FGWAS: Functional genome wide association analysis

the Alzheimer's Disease Neuroimaging Initiative

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Functional phenotypes (e.g., subcortical surface representation), which commonly arise in imaging genetic studies, have been used to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. However, existing statistical methods largely ignore the functional features (e.g., functional smoothness and correlation). The aim of this paper is to develop a functional genome-wide association analysis (FGWAS) framework to efficiently carry out whole-genome analyses of functional phenotypes. FGWAS consists of three components: a multivariate varying coefficient model, a global sure independence screening procedure, and a test procedure. Compared with the standard multivariate regression model, the multivariate varying coefficient model explicitly models the functional features of functional phenotypes through the integration of smooth coefficient functions and functional principal component analysis. Statistically, compared with existing methods for genome-wide association studies (GWAS), FGWAS can substantially boost the detection power for discovering important genetic variants influencing brain structure and function. Simulation studies show that FGWAS outperforms existing GWAS methods for searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. We have successfully applied FGWAS to large-scale analysis of data from the Alzheimer's Disease Neuroimaging Initiative for 708 subjects, 30,000 vertices on the left and right hippocampal surfaces, and 501,584 SNPs.

Original languageEnglish (US)
Pages (from-to)107-121
Number of pages15
JournalNeuroImage
Volume159
DOIs
StatePublished - Oct 1 2017

Fingerprint

Genome-Wide Association Study
Phenotype
Principal Component Analysis
Neuroimaging
Neurodegenerative Diseases
Single Nucleotide Polymorphism
Alzheimer Disease
Genome
Brain
Genes

Keywords

  • Computational complexity
  • Functional genome wide association analysis
  • Multivariate varying coefficient model
  • Wild bootstrap

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

Cite this

the Alzheimer's Disease Neuroimaging Initiative (2017). FGWAS: Functional genome wide association analysis. NeuroImage, 159, 107-121. https://doi.org/10.1016/j.neuroimage.2017.07.030

FGWAS : Functional genome wide association analysis. / the Alzheimer's Disease Neuroimaging Initiative.

In: NeuroImage, Vol. 159, 01.10.2017, p. 107-121.

Research output: Contribution to journalArticle

the Alzheimer's Disease Neuroimaging Initiative 2017, 'FGWAS: Functional genome wide association analysis', NeuroImage, vol. 159, pp. 107-121. https://doi.org/10.1016/j.neuroimage.2017.07.030
the Alzheimer's Disease Neuroimaging Initiative. FGWAS: Functional genome wide association analysis. NeuroImage. 2017 Oct 1;159:107-121. https://doi.org/10.1016/j.neuroimage.2017.07.030
the Alzheimer's Disease Neuroimaging Initiative. / FGWAS : Functional genome wide association analysis. In: NeuroImage. 2017 ; Vol. 159. pp. 107-121.
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