Triticeae Genomics and Genetics, 2025, Vol.16, No.6, 245-253 http://cropscipublisher.com/index.php/tgg 248 4.2 SNP array and resequencing data selection and processing for GWAS Not all genetic markers are suitable for GWAS, especially when the data quality is substandard. At present, there are two commonly used methods: one is SNP chips, and the other is high-throughput resequencing. The former is cheap and efficient, and is suitable for large samples. Although the latter is a bit more expensive, it has more comprehensive information and can also capture rare mutations (Yin et al., 2020). No matter which way it is, data cleaning is the first step: low-quality markers need to be removed and missing genotypes need to be filled. These may seem like technical details, but in fact, they directly affect whether reliable conclusions can be drawn in the subsequent analysis. 4.3 Common statistical models and control strategies in FHB resistance GWAS Once the phenotypic and genotypic data are in place, the next challenge lies in modeling. The initial GWAS method only measured each SNP one by one, but in plant research, the problem of too many false positives often troubles researchers. Thus, the mixed linear model (MLM) has become the mainstream choice because it can simultaneously consider the group structure and the kinship among individuals (Huang et al., 2025). In some studies with large sample sizes and complex backgrounds, more advanced methods are also employed, such as MLMM (Multilocus Mixed Model) or meta-analysis. Controlling confounding effects, conducting multiple tests and corrections reasonably, and combining them with clear result visualization all determine whether GWAS results are truly useful, especially when it comes to the breeding of Fusarium head blight resistance, which cannot be taken lightly. 5 Integrated Analysis of High-Throughput Phenotyping and GWAS 5.1 Enhancing mapping accuracy with multidimensional phenotypic data To precisely identify the genes related to FHB resistance, relying solely on traditional methods is often insufficient. Nowadays, many studies tend to combine high-throughput phenotypes with GWAS. Because this method can obtain various types of data - such as morphological, physiological and biochemical traits, and can also dynamically track plant changes (Merida-Garcia et al., 2024). Compared with traditional measurements, this type of phenotypic data contains much more information and can also capture some response details that would otherwise be easily overlooked. With these more "abundant" data, the accuracy of detecting marker-trait associations naturally improves, especially when studying complex traits like FHB that are controlled by multiple genes, the advantages are more obvious. 5.2 Principal component extraction and mixed model construction in joint analysis The "abundance" of multi-dimensional data does not mean that analysis is easy. Too much data and too many variables often lead researchers into an "information overload". In order to reduce interference and highlight key features, principal component analysis (PCA) is generally used for a round of dimensionality reduction first to extract the main variations (Zhang et al., 2020). These principal components are then incorporated into the mixed linear model to correct background noise and enhance the stability of the analysis. In this way, even if the research involves multiple traits and multiple time points, some gene loci with less obvious effects can be screened out (Wu et al., 2021). Especially for genes with pleiotropy or temporal dynamic effects, this method can capture signals more effectively. 5.3 Stability assessment of QTLs through multi-environment and temporal-spatial data integration No matter how well a QTL performs at a certain experimental point, if it becomes ineffective in a different year or environment, it is clearly not suitable for breeding. This situation is not uncommon. Therefore, before evaluating whether the QTLS related to FHB resistance are "reliable", multi-point validation across environments and years is very necessary. With the help of high-throughput phenotypic platforms, researchers can now repeatedly collect data under different field conditions, with more standardized operations and smaller errors. By combining these phenotypic data with GWAS results for analysis, it is possible to more clearly see which QTLS can be stably expressed in various environments (Xiao et al., 2021; Merida-Garcia et al., 2024). The ultimately selected "stable performance" genetic markers are more suitable for practical breeding applications and can also help us understand exactly how genes and the environment interact with each other.
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