Correction for Kessler et al., De novo mutations across 1,465 diverse genomes reveal mutational insights and reductions in the Amish founder population


Correction

GENETICS
Correction for “De novo mutations across 1,465 diverse genomes
reveal mutational insights and reductions in the Amish founder
population,” by Michael D. Kessler, Douglas P. Loesch, James
A. Perry, Nancy L. Heard-Costa, Daniel Taliun, Brian E. Cade,
Heming Wang, Michelle Daya, John Ziniti, Soma Datta, Juan C.
Celedón, Manuel E. Soto-Quiros, Lydiana Avila, Scott T. Weiss,
Kathleen Barnes, Susan S. Redline, Ramachandran S. Vasan,
Andrew D. Johnson, Rasika A. Mathias, Ryan Hernandez, James
G. Wilson, Deborah A. Nickerson, Goncalo Abecasis, Sharon R.
Browning, Sebastian Zöllner, Jeffrey R. O’Connell, Braxton D.

Mitchell, National Heart, Lung, and Blood Institute Trans-Omics
for Precision Medicine (TOPMed) Consortium, TOPMed Pop-
ulation Genetics Working Group, and Timothy D. O’Connor,
which first published January 21, 2020; 10.1073/pnas.1902766117
(Proc. Natl. Acad. Sci. U.S.A. 117, 2560–2569).
The authors note that a data error resulted in incorrect values

for the 3mer count totals shown in Fig. 1B and in Fig. S4 of the SI
Appendix. This error does not affect the conclusions of the article.
The corrected Fig. 1 and its legend appear below. The SI Appendix
has been corrected online to show the corrected Fig. S4.

C>TT>A T>CC>GC>AT>G

Mutation Type

3mers

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A B

Fig. 1. Distribution of single-base and 3-mer mutation types across SNV DNM call set. (A) The distribution of single-base mutation type counts across our SNV
DNM call set is shown. Colors represent mutation type, and stars represent associations with paternal age (red, P < 0.05 after Bonferroni correction). (B) The
counts across our DNM call set for each of 96 3-mer mutation types is shown. Colors represent the center base mutation, and are the same as those in A. Stars
represent associations with paternal age (red, P < 0.05 after Bonferroni correction).

Published under the PNAS license.

Published March 1, 2021.

www.pnas.org/cgi/doi/10.1073/pnas.2102070118

PNAS 2021 Vol. 118 No. 10 e2102070118 https://doi.org/10.1073/pnas.2102070118 | 1 of 1

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De novo mutations across 1,465 diverse genomes
reveal mutational insights and reductions in
the Amish founder population
Michael D. Kesslera,b,c,d, Douglas P. Loescha,b,c, James A. Perryb,c, Nancy L. Heard-Costae,f, Daniel Taliung, Brian E. Cadeh,i,
Heming Wangh,i, Michelle Dayaj, John Zinitik, Soma Dattak, Juan C. Celedónl, Manuel E. Soto-Quirosm, Lydiana Avilam,
Scott T. Weissk,n, Kathleen Barnesj, Susan S. Redlineh,o,p, Ramachandran S. Vasanf, Andrew D. Johnsonf,q,
Rasika A. Mathiasr,s, Ryan Hernandezt, James G. Wilsonu, Deborah A. Nickersonv, Goncalo Abecasisw,
Sharon R. Browningx, Sebastian Zöllnery,z, Jeffrey R. O’Connellb,c, Braxton D. Mitchellb,c,aa, National Heart,
Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) Consortium1, TOPMed Population Genetics
Working Group2, and Timothy D. O’Connora,b,c,d,3

aInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201; bDepartment of Medicine, University of Maryland School
of Medicine, Baltimore, MD 21201; cProgram for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD 21201;
dUniversity of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore,
MD 21201; eDepartment of Neurology, Boston University School of Medicine, Boston, MA 02118; fFramingham Heart Study, Framingham, MA 01702;
gDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109; hDivision of Sleep
and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA 02115; iProgram in Medical and Population Genetics, Broad Institute, Cambridge,
MA 02142; jDepartment of Medicine, University of Colorado Denver, Aurora, CO 80045; kChanning Division of Network Medicine, Department of Medicine,
Brigham and Women’s Hospital, Boston, MA 02115; lDivision of Pediatric Pulmonary Medicine, University of Pittsburgh School of Medicine, Pittsburgh,
PA 15213; mDepartment of Pediatrics, Hospital Nacional de Niños, 10103 San José, Costa Rica; nDepartment of Medicine, Harvard Medical School, Boston,
MA 02115; oDivision of Sleep Medicine, Harvard Medical School, Boston, MA 02115; pDivision of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel
Deaconess Medical Center, Boston, MA 02215; qPopulation Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute,
The Framingham Heart Study, Framingham, MA 01702; rDivision of Allergy and Clinical Immunology, The Johns Hopkins School of Medicine, Baltimore,
MD 21224; sBloomberg School of Public Health, The Johns Hopkins University, Baltimore, MD 21218; tQuantitative Life Sciences, McGill University, Montreal,
QC H3A OG4, Canada; uDepartment of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216; vDepartment of Genome
Sciences, University of Washington, Seattle, WA 98195; wSchool of Public Health, University of Michigan, Ann Arbor, MI 48109; xDepartment of Biostatistics,
University of Washington, Seattle, WA 98195; yDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109; zDepartment of Psychiatry,
University of Michigan, Ann Arbor, MI 48109; and aaGeriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center,
Baltimore, MD 21201

Edited by Stephen T. Warren, Emory University School of Medicine, Atlanta, GA, and approved December 17, 2019 (received for review February 19, 2019)

De novo mutations (DNMs), or mutations that appear in an individual
despite not being seen in their parents, are an important source of
genetic variation whose impact is relevant to studies of human evo-
lution, genetics, and disease. Utilizing high-coverage whole-genome
sequencing data as part of the Trans-Omics for Precision Medicine
(TOPMed) Program, we called 93,325 single-nucleotide DNMs across
1,465 trios from an array of diverse human populations, and used
them to directly estimate and analyze DNM counts, rates, and spectra.
We find a significant positive correlation between local recombina-
tion rate and local DNM rate, and that DNM rate explains a substan-
tial portion (8.98 to 34.92%, depending on the model) of the
genome-wide variation in population-level genetic variation from
41K unrelated TOPMed samples. Genome-wide heterozygosity
does correlate with DNM rate, but only explains <1% of variation.
While we are underpowered to see small differences, we do not
find significant differences in DNM rate between individuals of
European, African, and Latino ancestry, nor across ancestrally dis-
tinct segments within admixed individuals. However, we did find
significantly fewer DNMs in Amish individuals, even when com-
pared with other Europeans, and even after accounting for paren-
tal age and sequencing center. Specifically, we found significant
reductions in the number of C→A and T→C mutations in the
Amish, which seem to underpin their overall reduction in DNMs.
Finally, we calculated near-zero estimates of narrow sense herita-
bility (h2), which suggest that variation in DNM rate is significantly
shaped by nonadditive genetic effects and the environment.

de novo mutations | Amish | mutation rate | recombination | diversity

De novo mutations (DNMs) appear constitutively in an indi-vidual despite not being seen in their parents, and their
identification and study are critically important to our un-
derstanding of human genomic evolution (1–11). For example, it
is necessary to understand the rate at which DNMs accumulate

in order to calibrate evolutionary models of species divergence.
DNMs are also implicated in many diseases, including rare genetic
disorders (8, 12, 13) and common complex diseases, such as au-
tism and schizophrenia (13–15). Early studies indirectly inferred
mutation rate estimates from patterns of rare Mendelian diseases

Author contributions: M.D.K., N.L.H.-C., B.E.C., M.D., J.C.C., M.E.S.-Q., L.A., S.T.W.,
K.B., S.S.R., R.S.V., A.D.J., R.A.M., R.H., J.G.W., D.A.N., G.A., S.R.B., S.Z., J.R.O., B.D.M.,
N.H.L.a.B.I.T.-O.f.P.M.T.C., T.P.G.W.G., and T.D.O. designed research; M.D.K., D.P.L., and
J.R.O. performed research; M.D.K. and J.R.O. contributed new reagents/analytic tools;
M.D.K., D.P.L., J.A.P., N.L.H.-C., D.T., B.E.C., H.W., M.D., J.Z., S.D., J.C.C., M.E.S.-Q., S.T.W.,
K.B., A.D.J., R.A.M., R.H., G.A., S.R.B., S.Z., J.R.O., B.D.M., and T.D.O. analyzed data;
and M.D.K. and T.D.O. wrote the paper.

