A review of multivariate analyses in imaging genetics

Area of Science:

• Neuroscience
• Genetics
• Biostatistics

Background:

• Imaging genetics has rapidly expanded since 2000, integrating neuroimaging and molecular genetics.
• Numerous reviews exist, but a focus on multivariate analytical methods is needed.
• Proper implementation of analytical tools is crucial for successful imaging genetics studies.

Purpose of the Study:

• To survey recent publications in imaging genetics.
• To focus on multivariate methods for analyzing large-scale imaging and genetic datasets.
• To discuss methods for establishing genotype-phenotype associations.

Main Methods:

• Review of publications employing multivariate statistical techniques.
• Categorization of genetic data analysis into a priori and data-driven approaches.
• Summary of multivariate imaging data analysis, including Independent Component Analysis (ICA) and Independent Vector Analysis (IVA).
• Review of methods linking imaging and genetic data, such as sparse partial least squares and sparse canonical correlation analysis.

Main Results:

• Multivariate methods are essential for handling high-dimensional data in imaging genetics.
• Independent Component Analysis (ICA) and its extensions are prevalent for imaging data.
• Methods like sparse partial least squares and sparse canonical correlation analysis are key for multivariate genotype-phenotype associations.

Conclusions:

• Advanced multivariate methods are critical for advancing imaging genetics research.
• These methods facilitate the extraction of latent variables from genetic and imaging data.
• Understanding the assumptions, advantages, and limitations of these methods is vital for their effective application.