Until 2017, genome-wide polygenic scores derived from genome-wide association studies (GWAS) of intelligence were able to predict only 1% of the variance in intelligence in independent samples.
Polygenic scores derived from GWAS of intelligence can now predict 4% of the variance in intelligence.
More than 10% of the variance in intelligence can be predicted by multipolygenic scores derived from GWAS of both intelligence and years of education. This accounts for more than 20% of the 50% heritability of intelligence.
Polygenic scores are unique predictors in two ways. First, they predict psychological and behavioural outcomes just as well from birth as later in life. Second, polygenic scores are causal predictors in the sense that nothing in our brains, behaviour or environment can change the differences in DNA sequence that we inherited from our parents.
Polygenic scores for intelligence can bring the powerful construct of intelligence to any research in the life sciences without having to assess intelligence through the use of tests.
Intelligence — the ability to learn, reason and solve problems — is at the forefront of behavioural genetic research. Intelligence is highly heritable and predicts important educational, occupational and health outcomes better than any other trait. Recent genome-wide association studies have successfully identified inherited genome sequence differences that account for 20% of the 50% heritability of intelligence. These findings open new avenues for research into the causes and consequences of intelligence using genome-wide polygenic scores that aggregate the effects of thousands of genetic variants.
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The authors gratefully acknowledge the ongoing contribution of the participants in the Twins Early Development Study (TEDS) and their families. TEDS is supported by a programme grant to R.P. from the UK Medical Research Council (MR/M021475/1 and previously G0901245), with additional support from the US National Institutes of Health (AG046938). The research reported here has also received funding from the European Research Council (ERC) under the European Union's Seventh Framework Programme (FP7/2007-2013) grant agreement 602768 and ERC grant agreement 295366. R.P. is also supported by a Medical Research Council Professorship award (G19/2). S.v.S. is supported by a Jacobs Foundation Research Fellowship award (2017–2019).
The authors declare no competing financial interests.
- Twin studies
Studies comparing the resemblance of identical and fraternal twins to estimate genetic and environmental components of variance.
An index of how spread out scores are in a study population, which is calculated as the average of the squared deviations from the mean.
- Genome-wide association studies
(GWAS). Studies that aim to identify loci throughout the genome associated with an observed trait or disorder.
The proportion of observed differences among individuals that can be attributed to inherited differences in genome sequence.
- Genome-wide polygenic scores
(GPSs). Genetic indices of a trait for each individual that are the sum across the genome of thousands of single-nucleotide polymorphisms (SNPs) of the individual's increasing alleles associated with the trait, usually weighted by the effect size of each SNP's association with the trait in genome-wide association studies.
- Candidate gene studies
Studies that focus on genes for which the function suggests that they are associated with a trait, in contrast to genome-wide association studies.
- Effect sizes
Proportions of variance of traits in the study population accounted for by a particular factor such as a genome-wide polygenic score.
- Single-nucleotide polymorphisms
(SNPs). Single base pair differences in inherited DNA sequence between individuals.
- Linkage disequilibrium (LD) score regression analysis
Analysis that, for each single-nucleotide polymorphism in a genome-wide association study (GWAS), regresses χ2 statistics from GWAS summary statistics against LD scores.
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Plomin, R., von Stumm, S. The new genetics of intelligence. Nat Rev Genet 19, 148–159 (2018). https://doi.org/10.1038/nrg.2017.104
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