Weichen Song1,2, Kai Yuan1, Zhe Liu1, Wenxiang Cai1, Jue Chen1, Shunying Yu1,2, Min Zhao3,4, Guan Ning Lin5,6. 1. Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China. 2. Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China. 3. Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China. drminzhao@smhc.org.cn. 4. Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China. drminzhao@smhc.org.cn. 5. Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China. nickgnlin@sjtu.edu.cn. 6. Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China. nickgnlin@sjtu.edu.cn.
Abstract
BACKGROUND: We aimed to evaluate the potential role of antagonistic selection in polygenic diseases: if one variant increases the risk of one disease and decreases the risk of another disease, the signals of genetic risk elimination by natural selection will be distorted, which leads to a higher frequency of risk alleles. METHODS: We applied local genetic correlations and transcriptome-wide association studies to identify genomic loci and genes adversely associated with at least two diseases. Then, we used different population genetic metrics to measure the signals of natural selection for these loci and genes. RESULTS: First, we identified 2120 cases of antagonistic pleiotropy (negative local genetic correlation) among 87 diseases in 716 genomic loci (antagonistic loci). Next, by comparing with non-antagonistic loci, we observed that antagonistic loci explained an excess proportion of disease heritability (median 6%), showed enhanced signals of balancing selection, and reduced signals of directional polygenic adaptation. Then, at the gene expression level, we identified 31,991 cases of antagonistic pleiotropy among 98 diseases at 4368 genes. However, evidence of altered signals of selection pressure and heritability distribution at the gene expression level is limited. CONCLUSION: We conclude that antagonistic pleiotropy is widespread among human polygenic diseases, and it has distorted the evolutionary signal and genetic architecture of diseases at the locus level.
BACKGROUND: We aimed to evaluate the potential role of antagonistic selection in polygenic diseases: if one variant increases the risk of one disease and decreases the risk of another disease, the signals of genetic risk elimination by natural selection will be distorted, which leads to a higher frequency of risk alleles. METHODS: We applied local genetic correlations and transcriptome-wide association studies to identify genomic loci and genes adversely associated with at least two diseases. Then, we used different population genetic metrics to measure the signals of natural selection for these loci and genes. RESULTS: First, we identified 2120 cases of antagonistic pleiotropy (negative local genetic correlation) among 87 diseases in 716 genomic loci (antagonistic loci). Next, by comparing with non-antagonistic loci, we observed that antagonistic loci explained an excess proportion of disease heritability (median 6%), showed enhanced signals of balancing selection, and reduced signals of directional polygenic adaptation. Then, at the gene expression level, we identified 31,991 cases of antagonistic pleiotropy among 98 diseases at 4368 genes. However, evidence of altered signals of selection pressure and heritability distribution at the gene expression level is limited. CONCLUSION: We conclude that antagonistic pleiotropy is widespread among human polygenic diseases, and it has distorted the evolutionary signal and genetic architecture of diseases at the locus level.
Authors: G J Arason; R Kolka; A B Hreidarsson; H Gudjonsson; P M Schneider; L Fry; A Arnason Journal: Clin Exp Immunol Date: 2005-06 Impact factor: 4.330
Authors: Brendan K Bulik-Sullivan; Po-Ru Loh; Hilary K Finucane; Stephan Ripke; Jian Yang; Nick Patterson; Mark J Daly; Alkes L Price; Benjamin M Neale Journal: Nat Genet Date: 2015-02-02 Impact factor: 38.330
Authors: Yair Field; Evan A Boyle; Natalie Telis; Ziyue Gao; Kyle J Gaulton; David Golan; Loic Yengo; Ghislain Rocheleau; Philippe Froguel; Mark I McCarthy; Jonathan K Pritchard Journal: Science Date: 2016-10-13 Impact factor: 47.728
Authors: Alvaro N Barbeira; Rodrigo Bonazzola; Eric R Gamazon; Yanyu Liang; YoSon Park; Sarah Kim-Hellmuth; Gao Wang; Zhuoxun Jiang; Dan Zhou; Farhad Hormozdiari; Boxiang Liu; Abhiram Rao; Andrew R Hamel; Milton D Pividori; François Aguet; Lisa Bastarache; Daniel M Jordan; Marie Verbanck; Ron Do; Matthew Stephens; Kristin Ardlie; Mark McCarthy; Stephen B Montgomery; Ayellet V Segrè; Christopher D Brown; Tuuli Lappalainen; Xiaoquan Wen; Hae Kyung Im Journal: Genome Biol Date: 2021-01-26 Impact factor: 17.906
Authors: Hilary K Finucane; Brendan Bulik-Sullivan; Alexander Gusev; Gosia Trynka; Yakir Reshef; Po-Ru Loh; Verneri Anttila; Han Xu; Chongzhi Zang; Kyle Farh; Stephan Ripke; Felix R Day; Shaun Purcell; Eli Stahl; Sara Lindstrom; John R B Perry; Yukinori Okada; Soumya Raychaudhuri; Mark J Daly; Nick Patterson; Benjamin M Neale; Alkes L Price Journal: Nat Genet Date: 2015-09-28 Impact factor: 38.330
Authors: Hilary K Finucane; Yakir A Reshef; Verneri Anttila; Kamil Slowikowski; Alexander Gusev; Andrea Byrnes; Steven Gazal; Po-Ru Loh; Caleb Lareau; Noam Shoresh; Giulio Genovese; Arpiar Saunders; Evan Macosko; Samuela Pollack; John R B Perry; Jason D Buenrostro; Bradley E Bernstein; Soumya Raychaudhuri; Steven McCarroll; Benjamin M Neale; Alkes L Price Journal: Nat Genet Date: 2018-04-09 Impact factor: 38.330