We analyze data from the Minnesota Twin Registry (MTR) and the Socioeconomic Survey of Twins (SST), combined with new mortality data, and contribute to two bodies of literature. First, we demonstrate a beneficial casual effect of education on health and longevity. Literature results on this subject based on natural experiments and twin studies are controversial despite a long history of research. Second, we shed light on how families allocate resources among siblings, another controversial question. We show that if one twin has a poorer health endowment parents will compensate for the difference with more schooling. We argue that we can expect a bias towards detecting a reinforcement case. Despite the bias we still find evidence of compensating behavior. For identification we rely on identical genes in monozygotic twins, gene variation in dizygotic twins, and shared family background in all twins, and control for the key background characteristics of birth weight and disabling injury. We account for measurement error in reported years of schooling.

A Genome-wide association study (GWAS) estimates size and significance of the effect of common genetic variants on a phenotype of interest. A Polygenic Score (PGS) is a score, computed for each individual, summarizing the expected value of a phenotype on the basis of the individual’s genotype. The PGS is computed as a weighted sum of the values of the individual’s genetic variants, using as weights the GWAS estimated coefficients from a training sample. Thus, P GS carries information on the genotype, and only on the genotype, of an individual. In our case phenotypes of interest are measures of educational achievement, such as having a college degree, or the education years, in a sample of approximately 2700 adult twins and their parents. We set up the analysis in a standard model of optimal parental investment and intergenerational mobility, extended to include a fully specified genetic analysis of skill transmission, and show that the model’s predictions on mobility differ substantially from those of the standard model. For instance, the coefficient of intergenerational income elasticity may be larger, and may differ across countries because the distribution of the genotype is different, completely independently of any difference in institution, technology or preferences. We then study how much of the educational achievement is explained by the PGS for education, thus estimating how much of the variance of education can be explained by genetic factors alone. We find a substantial effect of P GS on performance in school, years of education and college. Finally we study the channels between PGS and the educational achievement, distinguishing how much is due to cognitive skills and to personality traits. We show that the effect of PGS is substantially stronger on Intelligence than on other traits, like Constraint, which seem natural explanatory factors of educational success. For educational achievement, both cognitive and non cognitive skills are important, although the larger fraction of success is channeled by Intelligence.

Genome-wide association studies involve detecting association between millions of genetic variants and a trait, which typically use univariate regression to test association between each single variant and the phenotype. Alternatively, Lasso penalized regression allows one to jointly model the relationship between all genetic variants and the phenotype. However, it is unclear how to best conduct inference on the individual Lasso coefficients, especially in high-dimensional settings. We consider six methods for testing the Lasso coefficients: two permutation (Lasso-Ayers, Lasso-PL) and one analytic approach (Lasso-AL) to select the penalty parameter for type-1-error control, residual bootstrap (Lasso-RB), modified residual bootstrap (Lasso-MRB), and a permutation test (Lasso-PT). Methods are compared via simulations and application to the Minnesota Center for Twins and Family Study. We show that for finite sample sizes with increasing number of null predictors, Lasso-RB, Lasso-MRB, and Lasso-PT fail to be viable methods of inference. However, Lasso-PL and Lasso-AL remain fast and powerful tools for conducting inference with the Lasso, even in high-dimensions. Our results suggest that the proposed permutation selection procedure (Lasso-PL) and the analytic selection method (Lasso-AL) are fast and powerful alternatives to the standard univariate analysis in genome-wide association studies.

BACKGROUND: Several studies in the new field of cognitive epidemiology have shown that higher intelligence predicts longer lifespan. This positive correlation might arise from socioeconomic status influencing both intelligence and health; intelligence leading to better health behaviours; and/or some shared genetic factors influencing both intelligence and health. Distinguishing among these hypotheses is crucial for medicine and public health, but can only be accomplished by studying a genetically informative sample. METHODS: We analysed data from three genetically informative samples containing information on intelligence and mortality: Sample 1, 377 pairs of male veterans from the NAS-NRC US World War II Twin Registry; Sample 2, 246 pairs of twins from the Swedish Twin Registry; and Sample 3, 784 pairs of twins from the Danish Twin Registry. The age at which intelligence was measured differed between the samples. We used three methods of genetic analysis to examine the relationship between intelligence and lifespan: we calculated the proportion of the more intelligent twins who outlived their co-twin; we regressed within-twin-pair lifespan differences on within-twin-pair intelligence differences; and we used the resulting regression coefficients to model the additive genetic covariance. We conducted a meta-analysis of the regression coefficients across the three samples. RESULTS: The combined (and all three individual samples) showed a small positive phenotypic correlation between intelligence and lifespan. In the combined sample observed r = .12 (95% confidence interval .06 to .18). The additive genetic covariance model supported a genetic relationship between intelligence and lifespan. In the combined sample the genetic contribution to the covariance was 95%; in the US study, 84%; in the Swedish study, 86%, and in the Danish study, 85%. CONCLUSIONS: The finding of common genetic effects between lifespan and intelligence has important implications for public health, and for those interested in the genetics of intelligence, lifespan or inequalities in health outcomes including lifespan.

The National Academy of Sciences-National Research Council Twin Registry\n(NAS-NRC Twin Registry) is a comprehensive registry of White male twin\npairs born in the USA between 1917 and 1927, both of the twins having\nserved in the military. The purpose was medical research and ultimately\nimproved clinical care. The cohort was assembled in the early 1960s with\nidentification of approximately 16 000 twin pairs, review of service\nrecords, a brief mailed questionnaire assessing zygosity, and a health\nsurvey largely comparable to questionnaires used at that time with\nScandinavian twin registries. Subsequent large-scale data collection\noccurred in 1974, 1985 and 1998, repeating the health survey and\nincluding information on education, employment history and earnings.\nSelf-reported data have been supplemented with mortality, disability and\nmedical data through record linkage. Potential collaborators should\naccess the study website\n{[}http://www.iom.edu/Activities/Veterans/TwinsStudy.aspx] or e-mail the\nMedical Follow-up Agency at {[}Twins@nas.edu]. Questionnaire data are\nbeing prepared for future archiving with the National Archive of\nComputerized Data on Aging (NACDA) at the Inter-University Consortium\nfor Political and Social Research (ICPSR), University of Michigan, MI.