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Article

Polygenic Genetic Analysis of Principal Genes for Yield Traits in Land Cotton

by
Xiaoman Ma
1,2,
Weifeng Guo
1,2,
Liangrong He
1,2 and
Xinchuan Cao
1,2,*
1
College of Agriculture, Tarim University, Alar 843300, China
2
Key Laboratory of Genetic Improvement and Efficient Production for Specialty Crops in Arid Southern Xinjiang of Xinjiang Corps, Alar 843300, China
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(11), 2749; https://doi.org/10.3390/agronomy14112749
Submission received: 15 October 2024 / Revised: 10 November 2024 / Accepted: 19 November 2024 / Published: 20 November 2024
(This article belongs to the Section Crop Breeding and Genetics)
Browse Figure
Versions Notes

Abstract

:
Objective: Yield traits are crucial for cotton breeding. Analyzing the yield traits of terrestrial cotton and exploring their genetic mechanisms through a primary gene + multigene hybrid genetic model provide a theoretical basis for selecting high-quality cotton varieties and identifying associated molecular markers. Methods: Completing the construction of the six populations (P1, P2, F1, F2, B1, B2) using Xinluzhong 37 as the female parent and Xinluzhong 51 as the male parent. Six yield traits were assessed: single boll weight, boll number per plant, lint yield per plant, seed cotton per plant, lint percentage, and seed index. Data were tested for normal distribution, and the inheritance patterns of yield traits were analyzed through combined primary gene + polygenic analysis. Results: The coefficients of variation for the six yield traits ranged from 37.368% to 53.905%, 33.335% to 58.524%, 34.132% to 57.686%, 8.721% to 12.808%, 1.842% to 6.283%, and 8.783% to 12.580%, respectively. These traits displayed either normal or skewed normal distributions. The optimal genetic model for single boll weight and seed index was PG-ADI, while MX2-ADI-AD best fit the traits of boll number per plant and lint percentage. For lint yield per plant and seed cotton per plant, the 2MG-ADI model was optimal. The polygenic heritability for single boll weight was 29.58%; for boll number per plant, main gene heritability was 25.19%, with 0% heritability for polygenes; for lint yield per plant, the heritability of the main gene was 23.47%. For seed cotton per plant, the heritability of main genes was 15.38%, with lint percentage showing 63.25% heritability for main genes and 0.08% for polygenes, and seed index with 45.93% heritability due to polygenes. Overall, single boll weight and seed index were predominantly polygenic, while boll number per plant and lint percentage were largely controlled by main gene inheritance. The inheritance of lint yield per plant and seed cotton per plant was also primarily governed by main genes.

1. Introduction

Xinjiang, with its unique climatic conditions and diverse geography, serves as the largest cotton production base in China. Globally, cotton plays an essential role in the production of textiles and oilseeds, acting as a primary source of textile raw materials and certain oilseeds. Land cotton cultivation, in particular, is widespread, forming the backbone of Xinjiang’s cotton industry. Cotton yield is closely tied to agronomic traits [1,2,3], making the study of these traits vital for improving cotton yield.
There exists a strong interrelationship between cotton yield and its related traits. Numerous studies have explored cotton yield traits, focusing on various factors such as environmental conditions [4,5], cultivation practices [6,7,8], and genetic determinants [9,10,11], with most research emphasizing genetic aspects. A comprehensive investigation into these traits improves our understanding of yield improvement, thereby boosting farmers’ economic returns and aligning with evolving market demands. Furthermore, optimizing cultivation practices and breeding strategies can support continued yield growth in Xinjiang’s cotton production while maintaining high-quality standards.
Research has demonstrated that cotton yield is influenced not by a single trait alone, but rather through interactions and constraints among multiple traits. The expression of individual traits in cotton is dependent on the genes controlling them. Quantitative traits are typically governed by multiple genes, often following a main gene + polygenic hybrid inheritance model [12,13]. This model has been validated by numerous scholars; for instance, Gai et al. [14] proposed that the main gene + polygenic model is commonly applicable to quantitative traits. The model facilitates not only the identification of main genes but also the detection of polygenes, enabling estimates of genetic parameters. Currently, this approach is widely employed in genetic studies across various crops, including chili [15,16,17,18], maize [19,20,21,22], soybean [23,24,25,26], oilseed rape [27,28,29], and rice [30]. However, its application to cotton trait research has been relatively limited. Previous research has mainly focused on fruit branch angle [31], low temperature tolerance [32], and resistance to Verticillium wilt [33]. Therefore, investigating the genetic patterns underlying yield traits in Xinjiang land cotton varieties could improve breeding efficiency and the selection of locally adapted cotton varieties. In this study, six populations were developed using two land cotton varieties as parents. Six yield traits were examined: single boll weight, boll number per plant, lint yield per plant, seed cotton per plant, lint percentage, and seed index. Through the main gene + polygenic hybrid model analysis for plant quantitative traits, we gained deeper insights into the inheritance patterns and genetic parameters of yield traits in Xinjiang land cotton. This study provides a theoretical basis for the genetic improvement of land cotton and the identification of molecular markers, thereby invigorating the genetic improvement and breeding of cotton varieties adapted to the Xinjiang region.

2. Materials and Methods

2.1. Test Materials

In this study, the long-fruited land cotton variety Xinluzhong 37 served as the maternal line (P1), while the short-fruited land cotton variety Xinluzhong 51 was used as the paternal line (P2). A cross between P1 and P2 produced the F1 generation, which was self-pollinated to create the F2 segregating population. The F1 generation was also backcrossed with P1 to obtain the B1 generation and with P2 to obtain the B2 generation, completing the construction of the six populations (P1, P2, F1, F2, B1, B2). Seeds from these populations were stored under cold conditions. Differences in yield trait performance between Xinluzhong 37 and Xinluzhong 51 highlight the importance of creating such populations for study. The test materials used to construct the genetic population, Xinluzhong 37 and Xinluzhong 51, were both validated varieties in Xinjiang, China [34,35].

