INFIMA

INFIMA integrates multi-omics data from Diversity Outbred (DO) mice to fine-map causal non-coding single nucleotide polymorphisms (SNPs) and nominate effector genes for human GWAS variants.

Key Features:

• Integration of multi-omics data: INFIMA combines ATAC-seq, RNA-seq, footprinting data, and in silico mutation analysis from founder mice to link regulatory elements to variants.
• Integrative fine-mapping: It performs fine-mapping by synthesizing chromatin accessibility, gene expression profiles, and regulatory element footprints to increase precision in causal SNP identification.
• Cross-species validation: It leverages human and mouse chromatin conformation capture datasets to validate candidate effector gene assignments.
• Diversity Outbred (DO) mouse modeling: INFIMA uses the genetic diversity of DO mice to model complex trait inheritance when mapping regulatory variants.

Scientific Applications:

• Effector gene nomination for GWAS loci: INFIMA maps non-coding GWAS variants to candidate effector genes by integrating regulatory and expression data.
• Functional interpretation of complex trait loci (e.g., diabetes): It enables mechanistic interpretation of loci underlying complex diseases by linking causal SNPs to regulatory changes and target genes.

Methodology:

INFIMA integrates ATAC-seq, RNA-seq, footprinting data, and in silico mutation analysis from founder mice, applies integrative fine-mapping by synthesizing chromatin accessibility, gene expression, and regulatory footprints, and compares candidate assignments to human and mouse chromatin conformation capture datasets while leveraging Diversity Outbred mouse genotypes.