Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2016 Feb;48(2):126-133.
doi: 10.1038/ng.3469. Epub 2015 Dec 14.

Timing, Rates and Spectra of Human Germline Mutation

Collaborators, Affiliations
Free PMC article

Timing, Rates and Spectra of Human Germline Mutation

Raheleh Rahbari et al. Nat Genet. .
Free PMC article


Germline mutations are a driving force behind genome evolution and genetic disease. We investigated genome-wide mutation rates and spectra in multi-sibling families. The mutation rate increased with paternal age in all families, but the number of additional mutations per year differed by more than twofold between families. Meta-analysis of 6,570 mutations showed that germline methylation influences mutation rates. In contrast to somatic mutations, we found remarkable consistency in germline mutation spectra between the sexes and at different paternal ages. In parental germ line, 3.8% of mutations were mosaic, resulting in 1.3% of mutations being shared by siblings. The number of these shared mutations varied significantly between families. Our data suggest that the mutation rate per cell division is higher during both early embryogenesis and differentiation of primordial germ cells but is reduced substantially during post-pubertal spermatogenesis. These findings have important consequences for the recurrence risks of disorders caused by de novo mutations.


Figure 1
Figure 1. Pedigrees of sequenced families
Identifiers and relationship between the individuals in the three families in this study. Individuals that were sequenced are symbolised by full circles and squares, other individuals by dotted circles and squares. Age of mother and father at the conception of each child and phasing information are summarised in the table. SFHS5165321 was only used for the part of the analysis related to mosaicism.
Figure 2
Figure 2. Paternal age vs. number of de novo mutations
The number of DNMs has been corrected to take into account genomic regions inaccessible to our methods. Red: Family 244. Yellow: Family 569. Blue: Family 603. Gray areas denote the regions covered by the 95% confidence interval of the intercept and slope of the linear regression line for each separate family. We note that the confidence intervals for families 244 and 603 do not overlap for younger fathers.
Figure 3
Figure 3. Detection of mutations mosaic in parents
(A) Simulation of detection power for ranges of mosaicism levels in the parents blood using the Miseq depth of coverage for all the de novo mutations (n=768). For mean validation coverage (Miseq platform) of 567X in the parents, we have >0.94 power to detect mosaicism of 2% and higher in the parents blood. (B) Comparison of parental Alt ratios between de novo mutations vs. germline mosaic sites. M is mosaic sites with significant excess of alt in the mother’s blood, P is the sites with significant excess of alt in the father’s blood, S is corresponding to the sites that are shared between the siblings but we could not detect any excess of alt allele in the either of the parents blood. SM/SP refer to the mosaic sites that are shared between the siblings and we have detected significant excess of alt in the mother’s blood (SM presented in pink dots) or father’s blood (SP shown in dark blue dot).
Figure 4
Figure 4. Mutational spectra
(A) Frequency of all mutation types in the catalogue of 6,570 high confidence DNMs (B) Difference in the frequency of maternal and paternal mutations for the subset of DNMs with phasing information (n=556) (C) Difference in the frequency of mutations of children fathers younger and older than 30 years (n=680). Error bars represent 95% confidence intervals.
Figure 5
Figure 5. Mutational spectrum and signatures
(A) High resolution mutational spectrum of de novo mutations. Each of the six possible point mutations is subdivided into 16 subclasses based on the 3′ and 5′ nucleotide flanking the mutation. We note that C:G>T:A and T:A>C:G transitions are more common. Within those categories, CpG sites are particularly frequent (B) Correlation of mutational signatures with observed mutations in mutational catalogue, correlation of each of the 30 signatures, with signatures 1 and 5 highlighted in orange (C) Combination of all possible pairs of signatures, with the combination of signatures 1 and 5 shown with an arrow.
Figure 6
Figure 6. Mutation rate model during gametogenesis
Comparison of mutation rate between spermatogenesis (blue-box) vs. oogenesis (red-box). μp and μm are mutation rate in paternal and maternal genome in respective order and mutation rate per each stage of gametogenesis is denoted by number. Gametogenesis is divided into three stages with different ranges of mutation rates. Stage 1: Pre-PGC specification (8-12 cell divisions in both maternal and paternal germline) ~0.2-0.6 mutations per haploid genome per cell division and this rate is the similar in both maternal and paternal gametogenesis, stage 2: post-PGC specification, in maternal germline there are ~20 cell divisions, in paternal germline there are ~24 cell divisions post-PGC up to puberty, mutation rate is similar at this stage in both sexes, (~0.5-0.7 mutations per haploid genome per cell division). Stage 3: post-puberty (only applicable to the paternal germline) sperm are continuously produced through the asymmetric division of self-renewing spermatogonial stem cells with ~23 cell divisions per year. The mutation rate falls to a range of ~0.09 to 0.17 mutations per haploid genome per cell division. This model is tentative and does not yet take all possible sources of uncertainty into account.

Similar articles

See all similar articles

Cited by 123 articles

See all "Cited by" articles


    1. Lindahl T, Wood RD. Quality control by DNA repair. Science. 1999;286:1897–905. - PubMed
    1. Hoeijmakers JH. Genome maintenance mechanisms for preventing cancer. Nature. 2001;411:366–74. - PubMed
    1. MacArthur DG, et al. Guidelines for investigating causality of sequence variants in human disease. Nature. 2014;508:469–76. - PMC - PubMed
    1. Scally A, Durbin R. Revising the human mutation rate: implications for understanding human evolution. Nat Rev Genet. 2012;13:745–53. - PubMed
    1. Michaelson JJ, et al. Whole-genome sequencing in autism identifies hot spots for de novo germline mutation. Cell. 2012;151:1431–42. - PMC - PubMed


    1. Ramu A, et al. DeNovoGear: de novo indel and point mutation discovery and phasing. Nat Methods. 2013;10:985–7. - PMC - PubMed
    1. Li H. Toward better understanding of artifacts in variant calling from high-coverage samples. Bioinformatics. 2014 - PMC - PubMed
    1. Koressaar T, Remm M. Enhancements and modifications of primer design program Primer3. Bioinformatics. 2007;23:1289–1291. - PubMed
    1. Li H, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25:2078–9. - PMC - PubMed
    1. Konfortov BA, Bankier AT, Dear PH. An efficient method for multi-locus molecular haplotyping. Nucleic Acids Res. 2007;35:e6. - PMC - PubMed

Publication types