2.2. Field Trials

The six populations, P1, P2, F1, F2, B1, and B2, were planted from west to east in 2023 at the experimental field of the Twelfth Regiment of Tarim University in Alar City, Xinjiang. Sowing occurred on 7 April 2023, with sampling on 24 September. During the growth period, the test plots received 11 irrigation cycles, along with applications of urea, phosphate, and potash as needed. The test site’s soil type was sandy loam, and cotton had been the previous crop. The row length in the test plot was 10 m, with a row spacing configuration of (10 cm + 66 cm + 10 cm + 66 cm + 10 cm + 66 cm), a plant spacing of 10.5 cm, and manual spot sowing as the planting method. The site consisted of one membrane, two tubes, and six rows. Field management practices for the experiment were aligned with standard production management and did not include any special treatments.
The irrigation and fertilization method chosen was drip irrigation. Urea, with a nitrogen content of ≥46.4%, was selected as the nitrogen fertilizer, while monoammonium phosphate (N-P2O5-K2O ≥ 58%) was used as the high-phosphorus fertilizer, and potassium sulphate (K2O ≥ 50%) was chosen as the high-potassium fertilizer. Drip irrigation was applied 11 times during the cotton growth period. Table 1 presents each growth stage, and Table 2 details the process of drip irrigation and fertilization throughout the cotton growth period.

2.3. Trait Determination

In the P1, P2, and F1 populations, 25 cotton plants with uniform growth were selected, while in the F2 population, 133 uniformly growing plants were chosen. In B1, 143 cotton plants with consistent growth were selected, and in B2, 147. The selected plants were evaluated for the six yield traits under study, as detailed in Table 3.

2.4. Statistical Analysis of Data

Data were organized using Excel 2021 software and subjected to preliminary analysis in IBM SPSS Statistics 20 to calculate the mean, coefficient of variation, kurtosis, and skewness for the six yield traits. Origin2021 software was used to create box-and-whisker plots with a superimposed normal curve, and these were combined to assess conformity to a normal distribution. Genetic model analysis was conducted on six populations across six yield traits following the mixed main gene + polygenic genetic model method proposed by Gai Junyi [36]. This analysis utilized the R package SEA2.0, developed by Wang et al. (2022) [37]. The analysis provided AIC values and maximum likelihood function values (MLV) for 24 genetic models across five categories: no main genes, one pair of main genes, two pairs of main genes, one pair of main genes + polygenes, and two pairs of main genes + polygenes. The three best models were selected based on the minimum information criterion (AIC) [38], and these models underwent uniformity tests U12, U22, and U32, along with the Smirnov test nW2 and Kolmogorov test Dn5 for fitness validation [39,40]. The optimal genetic model was then identified, and the corresponding genetic parameters were subsequently calculated.

3. Results

3.1. Phenotypic Data Analysis of Yield Traits in 6 Generations Population

The yield traits of the six populations (P1, P2, F1, F2, B1, and B2) were phenotyped, and the results are presented in Table 4. The mean values for boll number per plant, lint yield per plant, seed cotton per plant, and lint percentage were higher in P1 compared to P2. Conversely, P1 displayed lower mean values for single boll weight and seed index than P2, indicating a complementary relationship between the parental lines in terms of yield traits. Genetic variability was notably higher for boll number per plant (37.368–53.905%), lint yield per plant (33.335–58.524%), and seed cotton per plant (34.132–57.686%) compared to single boll weight (8.721–12.808%), lint percentage (1.842–6.283%), and seed index (8.783–12.580%). This suggests that boll number per plant, lint yield per plant, and seed cotton per plant exhibit greater potential for genetic variation among the six yield traits.
The Kolmogorov–Smirnov (K–S) normal distribution test, as shown in Table 1, indicated that all p values were greater than 0.05. Further analysis with box plots and normal curves, illustrated in Figure 1, confirmed that all six traits followed either a normal or skewed normal distribution within the six populations. This distribution aligns with the inheritance characteristics of quantitative traits, suggesting that the main gene + multigene inheritance model is suitable for genetic analysis of these six yield traits.

