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Cognitive brain networks such as the default-mode network (DMN), frontoparietal network, and salience network, are key functional networks of the human brain. Here we show that the rapid evolutionary cortical expansion of cognitive networks in the human brain, and most pronounced the DMN, runs parallel with high expression of human-accelerated genes (HAR genes). Using comparative transcriptomics analysis, we present that HAR genes are differentially more expressed in higher-order cognitive networks in humans compared to chimpanzees and macaques and that genes with high expression in the DMN are involved in synapse and dendrite formation. Moreover, HAR and DMN genes show significant associations with individual variations in DMN functional activity, intelligence, sociability, and mental conditions such as schizophrenia and autism. Our results suggest that the expansion of higher-order functional networks subserving increasing cognitive properties has been an important locus of genetic changes in recent human brain evolution. Several cortical association areas have rapidly expanded in size during human evolution, including elements of the central cognitive default mode network (DMN). Here, the authors show that genes highly divergent between humans and other primates (HAR genes) are particularly expressed in these brain regions.

Transcriptome-wide association studies (TWAS) aim to detect relationships between gene expression and a phenotype, and are commonly used for secondary analysis of genome-wide association study (GWAS) results. Results from TWAS analyses are often interpreted as indicating a genetic relationship between gene expression and a phenotype, but this interpretation is not consistent with the null hypothesis that is evaluated in the traditional TWAS framework. In this study we provide a mathematical outline of this TWAS framework, and elucidate what interpretations are warranted given the null hypothesis it actually tests. We then use both simulations and real data analysis to assess the implications of misinterpreting TWAS results as indicative of a genetic relationship between gene expression and the phenotype. Our simulation results show considerably inflated type 1 error rates for TWAS when interpreted this way, with 41% of significant TWAS associations detected in the real data analysis found to have insufficient statistical evidence to infer such a relationship. This demonstrates that in current implementations, TWAS cannot reliably be used to investigate genetic relationships between gene expression and a phenotype, but that local genetic correlation analysis can serve as a potential alternative.

The phenotypic correlation between human intelligence and brain volume (BV) is considerable ( r  ≈ 0.40), and has been shown to be due to shared genetic factors. To further examine specific genetic factors driving this correlation, we present genomic analyses of the genetic overlap between intelligence and BV using genome-wide association study (GWAS) results. First, we conduct a large BV GWAS meta-analysis (N = 47,316 individuals), followed by functional annotation and gene-mapping. We identify 18 genomic loci (14 not previously associated), implicating 343 genes (270 not previously associated) and 18 biological pathways for BV. Second, we use an existing GWAS for intelligence (N = 269,867 individuals), and estimate the genetic correlation ( r g ) between BV and intelligence to be 0.24. We show that the r g is partly attributable to physical overlap of GWAS hits in 5 genomic loci. We identify 92 shared genes between BV and intelligence, which are mainly involved in signaling pathways regulating cell growth. Out of these 92, we prioritize 32 that are most likely to have functional impact. These results provide information on the genetics of BV and provide biological insight into BV’s shared genetic etiology with intelligence. Brain volume and intelligence have been previously found to have shared genetic etiology, but the specific common genetic signals have not been identified. Here, the authors perform a genome-wide association study on brain volume, finding common genetic loci driving brain volume and intelligence.

Investigating the genetic underpinnings of functional brain connectivity is essential to understand how genetic variation influences brain health and disease. Here, a mass-univariate approach was adopted to study the genetic architecture of functional brain circuitry ( N total  = 28,159 subjects) with high spatial resolution (82 brain regions). Common genetic variants explained individual differences in 33% of all 3321 inter-regional functional pathways with 72 significant associations reflecting widespread, pleiotropic effects across the connectome. These associations were mapped to five genes— PAX8, EphA3, SLC39A12, THBS1 and APOE —with known associations with brain phenotypes and which converged in biological processes related to neurodevelopment and cardiovascular and cognitive traits (enrichment minimum p  = 3.0 × 10 −6 and p  = 1.6 × 10 −5 , respectively). Our findings show that the genetic component of individual differences in functional brain connectivity is largely shared throughout the brain, highlighting the importance of genetic variation in large-scale brain organisation and its relationship with cognitive function and overall health. Genetic variation shapes brain-wide patterns of functional networks. This study identifies key genes associated with macroscale functional brain connectivity and links genes and brain activity with cognitive and physical traits.

Proxy phenotypes allow for the utilization of genetic data from large population cohorts to analyze late-onset diseases by using parental diagnoses as a proxy for genetic disease risk. Proxy phenotypes based on parental diagnosis status have been used in previous studies to identify common variants associated with Alzheimer’s disease. As of yet, proxy phenotypes have not been used to identify genes associated with Alzheimer’s disease through rare variants. Here we show that a proxy Alzheimer’s disease/dementia phenotype can capture known Alzheimer’s disease risk genes through rare variant aggregation. We generated a proxy Alzheimer’s disease/dementia phenotype for 148,508 unrelated individuals of European ancestry in the UK biobank in order to perform exome-wide rare variant aggregation analyses to identify genes associated with proxy Alzheimer’s disease/dementia. We identified four genes significantly associated with the proxy phenotype, three of which were significantly associated with proxy Alzheimer’s disease/dementia in an independent replication cohort consisting of 197,506 unrelated individuals of European ancestry in the UK biobank. All three of the replicated genes have been previously associated with clinically diagnosed Alzheimer’s disease ( SORL1 , TREM2 , and TOMM40/APOE ). We show that proxy Alzheimer’s disease/dementia can be used to identify genes associated with Alzheimer’s disease through rare variant aggregation.

