Increasing power for voxel-wise genome-wide association studies: The random field theory, least square kernel machines and fast permutation procedures

• Published in: NeuroImage (Orlando, Fla.) Vol. 63; no. 2; pp. 858 - 873
• Main Authors: Ge, Tian, Feng, Jianfeng, Hibar, Derrek P., Thompson, Paul M., Nichols, Thomas E.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.11.2012; Elsevier Limited
• Subjects: ADNI; Aged; Alzheimer's disease; Association study; Brain - physiology; Cluster size inference; Diffusion Tensor Imaging; Female; Genes; Genetics; Genome-Wide Association Study - methods; Genomes; Genomics; GRIN2B; Humans; Image Interpretation, Computer-Assisted - methods; Imaging genetics; Least square kernel machines; Least-Squares Analysis; Male; Medical imaging; Methods; Models, Neurological; MRI; Nonstationarity; Parametric tail approximation; Pareto distribution; Permutation; Polymorphism, Single Nucleotide - genetics; Principal components analysis; Random field theory; Studies; Tensor-based morphometry; Theory; Voxel-wise inference; ADNI; Random field theory; Permutation; MRI; Imaging genetics; Association study; Parametric tail approximation; GRIN2B; Voxel-wise inference; Nonstationarity; Least square kernel machines; Pareto distribution; Tensor-based morphometry; Cluster size inference; Alzheimer's disease
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2012.07.012

Imaging traits are thought to have more direct links to genetic variation than diagnostic measures based on cognitive or clinical assessments and provide a powerful substrate to examine the influence of genetics on human brains. Although imaging genetics has attracted growing attention and interest, most brain-wide genome-wide association studies focus on voxel-wise single-locus approaches, without taking advantage of the spatial information in images or combining the effect of multiple genetic variants. In this paper we present a fast implementation of voxel- and cluster-wise inferences based on the random field theory to fully use the spatial information in images. The approach is combined with a multi-locus model based on least square kernel machines to associate the joint effect of several single nucleotide polymorphisms (SNP) with imaging traits. A fast permutation procedure is also proposed which significantly reduces the number of permutations needed relative to the standard empirical method and provides accurate small p-value estimates based on parametric tail approximation. We explored the relation between 448,294 single nucleotide polymorphisms and 18,043 genes in 31,662 voxels of the entire brain across 740 elderly subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Structural MRI scans were analyzed using tensor-based morphometry (TBM) to compute 3D maps of regional brain volume differences compared to an average template image based on healthy elderly subjects. We find method to be more sensitive compared with voxel-wise single-locus approaches. A number of genes were identified as having significant associations with volumetric changes. The most associated gene was GRIN2B, which encodes the N-methyl-d-aspartate (NMDA) glutamate receptor NR2B subunit and affects both the parietal and temporal lobes in human brains. Its role in Alzheimer's disease has been widely acknowledged and studied, suggesting the validity of the approach. The various advantages over existing approaches indicate a great potential offered by this novel framework to detect genetic influences on human brains. ► An efficient and accurate use of Random Field Theory in imaging genetics. ► A multi-locus approach modeling the interaction of nearby SNPs. ► A fast permutation procedure to reduce computational burden. ► An accurate parametric tail approximation to permutation distributions. ► Several genes identified with whole-brain genome-wide familywise error corrected significance.