Genetic variants linked to education predict longevity

Prevention of cardiovascular disease for healthy aging and longevity: A new scoring system and related "mechanisms-hallmarks-biomarkers"

Healthy "environment-sleep-emotion-exercise-diet" intervention [E(e)SEEDi] lifestyle can improve the quality of life, prolong aging and promote longevity due to improvement of human immunity and prevention of cardiovascular diseases (CVD). Here, the author reviewed the associations between these core elements with CVD and cardiovascular aging, and developed a new scoring system based on the healthy E(e)SEEDi lifestyle for prediction and evaluation of life expectancy. These core factors are assigned 20 points each (120 points in total), and a higher score predicts healthier aging and longevity. The E(e)SEEDi represents "a tree of life" bearing the fruits of longevity as well as "a rocket of anti-ageing" carrying people around the world on a journey of longevity. In conclusion, the E(e)SEEDi can delay aging and increase the life expectancy due to the role of a series of cellular and molecular "mechanisms-hallmarks-biomarkers". It's believed that the novel scoring system has a huge potential and beautiful prospects.

Multi-Trait Polygenic Risk Score, Nongenetic Determinants, and Cardiovascular Disease Death: A Cohort Study of 14 086 Japanese Individuals

BACKGROUND

Although utility of composite trait-specific polygenic risk score (multi-trait PRS) has been examined among European ancestries, few studies investigated among East Asians and incorporated modifiable risk factors. We examined the associations of multi-trait PRS for cardiometabolic factors with cardiovascular disease mortality by integrating nongenetic determinants.

METHODS

A total of 14 086 Japanese participants (mean age, 55±9; 55.8% women) of the J-MICC (Japan Multi-Institutional Collaborative Cohort) study were analyzed in this study. We calculated 6 PRSs for cardiometabolic traits (systolic blood pressure, body mass index, triglycerides, low-density lipoprotein cholesterol, estimated glomerular filtration rate, and hemoglobin A1c). Based on these PRSs, we developed multi-trait PRS and considered as a primary exposure. Three nongenetic factors (smoking, alcohol drinking, and educational attainment) from the self-reported questionnaire were also examined.

RESULTS

During a median 12.1-year follow-up period, a total of 472 all-cause and 79 cardiovascular disease mortality cases were documented. Compared with 0% to 90% of multi-trait PRSs, an adjusted hazard ratio (HR) among the top 10% of multi-trait PRSs was 1.32 (95% CI, 1.00-1.73) for all-cause death and 2.63 (95% CI, 1.48-4.67) for cardiovascular disease death. Incorporation of educational attainment with multi-trait PRSs showed null associations in those who went beyond high school (HR, 2.07 [95% CI, 0.44-9.66]) even in the top 10% of multi-trait PRS.

CONCLUSIONS

Our analysis combining both genetic and nongenetic determinants highlighted that lifestyle factors and educational attainment can slightly reduce an individual's composite genetic risk for cardiovascular disease death.

Examining Differences in the Predictive Capacity of Educational Polygenic Scores on Physical Limitations Among Older Adults With European or African Ancestry

This research examined whether educational polygenic scores were associated with physical limitations among older adults with European or African ancestry. In the European ancestry sample, we found that education polygenic scores were significantly associated with physical limitations, net of age, sex, and current socioeconomic status. In the African ancestry sample, education polygenic scores were not associated with physical limitations in any of the models. Observed educational attainment was a robust predictor of physical limitations in both samples. This research demonstrates the inequalities in the predictive capacity of educational polygenic scores for physical health. We hypothesize that this disparity is a result of structural barriers to educational attainment by race, selection bias, and/or racial inequities in data collection. All of these explanations stem from structural racism and highlight the limited usefulness of polygenic scores for clinical decision-making.

