A Latent Model for Prioritization of SNPs for Functional StudiesReportar como inadecuado




A Latent Model for Prioritization of SNPs for Functional Studies - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

One difficult question facing researchers is how to prioritize SNPs detected from genetic association studies for functional studies. Often a list of the top M SNPs is determined based on solely the p-value from an association analysis, where M is determined by financial-time constraints. For many studies of complex diseases, multiple analyses have been completed and integrating these multiple sets of results may be difficult. One may also wish to incorporate biological knowledge, such as whether the SNP is in the exon of a gene or a regulatory region, into the selection of markers to follow-up. In this manuscript, we propose a Bayesian latent variable model BLVM for incorporating -features- about a SNP to estimate a latent -quality score-, with SNPs prioritized based on the posterior probability distribution of the rankings of these quality scores. We illustrate the method using data from an ovarian cancer genome-wide association study GWAS. In addition to the application of the BLVM to the ovarian GWAS, we applied the BLVM to simulated data which mimics the setting involving the prioritization of markers across multiple GWAS for related diseases-traits. The top ranked SNP by BLVM for the ovarian GWAS, ranked 2nd and 7th based on p-values from analyses of all invasive and invasive serous cases. The top SNP based on serous case analysis p-value which ranked 197th for invasive case analysis, was ranked 8th based on the posterior probability of being in the top 5 markers 0.13. In summary, the application of the BLVM allows for the systematic integration of multiple SNP -features- for the prioritization of loci for fine-mapping or functional studies, taking into account the uncertainty in ranking.



Autor: Brooke L. Fridley , Ed Iversen, Ya-Yu Tsai, Gregory D. Jenkins, Ellen L. Goode, Thomas A. Sellers

Fuente: http://plos.srce.hr/



DESCARGAR PDF




Documentos relacionados