Age is the dominant risk factor for most chronic human diseases; yet the mechanisms by which aging confers this risk are largely unknown. 1 Recently, the age-related acquisition of somatic mutations in regenerating hematopoietic stem cell populations leading to clonal expansion was associated with both hematologic cancer 2 – 4 and coronary heart disease 5 , a phenomenon termed ‘Clonal Hematopoiesis of Indeterminate Potential’ (CHIP). 6 Simultaneous germline and somatic whole genome sequence analysis now provides the opportunity to identify root causes of CHIP. Here, we analyze high-coverage whole genome sequences from 97,691 participants of diverse ancestries in the NHLBI TOPMed program and identify 4,229 individuals with CHIP. We identify associations with blood cell, lipid, and inflammatory traits specific to different CHIP genes. Association of a genome-wide set of germline genetic variants identified three genetic loci associated with CHIP status, including one locus at TET2 that was African ancestry specific. In silico -informed in vitro evaluation of the TET2 germline locus identified a causal variant that disrupts a TET2 distal enhancer resulting in increased hematopoietic stem cell self-renewal. Overall, we observe that germline genetic variation shapes hematopoietic stem cell function leading to CHIP through mechanisms that are both specific to clonal hematopoiesis and shared mechanisms leading to somatic mutations across tissues.

Summary Blood cells play essential roles in human health, underpinning physiological processes such as immunity, oxygen transport, and clotting, which when perturbed cause a significant global health burden. Here we integrate data from UK Biobank and a large-scale international collaborative effort, including data for 563,085 European ancestry participants, and discover 5,106 new genetic variants independently associated with 29 blood cell phenotypes covering a range of variation impacting hematopoiesis. We holistically characterize the genetic architecture of hematopoiesis, assess the relevance of the omnigenic model to blood cell phenotypes, delineate relevant hematopoietic cell states influenced by regulatory genetic variants and gene networks, identify novel splice-altering variants mediating the associations, and assess the polygenic prediction potential for blood traits and clinical disorders at the interface of complex and Mendelian genetics. These results show the power of large-scale blood cell trait GWAS to interrogate clinically meaningful variants across a wide allelic spectrum of human variation.

Highlights d Blood cell traits differ by ancestry and are subject to selective pressure d We assessed 15 blood cell traits in 746,667 participants from 5 global populations d We identified more than 5,500 associations, including 100 associations not found in Europeans d These analyses improved risk prediction and identified potential causal variants

Widespread linkage disequilibrium and incomplete annotation of cell-to-cell state variation represent substantial challenges to elucidating mechanisms of trait-associated genetic variation. Here, we perform genetic fine-mapping for blood cell traits in the UK Biobank to identify putative causal variants. These variants are enriched in genes encoding for proteins in trait-relevant biological pathways and in accessible chromatin of hematopoietic progenitors. For regulatory variants, we explore patterns of developmental enhancer activity, predict molecular mechanisms, and identify likely target genes. In several instances, we localize multiple independent variants to the same regulatory element or gene. We further observe that variants with pleiotropic effects preferentially act in common progenitor populations to direct the production of distinct lineages. Finally, we leverage fine-mapped variants in conjunction with continuous epigenomic annotations to identify trait-cell type enrichments within closely related populations and in single cells. Our study provides a comprehensive framework for single-variant and single-cell analyses of genetic associations.