Centimorgans et proximité de l'ancêtre commun

Centimorgans et proximité de l'ancêtre commun

Centimorgans and proximity of the common ancestor

🎙 Un brin d'ADN 👥 790 📅 April 22, 2024 ⏱ 31 min 👁 435 📄 science communication 🧭 2026-10-09
Available in: English (current) Français

Keywords

centimorganshared cM projectcM explainerautosomal DNAcousin terminology

Summary

The video addresses whether higher centimorgan (cM) counts in DNA matches systematically indicate a closer common ancestor. The author begins with three golden rules: most matches are not with each other but through a third person (the common ancestor), the only vertical exception is direct parent-child or grandparent-grandchild relationships, and autosomal transmission is random except for the exact 50% parent-child case. Four real matches are analysed: 561 cM, 504 cM, 366 cM, and 75 cM. The first two (561 and 504 cM) both correspond to a first cousin once removed, with the common ancestor being great-grandparents for the author. The 366 cM match, although lower, corresponds to the child of a first cousin, meaning the common ancestor is the author’s grandparents—one generation closer. The 75 cM match is a second cousin, with great-grandparents shared on both sides, a relationship that some tools exclude entirely. The video uses the Shared cM Project histogram and MyHeritage’s cM Explainer to show probability ranges, and presents two simulation graphs from a blog article illustrating the random variation in ancestral DNA contribution (e.g., great-grandparents contributing between 6.1% and 16% instead of a fixed 12.5%). The conclusion is that cM count is not a reliable proxy for the genealogical proximity of the common ancestor; random recombination and the specific family tree structure matter more.

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Critical Evaluation

Value of the Information & Strength of the Argument

The core value of this video lies in its concrete, example-driven demonstration that centimorgan counts are not a straightforward indicator of how close a common ancestor is. The author uses four real matches from their own family tree, with documented paper trails, to show that a 366 cM match can lead to a closer common ancestor (grandparents) than two matches above 500 cM (great-grandparents). This directly challenges the intuitive assumption that more shared DNA always means a more recent common ancestor. The argument is built on a clear logical structure: first, the golden rules of DNA inheritance; second, the definition of cousin relationships; third, the statistical tools (Shared cM Project, cM Explainer); and finally, the simulations of random DNA contribution. The reasoning is internally consistent and the examples are well chosen to illustrate the point. However, the argument relies on a very small sample (four matches from one person), and the simulations are borrowed from a blog post rather than from a peer-reviewed study. The author acknowledges the randomness of autosomal transmission and the limitations of the tools, which strengthens the credibility of the argument. The distinction between what is mathematically expected (50%, 25%, 12.5%) and what actually happens due to recombination is well explained. The video does not overstate its conclusions; it presents the findings as illustrative rather than definitive, and encourages verification through paper records and other matches.

Scientific Rigor, Source Quality, Title Accuracy

The video demonstrates good scientific rigor in its use of established tools: the Shared cM Project (hosted on the DNA Painter website) and MyHeritage’s cM Explainer. These are widely used in the genetic genealogy community and are based on crowdsourced data from documented pedigrees. The author correctly explains that the Shared cM Project relies on submissions from individuals with proven paper trails, and that the histograms represent probability distributions rather than absolute rules. The simulations presented are attributed to a blog article titled ‘How many genetic ancestors do I have?’ from November 2013, and the author explicitly states that these are not their own work. However, no peer-reviewed scientific literature is cited, and the blog post is not a formal academic source. The title is fully adequate: it announces a discussion of centimorgans and common ancestor proximity, and the content delivers exactly that. The video does not overpromise or mislead. The author also notes that different tools give different probabilities (e.g., MyHeritage excludes the 75 cM second cousin relationship while Ancestry gives it 9%), which shows awareness of methodological variability. Overall, the sourcing is appropriate for a science communication video but lacks academic depth.

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Title / Content Match

The title accurately reflects the content: the video examines the relationship between centimorgan counts and the genealogical distance of the common ancestor, using four concrete match examples.

Quality & Reliability

7/10

The video relies on well-established genetic genealogy tools (Shared cM Project, MyHeritage cM Explainer) and on documented family trees, with a clear distinction between observed matches and statistical probabilities. However, the sample is tiny (four matches from a single individual), the simulations are borrowed from a blog post rather than peer-reviewed literature, and no primary scientific publication is cited. The reasoning is sound but the evidence base is anecdotal.

Chapters

Cited Sources

  • Shared cM Project (DNA Painter) — Tool used to enter a centimorgan value and obtain probability distributions for various relationships, based on crowdsourced data with documented paper trails.
  • MyHeritage cM Explainer — Tool used to estimate possible relationships for a given centimorgan value, showing probabilities for each relationship type.
  • How many genetic ancestors do I have? (blog article, November 2013) — Source of the two simulation graphs showing the random variation in ancestral DNA contribution across generations. The author mentions the title but does not provide a direct URL.

Concurring Sources

  • Shared cM Project — Provides statistical distributions of centimorgan values for known relationships, supporting the video's claim that cM ranges overlap and are not deterministic.
  • MyHeritage cM Explainer — Offers probability estimates for relationships based on cM values, consistent with the video's demonstration of overlapping probabilities.
  • International Society of Genetic Genealogy (ISOGG) — Provides educational resources on genetic genealogy, including explanations of autosomal DNA inheritance and cousin terminology.

Contribution & Novelties

The video’s original contribution is its practical, case-based demonstration that centimorgan counts alone do not determine the genealogical proximity of a common ancestor. By comparing four real matches from the same individual, it shows that a lower cM match (366 cM) can lead to a closer common ancestor (grandparents) than higher cM matches (561 and 504 cM, leading to great-grandparents). This challenges a common assumption among genetic genealogists and provides a concrete illustration of the random nature of autosomal DNA inheritance. The video also highlights the limitations of different tools, showing that some relationships (e.g., 75 cM second cousin) are excluded by some platforms but included by others.

Pour aller plus loin :

  • Centimorgan — Definition and explanation of the unit used to measure genetic linkage and shared DNA.
  • Genetic genealogy — Overview of the field and its methods, including autosomal DNA testing.
  • Shared cM Project — The crowdsourced project used in the video to estimate relationship probabilities.
  • Recombination — Explanation of the random process that causes variation in inherited DNA segments.
  • Identical by descent — Concept underlying the identification of shared segments between matches.

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Radar Profile

The radar profile shows a balanced emphasis on information quantity and quality, with a moderate technical level and good overall reliability. The video is strong in practical application and clear explanation, but the technical depth is limited by the absence of primary scientific literature and the small sample size. The reliability score reflects the use of established tools and transparent methodology, tempered by the anecdotal nature of the examples.

Reliability 7/10