The authors declare no competing interest.

This article is a PNAS Direct Submission.

This open access article is distributed under Creative Commons Attribution-NonCommercial-
NoDerivatives License 4.0 (CC BY-NC-ND).

Data deposition: Raw sequence data are available through dbGaP as part of the TOPMed
program, and is available via study-specific accessions. These are: Whole Genome Se-
quencing and Related Phenotypes in the “Framingham Heart Study” (phs000974) and
“NHLBI TOPMed, Genetics of Cardiometabolic Health in the Amish” (phs000956), “NHLBI
TOPMed: The Genetics and Epidemiology of Asthma in Barbados” (phs001143), “NHLBI
TOPMed: The Cleveland Family Study (WGS)” (phs000954), and “NHLBI TOPMed: The
Genetic Epidemiology of Asthma in Costa Rica” (phs000988). Centralized alignments
and variant calls are also available via these dbGaP accessions. After working with the
TOPMed Consortium, we have created DNM call-set files with chromosome, position,
reference allele, and alternative allele, as well as meta data files with paternal age,
maternal age, sequencing center, European ancestry proportion, African ancestry pro-
portion, Native American ancestry proportion, and self-reported ancestry. The cohort-
specific files are available upon request.
1A complete list of the National Heart, Lung, and Blood Institute Trans-Omics for Precision
Medicine (TOPMed) Consortium can be found in SI Appendix.

2A complete list of the TOPMed Population Genetics Working Group can be found in
SI Appendix.

3To whom correspondence may be addressed. Email: timothydoconnor@gmail.com.

This article contains supporting information online at https://www.pnas.org/lookup/suppl/
doi:10.1073/pnas.1902766117/-/DCSupplemental.

First published January 21, 2020.

2560–2569 | PNAS | February 4, 2020 | vol. 117 | no. 5 www.pnas.org/cgi/doi/10.1073/pnas.1902766117

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http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001143
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or from DNA substitution rates between species (4, 16, 17). More
recently, modern sequencing technologies have enabled the use of
pedigree data to directly estimate the number of new mutations
found across the genome (17–21). These pedigree-based studies
have identified both paternal and maternal age effects (8, 22), and
most recently estimate contributions of 1.51 and 0.37 DNMs per
year of paternal and maternal age, respectively (19). While these
effects are often explained on the basis of DNA replication errors,
recent studies have found that DNA repair processes are likely to
be major contributors to the mutations that accrue in both paternal
and maternal gametes (21, 23–25). Some of this repair-associated
mutation accumulation has been found to be due to maternal age-
dependent DNA damage in oocytes and to maternal age-dependent
postzygotic mutation increases (25). These studies have also iden-
tified specific mutation patterns, such as C→G transversions, that
strongly associate with DNA double-strand breaks and repair
(24, 25). In addition, these repair-associated mutations have
been found to cluster together in distinct patterns, to pre-
dominate in certain genomic regions, and to be associated with
recombination (21, 23, 24), which has itself been shown to in-
fluence mutation rates (26–28).
Other features have also been reported to influence variation

in mutation rates across the genome. GC content was recently
shown to directly increase single-nucleotide and structural mu-
tation rates, and was also shown to increase recombination rates
(29). Chromatin structure has also been shown to associate with
DNA mutations, with DNA replication times associating signif-
icantly with point mutations (30), and nucleotide positioning
found to significantly modulate mutation rate (31). Recent work
has tied a number of these features together by providing reso-
lute and individualized recombination maps, and using them to
demonstrate a positive relationship between maternal age and
the rates and locations of meiotic crossovers (32). Specifically,
older mothers have increased recombination that also shifts to-
ward late replicating regions and low GC regions, and numerous
loci seem to genetically influence meiotic recombination. Other
recent work evaluates nucleotide content, histone and chromatin
features, replication timing, and recombination to provide one of
the clearest pictures to date of the factors that shape genome-
wide variability in the human mutation rate (23). With regard to
base content, Amos (33) has proposed the “Heterozygote Instability”
hypothesis, which challenges the assumption that population size and
mutation rate are independent, and suggests their interdependence
on the basis of heterozygosity. According to this hypothesis, the
occurrence of gene conversion events at heterozygous sites during
meiosis could locally increase mutation rates, and Amos (34) uses
substitution rates to provide support for this. Yang et al. (35) test
this hypothesis using parent–offspring sequencing of Arabidopsis,
rice, and honey bee, and show support for the relationship between
heterozygosity and mutation rate by demonstrating an ∼3.5-fold
higher mutation rate in heterozygotes compared with homozygotes,

and mutation occurring closer to heterozygous sites and crossover
events.
While identifying these mutational correlates have helped us

better understand the biological processes that drive mutation,
genetic estimates of mutation rate are one-half the magnitude of
those originally inferred phylogenetically (8, 17–19, 36–38). This
has raised questions about the accuracy of these genetics esti-
mates, as well as about the accuracy of human evolutionary time
points calculated using phylogenetic estimates. While it has been
proposed that the failure of genetic methods to account for
postzygotic mutations in the parent might bias estimates down
and partly explain this discrepancy (19), recent work using sibling
recurrence suggests only minor mutation rate estimate increases
when accounting for a substantial portion of these mutations
(39). This discrepancy has also raised the possibility that muta-
tion rates have evolved more rapidly than previously assumed,
and that molecular clock-type analyses are therefore flawed (17,
36). For example, analyses of base pair substitution patterns have
identified mutational differences between human populations,
and showed most notably that the rate of TCC→TTC transitions
appears to have increased in Europeans thousands of years ago
for some finite period of time (40, 41).
These findings provide a rationale for how mutation rates

might differ between human populations. However, since most
studies of DNMs have used data from small cohorts of individ-
uals with predominantly European ancestry (8, 10, 42), little is
known about the role of DNMs in the evolution and health of
populations of predominantly non-European ancestry, and it is
unclear whether DNM rates vary across different human pop-
ulations. To address this and other questions about mutation, we
used a high-coverage whole-genome sequencing (WGS) dataset
generated by the National Heart, Lung, and Blood Institute
(NHLBI) Trans-Omics for Precision Medicine (TOPMed) pro-
gram (43) to directly estimate and analyze DNM accumulation
across multiple human ancestries and populations. After ana-
lyzing genome-wide patterns of mutation using a call set of
93,325 single-nucleotide variant (SNV) DNMs, we compared
DNM counts and rates across five TOPMed cohorts that rep-
resent European, African, and Native American (Latino) an-
cestry individuals, and that include Amish individuals from a
founder population with European ancestry. We also estimate
the correlation between heterozygosity and SNV DNM count,
and then test whether mutation rate is a heritable trait in an-
ticipation of using genome-wide association studies (GWAS) to
look for mutation rate-modifying loci.