Using the phenotype differences model to identify genetic effects in samples of partially genotyped sibling pairs

The identification of causal relationships between specific genes and social, behavioral, and health outcomes is challenging due to environmental confounding from population stratification and dynastic genetic effects. Existing methods to eliminate environmental confounding leverage random genetic variation resulting from recombination and require within-family dyadic genetic data (i.e., parent-child and/or sibling pairs), meaning they can only be applied in relatively small and selected samples. We introduce the phenotype differences model and provide derivations showing that it-under plausible assumptions-provides consistent (and, in certain cases, unbiased) estimates of genetic effects using just a single individual's genotype. Then, leveraging distinct samples of fully and partially genotyped sibling pairs in the Wisconsin Longitudinal Study, we use polygenic indices and phenotypic data for 24 different traits to empirically validate the phenotype differences model. Finally, we utilize the model to test the effects of 40 polygenic indices on lifespan. After a 10% false discovery rate correction, we find that polygenic indices for three traits-body mass index, self-rated health, chronic obstructive pulmonary disease-have a statistically significant effect on an individual's lifespan.

Rapid discrimination between deleterious and benign missense mutations in the CAGI 6 experiment

We describe the machine learning tool that we applied in the CAGI 6 experiment to predict whether single residue mutations in proteins are deleterious or benign. This tool was trained using only single sequences, i.e., without multiple sequence alignments or structural information. Instead, we used global characterizations of the protein sequence. Training and testing data for human gene mutations was obtained from ClinVar (ncbi.nlm.nih.gov/pub/ClinVar/), and for non-human gene mutations from Uniprot (www.uniprot.org). Testing was done on post-training data from ClinVar. This testing yielded high AUC and Matthews correlation coefficient (MCC) for well trained examples but low generalizability. For genes with either sparse or unbalanced training data, the prediction accuracy is poor. The resulting prediction server is available online at http://www.mamiris.com/Shoni.cagi6.

Polygenic Risk of Mental Disorders and Subject-Specific School Grades

BACKGROUND

Education is essential for socioeconomic security and long-term mental health; however, mental disorders are often detrimental to the educational trajectory. Genetic correlations between mental disorders and educational attainment do not always align with corresponding phenotypic associations, implying heterogeneity in the genetic overlap.

METHODS

We unraveled this heterogeneity by investigating associations between polygenic risk scores for 6 mental disorders and fine-grained school outcomes: school grades in language and mathematics in ninth grade and high school, as well as educational attainment by age 25, using nationwide-representative data from established cohorts (N = 79,489).

RESULTS

High polygenic liability of attention-deficit/hyperactivity disorder was associated with lower grades in language and mathematics, whereas high polygenic risk of anorexia nervosa or bipolar disorder was associated with higher grades in language and mathematics. Associations between polygenic risk and school grades were mixed for schizophrenia and major depressive disorder and neutral for autism spectrum disorder.

CONCLUSIONS

Polygenic risk scores for mental disorders are differentially associated with language and mathematics school grades.

Multi-polygenic prediction of frailty highlights chronic pain and educational attainment as key risk and protective factors

Frailty is a complex trait. Twin studies and recent Genome-Wide Association Studies have demonstrated a strong genetic basis of frailty but there remains a lack of genetic studies exploring genetic prediction of Frailty. Previous work has shown that a single polygenic predictor - represented by a Frailty polygenic score - predicts Frailty, measured via the frailty index, in independent samples within the United Kingdom. We extended this work, using a multi-polygenic score (MPS) approach to increase predictive power. Predictor variables - twenty-six polygenic scores (PGS) were modelled in regularised Elastic net regression models, with repeated cross-validation, to estimate joint prediction of the polygenic scores and order the predictions by their contributing strength to Frailty in two independent cohorts aged 65+ - the English Longitudinal Study of Ageing (ELSA) and Lothian Birth Cohort 1936 (LBC1936). Results showed that the MPS explained 3.6% and 4.7% of variance compared to the best single-score prediction of 2.6% and 2.2% of variance in ELSA and LBC1936 respectively. The strongest polygenic predictors of worsening frailty came from PGS for Chronic pain, Frailty and Waist circumference; whilst PGS for Parental Death, Educational attainment, and Rheumatoid Arthritis were found to be protective to frailty. Results from the predictors remaining in the final model were then validated using the longitudinal LBC1936, with equivalent PGS scores from the same GWAS summary statistics. Thus, this MPS approach provides new evidence for the genetic contributions to frailty in later life and sheds light on the complex structure of the Frailty Index measurement.

