Detecting and Adjusting for Hidden Biases due to Phenotype Misclassification in Genome-Wide Association Studies

Area of Science:

• Genetics and Genomics
• Statistical Genetics
• Bioinformatics

Background:

• Genome-wide association studies (GWAS) increasingly use large, diverse biobanks with potential phenotypic misclassification.
• The impact of phenotypic misclassification on large-scale GWAS is not well understood due to a lack of statistical methods.
• Current GWAS best practices may be affected by unaddressed phenotypic data quality issues.

Conclusions:

• PheMED provides a scalable method to assess and account for phenotypic misclassification in large-scale GWAS.
• Effective dilution biases GWAS replication and heritability studies, even with current best practices.
• PheMED is a valuable tool for improving phenotypic data quality assessment in genetic association studies.