Burak Yelmen
@burakyelmen.bsky.social
Research Fellow at the Institute of Genomics, University of Tartu. Genomics, complex traits, deep learning, artificial genomes. Occasionally music producer.
Hello, just wanted to chime in that larger sample sizes can increase false positives under model misspecification. In a recent preprint, we showed that standard GWAS that omit epistasis yield more spurious associations as N grows, even when epistasis is minimal.
www.biorxiv.org/content/10.1...
www.biorxiv.org/content/10.1...
Bias in genome-wide association test statistics due to omitted interactions
Over the past two decades, genome-wide association studies (GWAS) enabled the discovery of thousands of variants associated with many complex human traits. However, conventional GWAS are still widely ...
www.biorxiv.org
December 21, 2025 at 1:32 PM
Hello, just wanted to chime in that larger sample sizes can increase false positives under model misspecification. In a recent preprint, we showed that standard GWAS that omit epistasis yield more spurious associations as N grows, even when epistasis is minimal.
www.biorxiv.org/content/10.1...
www.biorxiv.org/content/10.1...
I think our results, in line with other recent findings, have important implications for GWAS methodology. Complex systems are rarely well captured by linear models; it’s reasonable to expect the same in complex trait genomics. Feedback on the manuscript is most welcome.
November 23, 2025 at 10:43 AM
I think our results, in line with other recent findings, have important implications for GWAS methodology. Complex systems are rarely well captured by linear models; it’s reasonable to expect the same in complex trait genomics. Feedback on the manuscript is most welcome.
We show that even if the variance explained by epistasis is modest, it's possible to get false hits. The problem is also positively correlated with sample size, suggesting caution when interpreting significant associations especially for large-scale GWAS.
November 23, 2025 at 10:43 AM
We show that even if the variance explained by epistasis is modest, it's possible to get false hits. The problem is also positively correlated with sample size, suggesting caution when interpreting significant associations especially for large-scale GWAS.