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Estimating Sampling Selection Bias in Human Genetics: A Phenomenological Approach.

Davide Risso1, Luca Taglioli2, Sergio De Iasio3

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Surname sampling in human genetics can introduce hidden biases. Researchers found that using historically documented surnames, especially for male-specific genome studies, is crucial to avoid skewed genetic inferences.

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Area of Science:

  • Population Genetics
  • Human Genetics
  • Bioinformatics

Background:

  • Surname-based sampling is common in human genetics research.
  • Existing sampling strategies may introduce hidden biases.
  • Understanding these biases is crucial for accurate genetic inference.

Purpose of the Study:

  • To empirically quantify hidden biases in human genetics sampling strategies.
  • To specifically assess biases associated with surname-based sampling methods.
  • To provide recommendations for optimal sampling in genetic studies.

Main Methods:

  • Reconstructed surname distributions for 26 Italian communities (1447-2001).
  • Quantified overlap between reference founding cores and sampled distributions.
  • Employed probabilistic and selective sampling methods under varying kinship models.

Main Results:

  • Significant discrepancies (average 59.5%, peak 84%) observed between founding cores and sampled distributions, particularly with random sampling (low kinship).
  • High kinship models reduced discrepancies but increased variance.
  • Methods maximizing patrilineages/residency were unexpectedly sensitive to recent gene flow.

Conclusions:

  • Surname sampling strategies can introduce substantial bias in human genetics.
  • The 'founders' method' (sampling individuals with historically documented surnames) is recommended, especially for male-specific genome studies.
  • This approach helps prevent over-stratification of genetic components and biases inferences.