3.2. Selection of Genetic Models

To investigate the genetic characteristics of yield traits in cotton, the yield traits of the six populations were analyzed using SEA 2.0 with a hybrid genetic model combining “main gene + multiple genes”. This analysis yielded a total of 24 genetic models across five categories (Table 5). The models were evaluated based on maximum likelihood values (MLV) and AIC values, with the three models showing the lowest AIC values chosen as alternative models. For single boll weight, the models 2MG-ADI, PG-ADI, and MX1-AD-ADI were selected as alternatives, with AIC values of 1184.352, 1175.595, and 1179.595, respectively. For boll number per plant, the alternative models were 2MG-ADI, MX2-ADI-AD, and MX2-A-AD, with AIC values of 2398.902, 2404.725, and 2406.324. In the case of lint yield per plant cotton, 2MG-ADI, MX2-ADI-AD, and MX2-A-AD were identified as alternative models, with AIC values of 3563.047, 3569.535, and 3576.968, respectively. For seed cotton per plant, 2MG-ADI, MX2-ADI-AD, and MX2-A-AD were selected, with AIC values of 4341.336, 4346.880, and 4352.878. For coat fraction, the models MX2-ADI-ADI, MX2-ADI-AD, and MX2-A-AD were chosen as alternatives, with AIC values of −2370.942, −2381.644, and −2369.066. Finally, for seed index, the alternative models selected were 2MG-ADI, PG-ADI, and MX1-AD-ADI, with AIC values of 1596.166, 1586.855, and 1590.855, respectively.
To determine the optimal model, the alternative genetic models for the six yield traits were subjected to fitness tests, with the results shown in Table 6. The model with the fewest significant-level fitness tests was selected as the optimal model. For single boll weight, none of the three alternative models reached a significant level. Based on the principle of the minimum AIC value, the PG-ADI model was chosen as the optimal model, indicating that single boll weight is controlled by multiple genes with additive-dominant-superiority effects. For boll number per plant, the alternative models had significant-level counts of 2, 1, and 3, respectively, and the MX2-ADI-AD model was selected as the optimal model. This suggests that boll number per plant is influenced by two pairs of main genes and polygenes, with the main genes exhibiting additive-dominant-superiority effects and the polygenes showing additive-dominant effects. For lint yield per plant cotton, none of the alternative models reached a significant level. Again, following the minimum AIC value criterion, the 2MG-ADI model was identified as the optimal model, indicating that lint yield per plant cotton is controlled by two pairs of main genes with additive-dominant-superiority effects. Similarly, for seed cotton per plant, none of the alternative models reached a significant level. The 2MG-ADI model was selected as optimal, suggesting that seed cotton per plant is governed by two pairs of main genes that exhibit additive-dominant-superiority effects. For lint percentage, none of the alternative models reached a significant level either. Based on the minimum AIC value, the MX2-ADI-AD model was chosen, indicating that coat score is influenced by two pairs of main genes and polygenes, with the main genes showing additive-dominant-superiority effects and the polygenes showing additive-dominant effects. Lastly, for seed index, none of the alternative models reached a significant level. The PG-ADI model was selected as the optimal model according to the minimum AIC value, indicating that seed index are controlled by multiple genes with additive-dominant-superiority effects.

3.3. Estimation of Genetic Parameters for Optimal Genetic Models

The first- and second-order genetic parameters of the six yield traits were estimated using the optimal model, as presented in Table 7. According to the data, the population mean for single boll weight was 5.27 g, with polygenic heritability at 29.58%, indicating that polygenes significantly influence the inheritance of single boll weight in cotton. Similarly, the population mean for seed index was 8.36 g, with polygenic heritability at 45.93%, highlighting the prominent role of polygenes in seed index inheritance. Since single boll weight and seed index are controlled solely by multiple genes, there are no available data on additive and dominant effect values for the two main gene pairs, nor on the additive × additive, additive × dominant, dominant × additive, and dominant × dominant interaction effects between these main genes. Additionally, there are no data on the variance or heritability of the main genes.
For boll number per plant, the population mean was 5.71 g. Both additive effects, da and db, were 1.35 > 0, indicating a positive additive effect from the two pairs of main genes. The dominant effects, ha and hb, were −0.24 and −0.45, respectively, both less than 0, indicating a negative dominant effect for the two main gene pairs. The interaction effect i was 1.64, indicating a positive additive × additive interaction between the main genes. The sums of jab and jba were −1.06 and −1.27, indicating negative additive × dominant and dominant × additive interaction effects. The interaction term I was 0.14, pointing to a positive dominant × dominant interaction effect. Heritability of the main genes was 25.19%, while polygenic heritability was 0%, suggesting that the main genes are the primary factors in the inheritance of boll number per plant in cotton.
The population mean for lint yield per plant cotton was 17.44 g. Both additive effects, da and db, were 1.58 > 0, indicating a positive additive effect from the two pairs of main genes. The dominant effects, ha and hb, were 5.97 and −3.05, respectively, with ha > 0 and hb < 0, suggesting that the dominant effect of the first main gene pair was positive, while that of the second pair was negative. The fact that |ha| > |hb| highlights that the dominant effect of the first gene pair was more pronounced. The interaction effect i was 2.71, indicating a positive additive × additive interaction among the main genes. The interaction terms jab, jba, and I were −1.20, −10.23, and −4.26, respectively, all negative, indicating that the additive × dominant, dominant × additive, and dominant × dominant interaction effects were negative. The heritability of the main genes was 23.47%, signifying that main genes are the primary contributors to the inheritance of linting in a seed cotton per plant. As lint yield per plant cotton is controlled by two pairs of main genes, data for σ2pg and h2pg (%) were unavailable.
The population mean for seed cotton per plant was 37.90 g. Both additive effects, da and db, were 2.80 > 0, indicating a positive additive effect from the two pairs of main genes. The dominant effects, ha and hb, were 13.15 and −6.39, respectively, with ha > 0 and hb < 0, suggesting that the dominant effect of the first main gene pair was positive, while that of the second pair was negative. The fact that |ha| > |hb| highlights the stronger positive dominant effect of the first main gene pair. The interaction effect i was 5.88, indicating a positive additive × additive interaction between the main genes. The values of jab, jba, and I were −2.49, −22.03, and −9.79, respectively, all negative, suggesting that the additive × dominant, dominant × additive, and dominant × dominant interaction effects were negative. The heritability of the main genes was 15.38%, indicating that they play a significant role in the inheritance of seed cotton per plant. As seed cotton per plant is controlled by two pairs of main genes, data for σ2pg and h2pg (%) were not available.
For lint percentage, the population mean was 46%. Both da and db were 0, indicating no additive effect from the two main gene pairs. The dominant effects, ha and hb, were both 0.03 > 0, suggesting a positive dominant effect for both main gene pairs, with |ha| = |hb|, indicating equal dominant effects from the first and second gene pairs. The interaction term i was −0.01, indicating a negative additive × additive interaction effect between the main genes. Both jab and jba were 0, showing no additive × dominant or dominant × additive interaction effects, while I was −0.02, indicating a negative dominant × dominant interaction effect. The heritability of the main genes was 63.25%, while polygenic heritability was 0.08%, indicating that main genes primarily influence the inheritance of the cotton lint percentage.

