Discovery of genes that affect human brain connectivity: A genome-wide analysis of the connectome

Abstract

Human brain connectivity is disrupted in a wide range of disorders – from Alzheimer’s disease to autism – but little is known about which specific genes affect it. Here we conducted a genome-wide association for connectivity matrices that capture information on the density of fiber connections between 70 brain regions. We scanned a large twin cohort (N=366) with 4-Tesla high angular resolution diffusion imaging (105-gradient HARDI). Using whole brain HARDI tractography, we extracted a relatively sparse 70×70 matrix representing fiber density between all pairs of cortical regions automatically labeled in co-registered anatomical scans. Additive genetic factors accounted for 1–58% of the variance in connectivity between 90 (of 122) tested nodes. We discovered genome-wide significant associations between variants and connectivity. GWAS permutations at various levels of heritability, and split-sample replication, validated our genetic findings. The resulting genes may offer new leads for mechanisms influencing aberrant connectivity and neurodegeneration.

Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA–DTI working group

“…A meta-analysis of neuroimaging measures has found that FA is heritable with relatively tight confidence intervals, making it extremely promising for further genetic study (Blokland et al, 2012). More recently, DTI measures have begun to be used as phenotypes for genome-wide association and linkage studies (Chiang et al, 2012; Jahanshad et al, 2012a; Kochunov et al, 2011b; Lopez et al, in press; Sprooten et al, 2012) in the quest to identify novel variants and molecular pathways associated with white matter microstructure. While these gene discovery studies have great potential, single-site studies have limited power to discover variants that reliably generalize to other cohorts.…”

Frequency Specific Effects of ApoE ε4 Allele on Resting-State Networks in Nondemented Elders

“…With the development of novel algorithms and theories, nowadays we can correlate the neuroimaging data with the genetic data and discover how genotype affecting brain function and connectivity just as the phonotype, and to identify risks for neurological and psychiatric diseases [30]. The frequency characteristics of functional connectivity may be distinct for different brain networks [13].…”

Fiberprint: A subject fingerprint based on sparse code pooling for white matter fiber analysis

“…Recent advances in dMRI acquisition protocols have lead to significant improvements 5 in signal reconstruction [2,3,4], driving the development of novel tools for processing and interpreting dMRI data. Among the many applications using dMRI data, the quantitative characterization of white matter geometry and its genetic basis [5,6,7] is an important step in the study of the human brain, essential to understanding the mechanisms of neurological function and disease [8,9,10,11]. 10 Over the years, several approaches have been proposed to provide a simplified quantitative description of white matter connections, to allow for crosspopulation inferences [12, 13,14,15].…”