Unravelling the complex genetics of common kidney diseases: from variants to mechanisms

Single murine and human intestinal stem cells can be expanded in culture over long time periods as genetically and phenotypically stable epithelial organoids. Increased cAMP levels induce rapid swelling of such organoids by opening the cystic fibrosis transmembrane conductor receptor (CFTR). This response is lost in organoids derived from cystic fibrosis (CF) patients. Here we use the CRISPR/Cas9 genome editing system to correct the CFTR locus by homologous recombination in cultured intestinal stem cells of CF patients. The corrected allele is expressed and fully functional as measured in clonally expanded organoids. This study provides proof of concept for gene correction by homologous recombination in primary adult stem cells derived from patients with a single-gene hereditary defect. Copyright © 2013 Elsevier Inc. All rights reserved.

During the past 12 years, genome-wide association studies (GWASs) have uncovered thousands of genetic variants that influence risk for complex human traits and diseases. Yet functional studies aimed at delineating the causal genetic variants and biological mechanisms underlying the observed statistical associations with disease risk have lagged. In this review, we highlight key advances in the field of functional genomics that may facilitate the derivation of biological meaning post-GWAS. We highlight the evidence suggesting that causal variants underlying disease risk often function through regulatory effects on the expression of target genes and that these expression effects might be modest and cell-type specific. We moreover discuss specific studies as proof-of-principle examples for current statistical, bioinformatic, and empirical bench-based approaches to downstream elucidation of GWAS-identified disease risk loci.

Transcriptome-wide association studies (TWAS) integrate genome-wide association studies (GWAS) and gene expression datasets to identify gene-trait associations. In this Perspective, we explore properties of TWAS as a potential approach to prioritize causal genes at GWAS loci, by using simulations and case studies of literature-curated candidate causal genes for schizophrenia, low-density-lipoprotein cholesterol and Crohn’s disease. We explore risk loci where TWAS accurately prioritizes the likely causal gene as well as loci where TWAS prioritizes multiple genes, some likely to be non-causal, owing to sharing of expression quantitative trait loci (eQTL). TWAS is especially prone to spurious prioritization with expression data from non-trait-related tissues or cell types, owing to substantial cross-cell-type variation in expression levels and eQTL strengths. Nonetheless, TWAS prioritizes candidate causal genes more accurately than simple baselines. We suggest best practices for causal-gene prioritization with TWAS and discuss future opportunities for improvement. Our results showcase the strengths and limitations of using eQTL datasets to determine causal genes at GWAS loci.