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Record W4414454131 · doi:10.1101/2025.09.19.25336188

Genetic consequences of serial sperm donation

2025· preprint· en· W4414454131 on OpenAlexaffabout
Thomas M Zheng, Alejandro Mejía‐García, Claude Bhérer, Catherine Laprise, Anne‐Marie Laberge, Simon Gravel

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsUniversité de MontréalUniversité du Québec à ChicoutimiMcGill Genome CentreMcGill University
Fundersnot available
KeywordsSperm donationSpermPopulationInbreedingDonationAccidentalRisk assessmentDisease

Abstract

fetched live from OpenAlex

Abstract Study question How does serial sperm donation impact genetic risk in the donor-conceived children and their descendants? Summary answer In addition to the psychological effects of serial sperm donation, donor-conceived children are at risk of unintentional inbreeding. This risk is compounded by the hard-to-quantify effect of social proximity between mothers. Such inbreeding would cause children to have up to 15% excess risk of childhood mortality or congenital morbidities. The risk to descendants after many generations is spread across many individuals and remains low as long as the number of donor-conceived children does not increase appreciably. What is known already Inbreeding increases the risk for a range of diseases among the offspring, with the risk increasing with the degree of inbreeding. Sperm donation increases the risk of accidental inbreeding, and thus likely increases disease risk. Study design, size, duration We performed a literature review of risks associated with consanguinity across a range of traits, together with a model-based mathematical analysis to estimate the short- and long-term risk associated with serial sperm donation. Participants/materials, setting, methods We used whole-genome sequencing and imputed sequence data from the CARTaGENE longitudinal study to estimate population prevalence of relevant risk alleles. We performed mathematical modelling based on these results on published estimates of the risk associated with inbreeding. Main results and the role of chance With over 600 children conceived in this serial sperm donation event, 0.1 consanguineous unions would be expected under the simplest model of random mating by generation within the province of Quebec. Preferential mating due to geographic and social proximity among the mothers could increase this rate appreciably, so that accidental inbreeding is not unlikely. Since the likelihood of inbreeding events increases quadratically with the number of children, active inbreeding avoidance by the offspring and interventions to reduce continued serial donation can reduce risk. Over generations, more distant inbreeding is unavoidable, but inbreeding coefficients are reduced. Our model predicts that the long-term excess number of serious adverse events will be fewer than one per generation. The short- and long-term rates of specific diseases may be affected, however, given public information about the donor carrier status, we expect an excess of 0.84 children per generation [95% CI: 0,3] affected by Hereditary Tyrosinemia of type 1. Large scale data CARTaGENE is a biobank based in Quebec, Canada, that is accessible following an independent data access protocol and can be found at: https://cartagene.qc.ca/en/ Limitations, reasons for caution Our analysis relies on uncertain estimates of the burden associated with inbreeding. We also rely on simplifying assumptions about future events, including migrations, social interactions between mothers, and future sperm donation events. As a result, our estimates should be seen as coarse estimates. Wider implications of the findings Serial sperm donation is not uncommon. Each documented instance has raised questions about the genetic burden associated with the practice. By quantifying this risk, this study will help inform the public health and genetic counselling response to these situations, in addition to being of interest from a population genetics perspective. Study funding/competing interest(s) This research was supported by the Canadian Institute for Health Research (CIHR) project grant 437576, NSERC grant RGPIN-2017-04816, the Canada Research Chair program to S.G., and the Canada Foundation for Innovation. T.M.Z was supported by the QLS Grad and Grad Excellence Award. The authors report no competing interests. Consanguinity The degree of relatedness between individuals, as measured by inheritance from recent ancestors. For example, second cousins share on average 3.125% of their DNA from their great-grandparents. Inbreeding The production of offspring from individuals with high consanguinity. Runs of Homozygosity (ROH) Stretches of the genome where identical alleles were received from both parents. The fraction of the genome in ROH is a measure of inbreeding. Donor-Conceived Child (DCC) Child born following sperm donation. DCC(X) refers to a child born following sperm donation by individual X. Congenital Morbidity Diseases or medical conditions present from birth, including physical, intellectual, or developmental. Specifically, does not include any diseases or conditions that arise from exposure to medications or chemicals during gestation or infections during pregnancy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.334
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes2
Has abstractyes

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