Rapid and accurate multi-phenotype imputation for millions of individuals

Lin-Lin Gu, Hong-Shan Wu, Tian-Yi Liu, Yong-Jie Zhang, Jing-Cheng He, Xiao-Lei Liu, Zhi-Yong Wang, Guo-Bo Chen (), Dan Jiang () and Ming Fang ()
Additional contact information
Lin-Lin Gu: Jimei University
Hong-Shan Wu: Jimei University
Tian-Yi Liu: Jimei University
Yong-Jie Zhang: Jimei University
Jing-Cheng He: Zhejiang University
Xiao-Lei Liu: Huazhong Agricultural University
Zhi-Yong Wang: Jimei University
Guo-Bo Chen: People’s Hospital of Hangzhou Medical College
Dan Jiang: Jimei University
Ming Fang: Jimei University

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Deep phenotyping can enhance the power of genetic analysis, including genome-wide association studies (GWAS), but the occurrence of missing phenotypes compromises the potential of such resources. Although many phenotypic imputation methods have been developed, the accurate imputation of millions of individuals remains challenging. In the present study, we have developed a multi-phenotype imputation method based on mixed fast random forest (PIXANT) by leveraging efficient machine learning (ML)-based algorithms. We demonstrate by extensive simulations that PIXANT is reliable, robust and highly resource-efficient. We then apply PIXANT to the UKB data of 277,301 unrelated White British citizens and 425 traits, and GWAS is subsequently performed on the imputed phenotypes, 18.4% more GWAS loci are identified than before imputation (8710 vs 7355). The increased statistical power of GWAS identified some additional candidate genes affecting heart rate, such as RNF220, SCN10A, and RGS6, suggesting that the use of imputed phenotype data from a large cohort may lead to the discovery of additional candidate genes for complex traits.

Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-55496-0 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-024-55496-0

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-55496-0

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().