 0.001 probability level, the expected number of spurious interactions would be 65. The interaction number of the traits analyzed in the four environments ranged from 70 to 234, although the number analyzed for boll weight of F_{2: 3} population at Handan (S1) and the number analyzed for lint percent of F_{2: 4} population at Cangzhou (N1) was 64 and 65, respectively. To further assess the likelihood of the interactions identified above as chance events, each of the declared significant interactions was subjected to randomization tests [41]. The numbers of significant interactions decreased after randomization tests, and the reductions were more than the expected numbers based on chance events in all the cases. Current result indicated that the randomization test was highly stringent in identification of the significant interactions. For example, the number of significant interactions identified for lint yield in F_{2: 3} populations was 109 and 106 at the two respective experimental sites, and reduced to 97 and 93, respectively, after randomization test. In F_{2: 4} populations, the number of significant interactions was from 99 and 160 at the two respective experimental sites, and reduced to 93 and 149, respectively, after randomization tests (Table 5). The interactions that survived randomization tests may therefore be regarded as the minimum number of significant interactions for each trait at the 0.001 probability level. In this way, large numbers of significant digenic interactions in the two populations were confirmed.

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Table 5. Number of significant interactions for yield and yield components detected at 0.001 probability by permutation tests of all possible two loci combinations in hybrids evaluated at Handan (S1) and Cangzhou (N1.).

https://doi.org/10.1371/journal.pone.0143548.t005

In the F_{2: 3} population, 97 digenic interactions for lint yield were detected at location of S1, and 93 digenic interactions were detected at location of N1 with one digenic interaction detected at both of the two locations. For the yield component of bolls per plant, 77 and 211 digenic interactions were detected at Handan (S1) and Cangzhou (N1), respectively, with one digenic interaction detected at both locations. For boll weight, 77 digenic interactions were detected at location of Handan, and 60 digenic interactions were detected at location of Cangzhou. There was no common digenic interaction detected at both locations for this yield component. For the yield component of lint percent, 98 digenic interactions were detected at location of Handan, and 61 digenic interactions were detected at location of Cangzhou with six common digenic interactions detected at both locations.

Interactions were partitioned by orthogonal contrasts into significant genetic components, and the significant genetic components were further confirmed by randomization tests (Table 6). In general, among two-locus interaction components, AA interactions for lint yield and yield components had the highest frequencies.

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Table 6. Summary of the significant (p≤0.001) interactions detected for lint yield and yield components by permutation tests of all possible two loci combinations.

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These results indicated that all genetic components, AA, AD/DA, DD, existed in F_{2: 3} generation for all traits detected with significant interactions. In contrast, DD interactions occurred with the lowest frequencies and AD/DA occurred with intermediate frequencies.

Effects of Epistatic Interaction

Significant heterosis was detected for boll number per plant, and this yield component contributed directly to lint yield [3]. This yield component was also identified as the most important yield component to lint yield in recent studies [70–71]. This yield component was used as an example in this study to illustrate digenic effects in F_{2: 3} population.

Effects of digenic interactions for the best double homozygotes, i.e., homozygotes of two-locus combinations with significant AA, are given in Table 7. A total of 56 two-locus combinations with significant AA, 33 interactions of complementary two-locus homozygotes (11/22 or 22/11) had distinct advantages over the means of two parental genotypes (11/11 or 22/22) (Table 7). Among them, twenty-two cases were for complementary two-locus homozygotes, 11/22, and eleven cases were for complementary two-locus homozygotes, 22/11. Among the 56 epistasis, sixteen and six cases were the best loci combination from homozygotes of 22/22 and 11/11, respectively. Occasionally, single heterozygotes (six and one cases out of 27 tests for 11/12 and 22/12, respectively) could be comparable to the best two-locus genotypes. Generally, the heterotic values of the two-locus combinations (11/22 or 22/11, eight cases out of 27 tests) and homozygotes (22/22, 11 cases out of 27 tests) with significant AD/DA interactions had larger heterosis effects than single heterozygotes (11/12, 22/12) (Table 8). These results indicated the complementary effects in two-locus homozygotes. In order to test the hypothesis that double heterozygotes (12/12) have advantage over single heterozygotes, two-locus combinations with significant DD effects were compared with two locus heterozygotes (Table 9). Most of the best two-locus genotypes were homozygous at one locus and heterozygous at the other locus (11/12 and 22/12). Advantage of the double heterozygotes over single heterozygotes was not observed. In summary of these results, the complementary two-locus homozygotes (11/22 or 22/11) frequently showed large effects in heterosis. Single heterozygotes (11/12 and 22/12) and homozygotes (22/22) were the best two-locus genotypes for yield in a few cases while in no case did double heterozygotes (12/12) showed larger effects of heterosis than double homozygotes and single heterozygotes.

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Table 7. The best and the worst double homozygotes in each of the two loci combinations showing significant AA interactions for bolls/plant in F_{2: 3} population.

https://doi.org/10.1371/journal.pone.0143548.t007

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Table 8. The best and the worst single heterozygotes in each of the two loci combinations showing significant AD/DA interactions for bolls/plant in F_{2: 3} population.

https://doi.org/10.1371/journal.pone.0143548.t008

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Table 9. The best and the worst double heterozygotes in the two loci combinations consistently showing significant DD interaction for bolls/plant in F_{2: 3} population.

https://doi.org/10.1371/journal.pone.0143548.t009

Discussion

Necessity for Constructing a High-Density Linkage Map in Upland Cotton

Microsatellite markers were widely used, even as framework markers, to construct linkage maps in crops [72], because of their relatively high polymorphisms, detectability, and stability in genome. Although the available EST-SSR sequences are now rich in GenBank, these sequences are so conserved that the polymorphism of SSR markers developed is comparatively low in Upland cotton [15].

Since the first genetic linkage map was constructed in an F₂ population derived from crosses between G. hirsutum and G. barbadense [5], a variety of linkage maps have been constructed and a number of QTLs for yield and fiber quality traits have been identified from these linkage maps. However, heterosis of yield and yield components had not been analyzed in these high-density maps.

In order to construct a high-density linkage map for analysis of yield heterosis in Upland cotton, 450 polymorphic primers were used to construct a linkage map in 173 F_{2: 3} progeny lines derived from two mapping parents ‘GX1135’ and ‘GX100-2’. The genetic linkage map was constructed with 421 loci mapped and coverage of 3814 cM genetic length in the cotton genome. In this research, the majority of primers with high rate of polymorphism were developed by genomic clones from microsatellite-enriched cotton DNA libraries which were sequenced to identify SSR-containing target regions and SSR-containing EST collections [73] (S1 Table). Physical map of D₅-genome cotton species, A₂-genome cotton species, and AD₁-genome in Upland cotton have been completed [21–25]. The physical map of whole genome in Sea Island cotton is not currently available, but the sequencing of this physical map will be released soon (www.cottongen.org). These physical maps will provide extensive information in constructing high-density genetic linkage map, molecular marker selection, and map-based cloning in Upland cotton. With emergence of next generation sequencing technology and more sequences from newly released expression DNA libraries, more and more functional markers will be developed for gene mapping and tagging.

Genetic Effects of QTL for Yield and Yield Components

In present study, only a few QTLs showed overdominance while majority of them showed partial dominance. Alleles from both female and male parents were in direction of increasing phenotypic variation, which has been observed in previous studies [39–41].

There were a number of QTLs for yield traits that were detected simultanuously in F_{2: 3} and F_{2: 4} populations with both additive and dominant types of genetic effects. QTLs identified for yield and yield components simultaneously in F_{2: 3} and F_{2: 4} populations as shown in Table 3 were further analyzed. Generally, QTLs detected in F_{2: 3} generation had larger effects than those in F_2:4 generation which indicated depression. For their QTL effects, the dominance effect of qSY-chr14-1 was 3.32 in F_{2: 3} generation, larger than its dominance effect, 0.10, in F_{2: 4} generation. For lint percent, seven QTLs were detected in both populations with similar magnitudes of additive and dominance effects in two populations. So the effects of the QTLs detected in F_{2: 3} population were substantially larger than those detected in F_{2: 4} population. Same phenomenon was observed in a previous study of QTLs for yield using F₂ and F_{2: 3} populations using a vegetatively reproduced rice [74]. These results are consistent with the contention that dominance effects were the genetic basis of heterosis and inbreeding depression in Upland cotton.