Results
TOPMed Dataset and Positive Correlation between DNMs and Parental
Age. Using WGS data for 1,465 individuals and their parents from
the TOPMed initiative (43), we identified a DNM call set and
compared DNM accumulation rates across ancestral background
(Table 1). The analyzed individuals belong to five TOPMed co-
horts with varied ancestral backgrounds: 1) The Amish, which are
an isolated European founder population; 2) the Barbados Asthma
Genetics Study (BAGS), which consists of individuals with pre-
dominantly African ancestry; 3) the Cleveland Family Study (CFS),
which consists of both European and African American individuals;
4) the Genetic Epidemiology of Asthma in Costa Rica and the
Childhood Asthma Management Program, which are collectively
referred to as the CRA cohort and consist of admixed Latino in-
dividuals; and 5) the Framingham Heart Study (FHS), which con-
sists of individuals with European American ancestry. For our
analyses, we treated the CFS study as two separate cohorts (signi-
fied as CFS_AFR and CFS_EUR, respectively). These make up our
six analysis cohorts (Amish, BAGS, CFS_AFR, CFS_EUR, CRA,
FHS) (Table 1). After removing samples with DNM counts that
were extreme outliers (often due to pedigree errors) (SI Appendix),
we were left with a DNM call set of 93,325 SNVs across 1,449

Significance

Here we provide the most diverse human de novo mutation
call set to date, and use it to quantify the genome-wide re-
lationship between local mutation rate and population-level
rare genetic variation. While we demonstrate that the human
single-nucleotide mutation rate is similar across numerous
human ancestries and populations, we also discovered a re-
duced mutation rate in the Amish founder population, which
shows that mutation rates can shift rapidly. Finally, we find
that variation in mutation rates is not heritable, which sug-
gests that the environment may influence mutation rates more
significantly than previously realized.

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individuals. This equates to an average of about 64.4 (95% CI:
63.6 to 65.2) mutations per individual, which is consistent with
previous findings (8, 22, 24). These 1,449 individuals come
from 1,201 independent nuclear families, and for our analysis
we treated each child and their two parents as a trio. To control
for any potential confounding effects from this sibling data, we
repeated all of our analyses that focused on per individual
DNM measures after randomly choosing one child per family.
The results from these repeated analyses are qualitatively the
same as those from our full analysis, and for simplicity, we only
report results from analyses run with our full dataset (except
when noted within our heritability models).
Using this call set, we calculated per sample mutation rate

estimates as SNV DNM count divided by the number of auto-
somal bases with depth ≥10 and quality ≥30, which serves as a
good representation of the number of bases evaluated for DNMs
by our filter heuristic (see SI Appendix for additional details).
This results in a mean SNV DNM rate estimate of 1.215 × 10−8

(95% CI: 1.201 × 10−8 to 1.230 × 10−8) mutations per base pair
per generation. While our DNM rate estimate is slightly lower
than previous genetic estimates (see SI Appendix for additional
details), our estimate is generally concordant with recent genetic
estimates (19, 20, 37), and lends support to the accuracy of our
filtering approach. As expected based on previous studies (8, 19),
we found a highly significant association between DNM rate per
individual and paternal age (linear regression, R2 = 0.410, P <
2.98 × 10−162) (SI Appendix, Fig. S1A), which provides an addi-
tional degree of validation for our approach and our call set.
While the high correlation in our dataset between paternal and
maternal age makes it difficult to evaluate the separate effect of
each on DNM count, linear modeling does succeed in identifying
significant paternal and maternal age effects that are consistent
in magnitude with those of recent studies (1.35 mutations per
year of father’s age, and 0.42 mutations per year of mother’s age)
(19, 21, 22). DNM totals per individual did not differ significantly
on the basis of the sex of the individual for whom the DNMs were
called (SI Appendix, Fig. S1B), their year of birth (SI Appendix,
Fig. S1C), or the age of their DNA (i.e., the individual’s age) at
the time of collection (SI Appendix, Fig. S1D).

DNM Mutation Types and Patterns. Using variant effect predictor
annotations (44) from the TOPMed Consortium (43) for loss-of-
function (LOF) variants found within the genomes of the 1,201 single
offspring trios, we found 8,499 LOF variants (SI Appendix, Fig.
S2). These 1,201 offspring also have 77,015 DNMs. Looking at
the intersection of these DNM and LOF call sets, we found 66
LOF DNM mutations. Therefore, 0.086% of DNMs are LOF
mutations, while 0.778% of LOF variants are DNMs, which
suggests that DNMs contribute significantly each generation to
the total number of segregating LOF variants. Using the set of
93,099 DNMs that remain after counting only once those DNMs
that are recurrent in siblings, and mapping our DNM set over to
hg38 so as to be compatible with recent genetic maps, we found
that 1,582 (1.7%) DNMs are in coding bases, which is consistent with
the proportion of the genome comprised of coding sequence.
We then used this set of 93,099 DNMs to evaluate mutational

patterns across the genome. Consistent with previous findings
(19, 21), the most frequent mutation types in our DNM call set
are C→T (43.31%) and T→C (25.42%) transitions (Fig. 1A and
Table S1). All mutations showed robust associations with pa-
ternal age, which we used as a proxy for the total parental age
effect due to the previously described confounding between pa-
ternal and maternal age (Fig. 1A, red stars). While DNM-type
composition is similar across the genome (SI Appendix, Fig. S3),
regions with significant deviations from the mean may serve as good
candidates for the identification and investigation of atypical mu-
tational processes (chromosomes [chrs] 2, 9, 16, and 19) (SI Ap-
pendix, Fig. S3B). The influence of base context on mutational
frequency can be better appreciated when viewing each DNM as a
3-mer by considering the bases in the human reference genome
immediately preceding and following each mutation (19, 21, 40, 41,
45) (Fig. 1B). For example, T→C mutations preceded by an A
appear to be more common than might have been predicted based
on their central base pair mutation-type alone. While CpG to TpG
transitions already comprise four of the five most common 3-mer
DNMs, their mutational potential is particularly highlighted by
normalizing each 3-mer DNM count for background 3-mer frequency,
which demonstrates an excess of CpG to TpG transitions com-
pared to the expectation based on genome frequency (SI Appendix,

Table 1. Cohort characteristics and mutation estimates

Study

name

TOPMed

project

No. of

children (after

outlier removal)

No. of

Nuclear

Families (after

outlier

removal)

Average

paternal

age at conception

(years ± SD)

Average

maternal

age at

conception

(years ± SD) Populations

Mutation

rate

Mutation

rate 95% CI

Parental

age effect

Parental

age effect

95% CI

Variance

explained

AMISH Genetics of

Cardiometabolic

Health in the Amish

115 (115) 59 (59) 29.24 ± 5.10 27.03 ± 5.24 Old Order

Amish large

extended

pedigrees

1.13E-08 1.084E-08,

1.176E-08

1.313 0.901, 1.726 0.398

BAGS Barbados Asthma

Genetics Study

210 (208) 125 (124) 31.74 ± 7.12 27.27 ± 5.92 African

Ancestry

(from Barbados)

1.27E-08 1.233E-08,

1.311E-08

1.503 1.273, 1.733 0.727

CFS_AFR The Cleveland

Family Study

31 (31) 22 (22) 28.27 ± 6.93 24.96 ± 4.76 African

American

1.20E-08 1.090E-08,

1.315E-08

1.932 1.283, 2.582 0.806

CFS_EUR The Cleveland

Family Study

100 (99) 52 (52) 29.97 ± 5.01 27.97 ± 4.92 European

American

1.22E-08 1.177E-08,

1.270E-08

1.613 1.223, 2.003 0.716

CRA Genetic Epidemiology

of Asthma in Costa

Rica (GACRS), Childhood

Asthma Management

Program (CAMP)

316 (310) 278 (276) 29.74 ± 6.56 26.77 ± 6.02 Costa Rican

(Latino/

Hispanic)

1.22E-08 1.187E-08,

1.250E-08

1.591 1.410, 1.772 0.696

FHS Whole Genome

Sequencing and

Related Phenotypes

in the Framingham

Heart Study

693 (686) 678 (672) 29.50 ± 5.28 27.39 ± 4.77 European

American

1.21E-08 1.189E-08,

1.232E-08

1.739 1.564, 1.913 0.495

Study cohorts and metadata are described. The six cohorts used in this study derive their names (“Study name”) from five TOPMed projects (“TOPMed project”), and represent a diversity of
populations and ancestries (“Populations”). Sample sizes are shown (“No. of children”) along with mean paternal and maternal age values per cohort (after the removal of DNM outliers). BAGS
individuals have the highest average paternal age, which seems to explain their elevated DNM rate, and CFS_AFR individuals have the lowest maternal ages. The estimated average mutation rate and
95% CI per cohort is also shown (calculated after removal of outliers), as are parental age effects (estimated using paternal age alone, due to confounding between paternal and maternal ages), and the
proportion of DNM variance explained by this parental age effect after accounting for Poisson variation.