Structural models of genome-wide covariance identify multiple common dimensions in autism

Common genetic variation has been associated with multiple phenotypic features in Autism Spectrum Disorder (ASD). However, our knowledge of shared genetic factor structures contributing to this highly heterogeneous phenotypic spectrum is limited. Here, we developed and implemented a structural equation modelling framework to directly model genomic covariance across core and non-core ASD phenotypes, studying autistic individuals of European descent with a case-only design. We identified three independent genetic factors most strongly linked to language performance, behaviour and developmental motor delay, respectively, studying an autism community sample (N = 5331). The three-factorial structure was largely confirmed in independent ASD-simplex families (N = 1946), although we uncovered, in addition, simplex-specific genetic overlap between behaviour and language phenotypes. Multivariate models across cohorts revealed novel associations, including links between language and early mastering of self-feeding. Thus, the common genetic architecture in ASD is multi-dimensional with overarching genetic factors contributing, in combination with ascertainment-specific patterns, to phenotypic heterogeneity.

Genetic endowments for social capital: An investigation accounting for genetic nurturing effects

Despite social capital having been shown to be important for health and well-being, relatively little research has examined genetic determinants. Genetic endowments for education have been shown to influence human, financial, and health capital, but few studies have examined social capital, and those conducted have yet to account for genetic nurturing. We used the Add-Health data to study the effect of genetic endowments on individual social capital using the education polygenic score (PGS). We used sibling fixed effects models and controlled for the family environment to account for genetic nurturing. After accounting for the family environment, we found moderately large significant associations between the education PGS and volunteering, but associations with religious service attendance and number of friends were completely attenuated in sibling fixed effects models. These findings highlight that genetic endowments play an important role in influencing volunteering and the importance of accounting for genetic nurturing.

The genetics of falling susceptibility and identification of causal risk factors

Falls represent a huge health and economic burden. Whilst many factors are associated with fall risk (e.g. obesity and physical inactivity) there is limited evidence for the causal role of these risk factors. Here, we used hospital and general practitioner records in UK Biobank, deriving a balance specific fall phenotype in 20,789 cases and 180,658 controls, performed a Genome Wide Association Study (GWAS) and used Mendelian Randomisation (MR) to test causal pathways. GWAS indicated a small but significant SNP-based heritability (4.4%), identifying one variant (rs429358) in APOE at genome-wide significance (P < 5e-8). MR provided evidence for a causal role of higher BMI on higher fall risk even in the absence of adverse metabolic consequences. Depression and neuroticism predicted higher risk of falling, whilst higher hand grip strength and physical activity were protective. Our findings suggest promoting lower BMI, higher physical activity as well as psychological health is likely to reduce falls.

Cross-National and Cross-Generational Evidence That Educational Attainment May Slow the Pace of Aging in European-Descent Individuals

OBJECTIVES

Individuals with more education are at lower risk of developing multiple, different age-related diseases than their less-educated peers. A reason for this might be that individuals with more education age slower. There are 2 complications in testing this hypothesis. First, there exists no definitive measure of biological aging. Second, shared genetic factors contribute toward both lower educational attainment and the development of age-related diseases. Here, we tested whether the protective effect of educational attainment was associated with the pace of aging after accounting for genetic factors.

METHODS

We examined data from 5 studies together totaling almost 17,000 individuals with European ancestry born in different countries during different historical periods, ranging in age from 16 to 98 years old. To assess the pace of aging, we used DunedinPACE, a DNA methylation algorithm that reflects an individual's rate of aging and predicts age-related decline and Alzheimer's disease and related disorders. To assess genetic factors related to education, we created a polygenic score based on the results of a genome-wide association study of educational attainment.

RESULTS

Across the 5 studies, and across the life span, higher educational attainment was associated with a slower pace of aging even after accounting for genetic factors (meta-analysis effect size = -0.20; 95% confidence interval [CI]: -0.30 to -0.10; p = .006). Further, this effect persisted after taking into account tobacco smoking (meta-analysis effect size = -0.13; 95% CI: -0.21 to -0.05; p = .01).

DISCUSSION

These results indicate that higher levels of education have positive effects on the pace of aging, and that the benefits can be realized irrespective of individuals' genetics.

