Unknown

Dataset Information

0

Exhaustive search of the SNP-sNP interactome identifies epistatic effects on brain volume in two cohorts.


ABSTRACT: The SNP-SNP interactome has rarely been explored in the context of neuroimaging genetics mainly due to the complexity of conducting approximately 10(11) pairwise statistical tests. However, recent advances in machine learning, specifically the iterative sure independence screening (SIS) method, have enabled the analysis of datasets where the number of predictors is much larger than the number of observations. Using an implementation of the SIS algorithm (called EPISIS), we used exhaustive search of the genome-wide, SNP-SNP interactome to identify and prioritize SNPs for interaction analysis. We identified a significant SNP pair, rs1345203 and rs1213205, associated with temporal lobe volume. We further examined the full-brain, voxelwise effects of the interaction in the ADNI dataset and separately in an independent dataset of healthy twins (QTIM). We found that each additional loading in the epistatic effect was associated with approximately 5% greater brain regional brain volume (a protective effect) in both the ADNI and QTIM samples.

SUBMITTER: Hibar DP 

PROVIDER: S-EPMC4109883 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

altmetric image

Publications

Exhaustive search of the SNP-sNP interactome identifies epistatic effects on brain volume in two cohorts.

Hibar Derrek P DP   Stein Jason L JL   Jahanshad Neda N   Kohannim Omid O   Toga Arthur W AW   McMahon Katie L KL   de Zubicaray Greig I GI   Montgomery Grant W GW   Martin Nicholas G NG   Wright Margaret J MJ   Weiner Michael W MW   Thompson Paul M PM  

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 20130101 Pt 3


The SNP-SNP interactome has rarely been explored in the context of neuroimaging genetics mainly due to the complexity of conducting approximately 10(11) pairwise statistical tests. However, recent advances in machine learning, specifically the iterative sure independence screening (SIS) method, have enabled the analysis of datasets where the number of predictors is much larger than the number of observations. Using an implementation of the SIS algorithm (called EPISIS), we used exhaustive search  ...[more]

Similar Datasets

| S-EPMC5651902 | biostudies-literature
| S-EPMC3551227 | biostudies-literature
| S-EPMC4938657 | biostudies-literature
| S-EPMC3665501 | biostudies-literature
| S-EPMC2394828 | biostudies-literature
| S-EPMC4229902 | biostudies-literature
| S-EPMC5860095 | biostudies-literature
| S-EPMC5417721 | biostudies-literature
| S-EPMC2762409 | biostudies-literature
| S-EPMC2818740 | biostudies-literature