Repeatability of QTLs among Different Studies

Although a number of QTLs for yield and yield components were identified in earlier reports using both inter-specific populations and intra-specific Upland cotton populations, only a limited number of markers were reported to be common among different populations in cotton genome. Therefore, it is necessary to fill this gap by identification of enough QTLs for yield traits consistent in different segregating populations and different environments. Some QTLs contributing to yield traits were identified on the same chromosomes in different studies. For example, QTLs for lint yield were detected on chromosome 1, 2, 3, 5, 6, 9, 12, 13, 14, 15, 16, 20, 23, 24, 25 and 26 [7, 75–80]. Among these, a common QTL for lint yield on chromosome 14 was detected in different mapping populations and environments [7, 77]. In current study, the QTL qLY-chr13-1 was also detected on chromosome 13 with interval between markers of GH157 and BNL 1495. Another QTL for lint yield was detected in a study by Wu et al. [79] with flanking markers BNL1421 and BNL1495. These two QTLs for lint yield may be overlapped or a common one due to their location proximity in the genome.

Digenic Interactions in F_{2: 3} and F_{2: 4} Populations

A total of nine genotypes were detected with significant interactions in three types of two-locus epistasis. In all interaction types, AA was an interaction for genotypes homozygous in both parents, including 11/11, 11/22, 22/11 and 22/22 (11 representing Parent 1 and 22 representing Parent 2); AD/DA was interaction for genotypes homozygous in one of parent and heterozygous in another parent, including 11/12, 22/12, 12/11 and 12/22; DD was interaction for genotypes heterozygous in both parents, i.e., 12/12 [41–42].

In F_{2: 3} and F_{2: 4} populations, three types of digenic interactions, AA, AD/DA and DD, were detected. AA interactions were detected with the highest frequency and DD interactions were detected with the lowest frequency. None of the double heterozygotes was detected as the best genotypes in terms of bolls per plant. The large number of significant digenic interactions for yield traits identified in F_{2: 3} and F_{2: 4} populations indicated that epistasis was the genetic basis of heterosis in Upland cotton.

In two-locus combinations showing significant AA effects, the best genotypes for bolls per plant were complementary two-locus homozygotes (11/22 or 22/11). Only one single heterozygote (11/12) was detected as the best genotype. The high heterotic values of two-locus combinations with significant AD/DA interactions indicated that the complementary two-locus homozygotes (11/22 or 22/11) were the best genotypes. In two-locus combinations with significant DD, the best genotypes were frequently single heterozygotes (11/12 and 22/12). No double heterozygote (12/12) was detected as the genotype with best performance. Similar results were also reported in previous studies by Hua et al. [41] and Zhou et al. [44]. These phenomena indicated a low correlation between heterozygosity of parental genotypes and their performance. However, single locus heterozygotes showed higher correlations between parental genotypes and their performance than those of double heterozygotes. These results implied that the double heterozygous does not necessarily favor the expression of a trait. The lack of correlation between heterozygosity and performance is possibly due to the small portion of heterozygous loci analyzed for heterosis [41]. The lack of correlation could also be caused by low association between marker heterozygosity and QTL [81].

Genetic basis of heterosis has been analyzed extensively by different research groups [37–41, 44, 82]. Due to different materials and methods used, these reported genetic and molecular mechanisms underlying heterosis were inconsistent in previous studies. In this research, we dissected the genetic basis of heterosis in F_{2: 3} and F_{2: 4} populations at single- and two-locus levels and concluded that dominance effects at single-locus and epistasis effects at two-locus level were the genetic basis of heterosis in Upland cotton.

Supporting Information

S1 Fig. Variation of lint yield and yield components in F_{2: 3} and F_{2: 4} populations.

(a). vertical axis: Number of lines, (b). horizontal axis: Traits values, (c). red arrows: GX1135, blue arrows: GX100-2, black arrows: F1.

https://doi.org/10.1371/journal.pone.0143548.s001

(TIFF)

S2 Fig. Locations of QTLs for yield and yield component traits in four environments.

SY, seed cotton yield; LY, lint yield; BNP, bolls/plant; BW, boll weight;; LP, lint percent. Markers underlined were published previously.

https://doi.org/10.1371/journal.pone.0143548.s002

(DOC)

S1 File. Traits phenotype value.

https://doi.org/10.1371/journal.pone.0143548.s003

(XLS)

S2 File. Molecular marker data.

https://doi.org/10.1371/journal.pone.0143548.s004

(XLS)

S1 Table. Percentages of polymorphism for the markers between two parents of hybrid ‘Xinza No. 1’.

https://doi.org/10.1371/journal.pone.0143548.s005

(DOC)

Acknowledgments

We thank Dr. Linghe Zeng (USDA-ARS, Crop Genetics Research Unit, Stoneville, MS 38776, USA) for his carefully critical revision on the manuscript. We thank Dr. Jihua Tang (Henan Agricultural University) and Dr. Huihui Li (Chinese Academy of Agricultural Sciences) for their kind helps in data analysis. We also thank Pengbo Li, Cheng Hu, and Hua Hua for the kind helps in genetic marker screening and analysis.

Author Contributions

Conceived and designed the experiments: JH. Performed the experiments: QL YW. Analyzed the data: QL LS. Contributed reagents/materials/analysis tools: JH. Wrote the paper: QL JH. Screened polymorphic primers: QL.

