Modern attempts in providing predictive risk for complex disorders, such as schizophrenia, integrate genetic and brain information in what is known as imaging genetics. In this work, we propose inferential and predictive methods to relate the presence of a complex disorder, schizophrenia, to genetic and imaging features and predict its risk for new individuals. Given functional Magnetic Resonance Image and Single Nucleotide Polymorphisms information of healthy and people diagnosed with schizophrenia, we use a Bayesian probit model to select discriminating variables, while to estimate the predictive risk, the most promising models are combined using a Bayesian model averaging scheme. For these purposes, we propose an informed reversible jump Markov chain Monte Carlo, named data driven or informed reversible jump, which is scalable to high-dimension data when the number of covariates is much larger than the sample size.

翻译：现代针对精神分裂症等复杂疾病预测风险的研究，通过整合遗传与脑部信息形成了成像遗传学领域。本文提出推断与预测方法，旨在建立复杂疾病（精神分裂症）与遗传和影像特征之间的关联，并预测新个体的患病风险。基于健康人群和精神分裂症患者的功能性磁共振成像与单核苷酸多态性数据，我们采用贝叶斯probit模型筛选判别性变量；同时，通过贝叶斯模型平均方案整合最具潜力的模型以评估预测风险。为此，我们提出一种名为数据驱动型或信息型可逆跳变的马尔可夫链蒙特卡洛方法，该算法在协变量数量远大于样本量的高维数据场景中具备可扩展性。