Inherited causes of clonal haematopoiesis in 97,691 whole genomes

Abstract

Age is the dominant risk factor for most chronic human diseases, but the mechanisms through which ageing confers this risk are largely unknown¹. The age-related acquisition of somatic mutations that lead to clonal expansion in regenerating haematopoietic stem cell populations has recently been associated with both haematological cancer^2-4 and coronary heart disease⁵-this phenomenon is termed clonal haematopoiesis of indeterminate potential (CHIP)⁶. Simultaneous analyses of germline and somatic whole-genome sequences provide the opportunity to identify root causes of CHIP. Here we analyse high-coverage whole-genome sequences from 97,691 participants of diverse ancestries in the National Heart, Lung, and Blood Institute Trans-omics for Precision Medicine (TOPMed) programme, and identify 4,229 individuals with CHIP. We identify associations with blood cell, lipid and inflammatory traits that are specific to different CHIP driver genes. Association of a genome-wide set of germline genetic variants enabled the identification of three genetic loci associated with CHIP status, including one locus at TET2 that was specific to individuals of African ancestry. In silico-informed in vitro evaluation of the TET2 germline locus enabled the identification of a causal variant that disrupts a TET2 distal enhancer, resulting in increased self-renewal of haematopoietic stem cells. Overall, we observe that germline genetic variation shapes haematopoietic stem cell function, leading to CHIP through mechanisms that are specific to clonal haematopoiesis as well as shared mechanisms that lead to somatic mutations across tissues.

Extended Data Fig. 1|. Characterizing TOPMed CHIP.

a, There was marked heterogeneity of CHIP clone size as measured by variant allele fraction by CHIP driver gene. Violin plot spanning minimum and maximum values calculated on full dataset (Supplementary Table 3). Sample size for each element in violin plot displayed in Fig. 1, b, 90% of individuals with CHIP had only one CHIP driver mutation identified c, CHIP prevalence with age was highly concordant across sequenced cohorts. CHIP prevalence was estimated from a logistic mixed model with spline-transformed age, sex, and cohort included as predictors. The cohort was included as a random intercept. Sample size for each cohort listed in Supplementary Table 1. d, CHIP prevalence with age in this study (blue triangles, N=82,807) was highly consistent with previously observed CHIP prevalence (dots represent mean point prevalence with shaded area represents 95% confidence interval; N_Genovese=12,380; N_Jaiswal = 17,182; N_Xie = 2,728).

Extended Data Fig. 2|. CHIP age association by mutational mechanism, gene and overlap with somatic chromosomal mosaicism.

a, cumulative density plot of CHIP incidence with age stratified by single nucleotide variant (SNV) vs frameshift mutations. SNVs were observed in younger individuals than Frameshift mutations (N=4,939; two-sided wilcox rank sum test p=0.01). b, cumulative density plot of CHIP incidence with age stratified by driver gene. c, 855 elderly WHI individuals (mean age: 70) with both whole genome and the array genotyping data available were interrogated for large-scale mosaic chromosomal rearrangements. The two somatic events did not co-occur more than would be expected by chance (hypergeometric p=0.25).

Extended Data Fig. 3|. CHIP associates with Blood, Lipid, and Inflammatory traits.

a, CHIP consistently associated with increased Red Cell Distribution Width (RDW). JAK2, SF3B1 and SRSF2 showed driver gene specific effects on blood traits (see Supplementary Table S5) b, CHIP status was not consistently associated with lipid traits, other than JAK2 CHIP which was associated with decreased total cholesterol and a trend towards decreased LDL (see Supplementary Table S6) c, CHIP status is associated with inflammatory markers, however notable heterogeneity existed across CHIP mutations (see Supplementary Table S7). Associations utilized a two-sided t-test from a multivariate general linear model including age, smoking, race and gender and study center and were not adjusted for multiple comparisons. Sample sizes and exact p-values for each phenotype are listed in Supplementary Tables 5–7.

Extended Data Fig. 4|. CHIP passenger somatic mutation spectrum.

a, Singleton mutation counts by nucleotide context in CHIP Cases and Controls. b, Signature contribution in CHIP cases and controls identified differential enrichment

Extended Data Fig. 5|. CHIP Single variant association regional association plots.

a, TERT locus b, TRIM59/KPNA4 locus c, TET2 locus. Two-sided association testing performed using SAIGE (N=65,405 individuals, see methods)

Extended Data Fig. 6|. CHIP transcriptome-wide association study (TWAS) results across 48 tissues identified 7 significant loci.

UTMOST algorithm applied to CHIP genome wide association study results from n=65,405 individuals (see methods). Genomic coordinates listed on x-axis. P-value from generalized Berk-Jones test on Y axis. Multiple hypothesis corrected threshold, p<2.9 × 10⁻⁶ displayed as dotted red line.

