Distinguishing different psychiatric disorders using DDx-PRS.
Distinguishing different psychiatric disorders using DDx-PRS.
复制标题
使用 DDx-PRS 区分不同的精神疾病。
DOI：
10.1101/2024.02.02.24302228
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Ruderfe
中科院分区：
文献类型：
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作者：
Peyrot,WouterJ;Panagiotaropoulou,Georgia;OldeLoohuis,LoesM;Adams,MarkJ;Awasthi,Swapnil;Ge,Tian;McIntosh,AndrewM;Mitchell,BrittanyL;Mullins,Niamh;O'Connell,KevinS;Penninx,BrendaWJH;Posthuma,Danielle;Ripke,Stephan;Ruderfe
尽管病例对照多基因预测方法（如精神分裂症与对照组）取得了很大进展，但仍然需要一种能够从基因上区分临床相关疾病的方法（如精神分裂症（SCZ）与双相情感障碍（BIP）与抑郁症（MDD）与对照组）；这种方法可能具有重要的临床价值，特别是在疾病发作时，鉴别诊断可能具有挑战性。在这里，我们引入了一种方法，即鉴别诊断-多基因风险评分（DDx-PRS），该方法通过对疾病之间的方差/协方差结构建模，利用每种疾病的病例对照多基因风险评分（PRS）（使用现有方法计算）和每种诊断类别的先验临床概率，联合估计每种可能诊断类别的后验概率（例如SCZ= 50%， BIP= 25%， MDD= 15%，对照= 10%）。DDx-PRS仅使用概要级培训数据，不使用调优数据，便于在临床环境中实施。在模拟中，DDx-PRS被很好地校准（而一种简单的方法，分析每一种疾病的边缘是很差的校准），并有效地区分每一种诊断类别与其他。然后，我们将DDx-PRS应用于精神病学基因组学联盟SCZ/BIP/MDD/对照数据，包括来自3个病例对照GWAS （N= 41,917-173,140例，总N= 1,048,683例）的总结水平训练数据，以及来自不同队列的每个诊断类别数量相同的测试数据（总N= 11,460）。相对于这些训练样本量，DDx-PRS得到了良好的校准和良好的动力，SCZ与休息的auc为0.66，BIP与休息的auc为0.64，MDD与休息的auc为0.59，对照与休息的auc为0.68。DDx-PRS产生的结果与利用调优数据的方法相当，证实了DDx-PRS是一种有效的方法。预测诊断概率的前十分位数的真实诊断概率大大大于先前的基线概率，特别是在对更大训练样本量的预测中，这意味着在某些情况下具有相当大的临床应用潜力。综上所述，DDx-PRS是鉴别临床相关疾病的有效方法。
Despite great progress on methods for case-control polygenic prediction (eg schizophrenia vs. control), there remains an unmet need for a method that genetically distinguishes clinically related disorders (eg schizophrenia (SCZ) vs. bipolar disorder (BIP) vs. depression (MDD) vs. control); such a method could have important clinical value, especially at disorder onset when differential diagnosis can be challenging. Here, we introduce a method, Differential Diagnosis-Polygenic Risk Score (DDx-PRS), that jointly estimates posterior probabilities of each possible diagnostic category (eg SCZ= 50%, BIP= 25%, MDD= 15%, control= 10%) by modeling variance/covariance structure across disorders, leveraging case-control polygenic risk scores (PRS) for each disorder (computed using existing methods) and prior clinical probabilities for each diagnostic category. DDx-PRS uses only summary-level training data and does not use tuning data, facilitating implementation in clinical settings. In simulations, DDx-PRS was well-calibrated (whereas a simpler approach that analyzes each disorder marginally was poorly calibrated), and effective in distinguishing each diagnostic category vs. the rest. We then applied DDx-PRS to Psychiatric Genomics Consortium SCZ/BIP/MDD/control data, including summary-level training data from 3 case-control GWAS (N= 41,917-173,140 cases; total N= 1,048,683) and held-out test data from different cohorts with equal numbers of each diagnostic category (total N= 11,460). DDx-PRS was well-calibrated and well-powered relative to these training sample sizes, attaining AUCs of 0.66 for SCZ vs. rest, 0.64 for BIP vs. rest, 0.59 for MDD vs. rest, and 0.68 for control vs. rest. DDx-PRS produced comparable results to methods that leverage tuning data, confirming that DDx-PRS is an effective method. True diagnosis probabilities in top deciles of predicted diagnosis probabilities were considerably larger than prior baseline probabilities, particularly in projections to larger training sample sizes, implying considerable potential for clinical utility under certain circumstances. In conclusion, DDx-PRS is an effective method for distinguishing clinically related disorders.