The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
New observations on maternal age effect on germline de novo mutations
Open accessAbstract
Germline mutations are the source of evolution and contribute substantially to many health-related processes. Here we use whole-genome deep sequencing data from 693 parents–offspring trios to examine the de novo point mutations (DNMs) in the offspring. Our estimate for the mutation rate per base pair per generation is 1.05 × 10−8, well within the range of previous studies. We show that maternal age has a small but significant correlation with the total number of DNMs in the offspring after controlling for paternal age (0.51 additional mutations per year, 95% CI: 0.29, 0.73), which was not detectable in the smaller and younger parental cohorts of earlier studies. Furthermore, while the total number of DNMs increases at a constant rate for paternal age, the contribution from the mother increases at an accelerated rate with age.These observations have implications related to the incidence of de novo mutations relating to maternal age.
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Cites
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Rate of de novo mutations and the importance of father’s age to disease risk
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Self-citationHuman Genome Sequencing Using Unchained Base Reads on Self-Assembling DNA Nanoarrays
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Whole-Genome Sequencing in Autism Identifies Hot Spots for De Novo Germline Mutation
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Estimating the human mutation rate using autozygosity in a founder population.
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Cited by
Prevalence and architecture of de novo mutations in developmental disorders
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Parental influence on human germline de novo mutations in 1,548 trios from Iceland
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Evolution of the germline mutation rate across vertebrates
Bergeron, L. A., Besenbacher, S., Zheng, J., Li, P., Bertelsen, M. F., Quintard, B., Hoffman, J. I., Li, Z., St. Leger, J., Shao, C., et al. (2023) · Nature · doi:10.1038/s41586-023-05752-y · Cited by 346
Parent-of-origin-specific signatures of de novo mutations
Goldmann, J. M., Wong, W. S. W., Pinelli, M., Farrah, T., Bodian, D., Stittrich, A. B., Glusman, G., Vissers, L. E. L. M., Hoischen, A., Roach, J. C., et al. (2016) · Nature Genetics · doi:10.1038/ng.3597 · Cited by 334
Self-citationWhole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk
Zhou, J., Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0420-0 · Cited by 284
Metadata
- Authors
- Wong, W. S. W., Solomon, B. D., Bodian, D. L., Kothiyal, P., Eley, G., Huddleston, K. C., Baker, R., Thach, D. C., Iyer, R. K., Vockley, J. G., & Niederhuber, J. E.
- Venue
- Nature Communications
- Published
- 2016-01-19
- Volume
- 7
- Issue
- 1
- Pages
- 10486-10486
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2342840727
- Cited by
- 203
- References
- 9
- FWCI
- 20.79
- Type
- journal article
- Language
- en
- Open access
- gold
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