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Fig. S4). This postnormalization-inflated mutational pattern can
also be seen for other CpG mutations (SI Appendix, Fig. S4, CpG to
GpG in gold, and CpG to ApG in salmon).
To evaluate the relationship between mutation and recombina-

tion, we used our DNM call set to estimate local mutation rate
across 1-Mb windows, and then tested the correlation between
these rates and local recombination rate estimates derived from the
recently published deCODE genetic map (32). We found a significant
positive correlation in which regions of higher recombination have
more DNMs (SI Appendix, Fig. S5A) (R2 = 0.057, P = 3.4 × 10−36)
(see SI Appendix for additional details). This relationship varies
across the autosomes, with chrs 1, 7, 8, 9, and 16, 18, 19 showing
strong positive correlations in which recombination explains as
much as 39% of the variation in DNM rates (SI Appendix, Fig.
S5B). Conversely, this correlation is either absent or limited across
a number of the other autosomes.
Evolutionary theory predicts that mutation, natural selection,

and genetic drift act in concert to shape genomic variation, and
that recombination can shape variation by breaking up linked
variants and enabling selection to act on them more efficiently
(46, 47). Therefore, we tested the degree to which local recombination
and mutation rate estimates explain the distribution of genomic vari-
ation seen at the human population level. To do this, we used
genomic variation data from the TOPMed Consortium that was
ascertained by performing WGS on ∼41K diverse unrelated in-
dividuals (43). We first calculated the total number of rare variants
(AF < 0.005) within 1-Mb windows across the genome, and used
standardized z-scores derived from these counts as a measure of
localized levels of recent human genomic variation. Local recombi-
nation and mutation rates explain up to 37.85% (95% CI: 33.13 to
42.58) of the local genomic variation in segregating rare variants (P <
7.25 × 10−277) (Fig. 2). As expected, DNM rate explains the majority
of this signal, accounting for 30.52% (95% CI: 26.08 to 34.92) of the
variation in rare variants after regressing out the effects of local
recombination rate (P < 9.0 × 10−214). Regions with the highest
levels of rare variation, such as megabases 1 to 7 on chr8 and 1 to
9 and 78 to 90 on chr16, have high DNM rates, and are largely com-
prised of regions in the top 10% of recombination values (Fig. 2).
When including both GC content and replication timing in the
model as covariates, we can explain up to 41.28% (95% CI:
37.13 to 45.16) of the variation in rare variant totals. Even after
adjusting for recombination rate, GC content, and replication

timing, which is conservative given the interconnected relation-
ships between these variables, DNMs still explain 27.95% (95%
CI: 23.90 to 32.30) of the variation in rare variants. Similar models
per chromosome can explain between 60% and 72% of the vari-
ation in rare variant totals across chromosomes that have seg-
ments with high local DNM rates (chrs 8, 9, 16, and 19) (Table
S2), with DNM rate as the dominant explanatory variable. In-
terestingly, replication timing seems to best explain variation in
rare variant levels across chrs 12 and 14.
In contrast to these rare variant models, when we ran similar

models with standardized z-scores derived from the counts of
common variants as our dependent variable, the most dominant
explanatory variable is recombination rate. That is, when using
recombination rate, GC content, replication timing, and DNM
rate as covariates to predict the distribution of common variation
across the genome, we explain a similar proportion of the vari-
ation in common variants as we did in rare variants (R2 = 0.3942,
95% CI: 0.3486 to 0.4389), but the relative contributions of
DNM rate and recombination rate seem to invert. In the former
set of models of the genomic distribution of rare variation, re-
combination plus replication timing explain 11.8% (95% CI:
9.85 to 14.00) of variation, whereas DNM rate explains 31.56%
(95% CI: 27.41 to 35.80) of the remaining variation. Conversely,
in the models of the genomic distribution of common variation,
recombination rate plus replication timing explain 30.03% (95%
CI: 26.28 to 33.76) of variation, whereas DNM rate explains only
the remaining 12.47% (95% CI: 8.98 to 16.01). This is consistent
with evolutionary predictions, as common variants typically repre-
sent older variants, and their distribution has been shaped more by
recombination, selection, or drift than by mutation. These patterns
hold when using linear regression to implement an adjustment for
coding proportion and mappability concerns, with each model
(DNM rate, recombination rate, replication timing, and GC as
covariates) explaining about 32% of the variation in common
and rare variants, respectively. DNM rates still explain the majority
of the variation in rare variant levels (17.77%, 95% CI: 13.28 to
22.57, after adjusting for recombination rate, replication timing, and
GC content), and recombination rates still explain the largest portion
of variation in common variant levels (14.32%, 95% CI: 11.51 to 17.45,
after adjusting for DNM rate, replication timing, and GC content).

The Relationship between DNM Rates and Heterozygosity. In testing
the relationship between heterozygosity and DNM rate across all
individuals, we found a significant positive correlation (P <
0.002) (SI Appendix, Fig. S6A). However, while this persists after
adjusting for parental age (P < 0.025), heterozygosity only ex-
plains ∼0.3% of the variation in DNM count. This relationship is
also entirely driven by the Amish population, and no significant
relationship persists when removing Amish individuals from the
analysis that adjusts for parental age (P > 0.13). Furthermore, we
found no relationship between heterozygosity and DNM count
when looking intracohort (see SI Appendix, Fig. S6B for a repre-
sentation of this across individuals from the FHS, our largest
cohort). The number of kilobases in an individual’s genome found
within runs of homozygosity (ROH) also correlates significantly
with DNM rate (P < 2.0 × 10−4) (SI Appendix, Fig. S7A) and
persists after adjusting for parental age (P < 3.1 × 10−4). However,
as was the case with heterozygosity, this is entirely driven by the
Amish, who happen to be outliers for ROH. Similar to hetero-
zygosity, additional analysis suggests that this association is confounded
and not reflective of a causal relationship. ROH only explains
∼1% of the variation in DNMs, and its association with DNMs is
no longer significant after filtering out the Amish (P > 0.14, R2 =
0.0017) or when evaluating intracohort (P ≥ 0.112).

DNM Rate Comparisons Across Ancestrally Diverse Cohorts. When
comparing DNM rate across all six ancestrally diverse cohorts (Table
1), we found significant differences (P < 1.6 × 10−3, ANOVA) (Fig. 3

Fig. 1. Distribution of single-base and 3-mer mutation types across SNV
DNM call set. (A) The distribution of single-base mutation type counts across
our SNV DNM call set is shown. Colors represent mutation type, and stars
represent associations with paternal age (red, P < 0.05 after Bonferroni
correction). (B) The counts across our DNM call set for each of 96 3-mer
mutation types is shown. Colors represent the center base mutation, and
are the same as those in A. Stars represent associations with paternal age
(red, P < 0.05 after Bonferroni correction).

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and Table 1), even after accounting for parental age (P < 0.01,
ANOVA). However, this appears to be driven by the Amish, who
have significantly fewer DNMs per individual than the other cohorts
(P < 0.05 between the Amish and the four largest populations
[BAGS, CFS_EUR, CRA, FHS] after correcting for multiple test-
ing). A reduced DNM rate persists in the Amish even when com-
paring DNM rates across population within regions of the genome
estimated not to be in ROH in either parent’s genome (P < 0.033,
ANOVA) (SI Appendix, Fig. S7B). Therefore, while recent work
suggests heterozygosity as a mutational driver, and increased ROH in
the Amish is an appealing explanation for their reduced mutation
rate, on the whole, differences in heterozygosity or ROH do not
seem to be sufficient to explain the reduced mutation rate in
the Amish.
To control for sequencing center differences that could potentially

influence the number of DNMs identified, we performed a sub-
analysis comparing DNMs between cohorts that were sequenced
at the same center. This subanalysis did not include individuals
from the BAGS cohort, since they alone were sequenced directly
by Illumina. This within-sequence center analysis also revealed
significantly lower DNM rates in the Amish compared with indi-
viduals from FHS (European ancestry; P < 0.005, ANOVA), even
after adjusting for parental age effects (P < 0.002, ANOVA),
whereas we found no significant differences in DNM rates before
and after adjusting for parental age effects between individuals
from CFS_EUR (European ancestry), individuals from CFS_AFR
(African ancestry), and individuals from CRA (Latino ancestry;
P > 0.56, ANOVA). While the BAGS cohort appears to have an
elevated mutation rate at first glance, this is a result of them having
higher average paternal ages (Table 1). While we are underpowered
to see smaller differences in mutation rate, we have reasonable power
to see moderate to large differences between all populations other
than CFS_AFR (SI Appendix, Fig. S8), so these results do suggest that
differences between ancestry thought to influence the DNM rate

