Bioscience Method 2024, Vol.15, No.1, 8-19 http://bioscipublisher.com/index.php/bm 16 (WGS) datasets to create a single reference panel that increases the depth of low-frequency and rare haplotypes. For example, the Haplotype Reference Consortium has combined low read depth WGS data from 20 studies of mainly European ancestry, which improves the accuracy of genotype imputation, especially at low frequency variants down to 0.1% MAF down and allows for smoother interpolation on existing servers (Bomba et al., 2017). In addition to imputation, custom genotyping arrays are another strategy used to investigate low-frequency and rare variants in association studies. These arrays are often designed for specific diseases and aim to enrich standard haplotype marker SNP panels with variants identified through sequencing and fine mapping efforts. 3.3 GWAS complexity study The complexity of interpreting genome-wide association study (GWAS) results involves multiple levels, the most critical of which is the confirmation and functional verification of candidate genes. Although GWAS can reveal genetic variations associated with specific traits, how to translate statistical correlations into biological mechanisms remains a huge challenge. This is mainly because most of the variants discovered by GWAS are located outside the coding region and involve non-coding variants, and the mechanism of their impact on traits is not as intuitive as coding region variants. Confirming candidate genes involves complex bioinformatics analysis, which requires further statistical fine mapping, transcriptome-wide association studies (TWAS) and other methods to screen out the true correlation with traits from a long list of related genetic markers identified by GWAS. candidate genes. However, even after candidate genes are identified, functional validation remains challenging. This is because functional verification in the laboratory requires tedious molecular biology experiments, such as gene expression, transcription factor binding, reporter gene assays, in vivo models, genome editing, and chromatin interactions, to confirm how these candidate genes or variants function. Affects trait performance at the molecular level (Alsheikh et al., 2022). A recent systematic review revealed the current research status in experimental verification of non-coding variants in GWAS, by examining 1 Screening and evaluation of 454 articles finally confirmed that 309 non-coding GWAS variants were experimentally verified. These variants regulate 252 genes and involve 130 human disease traits. These variations mainly work through multiple mechanisms such as cis-regulatory elements (70%), promoters (22%), and non-coding RNAs (8%). This study highlights the complexity of experimentally validating GWAS findings and the multifaceted approach required when prioritizing variants and nominating target genes (Alsheikh et al., 2022). These findings highlight that the translation from GWAS results to functional understanding is a multistep process that requires the integration of multiple bioinformatics and experimental methods. Despite some progress, validating thousands of GWAS associations remains a huge challenge for the field. In addition, with the development of functional genomics and gene editing technology, we have reason to expect that GWAS results will be more effectively parsed in the future and more knowledge about the genetic basis of complex traits will be revealed. 4 Future Development Directions of GWAS 4.1 The potential of integrating multi-omics data with GWAS Integrating multi-omics data (such as transcriptomics , proteomics) and GWAS are opening up new areas of crop trait analysis capabilities. The potential of this integrative approach lies in its ability to provide a more comprehensive view of how crop traits are co-regulated by genes, transcripts, proteins and metabolites. For example, by combining GWAS and transcriptome data, researchers are able to more accurately identify key genes associated with complex traits in crops, such as the DROUGHT1 (DROT1) gene in rice, which is associated with drought resistance, and the MADS26 gene in corn, which affects seed germination (Ahmad et al., 2017). mGWAS that combines genomic, transcriptomic and metabolomic data has been widely used in crops including rice, corn, wheat, barley and tomato, revealing metabolic pathways and genetic variations associated with complex traits, This provides a new perspective for metabolomics-related breeding. For example, by combining
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