References

1. Galanopoulou-Sendouca S, Roupakias D. Performance of cotton F1 hybrids and its relation to the mean yield of advanced bulk generations. Eur J Agron. 1999; 11(1): 53–62.
- View Article
- Google Scholar
2. Meredith WR, Bridge RR. Heterosis and gene action in cotton, Gossypium hirsutum L. Crop Sci. 1972; 12(3): 304–310.
- View Article
- Google Scholar
3. Sun J, Liu J, Zhang J. A review on research and utilization of hybrid vigor of cotton. Acta Gossypii Sinica. 1994; 3: 135–139.
- View Article
- Google Scholar
4. Paterson AH, Lander ES, Hewitt JD, Peterson S, Lincoln SE, Tanksley SD. Resolution of quantitative traits into mendelian factors by using a complete linkage map of restriction length polymorphisms. Nature. 1988; 335: 721–726.
- View Article
- Google Scholar
5. Reinisch AJ, Dong JM, Brubaker CL, Stelly DM, Wendel JF, Paterson AH. A detailed RFLP map of cotton, Gossypium hirsutum × Gossypium barbadense: chromosome organization and evolution in a disomic polyploid genome. Genetics. 1994; 138(3): 829–847. pmid:7851778
- View Article
- PubMed/NCBI
- Google Scholar
6. Guo WZ, Cai CP, Wang CB, Han ZG, Song XL, Wang K, et al. A microsatellite-based, gene-rich linkage map reveals genome structure, function, and evolution in Gossypium. Genetics. 2007; 176(1): 527–541. pmid:17409069
- View Article
- PubMed/NCBI
- Google Scholar
7. He DH, Lin ZX, Zhang XL, Nie YC, Guo XP, Zhang YX, et al. QTL mapping for economic traits based on a dense genetic map of cotton with PCR-based markers using the interspecific cross of Gossypium hirsutum × Gossypium barbadense. Euphytica. 2007; 153(1–2): 181–197.
- View Article
- Google Scholar
8. Lacape JM, Nguyen TB, Thibivilliers S, Bojinov B, Courtois B, Cantrell RG, et al. A combined RFLP SSR AFLP map of tetraploid cotton based on a Gossypium hirsutum×Gossypium barbadense backcross population. Genome. 2003; 46(4): 612–626. pmid:12897870
- View Article
- PubMed/NCBI
- Google Scholar
9. Rong J, Abbey C, Bowers JE, Brubaker CL, Chang C, Chee PW, et al. A 3347-locus genetic recombination map of sequence-tagged sites reveals features of genome organization, transmission and evolution of cotton (Gossypium). Genetics. 2004; 166(1): 389–417. pmid:15020432
- View Article
- PubMed/NCBI
- Google Scholar
10. Yu Y, Yuan DJ, Liang SG, Li XM, Wang XQ, Lin ZX, et al. Genome structure of cotton revealed by a genome-wide SSR genetic map constructed from a BC₁ population between Gossypium hirsutum and G. barbadense. BMC Genomics. 2011; 12(1): 15.
- View Article
- Google Scholar
11. Guo WZ, Cai CP, Wang CB, Zhao L, Wang L, Zhang TZ. A preliminary analysis of genome structure and composition in Gossypium hirsutum. BMC Genomics. 2008; 9(1): 314–332.
- View Article
- Google Scholar
12. Zhao L, Lv YD, Cai CP, Tong XC, Chen XD, Zhang W, et al. Toward allotetraploid cotton genome assembly, integration of a high-density molecular genetic linkage map with DNA sequence information. BMC Genomics. 2012; 13(1): 539–539.
- View Article
- Google Scholar
13. Ulloa M and Meredith WR. Genetic linkage map and QTL analysis of agronomic and fiber quality traits in an intraspecific population. J Cotton Sci. 2000; 4(3): 161–170.
- View Article
- Google Scholar
14. Zhang ZS, Hu MC, Zhang J, Liu DJ, Zheng J, Zhang K, et al. Construction of a comprehensive PCR-based marker linkage map and QTL mapping for fiber quality traits in upland cotton (Gossypium hirsutum L.). Mol Breeding. 2009; 24(1): 49–61.
- View Article
- Google Scholar
15. Lin Z, Zhang Y, Zhang X, Guo X. A high-density integrative linkage map for Gossypium hirsutum. Euphytica. 2009; 166(1): 35–45.
- View Article
- Google Scholar
16. Zhang K, Zhang J, Ma J, Tang S, Li D. Genetic mapping and quantitative trait locus analysis of fiber quality traits using a three-parent composite population in upland cotton (Gossypium hirsutum L.). Mol Breeding. 2012; 29(2): 335–348.
- View Article
- Google Scholar
17. Liang QZ, Hu C, Hua H, Li ZH, Hua JP. Construction of a linkage map and QTL mapping for fiber quality traits in upland cotton (Gossypium hirsutum L.). Chinese Sci Bull. 2013; 58(26): 3233–3243.
- View Article
- Google Scholar
18. Tang SY, Teng ZH, Zhai TF, Fang XM, Liu F, Liu DJ, et al. Construction of genetic map and QTL analysis of fiber quality traits for Upland cotton (Gossypium hirsutum L.). Euphytica. 2015; 201(2): 195–213.
- View Article
- Google Scholar
19. Liu RZ, Wang BH, Guo WZ, Qin YS, Wang LG, Zhang YM. Quantitative trait loci mapping for yield and its components by using two immortalized populations of a heterotic hybrid in Gossypium hirsutum L. Mol Breeding. 2012; 29(2): 297–311.
- View Article
- Google Scholar
20. Shang L, Liang Q, Wang Y, Wang X, Wang K, Abduweli A, et al. Identification of stable QTLs controlling fiber traits properties in multi-environment using recombinant inbred lines in Upland cotton (Gossypium hirsutum L.). Euphytica. 2015; 205(3): 877–888.
- View Article
- Google Scholar
21. Wang KB, Wang ZW, Li FG, Ye WW, Wang JY, Song GL, et al. The draft genome of a diploid cotton Gossypium raimondii. Nat Genet. 2012; 44(10): 1098–1103. pmid:22922876
- View Article
- PubMed/NCBI
- Google Scholar
22. Paterson AH, Wendel JF, Gundlach H, Guo H, Jenkins J, Jin DC, et al. Repeated polyploidization of Gossypium genomes and the evolution of spinnable cotton fibres. Nature. 2012; 492(7429): 423–427. pmid:23257886
- View Article
- PubMed/NCBI
- Google Scholar
23. Li FG, Fan GY, Wang KB, Sun FM, Yuan YL, Song GL, et al. Genome sequence of the cultivated cotton Gossypium arboreum. Nat Genet. 2014; 46(6): 567–572. pmid:24836287
- View Article
- PubMed/NCBI
- Google Scholar
24. Li FG, Fan GY, Lu CR, Xiao GH, Zou CS, Kohel RJ, et al. Genome sequence of cultivated Upland cotton (Gossypium hirsutum TM-1) provides insights into genome evolution. Nat Biotechnol. 2015; 33(5): 524–530. pmid:25893780
- View Article
- PubMed/NCBI
- Google Scholar
25. Zhang TZ, Hu Y, Jiang WK, Fang L, Guan XY, Chen JD, et al. Sequencing of allotetraploid cotton (Gossypium hirsutum L. acc. TM-1) provides a resource for fiber improvement. Nat Biotechnol. 2015; 33: 531–537. pmid:25893781
- View Article
- PubMed/NCBI
- Google Scholar
26. Basunanda P, Radoev M, Ecke W, Friedt W, Becker HC, Snowdon Rod. Comparative mapping of quantitative trait loci involved in heterosis for seedling and yield traits in oilseed rape (Brassica napus L.). Theor Appl Genet. 2010; 120(2): 271–281. pmid:19707740
- View Article
- PubMed/NCBI
- Google Scholar
27. Bruce A. The Mendelian theory of heredity and the augmentation of vigor. Science. 1910; 32: 627–628.
- View Article
- Google Scholar
28. Davenport C. Depopulation, albinism and inbreeding. Science. 1908; 28: 454–455. pmid:17771943
- View Article
- PubMed/NCBI
- Google Scholar
29. Jones DF. Dominance of linked factors as a means of accounting for heterosis. Genetics. 1917; 2(5): 466–479. pmid:17245892
- View Article
- PubMed/NCBI
- Google Scholar
30. Hull FH. Recurrent selection for specific combining ability in corn. J Am Soc Agron. 1945; 37(2): 134–145.
- View Article
- Google Scholar
31. Crow JF. Alternative hypotheses of hybrid vigor. Genetics. 1948; 33(5): 477–487. pmid:17247292
- View Article
- PubMed/NCBI
- Google Scholar
32. Fasoulas A, Allard RW. Nonallelic gene interactions in the inheritance of quantitative characters in barley. Genetics. 1962; 47(7): 899–907. pmid:17248119
- View Article
- PubMed/NCBI
- Google Scholar
33. Powers L. An expansion of Jones's theory for the explanation of heterosis. Am Nat. 1944; 78: 275–280.
- View Article
- Google Scholar
34. Williams W. Heterosis and the genetics of complex characters. Nature. 1959; 184: 527–530. pmid:13844942
- View Article
- PubMed/NCBI
- Google Scholar
35. Lu H, Romero-SeVerson J, Bernardo R. Genetic basis of heterosis explored by simple sequence repeat markers in a random-mated maize population. Theor Appl Genet. 2003; 107(3): 494–502. pmid:12759730
- View Article
- PubMed/NCBI
- Google Scholar
36. Meyer RC, Kusterer B, Lisec J, Steinfath M, Becher M, Scharr H, Melchinger AE, et al. QTL analysis of early stage heterosis for biomass in Arabidopsis. Theor Appl Genet. 2010; 120(2): 227–237. pmid:19504257
- View Article
- PubMed/NCBI
- Google Scholar
37. Krieger U, Lippman ZB, Zamir D. The flowering gene SINGLE FLOWER TRUSS drives heterosis for yield in tomato. Nat Genet. 2010; 42(5): 459–463. pmid:20348958
- View Article
- PubMed/NCBI
- Google Scholar
38. Xing YZ, Zhang QF. Genetic and molecular bases of rice yield. Plant Biology. 2010; 61: 421–442.
- View Article
- Google Scholar
39. Xiao J, Li J, Yuan L, Tanksley SD. Dominance is the major genetic basis of heterosis in rice as revealed by QTL analysis using molecular markers. Genetics. 1995; 140(2): 745–754. pmid:7498751
- View Article
- PubMed/NCBI
- Google Scholar
40. Stuber CW, Lincoln SE, Wolff D, Helentjaris T, Lander E. Identification of genetic factors contributing to heterosis in a hybrid from two elite maize inbred lines using molecular markers. Genetics. 1992; 132(3): 823–83. pmid:1468633
- View Article
- PubMed/NCBI
- Google Scholar
41. Hua JP, Xing YZ, Xu CG, Sun XL, Yu SB, Zhang QF. Genetic dissection of an elite rice hybrid revealed that heterozygotes are not always advantageous for performance. Genetics. 2002; 162(4): 1885–1895. pmid:12524357