Does Children's Education Improve Parental Health and Longevity? Causal Evidence from Great Britain

Parents with better-educated children are healthier and live longer, but whether there is a causal effect of children's education on their parents' health and longevity is unclear. First, we demonstrate an association between adults' offspring education and parental mortality in the 1958 British birth cohort study, which remains substantial-about two additional years of life-even when comparing parents with similar socioeconomic status. Second, we use the 1972 educational reform in England and Wales, which increased the minimum school leaving age from 15 to 16 years, to identify the presence of a causal effect of children's education on parental health and longevity using census-linked data from the Office for National Statistics Longitudinal Study. Results reveal that children's education has no causal effects on a wide range of parental mortality and health outcomes. We interpret these findings discussing the role of universal health care and education for socioeconomic inequality in Great Britain.

Co-Inheritance of Variation in All-Cause Mortality and Biochemical Risk Factors

Biomarkers may be useful endophenotypes for genetic studies if they share genetic sources of variation with the outcome, for example, with all-cause mortality. Australian adult study participants who had reported their parental survival information were included in the study: 14,169 participants had polygenic risk scores (PRS) from genotyping and up to 13,365 had biomarker results. We assessed associations between participants' biomarker results and parental survival, and between biomarker results and eight parental survival PRS at varying p-value cut-offs. Survival in parents was associated with participants' serum bilirubin, C-reactive protein, HDL cholesterol, triglycerides and uric acid, and with LDL cholesterol for participants' fathers but not for their mothers. PRS for all-cause mortality were associated with liver function tests (alkaline phosphatase, butyrylcholinesterase, gamma-glutamyl transferase), metabolic tests (LDL and HDL cholesterol, triglycerides, uric acid), and acute-phase reactants (C-reactive protein, globulins). Association between offspring biomarker results and parental survival demonstrates the existence of familial effects common to both, while associations between biomarker results and PRS for mortality favor at least a partial genetic cause of this covariation. Identification of genetic loci affecting mortality-associated biomarkers offers a route to the identification of additional loci affecting mortality.

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57.

High polygenic predisposition for ADHD and a greater risk of all-cause mortality: a large population-based longitudinal study

BACKGROUND

Attention deficit hyperactivity disorder (ADHD) is a highly heritable, neurodevelopmental disorder known to associate with more than double the risk of death compared with people without ADHD. Because most research on ADHD has focused on children and adolescents, among whom death rates are relatively low, the impact of a high polygenic predisposition to ADHD on accelerating mortality risk in older adults is unknown. Thus, the aim of the study was to investigate if a high polygenetic predisposition to ADHD exacerbates the risk of all-cause mortality in older adults from the general population in the UK.

METHODS

Utilising data from the English Longitudinal Study of Ageing, which is an ongoing multidisciplinary study of the English population aged ≥ 50 years, polygenetic scores for ADHD were calculated using summary statistics for (1) ADHD (PGS-ADHD_single) and (2) chronic obstructive pulmonary disease and younger age of giving first birth, which were shown to have a strong genetic correlation with ADHD using the multi-trait analysis of genome-wide association summary statistics; this polygenic score was referred to as PGS-ADHD_multi-trait. All-cause mortality was ascertained from the National Health Service central register that captures all deaths occurring in the UK.

RESULTS

The sample comprised 7133 participants with a mean age of 64.7 years (SD = 9.5, range = 50-101); of these, 1778 (24.9%) died during a period of 11.2 years. PGS-ADHD_single was associated with a greater risk of all-cause mortality (hazard ratio [HR] = 1.06, 95% CI = 1.02-1.12, p = 0.010); further analyses showed this relationship was significant in men (HR = 1.07, 95% CI = 1.00-1.14, p = 0.043). Risk of all-cause mortality increased by an approximate 11% for one standard deviation increase in PGS-ADHD_multi-trait (HR = 1.11, 95% CI = 1.06-1.16, p < 0.001). When the model was run separately for men and women, the association between PGS-ADHD_multi-trait and an increased risk of all-cause mortality was significant in men (HR = 1.10, 95% CI = 1.03-1.18, p = 0.003) and women (HR = 1.11, 95% CI = 1.04-1.19, p = 0.003).

