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. 2023 Apr 28;9(17):eabm4945.
doi: 10.1126/sciadv.abm4945. Epub 2023 Apr 26.

The genetic determinants of recurrent somatic mutations in 43,693 blood genomes

Joshua S Weinstock  1 Cecelia A Laurie  2 Jai G Broome  2   3 Kent D Taylor  4 Xiuqing Guo  4 Alan R Shuldiner  5 Jeffrey R O'Connell  5 Joshua P Lewis  5 Eric Boerwinkle  6 Kathleen C Barnes  7 Nathalie Chami  8   9 Eimear E Kenny  10 Ruth J F Loos  8   9 Myriam Fornage  11 Susan Redline  12   13 Brian E Cade  12   13   14 Frank D Gilliland  15 Zhanghua Chen  15 W James Gauderman  15 Rajesh Kumar  16   17 Leslie Grammer  17 Robert P Schleimer  17 Bruce M Psaty  18   19   20 Joshua C Bis  18 Jennifer A Brody  18 Edwin K Silverman  21 Jeong H Yun  21 Dandi Qiao  21 Scott T Weiss  12   21 Jessica Lasky-Su  12   21 Dawn L DeMeo  12   21 Nicholette D Palmer  22 Barry I Freedman  23 Donald W Bowden  22 Michael H Cho  24 Ramachandran S Vasan  25 Andrew D Johnson  25   26 Lisa R Yanek  27 Lewis C Becker  27 Sharon Kardia  28 Jiang He  29 Robert Kaplan  30 Susan R Heckbert  19   31 Nicholas L Smith  19   31   32 Kerri L Wiggins  33 Donna K Arnett  34 Marguerite R Irvin  35 Hemant Tiwari  35 Adolfo Correa  36 Laura M Raffield  37 Yan Gao  38 Mariza de Andrade  39 Jerome I Rotter  4 Stephen S Rich  40 Ani W Manichaikul  40 Barbara A Konkle  20 Jill M Johnsen  20   41 Marsha M Wheeler  42 Brian S Custer  43 Ravindranath Duggirala  44   45 Joanne E Curran  44   45 John Blangero  44   45 Hongsheng Gui  46   47 Shujie Xiao  46   47 L Keoki Williams  46   47 Deborah A Meyers  48 Xingnan Li  49 Victor Ortega  50 Stephen McGarvey  51 C Charles Gu  52 Yii-Der Ida Chen  4 Wen-Jane Lee  53 M Benjamin Shoemaker  54 Dawood Darbar  55 Dan Roden  56 Christine Albert  57 Charles Kooperberg  58 Pinkal Desai  59   60 Thomas W Blackwell  1 Goncalo R Abecasis  1   61 Albert V Smith  1 Hyun M Kang  1 Rasika Mathias  27 Pradeep Natarajan  14   62   63 Siddhartha Jaiswal  64 Alexander P Reiner  58   19 Alexander G Bick  65 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium
Affiliations

The genetic determinants of recurrent somatic mutations in 43,693 blood genomes

Joshua S Weinstock et al. Sci Adv. .

Abstract

Nononcogenic somatic mutations are thought to be uncommon and inconsequential. To test this, we analyzed 43,693 National Heart, Lung and Blood Institute Trans-Omics for Precision Medicine blood whole genomes from 37 cohorts and identified 7131 non-missense somatic mutations that are recurrently mutated in at least 50 individuals. These recurrent non-missense somatic mutations (RNMSMs) are not clearly explained by other clonal phenomena such as clonal hematopoiesis. RNMSM prevalence increased with age, with an average 50-year-old having 27 RNMSMs. Inherited germline variation associated with RNMSM acquisition. These variants were found in genes involved in adaptive immune function, proinflammatory cytokine production, and lymphoid lineage commitment. In addition, the presence of eight specific RNMSMs associated with blood cell traits at effect sizes comparable to Mendelian genetic mutations. Overall, we found that somatic mutations in blood are an unexpectedly common phenomenon with ancestry-specific determinants and human health consequences.

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Figures

Fig. 1.
Fig. 1.. Identification of recurrent somatic mutations from TOPMed whole genomes.
(A) Identification of RNMSMs. (B) The allele frequency spectrum of somatic mutations. These represent the frequency of sets of variants with the given allele count. (C) The average number of RNMSMs stratified across age bins with 95% CIs. (D) The histogram of the slopes of the individual RNMSMs with age. (E) Enrichments of chromHMM annotations across blood epigenomes from Roadmap Epigenomics. Enrichments are defined as the log odds ratio (OR) multiplied by the −log10 (P value) from the Fisher’s exact test. Treg, regulatory T cell; TH, T helper cell; IL-17pp, interleukin-17pp; PMA, phorbol 12-myristate 13-acetate; MACS, magnetic-activated cell sorting; Tmem, T memory.
Fig. 2.
Fig. 2.. Somatic genotype PCs are associated with genetic ancestry.
(A) Global ancestry labels, as estimated from RFMix, are regressed on the first five sPCs in a multinomial regression with a study indicator included as a covariate. The ORs are estimated with East Asian ancestry as the reference level. (B) A scatterplot of sPC4 and sPC5, with colors indicated by the RFMix global ancestry label. (C) A circular genome plot where the angle indicates genomic position, and the radius within a given track indicates the allele frequency, which ranges from 0 to 0.12. A separate track is plotted for the allele frequencies computed separately in European, Sub-Saharan African, and East Asian genomes, respectively, which are colored by the legend indicated in (B). The size of points indicates the fixation index (Fst) estimate, where larger points have larger Fst values.
Fig. 3.
Fig. 3.. Germline determinants of RNMSM burden.
(A) Manhattan plot from GWAS of RNMSM burden, computed using SAIGE. Germline variants included had a minor allele count ≥ 600 and were distinct from the set of RNMSMs. (B) Genetic determinants of individual RNMSMs are primarily in “cis,” where cis is defined as within 2 Mb of the RNMSM. Genomic coordinates of the associated linkage disequilibrium (LD)-clumped germline variants are plotted on the x axis, and coordinates of the RNMSMs are plotted on the y axis. Points falling along the diagonal indicate cis associations. Tick marks along the y axis indicate positions of the RNMSMs from the LD-clumped associations.
Fig. 4.
Fig. 4.. RNMSMs associate with blood cell traits.
Associations between eight RNMSMs (q values < 0.05) with harmonized blood cell traits in 2996 individuals. Units correspond to an increase of an SD of the phenotype associated with an increase in the VAF value of an RNMSM by 0.10. WBC, white blood cell; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume.

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