Ever since the availability of genomes from Neanderthals, Denisovans, and ancient humans, the field of evolutionary genomics has been searching for protein-coding variants that may hold clues to how our species evolved over the last ∼600,000 years. In this study, we identify such variants in the human-specific NOTCH2NL gene family, which were recently identified as possible contributors to the evolutionary expansion of the human brain. We find evidence for the existence of unique protein-coding NOTCH2NL variants in Neanderthals and Denisovans which could affect their ability to activate Notch signaling. Furthermore, in the Neanderthal and Denisovan genomes, we find unusual NOTCH2NL configurations, not found in any of the modern human genomes analyzed. Finally, genetic analysis of archaic and modern humans reveals ongoing adaptive evolution of modern human NOTCH2NL genes, identifying three structural variants acting complementary to drive our genome to produce a lower dosage of NOTCH2NL protein. Because copy-number variations of the 1q21.1 locus, encompassing NOTCH2NL genes, are associated with severe neurological disorders, this seemingly contradicting drive toward low levels of NOTCH2NL protein indicates that the optimal dosage of NOTCH2NL may have not yet been settled in the human population.

Cerebellar volume is highly heritable and associated with neurodevelopmental and neurodegenerative disorders. Understanding the genetic architecture of cerebellar volume may improve our insight into these disorders. This study aims to investigate the convergence of cerebellar volume genetic associations in close detail. A genome-wide associations study for cerebellar volume was performed in a discovery sample of 27,486 individuals from UK Biobank, resulting in 30 genome-wide significant loci and a SNP heritability of 39.82%. We pinpoint the likely causal variants and those that have effects on amino acid sequence or cerebellar gene-expression. Additionally, 85 genome-wide significant genes were detected and tested for convergence onto biological pathways, cerebellar cell types, human evolutionary genes or developmental stages. Local genetic correlations between cerebellar volume and neurodevelopmental and neurodegenerative disorders reveal shared loci with Parkinson’s disease, Alzheimer’s disease and schizophrenia. These results provide insights into the heritable mechanisms that contribute to developing a brain structure important for cognitive functioning and mental health. A genome-wide association study on MRI cerebellar volume in the UK Biobank cohort identifies 30 loci with genome-wide significance that might be relevant to brain structure and cognitive function.

Insomnia is a heritable, highly prevalent sleep disorder for which no sufficient treatment currently exists. Previous genome-wide association studies with up to 1.3 million subjects identified over 200 associated loci. This extreme polygenicity suggested that many more loci remain to be discovered. The current study almost doubled the sample size to 593,724 cases and 1,771,286 controls, thereby increasing statistical power, and identified 554 risk loci (including 364 novel loci). To capitalize on this large number of loci, we propose a novel strategy to prioritize genes using external biological resources and functional interactions between genes across risk loci. Of all 3,898 genes naively implicated from the risk loci, we prioritize 289 and find brain-tissue expression specificity and enrichment in specific gene sets of synaptic signaling functions and neuronal differentiation. We show that this novel gene prioritization strategy yields specific hypotheses on underlying mechanisms of insomnia that would have been missed by traditional approaches. Genome-wide association meta-analysis of insomnia in 593,724 cases and 1,771,286 controls identifies 554 risk loci and implicates specific biological pathways through gene prioritization.

Abstract Background Cognitive impairment is a clinically important feature of schizophrenia. Polygenic risk score (PRS) methods have demonstrated genetic overlap between schizophrenia, bipolar disorder (BD), major depressive disorder (MDD), educational attainment (EA), and IQ, but very few studies have examined associations between these PRS and cognitive phenotypes within schizophrenia cases. Methods We combined genetic and cognitive data in 3034 schizophrenia cases from 11 samples using the general intelligence factor g as the primary measure of cognition. We used linear regression to examine the association between cognition and PRS for EA, IQ, schizophrenia, BD, and MDD. The results were then meta-analyzed across all samples. A genome-wide association studies (GWAS) of cognition was conducted in schizophrenia cases. Results PRS for both population IQ (P = 4.39 × 10–28) and EA (P = 1.27 × 10–26) were positively correlated with cognition in those with schizophrenia. In contrast, there was no association between cognition in schizophrenia cases and PRS for schizophrenia (P = .39), BD (P = .51), or MDD (P = .49). No individual variant approached genome-wide significance in the GWAS. Conclusions Cognition in schizophrenia cases is more strongly associated with PRS that index cognitive traits in the general population than PRS for neuropsychiatric disorders. This suggests the mechanisms of cognitive variation within schizophrenia are at least partly independent from those that predispose to schizophrenia diagnosis itself. Our findings indicate that this cognitive variation arises at least in part due to genetic factors shared with cognitive performance in populations and is not solely due to illness or treatment-related factors, although our findings are consistent with important contributions from these factors.