(such as heterozygosity, demographic history, and so forth) are not
driving large differences in the accumulation of DNMs.
The amount of variation in DNM rate attributable to parental

age effects after adjustment for Poisson variation is lower in our
dataset (57.7%) than previously reported (>80%) (8). This
seems to be due to the fact that there is heterogeneity in the
parental age effects across our sample set (Table 1), which itself
becomes a contributor to the variation in DNM rate. In accor-
dance with this, and consistent with the reduced DNM rate we
found in the Amish, we estimate a lower parental age effect in
the Amish than in the other cohorts (Table 1). However, we have
limited power to detect this difference, which only reaches sig-
nificance when comparing effect sizes determined by Poisson
regression between the Amish and FHS (P < 0.035).

Evaluation of Batch Effects and Technical Artifacts. To evaluate the
robustness of the observation of a DNM rate reduction in the
Amish to technical artifacts, we took a multipronged approach.
First, we validated the consistency of our DNM call set by using
two offspring samples from the FHS cohort that had undergone
repeat sequencing at another sequence center (University of
Washington [UW]), as well as one offspring sample from the Amish
cohort that has a monozygotic twin in our dataset. For these three
samples, we evaluated the proportion of DNMs that are called as
heterozygous sites in the validation sample, as well as the concordance
between DNMs called in the initial and validation samples. In two of
the three samples, 100% of DNMs (one Amish, one FHS) were
called as heterozygous sites in the validation sample. In the third
sample (FHS), 5 of 50 DNMs were called as homozygous refer-
ence in the validation sample. However, upon further inspection,
all five of these DNMs had read counts and genotype likelihoods
suggesting that they were heterozygous sites. With regard to DNM
concordance, 95.3% of DNMs were called in both the initial and

DNM RATE

D
e
n
si

ty

5.0e 09 1.5e 08 2.5e 08

0.0e+00

5.0e+07

1.0e+08

1.5e+08

2.0e+08

Mean Rate = 1.13e 08

Mean Rate = 1.272e 08

Mean Rate = 1.203e 08

Mean Rate = 1.223e 08

Mean Rate = 1.218e 08

Mean Rate = 1.211e 08

Anova:
P = 0.0015808

AMISH
BAGS
CFS_AFR
CFS_EUR
CRA
FHS

Fig. 3. DNM rates across diverse cohorts. DNM rates per individual show
significant differences across cohort, which are driven by a reduction in
the Amish.

Fig. 2. City plot of rare variation, recombination rate, and DNM rate across
the genome. The relationship between DNM rate (blue to red color range),
rare variation (y axis, ranging from −5.83 to 9.06 z-scores), and recombina-
tion rate (z axis, ranging from 2.73 × 10−14 to 6.12 cM/Mb) across the ge-
nome (x axis, dotted vertical lines divide autosomes 1 to 22) is shown. In
moving from low to high rare variation levels across the y axis, a blue to red
gradient can be seen, which reflects the significant correlation between
DNM rate and population-level rare variation. Furthermore, regions with
high DNM rates and high variation levels generally have taller bars, which
reflects the positive relationship between DNM rate, variation level, and
recombination (a few exceptions to this can be seen as taller blueish bars).
Regions with the highest variation levels in the genome, such as those on
chromosomes 8 and 16, have the highest DNM rates.

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validation samples. Notably, this number is significantly higher
(98.2%) when excluding the five DNMs noted above as being im-
properly called as homozygous reference by the variant caller de-
spite strong evidence of heterozygosity, which is unlikely to be
a common issue.
We also assessed how a number of quality control measures

vary across population. Specifically, we compared measures of
average genome-wide sequence depth (SI Appendix, Fig. S9A),
average depth of variants called autosome-wide and passing filters
(SI Appendix, Fig. S9B), number of bases genome-wide with a
minimum depth ≥ 10 (SI Appendix, Fig. S9C), genome-wide Ts/Tv
ratio (SI Appendix, Fig. S9D), number of million reads genome-
wide per sample (SI Appendix, Fig. S9E), percent of the genome
covered (SI Appendix, Fig. S9F), percent of the genome with a
depth of 10 (SI Appendix, Fig. S9G), average depth of genome-wide
bases with Q ≥ 30 (SI Appendix, Fig. S9H), percent of the genome
with Q ≥ 20 (SI Appendix, Fig. S9I), and the number of autosomal
bases with depth ≥ 10 and Q ≥ 30 (SI Appendix, Fig. S9J). On the
whole, our study cohorts feature similar quality metrics, and the
Amish are not a notable outlier in any metric (SI Appendix, Fig. S9).
While the BAGS cohort seems to be a moderate outlier in Ts/Tv
and number of reads (SI Appendix, Fig. S9 D and E), and a more
pronounced outlier with regard to percent genome covered and
number of bases with Q ≥ 30 (SI Appendix, Fig. S9 F–H), adjusting
for these (as well as all of the other) quality control measures did not
significantly influence our signal. Similarly, when we used the number
of bases per sample with depth ≥ 10 + Q ≥ 30 to normalize our
DNM counts into DNM rate estimates, as described earlier, we ac-
tually saw an increased signal of a reduced DNM rate in the Amish.

DNM Rates by Ancestral Proportions of Individuals from Admixed
Populations. To test more directly how interancestral differences
might influence DNM accumulation, we used linear regression to
assess the relationships between DNM rate and African ancestry
proportion, and DNM rate and Native American ancestry pro-
portion. Within the BAGS, CFS_AFR, and CRA individuals,
which represent the three cohorts with significant African ances-
try, we found no significant correlation between African ancestry
proportion and DNM rate after adjusting for parental age (P >
0.36, ANOVA) (SI Appendix, Fig. S10A). This absence of a re-
lationship persists when looking only within BAGS individuals
(P > 0.72) or only within CRA individuals (P > 0.19), and there
was also no relationship when comparing Native American an-
cestry proportion and DNM rate before (P > 0.76) or after (P >
0.088) adjusting for parental age within Latino ancestry individ-
uals from the CRA cohort (SI Appendix, Fig. S10B).
Since we are also underpowered to see small-to-moderate

differences in single-base or 3-mer mutation frequencies between
populations, we instead tested the correlation between 3-mer
mutation type and European ancestry proportion within all indi-
viduals of admixed African ancestry (BAGS, CRA, CFS_AFR
cohorts). This should increase our power to detect mutational
associations with ancestral background, and specifically allow us to
test for a signal of ancestral association for the TCC→TTC 3-mer
mutation that was previously shown to positively correlate with
European ancestry (40, 41). Notably, we did not find a significant
association between TCC→TTC 3-mer mutation and European
ancestry (P ≥ 0.60). Interestingly, when testing this association in
individuals from BAGS, which is our cohort with the highest average
proportion of African ancestry, we found a negative correlation (β =
−4.08, 95% CI: −6.70 to −1.70, P < 0.0014, uncorrected, Poisson
regression) (SI Appendix, Fig. S11). Overall, this result is interesting
when contrasted with recent evidence that an ancestry-specific pulse
increased the occurrence of these 3-mer mutations in Europeans (41).

Shifts in the Mutational Spectrum of the Amish Founder Population.
To further assess the reduced DNM rates seen in the Amish, we
compared DNM counts for each single-base mutation type across

cohort. ANOVA revealed differences in mutation counts across
cohort, with differences in C→A (P < 0.03), T→C (P < 8 × 10−4),
and T→G (P < 3.2 × 10−8) mutations being most significant (SI
Appendix, Fig. S12). These differences are largely driven by de-
creases in these mutations in the Amish and increases in these
mutations in individuals from the BAGS cohort. To further eval-
uate these differences while controlling for sequencing center, we
compared mutations between the Amish and FHS cohorts, and
found the reduced number of C→A (P < 1 × 10−2) and T→C (P <
3 × 10−4) mutations in the Amish to persist (SI Appendix, Fig. S13).
Interestingly, the reduction in T→C mutations explains about 45%
of the overall DNM reduction seen in the Amish.

