Field Crop 2025, Vol.8, No.2, 82-92 http://cropscipublisher.com/index.php/fc 84 Fortunately, there is now high-throughput sequencing, which is much more convenient than using traditional methods in the past. However, the workload of data analysis has actually increased. 2.4 QTL mapping methods: multi-environment data analysis and statistical models When doing QTL positioning, we tried several methods. The interval plotting method is a basic skill, but relying solely on it still feels insufficient. Therefore, single-label analysis was added for cross-validation. The most troublesome factor is the environmental one-the same genotype may show vastly different manifestations in different plots. Therefore, in our analysis, we pay special attention to those QTLS that can be detected in multiple environments. These sites are usually more reliable (Li et al., 2019). The composite interval mapping method (CIM) was used to eliminate background interference. Later, the multi-QTL mapping method (MQM) was also attempted to see if those sites with small effects could be identified. In addition to the conventional additive effect, we also focused on the dominant effect and the upper-level effect-sometimes when two sites without a significant effect come together, they can produce unexpected results. To be honest, each of these statistical methods is more challenging to use than the last, but to avoid missing any important loci, all the necessary analyses must be carried out. 2.5 Software and analytical tools: genome data processing and visualization platforms When it comes to data analysis, we have really tinkered with software. TetraploidMap, a software specifically designed for tetraploids, is the main force. After all, ordinary diploid analysis tools would have problems when used to process potato data. Interval mapping mainly relies on QTL Cartographer. This veteran software is easy to use, but extra caution is needed when setting parameters. R/qtl is mainly used for drawing graphs for statistics, and the visualization effect is indeed good (Li et al., 2018a). Later, feeling it wasn't enough, I conducted a genome-wide association analysis using TASSEL and PLINK, hoping to find something new. To be honest, each of these software programs has its own advantages and disadvantages. We often have to verify the results with each other-sometimes the same data can be run with different software, and the results can vary quite a lot. The most troublesome thing is that when the software reports an error, just checking for bugs consumes a whole day. Fortunately, there are now many open-source tools available, so I can finally get a thorough understanding of the data. 3 Genetic Characteristics of Key Agronomic Traits in Potatoes 3.1 Genetic regulation and environmental adaptability of yield-related traits When it comes to potato yields, it's actually quite complicated. Not only should the genes of the variety itself be considered, but also the influence of the planting environment. Some varieties perform well in the laboratory but wilt as soon as they are in the field-that's why we pay special attention to environmental adaptability. Studies like those in Ethiopia have found that although the yield of local varieties is not the highest, genetic variability is particularly rich (Tessema et al., 2022), which is a treasure for breeding. Interestingly, there is a QTL locus related to maturity on the V chromosome (Hermeziu et al., 2023), and this discovery has been of great help because early-maturing varieties can avoid many pests and diseases. This is more obvious in drought years, and certain chromosomal markers are closely related to drought resistance. However, to be honest, even if these markers are found, they still need to be tested repeatedly during actual breeding. After all, there is always some unclear relationship between genotype and phenotype. 3.2 Genetic mechanisms of quality traits The quality of potatoes is no simple matter-especially the starch and dry matter content, which are the most valued indicators by processing plants. It is often found in the laboratory that the starch content of potatoes grown in the same plot of land can vary greatly. Through QTL localization, we did identify several key loci (Vanishree et al., 2021), some of which had a particularly significant impact. This was an unexpected gain. Interestingly, these quality traits are relatively less affected by the environment; it is mainly genes that play a role. It was recently discovered that the starch content varies with different cytoplasmic types (Alvarez-Morezuelas et al., 2023), which is quite surprising. To figure out the specific mechanism, we also investigated quite a few genes involved in sugar
RkJQdWJsaXNoZXIy MjQ4ODYzNA==