- View Article
- PubMed/NCBI
- Google Scholar
42. Hua J, Xing Y, Wu W, Xu C, Sun X, Yu S, et al. Single-locus heterotic effects and dominance by dominance interactions can adequately explain the genetic basis of heterosis in an elite rice hybrid. Proc Natl Acad Sci USA. 2003; 100(5): 2574–2579. pmid:12604771
- View Article
- PubMed/NCBI
- Google Scholar
43. Yu S, Li J, Xu C, Tan Y, Gao Y, Li X, et al. Importance of epistasis as the genetic basis of heterosis in an elite rice hybrid. Proc Natl Acad Sci USA. 1997; 94(17): 9226–9231. pmid:11038567
- View Article
- PubMed/NCBI
- Google Scholar
44. Zhou G, Chen Y, Yao W, Zhang C, Xie W, Hua J, et al. Genetic composition of yield heterosis in an elite rice hybrid. Proc Natl Acad Sci USA. 2012; 109(39): 15847–15852. pmid:23019369
- View Article
- PubMed/NCBI
- Google Scholar
45. Radoev M, Becker HC, Ecke W. Genetic analysis of heterosis for yield and yield components in rapeseed (Brassica napus L.) by quantitative trait locus mapping. Genetics. 2008; 179(3): 1547–1558. pmid:18562665
- View Article
- PubMed/NCBI
- Google Scholar
46. Guo TT, Yang N, Tong H, Pan QC, Yang XH, Tang JH, et al. Genetic basis of grain yield heterosis in an “immortalized F2” maize population. Theor Appl Genet. 2014; 127(10): 2149–2158. pmid:25104328
- View Article
- PubMed/NCBI
- Google Scholar
47. Carr DE, Dudash MR. Recent approaches into the genetic basis of inbreeding depression in plants. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences. 2003; 358(1434): 1071–1084. pmid:12831473
- View Article
- PubMed/NCBI
- Google Scholar
48. Li ZK, Luo LJ, Mei HW, Wang DL, Shu QY, Tabien R, et al. Overdominant epistatic loci are the primary genetic basis of inbreeding depression and heterosis in rice. I. Biomass and grain yield. Genetics. 2001; 158(4): 1737–1753. pmid:11514459
- View Article
- PubMed/NCBI
- Google Scholar
49. Luo LJ, Li ZK, Mei HW, Shu QY, Tabien R, Zhong DB, et al. Overdominant epistatic loci are the primary genetic basis of inbreeding depression and heterosis in rice. II. Grain yield components. Genetics. 2001; 158(4): 1755–1771. pmid:11514460
- View Article
- PubMed/NCBI
- Google Scholar
50. Rousselle Y, Thomas M, Galic N, Bonnin I, Goldringer I. Inbreeding depression and low between-population heterosis in recently diverged experimental populations of a selfing species. Heredity. 2010; 106(2): 289–299. pmid:20531445
- View Article
- PubMed/NCBI
- Google Scholar
51. Wright S. Evolution and the genetics of populations, Volume 3: experimental results and evolutionary deductions. University of Chicago Press. 1984.
52. Zhou SH, Pannell JR. Inbreeding depression and genetic load at partially linked loci in a mentapopulation. Genet Res. 2010; 92(02): 127–140.
- View Article
- Google Scholar
53. Burton JW, Browine C. Heterosis and inbreeding depression in two soybean single crosses. Crop Sci. 2006; 46(6): 2643–2648.
- View Article
- Google Scholar
54. Li ZK, Pinson SRM, Paterson AH, Park WD, Stansel JW. Genetics of hybrid sterility and hybrid breakdown in an inter-subspecific rice (Oryza sativa L.) population. Genetics. 1997; 145(4): 1139–1148. pmid:9093864
- View Article
- PubMed/NCBI
- Google Scholar
55. Shang LG, Cai SH, Ma LL, Wang YM, Abduweli A, Wang MY, et al. Seedling root QTLs analysis on dynamic development and upon nitrogen deficiency stress in Upland cotton. Euphytica. 2015;
- View Article
- Google Scholar
56. Shang LG, Liu F, Wang YM, Abduweli A, Cai SH, Wang KB, et al. Dynamic QTL mapping for plant height in Upland cotton (Gossypium hirsutum). Plant Breeding. 2015;
- View Article
- Google Scholar
57. Hoagland DR, Arnon DI. The water-culture method for growing plants without soil. California Agricultural Experiment Station Circular Number 347. College of Agriculture, University of California, Berkeley, Calif. 1950.
58. Paterson AH, Brubaker CL, Wendel JF. A rapid method for extraction of cotton (Gossypium spp.) genomic DNA suitable for RFLP or PCR analysis. Plant Mol Biol Rep. 1993; 11(2): 122–127.
- View Article
- Google Scholar
59. Blenda A, Fang DD, Rami JF, Garsmeur O, Luo F, Lacape JM. A high density consensus genetic map of tetraploid cotton that integrates multiple component maps through molecular marker redundancy check. PLoS ONE. 2012; 7: e4573.
- View Article
- Google Scholar
60. Xiao J, Wu K, Fang DD, Stelly DM, Yu J. New SSR markers for use in cotton (Gossypium spp.) improvement. J Cotton Sci. 2009; 13: 75–157.
- View Article
- Google Scholar
61. Lander ES, Green P, Abrahamson J, Barlow A, Daly MJ, Lincoln SE, et al. MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics. 1987; 1(2): 174–181. pmid:3692487
- View Article
- PubMed/NCBI
- Google Scholar
62. Tan ZY, Fang XM, Tang SY, Zhang J, Liu DJ, Teng ZH, et al. Genetic map and QTL controlling fiber quality traits in upland cotton (Gossypium hirsutum L.). Euphytica. 2014; 203(3): 615–628.
- View Article
- Google Scholar
63. Zeng ZB. Precision mapping of quantitative trait loci. Genetics. 1994; 136(4): 1457–1468. pmid:8013918
- View Article
- PubMed/NCBI
- Google Scholar
64. Wang SC, Basten CJ, Zeng ZB. Windows QTL cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh, NC. 2005.
65. Lander LE, Kruglyak L. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet. 1995; 11(3): 241–247. pmid:7581446
- View Article
- PubMed/NCBI
- Google Scholar
66. Shen XL, Guo WZ, Zhu XF, Yuan YL, Yu JZ, Kohel R. Molecular mapping of QTLs for fiber qualities in three diverse lines in Upland cotton using SSR markers. Mol Breeding. 2005; 15(2): 169–181.
- View Article
- Google Scholar
67. Voorrips R. MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered. 2002; 93(1): 77–78. pmid:12011185
- View Article
- PubMed/NCBI
- Google Scholar
68. Soft. STATISTICA for Windows. StatSoft Inc, Tulsa, Oklahoma, USA. 1997.
69. Zeng LH, Meredith WR. Relationship between SSR-based genetic distance and cotton F₂ hybrid performance for lint yield and fiber properties. Crop Sci. 2011; 51(6): 2362–2370.
- View Article
- Google Scholar
70. Wu YT, Yin JM, Guo WZ, Zhu XF, Zhang TZ. Heterosis performance of yield and fibre quality in F1 and F2 hybrids in upland cotton. Plant Breeding. 2004; 123(3): 285–289.
- View Article
- Google Scholar
71. McCarty JC, Jenkin JN, Wu JX. Genetic association of cotton yield with its component traits in derived primitive accessions crossed by elite upland cultivars using the conditional ADAA genetic model. Euphytica. 2008; 161(3): 337–352.
- View Article
- Google Scholar
72. Mei M, Syed NH, Gao W, Thaxton PM, Smith CW, Stelly DM, et al. Genetic mapping and QTL analysis of fiber-related traits in cotton (Gossypium). Theor Appl Genet. 2004; 108(2): 280–291. pmid:14513220
- View Article
- PubMed/NCBI
- Google Scholar
73. Bowman T, May O, Calhoun D. Genetic base of upland cotton cultivars released between 1970 and 1990. Crop Sci. 1996; 36(3): 577–581.
- View Article
- Google Scholar
74. Li JX, Yu SB, Xu CG, Tan YF, Gao YJ, Li XH, et al. Analyzing quantitative trait loci for yield using a vegetatively replicated F₂ population from a cross between the parents of an elite rice hybrid. Theor Appl Genet. 2000; 101(1–2): 248–254.
- View Article
- Google Scholar
75. Shen XZ, Guo T, Zhu W, Zhang XF. Mapping fiber and yield QTLs with main epistatic and QTL × environment interaction effects in recombinant inbred lines of upland cotton. Crop Sci. 2006; 46(1): 61–66.
- View Article
- Google Scholar
76. Qin H, Guo W, Zhang YM, Zhang TZ. QTL mapping of yield and fiber traits based on a four-way cross population in Gossypium hirsutum L. Theor Appl Genet. 2008; 117(6): 883–894. pmid:18604518
- View Article
- PubMed/NCBI
- Google Scholar
77. He DH, Lin ZX, Zhang XL, Nie YC, Guo XP, Fang CD, et al. Mapping QTLs of traits contributing to yield and analysis of genetic effects in tetraploid cotton. Euphytica. 2005; 144(1–2): 141–149.
- View Article
- Google Scholar
78. Li CQ, Guo WZ, Ma XL, Zhang TZ. Tagging and mapping of QTL for yield and its components in upland cotton (Gossypium hirsutum L.) population with varied lint percentage. Cotton Sci. 2008; 20: 163–169.
- View Article
- Google Scholar
79. Wu J, Gutierrez OA, Jenkins JN, McCarty JC, Zhu J. Quantitative analysis and QTL mapping for agronomic and fiber traits in an RI population of upland cotton. Euphytica. 2009; 165(2): 231–245.
- View Article
- Google Scholar
80. Wang BH, Guo WZ, Zhu XF, Wu YT, Huang NT, Zhang TT. QTL mapping of yield and yield components for elite hybrid derived-RILs in upland cotton. J Genet Genomics. 2007; 34(1): 35–45. pmid:17469776
- View Article
- PubMed/NCBI
- Google Scholar
81. Bernado R. Relationship between single-cross performance and molecular marker heterozygosity. Theor Appl Genet. 1992; 83(5): 628–634. pmid:24202681
- View Article
- PubMed/NCBI
- Google Scholar
82. Hochholdinger F, Hoecker N. Towards the molecular basis of heterosis. Trends Plant Sci. 2007; 12(9): 427–432. pmid:17720610
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Galanopoulou-Sendouca S, Roupakias D. Performance of cotton F1 hybrids and its relation to the mean yield of advanced bulk generations. Eur J Agron. 1999; 11(1): 53–62.
View Article
Google Scholar

