Author
Listed:
- Bao-Lin Zhang
(Chinese Academy of Sciences
Chinese Academy of Sciences
Chinese Academy of Sciences)
- Yongxuan Chen
(Chinese Academy of Sciences
University of Chinese Academy of Sciences)
- Yali Zhang
(Chinese Academy of Sciences)
- Yicheng Qiao
(University of Chinese Academy of Sciences
Chinese Academy of Sciences)
- Yang Wu
(West China Hospital of Sichuan University)
- Yi Zhang
(Chinese Academy of Sciences
Chinese Academy of Sciences)
- Yizheng Lu
(Chinese Academy of Sciences
University of Chinese Academy of Sciences)
- Xinran You
(Chinese Academy of Sciences)
- Yanling Li
(Chinese Academy of Sciences)
- Hong-Di Huang
(Chinese Academy of Sciences
University of Chinese Academy of Sciences)
- Qiong Wang
(Chinese Academy of Sciences
Chinese Academy of Sciences)
- Yijiang Li
(Chinese Academy of Sciences
Chinese Academy of Sciences)
- Yun Wang
(Chinese Academy of Sciences
Chinese Academy of Sciences)
- Wenxian Xiao
(Chinese Academy of Sciences
Chinese Academy of Sciences)
- Hexian Duan
(Chinese Academy of Sciences
Chinese Academy of Sciences)
- Ming-Hao Qiu
(Chinese Academy of Sciences)
- Nan-Hui Chen
(Chinese Academy of Sciences)
- Xiaomei Yu
(Chinese Academy of Sciences)
- Min-Min Yang
(Chinese Academy of Sciences)
- Longbao Lv
(Chinese Academy of Sciences
University of Chinese Academy of Sciences
Chinese Academy of Sciences)
- David N. Cooper
(Cardiff University)
- Ping Zheng
(Chinese Academy of Sciences
Chinese Academy of Sciences
Chinese Academy of Sciences
Chinese Academy of Sciences)
- Yong-Gang Yao
(Chinese Academy of Sciences
Chinese Academy of Sciences
University of Chinese Academy of Sciences
Chinese Academy of Sciences)
- Ning Liu
(University of Chinese Academy of Sciences
Chinese Academy of Sciences)
- Jian-Hong Wang
(Chinese Academy of Sciences
Chinese Academy of Sciences)
- Dong-Dong Wu
(Chinese Academy of Sciences
Chinese Academy of Sciences
Chinese Academy of Sciences
Chinese Academy of Sciences)
Abstract
Combining genotype and phenotype data promises to greatly increase the value of macaque as biomedical models for human disease. Here we launch the Macaque Biobank project by deeply sequencing 919 captive Chinese rhesus macaques (CRM) while assessing 52 phenotypic traits. Genomic analyses reveal the captive CRMs are a mixture of multiple wild sources and exhibit significantly lower mutational load than their Indian counterparts. We identify hundreds of loss-of-function variants linked to human inherited disease and drug targets, and at least seven exert significant effects on phenotypes using forward genomic screens. Genome-wide association analyses reveal 30 independent loci associated with phenotypic variations. Using reverse genomic approaches, we identify DISC1 (p.Arg517Trp) as a genetic risk factor for neuropsychiatric disorders, with macaques carrying this deleterious allele exhibiting impairments in working memory and cortical architecture. This study demonstrates the potential of macaque cohorts for the investigation of genotype-phenotype relationships and exploring potential spontaneous models of human genetic disease.
Suggested Citation
Bao-Lin Zhang & Yongxuan Chen & Yali Zhang & Yicheng Qiao & Yang Wu & Yi Zhang & Yizheng Lu & Xinran You & Yanling Li & Hong-Di Huang & Qiong Wang & Yijiang Li & Yun Wang & Wenxian Xiao & Hexian Duan , 2025.
"Forward and reverse genomic screens enhance the understanding of phenotypic variation in a large Chinese rhesus macaque cohort,"
Nature Communications, Nature, vol. 16(1), pages 1-14, December.
Handle:
RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-63747-x
DOI: 10.1038/s41467-025-63747-x
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