Local Ancestry Analysis Does Not Identify Ancestrally Distinct
Mutation Rates. To look more closely at whether mutation rates
differ between ancestries, we compared the rates of DNM ac-
cumulation across ancestrally distinct genomic segments within
admixed samples. Using local ancestry assignments, we counted
DNMs across all possible diploid ancestral segment combina-
tions (e.g., homozygous African, heterozygous African and Eu-
ropean, and so forth) (SI Appendix, Fig. S14), and limited each
comparison to individuals with at least 80 Mb of each diploid
category. The resulting intraindividual comparisons allow for the
control of unmeasured variables that may otherwise confound
interindividual analyses, such as environmental exposures that
might influence mutation rate. Therefore, the only variable that
should differ between diploid segments is their ancestral origin,
which should allow us to isolate and test for any effects of this
ancestral background on local sequence context (i.e., this is a test
for cis effects). After estimating DNM rates per individual by
normalizing DNM counts by diploid category base total (i.e.,
rates per base pair), we did not find evidence of ancestry specific
differences in DNM rate (SI Appendix, Fig. S15). While we did find
a significant reduction in the mutation rate in African/European
segments compared with African/African segments (P = 7.33 ×
10−4), other comparisons between African and European seg-
ments showed no differences. Given the absence of consistent
differences in DNM rate across local ancestral segments, these
results do not provide compelling support for the existence of
ancestry-based differences in DNM rate.

The Heritability of DNM Accumulation. To assess whether we could
use GWAS to detect any genetic loci that might underpin the
interindividual variation we see in DNM accumulation, we first
wanted to test for what proportion of the variation in DNM
accumulation was explained by genetics. One measure of this is
narrow-sense heritability (h2), which represents the proportion of
phenotypic variation explained by additive genetic effects (48),
and which can be used as a null model for running associations at
every locus (i.e., each locus is tested for the proportion of h2 that
it explains). Treating the number of DNMs per individual as a
phenotype reflecting DNM accumulation, we used the MMAP
software (49) to estimate the h2 of DNM accumulation via a
restricted maximum likelihood-based (REML) method (SI Appendix).
When using all samples across all cohorts, we used paternal age at
offspring’s conception, maternal age at offspring’s conception, and
cohort label as fixed effects, and a genetic relatedness matrix esti-
mated from the sequence data. We estimate an h2 = 0.0 (SE = 0.028),
and we reaffirmed a paternal age effect (P < 8.51 × 10−55), a maternal
age effect (P < 6.42 × 10−7), and an effect of Amish cohort status
(P < 1.13 × 10−3) (Table 2 and SI Appendix, Table S3). When running
this base model on each cohort separately, h2 estimates are zero for
each cohort other than CRA (h2 = 0.42, P = 0.122) (Table S4).
However, the h2 result for CRA was 0.0 when further restricting to
only unrelated subjects (Table S5).
To evaluate whether the portion of mutational variation

explained by parental age might be shaped by additive genetic
factors, we repeated these heritability models without including

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parental ages as fixed effects. Interestingly, these models estimated
nonzero heritability (h2 = 0.137, P < 3.93 × 10−2), with the BAGS
and CFS_EUR cohorts contributing most significantly to this
nonzero heritability estimate (Tables S3 and S4). However, when
repeating analyses with only one offspring randomly chosen per
nuclear family (i.e., removing sibling data) (Table S6), or with
only unrelated samples (Table S5), h2 is zero in nearly all models.
Exceptions include the Amish and CFS_EUR per cohort analy-
ses, but these have notably small sample sizes as well as non-
significant (P > 0.129) h2 estimates with wide SEs. Overall, this
suggests that the significant nonzero heritability we see when not
adjusting for parental age effects is driven by the shared parental
contribution of siblings and or confounding due to relatedness, and
not by genotypic similarity. While statistical power is a concern as a
result of our sample sizes, we do have reasonable power (≥0.6) to
detect moderate h2 across all samples as well as across larger co-
horts and cohort combinations (SI Appendix, Fig. S16). Simulations
across the known pedigree of nearly 6,000 Amish individuals also
suggest that we would only expect to estimate zero heritability
across our samples if the true heritability was ≤0.18 (empirical P <
0.05) (SI Appendix, Fig. S17).

Discussion
Here we call DNMs across 1,465 individuals from diverse cohorts
sequenced through the TOPMed program. Our call set is de-
termined by a filtering heuristic that is similar to previous ap-
proaches (19), its specificity is supported by a Bayesian approach
implemented in the TrioDeNovo software (50) that called
99.39% of our DNMs, and its overall accuracy is supported by
validation sequencing across two repeated samples and one pair
of monozygotic twins. In addition to this, quality assessments
done as part of the TOPMed Consortium sequence analysis efforts
used repeatedly sequenced samples to demonstrate similar geno-
typic concordance rates within and between sequence centers (43).
While this was done for both variants passing calling filters and
variants failing calling filters, variants passing calling filters have
even larger levels of concordance than those failing calling filters,
which reflects the efficacy of these calling filters in identifying
errors. This suggests that even the differences in some quality
measures mentioned above, such as the average depth of Q30

bases in BAGS, are already effectively handled by our variant
calling quality control. This is directly in accordance with the facts
that controlling for these covariates doesn’t qualitatively change
our signal, and that using DP10Q30 metrics to estimate normal-
ized per sample mutation rates in fact increases our signal of a
reduced mutation rate in the Amish. Other TOPMed Consortium
results using principal components analysis to evaluate the influ-
ence of sequence center on genotypic variance further support a
very limited influence of sequencing center (43).
Despite the predominance of C→T mutations among DNMs,

we did not find any differences between populations or ancestral
backgrounds in C→T mutations. This is notable given the high
frequency of this mutational class, and the concomitant in-
creased power to detect differences. This suggests that processes
driving cytosine deamination are fairly conserved, and is con-
sistent with the assertion by others that CpG mutation rates may
serve as a better “molecular clock” than the base substitution rates
that have previously been used (17, 40, 45, 51). Similarly, we did
not find differences in other single-base and 3-mer mutation types
across ancestral background. While we did have somewhat limited
statistical power to see smaller effect sizes (SI Appendix, Fig. S8),
we did have reasonable power to see moderate-to-large differ-
ences when comparing most populations, and potentially even had
good power to see small-to-moderate differences when comparing
our larger populations. For example, when comparing DNM rates
between the BAGS and FHS cohorts (our largest African ancestry
and European ancestry cohorts, respectively), we had 80% power
to see an effect size difference reflected by a Cohen’s d of 0.3. This
represents an effect size small enough so that only 61.79% of the
DNM rates in the population with the larger DNM rate would be
larger than the mean in the other population (Cohen’s U3), there
would only be a 58.4% chance that a random individual from the
population with the larger DNM rate has a higher DNM rate than
a random person from the other population (probability of su-
periority), and there would be an 88.08% overlap between the
DNM rate distributions of the two populations (52). This is gen-
erally considered a small-to-moderate effect size, and helps to
appreciate what kind of DNM rate differences we have power to
see. However, we likely only have this level of power to detect
differences in the overall SNV DNM rate (and possibly certain

Table 2. Heritability model across all cohorts

Variable Value Variable Value

Mean DNMs 64.39 βpaternal_age P value 8.51E-55
SD 14.94 βmaternal_age 0.45
Minimum 14.00 βmaternal_age SE 0.09
Maximum 164.00 βmaternal_age P value 6.42E-07
Kurtosis 3.64 βAmish −4.06
DNMs > 3 SD 12.00 βAmish SE 1.24
DNMs < 3 SD 1.00 βAmish P value 1.13E-03
Sample size 1,389 βBAGS 0.78
h2 0.00 βBAGS SE 1.13
h2 P value 0.00 βBAGS P value 0.49
h2 P value SE 0.03 βCFS_AFR 1.63
Proportion variance explained by covariates 0.45 βCFS_AFR SE 2.33
Adjusted proportion variance explained by vovariates 0.45 βCFS_AFR P value 0.48
ln likelihood −4,067.84 βCRA −0.39
Intercept 13.36 βCRA SE 1.33
Intercept SE 1.79 βCRA P value 0.77
Intercept P value 1.27E-13 βFHS −0.32
βpaternal_age 1.32 βFHS SE 0.78
βpaternal_age SE 0.08 βFHS P value 0.68

Results are shown from heritability models run with MMAP across all samples with paternal and maternal
ages available (n = 1,389). Heritability is estimated as zero (h2 = 0.00), with an SE of 0.03). These models confirm
that paternal age at offspring’s conception (P = 8.51 × 10−55), maternal age at offspring’s conception (P = 6.42 ×
10−7), and Amish cohort status (P = 1.13 × 10−3) are significantly correlated with DNM total per individual.

