在精神疾病协会研究中代表性不足的人群 (PUMAS项目) 的表型协调和分析
Ana M Ramirez-Diaz1, Ana M Diaz-Zuluaga1, Rocky E Stroud2,3
1Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA.
medRxiv : the preprint server for health sciences
|November 6, 2024
概括
普马斯项目协调了来自不同队列的数据,以使严重精神疾病 (SMI) 的代表人数不足的跨诊断遗传研究成为可能. 这项努力允许对精神分裂症和双相情感障碍进行遗传分析,改善了精神病遗传学研究.
科学领域:
- 精神病学遗传学 精神病学遗传学
- 人口遗传学 人口遗传学
- 计算生物学 计算生物学
背景情况:
- 精神疾病协会研究中代表性不足的人口 (PUMAS) 项目解决了非洲和拉丁美洲人口在精神病遗传学研究中的历史代表性不足问题.
- 普马斯对严重精神疾病 (SMI) 进行大规模遗传关联研究,包括精神分裂症 (SCZ),双相情感障碍 (BP) 和严重抑郁障碍 (MDD).
研究的目的:
- 通过调整和协调来自四个不同的队列的表型数据,使PUMAS数据的跨诊断基因分析成为可能.
- 为了促进多个层面的遗传变异分析:SMI总体,具体诊断和个别症状.
主要方法:
- 用不同的评估方法对89320名参与者在四个队列 (PAISA,PUMAS-LATAM,NGAP,GPC) 的数据进行表型对齐和协调.
- 标准化了19个核心SMI项目级表型,来自>6,895个映射的项目级表型,包括内容,分支和时间框架.
- 通过多重对应分析 (MCA),并发分析和项目级认可来评估协调.
主要成果:
- 从各种评估工具中成功协调了广泛的表型数据,使SMI,SCZ和BP的遗传分析成为可能.
- 精神分裂症 (44.97%) 和双相I型精神障碍 (31.53%) 是最常见的诊断. 十二个核心表型发生在诊断频率>10%,适合跨诊断分析.
- MCA 显示了整个队列的一致性,MDD 和 SCZ 形成了单独的集群,而其他诊断显示没有显著的集群.
结论:
- 调整策略有效地汇总了表型数据,为跨诊断遗传分析提供了协调的数据集.
- 来自MCA和项目级表型的维度得分将用于基因分析.
- 剩余异质性突出了诊断仪器结构,招募和文化差异对表型数据的影响.
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