Cross-ancestry analysis of brain QTLs enhances interpretation of schizophrenia genome-wide association studies

Research on brain expression quantitative trait loci (eQTLs) has illuminated the genetic underpinnings of schizophrenia (SCZ). Yet most of these studies have been centered on European populations, leading to a constrained understanding of population diversities and disease risks. To address this gap, we examined genotype and RNA-seq data from African Americans (AA, n = 158), Europeans (EUR, n = 408), and East Asians (EAS, n = 217). When comparing eQTLs between EUR and non-EUR populations, we observed concordant patterns of genetic regulatory effect, particularly in terms of the effect sizes of the eQTLs. However, 343,737 cis-eQTLs linked to 1,276 genes and 198,769 SNPs were found to be specific to non-EUR populations. Over 90% of observed population differences in eQTLs could be traced back to differences in allele frequency. Furthermore, 35% of these eQTLs were notably rare in the EUR population. Integrating brain eQTLs with SCZ signals from diverse populations, we observed a higher disease heritability enrichment of brain eQTLs in matched populations compared to mismatched ones. Prioritization analysis identified five risk genes (SFXN2, VPS37B, DENR, FTCDNL1, and NT5DC2) and three potential regulatory variants in known risk genes (CNNM2, MTRFR, and MPHOSPH9) that were missed in the EUR dataset. Our findings underscore that increasing genetic ancestral diversity is more efficient for power improvement than merely increasing the sample size within single-ancestry eQTLs datasets. Such a strategy will not only improve our understanding of the biological underpinnings of population structures but also pave the way for the identification of risk genes in SCZ.

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Publication Details

Journal
The American Journal of Human Genetics
Published
2024-10-02
DOI
https://doi.org/10.1016/j.ajhg.2024.09.001
Citations
12
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
6.60

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article

Cross-ancestry analysis of brain QTLs enhances interpretation of schizophrenia genome-wide association studies

Jiapei Dai, Kai Yuan, Tim B. Bigdeli, Richard F. Kopp et al.
12 citations
The American Journal of Human Genetics
Genetic Associations and Epidemiology
6.60
article

Cross-ancestry analysis of brain QTLs enhances interpretation of schizophrenia genome-wide association studies

Jiapei Dai, Kai Yuan, Tim B. Bigdeli, Richard F. Kopp, Fangyuan Duan, Hailiang Huang, Elliot S. Gershon, Shuhua Xu, Chao Ma, Rujia Dai, Xiao‐Xin Yan, Lingqian Wu, Jufang Huang, Cong Han, Zongyao Ren, Yi Jiang, Wenying Qiu, Sihan Liu, Beisha Tang, Qiuman Liang, Eric R. Gamazon, Chunyu Liu, Yu Chen, Chao Chen, Feiran Wang, Yan Xia, Miao Li, Zhilin Ning
article en
12 citations

Abstract

Research on brain expression quantitative trait loci (eQTLs) has illuminated the genetic underpinnings of schizophrenia (SCZ). Yet most of these studies have been centered on European populations, leading to a constrained understanding of population diversities and disease risks. To address this gap, we examined genotype and RNA-seq data from African Americans (AA, n = 158), Europeans (EUR, n = 408), and East Asians (EAS, n = 217). When comparing eQTLs between EUR and non-EUR populations, we observed concordant patterns of genetic regulatory effect, particularly in terms of the effect sizes of the eQTLs. However, 343,737 cis-eQTLs linked to 1,276 genes and 198,769 SNPs were found to be specific to non-EUR populations. Over 90% of observed population differences in eQTLs could be traced back to differences in allele frequency. Furthermore, 35% of these eQTLs were notably rare in the EUR population. Integrating brain eQTLs with SCZ signals from diverse populations, we observed a higher disease heritability enrichment of brain eQTLs in matched populations compared to mismatched ones. Prioritization analysis identified five risk genes (SFXN2, VPS37B, DENR, FTCDNL1, and NT5DC2) and three potential regulatory variants in known risk genes (CNNM2, MTRFR, and MPHOSPH9) that were missed in the EUR dataset. Our findings underscore that increasing genetic ancestral diversity is more efficient for power improvement than merely increasing the sample size within single-ancestry eQTLs datasets. Such a strategy will not only improve our understanding of the biological underpinnings of population structures but also pave the way for the identification of risk genes in SCZ.

The American Journal of Human GeneticsVol. 111(11)
Broad Institute (US), Central South University (CN), South Central Minzu University (CN), SUNY Downstate Health Sciences University (US), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Vanderbilt University (US), SUNY Upstate Medical University (US), Fudan University (CN), Yale University (US), University of Chicago (US), Shanghai Institute of Nutrition and Health (CN), Second Xiangya Hospital of Central South University (CN), Xiangya Hospital Central South University (CN), University of Chinese Academy of Sciences (CN), State Key Laboratory of Genetic Engineering
National Natural Science Foundation of China, National Institutes of Health
Openalex Percentile: Top 3%
Genetic Associations and Epidemiology
6.60
DOIOpen Access PDFOpenAlex
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