4. Discussion

Single boll weight, boll number per plant, lint yield per plant, seed cotton per plant, lint percentage, and seed index are crucial factors influencing cotton yield [41,42,43]. These traits significantly contribute to the selection and improvement of high-yield, high-quality cotton varieties. An in-depth understanding of the genetic mechanisms underlying each yield trait forms the foundation for the effective selection and breeding of cotton with superior yield and quality characteristics.
The findings of Di et al. [44] differ from this study’s results in that they concluded single boll weight is inherited through two pairs of main genes and polygenes, whereas this study aligns more closely with Gong et al. [45], who found that single boll weights in the RIL population of CCM 70 are primarily influenced by multiple genes. This suggests that increasing cotton yield in breeding efforts should consider not only main genes but also the subtle contributions of polygenes. Our study’s conclusion also corresponds with Li et al. [46], who found that coat score is regulated by 1–2 pairs of additive main genes combined with additive-dominant polygenes, indicating that both main genes and polygenes contribute to inheritance, although with varying gene effects. In contrast, our findings on seed index inheritance, which indicate control by polygenes alone, diverge from Li Chengqi et al.’s results, which suggest control by two pairs of additive-dominant main genes plus additive-dominant polygenes or two pairs of additive main genes with polygenes. This study reports heritability of the main gene for coat score at 63.25%, indicating relatively stable inheritance, minimally influenced by environmental factors. Consequently, selecting for superior coat score in progeny populations could be advantageous. However, the results from Li et al. [47] and Zhe et al. [48] on the inheritance of boll number per plant, seed cotton per plant, and lint yield per plant cotton traits, where they identified control by 1–2 pairs of main genes plus polygenes, diverge from the current study, which found that boll number per plant is governed by two pairs of main genes plus polygenes, while seed cotton per plant and lint yield per plant cotton are controlled solely by two pairs of main genes. Overall, these findings highlight that gene expression may vary across different environments, reflecting genetic diversity and potential environmental impact on the expression of main and polygenes. Therefore, to increase selection efficiency in breeding for cotton yield traits, it is crucial to ensure consistent land fertility and field management practices within a single location for progeny populations. Additionally, trials across multiple locations with significant environmental differences should be conducted.
This study has provided a clearer understanding of the individual and interactive effects of main and multiple genes on cotton yield traits, contributing valuable insights to their genetic characteristics and potential for yield improvement. Wang et al. [49] demonstrated that when plant traits are controlled by 1–3 main genes, the number of main genes identified through QTL localization is generally consistent with predictions from the main gene + polygenic inheritance model. However, genetic analysis alone offers a conceptual framework, making molecular marker-based QTL localization studies essential. Thus, this study holds significant value for localizing six yield traits, identifying relevant genes, and advancing molecular-assisted breeding through the genetic analysis of cotton yield traits.

5. Conclusions

In this study, six yield traits, single boll weight per boll, boll number per plant, lint yield per plant, seed cotton per plant, lint percentage, and seed index, were analyzed using a mixed main gene + polygenic inheritance model in six populations constructed from Xinluzhong 37 and Xinluzhong 51. The findings indicate that all six traits exhibit quantitative inheritance patterns. Specifically, single boll weight and seed index were influenced by additive-dominant polygenes; boll number per plant and lint percentage were governed by two pairs of additive-dominant primary genes combined with additive-dominant polygenes; and both lint yield per plant and seed cotton per plant were regulated by two pairs of additive-dominant primary genes. Overall, single boll weight and seed index were predominantly polygenic, while single boll weight and lint percentage were primarily controlled by main gene inheritance, as were lint yield per plant and seed cotton per plant. This study provides a foundation for gene discovery and molecular marker development in cotton yield traits.

Author Contributions

Conceptualization, X.C.; methodology, X.M.; software, X.M.; validation, W.G. and L.H.; formal analysis, X.M.; investigation, X.M., W.G., L.H. and X.C.; resources, X.C.; data curation, X.M.; writing—original draft preparation, X.M.; writing—review and editing, X.C.; visualization, X.M.; supervision, X.C.; project administration, X.M.; funding acquisition, X.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by BTNYGG (NYHXGG, 2023AA102) and National Key R&D Program of China (2023YFD2301200).

