Background: Brain image genetics provides enormous opportunities for examining the effects of genetic
variations on the brain. Many studies have shown that the structure, function, and abnormality (e.g., those
related to Alzheimer's disease) of the brain are heritable. However, which genetic variations contribute to these
phenotypic changes is not completely clear. Advances in neuroimaging and genetics have led us to obtain
detailed brain anatomy and genome-wide information. These data offer us new opportunities to identify genetic
variations such as single nucleotide polymorphisms (SNPs) that affect brain structure. In this paper, we perform
a genome-wide variant-based study, and aim to identify top SNPs or SNP sets which have genetic effects with
the largest neuroanotomic coverage at both voxel and region-of-interest (ROI) levels. Based on the voxelwise
genome-wide association study (GWAS) results, we used the exhaustive search to nd the top SNPs or SNP
sets that have the largest voxel-based or ROI-based neuroanatomic coverage. For SNP sets with >2 SNPs, we
proposed an efficient genetic algorithm to identify top SNP sets that can cover all ROIs or a specific ROI.
Results: We identified an ensemble of top SNPs, SNP-pairs and SNP-sets, whose effects have the largest
neuroanatomic coverage. Experimental results on real imaging genetics data show that the proposed genetic
algorithm is superior to the exhaustive search in terms of computational time for identifying top SNP-sets.
Conclusions: We proposed and applied an informatics strategy to identify top SNPs, SNP-pairs and SNP-sets
that have genetic effects with the largest neuroanatomic coverage. The proposed genetic algorithm others an
efficient solution to accomplish the task, especially for identifying top SNP-sets.