Preface
Harnessing Large Cohorts and AI to Bridge Genomic Discovery and Clinical Practice
Bitao Zhong, Shaoqi Wang, Xiaoxi Jing, Aniruddh P Patel, Yajie Zhao, Minxian Wang
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abstract
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Perspective
Toward Responsible and Sustainable Data Sharing in Large-scale Cohort-based Genomic Research
Jie Song, Wenwen Chen, Jin Huang, Huan Song
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abstract
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Original Research
Whole-genome Sequence Analysis Revealed Novel Subjective Cognitive Decline-associated Genes in 10,763 Chinese
Mengying Wang, Liyang Sun, Xin Xu, Ruoqi Dai , Qilong Tan , Yun Zhu , Andi Xu, Weifang Zheng, Yuanxing Tu, Dan Zhou, Wenyuan Li, Xifeng Wu
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abstract
Subjective cognitive decline (SCD) is widely regarded as a potential preclinical stage of Alzheimer’s disease (AD), yet its genetic basis remains poorly understood. To address this gap, we investigated genetic biomarkers associated with SCD using whole-genome sequencing (WGS) in 10,763 Chinese participants from the Healthy Zhejiang One Million People Cohort (HOPE Cohort). The discovery stage included 9284 samples, with 1479 samples used for validation. Using a two-stage design, we systematically investigated both common and rare variants associated with SCD. In rare variant analyses, we identified and replicated an association between the upstream region of SEPHS2 and SCD. SEPHS2 is involved in selenophosphate synthesis, and a Mendelian randomization analysis reveals that its expression levels in both blood and brain cerebellum are associated with AD. Additionally, we identified CLVS2, which encodes a protein primarily expressed in neuronal cells, as a potential regulator for SCD based on missense rare variants. Multi-omics evidence suggests that both SEPHS2 and CLVS2 may play roles in neurodegenerative diseases. For common variants, we validated 8 known loci related to cognitive decline, 3 of which originated from the only existing SCD genetic study conducted under a migraine background. Overall, our WGS-based study fills the gap in SCD research by providing vital genetic evidence from an East Asian population and offers insights into the pathogenic mechanisms of SCD.
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Original Research
Regulatory Genomic Circuitry of Brain Age by Integrative Functional Genomic Analyses
Xingzhong Zhao, Anyi Yang, Jing Ding, Yucheng T Yang, Xing-Ming Zhao
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abstract
Brain age gap (BAG) is a valuable biomarker for evaluating brain healthy status and detecting age-associated cognitive degeneration. However, the genetic architecture of BAG and the underlying mechanisms are poorly understood. Here, we estimated brain age from magnetic resonance imaging with improved accuracy using our proposed adversarial convolution network (ACN), and applied the ACN model to an elderly cohort from the UK Biobank. The genetic heritability of BAG was significantly enriched in regulatory regions and implicated in glial cells. We prioritized a set of BAG-associated genes, and further characterized their expression patterns across brain cell types and regions. Two BAG-associated genes, RUNX2 and KLF3, were found to be associated with epigenetic clock and diverse aging-related biological pathways. Finally, two BAG-associated hub transcription factor genes, KLF3 and SOX10, were identified as regulators of pleiotropic risk genes for diverse brain disorders. Altogether, we improve the estimation of BAG, and identify BAG-associated genes and regulatory networks implicated in brain disorders.
研究问题：
随着人类寿命延长，保持大脑健康成为延缓衰老的重要研究方向。大脑年龄差（brain age gap，BAG）是指利用大脑磁共振成像预测的大脑年龄与实际年龄之间的差值，它是衡量脑衰老程度的有力生物标志物。然而BAG的遗传结构和调控关系尚未被系统揭示，限制了其在疾病预防和干预中的应用。研究团队希望回答以下问题：脑龄差的遗传基础是什么？哪些基因和调控网络在大脑衰老中发挥关键作用？脑龄差与精神疾病及衰老相关疾病之间有何因果关系？
研究方法：
本研究构建域对抗卷积网络（adversarial convolution network，ACN），将性别与采集站点视为“域”进行对抗训练以提取与年龄相关且去偏的特征；模型在五个独立队列（西南大学成人生命周期数据集SALD、花旗集团生物医学影像中心CBIC、澳大利亚影像生物标志物与生活方式老龄化研究AIBL、图像信息提取IXI、开放获取影像研究系列OASIS）共2011名4–95岁健康个体上训练，并在英国生物银行3.57万人（45–85岁）上预测BAG，平均绝对误差（mean absolute error, MAE）≈2.70岁；随后整合基因型数据进行全基因组关联研究（genome-wide association study, GWAS）分析，并进行多层次的功能基因组整合分析，包括大脑衰老遗传力的组织与细胞类型富集分析，大脑衰老与多种脑疾病的遗传关联，大脑衰老相关基因的鉴定及表达动态特征分析，大脑衰老相关遗传变异/基因与表观遗传时钟的关联，以及大脑衰老相关基因的调控网络分析。
主要结果：
1. ACN模型在跨数据集上表现优异。与LASSO、弹性网络、GPR、SVR、3D ResNet等模型比较，ACN在多个数据集上的平均绝对误差最低。该模型学习到的特征可在支持向量机（support vector machine, SVM）中以约79%的准确率区分阿尔茨海默病患者和健康人群。
2. GWAS鉴定大量新遗传变异。在3.6万名个体的GWAS中，共发现3868个达到全基因组显著性的单核苷酸多态性（single nucleotide polymorphism, SNP），并筛选出10个独立的lead SNP，其中9个为首次报道；BAG的遗传力约为0.21。
3. BAG的遗传力主要富集在调控区和胶质细胞中。遗传力富集分析显示BAG的遗传贡献主要富集在超级增强子（富集倍数为2.61，假发现率FDR=2.97E−08）和H3K27ac标记的增强子区域；在细胞层面，在胶质细胞的富集度明显高于神经元。
4. BAG与多种脑病和代谢疾病的遗传风险相关。多基因风险评分分析表明BAG与阿尔茨海默病、帕金森病、2型糖尿病、重度抑郁症、精神分裂症等的风险显著正相关。Mendelian随机化分析进一步指出精神分裂症和双相情感障碍可能促进大脑加速衰老。
5. 识别关键基因及其调控网络。基于基因的分析和功能映射，共确认近200个BAG相关基因，其富集于WNT信号通路、突触传递、细菌脂蛋白代谢等功能；这些基因在成人大脑和胶质细胞中的表达显著高于幼年期。RUNX2、KLF3、SOX10等枢纽基因不仅与DNA甲基化年龄差相关，还调控多种神经疾病风险基因。
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