Presenter: Dr. Saori Sakaue
Affiliation: Harvard Medical School, Broad Institute
Title: Finding causal mechanisms of human diseases by integrative analyses of genetics and single-cell genomics
Human genetic studies in these 20 years have been hugely successful in systematically identifying hundreds of thousands of causal loci for human diseases even without any prior hypothesis about biology (1). We can now genetically predict our future risk of diseases and traits more precisely than ever (2). However, a critical challenge in the current genetics is to build a strategy to define causal mechanisms of human diseases from each genetic locus we identify through genome-wide association studies (GWAS), which could lead to novel therapeutics. This has been an extremely challenging and unsolved task, since >90% of the loci we identify through GWAS lie within non-coding regions, where we do not have a consensus “grammar” for defining the consequence and impact of genetic variants on gene regulation. With the current statistical genetics methods alone, only rarely are we able to define causal variants or their target genes due to the complex linkage disequilibrium structure and potential long-range gene regulations in a cell-type-specific manner.
Therefore, it is essential to integrate large-scale experimental genomic data with genetic studies to nail down to the causal mechanisms. In particular, we have seen a huge opportunity in recent advances in single-cell technologies for building accurate cell-type-specific gene regulatory maps. We thus developed a new non-parametric statistical method, SCENT (Single-Cell ENhancer Target gene mapping) which models association between enhancer chromatin accessibility and gene expression in single-cell multimodal RNA-seq and ATAC-seq data (3). We applied SCENT to all available multimodal datasets and created 23 cell-type-specific enhancer-gene maps. These maps were highly enriched for causal variants in expression quantitative loci (eQTLs) and GWAS for 1,143 diseases and traits, which outperformed previous bulk-tissue based enhancer maps and single-cell based method. We identified novel likely causal genes for both common and rare diseases. In addition, we were able to link somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer-gene maps, essential for defining non-coding variant function.
- Sakaue et al. Nature Genetics 2021
- Sakaue et al. Nature Medicine 2020
- Sakaue et al. medRxiv 2022