Making sense of the GWAS: colocalization and fine mapping of autoimmunity risk alleles with molecular QTLs

Identification: Tardaguila, Manuel


Description

Making sense of the GWAS: colocalization and fine mapping of autoimmunity risk alleles with molecular QTLs
 
Manuel Tardaguila1, Kousik Kundu1, Stephen Sawcer2, Nicole Soranzo1*
1Department of Human Genetics, The Wellcome Sanger Institute; 2Department of Clinical Neurosciences, Cambridge University
      
Resolving the genetic basis of human disease is one of the main challenges of present-day medicine. In the last decade, several hundreds of genomic loci have been robustly associated with susceptibility to autoimmune diseases, predominantly mapping to non-coding regulatory regions of the genome that are active in immune cells, but very few have yielded detailed insights into disease biology. Here we describe an analysis framework to identify and prioritise causal genetic variants and disease genes underpinning associations with fourteen autoimmune diseases e.g. multiple sclerosis (MS), celiac disease, IBD, T1D, and RA, extending published work1. We integrate genetic information with Quantitative Trait Locus (QTL) analyses of molecular phenotypes of gene expression, histone modifications, DNA methylation and transcription factor binding in neutrophils, monocytes and naïve CD4-T cells. Through colocalisation and fine-mapping, we identify putative molecular mechanisms for 346 unique disease loci, and resolve 110 to credible sets of 5 or less causal genetic variants to be assayed in targeted functional experiments. We describe the analytical rationale and results of this large scale effort leveraging high throughput sequencing at population scale, and show how this enhances the functional and mechanistic interpretation of genetic associations in the context of MS. As genetically informed linkage of disease and target gene almost doubles the success of phase II clinical trials2, we anticipate that population genomics-based integrative approaches will be central for target identification and prioritization in drug development pipelines of the omics era.
 
1.      Chen, L. et al. Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells. Cell 167, 1398-1414.e24 (2016).
2.      Cook, D. et al. Lessons learned from the fate of AstraZeneca's drug pipeline: A five-dimensional framework. Nature Reviews Drug Discovery 13, 419-431 (2014).
 

Credits

Credits: None available.

You must be logged in and own this product in order to post comments.