Written by: Stephen Hsu
Primary Source: Information Processing
A quiet revolution has begun. We now know enough about the genetic architecture of human intelligence to make predictions based on DNA alone. While it is a well-established scientific fact that variations in human cognitive ability are influenced by genes, many have doubted whether scientists would someday decipher the genetic code sufficiently to be able to identify individuals with above or below average intelligence using only their genotypes. That day is nearly upon us.
The figures below are taken from a recently published paper (see bottom), which examined genomic prediction on a longitudinal cohort of ~1000 individuals of European ancestry, followed from childhood into adulthood. (The study, based in Dunedin, New Zealand, extends over 40 years.) The genomic predictor (or polygenic score) was constructed using SSGAC GWAS analysis of a sample of more than one hundred thousand individuals. (Already, significantly more powerful predictors are available, based on much larger sample size.) In machine learning terminology, the training set includes over a hundred thousand individuals, and the validation set roughly one thousand.
These graphs show that individuals with higher polygenic score exhibit, on average, higher IQ scores than individuals with lower polygenic scores.
This figure shows that polygenic scores predict adult outcomes even when analyses account for social-class origins. Each dot represents ten individuals.
From an earlier post, Genomic Prediction of Adult Life Outcomes:
Genomic prediction of adult life outcomes using SNP genotypes is very close to a reality. This was discussed in an earlier post The Tipping Point. The previous post, Prenatal and pre-implantation genetic diagnosis (Nature Reviews Genetics), describes how genotyping informs the Embryo Selection Problem which arises in In Vitro Fertilization (IVF).
The Adult-Attainment factor in the figure above is computed using inputs such as occupational prestige, income, assets, social welfare benefit use, etc. See Supplement, p.3. The polygenic score is computed using estimated SNP effect sizes from the SSGAC GWAS on educational attainment (i.e., a simple linear model).
A genetic test revealing that a specific embryo is, say, a -2 or -3 SD outlier on the polygenic score would probably give many parents pause, in light of the results in the figure above. The accuracy of this kind of predictor will grow with GWAS sample size in coming years.
Psychological Science 2016, Vol. 27(7) 957–972
A previous genome-wide association study (GWAS) of more than 100,000 individuals identified molecular-genetic predictors of educational attainment. We undertook in-depth life-course investigation of the polygenic score derived from this GWAS using the four-decade Dunedin Study (N = 918). There were five main findings. First, polygenic scores predicted adult economic outcomes even after accounting for educational attainments. Second, genes and environments were correlated: Children with higher polygenic scores were born into better-off homes. Third, children’s polygenic scores predicted their adult outcomes even when analyses accounted for their social-class origins; social-mobility analysis showed that children with higher polygenic scores were more upwardly mobile than children with lower scores. Fourth, polygenic scores predicted behavior across the life course, from early acquisition of speech and reading skills through geographic mobility and mate choice and on to financial planning for retirement. Fifth, polygenic-score associations were mediated by psychological characteristics, including intelligence, self-control, and interpersonal skill. Effect sizes were small. Factors connecting DNA sequence with life outcomes may provide targets for interventions to promote population-wide positive development.
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