基于电子健康记录的多维精神病理学全基因组关联研究。
Genome-wide Association Study of Dimensional Psychopathology Using Electronic Health Records.
机构信息
Center for Quantitative Health and Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
Center for Quantitative Health and Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
出版信息
Biol Psychiatry. 2018 Jun 15;83(12):1005-1011. doi: 10.1016/j.biopsych.2017.12.004. Epub 2018 Feb 26.
BACKGROUND
Genetic studies of neuropsychiatric disease strongly suggest an overlap in liability. There are growing efforts to characterize these diseases dimensionally rather than categorically, but the extent to which such dimensional models correspond to biology is unknown.
METHODS
We applied a newly developed natural language processing method to extract five symptom dimensions based on the National Institute of Mental Health Research Domain Criteria definitions from narrative hospital discharge notes in a large biobank. We conducted a genome-wide association study to examine whether common variants were associated with each of these dimensions as quantitative traits.
RESULTS
Among 4687 individuals, loci in three of five domains exceeded a genome-wide threshold for statistical significance. These included a locus spanning the neocortical development genes RFPL3 and RFPL3S for arousal (p = 2.29 × 10) and one spanning the FPR3 gene for cognition (p = 3.22 × 10).
CONCLUSIONS
Natural language processing identifies dimensional phenotypes that may facilitate the discovery of common genetic variation that is relevant to psychopathology.
背景
神经精神疾病的遗传学研究强烈表明易感性存在重叠。人们越来越努力地从维度上而不是类别上描述这些疾病，但这些维度模型与生物学的对应程度尚不清楚。
方法
我们应用一种新开发的自然语言处理方法，根据国家心理健康研究所研究领域标准的定义，从大型生物库中的住院记录中提取五个症状维度。我们进行了全基因组关联研究，以检查常见变体是否与这些维度中的每一个作为定量特征相关联。
结果
在 4687 个人中，五个域中的三个域的位点超过了全基因组统计显著性阈值。其中包括跨越新皮层发育基因 RFPL3 和 RFPL3S 的唤醒的一个位点（p=2.29×10）和一个跨越 FPR3 基因的认知的一个位点（p=3.22×10）。
结论
自然语言处理可以识别出维度表型，这可能有助于发现与精神病理学相关的常见遗传变异。
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