Molecular Plant Breeding 2024, Vol.15, No.2, 52-62 http://genbreedpublisher.com/index.php/mpb 59 Figure 3 Application of new statistical multi-locus model in sesame (Adopted from Berhe et al., 2021) Another example is the whole-genome resequencing, diversity analysis and stress resistance trait analysis of 77 grape root stock genotypes. GWAS analysis revealed SNP loci and genes related to grape root stock resistance to various stresses such as root nodule nematodes, salinity, drought, cold and flooding. In particular, the study found that genes related to salt stress are distributed on multiple chromosomes. These genes include multiple cysteine-rich receptor-like protein kinase genes and the sodium/hydrogen exchanger 2 gene located on chromosome 5. These findings provide important clues for further exploring the tolerance mechanism of grapes to salt stress. In addition, for cold tolerance traits, the study identified loci related to the C-repeat binding factor 1 gene, which is the main regulator of plant cold resistance mechanisms. These results are of great significance for improving the stress resistance traits of grapes (Figure 4) (Wang et al., 2024). Figure 4 Genome-wide association analysis of six resistance traits (Adopted from Wang et al., 2024) Advances in bioinformatics have provided key technologies for processing and analyzing high-throughput sequencing data. As the amount of data increases, the development of bioinformatics tools and algorithms makes it possible to extract useful information from complex data. This includes preprocessing of data, identification and annotation of variants, and sophisticated statistical analysis to reveal associations between genetic variants and specific traits. In the future, with the continued advancement of high-throughput sequencing technology and the continuous innovation of bioinformatics methods, we expect that GWAS will be able to more accurately identify and explain the genetic basis that affects complex diseases and traits. This will include utilizing larger sample sizes, more comprehensive coverage of genetic variation, and integrated analysis of multi-omics data (such as transcriptome, proteome, etc.) to gain a deeper understanding of the molecular mechanisms of disease and promote personalized medicine. and the development of precision treatments.
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