Combining genome-wide association studies highlight novel loci involved in human facial variation

Ziyi Xiong, Xingjian Gao, Yan Chen, Zhanying Feng, Siyu Pan, Haojie Lu, Andre G. Uitterlinden, Tamar Nijsten, Arfan Ikram, Fernando Rivadeneira, Mohsen Ghanbari, Yong Wang, Manfred Kayser () and Fan Liu ()
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Ziyi Xiong: University Medical Center Rotterdam
Xingjian Gao: Chinese Academy of Sciences
Yan Chen: University Medical Center Rotterdam
Zhanying Feng: Chinese Academy of Sciences
Siyu Pan: Chinese Academy of Sciences
Haojie Lu: University Medical Center Rotterdam
Andre G. Uitterlinden: University Medical Center Rotterdam
Tamar Nijsten: University Medical Center Rotterdam
Arfan Ikram: University Medical Center Rotterdam
Fernando Rivadeneira: University Medical Center Rotterdam
Mohsen Ghanbari: University Medical Center Rotterdam
Yong Wang: Chinese Academy of Sciences
Manfred Kayser: University Medical Center Rotterdam
Fan Liu: University Medical Center Rotterdam

Nature Communications, 2022, vol. 13, issue 1, 1-20

Abstract: Abstract Standard genome-wide association studies (GWASs) rely on analyzing a single trait at a time. However, many human phenotypes are complex and composed by multiple correlated traits. Here we introduce C-GWAS, a method for combining GWAS summary statistics of multiple potentially correlated traits. Extensive computer simulations demonstrated increased statistical power of C-GWAS compared to the minimal p-values of multiple single-trait GWASs (MinGWAS) and the current state-of-the-art method for combining single-trait GWASs (MTAG). Applying C-GWAS to a meta-analysis dataset of 78 single trait facial GWASs from 10,115 Europeans identified 56 study-wide suggestively significant loci with multi-trait effects on facial morphology of which 17 are novel loci. Using data from additional 13,622 European and Asian samples, 46 (82%) loci, including 9 (53%) novel loci, were replicated at nominal significance with consistent allele effects. Functional analyses further strengthen the reliability of our C-GWAS findings. Our study introduces the C-GWAS method and makes it available as computationally efficient open-source R package for widespread future use. Our work also provides insights into the genetic architecture of human facial appearance.

Date: 2022
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DOI: 10.1038/s41467-022-35328-9

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