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Selection schemes can bias genetic inferences and predictions. This study introduces a Bayesian approach integrating fitness and missing data to better account for selection, improving quantitative genetics analysis.

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Area of Science:

  • Quantitative genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Selection schemes can distort genetic inferences and predictions, particularly in breeding and trait association studies.
  • Biased inferences arise when data are not collected from random samples.
  • Existing methods may not adequately address distortions caused by selection processes.

Purpose of the Study:

  • To revisit inference in quantitative genetics under selection processes.
  • To develop a unified Bayesian framework for inference and prediction that accounts for selection.
  • To explore the impact of selection on genetic analyses in animal and plant breeding.

Main Methods:

  • Integration of classical fitness concepts with missing data techniques.
  • A fully Bayesian approach for unified inference and prediction.
  • Development of a flexible "soft selection" model to diagnose selection's impact.

Main Results:

  • The "soft selection" model helps assess the extent to which selection can be ignored.
  • Highlights the link between missingness probability and fitness in selection scenarios.
  • Demonstrates that a fixed selection threshold is often unrealistic; soft selection accounts for data variability.

Conclusions:

  • The proposed Bayesian approach offers an integrated solution for inference and prediction under selection.
  • While the quality of inferences under selection remains challenging to ascertain unambiguously, predictions can be empirically validated.
  • The methods are applicable to natural selection and complex breeding data structures.