Myeloproliferative neoplasms (MPNs) are rare chronic hematological cancers characterized by the overproduction of mature blood cells leading to elevated blood cell parameters. They are typically driven by somatically mutated JAK2-mediated, calreticulin (CALR)-mediated or MPL-mediated clonal expansion¹. JAK2 mutations are found in both polycythemia vera (PV) and essential thrombocythemia (ET), which are distinct but overlapping MPNs characterized by increased numbers of red blood cells and platelets, respectively. Mutant JAK2 is commonly detectable in 0.1–3% of the healthy population as clonal hematopoiesis (CH)^2,3,4,5,6,7, with the vast majority of carriers not meeting or going on to develop disease-defining characteristics of MPN. Little is understood about why only a minority of individuals with mutated JAK2 develop more severe hematological manifestations of MPN and the factors that influence blood count heterogeneity in MPNs.

The 46/1 haplotype near JAK2 is a known germline risk factor for MPNs in the population⁸. Genome-wide association studies (GWAS) have identified additional disease-associated germline risk loci, estimating the liability-scale heritability of MPNs based on common single-nucleotide polymorphisms (SNPs) to be ~6.5% (refs. ^9,10,11). However, these germline risk loci insufficiently explain the phenotypic heterogeneity observed within MPNs and in JAK2-mutated healthy carriers.

Blood cell traits vary widely in the healthy population. The genetic architecture underlying these traits is highly polygenic, with more than 11,000 independently associated genetic variants discovered so far^12,13,14. These genome-wide associated variants, when combined in polygenic scores (PGSs), explain a large proportion of phenotypic variance among healthy individuals (from 2.5% for basophil count to 27.3% for mean platelet volume) and are associated with multiple common diseases and rare hematological disorders¹⁴. We hypothesized that a genetic burden of germline variants associated with extreme hematological traits could influence phenotypic heterogeneity in association with mutated JAK2, by influencing the clonal dynamics of mutant JAK2 and/or modifying its downstream consequences. In this study, we integrate information on somatic driver mutations, germline genetic variants associated with MPNs, and CH and hematological trait PGSs to study how inherited polygenic variation underlying blood cell traits influences clonal selection on mutated JAK2 and MPN disease phenotypes (Supplementary Fig. 1).

Inherited polygenic contribution to JAK2 ^V617F positivity

One in 30 healthy individuals reportedly harbors JAK2^V617F in their blood, as determined using sensitive assays⁶. The majority of such individuals have low levels of JAK2^V617F and do not meet clinical criteria for MPN due to the absence of elevated blood cell parameters. We wished to understand whether inherited polygenic loci that underlie blood cell traits influence the strength of clonal selection on JAK2^V617F.

We studied the germline characteristics of individuals in UK Biobank (UKBB) with and without JAK2^V617F. From 162,534 genetically unrelated individuals of European ancestry within the UKBB whole-exome sequencing cohort (‘200k UKBB-WES cohort’; Methods), we identified 540 individuals with one or more mutant reads for JAK2^V617F (0.3%, median variant allele frequency (VAF) = 0.056, range = 0.019–1; Supplementary Fig. 2; ‘UKBB-JAK2^V617F cohort’). The lower rate of JAK2^V617F in the UKBB-WES cohort compared to other population studies^6,7 could be explained by its low sequencing coverage (21.5× depth), as also reported previously¹⁵ (Supplementary Fig. 3). As expected, there was some overlap among individuals with JAK2^V617F and those with a diagnosis of MPN. Of the 423 individuals labeled with a diagnosis of MPN (156 with ET, 161 with PV and 106 with myelofibrosis (MF)), 72 were positive for JAK2^V617F (Supplementary Table 1).

We built PGSs for 29 blood cell traits covering a wide range of hematopoietic parameters (Supplementary Table 2). Blood cell trait-specific PGSs were then weighted (by effect size) by the sum of all common (minor allele frequency (MAF) > 0.01) variants that were independently associated with a blood cell trait at genome-wide significance (P < 5 × 10⁻⁸) in UKBB (Methods)¹⁴. To assess the association between hematological PGSs and small (VAF < 0.1, n = 397) or large (VAF ≥ 0.1, n = 143) JAK2^V617F clones, we used multinomial logistic regression including PGSs for each hematological trait (units of s.d.), together with previously reported germline sites associated with MPN⁹ and CH¹⁶ (PGS_MPN and PGS_CH) as covariates. To account for the recognized predisposition risk for MPN driven by the JAK2 46/1 haplotype⁸, we computed two PGS_MPN scores, separating rs1327494 (tagging the JAK2 46/1 haplotype; PGS_MPN–46/1) from nontagging JAK2 variants (PGS_MPN-other). We found a negative association between the PGSs for both mean reticulocyte volume (PGS_MRV) and immature reticulocyte fraction (PGS_IRF) and small JAK2^V617F clones (P = 6.2 × 10⁻⁴ and 0.0018, false discovery rate (FDR) < 0.05; Supplementary Table 3). We also found significant positive associations with small JAK2^V617F clones for the PGSs of plateletcrit (PGS_PCT) and monocyte count (PGS_MONO) (P = 9.5 × 10⁻⁴ and 0.0036, FDR < 0.05). Germline predisposition to high MONO and PCT values was also positively associated with large JAK2^V617F clones at modest significance (P = 0.033 and 0.0022, FDR-adjusted P = 0.31 and 0.064; Fig. 1a). Repeating the analysis above excluding MPN cases still demonstrated a significant association between PGS_PCT or PGS_MONO and small JAK2^V617F clones (P < 0.013, Bonferroni corrected; Supplementary Table 4), suggesting that the inherited effects on JAK2^V617F were not driven by the subset of MPN cases. These associations were independent of the known germline risk loci associated with MPN and CH (Supplementary Table 3). Validating these associations in the full UKBB-WES dataset (n = 799 and 326 for small and large clones, respectively, and n = 338,919 for controls), we again replicated the associations between PGS_PCT and small JAK2^V617F clones and between PGS_MONO and large JAK2^V617F clones at FDR < 0.05 (PCT: odds ratio (OR) = 1.15 (change in odds per increase of 1 s.d. in PGS), 95% confidence interval (CI) = 1.07–1.24, P = 1.4 × 10⁻⁴; MONO: OR = 1.20, 95% CI = 1.07–1.34, P = 0.0014; Supplementary Table 5).

