Retirement Makes You Old? Causal Effect of Retirement on Biological Age.

Retirement is a critical life event for older people. Health scholars have scrutinized the health effects of retirement, but its consequences on age-related diseases and mortality are unclear. We extend this body of research by integrating measurements of biological age, representing the physiological decline preceding disease onset. Using data from the UK Biobank and a fuzzy regression discontinuity design, we estimated the effects of retirement on two biomarker-based biological age measures. Results showed that retirement significantly increases biological age for those induced to retire by the State Pension eligibility by 0.871-2.503 years, depending on sex and specific biological age measurement. Given the emerging scientific discussion about direct interventions to biological age to achieve additional improvements in population health, the positive effect of retirement on biological age has important implications for an increase in the State Pension eligibility age and its potential consequences on population health, public health care policy, and older people's labor force participation. Overall, this study provides novel empirical evidence contributing to the question of what social factors make people old.

Separating Scarring Effect and Selection of Early-Life Exposures With Genetic Data.

Causal life course research examining consequences of early-life exposures has largely relied on associations between early-life environments and later-life outcomes using exogenous environmental shocks. Nonetheless, even with (quasi-)randomized early-life exposures, these associations may reflect not only causation ("scarring") but also selection (i.e., which members are included in data assessing later life). Investigating this selection and its impacts on estimated effects of early-life conditions has, however, often been ignored because of a lack of pre-exposure data. This study proposes an approach for assessing and correcting selection, separately from scarring, using genetic measurements. Because genetic measurements are determined at the time of conception, any associations with early-life exposures should be interpreted as selection. Using data from the UK Biobank, we find that in utero exposure to a higher area-level infant mortality rate is associated with genetic predispositions correlated with better educational attainment and health. These findings point to the direction and magnitude of selection from this exposure. Corrections for this selection in examinations of effects of exposure on later educational attainment suggest underestimates of 26-74%; effects on other life course outcomes also vary across selection correction methods.

Controlling for polygenic genetic confounding in epidemiologic association studies.

Epidemiologic associations estimated from observational data are often confounded by genetics due to pervasive pleiotropy among complex traits. Many studies either neglect genetic confounding altogether or rely on adjusting for polygenic scores (PGS) in regression analysis. In this study, we unveil that the commonly employed PGS approach is inadequate for removing genetic confounding due to measurement error and model misspecification. To tackle this challenge, we introduce PENGUIN, a principled framework for polygenic genetic confounding control based on variance component estimation. In addition, we present extensions of this approach that can estimate genetically-unconfounded associations using GWAS summary statistics alone as input and between multiple generations of study samples. Through simulations, we demonstrate superior statistical properties of PENGUIN compared to the existing approaches. Applying our method to multiple population cohorts, we reveal and remove substantial genetic confounding in the associations of educational attainment with various complex traits and between parental and offspring education. Our results show that PENGUIN is an effective solution for genetic confounding control in observational data analysis with broad applications in future epidemiologic association studies.

Understanding Internal Migration: A Research Note Providing an Assessment of Migration Selection With Genetic Data.

Migration is selective, resulting in inequalities between migrants and nonmigrants. However, investigating migration selection is empirically challenging because combined pre- and post-migration data are rarely available. We propose an alternative approach to assessing internal migration selection by integrating genetic data, enabling an investigation of migration selection with cross-sectional data collected post-migration. Using data from the UK Biobank, we utilized standard tools from statistical genetics to conduct a genome-wide association study (GWAS) for migration distance. We then calculated genetic correlations to compare GWAS results for migration with those for other characteristics. Given that individual genetics are determined at conception, these analyses allow a unique exploration of the association between pre-migration characteristics and migration. Results are generally consistent with the healthy migrant literature: genetics correlated with longer migration distance are associated with higher socioeconomic status and better health. We also extended the analysis to 53 traits and found novel correlations between migration and several physical health, mental health, personality, and sociodemographic traits.

The Big (Genetic) Sort? A Research Note on Migration Patterns and Their Genetic Imprint in the United Kingdom.

This research note reinvestigates Abdellaoui et al.'s (2019) findings that genetically selective migration may lead to persistent and accumulating socioeconomic and health inequalities between types (coal mining or non-coal mining) of places in the United Kingdom. Their migration measure classified migrants who moved to the same type of place (coal mining to coal mining or non-coal mining to non-coal mining) into "stay" categories, preventing them from distinguishing migrants from nonmigrants. We reinvestigate the question of genetically selective migration by examining migration patterns between places rather than place types and find genetic selectivity in whether people migrate and where. For example, we find evidence of positive selection: people with genetic variants correlated with better education moved from non-coal mining to coal mining places with our measure of migration. Such findings were obscured in earlier work that could not distinguish nonmigrants from migrants.