[2] View Article

[3] Google Scholar

[ref2] 2. Meredith WR, Bridge RR. Heterosis and gene action in cotton, Gossypium hirsutum L. Crop Sci. 1972; 12(3): 304–310.
View Article
Google Scholar

[5] View Article

[6] Google Scholar

[ref3] 3. Sun J, Liu J, Zhang J. A review on research and utilization of hybrid vigor of cotton. Acta Gossypii Sinica. 1994; 3: 135–139.
View Article
Google Scholar

[8] View Article

[9] Google Scholar

[ref4] 4. Paterson AH, Lander ES, Hewitt JD, Peterson S, Lincoln SE, Tanksley SD. Resolution of quantitative traits into mendelian factors by using a complete linkage map of restriction length polymorphisms. Nature. 1988; 335: 721–726.
View Article
Google Scholar

[11] View Article

[12] Google Scholar

[ref5] 5. Reinisch AJ, Dong JM, Brubaker CL, Stelly DM, Wendel JF, Paterson AH. A detailed RFLP map of cotton, Gossypium hirsutum × Gossypium barbadense: chromosome organization and evolution in a disomic polyploid genome. Genetics. 1994; 138(3): 829–847. pmid:7851778
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref6] 6. Guo WZ, Cai CP, Wang CB, Han ZG, Song XL, Wang K, et al. A microsatellite-based, gene-rich linkage map reveals genome structure, function, and evolution in Gossypium. Genetics. 2007; 176(1): 527–541. pmid:17409069
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref7] 7. He DH, Lin ZX, Zhang XL, Nie YC, Guo XP, Zhang YX, et al. QTL mapping for economic traits based on a dense genetic map of cotton with PCR-based markers using the interspecific cross of Gossypium hirsutum × Gossypium barbadense. Euphytica. 2007; 153(1–2): 181–197.
View Article
Google Scholar

[22] View Article

[23] Google Scholar

[ref8] 8. Lacape JM, Nguyen TB, Thibivilliers S, Bojinov B, Courtois B, Cantrell RG, et al. A combined RFLP SSR AFLP map of tetraploid cotton based on a Gossypium hirsutum×Gossypium barbadense backcross population. Genome. 2003; 46(4): 612–626. pmid:12897870
View Article
PubMed/NCBI
Google Scholar

[25] View Article

[26] PubMed/NCBI

[27] Google Scholar

[ref9] 9. Rong J, Abbey C, Bowers JE, Brubaker CL, Chang C, Chee PW, et al. A 3347-locus genetic recombination map of sequence-tagged sites reveals features of genome organization, transmission and evolution of cotton (Gossypium). Genetics. 2004; 166(1): 389–417. pmid:15020432
View Article
PubMed/NCBI
Google Scholar

[29] View Article

[30] PubMed/NCBI

[31] Google Scholar

[ref10] 10. Yu Y, Yuan DJ, Liang SG, Li XM, Wang XQ, Lin ZX, et al. Genome structure of cotton revealed by a genome-wide SSR genetic map constructed from a BC₁ population between Gossypium hirsutum and G. barbadense. BMC Genomics. 2011; 12(1): 15.
View Article
Google Scholar

[33] View Article

[34] Google Scholar

[ref11] 11. Guo WZ, Cai CP, Wang CB, Zhao L, Wang L, Zhang TZ. A preliminary analysis of genome structure and composition in Gossypium hirsutum. BMC Genomics. 2008; 9(1): 314–332.
View Article
Google Scholar

[36] View Article

[37] Google Scholar

[ref12] 12. Zhao L, Lv YD, Cai CP, Tong XC, Chen XD, Zhang W, et al. Toward allotetraploid cotton genome assembly, integration of a high-density molecular genetic linkage map with DNA sequence information. BMC Genomics. 2012; 13(1): 539–539.
View Article
Google Scholar

[39] View Article

[40] Google Scholar

[ref13] 13. Ulloa M and Meredith WR. Genetic linkage map and QTL analysis of agronomic and fiber quality traits in an intraspecific population. J Cotton Sci. 2000; 4(3): 161–170.
View Article
Google Scholar

[42] View Article

[43] Google Scholar

[ref14] 14. Zhang ZS, Hu MC, Zhang J, Liu DJ, Zheng J, Zhang K, et al. Construction of a comprehensive PCR-based marker linkage map and QTL mapping for fiber quality traits in upland cotton (Gossypium hirsutum L.). Mol Breeding. 2009; 24(1): 49–61.
View Article
Google Scholar

