Blood trait regulatory genes and hematopoietic contributions to disease phenotypes revealed through genetic colocalization analysis

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Genetic associations link hematopoietic traits and disease end-points, but causal variants and related genes are largely unknown. Genome-wide significant signals for human traits sometimes directly overlap. We hypothesized that such 'genetic colocalization' sites and related genes could explain shared genetic bases for hematopoietic, cardiovascular, autoimmune, neuropsychiatric, and cancer phenotypes.


Using genome wide association study (GWAS) summary statistics and a recently described colocalization algorithm, we determined multi-trait overlap sites (false discovery rate <5%). We then identified quantitative trait loci (QTLs) among these sites to highlight related genes. 


From 2706 genome-wide significant loci for ³1 blood trait, we identified 1779 sites (66%) where ³2 trait signals colocalized. We could assign genetic effects of some sites to developmental cell types. For example, SH2B3- and ATM-related sites affected white blood cell, erythroid and platelet traits, consistent with previously described impacts on hematopoietic progenitor cells. We also parsed colocalization sites affecting specific terminal blood cell types. Among 439 erythroid-specific sites were QTLs for genes influencing erythroid stability (RHD, LPL) or development (SP1, FANCA). 

Analysis of 9852 genome-wide significant sites among 70 human traits defined 2123 loci (22%) where ³2 traits colocalized. We identified known trait relationships, and a novel link between eosinophil percentage and eczema. We also identified colocalizations at clinically relevant coding mutations, including sites linking PTPN22 with white blood cell traits and Crohn's disease, NIPA with heart disease and platelet traits, and HFE with altered erythroid traits and lipid levels. Finally, we anticipate potential off-target effects on blood traits related novel therapeutic targets, including TRAIL.


Our results findings provide a road map for gene validation experiments and novel therapeutics related to hematopoietic development, and offer a rationale for pleiotropic interactions between hematopoietic loci and disease end-points. Application of our pipeline to other disease-specific data may define related genes and pathways. For example, this approach could be used to pinpoint causal genes underlying pediatric disease phenotypes and anticipate off-target effects on blood traits in the pediatric population. 

Children's Hospital of Philadelphia
University of Pennsylvania Perelman School of Medicine

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