Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease

Abstract

2Genetic determinants of cognition are poorly characterized and their relationship to genes that confer risk for neurodevelopmental disease is unclear. Here, we used a systems-level analysis of genome-wide gene expression data to infer gene-regulatory networks conserved across species and brain regions. Two of these networks, M1 and M3, showed replicable enrichment for common genetic variants underlying healthy human cognitive abilities including memory. Using exome sequence data from 6,871 trios, we find that M3 genes are also enriched for mutations ascertained from patients with neurodevelopmental disease generally, and intellectual disability and epileptic encephalopathy in particular. M3 consists of 150 genes whose expression is tightly developmentally regulated, but which are collectively poorly annotated for known functional pathways. These results illustrate how systems-level analyses can reveal previously unappreciated relationships between neurodevelopmental disease genes in the developed human brain, and provide empirical support for a convergent generegulatory network influencing cognition and neurodevelopmental disease.Cognition refers to human mental abilities such as memory, attention, processing speed, reasoning and executive function. Performance on cognitive tasks varies between individuals and is highly heritable 1 and polygenic 2,3 . However, to date, progress in identifying molecular genetic contributions to healthy human cognitive abilities has been limited 4,5 .A distinction can be made between cognitive domains such as the ability to apply acquired knowledge and learned skills (so called crystallized abilities) and fluid cognitive abilities such as the capacity to establish new memories, reason in novel situations or perform cognitive tasks accurately and quickly 6 . Notably, within individuals, performance on different measures of cognitive ability tend to be positively correlated such that people who do well in one domain, such as memory, tend to do well in other domains 7 . Seemingly disparate domains of cognitive ability also show high levels of genetic correlation in twin studies, typically in excess of 0.6 8 , and analyses using genome-wide similarity between unrelated individuals 3 (genome-wide complex trait analysis, GCTA) has also demonstrated substantial genetic correlation between diverse cognitive and learning abilities 9,10 . These studies suggest genes that influence human cognition may exert pleiotropic effects across diverse cognitive domains, such that genes regulating one cognitive ability might influence other cognitive abilities.Since impairment of cognitive function is a core clinical feature of many neurodevelopmental diseases including schizophrenia 11 , autism 12 , epilepsy 13 and intellectual disability (by definition), we sought to investigate gene-regulatory networks for human cognition and to determine their relationship to neurodevelopmental disease. An overview of our experimental design is provided in Supplementary Fig. 1. RESULTS Gene co-expression network a...

Genomic analysis of family data reveals additional genetic effects on intelligence and personality

“…Future work can use the SNPs known to affect intelligence and personality [17,18] to empirically quantify the coupling between allele frequency (indicating selection strength) and effect size in order to test this explanation directly, as has been demonstrated for height and BMI [62]. Targeted resequencing of enriched genetic regions [24,77,78] might be necessary to find very rare genetic variants associated with intelligence and personality, as has proven fruitful for example in prostate cancer research [79].…”

Genome-wide analysis of differential RNA editing in epilepsy

“…To this end, we tested the set of genes DRE between epileptic and control hippocampi for enrichment of validated nonpolymorphic de novo single nucleotide variant mutations (DNMs) identified in neurodevelopmental whole-exome sequencing (WES) studies that shared similar sequencing technologies, coverage criteria, and variant calling methodology (Johnson et al 2015b). Collectively, the neurodevelopmental disease cohort consisted of 5738 nonoverlapping published parent-offspring trios across four disease phenotypes; epileptic encephalopathy (EE, n = 356), autism spectrum disorder (ASD, n = 4186), schizophrenia (SCZ, n = 1004), and intellectual disability (ID, n = 192) (see Methods for cohort references).…”

“…Collectively, the neurodevelopmental disease cohort consisted of 5738 nonoverlapping published parent-offspring trios across four disease phenotypes; epileptic encephalopathy (EE, n = 356), autism spectrum disorder (ASD, n = 4186), schizophrenia (SCZ, n = 1004), and intellectual disability (ID, n = 192) (see Methods for cohort references). For controls, we used 1891 nonneurological control samples as previously reported (Johnson et al 2015b). The genetic relationship of DRE genes to epilepsy was tested using a FET (two-tailed) to empirically compare the rates of DNMs overlapping the consensus CDS of DRE genes in case and control cohorts.…”

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