[45] View Article

[46] Google Scholar

[ref15] 15. Lin Z, Zhang Y, Zhang X, Guo X. A high-density integrative linkage map for Gossypium hirsutum. Euphytica. 2009; 166(1): 35–45.
View Article
Google Scholar

[48] View Article

[49] Google Scholar

[ref16] 16. Zhang K, Zhang J, Ma J, Tang S, Li D. Genetic mapping and quantitative trait locus analysis of fiber quality traits using a three-parent composite population in upland cotton (Gossypium hirsutum L.). Mol Breeding. 2012; 29(2): 335–348.
View Article
Google Scholar

[51] View Article

[52] Google Scholar

[ref17] 17. Liang QZ, Hu C, Hua H, Li ZH, Hua JP. Construction of a linkage map and QTL mapping for fiber quality traits in upland cotton (Gossypium hirsutum L.). Chinese Sci Bull. 2013; 58(26): 3233–3243.
View Article
Google Scholar

[54] View Article

[55] Google Scholar

[ref18] 18. Tang SY, Teng ZH, Zhai TF, Fang XM, Liu F, Liu DJ, et al. Construction of genetic map and QTL analysis of fiber quality traits for Upland cotton (Gossypium hirsutum L.). Euphytica. 2015; 201(2): 195–213.
View Article
Google Scholar

[57] View Article

[58] Google Scholar

[ref19] 19. Liu RZ, Wang BH, Guo WZ, Qin YS, Wang LG, Zhang YM. Quantitative trait loci mapping for yield and its components by using two immortalized populations of a heterotic hybrid in Gossypium hirsutum L. Mol Breeding. 2012; 29(2): 297–311.
View Article
Google Scholar

[60] View Article

[61] Google Scholar

[ref20] 20. Shang L, Liang Q, Wang Y, Wang X, Wang K, Abduweli A, et al. Identification of stable QTLs controlling fiber traits properties in multi-environment using recombinant inbred lines in Upland cotton (Gossypium hirsutum L.). Euphytica. 2015; 205(3): 877–888.
View Article
Google Scholar

[63] View Article

[64] Google Scholar

[ref21] 21. Wang KB, Wang ZW, Li FG, Ye WW, Wang JY, Song GL, et al. The draft genome of a diploid cotton Gossypium raimondii. Nat Genet. 2012; 44(10): 1098–1103. pmid:22922876
View Article
PubMed/NCBI
Google Scholar

[66] View Article

[67] PubMed/NCBI

[68] Google Scholar

[ref22] 22. Paterson AH, Wendel JF, Gundlach H, Guo H, Jenkins J, Jin DC, et al. Repeated polyploidization of Gossypium genomes and the evolution of spinnable cotton fibres. Nature. 2012; 492(7429): 423–427. pmid:23257886
View Article
PubMed/NCBI
Google Scholar

[70] View Article

[71] PubMed/NCBI

[72] Google Scholar

[ref23] 23. Li FG, Fan GY, Wang KB, Sun FM, Yuan YL, Song GL, et al. Genome sequence of the cultivated cotton Gossypium arboreum. Nat Genet. 2014; 46(6): 567–572. pmid:24836287
View Article
PubMed/NCBI
Google Scholar

[74] View Article

[75] PubMed/NCBI

[76] Google Scholar

[ref24] 24. Li FG, Fan GY, Lu CR, Xiao GH, Zou CS, Kohel RJ, et al. Genome sequence of cultivated Upland cotton (Gossypium hirsutum TM-1) provides insights into genome evolution. Nat Biotechnol. 2015; 33(5): 524–530. pmid:25893780
View Article
PubMed/NCBI
Google Scholar

[78] View Article

[79] PubMed/NCBI

[80] Google Scholar

[ref25] 25. Zhang TZ, Hu Y, Jiang WK, Fang L, Guan XY, Chen JD, et al. Sequencing of allotetraploid cotton (Gossypium hirsutum L. acc. TM-1) provides a resource for fiber improvement. Nat Biotechnol. 2015; 33: 531–537. pmid:25893781
View Article
PubMed/NCBI
Google Scholar

[82] View Article

[83] PubMed/NCBI

[84] Google Scholar

[ref26] 26. Basunanda P, Radoev M, Ecke W, Friedt W, Becker HC, Snowdon Rod. Comparative mapping of quantitative trait loci involved in heterosis for seedling and yield traits in oilseed rape (Brassica napus L.). Theor Appl Genet. 2010; 120(2): 271–281. pmid:19707740
View Article
PubMed/NCBI
Google Scholar

[86] View Article

[87] PubMed/NCBI

[88] Google Scholar

[ref27] 27. Bruce A. The Mendelian theory of heredity and the augmentation of vigor. Science. 1910; 32: 627–628.
View Article
Google Scholar

[90] View Article

[91] Google Scholar

[ref28] 28. Davenport C. Depopulation, albinism and inbreeding. Science. 1908; 28: 454–455. pmid:17771943
View Article
PubMed/NCBI
Google Scholar

[93] View Article

[94] PubMed/NCBI

[95] Google Scholar

[ref29] 29. Jones DF. Dominance of linked factors as a means of accounting for heterosis. Genetics. 1917; 2(5): 466–479. pmid:17245892
View Article
PubMed/NCBI
Google Scholar

[97] View Article

[98] PubMed/NCBI

[99] Google Scholar

[ref30] 30. Hull FH. Recurrent selection for specific combining ability in corn. J Am Soc Agron. 1945; 37(2): 134–145.
View Article
Google Scholar

[101] View Article

[102] Google Scholar

[ref31] 31. Crow JF. Alternative hypotheses of hybrid vigor. Genetics. 1948; 33(5): 477–487. pmid:17247292
View Article
PubMed/NCBI
Google Scholar

[104] View Article

[105] PubMed/NCBI

[106] Google Scholar

[ref32] 32. Fasoulas A, Allard RW. Nonallelic gene interactions in the inheritance of quantitative characters in barley. Genetics. 1962; 47(7): 899–907. pmid:17248119
View Article
PubMed/NCBI
Google Scholar

[108] View Article

[109] PubMed/NCBI

[110] Google Scholar

[ref33] 33. Powers L. An expansion of Jones's theory for the explanation of heterosis. Am Nat. 1944; 78: 275–280.
View Article
Google Scholar

[112] View Article

[113] Google Scholar

[ref34] 34. Williams W. Heterosis and the genetics of complex characters. Nature. 1959; 184: 527–530. pmid:13844942
View Article
PubMed/NCBI
Google Scholar

[115] View Article

[116] PubMed/NCBI

[117] Google Scholar

[ref35] 35. Lu H, Romero-SeVerson J, Bernardo R. Genetic basis of heterosis explored by simple sequence repeat markers in a random-mated maize population. Theor Appl Genet. 2003; 107(3): 494–502. pmid:12759730
View Article
PubMed/NCBI
Google Scholar

[119] View Article

[120] PubMed/NCBI

[121] Google Scholar

[ref36] 36. Meyer RC, Kusterer B, Lisec J, Steinfath M, Becher M, Scharr H, Melchinger AE, et al. QTL analysis of early stage heterosis for biomass in Arabidopsis. Theor Appl Genet. 2010; 120(2): 227–237. pmid:19504257
View Article
PubMed/NCBI
Google Scholar

[123] View Article

[124] PubMed/NCBI

[125] Google Scholar

[ref37] 37. Krieger U, Lippman ZB, Zamir D. The flowering gene SINGLE FLOWER TRUSS drives heterosis for yield in tomato. Nat Genet. 2010; 42(5): 459–463. pmid:20348958
View Article
PubMed/NCBI
Google Scholar

[127] View Article

[128] PubMed/NCBI

[129] Google Scholar

[ref38] 38. Xing YZ, Zhang QF. Genetic and molecular bases of rice yield. Plant Biology. 2010; 61: 421–442.
View Article
Google Scholar

