Sparse models for imaging genetics

J. Wang, T. Yang, P. Thompson, J. Ye

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Scopus citations


Imaging genetics is an emerging and promising technique that investigates how genetic variations affect brain development, structure, and function. Many imaging genetic studies involve disorder-related neuroimaging phenotypes and molecular genetics data, such as single nucleotide polymorphism data. However, this class of studies is challenging due to the relatively small number of subjects but extremely high-dimensionality of both imaging and genetic data. In this chapter, we introduce a suite of sparse methods-that can produce interpretable models and are robust to overfitting-for imaging genetics. Moreover, we can incorporate various biological prior knowledge-such as linkage disequilibrium information-into the analysis. However, due to the nonsmooth and highly complex regularizers, the applications of sparse models to large-scale problems remain challenging. Thus we introduce a suite of novel optimization techniques, that is, sparse screening, which can boost the efficiency of many sparse models on large-scale data sets by several orders of magnitude.

Original languageEnglish
Title of host publicationMachine Learning and Medical Imaging
PublisherElsevier Inc.
Number of pages23
ISBN (Electronic)9780128041147
ISBN (Print)9780128040768
StatePublished - 9 Aug 2016
Externally publishedYes


  • High-dimensionality
  • Imaging genetics
  • Large-scale optimization
  • Machine learning
  • Screening
  • Sparse learning


Dive into the research topics of 'Sparse models for imaging genetics'. Together they form a unique fingerprint.

Cite this