The predicament of heritable confounders.

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.