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Causal inference in genetic trio studies.

Stephen Bates1, Matteo Sesia2, Chiara Sabatti3,4

  • 1Department of Statistics, Stanford University, Stanford, CA 94305; stephenbates@berkeley.edu candes@stanford.edu.

Proceedings of the National Academy of Sciences of the United States of America
|September 19, 2020
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Summary

We developed a novel method for causal inference using genetic data from parents and offspring. This approach uses a "digital twin" test to identify causal genetic variants with enhanced power and accuracy.

Keywords:
causal discoveryconditional independence testingfalse discovery rate (FDR)family-based association test (FBAT)transmission disequilibrium test (TDT)

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Causal inference from genetic data is challenging due to potential confounding factors.
  • Existing methods like the Transmission Disequilibrium Test (TDT) have limitations in power and localization.
  • Genetic data including parent-offspring trios offers a unique opportunity for robust causal inference.

Purpose of the Study:

  • To introduce a novel method for drawing causal inferences from genetic data, immune to confounding.
  • To identify specific genomic regions containing distinct causal variants.
  • To improve upon existing methods in terms of statistical power and localization accuracy.

Main Methods:

  • Developed a conditional independence test based on a "digital twin" approach.
  • The digital twin test compares observed offspring to synthetic offspring generated from the same parents.
  • The method leverages a well-established mathematical model of recombination and can incorporate black-box models and additional genetic data.

Main Results:

  • The digital twin test successfully identifies regions of the genome containing distinct causal variants.
  • Demonstrated enhanced statistical power compared to the widely used Transmission Disequilibrium Test.
  • Achieved improved localization of causal variants.

Conclusions:

  • The proposed digital twin method enables robust causal inference from genetic data, overcoming confounding issues.
  • This approach offers significant advantages in power and precision for identifying causal genetic variants.
  • The method relies on a fundamental model of recombination, making minimal assumptions about genotype-phenotype relationships.