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single-base mutation rates), as we are notably more underpowered
to detect differences in 3-mer rates due to their reduced count
sizes. Nonetheless, we did not see significant mutation rate dif-
ferences between populations with distinct ancestral backgrounds,
which suggests that overall and single-base mutation rate differ-
ences are not likely to be large.
When leveraging quantitative estimates of genetic ancestry within a

framework with increased power to see potential differences, we also
did not find significant associations between European ancestry pro-
portion and 3-mer mutation type across individuals from admixed
cohorts (i.e., BAGS, CFS_AFR, CRA). This result is interesting
when contrasted with recent findings of Europeans having had a
significantly higher rate of TCC→TTC mutations in the past (40,
41). First, the recent identification of a significant batch effect in the
1000 Genomes data used to identify these mutational differences
raises some questions about their legitimacy (53). Nevertheless,
assuming TCC→TTC mutations did in fact increase in Europeans
at some point in the past, our results suggest that there is currently
no difference, which is consistent with findings that the increase in
Europeans happened as a pulse that ended around 2,000 y ago
(41). It is also possible that differences in the rate of this mutation
across populations has been influenced by differential shifts in mean
parental age at conception, which is consistent with recent findings by
Jónsson et al. (19) and Agarwal and Przeworski (54) that this muta-
tion is enriched for sex differences. Ultimately, additional research is
needed to better understand the evolutionary history of TCC→TTC
and other mutations across divergent human populations.
The correlations we found between local recombination and DNM

rates are concordant with research showing that processes underpin-
ning recombination are mutagenic (26, 27, 32), and our models
comparing population-level variation with recombination rates, DNM
rates, replication timing, and GC content add further resolution to the
relationship between these intertwined covariates and genomic evo-
lution. Areas of the genome with the highest local DNM rate are in
regions with high recombination that have recently been identified as
featuring specific recombination-based processes that drive mutation
(24). Alternatively, regions with high recombination rate estimates
that have low DNM rates (Fig. 2, tall blue bars) are good candidates
for the identification of genomic features that may explain a reduced
mutation rate, including contexts in which recombination itself po-
tentially drives less mutagenesis. While recent research into the re-
lationship between DNMs and fine-scale local recombination rates
identified up to 50-fold increases in DNM rates around meiotic
crossover events (32), the fact that these events are rare is likely why
we only explain about 5% of the variation in DNMs on the basis of
recombination. This recombination study also found 35 loci influ-
encing recombination rate, which may lead one to ask why we did not
find any signal of genetic influence over DNM accumulation (i.e., we
estimate h2 = 0), given that recombination is mutagenic and associates
with DNMs in our dataset. First, this is likely due to the fact that
recombination only explains a small portion of the overall variation in
DNMs, and small effect-size modulators of recombination are unlikely
to be detected as correlates of DNM rate without very large sample
sizes. Second, we adjusted for parental age effects, which might
eliminate from many of our heritability models the portion of varia-
tion in DNM rate influenced by meiotic recombination. Furthermore,
multivariate models have been able to explain >50% of the variation
in regional mutation rates on the basis of genomic features, such as
GC content, exon density, sex chromosome status, recombination, and
distance to telomeres (55). Given that we excluded most centromeric/
telomeric segments from our analyses, didn’t consider exon density or
sex chromosome status, and used metadata from multiple studies
across multiple populations, we expected our model to explain a lower
proportion of the variation in mutation rate than these models.
Nonetheless, these and other covariates potentially explain the exis-
tence of high recombination regions with low DNM rates.
Via these models, we also estimated the quantitative contri-

butions of mutation and recombination to population-level variation,

and were able to explain up to 38% of the variation across the ge-
nome in population-level rare variation using local recombination
and DNM rates, and 41% when including replication timing as an
additional covariate in the model. The remaining proportion of
variation is likely the result of a combination of genetic drift, selection,
or measurement error. Of this explained variation, ∼30.5% is attrib-
utable to variation in local DNM rates. Regions with high mutation
rates have the most variation in the genome (e.g., chr8, chr16), and
often feature high recombination rates that likely increase mutation
accumulation. Numerous recent studies have found complementary
results that describe the types of mutations predominating in high-
variation regions, and identify the likely sources of these mutations
(21, 23, 24). Some of this recent work demonstrated that clustered
DNMs contribute very significantly to the clustering of genomic
SNPs (specifically C→G SNPs), although clustered DNMs are es-
timated to make up less than 2.5% of DNMs (24) and may only
have limited impact on the findings presented here. Here, we show
that single-nucleotide DNMs contribute profoundly to the entire
genomic distribution of common and rare single-nucleotide vari-
ation, but that recombination is a larger driver of the variation in
common variant levels than is mutation. This is consistent with
evolutionary theory about the ability of recombination to discon-
nect linked variants and enable selection to act more efficiently, as
well as the expectation that common variants are older and have
been subjected to nonmutational evolutionary forces for longer.
While the Heterozygosity Instability hypothesis (33, 34, 56, 57)

and recent related findings (35) predict that increasingly het-
erozygous genomes will have higher mutation rates, we only
found evidence of a modest relationship between heterozygosity and
DNM rate. Furthermore, we did not find the differences in DNM
rate between ancestral background that differences in heterozygosity
across ancestry would have predicted, nor a significant correlation
between heterozygosity and DNM rate between samples from the
same population. While runs of homozygosity do seem to shape
DNM rates more so than levels of heterozygosity, this relationship
is entirely driven by the Amish, and additional analysis of mutation
rate across non-ROH–bearing regions of the genome suggests that
this is likely due to confounding between the high ROH in the
Amish and whatever else is reducing their mutation rate. Therefore,
while this finding of a potential relationship between ROH and
DNM rate does raise the possibility that the absence of hetero-
zygosity may drive significant reductions in DNM rate, additional
research is needed to more directly address this question.
In our heritability analyses, we estimate the heritability of DNM

rates to be zero across nearly all models. Due to sample size con-
cerns, we conducted power analyses and used simulations to esti-
mate the likelihood of estimating zero heritability across increasing
values of true heritability. For the full dataset, most power calcu-
lation approaches (SI Appendix, Fig. S16 A–D) suggest we have
good power to detect low to moderate levels of heritability [i.e.,
(h2 ≥ 0.1)], although we likely have very little power to detect her-
itability across the smaller cohorts. Similarly, given sample sizes
similar to that of our full dataset, a trait with an h2 ≥ 0.13 will be
estimated as zero ≤20% of the time. Even in circumstances with
limited power, we would still expect h2 estimates centered off of zero
when working with traits that have true heritability greater than zero.
Therefore, given our simulation results, and that our h2 estimates
are consistently centered around 0, we find it more likely that the
true heritability of DNM rates across our dataset are low or
approaching zero. While future initiatives might still consider large-
scale GWAS efforts in order to search for local mutation events
under genetic control, and larger studies with increased power are
needed to confirm the heritability of DNM rates, we did not find
evidence that genetic similarity explains the variation in mutation
rates we see within or between populations.
Despite being a founder population that diverged from other

