Introduction of Fine Mapping
Transcriptomics research and GWAS (Genome-Wide Association Study) integration through putative expression have been widely used in recent years, enabling the functional characterization of GWAS loci and the prioritization of causal genes. However, the techniques to infer transcriptome traits from DNA variation is still underdeveloped. In addition, the associations found when linking eQTL studies with complex traits through methods similar to PrediXcan may lead to false positives due to linkage disequilibrium between distinct causal variants. Research shows that informing prediction models with posterior causal probability from fine-mapping and borrowing information across organizations lead to better performance in terms of number and proportion of significant associations that are colocalized and the proportion of silver standard genes identified as indicated by precision recall and receiver operating characteristic (ROC) curves.
Fine mapping informed models are sparse and parsimonious, various of fine mapping methods were developed, including CAVIAR and PAINTOR and Bayesian fine mapping (such as BIMBAM, CAVIARBF). Fine-mapping requires three essential components: Fistly, all the common SNPs in the region need to be genotyped or imputed with high confidence. Secondly, very stringent quality control. Lastly, large sample sizes to provide enough power to differentiate between SNPs in high LD. There are five methods for fine mapping to analyze variation, as shown follows:
Fig 1. An overview of procedures for fine-mapping of GWAS loci. (Spain S L, Barrett J C. 2015)
Genome-wide association analysis (GWAS) is used to find out the set of gene loci (SNP) most associated with a disease, but in these loci, some loci are not associated with the phenotype. Therefore, through fine mapping, it is necessary to further reduce the susceptibility sites of candidate genes and exclude some useless sites. Fine mapping is to use statistical models (such as Bayesian models) to guess what is the real casual variation. This will enable more accurate pathway and functional analysis and facilitate the understanding of disease biology and identification of drug targets to help ameliorate the symptoms of complex diseases.
MedAI uses machine learning statistical models (such as Bayesian models) for fine mapping analysis. Further reduce the gene locus (SNP) that is most associated with a disease through genome-wide association analysis (GWAS), so as to help you quickly obtain effective phenotype-related genotypes and accelerate your research. In addition, we can use different fine mapping methods (such as CAVIAR, PAINTOR, BIMBAM, CAVIARBF, etc.) to analyze phenotypes and traits according to your needs. If you have any questions about fine mapping, please feel free to contact us, we look forward to working with you, and we will provide you with satisfactory services.
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