GWAS Data Analysis Services That Catch Hidden Errors Before They Derail Your Study

Hidden confounding, imputation mismatches, and flawed LD assumptions quietly corrupt GWAS results - even when Manhattan plots look perfect and p-values hit genome-wide significance. False positives dominate follow-up, true signals disappear, and entire studies collapse under re-analysis. These failures don’t show up in summary stats or QC reports - but they waste resources, misdirect experiments, and destroy confidence in your findings.

At AccuraScience, we catch high-impact errors in GWAS data analysis before they collapse your GWAS study. Whether you're mapping traits in population cohorts, case-control panels, or biobank-scale datasets, we ensure your associations are valid, robust, and biologically meaningful - not statistical illusions.

Eight Common GWAS Analysis Pitfalls That Sabotage Results - Unless You Catch Them Early

We’ve stepped in to rescue GWAS projects that seemed rock-solid - until key assumptions quietly collapsed under closer scrutiny. These are eight hidden traps we routinely detect and correct:

1. Confounding by Population Structure Despite PCA Correction

We test for inflation, apply mixed models, and halt analysis if stratification isn’t under control.

2. Misuse of Genotype Imputation or Reference Panels

We check ancestry match, apply strict INFO filters, and re-impute if necessary - no black-box assumptions.

3. Incorrect Treatment of Related Samples or Hidden Kinship

We detect cryptic relatedness and apply appropriate kinship-aware models or pruning strategies.

4. Inadequate Handling of Rare Variants and Low MAF SNPs

We set MAC and MAF thresholds, apply rare variant tests, and manually verify all top hits.

5. Multiple Testing Correction Misunderstood or Misapplied

We explain genome-wide thresholds, FDR logic, and clearly separate “suggestive” from “significant.”

6. Naive Interpretation of Intergenic or Noncoding Variants

We annotate with eQTL, chromatin, and LD data - not just assign SNPs to the nearest gene.

7. Improper LD Pruning, Clumping, or Locus Definition

We use ancestry-matched panels and visualize LD structure - avoiding over- or under-calling loci.

8. Overconfident Biological Claims Without Functional Evidence

We connect hits to functional context and guide clients on what’s solid - and what’s speculation.

Read the Full Breakdown in Our Expert Blog

We’ve published a detailed, expert-level blog that walks through each of these eight pitfalls - with real-world examples, root causes, and how we handle them. It’s designed for researchers who want results that hold up under scrutiny.

→ Read the full article here

Why Researchers Trust AccuraScience

Founded in 2013, AccuraScience was the first bioinformatics service company in the U.S. offering broad-spectrum customized solutions to academic and industry researchers. Our team of senior bioinformaticians brings over 200 years of combined experience - with deep biological insight and computational rigor. We’ve completed projects for over 180 research institutions across five continents, contributed to NIH-funded grants, and supported peer-reviewed publications and clinical applications.

When It Has to Be Right

A GWAS study can look clean - but be fundamentally flawed. If your signals don’t survive population adjustment, or your lead SNP lacks functional support, the damage can be hard to fix. We can help - before these issues compromise your manuscript, your grant, or your reputation.