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The predicament of heritable confounders.

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Large genetic studies of psychiatric disorders are confounded by heritable factors unrelated to the disease. This can lead to inaccurate genetic mapping and flawed conclusions about disease relationships, necessitating new data collection and analysis methods.

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Area of Science:

  • Psychiatric Genetics
  • Genomic Epidemiology
  • Biostatistics

Background:

  • Genome-wide association studies (GWAS) require large sample sizes to identify genetic loci associated with psychiatric disorders.
  • Current diagnostic methods, including self-assessment and electronic health records, introduce non-specific heritable variation, confounding genetic analyses.
  • This leads to the identification of irrelevant genetic loci and missed relevant ones, impacting the accuracy of genetic correlations between disorders and cohorts.

Purpose of the Study:

  • To highlight the problem of heritable confounders in large-scale psychiatric genetics studies.
  • To propose methods for identifying and flagging affected cohorts in genetic research.
  • To discuss future strategies for data collection and machine learning to mitigate these confounding effects.

Main Methods:

  • Review of current methodologies in psychiatric disorder diagnosis and genetic mapping.
  • Analysis of how non-specific heritable variation propagates as sample sizes increase.
  • Conceptual framework for identifying shared biases as shared etiology in genetic correlations.

Main Results:

  • Genetic mapping studies are susceptible to identifying loci unrelated to the target psychiatric disorder.
  • Genetic correlations can be misleading, appearing as shared etiology due to common confounding factors.
  • Confounding effects are amplified in larger sample sizes, leading to potentially unreproducible findings.

Conclusions:

  • Heritable confounders pose a significant challenge to genetic studies of psychiatric disorders.
  • Strategies for cohort flagging and improved data collection are crucial.
  • Machine learning approaches may offer solutions for mitigating the impact of these confounders in future research.