Department of Pharmacology, Vanderbilt University, Nashville, TN 37235, USA.
Departments of Psychiatry and Behavioral Medicine and Pharmacology, Toxicology and Neuroscience, LSU Health Shreveport, Shreveport, LA 71103, USA.
Genes (Basel). 2022 Jun 10;13(6):1040. doi: 10.3390/genes13061040.
Schizophrenia is a highly heritable polygenic psychiatric disorder. Characterization of its genetic architecture may lead to a better understanding of the overall burden of risk variants and how they determine susceptibility to disease. A major goal of this project is to develop a modeling approach to compare and quantify the relative effects of single nucleotide polymorphisms (SNPs), copy number variants (CNVs) and other factors. We derived a mathematical model for the various genetic contributions based on the probability of expressing a combination of risk variants at a frequency that matched disease prevalence. The model included estimated risk variant allele outputs (VAOs) adjusted for population allele frequency. We hypothesized that schizophrenia risk genes would be more interactive than random genes and we confirmed this relationship. Gene-gene interactions may cause network ripple effects that spread and amplify small individual effects of risk variants. The modeling revealed that the number of risk alleles required to achieve the threshold for susceptibility will be determined by the average functional locus output (FLO) associated with a risk allele, the risk allele frequency (RAF), the number of protective variants present and the extent of gene interactions within and between risk loci. The model can account for the quantitative impact of protective variants as well as CNVs on disease susceptibility. The fact that non-affected individuals must carry a non-trivial burden of risk alleles suggests that genetic susceptibility will inevitably reach the threshold for schizophrenia at a recurring frequency in the population.
精神分裂症是一种高度遗传性的多基因精神疾病。对其遗传结构的特征描述可能有助于更好地理解风险变异的总体负担以及它们如何决定疾病易感性。该项目的主要目标之一是开发一种建模方法来比较和量化单核苷酸多态性 (SNP)、拷贝数变异 (CNV) 和其他因素的相对影响。我们根据与疾病流行率相匹配的风险变异频率表达组合的概率,为各种遗传贡献推导了一个数学模型。该模型包括针对群体等位基因频率进行调整的估计风险变异等位基因输出 (VAO)。我们假设精神分裂症风险基因的相互作用会比随机基因更频繁,我们证实了这种关系。基因-基因相互作用可能导致网络涟漪效应,从而传播和放大风险变异的微小个体效应。建模结果表明,达到易感性阈值所需的风险等位基因数量将取决于与风险等位基因相关的平均功能位点输出 (FLO)、风险等位基因频率 (RAF)、存在的保护变异数量以及风险基因座内和基因座之间的基因相互作用程度。该模型可以解释保护变异和 CNV 对疾病易感性的定量影响。事实上,未受影响的个体必须携带大量非风险等位基因,这表明遗传易感性将不可避免地以反复出现的频率达到精神分裂症的阈值。