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Physicist, Startup Founder, Blogger, Dad

Monday, July 09, 2018

Game Over: Genomic Prediction of Social Mobility

[ NOTE: The PNAS paper discussed below uses the SSGAC EA3 genomic predictor, trained on over a million genomes. The EA3 paper has now appeared in Nature Genetics. ]

The figure below shows SNP-based polygenic score and life outcome (socioeconomic index, on vertical axis) in four longitudinal cohorts, one from New Zealand (Dunedin) and three from the US. Each cohort (varying somewhat in size) has thousands of individuals, ~20k in total (all of European ancestry). The points displayed are averages over bins containing 10-50 individuals. For each cohort, the individuals have been grouped by childhood (family) social economic status. Social mobility can be predicted from polygenic score. Note that higher SES families tend to have higher polygenic scores on average -- which is what one might expect from a society that is at least somewhat meritocratic. The cohorts have not been used in training -- this is true out-of-sample validation. Furthermore, the four cohorts represent different geographic regions (even, different continents) and individuals born in different decades.

Everyone should stop for a moment and think carefully about the implications of the paragraph above and the figure below.


Caption from the PNAS paper.
Fig. 4. Education polygenic score associations with social attainment for Add Health Study, WLS, Dunedin Study, and HRS participants with low-, middle-, and high-socioeconomic status (SES) social origins. The figure plots polygenic score associations with socioeconomic attainment for Add Health Study (A), Dunedin Study (B), WLS (C), and HRS (D) participants who grew up in low-, middle-, and high-SES households. For the figure, low- middle-, and high-SES households were defined as the bottom quartile, middle 50%, and top quartile of the social origins score distributions for the Add Health Study, WLS, and HRS. For the Dunedin Study, low SES was defined as a childhood NZSEI of two or lower (20% of the sample), middle SES was defined as childhood NZSEI of three to four (63% of the sample), and high SES was defined as childhood NZSEI of five or six (17% of the sample). Attainment is graphed in terms of socioeconomic index scores for the Add Health Study, Dunedin Study, and WLS and in terms of household wealth in the HRS. Add Health Study and WLS socioeconomic index scores were calculated from Hauser and Warren (34) occupational income and occupational education scores. Dunedin Study socioeconomic index scores were calculated similarly, according to the Statistics New Zealand NZSEI (38). HRS household wealth was measured from structured interviews about assets. All measures were z-transformed to have mean = 0, SD = 1 for analysis. The individual graphs show binned scatterplots in which each plotted point reflects average x and y coordinates for a bin of 50 participants for the Add Health Study, WLS, and HRS and for a bin of 10 participants for the Dunedin Study. The red regression lines are plotted from the raw data. The box-and-whisker plots at the bottom of the graphs show the distribution of the education polygenic score for each childhood SES category. The blue diamond in the middle of the box shows the median; the box shows the interquartile range; and the whiskers show upper and lower bounds defined by the 25th percentile minus 1.5× the interquartile range and the 75th percentile plus 1.5× the interquartile range, respectively. The vertical line intersecting the x axis shows the cohort average polygenic score. The figure illustrates three findings observed consistently across cohorts: (i) participants who grew up in higher-SES households tended to have higher socioeconomic attainment independent of their genetics compared with peers who grew up in lower-SES households; (ii) participants’ polygenic scores were correlated with their social origins such that those who grew up in higher-SES households tended to have higher polygenic scores compared with peers who grew up in lower-SES households; (iii) participants with higher polygenic scores tended to achieve higher levels of attainment across strata of social origins, including those born into low-SES families.

The paper:
Genetic analysis of social-class mobility in five longitudinal studies, Belsky et al.

PNAS July 9, 2018. 201801238; published ahead of print July 9, 2018. https://doi.org/10.1073/pnas.1801238115

A summary genetic measure, called a “polygenic score,” derived from a genome-wide association study (GWAS) of education can modestly predict a person’s educational and economic success. This prediction could signal a biological mechanism: Education-linked genetics could encode characteristics that help people get ahead in life. Alternatively, prediction could reflect social history: People from well-off families might stay well-off for social reasons, and these families might also look alike genetically. A key test to distinguish biological mechanism from social history is if people with higher education polygenic scores tend to climb the social ladder beyond their parents’ position. Upward mobility would indicate education-linked genetics encodes characteristics that foster success. We tested if education-linked polygenic scores predicted social mobility in >20,000 individuals in five longitudinal studies in the United States, Britain, and New Zealand. Participants with higher polygenic scores achieved more education and career success and accumulated more wealth. However, they also tended to come from better-off families. In the key test, participants with higher polygenic scores tended to be upwardly mobile compared with their parents. Moreover, in sibling-difference analysis, the sibling with the higher polygenic score was more upwardly mobile. Thus, education GWAS discoveries are not mere correlates of privilege; they influence social mobility within a life. Additional analyses revealed that a mother’s polygenic score predicted her child’s attainment over and above the child’s own polygenic score, suggesting parents’ genetics can also affect their children’s attainment through environmental pathways. Education GWAS discoveries affect socioeconomic attainment through influence on individuals’ family-of-origin environments and their social mobility.

Note Added from comments: Plots would look much noisier if not for averaging many individuals into single point. Keep in mind that socioeconomic success depends on a lot more than just cognitive ability, or even cognitive ability + conscientiousness.

But, underlying predictor correlates ~0.35 with actual educational attainment, IIRC. That is, the polygenic score predicts EA about as well as standardized tests predict success in schooling.

This means you can at least use it to identify outliers: just as a very high/low test score (SAT, ACT, GRE) does not *guarantee* success/failure in school, nevertheless the signal is useful for selection = admissions.

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