Abstract
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.
Highlights
While most genetic studies have focused on phenotypes as diagnoses and clinical symptoms, it is relatively recent that intermediate phenotypes have become an ever increasing focus
Methods to capture the interactive or integrated genetic effects of a set of genetic variants, methods to extract brain networks formed from individual voxels or regions, and methods to detect, possibly, multiple genotype-phenotype connections have been developed with their limitations and advantages (Hardoon et al, 2009; Liu et al, 2009b; Vounou et al, 2010; Le Floch et al, 2012)
We present the analytical methods implemented in studies of the last three categories, category 2: sets of genotypes with candidate phenotype, category 3: candidate genotype with multiple imaging phenotypes, A priori BASED MULTIVARIATE ANALYSES ON
Summary
While most genetic studies have focused on phenotypes as diagnoses and clinical symptoms, it is relatively recent that intermediate phenotypes have become an ever increasing focus. Methods to capture the interactive or integrated genetic effects of a set of genetic variants, methods to extract brain networks formed from individual voxels or regions, and methods to detect, possibly, multiple genotype-phenotype connections have been developed with their limitations and advantages (Hardoon et al, 2009; Liu et al, 2009b; Vounou et al, 2010; Le Floch et al, 2012).
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