[Paper Review] The network architecture of the human brain is modularly encoded in the genome

Maxwell A. Bertolero, Ann S. Blevins+6 more|arXiv (Cornell University)|May 18, 2019

Functional Brain Connectivity Studies54 references6 citations

TL;DR

This study reveals that the human brain's functional connectivity is modularly encoded in the genome, with gene coexpression patterns and SNPs predicting regional connectivity more strongly than structural connectivity. Critically, genetic signatures of brain regions align with their network roles—especially connector hubs—while SNPs at coexpressed genes explain greater variance in connectivity and track with cognitive performance, heritability, and evolution.

ABSTRACT

The form of genotype-phenotype maps are typically modular. However, the form of the genotype-phenotype map in human brain connectivity is unknown. A modular mapping could exist, in which distinct sets of genes' coexpression across brain regions is similar to distinct brain regions' functional or structural connectivity, and in which single nucleotide polymorphisms (SNPs) at distinct sets of genes alter distinct brain regions' functional or structural connectivity. Here, we leverage multimodal human neuroimaging, genotype, and post-mortem gene expression datasets to determine the form of the genotype-phenotype map of human brain connectivity. Across multiple analytic approaches, we find that both gene coexpression and SNPs are consistently more strongly related to functional brain connectivity than to structural brain connectivity. Critically, network analyses of genes and brain connectivity demonstrate that different sets of genes account for the connectivity of different regions in the brain. Moreover, the genetic signature of each brain region reflects its community affiliation and role in network communication. Connector hubs have genetic signatures that are similarly connector-like, in that they are representative of the genetic signature of nodes in multiple other modules. Remarkably, we find a tight relationship between gene coexpression and genetic variance: SNPs that are located at the genes whose coexpression is similar to a region's connectivity across cortex tend to predict more variance in that region's connectivity across subjects than SNPs at other genes. Finally, brain regions whose connectivity are well explained by gene coexpression and genetic variance (SNPs) also display connectivity variance across subjects that tracks variance in human performance on cognitively demanding tasks, heritability, development, and evolutionary expansion.

Motivation & Objective

• To determine the form of the genotype-phenotype map in human brain connectivity.
• To investigate whether distinct gene coexpression modules correspond to functional or structural brain network modules.
• To assess whether SNPs in specific genes predict variance in regional brain connectivity.
• To examine the relationship between genetic signatures, network roles (e.g., connector hubs), and cognitive performance.
• To explore how genetic variance in connectivity relates to heritability, development, and evolutionary expansion.

Proposed method

• Integration of multimodal datasets: post-mortem gene expression, functional and structural neuroimaging, and genotype data from human subjects.
• Computation of gene coexpression networks across brain regions using post-mortem transcriptomic data.
• Construction of functional and structural brain connectivity matrices from resting-state fMRI and diffusion MRI data.
• Application of network analysis to identify modular brain communities and characterize hub regions (especially connector hubs).
• Statistical modeling to test whether SNPs in genes with coexpression patterns matching a region’s connectivity predict greater variance in that region’s connectivity across individuals.
• Correlation of genetic variance, connectivity variance, and phenotypic measures including cognitive task performance, heritability, developmental trajectories, and evolutionary expansion.

Experimental results

Research questions

• RQ1 Is there a modular genotype-phenotype map in which gene coexpression patterns correspond to functional brain network modules?
• RQ2 Do SNPs in genes whose coexpression mirrors a brain region’s connectivity predict greater variance in that region’s functional connectivity?
• RQ3 Do genetic signatures of brain regions reflect their network roles, such as those of connector hubs?
• RQ4 How does the variance in functional connectivity explained by gene coexpression and SNPs relate to cognitive performance?
• RQ5 To what extent do connectivity variance, heritability, development, and evolutionary expansion co-vary with genetic variance in connectivity?

Key findings

• Gene coexpression and SNPs are more strongly related to functional brain connectivity than to structural brain connectivity across all analytical approaches.
• Different sets of genes account for the connectivity of distinct brain regions, indicating a modular genetic encoding of network organization.
• The genetic signature of each brain region reflects its community affiliation and role in network communication, with connector hubs showing genetically representative signatures of multiple modules.
• SNPs located at genes whose coexpression pattern matches a region’s cortical connectivity predict significantly more variance in that region’s functional connectivity than SNPs at other genes.
• Brain regions with high explanatory power from gene coexpression and SNPs display connectivity variance that co-occurs with variance in cognitive performance, heritability, developmental changes, and evolutionary expansion.