Genetic Epidemiology and, Queensland Institute of Medical Research, Herston Road, Brisbane, Queensland 4006, Australia.
Genome Med. 2010 Feb 2;2(2):10. doi: 10.1186/gm131.
Evidence for genetic contribution to complex diseases is described by recurrence risks to relatives of diseased individuals. Genome-wide association studies allow a description of the genetics of the same diseases in terms of risk loci, their effects and allele frequencies. To reconcile the two descriptions requires a model of how risks from individual loci combine to determine an individual's overall risk.
We derive predictions of risk to relatives from risks at individual loci under a number of models and compare them with published data on disease risk.
The model in which risks are multiplicative on the risk scale implies equality between the recurrence risk to monozygotic twins and the square of the recurrence risk to sibs, a relationship often not observed, especially for low prevalence diseases. We show that this theoretical equality is achieved by allowing impossible probabilities of disease. Other models, in which probabilities of disease are constrained to a maximum of one, generate results more consistent with empirical estimates for a range of diseases.
The unconstrained multiplicative model, often used in theoretical studies because of its mathematical tractability, is not a realistic model. We find three models, the constrained multiplicative, Odds (or Logit) and Probit (or liability threshold) models, all fit the data on risk to relatives. Currently, in practice it would be difficult to differentiate between these models, but this may become possible if genetic variants that explain the majority of the genetic variance are identified.
通过患病个体亲属的复发风险来描述遗传因素对复杂疾病的影响。全基因组关联研究可以根据风险位点、其效应和等位基因频率来描述相同疾病的遗传学。要协调这两种描述，需要建立一个模型，说明个体位点的风险如何组合来确定个体的整体风险。
我们根据多种模型推导了个体位点风险对亲属风险的预测，并将其与疾病风险的已发表数据进行了比较。
在风险尺度上风险呈乘法关系的模型意味着同卵双胞胎的复发风险与同胞复发风险的平方相等，这种关系并不常见，尤其是对于低流行疾病。我们表明，通过允许疾病的概率不可能达到，就可以实现这种理论上的相等。其他模型，其中疾病的概率受到限制，最大值为一，对于一系列疾病的结果与经验估计更为一致。
无约束乘法模型，由于其数学上的可处理性，经常在理论研究中使用，但它不是一个现实的模型。我们发现三个模型，即约束乘法模型、优势（或对数比）模型和概率（或发病阈值）模型，都符合亲属风险的数据。目前，在实践中很难区分这些模型，但如果能确定解释大部分遗传变异的遗传变异，这可能变得可行。
