Kendler Kenneth S, Ohlsson Henrik, Sundquist Jan, Sundquist Kristina
Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA.
Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA.
Mol Psychiatry. 2023 Dec;28(12):5195-5205. doi: 10.1038/s41380-023-02156-2. Epub 2023 Jul 6.
We investigate how selection of psychiatric cases by phenotypic criteria can alter the strength and specificity of their genetic risk by examining samples from national Swedish registries for five disorders: major depression (MD, N = 158,557), drug use disorder (DUD, N = 69,841), bipolar disorder (BD, N = 13,530)) ADHD (N = 54,996) and schizophrenia (N = 11,227)). We maximized the family genetic risk score (FGRS) for each disorder and then the specificity of the FGRS in six disorder pairs by univariable and multivariable regression. We use split-half methods to divide our cases for each disorder into deciles for prediction of genetic risk magnitude and quintiles for prediction of specificity by FGRS differences between two disorders. We utilized seven predictor groups: demography/sex, # registrations, site of diagnosis, severity, comorbidity, treatment, and educational/social variables. The ratio of the FGRS in the upper vs two lower deciles from our multivariable prediction model was, in order, DUD - 12.6, MD - 4.9, BD - 4.5, ADHD - 3.3 and schizophrenia 1.4. From the lowest to highest quintile, our measures of genetic specificity increased more than five-fold for i) MD vs. Anxiety Disorders, ii) MD vs BD, iii) MD versus alcohol use disorder (AUD), iv) BD vs schizophrenia and v) DUD vs AUD. This increase was nearly two-fold for ADHD vs DUD. We conclude that the level of genetic liability for our psychiatric disorders could be substantially enriched by selection of cases with our predictors. Specificity of genetic risk could also be substantially impacted by these same predictors.
我们通过检查瑞典国家登记处的五种疾病样本,研究了根据表型标准选择精神疾病病例如何改变其遗传风险的强度和特异性:重度抑郁症(MD,N = 158,557)、药物使用障碍(DUD,N = 69,841)、双相情感障碍(BD,N = 13,530)、注意力缺陷多动障碍(ADHD,N = 54,996)和精神分裂症(N = 11,227)。我们通过单变量和多变量回归,最大化了每种疾病的家族遗传风险评分(FGRS),然后是六个疾病对中FGRS的特异性。我们使用折半法将每种疾病的病例分为十分位数以预测遗传风险大小,分为五分位数以通过两种疾病之间的FGRS差异预测特异性。我们使用了七个预测变量组:人口统计学/性别、登记次数、诊断地点、严重程度、共病、治疗以及教育/社会变量。我们多变量预测模型中FGRS在最高十分位数与两个最低十分位数中的比率依次为:DUD - 12.6、MD - 4.9、BD - 4.5、ADHD - 3.3和精神分裂症1.4。从最低到最高五分位数,我们的遗传特异性指标在以下情况中增加了五倍多:i)MD与焦虑症、ii)MD与BD、iii)MD与酒精使用障碍(AUD)、iv)BD与精神分裂症以及v)DUD与AUD。对于ADHD与DUD,这种增加近两倍。我们得出结论,通过使用我们的预测变量选择病例,可以大幅提高我们精神疾病的遗传易感性水平。这些相同的预测变量也可能对遗传风险的特异性产生重大影响。