Gaining insight into metabolic diseases from human genetic discoveries

Abstract

Human large-scale genetic association studies have identified sequence variations at thousands of genetic risk loci that are more common in patients with diverse metabolic disease compared with healthy controls. While these genetic associations have been replicated in multiple large cohorts and sometimes can explain up to 50% of heritability, the molecular and cellular mechanisms affected by common genetic variation associated with metabolic disease remains mostly unknown. A variety of new genome-wide data types, in conjunction with novel biostatistical and computational analytical methodologies and foundational experimental technologies, are paving the way for a principled approach to systematic variant-to-function (V2F) studies for metabolic diseases, turning associated regions into causal variants, cell types and states of action, effector genes, and cellular and physiological mechanisms. Identification of new target genes and cellular programs for metabolic risk loci will improve mechanistic understanding of disease biology and identification of novel therapeutic strategies.

Keywords: GWAS; common variants; epigenetics; functional annotation; genetic variants.

Figure1

(A). Five-step model for linking Variants – to – Function (V2F) at genetic risk loci associated with metabolic traits implicates to address the following five questions at each metabolic risk individually, and all GWAS loci globally: (1) Which of the metabolic disease-associated variants at a non-coding locus play a causal role (V)? (2) Which regulatory elements are affected by these variants (RE)? (3) Which effector genes are affected (G)? (4) In which cell types and cell contexts do the causal variants act (C)? (5) What cellular functions are affected (F)? (B) Establishing chain-of-causations that link variants to regulatory elements to genes to cell types and states and functions involve the generation of observatory data sets assayed in cells from many individuals for large-scale QTL mapping and fine-mapping approaches followed by systematic pooled and arrayed CRISPR based perturbations as well as multiplexed assays of variant effects (MAVEs). (C) Pooled CRISPR screen in disease relevant cell types allow to connect genes to specific cellular functions, exemplified by a genome-wide pooled CRISPR knockout screens in a human beta-cell line with insulin content as the cellular phenotype read-out [92].

Figure 2.

Learning the rules of how metabolic risk variants affect effector genes and cellular programs requires massive data generation in cells relevant for metabolic disease across many physiologically relevant stimulations using QTL mapping and CRISPR based high-throughput perturbation screens combined with multiple phenotypic read-outs (left). Such phenotypic read-outs can span generic read-outs of transcriptomics, epigenomics, metabolomics, proteomics and high-content image-based profiling as well as specific, bespoke cell-based assays (middle). Data analyses and computational integration strategies across these data are poised to provide large-scale QTL data sets, predict cell type and states of action, candidate target genes and cellular functions.