Pervasive biases in proxy GWAS based on parental history of Alzheimer's disease.

Almost every recent Alzheimer's disease (AD) genome-wide association study (GWAS) has performed meta-analysis to combine studies with clinical diagnosis of AD with studies that use proxy phenotypes based on parental disease history. Here, we report major limitations in current GWAS-by-proxy (GWAX) practices due to uncorrected survival bias and non-random participation of parental illness survey, which cause substantial discrepancies between AD GWAS and GWAX results. We demonstrate that current AD GWAX provide highly misleading genetic correlations between AD risk and higher education which subsequently affects a variety of genetic epidemiologic applications involving AD and cognition. Our study sheds important light on the design and analysis of mid-aged biobank cohorts and underscores the need for caution when interpreting genetic association results based on proxy-reported parental disease history.

The production of within-family inequality: Insights and implications of integrating genetic data.

The integration of genetic data within large-scale social and health surveys provides new opportunities to test long-standing theories of parental investments in children and within-family inequality. Genetic predictors, called polygenic scores, allow novel assessments of young children's abilities that are uncontaminated by parental investments, and family-based samples allow indirect tests of whether children's abilities are reinforced or compensated. We use over 16,000 sibling pairs from the UK Biobank to test whether the relative ranking of siblings' polygenic scores for educational attainment is consequential for actual attainments. We find evidence consistent with compensatory processes, on average, where the association between genotype and phenotype of educational attainment is reduced by over 20% for the higher-ranked sibling compared to the lower-ranked sibling. These effects are most pronounced in high socioeconomic status areas. We find no evidence that similar processes hold in the case of height or for relatives who are not full biological siblings (e.g. cousins). Our results provide a new use of polygenic scores to understand processes that generate within-family inequalities and also suggest important caveats to causal interpretations the effects of polygenic scores using sibling difference designs. Future work should seek to replicate these findings in other data and contexts.

Re-examining the relationship between education and adult mental health in the UK: A research note.

Previous studies using variation in education arising from compulsory schooling laws have found no causal effects of education on mental health in the UK. We re-examine the relationship between education and mental health in the UK by taking a different approach: sibling fixed-effects with controls for polygenic scores (summary measures of genetic predisposition) for educational attainment and adult depressive symptoms. We find that higher educational attainment is associated with better adult mental health, that sibling controls reduce these associations by ~40-70% but important associations remain and find evidence for non-monotonic effects. We also find suggestive evidence that education partially "rescues" genetic predictors of poor mental health.

Understanding Geographic Disparities in Mortality.

A rich literature shows that early-life conditions shape later-life outcomes, including health and migration events. However, analyses of geographic disparities in mortality outcomes focus almost exclusively on contemporaneously measured geographic place (e.g., state of residence at death), thereby potentially conflating the role of early-life conditions, migration patterns, and effects of destinations. We employ the newly available Mortality Disparities in American Communities data set, which links respondents in the 2008 American Community Survey to official death records, and estimate consequential differences based on the method of aggregation we use: the unweighted mean absolute deviation of the difference in life expectancy at age 50 measured by state of birth versus state of residence is 0.58 years for men and 0.40 years for women. These differences are also spatially clustered, and we show that regional inequality in life expectancy is higher based on life expectancies by state of birth, implying that interstate migration mitigates baseline geographic inequality in mortality outcomes. Finally, we assess how state-specific features of in-migration, out-migration, and nonmigration together shape measures of mortality disparities by state (of residence), further demonstrating the difficulty of clearly interpreting these widely used measures.

Decomposing heritability and genetic covariance by direct and indirect effect paths.

Estimation of heritability and genetic covariance is crucial for quantifying and understanding complex trait genetic architecture and is employed in almost all recent genome-wide association studies (GWAS). However, many existing approaches for heritability estimation and almost all methods for estimating genetic correlation ignore the presence of indirect genetic effects, i.e., genotype-phenotype associations confounded by the parental genome and family environment, and may thus lead to incorrect interpretation especially for human sociobehavioral phenotypes. In this work, we introduce a statistical framework to decompose heritability and genetic covariance into multiple components representing direct and indirect effect paths. Applied to five traits in UK Biobank, we found substantial involvement of indirect genetic components in shared genetic architecture across traits. These results demonstrate the effectiveness of our approach and highlight the importance of accounting for indirect effects in variance component analysis of complex traits.

