Eight Common GWAS Analysis Pitfalls That Sabotage Your Results - And How Seasoned Bioinformaticians Avoid Them

Introduction

Genome-wide association studies (GWAS) have transformed our ability to link genetic variation with complex human traits. Over the past decade, the field has produced thousands of publications, from schizophrenia and diabetes to height, BMI, and even facial morphology. The statistical frameworks are mature. The data platforms are powerful. And many bioinformatic pipelines have become standardized.

But despite this progress, we repeatedly see GWAS studies fail in ways that are surprisingly basic - or surprisingly subtle. A Manhattan plot may look impressive. The top hits may pass the genome-wide threshold. But when we dig deeper, something feels wrong: population stratification not fully controlled, imputation done with wrong panel, low-frequency variants misinterpreted, LD structure ignored, or post-GWAS modeling applied without checking assumptions.

This article is not a tutorial for PLINK or SAIGE. Instead, it summarizes eight common pitfalls we encounter in real-world GWAS analysis projects. For each, we explain what the problem is, why it happens even with experienced researchers, a real example (anonymized), and how seasoned analysts avoid or fix the issue. We hope this can help others avoid costly mistakes - before they spread into manuscripts, grants, or reviewer comments.

Table of Contents

1. Confounding by Population Structure Despite PCA Correction

The Problem

Even after adjusting for top principal components (PCs), false-positive associations remain due to unmodeled ancestry differences or residual stratification.

Why It Happens

PCA captures major ancestry axes, but subtle structure often persists. Including too few PCs leaves confounding; too many introduces noise. Also, PC correction assumes linear effects - non-linear ancestry structure (e.g., admixed populations) may not be well controlled. Tools like EIGENSTRAT or fastPCA help, but model choice still matters.

Real Example

In a European obesity study, after adjusting for PC1–PC4, the top SNP was in the HLA region. But the same SNP was also a strong ancestry marker. When we re-ran the analysis with a mixed model (e.g., SAIGE), the signal disappeared. It was not obesity-linked - it was ancestry-linked.

What We Do Differently

We don't just throw in 10 PCs. We plot PC vs phenotype, test for inflation (λGC), run QQ plots, and evaluate mixed models if structure persists. For admixed samples, we model ancestry proportions directly or use methods like GEMMA or REGENIE. If population stratification isn't under control, we halt GWAS - even if results look interesting.

2. Misuse of Genotype Imputation or Reference Panels

The Problem

Poor-quality imputation introduces false associations or removes real ones. Researchers may assume imputed data is “better” by default.

Why It Happens

Imputation accuracy depends on the match between sample ancestry and reference panel (e.g., 1000 Genomes vs HRC vs TOPMed). Also, filtering by INFO score and hard-calling thresholds is often done inconsistently. Some tools output dosage, others hard genotype calls; misusing them in downstream tools leads to bias.

Real Example

In a blood trait GWAS of South Asian samples, the analyst used the HRC panel (mostly European). The imputed genotypes had low INFO scores, and rare variants were mostly noise. When we re-imputed using 1000 Genomes Phase 3 and filtered properly, top hits changed significantly.

What We Do Differently

We always evaluate ancestry match before selecting imputation panel. We QC imputed data thoroughly: filter by INFO > 0.8, remove low MAC SNPs, and track hard call conversion methods. We also document every step - so imputation isn’t a black box. If imputation is weak, we may re-impute or fall back to directly genotyped SNPs.

3. Incorrect Treatment of Related Samples or Hidden Kinship

The Problem

Unrecognized relatedness among samples inflates association signals. Siblings, cousins, or cryptic kinships distort allele distributions.

Why It Happens

In some datasets (e.g., hospital-based cohorts, rural populations), related samples are common. If relatedness isn’t accounted for, linear models assume independence and produce false positives. Identity-by-descent (IBD) estimation helps, but not always used.

Real Example

In one pharmacogenetics GWAS, the top variant was in a detox gene. However, when we checked IBD, we found 6 sibling pairs and 3 cousins - all with similar phenotype and genotype. After removing or modeling related samples (via KING and SAIGE), the association dropped below threshold.

What We Do Differently

We always check for kinship using KING or PLINK --rel-cutoff. Depending on sample size and goal, we either (1) remove one from each pair, or (2) use mixed models with genetic relationship matrix (GRM). For family-based designs, we apply transmission disequilibrium tests or within-family models.

4. Inadequate Handling of Rare Variants and Low MAF SNPs

The Problem

Rare variants often show “significant” p-values in small cohorts - but are statistical artifacts or genotyping errors.

Why It Happens

Low minor allele frequency (MAF) means small number of carriers - often just 2–3 individuals. With noisy phenotypes or poor imputation, these signals are unstable. Some pipelines use default filters like MAF > 0.01 but don’t check MAC (minor allele count). Even dosage rounding can create false calls.

Real Example

In a liver trait GWAS of 1,500 samples, a rare SNP with MAF 0.003 showed p < 1e-8. But on inspection, only 3 carriers existed - all from one center. The phenotype was also noisy (ALT levels with batch effects). When we filtered by MAC ≥ 20 and checked site QC, the signal disappeared.

What We Do Differently

We filter by both MAF and MAC thresholds (e.g., MAC ≥ 20). We use Firth logistic regression or burden tests for rare variant modeling. We also flag variants with imputation INFO < 0.9 and manually inspect top rare hits. If a rare variant is significant, it must survive multiple layers of QC before we trust it.