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Agronomy 14 02749 g001
Figure 1. Box line plots and normal curves for the six yield traits.
Figure 1. Box line plots and normal curves for the six yield traits.
Agronomy 14 02749 g001
Table 1. Time periods for cotton growth stages.
Table 1. Time periods for cotton growth stages.
Growth PeriodTimeDays/d
Sowing-Emergence8 April 2023–27 April 202319
Seedling-Flowering28 April 2023–25 June 202358
Flowering-Boll Formation26 June 2023–10 August 202345
Boll Opening Stage11 August 2023–14 September 202334
Table 2. Drip irrigation and fertilization during cotton growth period.
Table 2. Drip irrigation and fertilization during cotton growth period.
Fertilisation FrequencyFertilisation DateUrea (kg/hm2)High Phosphate Fertiliser (kg/hm2)High Potash Fertiliser (kg/hm2)Water (m3/hm2)
18 June450025
218 June6037.5025
327 June4556.25025
45 July7575025
511 July6056.25020
620 July6028.12546.87525
727 July60065.62520
83 August6007520
99 August60056.2520
1015 August60037.525
1121 August00020
Table 3. Determination of six yield traits.
Table 3. Determination of six yield traits.
TraitsMeasurement Methods
Single Boll WeightEach boll on a plant was weighed using an electronic balance.
Boll Number per PlantThe number of bolls on each plant was counted.
Lint Yield Per PlantSeed cotton per plant from each plant was ginned in a lint roller gin to separate lint and seed; lint was then weighed on an electronic balance.
Seed Cotton Per PlantThe entire seed cotton per plant yield from each plant was harvested and weighed using an electronic balance.
Lint PercentageCalculated as the lint yield as a percentage of seed cotton per plant.
Seed IndexA sample of 100 seeds was randomly selected, and their total weight was measured using an electronic balance.
Table 4. Phenotypic data for yield traits in six generations.
Table 4. Phenotypic data for yield traits in six generations.
TraitsGenerationMeanSDCV (%)K–S (p Value)
Single Boll Weight (g)P15.2670.4678.8710.200
P26.0360.70111.6160.200
F16.1190.78412.8080.200
F26.4540.5638.7210.200
B16.1040.73412.0200.200
B26.4980.81712.5700.200
Boll Number Per Plant (PCS)P18.2003.13638.2420.200
P26.3202.73443.2650.090
F15.9602.28238.2850.171
F26.2803.38553.9050.069
B14.9202.32647.2750.200
B26.6402.48137.3680.088
Lint Yield Per Plant (g)P121.8407.28033.3350.054
P217.5128.53048.7090.200
F116.9287.14242.1890.200
F219.5399.88550.5880.200
B113.7118.02458.5240.200
B220.1738.50242.1450.200
Seed Cotton Per Plant (g)P146.95216.02634.1320.200
P239.29119.34249.2290.200
F136.85315.77242.7970.200
F241.82320.76649.6510.123
B129.01916.74057.6860.200
B244.64819.01842.5950.200
Lint Percentage (%)P10.4660.0163.4760.200
P20.4390.0081.8420.052
F10.4610.0143.0720.112
F20.4710.0183.8810.200
B10.4710.0306.2830.200
B20.4520.0286.1150.200
Seed Index (g)P18.3610.91010.8790.200
P29.6571.05410.9120.200
F19.4710.8328.7830.200
F210.6150.9569.0050.200
B19.5851.20612.5800.200
B210.4071.10310.5970.200
Note: mean, mean value; SD, standard deviation; CV, coefficient of variation; K–S, Kolmogorov–Smirnov.
Table 5. Great likelihood values (MLV) and AIC values of 24 genetic models for six yield traits.
Table 5. Great likelihood values (MLV) and AIC values of 24 genetic models for six yield traits.
ModelSingle Boll WeightBoll Number Per PlantLint Yield Per Plant
MLVAICMLVAICMLVAIC
1MG-AD−597.7741203.549−1200.5622409.125−1790.1153588.229
1MG-A−601.2451208.491−1204.0202414.041−1791.8203589.640
1MG-EAD−607.5821221.164−1204.1192414.239−1791.4693588.937
1MG-NCD−595.9131197.826−1203.0442412.088−1791.6233589.246
2MG-ADI−582.1761184.352−1189.4512398.902−1771.5243563.047
2MG-AD−592.6091197.218−1199.8712411.742−1790.0853592.171
2MG-A−641.3901290.779−1224.7242457.447−1815.5033639.006
2MG-EA−601.2641208.528−1204.0212414.043−1791.8203589.640
2MG-CD−608.0361224.073−1204.1182416.235−1788.0913584.182
2MG-EAD−608.0701222.140−1204.1172414.234−1791.5343589.068
PG-ADI−577.7971175.595−1193.9402407.881−1780.2883580.576
PG-AD−592.2811198.563−1200.7152415.430−1784.7873583.575
MX1-AD-ADI−577.7981179.595−1194.0602412.120−1780.2883584.576
