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 brainregions'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 .
For much of biology, the manner in which genotype maps to phenotype remains a fundamental mystery. The few maps that are known tend to show modular pleiotropy: sets of phenotypes are determined by distinct sets of genes . One key map that has evaded discovery is that of the human brain's network architecture . Here, we determine the form of this map for gene coexpression and single nucleotide polymorphisms. We discover that mostly non-overlapping sets of genes encode the connectivity of brain network modules (or so-called communities), suggesting that brain network communities demarcate genetic transitions. We find that these clean boundaries break down at connector hubs, whose integrative connectivity is encoded by pleiotropic genes from mostly non-overlapping sets. Broadly, this study opens fundamentally new directions in the study of genetic encoding of brain development, evolution, and disease .
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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
For much of biology, the manner in which genotype maps to phenotype remains a fundamental mystery. The few maps that are known tend to show modular pleiotropy: sets of phenotypes are determined by distinct
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alter distinct brainregions'functional or structural connectivity
. One key map that has evaded discovery is that of the human brain's network architecture
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leverage multimodal human neuroimaging, genotype, and post-mortem gene expression datasets to
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the genotype-phenotype map of human brain connectivity. Across multiple analytic approaches, we find that both
this map for
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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
single nucleotide polymorphisms. We discover that mostly non-overlapping
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account for
encode
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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
brain network modules (or so-called communities), suggesting that brain network communities demarcate genetic transitions. We find that these clean boundaries break down at connector hubs, whose integrative connectivity is encoded by pleiotropic genes from mostly non-overlapping sets. Broadly, this study opens fundamentally new directions in the study of genetic encoding of brain development, evolution, and disease