Early life environments and cognition in adulthood: New evidence using a semiparametric approach and quantile regression.

Theories and empirical evidence document the importance of early life environmental factors on later life cognition. A next question is how and in what dimension associations between early life environments and later life cognition vary. Using data from the UK Biobank in conjunction with time-place-specific infant mortality rates, we assessed heterogeneous and non-linear associations between early life conditions and later life cognition. We found that the association between the infant mortality rate and later life cognition increased once the UK achieved very low infant mortality rates, suggesting that additional decreases in infant mortality rates in an industrialized society continue to improve later life cognition. We also found that infant mortality rates have stronger effects at upper quantiles of the cognition distribution. This implies that adverse early life environments may have an important role for an early manifestation of cognitive aging.

A quantile integral linear model to quantify genetic effects on phenotypic variability.

Detecting genetic variants associated with the variance of complex traits, that is, variance quantitative trait loci (vQTLs), can provide crucial insights into the interplay between genes and environments and how they jointly shape human phenotypes in the population. We propose a quantile integral linear model (QUAIL) to estimate genetic effects on trait variability. Through extensive simulations and analyses of real data, we demonstrate that QUAIL provides computationally efficient and statistically powerful vQTL mapping that is robust to non-Gaussian phenotypes and confounding effects on phenotypic variability. Applied to UK Biobank (n = 375,791), QUAIL identified 11 vQTLs for body mass index (BMI) that have not been previously reported. Top vQTL findings showed substantial enrichment for interactions with physical activities and sedentary behavior. Furthermore, variance polygenic scores (vPGSs) based on QUAIL effect estimates showed superior predictive performance on both population-level and within-individual BMI variability compared to existing approaches. Overall, QUAIL is a unified framework to quantify genetic effects on the phenotypic variability at both single-variant and vPGS levels. It addresses critical limitations in existing approaches and may have broad applications in future gene-environment interaction studies.

What is driving the relationship between height and cognition? Evidence from the Twins Early Development Study.

Taller children tend to have better cognitive ability, and the relationship between height and cognition has been proposed as an explanation for the height-wage labor market premium. Height-cognition associations may arise due to social factors that favor taller individuals or be driven by "common factors" that are correlated with height and cognition. Indeed, there is now evidence of a genetic correlation between height and cognition that provides specific evidence for this concern. We examine whether genetic factors explain the relationship by estimating associations between childhood height and cognition in the Twins Early Development Study. We find that height is associated with better cognition even after controlling for genetic and environmental factors shared by twins. The association between height and cognition within fraternal twin pairs is also robust to controlling for individual genetic predictors of height and cognition. These results suggest that genetic factors are not solely responsible for driving the relationship between height and cognition.

Heterogeneity in disease resistance and the impact of antibiotics in the US.

We hypothesize that the impact of antibiotics is moderated by a population's inherent (genetic) resistance to infectious disease. Using the introduction of sulfa drugs in 1937, we show that US states that are more genetically susceptible to infectious disease saw larger declines in their bacterial mortality rates following the introduction of sulfa drugs in 1937. This suggests area-level genetic endowments of disease resistance and the discovery of medical technologies have acted as substitutes in determining levels of health across the US. We also document immediate effects of sulfa drug exposure to the age of the workforce and cumulative effects on educational attainment for cohorts exposed to sulfa drugs in early life.

Network resources and educational outcomes among Mexican-origin youth.

Despite schooling gains over the last two decades, Mexican-origin adults complete fewer years of schooling than adults from other ethnic backgrounds. Explanations emphasizing network resources suggest Mexican-origin adolescents have social ties that are more likely to be "closed" from adults with experience in higher education-and this, in turn, inhibits the transition to college. In this study, we draw on unusual network data measuring characteristics of students' peers and friends, as well as the socioeconomic background of peers' and friends' parents. We demonstrate that Mexican-origin adolescents are much less likely to have friends whose parents have college educations. 83% of non-Hispanic Asian students and 72% of non-Hispanic white students have nominated friends with college-educated mothers; about half of Mexican-origin students do. These patterns are the result of socioeconomic segregation in social networks both across and within schools. Within schools, we observe that the educational background of friends is predictive of schooling outcomes for non-Mexican students. We find evidence that this network resource shapes non-Mexican students' educational expectations in high school and longer-run completed schooling as adults more so than it shapes the outcomes among Mexican-origin students.