Data are presented as ORs (solid dots) with 95% CIs (error bars). a, PGSs with significant associations with small clone size of JAK2^V617F (FDR < 0.05) compared to the CH and MPN PGSs (Supplementary Table 3). OR was defined as the change in odds per increase of 1 s.d. in PGS. b, Causal effects estimated by four MR methods for the exposure traits whose PGSs were found to have significant predisposition risk for JAK2^V617F positivity (Supplementary Table 7). OR was defined as the change in odds per increase of 1 s.d. in exposure. The MR results shown were based on GWAS summary statistics for JAK2^V617F positivity in the full UKBB (Supplementary Fig. 4). Results based on the main discovery set (200k UKBB-WES cohort) are shown in Supplementary Table 6. The MR result for MRV was not available due to a lack of corresponding GWAS summary data in INTERVAL.

To understand the causal relationship among these associations, we undertook Mendelian randomization (MR) analyses with GWAS estimates for the exposure (blood traits) and the outcome (JAK2^V617F positivity; Supplementary Fig. 4) obtained from two independent sources. We used genetic instruments for hematological traits identified from UKBB, with effect size estimates from INTERVAL¹⁷ (n = 30,305), an external independent cohort. MRV was excluded due to a lack of data in INTERVAL. Both PCT and MONO showed significant causality on the presence of a JAK2^V617F clone based on inverse variance-weighted (IVW)¹⁸ MR and demonstrated consistent effect estimates using two other MR methods (simple median and weighted median), suggesting that higher MONO and higher PCT values cause a detectable JAK2^V617F clone (Supplementary Table 6).

Extending this analysis to the full UKBB-WES cohort (JAK2^V617F, n = 1,125; controls, n = 338,919) validated these causal associations with greater estimation accuracy (PCT: OR_IVW = 1.52, 95% CI = 1.29–1.78, P = 3.0 × 10⁻⁷; MONO: OR_IVW = 1.3, 95% CI = 1.15–1.49, P = 4.6 × 10⁻⁵; Fig. 1b and Supplementary Table 7). The IVW method of MR (Methods) assumes that the germline loci that drive MONO and PCT have no direct causal effect on driving a JAK2^V617F clone (that is, there are no direct causal effects of the genetic instruments on the outcome). We found no evidence of pleiotropy using the MR-Egger¹⁹ test; the estimated intercept was not significantly different from zero with P = 0.84 and P = 0.90 for PCT and MONO, respectively. The causal relationship was also significant for PCT and MONO (P < 0.05; Supplementary Table 7 and Supplementary Fig. 5). Additionally, the estimates were not biased by any potential pleiotropic outlier variants and were highly consistent with outlier-corrected causal estimates (Supplementary Table 7 and Methods). Lastly, to ensure the results were not confounded by the possibility that the genetic loci used as instruments for MR directly promoted the outcome (that is, JAK2^V617F positivity), we repeated the analysis excluding genetic instruments associated with JAK2^V617F positivity (P_association < 10⁻⁶), as well as those that correlated with JAK2^V617F variants (that is, those variants and JAK2^V617F variants are in linkage disequilibrium (LD) r² > 0.01) or were in proximity to JAK2^V617F variants (in the 10-Mb region centered on each variant), and found no major changes (Supplementary Table 8). Importantly, any reverse causal effect we detected for MONO and PCT was subtle and with pleiotropic effects (P_Egger > 0.05 and P_{Egger-intercept} < 0.05; Supplementary Table 9 and Supplementary Fig. 6).

Overall, the association results combined with MR suggest that higher PCT and MONO are causal for the presence of a JAK2^V617F clone. This would also explain why individuals with germline predisposition to high PCT and MONO are also more likely to harbor a JAK2^V617F clone. Given that acquisition of somatic mutations in blood is largely stochastic in healthy populations²⁰, our data suggest that genetically predicted PCT and MONO influence clonal selection on nascent JAK2^V617F cells to promote mutation acquisition.

Germline contribution to blood cell count variation in MPNs

Having shown that polygenic germline loci can predispose to JAK2 clone positivity through their influence on blood cell trait levels, we next studied the contribution of these inherited sites to clinical phenotypes of MPN. We first considered the four blood cell traits that are used to define MPN subcategories clinically²¹ as follows: hemoglobin concentration (HGB) (g dl^–1 divided by 10), hematocrit (HCT) (%), platelet count (PLT) (×10⁹ divided by 1,000) and white blood cell count (WBC) (×10⁹ divided by 100). We used SNP arrays to measure genome-wide polymorphism in an MPN cohort of 761 patients (PV, n = 112; ET, n = 581; MF, n = 68), in whom diagnostic blood cell counts were available and mutation status for a panel of cancer-associated genes (Fig. 2a) had previously been characterized²².

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