Liu Jingyu, Calhoun Vince D
The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA ; Department of Electrical and Computer Engineering, University of New Mexico Albuquerque, NM, USA.
Front Neuroinform. 2014 Mar 26;8:29. doi: 10.3389/fninf.2014.00029. eCollection 2014.
Recent advances in neuroimaging technology and molecular genetics provide the unique opportunity to investigate genetic influence on the variation of brain attributes. Since the year 2000, when the initial publication on brain imaging and genetics was released, imaging genetics has been a rapidly growing research approach with increasing publications every year. Several reviews have been offered to the research community focusing on various study designs. In addition to study design, analytic tools and their proper implementation are also critical to the success of a study. In this review, we survey recent publications using data from neuroimaging and genetics, focusing on methods capturing multivariate effects accommodating the large number of variables from both imaging data and genetic data. We group the analyses of genetic or genomic data into either a priori driven or data driven approach, including gene-set enrichment analysis, multifactor dimensionality reduction, principal component analysis, independent component analysis (ICA), and clustering. For the analyses of imaging data, ICA and extensions of ICA are the most widely used multivariate methods. Given detailed reviews of multivariate analyses of imaging data available elsewhere, we provide a brief summary here that includes a recently proposed method known as independent vector analysis. Finally, we review methods focused on bridging the imaging and genetic data by establishing multivariate and multiple genotype-phenotype-associations, including sparse partial least squares, sparse canonical correlation analysis, sparse reduced rank regression and parallel ICA. These methods are designed to extract latent variables from both genetic and imaging data, which become new genotypes and phenotypes, and the links between the new genotype-phenotype pairs are maximized using different cost functions. The relationship between these methods along with their assumptions, advantages, and limitations are discussed.
神经成像技术和分子遗传学的最新进展为研究基因对大脑属性变异的影响提供了独特的机会。自2000年发表首篇关于脑成像与遗传学的文章以来，成像遗传学已成为一种快速发展的研究方法，每年的相关出版物不断增加。已有多篇综述为研究界提供了针对各种研究设计的内容。除了研究设计外，分析工具及其正确应用对于研究的成功也至关重要。在本综述中，我们使用神经成像和遗传学数据调查近期的出版物，重点关注能够适应来自成像数据和基因数据的大量变量的多变量效应捕获方法。我们将基因或基因组数据的分析分为先验驱动或数据驱动方法，包括基因集富集分析、多因素降维、主成分分析、独立成分分析（ICA）和聚类。对于成像数据的分析，ICA及其扩展是最广泛使用的多变量方法。鉴于其他地方已有关于成像数据多变量分析的详细综述，我们在此提供简要总结，包括一种最近提出的称为独立向量分析的方法。最后，我们回顾通过建立多变量和多基因-表型关联来衔接成像和基因数据的方法，包括稀疏偏最小二乘法、稀疏典型相关分析、稀疏降秩回归和平行ICA。这些方法旨在从基因和成像数据中提取潜在变量，这些潜在变量成为新的基因型和表型，并使用不同的代价函数使新基因型-表型对之间的联系最大化。我们还讨论了这些方法之间的关系及其假设、优点和局限性。
