Cotton Genomics and Genetics 2025, Vol.16, No.3, 117-125 http://cropscipublisher.com/index.php/cgg 122 selection, some people have indeed done it. A batch of upland cotton varieties were selected under this strategy. By aggregating key gene loci, they finally showed that the yield and fiber quality were 15%-20% higher than the old varieties (Wang et al., 2021). This was difficult to achieve in the past, after all, the two traits always have a sense of "constraining each other". Now, technical means have broken through this hurdle, not only providing breeders with a clear operation path, but also allowing the cotton industry to take a big step towards "high efficiency and stable production". Figure 2 Phylogenetic relationships of 316 cotton accessions: (a) A neighbor-joining tree was constructed using whole-genome SNP data. The accessions were divided into three groups, group-1 (red), group-2 (cyan) and group-3 (blue); (b) Population structure of cotton accessions. The cotton samples were divided into three groups when k = 3; (c) Geographic origin of the three groups, Central Asia (CA), the United States (US), the Yellow River (YR), the Yangtze River (YZR) and other places (OTH); (d) Phenotype distributions of yield and fiber quality traits, the group divided by the structure of the 316 accessions, Boll weight (BW), Seed index (SI), Lint PC (LP), Fiber length (FL), Fiber strength (FS) and Flowering data (FD). Within boxplots, the bold line represents the median, box edges represent upper and lower quantiles, and whiskers are 1.5 times the quantile of the data. Outliers are shown as open dots (*P < 0.05, **P < 0.01 and ***P < 0.001, two-sided t-test). The neighbor-joining tree (a) was constructed using the software PHYLIP (v3.696, https://evolution.genetics.washington.edu/phylip.html). Population structure of cotton accessions (b) was determined using the software Admixture (v1.30, http://dalexander.github.io/admixture/index.html). The others were created by the software GraphPad Prism 9 (ver. 9.0.0, http://www.graphpad.com) (Adopted from Wang et al., 2021) 8 Challenges and Future Prospects 8.1 Managing genotype-by-environment interactions in predictive breeding It is not uncommon for the same cotton plant to grow differently in different fields. The problem is that it is difficult for breeders to predict its performance. Especially when genotype and environment are mixed together,
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