MX1-AD-AD−594.6721207.344−1200.5662419.132−1784.4673586.935
MX1-A-AD−591.7641199.527−1200.6762417.353−1784.6983585.396
MX1-EAD-AD−592.2081200.416−1200.6792417.359−1784.7583585.516
MX1-NCD-AD−592.2051200.410−1200.6802417.360−1784.7603585.520
MX2-ADI-ADI−577.4271190.854−1185.6242407.247−1773.6273583.253
MX2-ADI-AD−579.6591189.319−1187.3632404.725−1769.7673569.535
MX2-AD-AD−592.2071206.413−1200.6782423.357−1784.7583591.517
MX2-A-AD−583.7241185.447−1194.1622406.324−1779.4843576.968
MX2-EA-AD−592.0341200.068−1200.6762417.353−1784.6713585.343
MX2-CD-AD−643.9671305.933−1202.7712423.541−1791.1593600.318
MX2-EAD-AD−592.2041200.408−1200.6792417.358−1784.7563585.512
modelSeed cotton per plantLint percentageSeed index
MLVAICMLVAICMLVAIC
1MG-AD−2179.3454366.6911166.694−2325.389−818.7861645.572
1MG-A−2181.8584369.7161163.959−2321.917−819.8751645.750
1MG-EAD−2180.7764367.5531164.983−2323.965−829.8271665.653
1MG-NCD−2182.0744370.1481150.963−2295.927−811.4361628.871
2MG-ADI−2160.6684341.3361176.407−2332.814−788.0831596.166
2MG-AD−2178.3474368.6931171.733−2331.466−813.1481638.296
2MG-A−2203.5414415.0811131.330−2254.660−838.9581685.915
2MG-EA−2181.8594369.7171162.845−2319.689−819.4461644.892
2MG-CD−2177.0024362.0041165.343−2322.686−830.2681668.536
2MG-EAD−2181.0644368.1281165.116−2324.231−830.2701666.539
PG-ADI−2167.2874354.5731189.113−2358.226−783.4281586.855
PG-AD−2172.6464359.2911183.151−2352.302−812.0241638.048
MX1-AD-ADI−2167.2874358.5731194.907−2365.814−783.4281590.855
MX1-AD-AD−2171.0684360.1351176.055−2334.109−815.3121648.623
MX1-A-AD−2172.5904361.1811186.007−2356.014−811.9611639.921
MX1-EAD-AD−2172.6064361.2121188.784−2361.567−811.9481639.896
MX1-NCD-AD−2172.6084361.2161183.187−2350.375−811.9631639.926
MX2-ADI-ADI−2161.4374358.8741203.471−2370.942−780.8061597.612
MX2-ADI-AD−2158.4404346.8801205.822−2381.644−787.4721604.943
MX2-AD-AD−2172.6064367.2131183.192−2344.385−811.9611645.922
MX2-A-AD−2167.4394352.8781193.533−2369.066−794.1331606.266
MX2-EA-AD−2172.5904361.1801187.211−2358.422−811.9611639.921
MX2-CD-AD−2177.8114373.6221190.768−2363.537−1032.4332082.866
MX2-EAD-AD−2172.6044361.2081188.860−2361.720−811.9531639.907
Table 6. Suitability tests for alternative models.
Table 6. Suitability tests for alternative models.
TraitsSingle Boll WeightBoll Number Per Plant
GenerationModel2MG-ADIPG-ADIMX1-AD-ADI2MG-ADIMX2-ADI-ADMX2-A-AD
P1U123.036 (0.081)0.048 (0.826)0.048 (0.826)0.512 (0.474)0.014 (0.906)0.035 (0.851)
U222.056 (0.152)0.016 (0.900)0.016 (0.900)0.370 (0.543)0.002 (0.966)0.065 (0.799)
U321.027 (0.311)0.121 (0.728)0.121 (0.728)0.114 (0.736)0.082 (0.774)0.086 (0.769)
nW20.388 (0.081)0.067 (0.773)0.067 (0.773)0.086 (0.670)0.040 (0.931)0.043 (0.917)
Dn0.237 (0.102)0.121 (0.818)0.121 (0.819)0.147 (0.601)0.100 (0.941)0.103 (0.931)
F1U120.007 (0.934)0.036 (0.849)0.036 (0.849)0.449 (0.503)0.296 (0.586)0.009 (0.923)
U220.003 (0.957)0.115 (0.735)0.115 (0.735)0.293 (0.588)0.406 (0.524)0.001 (0.977)
U320.291 (0.590)0.380 (0.538)0.380 (0.538)0.184 (0.668)0.195 (0.659)0.242 (0.623)
nW20.090 (0.648)0.103 (0.583)0.103 (0.583)0.127 (0.471)0.114 (0.526)0.085 (0.675)
Dn0.149 (0.584)0.141 (0.652)0.141 (0.652)0.210 (0.190)0.165 (0.454)0.165 (0.453)
P2U120.002 (0.962)0.007 (0.932)0.007 (0.932)0.010 (0.920)0.127 (0.721)0.038 (0.846)
U220.002 (0.964)0.007 (0.934)0.007 (0.934)0.002 (0.970)0.151 (0.698)0.057 (0.812)
U320.000 (0.998)0.000 (1.000)0.000 (1.000)0.056 (0.813)0.030 (0.864)0.041 (0.840)
nW20.035 (0.957)0.036 (0.954)0.036 (0.954)0.066 (0.780)0.084 (0.682)0.073 (0.743)
Dn0.097 (0.954)0.094 (0.965)0.094 (0.965)0.138 (0.674)0.174 (0.390)0.161 (0.485)
B1U120.414 (0.520)0.003 (0.954)0.003 (0.954)0.097 (0.755)0.011 (0.917)0.128 (0.720)
U220.201 (0.654)0.018 (0.893)0.015 (0.903)0.271 (0.603)0.149 (0.700)0.338 (0.561)
U320.487 (0.485)0.101 (0.751)0.071 (0.790)0.766 (0.382)3.790 (0.052)0.880 (0.348)
nW20.100 (0.594)0.053 (0.861)0.052 (0.868)0.271 (0.170)0.286 (0.155)0.299 (0.143)
Dn0.067 (0.534)0.049 (0.881)0.049 (0.887)0.116 (0.042) *0.109 (0.065)0.121 (0.030) *
B2U120.573 (0.449)0.001 (0.982)0.001 (0.982)0.050 (0.823)0.001 (0.971)0.007 (0.936)
