Tens of thousands of studies correlate family socioeconomic status with later child outcomes like income, wealth and attainment and then claim the correlation is causal. Very few such studies control for genetics, although twin adoption studies suggest that genetics is important. Cheap genomic scanning, however, has made it possible to go beyond twin studies. A new paper, for example, looks at differences in education-associated genes between non-identical twins raised in the same family and they find that children with more education-associated genes tend to have greater educational attainment and higher income later in life. In other words, differences in child outcomes both across families and within the same family are in part driven by genetics.
Surprisingly, however, the authors also find evidence for “genetic nurture” the idea that parental genes drive child environment which drives outcomes. That’s surprising because it’s hard to find strong evidence for big environmental effects in adoption studies but here the authors can rely on more precise data. Specifically, the authors look at maternal education-associated genes that are NOT passed on to the children and yet they find that such genes are also correlated with important child outcomes (fyi, they only have maternal genes). So smart parents benefit children twice. First by passing on smart genes and second–even when they do not pass on smart genes–by passing on a smart environment. Previous studies missed the latter effect perhaps because they focused on rich parents rather than smart parents (the former being easier to measure). The authors suggest that by looking at how smart parents help kids without smart genes we may be able to figure out smart environments and generalize them to everyone. That strikes me as optimistic.
Here is the paper abstract:
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.
You can find the appendix with the key results here. I find the lab style difficult to follow. The authors run regressions, for example, but you won’t find a regression equation followed by a table with all the results. Instead the regression is described in the appendix and then some coefficients, but by no means all, are presented later in the appendix.