Europeans only very recently (58), the Amish show a mutation rate
reduction of about 7%. This reduction persists when controlling

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for parental age effects and sequence quality metrics, and seems
to be driven by reductions in C→A and T→C mutations. Together
with our estimation that DNM rate has zero narrow-sense heri-
tability, this suggests that the environment may play a bigger role
in modulating the mutation rate than previously appreciated. The
Amish lifestyle features preindustrial era aspects, and while
modern Amish communities are diverse and have adapted to the
usage of some modern items, they continue to limit the influence of
technology in their daily lives (59, 60). Given this, it is possible that
the Amish are exposed to fewer mutagens, and that this “clean
living” may be partially responsible for the reduced mutation rate
we report here. For example, studies have shown that rural areas,
such as those similar to the areas occupied by the Amish, have
fewer carcinogens and mutagens than industrialized areas (61–63).
Recent analysis of mutation patterns has also called into question
the canonical view that DNMs rise predominantly from replicative
errors, and suggests that exogenous mutagens may play a larger
role in mutation accumulation than previously appreciated (25). If
the Amish do in fact experience less environmentally driven mu-
tagenesis, then one would predict a significant reduction in the rate
of cancer in the Amish. This is exactly what has been found in
multiple Old Order Amish populations, with a particularly large
reduction in cancer rates found in men (64, 65). A similar re-
duction in overall mortality has been found in Amish men com-
pared with FHS men, which is hypothesized to be due to lifestyle
factors, such as reduced tobacco use and increased physical activity
(59). Given that DNM mutation in sperm is the single largest driver
of DNM accumulation, an Amish environment that potentially
limits DNA damage in Amish men is consistent with a lower DNM
rate. In accordance with this, the Amish have the lowest estimated
parental age effect (Table 1). This is also consistent with the recent
finding of significant variability in parental age effects across an-
cestrally similar families, which also suggests the possibility that
environmental factors influence DNM rates (66).
It is important to note that considerations of the environment

as a potential explanation for the reduced DNM rate we detect
in the Amish are entirely speculative, and that batch effects or
other technical artifacts remain possible despite our significant
efforts to control for them. Nonetheless, our findings as well as the
aforementioned context do suggest environmental influence as a
possible explanation, and one that is worthy of additional consid-
eration and follow-up. The fact that the only signal of DNM her-
itability we detect is driven by siblings when not accounting for
parental age effects further suggests that the environmental simi-
larity shared by siblings (including parental age effects) is signifi-
cantly influencing DNM rates. In sum, the mutational differences
we found in the Amish stand in contrast to the relative homoge-
neity seen across the other diverse human populations we analyzed,
and suggest that additional work is needed to better appreciate the
forces shaping human mutational processes at fine scales.

Methods
WGS was performed using samples previously collected and consented across 90
NHLBI-funded research projects as part of the TOPMed program (43). Using variant
data from a jointly called variant call set for samples from five of these TOPMed
research projects, we implemented a filtering heuristic to call 93,325 DNMs across
1,449 samples after the removal of outliers and pedigree errors. We then used two
resequenced samples and one monozygotic twin pair to validate our call set by

evaluating the proportion of DNMs in one sample that were called as heterozy-
gous sites in the second sample, and the percent of DNMs that were called by our
filtering approach in both the initial and validation samples. After demonstrating
high concordance between our call set and DNMs called with the TrioDeNovo
Bayesian mutation caller, we compared single-nucleotide DNM counts and rates
using ANOVA, t tests, and linear and Poisson regression. DNM rates were estimated
as the number of single-nucleotide DNMs autosome-wide divided by the number of
autosomal bases with depth ≥ 10 and quality ≥ 30. To test whether more hetero-
zygous genomes have a higher DNM rate, we calculated genome-wide heterozy-
gosity scores for each of the 1,449 samples included in our analysis, and compared
these with estimated DNM rates per individual using linear regression. For com-
parison within only a particular ancestry, samples were subset down accordingly. For
genome-wide analyses, we used the University of California, Santa Cruz’s liftOver
tool (67) to lift our DNM call set over to hg38 coordinates, and calculated local DNM
rates, local recombination rates (32), local replication timing rates (68), GC
content, and rare variation levels (43) for 1-Mb windows across the autosomes.
To call local ancestry across our samples, we first phased the data using the
Eagle2 algorithm as implemented in the Eagle software (69), combined WGS
data from the 1000 Genomes Project (70) with high coverage WGS data from
the Peruvian Genome Project (71), and used this genotype data as reference
input to the RFMIX software (72). Heritability was estimated using MMAP
(49) and GCTA (73) with DNM count per individual as the quantitative mutation
phenotype. Additional methodological details are described in SI Appendix.
Data can be accessed via dbGaP (https://www.ncbi.nlm.nih.gov/gap/) using the
“phs” accession numbers listed for each study cohort in the Acknowledgments.

ACKNOWLEDGMENTS. Whole genome sequencing (WGS) for the Trans-Omics
in Precision Medicine (TOPMed) program was supported by the National
Heart, Lung, and Blood Institute (NHLBI). WGS for “NHLBI TOPMed: Whole
Genome Sequencing and Related Phenotypes in the Framingham Heart
Study” (phs000974) and “NHLBI TOPMed: Genetics of Cardiometabolic Health
in the Amish” (phs000956) were performed at the Broad Institute of MIT and
Harvard (3R01HL092577-06S1, 3R01HL121007-01S1). WGS for “NHLBI TOPMed:
The Genetics and Epidemiology of Asthma in Barbados” (phs001143) was per-
formed by Illumina, Inc. (3R01HL104608-04S1). WGS for “NHLBI TOPMed: The
Cleveland Family Study (WGS)” (phs000954) and “NHLBI TOPMed: The Ge-
netic Epidemiology of Asthma in Costa Rica” (phs000988) were performed at
the University of Washington Northwest Genomics Center (3R01HL098433-
05S1, 3R37HL066289-13S1). Centralized read mapping and genotype calling,
along with variant quality metrics and filtering, were provided by the
TOPMed Informatics Research Center (3R01HL-117626-02S1). Phenotype har-
monization, data management, sample-identity quality control, and general
study coordination, were provided by the TOPMed Data Coordinating Cen-
ter (3R01HL-120393-02S1). We gratefully acknowledge the studies and par-
ticipants who provided biological samples and data for TOPMed. A full list of
TOPMed collaborators can be found at https://www.nhlbiwgs.org/topmed-
banner-authorship. M.D.K. was supported by NIH grant T32CA154274. M.D.K.
and T.D.O. were supported by funding from the Center for Health Related
Informatics and Bioimaging at the University of Maryland School of Medicine,
institutional support for the Institute for Genome Sciences and Program in
Personalized Genomic Medicine at the University of Maryland School of Med-
icine, NIH Genomic Commons Award OT3 OD025459-01, NHLBI Trans-Omics for
Precision Medicine Program High-performance Grant U01 HL137181-01, and
National Human Genome Research Institute Genomic Innovator Grant 1 R35
HG010692-01 (to T.D.O.). D.P.L. was supported by NIH T32HL007698. This work
was further supported by grants to the Amish research program (R01 HL121007,
R01 AG18728, and U01 HL072515); a grant for the study of Asthma in Costa
Rica (1P01HL132825-01 to S.T.W.); grants to study sleep apnea (R01-
HL113338 to S.S.R., R35-HL135818 from Sleep Research Society Foundation
to S.S.R. and with support for H.W., and K01-HL135405 from American
Thoracic Society Foundation to B.E.C.); and Framingham Heart Study Grant
HHSN268201500001 (to R.S.V.). Sequencing was funded by grants to the
Genetics of Cardiometabolic Health in the Amish study (3R01HL121007-
01S1), The Genetics and Epidemiology of Asthma in Barbados study
(3R01HL104608-04S1), The Cleveland Family Study (3R01HL098433-05S1),
The Genetic Epidemiology of Asthma in Costa Rica study (3R37HL066289-
13S1), and Whole Genome Sequencing and Related Phenotypes in the Framing-
ham Heart Study (3R01HL092577-06S1).

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