Identifying genetic variants for brain connectivity using Ball Covariance Ranking and Aggregation.

Understanding the genetic architecture of brain functions is essential to clarify the biological etiologies of behavioral and psychiatric disorders. Functional connectivity, representing pairwise correlations of neural activities between brain regions, is moderately heritable. Current methods to identify single nucleotide polymorphisms (SNPs) linked to functional connectivity either neglect the complex structure of functional connectivity or fail to control false discoveries. Therefore, we propose a SNP-set hypothesis test, Ball Covariance Ranking and Aggregation (BCRA), to select and test the significance of SNP sets related to functional connectivity, incorporating matrix structure and controlling false discovery rate. Additionally, we present subsample-BCRA, a faster version for large-scale datasets. Simulation studies show both methods effectively detect SNPs with interactive structures, with subsample-BCRA shortens the running time by 700 folds. Applying our method to UK Biobank data from 34,129 individuals, we identify 10 SNP-sets with 29 SNPs significantly impacting functional connectivity. Gene-based analyses reveal three SNPs as eQTLs of gene , known to change functional connectivity. We also detect nine novel genes associated with behavioral and psychiatric disorders, whose connections to brain functions remain unexplored. Our findings improve our understanding of the genetic basis for brain connectivity and showcase our method's utility for broader applications.