Variation in cognitive traits and genetics

2 minute read

A new study of more than 50,000 people has identified some of the genetic variations that underlie cognitive variation among middle-aged and older adults (Davies et al. 2015). This cohort was developed by combining several different large cohort studies to look for the factors that underlie cognitive decline and heart disease.

To date, behavior genetic research—using twin, adoption and family designs—shows that general cognitive ability is substantially heritable across the life course, from late childhood to old age. The heritability of general cognitive functioning in old age might decrease slightly from its levels in young and middle adulthood. Candidate gene studies have found that variation in APOE genotype is the only reliable individual genetic associate of cognitive function in older age, but that might apply especially to cognitive change rather than cognitive level in old age. Using the genome-wide complex trait analysis procedure (GCTA), genome-wide association studies (GWAS) found that ~51% (the s.e. was large, at 11%) of the variation in general fluid cognitive function in late middle age and older age could be accounted for by genetic variation that is tagged by single-nucleotide polymorphisms (SNPs) on the Illumina610-Quadv1 chip. That study was conducted in a total discovery sample of 3511 individuals, with replication in 670 independent individuals. It found no genome-wide significant single SNP associations. From other GWAS studies of complex traits, we now know that this sample size is likely to be too small, by an order of magnitude, to detect genome-wide significant SNPs.

Twin studies have shown that on the order of half the variance in cognitive performance is additive. Until the last couple of years, no one had any real success discovering which genes might explain this additive variance. Many other complex traits were similar stories: From stature to schizophrenia, these traits are affected by many genes, with no single common allele having a very large effect on the phenotype. A sample of a thousand individuals may sound large, but is actually very small for this kind of investigation, capable of demonstrating large effects of common alleles, but not small effects or effects of rare alleles. By looking at 50,000 people, this cohort study made it possible to test smaller effect sizes on common alleles. Several genes stuck out as having significant effects in this large sample, and across the whole genome the genotypes explain 29% of the phenotypic variance in the study’s measures of cognitive function. That’s a very high proportion of the additive variance, although the genes that explain the variance in this particular sample may not replicate in other populations.

I mentioned last month the move in Congress toward funding research into personalized medicine (“Link: Personalized medicine going to Congress”). That effort is centered around the development of a super large cohort, with genotyping or sequencing of a million individuals, including many who are parts of existing cohort studies with phenotype data. Massive cohorts may or may not help to find effective treatments for complex disorders. But with good phenotype data, massive cohorts will certainly help to uncover the small-effect genes that underlie normal human variation.


Davies, G. and lots of others. (2015). Genetic contributions to variation in general cognitive function: a meta-analysis of genome-wide association studies in the CHARGE consortium (N=53 949). Molecular Psychiatry (online) doi:10.1038/mp.2014.188