Scalable and Robust Regression Methods for Phenome-Wide Association Analysis on Large-Scale Biobank Data

Area of Science:

• Genomics and Bioinformatics
• Human Genetics
• Computational Biology

Background:

• Large biobanks integrate genotyping data with electronic health records (EHRs).
• Phenome-wide association studies (PheWAS) leverage this data to explore genetic associations across numerous phenotypes.
• Existing PheWAS approaches face challenges like computational demands and data imbalance.

Purpose of the Study:

• To discuss the challenges encountered in large-scale biobank data analysis for PheWAS.
• To summarize scalable and robust methodologies for Genome-Wide Association Studies (GWAS) and PheWAS.
• To provide a practical guide for researchers analyzing genetic variations and health-related phenotypes.

Main Methods:

• Review of current computational and statistical approaches for GWAS and PheWAS.
• Discussion of challenges including computational burden, phenotypic distribution, and genetic relationships.
• Identification of scalable and robust methods for large-scale biobank data analysis.

Main Results:

• Identification of key challenges in large-scale PheWAS, including computational scalability and data heterogeneity.
• Summary of advanced analytical strategies applicable to GWAS and PheWAS.
• Guidance on selecting appropriate methods for genetic variation and phenotype association studies.

Conclusions:

• Large biobanks offer unprecedented opportunities for genetic discovery.
• Addressing computational and statistical challenges is crucial for effective PheWAS.
• This review provides essential insights for researchers utilizing biobank data to understand human genetics and health.