5. Multiple Testing Correction Misunderstood or Misapplied

The Problem

Genome-wide significance thresholds are misused - either too lenient (false positives) or too strict (false negatives).

Why It Happens

Standard GWAS threshold is p < 5e-8, based on ~1 million independent tests. But if analysis is limited (e.g., candidate region, exome array), this is too conservative. Some use Bonferroni correction improperly. Others ignore it entirely. False discovery rate (FDR) is often misunderstood.

Real Example

A metabolic GWAS with 250,000 SNPs used Bonferroni p < 2e-7 (which was fine). But in the paper, all SNPs with p < 1e-5 were called “significant.” Reviewers challenged this, and manuscript was delayed. After applying FDR and clarifying terminology, the interpretation changed.

What We Do Differently

We report genome-wide significance (p < 5e-8), suggest study-specific thresholds if appropriate, and always distinguish “suggestive” vs “significant” hits. For small studies or gene-based tests, we use FDR carefully. We also explain to the client what the thresholds actually mean - in practice.

6. Naive Interpretation of Intergenic or Noncoding Variants

The Problem

Top GWAS SNPs fall in “junk” DNA - intergenic regions. Analysts struggle to assign meaning, and may assign wrong gene.

Why It Happens

Many tools assign SNPs to nearest gene. But regulatory elements (enhancers, silencers) may act over long distances. SNPs in LD may affect distant genes through eQTLs or 3D chromatin loops. Without functional annotation or QTL lookup, interpretation is speculative.

Real Example

In an asthma GWAS, the lead SNP was 100 kb upstream of gene A. Authors claimed A was the causal gene. But GTEx showed the SNP was a strong eQTL for gene B, 450 kb away, in lung tissue. Gene A had no expression in relevant cell types. The true biology was missed.

What We Do Differently

We annotate top SNPs using eQTL and chromatin data (e.g., GTEx, ENCODE, Hi-C). We also look at LD block structure to find credible SNPs, not just lead SNP. For functional interpretation, we prioritize tissue-specific expression and regulatory context - not just distance to gene.

7. Improper LD Pruning, Clumping, or Locus Definition

The Problem

Too many “independent” signals are reported - or too few - because LD pruning/clumping is done wrong.

Why It Happens

Clumping depends on LD window and r² threshold. If population-specific LD is ignored, SNPs appear independent when they’re not. Sometimes clumping is done on imputed dosages without proper reference panel. Others mix datasets from different ethnic groups without adjusting LD.

Real Example

In a cardiovascular GWAS, 6 SNPs in a 120 kb region were reported as independent. But r² among them was 0.95 in European samples. When we re-clumped using proper LD panel, only one signal remained. Interpretation and downstream analysis changed dramatically.

What We Do Differently

We use ancestry-matched LD panels for clumping (e.g., PLINK --clump with 1000G EUR or EAS). We visualize LD structure with LocusZoom or custom heatmaps. We also provide credible sets for fine-mapping when needed. We never report inflated number of loci.

8. Overconfident Biological Claims Without Functional Evidence

The Problem

GWAS hits are turned into bold biological stories - but without any functional validation. This weakens papers and raises reviewer concerns.

Why It Happens

Once a “significant SNP” is found, pressure builds to publish. Authors may overinterpret the signal: link it to a famous gene, create a pathway, or propose mechanism. But without replication, QTL support, or functional assay, it’s just a statistical association.

Real Example

A brain aging study claimed a SNP near FOXP2 altered language processing. But the SNP was not replicated, had no eQTL, and no functional data. Reviewers rejected the story. After reanalysis and more cautious framing, the paper was accepted - but claims were reduced.

What We Do Differently

We guide clients on what conclusions are supported and what are speculative. We help identify supporting evidence (e.g., eQTL, chromatin, conservation, prior GWAS). If needed, we suggest replication cohorts or functional follow-up. We also prepare figures and tables that reflect the real strength of findings - not wishful thinking. Final Remarks GWAS analysis may seem routine today. The tools are standardized. The pipelines are well documented. And the output - Manhattan plots, QQ plots, summary tables - can look professional even when the analysis is flawed. But experienced researchers know: behind every clean plot can hide deep problems. Population confounding. Imputation errors. Misassigned genes. Misinterpreted variants. Reviewers see through these quickly. And if they don’t, the next study may collapse when replication fails. We’ve recovered many GWAS projects that looked finished - but were not truly solid. Fixing those takes more than rerunning PLINK. It takes deep understanding of the biology, population genetics, and statistics behind the data. We hope this article helps clarify where things go wrong - and how careful analysts prevent them. GWAS is still powerful, but it’s not plug-and-play. Getting it right is harder than it looks.

Final Remarks

GWAS analysis may seem routine today. The tools are standardized. The pipelines are well documented. And the output - Manhattan plots, QQ plots, summary tables - can look professional even when the analysis is flawed.

But experienced researchers know: behind every clean plot can hide deep problems. Population confounding. Imputation errors. Misassigned genes. Misinterpreted variants. Reviewers see through these quickly. And if they don’t, the next study may collapse when replication fails.

We’ve recovered many GWAS projects that looked finished - but were not truly solid. Fixing those takes more than rerunning PLINK. It takes deep understanding of the biology, population genetics, and statistics behind the data.

We hope this article helps clarify where things go wrong - and how careful analysts prevent them. GWAS is still powerful, but it’s not plug-and-play. Getting it right is harder than it looks.

This blog article was authored by William Gong, Ph.D., Lead Bioinformatician. To learn more about AccuraScience's Lead Bioinformaticians, visit https://www.accurascience.com/our_team.html.