Robust meta-analysis of biobank-based genome-wide association studies with unbalanced binary phenotypes

Genetic Epidemiology
Rounak DeySeunggeun Lee

Abstract

With the availability of large-scale biobanks, genome-wide scale phenome-wide association studies are being instrumental in discovering novel genetic variants associated with clinical phenotypes. As increasing number of such association results from different biobanks become available, methods to meta-analyse those association results is of great interest. Because the binary phenotypes in biobank-based studies are mostly unbalanced in their case-control ratios, very few methods can provide well-calibrated tests for associations. For example, traditional Z-score-based meta-analysis often results in conservative or anticonservative Type I error rates in such unbalanced scenarios. We propose two meta-analysis strategies that can efficiently combine association results from biobank-based studies with such unbalanced phenotypes, using the saddlepoint approximation-based score test method. Our first method involves sharing the overall genotype counts from each study, and the second method involves sharing an approximation of the distribution of the score test statistic from each study using cubic Hermite splines. We compare our proposed methods with a traditional Z-score-based meta-analysis strategy using numerical simulations and re...Continue Reading

References

May 14, 2011·American Journal of Human Genetics·Buhm Han, Eleazar Eskin
Aug 11, 2012·International Journal of Epidemiology·S KrokstadJ Holmen
May 10, 2013·Nature Reviews. Genetics·Evangelos Evangelou, John P A Ioannidis
Aug 30, 2016·Nature Genetics·Sayantan DasChristian Fuchsberger
Jun 13, 2017·American Journal of Human Genetics·Rounak DeySeunggeun Lee
Jul 25, 2017·International Journal of Obesity : Journal of the International Association for the Study of Obesity·C A Celis-MoralesN Sattar

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Citations

Dec 13, 2019·Genetic Epidemiology·Tianzhong YangWei Pan
Jun 27, 2020·American Journal of Human Genetics·Wenjian BiSeunggeun Lee

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