Extended Data Fig. 7|. Tissue-specific results from the top 9 overall UTMOST-significant genes.

UTMOST algorithm applied to CHIP genome wide association study results from n=65,405 individuals. P-value from generalized Berk-Jones test. eQTL z-scores for associations with P<0.05 are displayed in each bar. GTEX eQTL tissue listed on Y-axis.

Extended Data Fig. 8 |. CRISPR/Cas9 editing efficiency of TET2 Enhancer deletion in primary CD34+ HSPCs.

a, Schematic showing the position of the two sgRNAs used to delete the TET2 enhancer (512bp) containing rs79901204. B, Gel electrophoresis image of PCR products from genomic DNA of edited HSPCs indicating unedited (WT) and deletion bands at sgRNA target site. Percentages of deletion alleles determined by band intensity and is shown below each lane. The experiment contains 3 biological replicates and was performed once.

Extended Data Fig. 9 |. rs79901204 associated with genome wide differential methylation signal,

Methylation Quantitative Trait association results of rs79901204 variant with cpg methylation probes identify an altered peripheral leukocyte methylation profile genome wide in N = 1747 individuals. The strongest signal is at the chr4 TET2 locus. P-values on Y-axis derived from two-sided linear mixed effects model (see methods). To account for multiple hypothesis testing, a Bonferroni threshold of p < 5.8 × 10⁻⁸ was used to establish statistical significance.

Extended Data Fig. 10 |. Sensitivity of CHIP detection at various VAFs across sequencing depths.

A set of 30 samples from a previously published CHIP cohort (Gibbons et al, 2017) were computationally down sampled to 30x, 40x, 50x, 100x and 400x sequencing depth. TOPMed WGS data was typically in the 40x depth range across CHIP genes. WGS data has excellent sensitivity to detect CHIP clones with VAF >10%, and ~50% sensitivity to detect CHIP VAF 5–10%, with minimal ability to detect CHIP clones <5%.

Fig. 1|. Identifying CHIP in TOPMed Genomes.

CHIP was identified in 97,631 whole genome sequenced peripheral blood samples through the curation of somatic driver mutations. Counts for 8 most common driver genes plotted. inset, CHIP prevalence increased with age. Center line represents general additive model spline, 95% confidence interval is shaded (n=82,807 individuals; two-sided t-test: p<10⁻³⁰⁰).

Fig. 2|. Genetic Determinants of CHIP.

Single variant genetic association analyses of CHIP identified three genome wide significant loci. Two-sided association testing performed using SAIGE (n=65,405 individuals, see methods)

Fig. 3|. African ancestry specific TET2 locus risk variant disrupts hematopoietic stem cell TET2 enhancer decreasing TET2 expression and increasing self-renewal.

a, the TET2 locus with fine-mapped risk variants, Activity-by-Contact (ABC) hematopoietic stem and progenitor cell (HSPC) enhancers, DNase-Seq CD34+ HSPC and RefSeq genes. ABC model predicts that rs79901204 disrupts a TET2 enhancer resulting in decreased TET2 expression (see methods). b, expanded view of TET2 enhancer element. c, rs79901204 disrupts a GATA motif/E-Box motif. d, rs79901204 is associated with decreased TET2 expression in human peripheral blood RNA-seq (N_A/A=230, N_A/T=16, N_T/T=1, two-sided linear mixed model p=0.012). TPM, transcripts per million. Boxplot displays median, 25^th and 75^th percentiles, mean (diamond symbol) and outlier observations (black dots) e, luciferase assay in CD34+ primary cells demonstrates four-fold attenuation of enhancer activity by the rs79901204 T risk allele relative to the A reference allele (N=3, two-sided t-test p=0.007). f, deleting the TET2 enhancer (ENH) in CD34+ primary cells results in decreased TET2 expression relative to deletion of control locus AAVS1 (N=3, two-sided t-test, p=0.04). g, Human HSPCs were electroporated with Cas9 targeting a coding region of TET2 and AAVS1 (a control locus) and plated for primary and secondary colony-forming assays. h, two TET2 guides had differential editing efficiency. i, TET2 coding disruption leads to expanded secondary colony formation compared to AAVS1 controls (N=3, two-sided t-test p=0.01, p=0.002 for g1 and g2 respectively, with greater expansion identified in the TET2 guide with greater editing efficiency (two-sided t-test p=0.04). Mean and standard deviation of number of each colony type plotted. CFU-M, colony forming unit-macrophage; CFU-GM, granulocyte macrophage; CFU-GEMM, granulocyte erythrocyte macrophage megakaryocyte; CFU-G, granulocyte; BFU-E, burst forming unit-erythroid. In e, f, h, points represent independent replicates, mean values and error bars represent standard error are plotted.