TY - JOUR
T1 - FGWAS
T2 - Functional genome wide association analysis
AU - The Alzheimer's Disease Neuroimaging Initiative
AU - Huang, Chao
AU - Thompson, Paul
AU - Wang, Yalin
AU - Yu, Yang
AU - Zhang, Jingwen
AU - Kong, Dehan
AU - Colen, Rivka R.
AU - Knickmeyer, Rebecca C.
AU - Zhu, Hongtu
N1 - Publisher Copyright:
© 2017 Elsevier Inc.
PY - 2017/10/1
Y1 - 2017/10/1
N2 - 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.
AB - 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.
KW - Computational complexity
KW - Functional genome wide association analysis
KW - Multivariate varying coefficient model
KW - Wild bootstrap
UR - http://www.scopus.com/inward/record.url?scp=85026417178&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85026417178&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2017.07.030
DO - 10.1016/j.neuroimage.2017.07.030
M3 - Article
C2 - 28735012
AN - SCOPUS:85026417178
SN - 1053-8119
VL - 159
SP - 107
EP - 121
JO - NeuroImage
JF - NeuroImage
ER -