Introduction
Genome-wide association studies (GWAS) have become a standard method for uncovering genetic variants linked to disease and trait variation. With free software and publicly available data, many researchers are able to run GWAS pipelines. However, generating a Manhattan plot is only the start. Designing a rigorous GWAS - and interpreting its results correctly - requires addressing a series of challenges that go far beyond basic statistical tests.
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This is the second article in our three-part GWAS series:
These issues may not be fully described in standard reviews or tool manuals, but they are crucial if one hopes to achieve solid and biologically useful results.
Challenge #1: Controlling Population Structure and Relatedness
Population structure and kinship among samples can lead to false positive signals in GWAS. Early studies often misinterpreted subpopulation differences as associations, resulting in misleading conclusions. This is still one of the most critical - and frequently mishandled - issues in practice.
Solution: Use a mixed linear model (MLM) that includes both fixed and random effects. Include principal components (PCs) to model structure and a kinship matrix for relatedness. Tools such as GEMMA, EMMAX, GAPIT, and SAIGE support this. But success depends on data quality, ancestry knowledge, and careful parameter settings.
Challenge #2: Interpreting Non-Coding or Regulatory Variants
A large proportion of GWAS hits fall into non-coding regions. These variants may lie in enhancers, promoters, or other regulatory elements, making function hard to interpret.
Solution: Use annotations from ENCODE, Roadmap, and FANTOM5 via tools like HaploReg or RegulomeDB. When possible, perform colocalization with expression QTLs (eQTLs) using COLOC, eCAVIAR, or SMR. Prioritize context-specific interpretation - tissues matter.
Challenge #3: Handling Rare Variants in GWAS
Rare variants (MAF < 1-5%) are hard to detect due to low power, yet some may have large effects.
Solution: Use burden or variance component tests (e.g., SKAT, EPACTS). Report only high-confidence rare hits. Emphasize strong quality control, especially for imputation and genotyping.
Challenge #4: Integrating Multi-Omic Data for Causal Inference
GWAS identifies association, not causality. Integration with transcriptomics or proteomics is often needed to infer mechanism.
Solution: Perform colocalization with QTLs or use frameworks like TWAS, MR, or MTAG. Use ancestry- and tissue-matched data. Interpretation still requires expert judgment and biological context.
Challenge #5: Fine-Mapping and Functional Prioritization
GWAS signals span many variants in LD. Pinpointing causal ones is difficult.
Solution: Use fine-mapping tools (e.g., SuSiE, CAVIAR) to define credible sets. Combine with functional annotations or experiments (e.g., CRISPR screens). Use LD-aware strategies and population-matched panels.
Conclusion
Running a GWAS pipeline is relatively easy today. But conducting a scientifically robust, biologically meaningful, and publishable GWAS remains challenging. Many researchers struggle with issues that are hard to see in tutorials or review articles - but that make the difference between noise and insight.
This is the second part of our GWAS blog series. You can:
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About the Author: Justin T. Li received his Ph.D. in Neurobiology from the University of Wisconsin-Madison in 2000 and a M.S. in Computer Science from the University of Houston in 2001. Between 2004 and 2009, he served as an Assistant Professor in the Medical School at the University of Minnesota Twin Cities campus. From 2009 to 2013, he was the Chief Bioinformatics Officer at LC Sciences in Houston. In June 2013, Justin joined AccuraScience as a Lead Bioinformatician. He has published over 50 research articles in bioinformatics, computational biology, and related fields. More information about Justin can be found at https://www.accurascience.com/our_team.html.
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