U220.449 (0.503)0.031 (0.860)0.027 (0.869)0.198 (0.656)0.008 (0.930)0.013 (0.908)
U320.063 (0.802)0.631 (0.427)0.557 (0.456)0.836 (0.361)0.044 (0.834)0.598 (0.439)
nW20.100 (0.598)0.053 (0.861)0.050 (0.874)0.227 (0.225)0.214 (0.244)0.225 (0.227)
Dn0.071 (0.445)0.048 (0.886)0.048 (0.894)0.101 (0.100)0.097 (0.128)0.098 (0.118)
F2U120.821 (0.365)0.027 (0.869)0.027 (0.869)0.873 (0.350)1.037 (0.309)3.316 (0.069)
U221.495 (0.222)0.004 (0.952)0.006 (0.940)0.972 (0.324)0.809 (0.369)3.775 (0.052)
U321.908 (0.167)0.157 (0.692)0.117 (0.733)0.105 (0.746)0.121 (0.728)0.518 (0.472)
nW20.158 (0.367)0.049 (0.882)0.048 (0.888)0.299 (0.143)0.323 (0.123)0.607 (0.022) *
Dn0.089 (0.244)0.051 (0.886)0.050 (0.888)0.119 (0.048) *0.127 (0.027) *0.005 (0.005) **
TraitsLint yield per plantSeed cotton per plant
Generationmodel2MG-ADIMX2-ADI-ADMX2-A-AD2MG-ADIMX2-ADI-ADMX2-A-AD
P1U121.124 (0.289)0.274 (0.601)0.142 (0.707)0.759 (0.384)0.232 (0.630)0.049 (0.824)
U220.573 (0.449)0.075 (0.784)0.020 (0.888)0.329 (0.566)0.053 (0.819)0.000 (0.997)
U321.163 (0.281)0.861 (0.354)0.797 (0.372)1.165 (0.280)0.901 (0.343)0.762 (0.383)
nW20.203 (0.265)0.121 (0.496)0.108 (0.558)0.178 (0.315)0.122 (0.492)0.100 (0.599)
Dn0.234 (0.110)0.199 (0.243)0.189 (0.298)0.187 (0.306)0.162 (0.483)0.144 (0.630)
F1U120.141 (0.708)0.023 (0.880)0.460 (0.498)0.177 (0.674)0.012 (0.914)0.226 (0.634)
U220.077 (0.782)0.051 (0.822)0.501 (0.479)0.100 (0.752)0.036 (0.850)0.279 (0.597)
U320.118 (0.731)0.099 (0.754)0.042 (0.838)0.134 (0.715)0.114 (0.736)0.073 (0.787)
nW20.034 (0.959)0.028 (0.983)0.076 (0.727)0.042 (0.924)0.031 (0.973)0.056 (0.841)
Dn0.094 (0.966)0.113 (0.871)0.155 (0.533)0.107 (0.910)0.118 (0.836)0.147 (0.598)
P2U120.011 (0.916)0.040 (0.842)0.137 (0.712)0.001 (0.973)0.011 (0.917)0.178 (0.673)
U220.006 (0.938)0.028 (0.867)0.133 (0.715)0.000 (0.999)0.004 (0.950)0.174 (0.677)
U320.009 (0.924)0.010 (0.920)0.001 (0.978)0.019 (0.890)0.022 (0.881)0.001 (0.972)
nW20.049 (0.886)0.050 (0.876)0.067 (0.777)0.036 (0.954)0.035 (0.956)0.062 (0.805)
Dn0.119 (0.834)0.126 (0.776)0.131 (0.736)0.081 (0.992)0.087 (0.983)0.111 (0.883)
B1U120.012 (0.911)0.005 (0.946)0.012 (0.914)0.098 (0.754)0.001 (0.970)0.002 (0.968)
U220.002 (0.964)0.003 (0.959)0.008 (0.928)0.065 (0.798)0.004 (0.949)0.012 (0.912)
U320.375 (0.540)0.003 (0.954)0.003 (0.954)0.037 (0.848)0.161 (0.688)0.084 (0.773)
nW20.046 (0.900)0.027 (0.984)0.031 (0.974)0.066 (0.783)0.039 (0.939)0.046 (0.898)
Dn0.042 (0.963)0.040 (0.979)0.045 (0.931)0.061 (0.658)0.040 (0.975)0.047 (0.905)
B2U120.060 (0.806)0.178 (0.673)0.009 (0.923)0.041 (0.840)0.211 (0.646)0.070 (0.792)
U220.026 (0.872)0.120 (0.729)0.000 (0.983)0.012 (0.915)0.187 (0.665)0.006 (0.937)
U320.095 (0.758)0.062 (0.804)0.210 (0.647)0.124 (0.725)0.002 (0.963)0.498 (0.480)
nW20.058 (0.829)0.065 (0.788)0.083 (0.686)0.048 (0.888)0.058 (0.829)0.068 (0.770)
Dn0.052 (0.829)0.049 (0.867)0.060 (0.660)0.047 (0.897)0.049 (0.876)0.065 (0.565)
F2U120.302 (0.583)0.219 (0.640)2.095 (0.148)0.423 (0.516)0.418 (0.518)2.726 (0.099)
U220.472 (0.492)0.297 (0.586)2.180 (0.140)0.777 (0.378)0.564 (0.453)2.846 (0.092)
U320.385 (0.535)0.133 (0.716)0.090 (0.764)1.013 (0.314)0.250 (0.617)0.125 (0.724)
nW20.133 (0.451)0.120 (0.503)0.357 (0.099)0.154 (0.379)0.139 (0.429)0.432 (0.062)
Dn0.071 (0.521)0.070 (0.536)0.136 (0.136)0.073 (0.484)0.074 (0.461)0.107 (0.107)
TraitsLint percentageSeed index
GenerationmodelMX2-ADI-ADIMX2-ADI-ADMX2-A-AD2MG-ADIPG-ADIMX1-AD-ADI
P1U120.050 (0.822)0.154 (0.695)0.021 (0.884)0.897 (0.344)0.001 (0.978)0.001 (0.978)
U220.039 (0.843)0.139 (0.710)0.014 (0.906)0.517 (0.472)0.045 (0.832)0.045 (0.832)
U320.006 (0.939)0.001 (0.975)0.009 (0.925)0.627 (0.428)0.548 (0.459)0.548 (0.459)
nW20.049 (0.882)0.063 (0.800)0.045 (0.909)0.142 (0.419)0.063 (0.799)0.063 (0.799)
Dn0.120 (0.827)0.133 (0.719)0.113 (0.871)0.172 (0.402)0.133 (0.716)0.133 (0.716)
F1U120.053 (0.818)0.359 (0.549)0.377 (0.539)2.732 (0.098)0.020 (0.889)0.020 (0.889)
U220.047 (0.828)0.297 (0.586)0.309 (0.579)2.331 (0.127)0.034 (0.854)0.034 (0.854)
U320.000 (0.983)0.020 (0.889)0.024 (0.877)0.087 (0.768)0.038 (0.845)0.038 (0.845)
