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*, Fan Liu*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Scopus)
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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.

Original languageEnglish
Article number7832
JournalNature Communications
Issue number1
Publication statusPublished - 20 Dec 2022

Bibliographical note

The authors are grateful for the dedication, commitment and contribution of the study participants, the general practitioners, pharmacists, and the staff from the Rotterdam Study. The Rotterdam Study is supported by the Erasmus MC; the Erasmus University Rotterdam; the Netherlands Organization for Scientific Research (NWO); the Netherlands Organization for Health Research and Development (ZonMw); the Research Institute for Diseases in the Elderly (RIDE); the Netherlands Genomics Initiative (NGI); the Ministry of Education, Culture and Science; the Ministry of Health Welfare and Sport; the European Commission (DG XII); and the Municipality of Rotterdam. The generation and management of GWAS genotype data for the Rotterdam Study were executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC. We thank Susan Walsh and Mark Shriver for sharing with us for replication purposes in this study their published data5 from the Indiana University-Purdue University Indianapolis (IUPUI) and Pennsylvania State University (PSU) cohorts. The authors of Zhang et al.6 for making their GWAS summary statistics publicly available allowing us to use them here for replication purposes. FL was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (Grant Nos. XDB38010400, XDC01000000), Shanghai Municipal Science and Technology Major Project (Grant No. 2017SHZDZX01), National Natural Science Foundation of China (NSFC) (81930056), Science and Technology Service Network Initiative of Chinese Academy of Sciences (KFJ-STS-QYZD-2021-08-001, KFJ-STS-ZDTP-079).

Publisher Copyright: © 2022, The Author(s).


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