Differences between germline genomes of monozygotic twins


Despite the important role that monozygotic twins have played in genetics research, little is known about their genomic differences. Here we show that monozygotic twins differ on average by 5.2 early developmental mutations and that approximately 15% of monozygotic twins have a substantial number of these early developmental mutations specific to one of them. Using the parents and offspring of twins, we identified pre-twinning mutations. We observed instances where a twin was formed from a single cell lineage in the pre-twinning cell mass and instances where a twin was formed from several cell lineages. CpG>TpG mutations increased in frequency with embryonic development, coinciding with an increase in DNA methylation. Our results indicate that allocations of cells during development shapes genomic differences between monozygotic twins.

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Fig. 1: The timing of postzygotic mutations.
Fig. 2: Number of postzygotic mutations per individual.
Fig. 3: Timing of pre-PGCS and pre-twinning mutations in twins.
Fig. 4: Number of mutations transmitted to the offspring and VAF of pre-PGCS mutations.
Fig. 5: The three-generation approach.
Fig. 6: Mutation classes of pre-PGCS versus trio mutations.
Fig. 7: Cell allocation in early human development.

Data availability

Access to these data is controlled; the sequence data cannot be made publicly available because Icelandic law and the regulations of the Icelandic Data Protection Authority prohibit the release of individual-level and personally identifying data. Data access can be granted only at the facilities of deCODE genetics in Iceland, subject to Icelandic law regarding data usage. Anyone wanting to gain access to the data should contact Kári Stefánsson (kstefans@decode.is). Data access consists of the lists of mutations identified in monozygotic twins with numbered proband identifiers. The lists of mutations are provided in Supplementary Data 13.

Code availability

The major components in our sequence data processing pipeline consist of publicly available software, notably Burrows–Wheeler Aligner-MEM for the alignment (https://github.com/lh3/bwa), Samtools for the processing of BAM files (http://samtools.github.io/), Picard for PCR duplication marking (https://broadinstitute.github.io/picard/) and GraphTyper for sequence variant calling (https://github.com/DecodeGenetics/graphtyper). The implementation of the phasing and imputation of sequence variants is described in the data descriptor32.


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We thank everyone who participated in our studies.

Author information




H.J., U.T., D.F.G. and K.S. wrote the manuscript with input from E.M., T.S., H.P.E., O.A.S., O.E., G.A.A., F.Z., E.A.H., I.J., A.G., Adalbjorg Jonasdottir, Aslaug Jonasdottir, D.B., G.L.N., O.T.M., G.M., B.V.H., A.H. and P.S. H.J., O.E. and E.A.H. analyzed the data. H.J., H.P.E., O.E., F.Z., E.A.H., A.G., G.M. and P.S. developed the methods. H.J., Adalbjorg Jonasdottir, Aslaug Jonasdottir, O.T.M. and G.L.N. performed the experiments. G.A.A. and I.J. provided samples and information. H.J., P.S., D.F.G. and K.S. designed the study.

Corresponding authors

Correspondence to Hakon Jonsson or Daniel F. Gudbjartsson or Kari Stefansson.

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Competing interests

H.J., H.P.E., O.A.S., O.E., G.A.A., F.Z., E.A.H., I.J., A.G., Adalbjorg Jonasdottir, Aslaug Jonasdottir, D.B., G.L.N., O.T.M., G.M., B.V.H., U.T., A.H., P.S., D.F.G. and K.S. are employed by deCODE genetics/Amgen.

Additional information

Peer review information Nature Genetics thanks Jeffrey Beck, Dorret Boomsma, Ziyue Gao, Brandon Johnson, and Amy Williams for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Histogram of the genome-wide sequence coverage of the twins.

Histogram of the genome-wide sequence, coverage of the twins. Note that the sequence coverage for the monoyzygotic twins was aggregated across several sequencing runs, and the aggregated sequence data were used for the subsequent analysis.

Extended Data Fig. 2 The genome-wide sequence coverage of the probands’ family members.

The genome-wide sequence coverage of the, probands’ family members. The family members of the probands were used to detect pre-PGCS mutations. Note, that if both twins of a pair have sequenced children then they will appear as ‘Proband’ and as ‘Twin’.

Extended Data Fig. 3 Number of children with a pre-PGCS mutation.

Number of children with a pre-PGCS mutation. a, We counted how many children have a pre-PGCS mutation with VAF higher than a cutoff. b, We restricted to children where at least one pre-PGCS mutation was detected.

Extended Data Fig. 4 The maximum VAF of pre-PGCS mutations per proband/mate pair.

The maximum VAF of pre-PGCS mutations per proband/mate pair. a, The maximum VAF of pre-PGCS mutations per proband/mate pair. b, The standard deviation of the maximum VAF per proband/mate pair against the average of the maximum VAF.

Extended Data Fig. 5 Alternative calculations of the slopes from the three-generation approach.

Alternative calculations of the slopes from the three-generation approach. a, Histogram of the slopes as Fig. 5e, except the slopes are transformed with atan. b, The slopes in three generation approach with swapped roles. Note that the reciprocal slopes are not defined for near constitutional probands due to zero sample variance.

Supplementary information

Supplementary Information

Supplementary Note and Tables 1–9

Reporting Summary

Supplementary Table 10

A summary of the simulation results in each scenario, compared to the relevant quantities in our observed data

Supplementary Data 1

The mutations identified by comparing the somatic tissues of the twins.

Supplementary Data 2

The mutations identified by the quad approach.

Supplementary Data 3

The mutations identified by the three generation approach.

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Jonsson, H., Magnusdottir, E., Eggertsson, H.P. et al. Differences between germline genomes of monozygotic twins. Nat Genet 53, 27–34 (2021). https://doi.org/10.1038/s41588-020-00755-1

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