Genome-wide meta-analysis of brain volume identifies genomic loci and genes shared with intelligence

Abstract

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 (rg) between BV and intelligence to be 0.24. We show that the rg 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.

Genome scans of dog behavior implicate a gene network underlying psychopathology in mammals, including humans

“…In humans, height is genetically weakly-correlated with brain traits including neuroticism, risk tolerance and smoking cessation [57][58][59] . As intracranial volume is correlated with body size in dogs 19 , it is with height in humans (phenotypic r=0.55; genetic r=0.26) 60 . Human brain volume is also genetically correlated with intelligence (r g =0.24) and the PI3K growth signaling pathway was implicated for both these traits 60 .…”

“…As intracranial volume is correlated with body size in dogs 19 , it is with height in humans (phenotypic r=0.55; genetic r=0.26) 60 . Human brain volume is also genetically correlated with intelligence (r g =0.24) and the PI3K growth signaling pathway was implicated for both these traits 60 . That supports the interpretation that correlations of canine behavior and body size are due to pleiotropy and not population structure (Suppl.…”

Adolescent Cranial Volume as a Sensitive Marker of Parental Investment: The Role of Non-material Resources?

“…A Swedish study showed that infants of mothers with a history of eating disorders had delayed head growth until at least 18 months of age while height of infants was not associated with maternal condition ( Koubaa et al, 2013 ). Cranial volume predicts brain size, intelligence, and educational attainment (reviewed by Valge et al, 2019 ; Jansen et al, 2020 ) and positive effects of nutritional supplementation on intelligence or achievement have been reported in lactants, school children, and adolescents as well as pregnant mothers in both developed or developing countries (reviewed by Arija et al, 2006 ; DiGirolamo et al, 2020 ). Further, even in developed countries, the surplus of dietary energy is not always matched with proportional availability of micronutrients, and some micronutrients, including iron, are particularly sensitive to marginal dietary intake, since their needs increase substantially during adolescent growth ( Caballero, 2002 ; Arija et al, 2006 ).…”

“…Understanding how external constraints via parental investment relate to head and body growth is important because both stature and cranial volume (a proxy for brain size) correlate phenotypically and genetically with cognitive abilities and/or educational attainment ( Nave et al, 2019 ; Valge et al, 2019 ; Jansen et al, 2020 ). Intelligence and education, in turn, strongly and robustly predict essential life outcomes including occupational status, happiness, health, and life expectancy ( Deary et al, 2019 ).…”

Statistical testing in gene transcriptomic-neuroimaging associations: an evaluation of methods that assess spatial and gene specificity

“…Most of such examinations will thus be centered on testing a GOI pre-selected based on the findings from previous brain related studies, e.g. GWAS of a disease phenotype like schizophrenia (Schizophrenia Working Group of the Psychiatric Genomics, Consortium, 2014) or Alzheimer's disease (Jansen et al, 2019), a specific pathway related to neuronal properties (Kepecs and Fishell, 2014) as in our first HSE example, genetic variants related to brain volume (Jansen et al, 2020), etcetera. The result of such pre-selected GOI is that most of them will likely be related to processes related to the brain, and as such reflect genes likely over-expressed in brain tissue.…”