nW20.069 (0.766)0.079 (0.707)0.108 (0.555)0.262 (0.181)0.030 (0.977)0.030 (0.977)
Dn0.157 (0.516)0.128 (0.761)0.183 (0.329)0.167 (0.438)0.085 (0.987)0.085 (0.987)
P2U120.014 (0.907)1.635 (0.201)0.187 (0.665)1.023 (0.312)0.034 (0.854)0.034 (0.854)
U220.013 (0.909)1.217 (0.270)0.420 (0.517)0.730 (0.393)0.118 (0.731)0.118 (0.731)
U320.834 (0.361)0.292 (0.589)0.840 (0.360)0.248 (0.619)0.438 (0.508)0.438 (0.508)
nW20.127 (0.473)0.298 (0.144)0.143 (0.414)0.128 (0.469)0.066 (0.781)0.066 (0.781)
Dn0.173 (0.397)0.263 (0.052)0.207 (0.205)0.165 (0.453)0.130 (0.747)0.130 (0.747)
B1U120.197 (0.657)0.004 (0.948)0.273 (0.601)0.004 (0.953)0.018 (0.893)0.019 (0.892)
U220.242 (0.623)0.001 (0.980)0.198 (0.656)0.031 (0.861)0.036 (0.850)0.031 (0.860)
U320.061 (0.805)0.125 (0.724)0.059 (0.808)0.864 (0.353)0.056 (0.814)0.033 (0.856)
nW20.086 (0.672)0.032 (0.971)0.196 (0.276)0.052 (0.863)0.026 (0.987)0.026 (0.988)
Dn0.057 (0.742)0.037 (0.991)0.077 (0.362)0.043 (0.951)0.038 (0.987)0.037 (0.989)
B2U120.000 (0.990)0.082 (0.775)0.475 (0.491)0.704 (0.401)0.222 (0.638)0.224 (0.636)
U220.027 (0.870)0.203 (0.653)0.415 (0.520)0.120 (0.729)0.462 (0.497)0.450 (0.503)
U320.493 (0.482)0.483 (0.487)0.009 (0.925)3.482 (0.062)0.805 (0.370)0.719 (0.397)
nW20.083 (0.688)0.078 (0.716)0.330 (0.117)0.209 (0.253)0.106 (0.564)0.104 (0.575)
Dn0.059 (0.679)0.050 (0.861)0.111 (0.053)0.089 (0.195)0.055 (0.775)0.055 (0.772)
F2U120.001 (0.983)0.007 (0.931)0.210 (0.647)0.001 (0.971)0.029 (0.865)0.029 (0.864)
U220.049 (0.825)0.011 (0.915)0.086 (0.769)0.005 (0.942)0.088 (0.766)0.081 (0.776)
U320.639 (0.424)0.008 (0.928)0.358 (0.550)0.185 (0.667)0.283 (0.595)0.227 (0.634)
nW20.046 (0.903)0.022 (0.994)0.077 (0.721)0.054 (0.851)0.065 (0.789)0.064 (0.795)
Dn0.048 (0.917)0.033 (0.998)0.618 (0.618)0.053 (0.846)0.059 (0.749)0.058 (0.766)
Note: “*” indicates significant difference; “**” indicates a highly significant difference.
Table 7. Genetic parameters of the optimal model.
Table 7. Genetic parameters of the optimal model.
TraitsSingle Boll WeightBoll Number per PlantLint Yield per PlantSeed Cotton per PlantLint PercentageSeed Index
ModelPG-ADIMX2-ADI-AD2MG-ADI2MG-ADIMX2-ADI-ADPG-ADI
1storder genetic parameterm5.275.7117.4437.900.468.36
da—1.351.582.800.00—
db—1.351.582.800.00—
ha—−0.245.9713.150.03—
hb—−0.45−3.05−6.390.03—
i—1.642.715.88−0.01—
jab—−1.06−1.20−2.490.00—
jba—−1.27−10.23−22.030.00—
I—0.14−4.26−9.79−0.02—
2ndorder genetic parameterσ2mg—1.7615.4444.810.00—
h2mg(%)—25.1923.4715.3863.25—
σ2pg0.180.00——0.000.71
h2pg(%)29.580.00——0.0845.93
Note: m, group mean; da, additive effect value of the first pair of major genes; db, additive effect value on the second major gene; ha, the dominant effect value of the first major gene; hb, the dominant effect value of the second major gene; i, additive × additive epistasis (interaction) effect value between two major genes; jab, the interaction effect of additive × dominant of two major genes; jba, the interaction effect of two major genes dominant × additive; I, the interaction effect of two major gene dominance × dominance; σ2mg, major gene variance; h2mg(%), heritability of major gene; σ2Pg, polygene variance; h2Pg(%), heritability of polygene-Var.; —, No date.
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Ma, X.; Guo, W.; He, L.; Cao, X. Polygenic Genetic Analysis of Principal Genes for Yield Traits in Land Cotton. Agronomy 2024, 14, 2749. https://doi.org/10.3390/agronomy14112749

AMA Style

Ma X, Guo W, He L, Cao X. Polygenic Genetic Analysis of Principal Genes for Yield Traits in Land Cotton. Agronomy. 2024; 14(11):2749. https://doi.org/10.3390/agronomy14112749

Chicago/Turabian Style

Ma, Xiaoman, Weifeng Guo, Liangrong He, and Xinchuan Cao. 2024. "Polygenic Genetic Analysis of Principal Genes for Yield Traits in Land Cotton" Agronomy 14, no. 11: 2749. https://doi.org/10.3390/agronomy14112749

APA Style

Ma, X., Guo, W., He, L., & Cao, X. (2024). Polygenic Genetic Analysis of Principal Genes for Yield Traits in Land Cotton. Agronomy, 14(11), 2749. https://doi.org/10.3390/agronomy14112749

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