[131] View Article

[132] Google Scholar

[ref39] 39. Xiao J, Li J, Yuan L, Tanksley SD. Dominance is the major genetic basis of heterosis in rice as revealed by QTL analysis using molecular markers. Genetics. 1995; 140(2): 745–754. pmid:7498751
View Article
PubMed/NCBI
Google Scholar

[134] View Article

[135] PubMed/NCBI

[136] Google Scholar

[ref40] 40. Stuber CW, Lincoln SE, Wolff D, Helentjaris T, Lander E. Identification of genetic factors contributing to heterosis in a hybrid from two elite maize inbred lines using molecular markers. Genetics. 1992; 132(3): 823–83. pmid:1468633
View Article
PubMed/NCBI
Google Scholar

[138] View Article

[139] PubMed/NCBI

[140] Google Scholar

[ref41] 41. Hua JP, Xing YZ, Xu CG, Sun XL, Yu SB, Zhang QF. Genetic dissection of an elite rice hybrid revealed that heterozygotes are not always advantageous for performance. Genetics. 2002; 162(4): 1885–1895. pmid:12524357
View Article
PubMed/NCBI
Google Scholar

[142] View Article

[143] PubMed/NCBI

[144] Google Scholar

[ref42] 42. Hua J, Xing Y, Wu W, Xu C, Sun X, Yu S, et al. Single-locus heterotic effects and dominance by dominance interactions can adequately explain the genetic basis of heterosis in an elite rice hybrid. Proc Natl Acad Sci USA. 2003; 100(5): 2574–2579. pmid:12604771
View Article
PubMed/NCBI
Google Scholar

[146] View Article

[147] PubMed/NCBI

[148] Google Scholar

[ref43] 43. Yu S, Li J, Xu C, Tan Y, Gao Y, Li X, et al. Importance of epistasis as the genetic basis of heterosis in an elite rice hybrid. Proc Natl Acad Sci USA. 1997; 94(17): 9226–9231. pmid:11038567
View Article
PubMed/NCBI
Google Scholar

[150] View Article

[151] PubMed/NCBI

[152] Google Scholar

[ref44] 44. Zhou G, Chen Y, Yao W, Zhang C, Xie W, Hua J, et al. Genetic composition of yield heterosis in an elite rice hybrid. Proc Natl Acad Sci USA. 2012; 109(39): 15847–15852. pmid:23019369
View Article
PubMed/NCBI
Google Scholar

[154] View Article

[155] PubMed/NCBI

[156] Google Scholar

[ref45] 45. Radoev M, Becker HC, Ecke W. Genetic analysis of heterosis for yield and yield components in rapeseed (Brassica napus L.) by quantitative trait locus mapping. Genetics. 2008; 179(3): 1547–1558. pmid:18562665
View Article
PubMed/NCBI
Google Scholar

[158] View Article

[159] PubMed/NCBI

[160] Google Scholar

[ref46] 46. Guo TT, Yang N, Tong H, Pan QC, Yang XH, Tang JH, et al. Genetic basis of grain yield heterosis in an “immortalized F2” maize population. Theor Appl Genet. 2014; 127(10): 2149–2158. pmid:25104328
View Article
PubMed/NCBI
Google Scholar

[162] View Article

[163] PubMed/NCBI

[164] Google Scholar

[ref47] 47. Carr DE, Dudash MR. Recent approaches into the genetic basis of inbreeding depression in plants. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences. 2003; 358(1434): 1071–1084. pmid:12831473
View Article
PubMed/NCBI
Google Scholar

[166] View Article

[167] PubMed/NCBI

[168] Google Scholar

[ref48] 48. Li ZK, Luo LJ, Mei HW, Wang DL, Shu QY, Tabien R, et al. Overdominant epistatic loci are the primary genetic basis of inbreeding depression and heterosis in rice. I. Biomass and grain yield. Genetics. 2001; 158(4): 1737–1753. pmid:11514459
View Article
PubMed/NCBI
Google Scholar

[170] View Article

[171] PubMed/NCBI

[172] Google Scholar

[ref49] 49. Luo LJ, Li ZK, Mei HW, Shu QY, Tabien R, Zhong DB, et al. Overdominant epistatic loci are the primary genetic basis of inbreeding depression and heterosis in rice. II. Grain yield components. Genetics. 2001; 158(4): 1755–1771. pmid:11514460
View Article
PubMed/NCBI
Google Scholar

[174] View Article

[175] PubMed/NCBI

[176] Google Scholar

[ref50] 50. Rousselle Y, Thomas M, Galic N, Bonnin I, Goldringer I. Inbreeding depression and low between-population heterosis in recently diverged experimental populations of a selfing species. Heredity. 2010; 106(2): 289–299. pmid:20531445
View Article
PubMed/NCBI
Google Scholar

[178] View Article

[179] PubMed/NCBI

[180] Google Scholar

[ref51] 51. Wright S. Evolution and the genetics of populations, Volume 3: experimental results and evolutionary deductions. University of Chicago Press. 1984.

[ref52] 52. Zhou SH, Pannell JR. Inbreeding depression and genetic load at partially linked loci in a mentapopulation. Genet Res. 2010; 92(02): 127–140.
View Article
Google Scholar

[183] View Article

[184] Google Scholar

[ref53] 53. Burton JW, Browine C. Heterosis and inbreeding depression in two soybean single crosses. Crop Sci. 2006; 46(6): 2643–2648.
View Article
Google Scholar

[186] View Article

[187] Google Scholar

[ref54] 54. Li ZK, Pinson SRM, Paterson AH, Park WD, Stansel JW. Genetics of hybrid sterility and hybrid breakdown in an inter-subspecific rice (Oryza sativa L.) population. Genetics. 1997; 145(4): 1139–1148. pmid:9093864
View Article
PubMed/NCBI
Google Scholar

[189] View Article

[190] PubMed/NCBI

[191] Google Scholar

[ref55] 55. Shang LG, Cai SH, Ma LL, Wang YM, Abduweli A, Wang MY, et al. Seedling root QTLs analysis on dynamic development and upon nitrogen deficiency stress in Upland cotton. Euphytica. 2015;
View Article
Google Scholar

[193] View Article

[194] Google Scholar

[ref56] 56. Shang LG, Liu F, Wang YM, Abduweli A, Cai SH, Wang KB, et al. Dynamic QTL mapping for plant height in Upland cotton (Gossypium hirsutum). Plant Breeding. 2015;
View Article
Google Scholar

[196] View Article

[197] Google Scholar

[ref57] 57. Hoagland DR, Arnon DI. The water-culture method for growing plants without soil. California Agricultural Experiment Station Circular Number 347. College of Agriculture, University of California, Berkeley, Calif. 1950.

[ref58] 58. Paterson AH, Brubaker CL, Wendel JF. A rapid method for extraction of cotton (Gossypium spp.) genomic DNA suitable for RFLP or PCR analysis. Plant Mol Biol Rep. 1993; 11(2): 122–127.
View Article
Google Scholar

[200] View Article

[201] Google Scholar

[ref59] 59. Blenda A, Fang DD, Rami JF, Garsmeur O, Luo F, Lacape JM. A high density consensus genetic map of tetraploid cotton that integrates multiple component maps through molecular marker redundancy check. PLoS ONE. 2012; 7: e4573.
View Article
Google Scholar

[203] View Article

[204] Google Scholar

[ref60] 60. Xiao J, Wu K, Fang DD, Stelly DM, Yu J. New SSR markers for use in cotton (Gossypium spp.) improvement. J Cotton Sci. 2009; 13: 75–157.
View Article
Google Scholar

[206] View Article

[207] Google Scholar

[ref61] 61. Lander ES, Green P, Abrahamson J, Barlow A, Daly MJ, Lincoln SE, et al. MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics. 1987; 1(2): 174–181. pmid:3692487
View Article
PubMed/NCBI
Google Scholar

[209] View Article

[210] PubMed/NCBI

[211] Google Scholar

[ref62] 62. Tan ZY, Fang XM, Tang SY, Zhang J, Liu DJ, Teng ZH, et al. Genetic map and QTL controlling fiber quality traits in upland cotton (Gossypium hirsutum L.). Euphytica. 2014; 203(3): 615–628.
View Article
Google Scholar

[213] View Article

[214] Google Scholar

[ref63] 63. Zeng ZB. Precision mapping of quantitative trait loci. Genetics. 1994; 136(4): 1457–1468. pmid:8013918
View Article
PubMed/NCBI
Google Scholar

[216] View Article

[217] PubMed/NCBI

[218] Google Scholar

[ref64] 64. Wang SC, Basten CJ, Zeng ZB. Windows QTL cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh, NC. 2005.

