治疗耐药抑郁症的全基因组协会研究:具有代谢特征的共享生物学
JooEun Kang1, Victor M Castro1, Michael Ripperger1
1Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute (Kang, Morley, Han, Ruderfer), Department of Psychiatry (Castro, Kim, Ge, McCoy, Perlis) and Center for Quantitative Health (Castro, Kim, McCoy, Perlis), Massachusetts General Hospital, Boston; Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); Department of Psychiatry, Center for Disease Neurogenomics, Friedman Brain Institute, Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, and Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York (Venkatesh, Burstein, Voloudakis, Roussos); Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, N.Y. (Venkatesh, Burstein, Voloudakis, Roussos); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Linnér, Chabris); Department of Economics, Leiden University, Leiden, the Netherlands (Linnér); Phenomic Analytics and Clinical Data Core (Rocha) and Population Health Sciences (Hu), Geisinger, Danville, Pa.; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei (Feng).
治疗耐药性抑郁症 (TRD) 的遗传分析揭示了与代谢特征共同的生物学,并确定了两个新的遗传位置. 这项研究提高了我们对TRD的理解.
科学领域:
- 精神病学是一个精神病学.
- 遗传学 是一个遗传学.
- 计算生物学 计算生物学
背景情况:
- 耐治疗抑郁症 (TRD) 影响了三分之一的大型抑郁症 (MDD) 患者.
- TRD的遗传结构尚不清楚,之前的遗传发现有限.
- 缺乏大型,强大的队伍阻碍了TRD遗传研究.
研究的目的:
- 为TRD.进行一项强大的全基因组关联研究 (GWAS).
- 通过一种新的机器学习方法,研究TRD的遗传基础.
主要方法:
- 使用电疗法 (ECT) 收据作为TRD的代用表型.
- 将机器学习应用于电子健康记录,以预测ECT概率.
- 在四个生物库中对154,433个基因型个体进行了GWAS.
主要成果:
- 确定了两种与TRD相关的全基因组显著位点,这两种位点以前都与代谢特征有关.
- 估计TRD遗传率在2%至4.2%之间.
- 观察到TRD和包括认知,ADHD,精神分裂症和BMI在内的特征之间的显著遗传重叠.
结论:
- 支持在基因组研究中使用预测疾病概率.
- 提供了对TRD的遗传结构和生物途径的新见解.
- 表明TRD和代谢条件之间有共同的生物机制.
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