Integrative analysis of functional genomic annotations and sequencing data to identify rare causal variants via hierarchical modeling

Integrative analysis of functional genomic annotations and sequencing data to identify rare causal variants via hierarchical modeling

Blog Article

Identifying the small number of rare causal variants contributing to disease has beena major focus of investigation in recent years, but represents a formidable statisticalchallenge due to the rare frequencies with which these variants are observed.In thiscommentary we draw attention to a formal statistical framework, namely houston texans shorts hierarchicalmodeling, to combine functional genomic annotations with sequencing data with theobjective of enhancing our ability to identify rare causal variants.Using simulations weshow that in all here configurations studied, the hierarchical modeling approach has superiordiscriminatory ability compared to a recently proposed aggregate measure of deleteriousness,the Combined Annotation-Dependent Depletion (CADD) score, supportingour premise that aggregate functional genomic measures can more accurately identifycausal variants when used in conjunction with sequencing data through a hierarchicalmodeling approach.