DOI: 10.5061/dryad.83bk3j9mw
title: The evolutionary dynamics and fitness landscape of clonal hematopoiesis

description: Somatic mutations acquired in healthy tissues as we age are major
 determinants of cancer risk. Whether variants confer a fitness advantage
 or rise to detectable frequencies by change remains largely unknown. Blood
 sequencing data from ∼50,000 individuals reveals how mutation,
 genetic drift and fitness shape the genetic diversity of healthy blood
 (clonal hematopoiesis). We show that positive selection, not drift, is the
 major force shaping clonal hematopoiesis, provide bounds on the number of
 hematopoietic stem cells, and quantify the fitness advantages of key
 pathogenic variants, at single-nucleotide resolution, as well as the
 distribution of fitness effects (fitness landscape) within commonly
 mutated driver genes. These data are consistent with clonal hematopoiesis
 being driven by a continuing risk of mutations and clonal expansions that
 become increasingly detectable with age.
description: This repository contains the necessary data files to accompany
 the code for analyses in the manuscript <strong>"The
 Evolutionary Dynamics and Fitness Landscape of Clonal
 Hematopoiesis"</strong>.  Accompanying code can be found in
 folders of the same corresponding name on the Blundell Lab Github page
 (10.5281/zenodo.3706791).    
 Data used for generation of figures/ analyses in main
 text: Figure 1: 
 <strong>Figure 1b (distribution of variants across
 DNMT3A)</strong>: DRYAD
 data folder: <em>Distribution of variants across DNMT3A - data
 files</em> 
 <strong>Figure 1e (VAF-density histrogram)</strong>:
 DRYAD data folder: <em>Distribution of variants
 across DNMT3A</em>. 
 Figure 2: <strong>Figure
 2a (fitness effects & mutation rates for top 20 most commonly
 observed CH variants)</strong>: DRYAD data
 folder: <em>Maximum likelihood estimations/ Fitness landscape of top
 20 most commonly observed variants in CH</em>.
 <strong>Figure 2b (distribution
 of fitness effects in key CH driver genes)</strong>: 
 DRYAD data folder: <em>Maximum likelihood estimations/
 Fitness landscape of key CH driver genes (DNMT3A, TET2, ASXL1,
 TP53)</em>. 
 Figure 3: predicted prevalence of CH as a function of
 age): DRYAD data
 folder: <em>Predicted prevalence of CH and double
 mutations</em>.  
 Code & data for generation of figures/ analyses
 in supplement: Supplementary Methods 1
 (data trimming): DRYAD data
 folder: <em>Trimming the data</em>. 
 Supplementary Methods 5 (study-specific mutation
 rates): DRYAD data
 folder: <em>Mutation rate calculations</em>.
 Supplementary Methods 6 (emergence of clones with
 multiple mutations): DRYAD data
 folder: <em>Predicted prevalence of CH and double
 mutations</em>. Supplementary
 Methods 7: <strong>Figure S9
 (DNMT3A R882H)</strong>: DRYAD data
 folder: <em>Maximum likelihood estimations/ DNMT3A R882H, DNMT3A
 nonsynonymous, all synonymous</em>. 
 <strong>Figure S10 (top 20 most commonly
 observed variants)</strong>: DRYAD data
 folder: <em>Maximum likelihood estimations/ Fitness landscape of top
 20 most commonly observed variants</em>. 
 <strong>Figure S11 (R882H vs
 R882HC)</strong>: DRYAD data
 folder: <em>Maximum likelihood estimations/ Fitness of DNMT3A R882H
 vs R882C</em>. 
 <strong>Figure S12 (nonsynonymous variants parameter
 estimation)</strong>: DNMT3A: 
 DRYAD data folder: <em>Maximum likelihood estimations/
 DNMT3A R882H, DNMT3A nonsynonymous, all synonymous</em>.
 TET2, ASXL1, TP53: 
 DRYAD data folder: <em>Maximum likelihood estimations/
 Fitness landscape of key CH driver genes (DNMT3A, TET2, ASXL1,
 TP53)</em>. 
 <strong>Figure S13 (synoymous variants
 parameter estimation)</strong>: DRYAD data
 folder: <em>Maximum likelihood estimations/ DNMT3A R882H, DNMT3A
 nonsynonymous, all synonymous</em>. 
 Supplementary Methods 8:
 <strong>Figure S16 (hitchhiker variants
 parameter estimation)</strong>: DRYAD data
 folder: <em>Maximum likelihood estimations/ DNMT3A R882H, DNMT3A
 nonsynonymous, all synonymous</em>. 
 Supplemetary Methods 9 (odds ratio of
 AML stratified by variant fitness): DRYAD
 data folder: <em>Highly fit variants are enriched in pre-AML blood
 samples</em>. Supplementary
 Methods 10 (age prevalence of R882H and R882C mutations):
 DRYAD data folder: <em>Age prevalence of DNMT3A
 R882H and R882C variants</em>. 
 Supplementary Methods 11 (parameter estimation for 10 commonly
 mutated CH genes): DRYAD
 data folder: <em>Predicted prevalence of CH and double
 mutants</em>. Supplementary Methods
 12: (estimating fitness effects of infrequently mutated sites)
 DRYAD data folder: <em>Estimating fitness
 effects of infrequently mutated sites</em>. 
 Supplementary Methods 13: (limitations of study size and
 sequencing limit) DRYAD data
 folder: <em>Limitations of study size and sequencing
 limit</em>.