阿姆斯特丹自由大学Danielle Posthuma研究小组取得一项新突破。他们的论文发现了利用多模态证据对性状相关位点的效应基因进行优先排序。2025年2月10日，国际知名学术期刊《自然—遗传学》发表了这一成果。

在这里，研究团队提出了FLAMES（效应基因的精细定位评估模型）框架，它预测了一个位点中最可能的效应基因。FLAMES从连接单核苷酸多态性与基因的生物数据中创建机器学习预测，然后将这些分数与功能网络中GWAS信号收敛的以基因为中心的证据一起评估。小组在专家整理、罕见变异暗示和分子特征领域知识衍生的基因位点对上对FLAMES进行基准测试。

研究团队证明，结合基于单核苷酸多态性和基于收敛的模式优于优先级策略主题单一的证据。应用FLAMES，该课题组研究人员在异卵双胞胎的GWAS中解析了FSHB位点，并进一步利用这一框架寻找与罕见编码证据趋同的精神分裂症风险基因，这些基因与生命的不同阶段相关。

据悉，全基因组关联研究（GWAS）产生了大量与性状和疾病相关的遗传位点。预测介导这些位点-性状关联的效应基因仍然具有挑战性。

附：英文原文

Title: Prioritizing effector genes at trait-associated loci using multimodal evidence

Author: Schipper, Marijn, de Leeuw, Christiaan A., Maciel, Bernardo A. P. C., Wightman, Douglas P., Hubers, Nikki, Boomsma, Dorret I., ODonovan, Michael C., Posthuma, Danielle

Issue&Volume: 2025-02-10

Abstract: Genome-wide association studies (GWAS) yield large numbers of genetic loci associated with traits and diseases. Predicting the effector genes that mediate these locus-trait associations remains challenging. Here we present the FLAMES (fine-mapped locus assessment model of effector genes) framework, which predicts the most likely effector gene in a locus. FLAMES creates machine learning predictions from biological data linking single-nucleotide polymorphisms to genes, and then evaluates these scores together with gene-centric evidence of convergence of the GWAS signal in functional networks. We benchmark FLAMES on gene-locus pairs derived by expert curation, rare variant implication and domain knowledge of molecular traits. We demonstrate that combining single-nucleotide-polymorphism-based and convergence-based modalities outperforms prioritization strategies using a single line of evidence. Applying FLAMES, we resolve the FSHB locus in the GWAS for dizygotic twinning and further leverage this framework to find schizophrenia risk genes that converge with rare coding evidence and are relevant in different stages of life.

DOI: 10.1038/s41588-025-02084-7

Source: https://www.nature.com/articles/s41588-025-02084-7