Impact of common variants on brain gene expression from RNA to protein to schizophrenia risk

Qiuman Liang, Yi Jiang, Annie W. Shieh, Dan Zhou, Rui Chen, Feiran Wang, Meng Xu, Mingming Niu, Xusheng Wang, Dalila Pinto, Yue Wang, Lijun Cheng, Ramu Vadukapuram, Chunling Zhang, Kay Grennan, Gina Giase, Kevin P. White, Junmin Peng, Bingshan Li, Chunyu Liu (), Chao Chen () and Sidney H. Wang ()
Additional contact information
Qiuman Liang: Central South University, MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital
Yi Jiang: Central South University, MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital
Annie W. Shieh: The University of Texas Health Science Center at Houston, Center for Human Genetics, The Brown foundation Institute of Molecular Medicine
Dan Zhou: Zhejiang University School of Medicine, School of Public Health and the Second Affiliated Hospital
Rui Chen: Vanderbilt University, Department of Molecular Physiology and Biophysics, Vanderbilt Genetics Institute
Feiran Wang: Central South University, MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital
Meng Xu: Central South University, MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital
Mingming Niu: St. Jude Children’s Research Hospital, Department of Structural Biology, Center for Proteomics and Metabolomics
Xusheng Wang: University of Tennessee Health Science Center, Department of Neurology
Dalila Pinto: Icahn School of Medicine at Mount Sinai, Department of Psychiatry, and Seaver Autism Center for Research and Treatment
Yue Wang: Virginia Polytechnic Institute and State University, Department of Electrical and Computer Engineering
Lijun Cheng: University of Chicago, Institute for Genomics and Systems Biology
Ramu Vadukapuram: The University of Texas Rio Grande Valley, Department of Psychiatry
Chunling Zhang: SUNY Upstate Medical University, Department of Neuroscience and Physiology
Kay Grennan: SUNY Upstate Medical University, Department of Psychiatry
Gina Giase: Northwestern University, The Feinberg School of Medicine
Kevin P. White: National University of Singapore, Department of Biochemistry, Yong Loo Lin School of Medicine
Junmin Peng: St. Jude Children’s Research Hospital, Department of Structural Biology, Center for Proteomics and Metabolomics
Bingshan Li: Vanderbilt University, Department of Molecular Physiology and Biophysics, Vanderbilt Genetics Institute
Chunyu Liu: Central South University, MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital
Chao Chen: Central South University, MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital
Sidney H. Wang: The University of Texas Health Science Center at Houston, Center for Human Genetics, The Brown foundation Institute of Molecular Medicine

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract Genetic variants influencing gene expression have been extensively studied at the transcriptional level. How these variants affect downstream processes remains unclear. We quantitated ribosome occupancy in prefrontal cortex samples from the BrainGVEX cohort and integrated these data with transcriptomic and proteomic profiles from the same individuals. Through cis-QTL mapping, we identified genetic variants associated with transcript level (eQTLs), ribosome occupancy (rQTLs), and protein level (pQTLs). Notably, only 34% of eQTLs have their effects propagated to the protein levels, suggesting widespread post-transcriptional attenuation. Using both a gene-based approach and a variant-based approach we identified omics-specific QTLs that associated with brain disorder GWAS signals and found the majority of them to be driven predominantly by transcriptional regulation. Consistently, using a TWAS approach, we identified 74 SCZ risk genes across the three omics layers, 52 were discovered using transcriptome with 68% showing limited impact on protein expression. Our findings indicated that many disease-associated variants act through regulatory mechanisms that do not lead to an observable impact on the protein level.

Date: 2025
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DOI: 10.1038/s41467-025-65818-5

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