[ref65] 65. Lander LE, Kruglyak L. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet. 1995; 11(3): 241–247. pmid:7581446
View Article
PubMed/NCBI
Google Scholar

[221] View Article

[222] PubMed/NCBI

[223] Google Scholar

[ref66] 66. Shen XL, Guo WZ, Zhu XF, Yuan YL, Yu JZ, Kohel R. Molecular mapping of QTLs for fiber qualities in three diverse lines in Upland cotton using SSR markers. Mol Breeding. 2005; 15(2): 169–181.
View Article
Google Scholar

[225] View Article

[226] Google Scholar

[ref67] 67. Voorrips R. MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered. 2002; 93(1): 77–78. pmid:12011185
View Article
PubMed/NCBI
Google Scholar

[228] View Article

[229] PubMed/NCBI

[230] Google Scholar

[ref68] 68. Soft. STATISTICA for Windows. StatSoft Inc, Tulsa, Oklahoma, USA. 1997.

[ref69] 69. Zeng LH, Meredith WR. Relationship between SSR-based genetic distance and cotton F₂ hybrid performance for lint yield and fiber properties. Crop Sci. 2011; 51(6): 2362–2370.
View Article
Google Scholar

[233] View Article

[234] Google Scholar

[ref70] 70. Wu YT, Yin JM, Guo WZ, Zhu XF, Zhang TZ. Heterosis performance of yield and fibre quality in F1 and F2 hybrids in upland cotton. Plant Breeding. 2004; 123(3): 285–289.
View Article
Google Scholar

[236] View Article

[237] Google Scholar

[ref71] 71. McCarty JC, Jenkin JN, Wu JX. Genetic association of cotton yield with its component traits in derived primitive accessions crossed by elite upland cultivars using the conditional ADAA genetic model. Euphytica. 2008; 161(3): 337–352.
View Article
Google Scholar

[239] View Article

[240] Google Scholar

[ref72] 72. Mei M, Syed NH, Gao W, Thaxton PM, Smith CW, Stelly DM, et al. Genetic mapping and QTL analysis of fiber-related traits in cotton (Gossypium). Theor Appl Genet. 2004; 108(2): 280–291. pmid:14513220
View Article
PubMed/NCBI
Google Scholar

[242] View Article

[243] PubMed/NCBI

[244] Google Scholar

[ref73] 73. Bowman T, May O, Calhoun D. Genetic base of upland cotton cultivars released between 1970 and 1990. Crop Sci. 1996; 36(3): 577–581.
View Article
Google Scholar

[246] View Article

[247] Google Scholar

[ref74] 74. Li JX, Yu SB, Xu CG, Tan YF, Gao YJ, Li XH, et al. Analyzing quantitative trait loci for yield using a vegetatively replicated F₂ population from a cross between the parents of an elite rice hybrid. Theor Appl Genet. 2000; 101(1–2): 248–254.
View Article
Google Scholar

[249] View Article

[250] Google Scholar

[ref75] 75. Shen XZ, Guo T, Zhu W, Zhang XF. Mapping fiber and yield QTLs with main epistatic and QTL × environment interaction effects in recombinant inbred lines of upland cotton. Crop Sci. 2006; 46(1): 61–66.
View Article
Google Scholar

[252] View Article

[253] Google Scholar

[ref76] 76. Qin H, Guo W, Zhang YM, Zhang TZ. QTL mapping of yield and fiber traits based on a four-way cross population in Gossypium hirsutum L. Theor Appl Genet. 2008; 117(6): 883–894. pmid:18604518
View Article
PubMed/NCBI
Google Scholar

[255] View Article

[256] PubMed/NCBI

[257] Google Scholar

[ref77] 77. He DH, Lin ZX, Zhang XL, Nie YC, Guo XP, Fang CD, et al. Mapping QTLs of traits contributing to yield and analysis of genetic effects in tetraploid cotton. Euphytica. 2005; 144(1–2): 141–149.
View Article
Google Scholar

[259] View Article

[260] Google Scholar

[ref78] 78. Li CQ, Guo WZ, Ma XL, Zhang TZ. Tagging and mapping of QTL for yield and its components in upland cotton (Gossypium hirsutum L.) population with varied lint percentage. Cotton Sci. 2008; 20: 163–169.
View Article
Google Scholar

[262] View Article

[263] Google Scholar

[ref79] 79. Wu J, Gutierrez OA, Jenkins JN, McCarty JC, Zhu J. Quantitative analysis and QTL mapping for agronomic and fiber traits in an RI population of upland cotton. Euphytica. 2009; 165(2): 231–245.
View Article
Google Scholar

[265] View Article

[266] Google Scholar

[ref80] 80. Wang BH, Guo WZ, Zhu XF, Wu YT, Huang NT, Zhang TT. QTL mapping of yield and yield components for elite hybrid derived-RILs in upland cotton. J Genet Genomics. 2007; 34(1): 35–45. pmid:17469776
View Article
PubMed/NCBI
Google Scholar

[268] View Article

[269] PubMed/NCBI

[270] Google Scholar

[ref81] 81. Bernado R. Relationship between single-cross performance and molecular marker heterozygosity. Theor Appl Genet. 1992; 83(5): 628–634. pmid:24202681
View Article
PubMed/NCBI
Google Scholar

[272] View Article

[273] PubMed/NCBI

[274] Google Scholar

[ref82] 82. Hochholdinger F, Hoecker N. Towards the molecular basis of heterosis. Trends Plant Sci. 2007; 12(9): 427–432. pmid:17720610
View Article
PubMed/NCBI
Google Scholar

[276] View Article

[277] PubMed/NCBI

[278] Google Scholar

Figures

Abstract

Introduction

Materials and Methods

Ethics Statement

Plant Materials

Field Planting and Traits Examination

DNA Isolation and Genotype Analysis

Map Construction and QTL Analysis

Analysis, Confirmation of Digenic Interaction and Components Partition

Results

Measurements of Yield and Yield Components in F2: 3 and F2: 4 Populations and Mid-Parent Heterosis of Hybrid ‘Xinza No. 1’

Linkage Map and QTL Mapping for Yield and Yield Components

Dominance and Overdominance Effects of QTL

Relationship between Marker Heterozygosity and Performance

Identification and Verification of Digenic Interactions

Effects of Epistatic Interaction

Discussion

Necessity for Constructing a High-Density Linkage Map in Upland Cotton

Genetic Effects of QTL for Yield and Yield Components

Repeatability of QTLs among Different Studies

Digenic Interactions in F2: 3 and F2: 4 Populations

Supporting Information

S1 Fig. Variation of lint yield and yield components in F2: 3 and F2: 4 populations.

S2 Fig. Locations of QTLs for yield and yield component traits in four environments.

S1 File. Traits phenotype value.

S2 File. Molecular marker data.

S1 Table. Percentages of polymorphism for the markers between two parents of hybrid ‘Xinza No. 1’.

Acknowledgments

Author Contributions

References

Measurements of Yield and Yield Components in F_{2: 3} and F_{2: 4} Populations and Mid-Parent Heterosis of Hybrid ‘Xinza No. 1’

Digenic Interactions in F_{2: 3} and F_{2: 4} Populations

S1 Fig. Variation of lint yield and yield components in F_{2: 3} and F_{2: 4} populations.