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

Abstract

Pedigree-based analyses of intelligence have reported that genetic differences account for 50-80% of the phenotypic variation. For personality traits these effects are smaller, with 34-48% of the variance being explained by genetic differences. However, molecular genetic studies using unrelated individuals typically report a heritability estimate of around 30% for intelligence and between 0% and 15% for personality variables. Pedigree-based estimates and molecular genetic estimates may differ because current genotyping platforms are poor at tagging causal variants, variants with low minor allele frequency, copy number variants, and structural variants. Using ∼20 000 individuals in the Generation Scotland family cohort genotyped for ∼700 000 single nucleotide polymorphisms (SNPs), we exploit the high levels of linkage disequilibrium (LD) found in members of the same family to quantify the total effect of genetic variants that are not tagged in GWASs of unrelated individuals. In our models, genetic variants in low LD with genotyped SNPs explain over half of the genetic variance in intelligence, education, and neuroticism. By capturing these additional genetic effects our models closely approximate the heritability estimates from twin studies for intelligence and education, but not for neuroticism and extraversion. We then replicated our finding using imputed molecular genetic data from unrelated individuals to show that ∼50% of differences in intelligence, and ∼40% of the differences in education, can be explained by genetic effects when a larger number of rare SNPs are included. From an evolutionary genetic perspective, a substantial contribution of rare genetic variants to individual differences in intelligence and education is consistent with mutation-selection balance.

Familial influences on Neuroticism and Education in the UK Biobank

“…Scotland revealed a large increase in heritability compared to a standard GREML analysis of unrelated individuals (Hill et al 2018). For neuroticism, the total heritability from the best-fitting model was 30%, primarily accounted for by kin-based genetic effects (19%), as well as common variant effects tagged in studies of unrelated individuals (11% -akin to SNP heritability).…”

“…Moreover, a replication of these results would suggest that most of the variance in education (86%) can be captured with measured parameters. Notably, the authors also found that rarer variants (0.1%-1% in frequency) explained 12% of the variance in education, but did not influence variation in neuroticism (Hill et al 2018).…”

“…One study applied the GREML-KIN method to neuroticism and years of education (Hill et al 2018) in Generation Scotland, a large family-based study (Smith et al 2006). Their sample was selected to capture dense kinships, where many individuals have siblings, parents and spouses who are also study participants.…”

Personality and local brain structure: their shared genetic basis and reproducibility

“…Second, it is possible that similar functional processes have diverging neural signatures across samples. (Part of) variation in personality is due to rare genetic variants (Penke L and M Jokela 2016;Boyle EA et al 2017;Hill WD et al 2018) which could lead to differences between samples leading to variable patterns of brain-behavior correspondences.…”

Screening human embryos for polygenic traits has limited utility

“…Current imputation panels are limited in their ability to accurately assess variants with frequencies below 1%, but will continuously improve as imputation panels increase in size and representation of varying populations [41,42]. For example, a recent family-based study [43] has demonstrated that more than half of the variation in cognitive ability is attributable to rare variation not captured by current GWASs (see also [44]). Importantly, very little of this variation is private to individual families; most could be captured by population-based reference panels of sufficient size to accurately impute variants at 0.1% minor allele frequency and greater.…”