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.

中文翻译：

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脑 QTL 的交叉祖先分析加强了对精神分裂症全基因组关联研究的解释


对脑表达数量性状位点 （eQTL） 的研究阐明了精神分裂症 （SCZ） 的遗传基础。然而，这些研究大多以欧洲人群为中心，导致对人口多样性和疾病风险的理解有限。为了解决这一差距，我们检查了来自非裔美国人 （AA， n = 158）、欧洲人 （EUR， n = 408） 和东亚人 （EAS， n = 217） 的基因型和 RNA-seq 数据。在比较 EUR 和非 EUR 群体之间的 eQTL 时，我们观察到遗传调控效应的一致模式，特别是在 eQTLs 的效应大小方面。然而，发现与 1,276 个基因相关的 343,737 个 cis-eQTL 和 198,769 个 SNP 对非 EUR 人群具有特异性。超过 90% 观察到的 eQTL 群体差异可以追溯到等位基因频率的差异。此外，这些 eQTL 中有 35% 在 EUR 人群中非常罕见。将脑 eQTLs 与来自不同群体的 SCZ 信号整合，我们观察到与不匹配的群体相比，匹配群体中大脑 eQTLs 的疾病遗传性富集更高。优先级分析确定了 EUR 数据集中遗漏的 5 个风险基因（SFXN2、VPS37B、DENR、FTCDNL1 和 NT5DC2）和已知风险基因（CNNM2、MTRFR 和 MPHOSPH9）中的 3 个潜在调节变异。我们的研究结果强调，增加遗传祖先多样性比仅仅增加单祖先 eQTL 数据集中的样本量更有效地提高功效。这样的策略不仅会提高我们对种群结构生物学基础的理解，还会为 SCZ 中风险基因的鉴定铺平道路。