Inherited causes of clonal haematopoiesis in 97,691 whole genomes

Abstract

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.

Increased CHIP Prevalence Amongst People Living with HIV

“…We began by considering the fraction of CHIP across the entire SHCS PLWH cohort (N=600) and ARIC cohort (N=8111) ( Figure 1). SHCS PLWH and ARIC participants had mean (SD) age 44 (11) and 6years (p=1.8 x 10 -167 ), were 25% and 56% female (p=1.9 x 10 -46 ), and were 95% and 74% of European ancestry (p=5.2 x 10 -36 ) respectively. With adjustment for age, age 2 , sex and ethnicity, we observed a significant association between HIV case status and CHIP (OR: 1.77, 95% CI: 1.33-2.21, p=0.02).…”

“…CHIP was called in both exome sequenced cohorts using a previously described pipeline. 4,11 Briefly, short read sequence data was aligned to the hg19 reference genome using the BWA-mem algorithm and processed with the Genome Analysis Toolkit MuTect2 tool to detect somatic variants. 12 Identification of individuals with CHIP, used a pre-specified list of variants in 74 genes known to be recurrent drivers of myeloid malignancies.…”

Discovering the drivers of clonal hematopoiesis

“…Mutations in some CH-related genes are indeed known to provide an advantage to hematopoietic cells under exposure to certain cytotoxic treatments. Other differences, such as the different composition of both cohorts, in terms of human populations and tumor types represented may also have a bearing in the differences in CH-related genes discovered in each 56 . Further studies are needed to clarify this point, which the availability of the discovery presented here now make possible to undertake.…”

From GWAS Variant to Function: a Study of ~148,000 Variants for Blood Cell Traits

“…In addition to a priori providing one category of coding variants and 5 categories of non-coding variants that warrant prioritization consideration, VAMPIRE allows users to decide their own categories based on arbitrary combinations of the annotations at adjustable thresholds (for example, prioritizing high CADD score variants, or variants in open chromatin in blood cells based on ATAC-seq). Along with the addition of more blood cell trait genetics papers published in the future, VAMPIRE could also be used as written to annotate GWAS results for other blood related phenotypes, such as recent GWAS of risk of myeloproliferative neoplasm or clonal hematopoiesis [6,7]. As we accumulate additional functional validation data, including high-throughput massively parallel reporter assays (MPRA), medium-throughput CRISPRi/CRISPRa and low throughput mouse xenotransplant experiments, we will provide statistics summarizing experimental validation results (e.g., number of variants in the category followed-up, proportion that show evidence of functional impact in their experiments) for each of the 6 VAMPIRE categories and for user defined categories.…”