This study demonstrates body mass in middle and late adulthood as a consequence of the complex interplay among individuals’ genes, lifetime socioeconomic experiences, and the historical context in which they live. Drawing on approximately 9,000 genetic samples from the Health and Retirement Study, we first investigate how socioeconomic status (SES) over the life course moderates the impact of 32 established obesity-related genetic variants on body mass index (BMI) in middle and late adulthood. Further, we consider differences across birth cohorts in the genetic influence on BMI and cohort variations in the moderating effects of life-course SES on the genetic influence. Our analyses suggest that persistently low SES over the life course or downward mobility (e.g., high SES in childhood but low SES in adulthood) amplified the genetic influence on BMI, while persistently high SES or upward mobility (e.g., low SES in childhood but high SES in adulthood) compensated for such influence. For more recent birth cohorts, while the genetic influence on BMI became stronger, the moderating effects of lifetime SES on the genetic influence were weaker compared to earlier cohorts. We discuss these findings in light of social changes during the obesity epidemic in the United States.

This study investigates the complex roles of the social environment and genes in the multigenerational transmission of educational attainment. Drawing on genome-wide data and educational attainment measures from the Framingham Heart Study (FHS) and the Health and Retirement Study (HRS), I conduct polygenic score analyses to examine genetic confounding in the estimation of parents’ and grandparents’ influences on their children’s and grandchildren’s educational attainment. I also examine social genetic effects (i.e., genetic effects that operate through the social environment) in the transmission of educational attainment across three generations. Two-generation analyses produce three important findings. First, about one-fifth of the parent-child association in education reflects genetic inheritance. Second, up to half of the association between parents’ polygenic scores and children’s education is mediated by parents’ education. Third, about one-third of the association between children’s polygenic scores and their educational attainment is attributable to parents’ genotypes and education. Three-generation analyses suggest that genetic confounding on the estimate of the direct effect of grandparents’ education on grandchildren’s education (net of parents’ education) may be inconsequential, and I find no evidence that grandparents’ genotypes significantly influence grandchildren’s education through non-biological pathways. The three-generation results are suggestive, and the results may change when different samples are used.

In this analysis, guided by an evolutionary framework, we investigate how the human genome as a whole interacts with historical period, age, and physical activity to influence body mass index (BMI). The genomic influence is estimated by (1) heritability or the proportion of variance in BMI explained by genome-wide genotype data, and (2) the random effects or the best linear unbiased predictors (BLUPs) of genome-wide association studies (GWAS) data on BMI. Data were used from the Framingham Heart Study (FHS) in the United States. The study was initiated in 1948, and the obesity data were collected repeatedly over the subsequent decades. The analyses draw analysis samples from a pool of >8,000 individuals in the FHS. The hypothesis testing based on Pitman test, permutation Pitman test, F test, and permutation F test produces three sets of significant findings. First, the genomic influence on BMI is substantially larger after the mid-1980s than in the few decades before the mid-1980s within each age group of 21-40, 41-50, 51-60, and >60. Second, the genomic influence on BMI weakens as one ages across the life course, or the genomic influence on BMI tends to be more important during reproductive ages than after reproductive ages within each of the two historical periods. Third, within the age group of 21-50 and not in the age group of >50, the genomic influence on BMI among physically active individuals is substantially smaller than the influence on those who are not physically active. In summary, this study provides evidence that the influence of human genome as a whole on obesity depends on historical period, age, and level of physical activity.

Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

Objective Experience of stressful life events is associated with risk of depression. Yet many exposed individuals do not become depressed. A controversial hypothesis is that genetic factors influence vulnerability to depression following stress. This hypothesis is often tested with a “diathesis-stress” model, in which genes confer excess vulnerability. We tested an alternative formulation of this model: genes may buffer against depressogenic effects of life stress. Method We measured the hypothesized genetic buffer using a polygenic score derived from a published genome-wide association study (GWAS) of subjective wellbeing. We tested if married older adults who had higher polygenic scores were less vulnerable to depressive symptoms following the death of their spouse as compared to age-peers who had also lost their spouse and who had lower polygenic scores. We analyzed data from N=8,588 non-Hispanic white adults in the Health and Retirement Study (HRS), a population-representative longitudinal study of older adults in the United States. Results HRS adults with higher wellbeing polygenic scores experienced fewer depressive symptoms during follow-up. Those who survived death of their spouses (n=1,647) experienced a sharp increase in depressive symptoms following the death and returned toward baseline over the following two years. Having a higher polygenic score buffered against increased depressive symptoms following a spouse’s death. Conclusions Effects were small and clinical relevance is uncertain, although polygenic score analyses may provide clues to behavioral pathways that can serve as therapeutic targets. Future studies of gene-environment interplay in depression may benefit from focus on genetics discovered for putative protective factors.