GWAS on birth year infant mortality rates provides evidence of recent natural selection.

Following more than a century of phenotypic measurement of natural selection processes, much recent work explores relationships between molecular genetic measurements and realized fitness in the next generation. We take an innovative approach to the study of contemporary selective pressure by examining which genetic variants are "sustained" in populations as mortality exposure increases. Specifically, we deploy a so-called "regional GWAS" (genome-wide association study) that links the infant mortality rate (IMR) by place and year in the United Kingdom with common genetic variants among birth cohorts in the UK Biobank. These cohorts (born between 1936 and 1970) saw a decline in IMR from above 65 to under 20 deaths per 1,000 live births, with substantial subnational variations and spikes alongside wartime exposures. Our results show several genome-wide significant loci, including LCT and TLR10/1/6, related to area-level cohort IMR exposure during gestation and infancy. Genetic correlations are found across multiple domains, including fertility, cognition, health behaviors, and health outcomes, suggesting an important role for cohort selection in modern populations.

Higher educational attainment is associated with longer telomeres in midlife: Evidence from sibling comparisons in the UK Biobank.

Prior studies have established that higher educational attainment is associated with a longer telomere length (TL), a marker of cellular aging. However, it is unclear whether extant associations are causal, since they are likely confounded by unobserved genetic, early-life and family background factors that are correlated with education and TL. We leverage sibling differences in TL, education and measured genetics (polygenic scores for educational attainment and TL) to estimate associations between educational attainment and TL in midlife for European ancestry individuals in the UK Biobank, while controlling for unobserved confounders shared by siblings. After controlling for genetics and shared background between siblings, we find suggestive evidence that high school graduates have longer telomeres than high school dropouts, but we find no differences in TL between high school dropouts and college graduates.

Detecting genetic heterogeneities in response to trauma: The case of 9/11.

The current study evaluates genetic heterogeneities in response to trauma among U.S. young adults. Using Add Health Wave III, which coincidently overlapped with the September 11 attacks, and a depression mean and variance polygenic scores, we investigate how the polygenic scores moderate the causal effect of 9/11 on mental health. Our results show the presence of genetic heterogeneity, where those with high genetic plasticity experience an increase in depressive symptoms following trauma while those with low genetic plasticity do not. While the documented differences in reactions to trauma are important, we also note our ability to predict responses based only on genetic measures are too imprecise to identify susceptible clinical patients. We, therefore, contend that the expected benefits from genetic screening to identify susceptible individuals after trauma are limited. Our results provide novel evidence of a specific source of an additional heterogeneity contributing to the inequality of health following trauma.

What's the use?

A theoretical framework predicts that using polygenic screening to select embryos against traits that depend on many genes has few benefits.

The effects of relative body weight on socioemotional and schooling outcomes among female adolescents in the United States.

RATIONALE: An extensive literature has shown that individuals, especially women, with higher body mass index (BMI) face a range of negative life outcomes. Most previous studies rely on absolute measures of body weight, such as BMI and obesity status, to estimate the social impact of body weight. Using absolute measures of body weight, however, is inconsistent with social-psychological theories that explain the effects of body weight because they conflate the social effects of body weight with biological processes of body weight. OBJECTIVE: This study extends the literature by utilizing a relative measure of body weight, or a student's BMI rank within her relevant peer group, to examine the impact of body weight on youth outcomes. METHODS: Using data from the National Longitudinal Study of Adolescent to Adult Health and leveraging a quasi-experimental research design, this study examines the association between relative BMI and socioemotional and schooling outcomes among female adolescents in the United States. RESULTS: Results show that female students with high relative BMI are more likely to experience a decrease in self-esteem and an increase in depressive symptoms, even after adjusting for absolute BMI and weight perceptions. These effects are partially explained by lower levels of school attachment (∼26% for self-esteem and ∼15% for depressive symptoms). This study also finds that relative BMI is associated with an increased risk of high school dropout, but not college attainment and completed years of schooling. The association between relative BMI and high school dropout is partially explained by a combination of lowered self-esteem (∼7%), increased depressive symptoms (∼12%), as well as a decline in academic achievement (∼33%) and aspirations (∼12%). CONCLUSION: We argue that to better understand how body weight affects one' life outcomes, it is essential to take into consideration the social contexts in which one is embedded.