Search bioRxiv⌕ Search

bioRxiv · 10.1101/2022.07.26.501520

The PREGCARE study: Personalized recurrence risk assessment following the birth of a child with a pathogenic de novo mutation

Abstract

Next-generation sequencing has led to a dramatic improvement in molecular diagnoses of serious pediatric disorders caused by apparently de novo mutations (DNMs); by contrast, clinicians ability to counsel the parents about the risk of recurrence in a future child has lagged behind. Owing to the possibility that one of the parents could be mosaic in their germline, a recurrence risk of 1-2% is frequently quoted, but for any specific couple, this figure is usually incorrect. We present a systematic approach to providing individualized recurrence risk stratification, by combining deep-sequencing of multiple tissues in the mother-father-child trio with haplotyping to determine the parental origin of the DNM. In the first 58 couples analysed (total of 59 DNMs in 49 different genes), the risk for 35 (59%) DNMs was decreased below 0.1% but for 6 (10%) couples it was increased owing to parental mosaicism - that could be quantified in semen (recurrence risks of 5.6-12.1%) for the paternal cases. Deep-sequencing of the DNM efficiently identifies couples at greatest risk for recurrence and may qualify them for additional reproductive technologies. Haplotyping can further reassure many other couples that their recurrence risk is very low, but its implementation is more technically challenging and will require better understanding of how couples respond to information that reduces their risks.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bernkopf, M., Abdullah, U. B., Bush, S. J., Wood, K., Ghaffari, S., Giannoulatou, E., Koelling, N., Maher, G., Thibaut, L. M., Williams, J., Blair, E. M., Blanco Kelly, F., Bloss, A., Burkitt-Wright, E., Canham, N., Deng, A. T., Dixit, A., Eason, J., Elmslie, F., Gardham, A., Hay, E., Holder, M., Homfray, T., Hurst, J. A., Johnson, D., Jones, W. D., Kini, U., Kivuva, E., Kumar, A., Lees, M. M., Leitch, H. G., Morton, J. E., Nemeth, A. H., Ramachandrappa, S., Saunders, K., Shears, D. J., Side, L., Splitt, M., Stewart, A., Stewart, H., Suri, M., Clouston, P., Davies, R. W., Wilkie, A. O., Goriel. 2022-07-27. The PREGCARE study: Personalized recurrence risk assessment following the birth of a child with a pathogenic de novo mutation. https://doi.org/10.1101/2022.07.26.501520

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Generation of a transgenic cephalopod

Coleoid cephalopods (cuttlefish, octopus, and squid) are marine mollusks with elaborate nervous systems that support a diverse repertoire of complex behaviors. These include the neural control of the color, pattern, and texture of the skin, facilitating both adaptive camouflage and innate patterning that may reflect internal state. The development of transgenic cephalopods expressing fluorescent proteins, optogenetic actuators, and reporters of neural activity would contribute a new and important technology to cephalopod biology. The generation of transgenic cephalopods, however, has remained a major challenge. Here, we report the development of stable transgenic dwarf cuttlefish (Ascarosepion bandense) expressing ubiquitous nuclear-localized mScarlet, a red fluorescent protein. We evaluated multiple strategies for transgenesis, and established cuttlefish lines using both CRISPR and the transposons Sleeping Beauty and Minos. The stable expression of transgenes enabled live imaging of cell dynamics during embryonic development. The Minos transposon emerged as the most efficient transgenesis strategy and is adaptable to promoters and transgenes of choice. These strategies now enable the generation of diverse genetic tools for mechanistic studies of cephalopod biology.

genetics↗

Large language model-based bibliometric evaluation of population descriptors in human genetics

As the use of population descriptors such as race, ethnicity, and ancestry have become increasingly common in modern genetics research, there have been growing calls to critically examine their use. Most notably, in 2023, the National Academies of Science, Engineering, and Medicine (NASEM) published a report titled Using Population Descriptors in Genetics and Genomics Research: A New Framework for an Evolving Field, which included eight specific and actionable recommendations for researchers to implement the ethical and accurate use of population descriptors in genetic research. Here, we use the 2023 NASEM report as a benchmark to analyze the use of population descriptors in genome-wide association studies (GWAS). We develop a general toolkit for large language model-based bibliometrics, operationalize the report's recommendations into an evaluation framework, and apply this framework to evaluate all 4,007 papers from the GWAS Catalog published between 2007 and 2025 with full text available on PubMedCentral. We find significant improvements in adherence to NASEM report recommendations over time. However, most improvements predate the publication of the NASEM report itself, suggesting the report functioned primarily as a synthesis of existing best practices rather than a catalyst for change. We conclude by highlighting opportunities for growth in the field of human genetics.

genetics↗

Mitigating biases of rescaling in forward-in-time population genetic simulations

Forward-in-time population genetic simulations are widely used in evolutionary analyses, but simulating large populations and long genomic regions remains computationally demanding. To reduce this cost, parameter rescaling is widely employed, in which the original evolutionary process is approximated by one with a smaller population size and fewer generations. Recently, several studies using the SLiM simulator have raised concerns about the accuracy of this rescaling approach. In this study, we show that many of the biases reported in these studies can be mitigated by using a different simulation algorithm. These results reveal that the accuracy of parameter rescaling depends on how well the simulation algorithm preserves diffusion-limit properties under rescaling.

genetics↗