CONCLUSIONS

A high polygenetic predisposition to ADHD is a risk factor for all-cause mortality in older adults. This risk is better captured when incorporating genetic information from correlated traits.

Genetic risk scores in life insurance underwriting

Genetic tests that predict the lifetime risk of common medical conditions are fast becoming more accurate and affordable. The life insurance industry is interested in using predictive genetic tests in the underwriting process, but more research is needed to establish whether this nascent form of genetic testing can refine the process over conventional underwriting factors. Here, we perform Cox regression of survival on a battery of genetic risk scores for common medical conditions and mortality risks in the Health and Retirement Study, without returning results to participants. Adjusted for covariates in a relevant insurance scenario, the scores could improve mortality risk classification by identifying 2.6 years shorter median lifespan in the highest decile of total genetic liability. We conclude that existing genetic risk scores can already improve life insurance underwriting, which stresses the urgency of policymakers to balance competing interests between stakeholders as this technology develops.

Causal relationship between educational attainment and the risk of rheumatoid arthritis: a Mendelian randomization study

Background

Educational attainment is moderately heritable and inversely associated with the risk of rheumatoid arthritis. However, the causality from educational attainment on rheumatoid arthritis remained unknown. Here, we aimed to determine whether educational attainment is causally associated with rheumatoid arthritis (RA) by using Mendelian randomization (MR) approach.

Methods

Summary statistics data for RA were obtained from an available, published meta-analysis of genome-wide association studies (GWAS) that included 14,361 RA cases and 43,923 controls of European ancestry. The instrumental variables for educational attainment were obtained from a GWAS meta-analysis that included over 1 million individuals (N = 1,131,881) of European ancestry. MR analyses were mainly performed using the inverse-variance weighted (IVW) method. Sensitivity analyses were further performed to test the robustness of the association using the weighted median method, MR-Egger, Cochran Q test, “leave-one-out” analysis and MR-PRESSO test.

Results

A total of 387 SNPs were employed as instrumental variables in our MR analysis. Genetically predicted higher educational attainment was associated with a significantly lower risk of RA using the IVW method (odds ratio [OR] = 0.42, 95% confidence interval [CI]: 0.34–0.52; p = 1.78 × 10^− 14). The weighted median method and MR Egger regression analysis yielded consistent results. The effect estimate remained robust after the outlier variants and SNPs (associated with the confounding factors) were excluded. “Leave-one-out” analysis confirmed the stability of our results. Additionally, the results suggested the absence of the horizontal pleiotropy.

Conclusions

The MR analysis supported a potential inverse causative relationship between educational attainment and the risk of RA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41927-021-00216-0.

Genes Related to Education Predict Frailty Among Older Adults in the United States

OBJECTIVE

This article expands on research that links education and frailty among older adults by considering the role of genes associated with education.

METHOD

Data come from a sample of 7,064 non-Hispanic, white adults participating in the 2004-2012 waves of the Health and Retirement Study. Frailty was measured with two indices: (a) The Frailty Index which corresponds to a deficit accumulation model; and (b) The Paulson-Lichtenberg Frailty Index which corresponds to the biological syndrome/phenotype model. Genes associated with education were quantified using an additive polygenic score. Associations between the polygenic score and frailty indices were tested using a series of multilevel models, controlling for multiple observations for participants across waves.

RESULTS

Results showed a strong and negative association between genes for education and frailty symptoms in later life. This association exists above and beyond years of completed education and we demonstrate that this association becomes weaker as older adults approach their 80s.

DISCUSSION

The results contribute to the education-health literature by highlighting new and important pathways through which education might be linked to successful aging.

Transpersonal Genetic Effects Among Older U.S. Couples: A Longitudinal Study

BACKGROUND

Emerging social genetics research suggests one's genes may influence not just one's own outcomes but also those of close social alters. Health implications, particularly in late life, remain underexplored. Using combined genetic and survey data, this study examined such transpersonal genetic associations among older U.S. couples.

METHOD

Data were from married or cohabiting couples in the 2006-2016 waves of the Health and Retirement Study, nationally representative of U.S. adults over 50. Measures included a polygenic score for educational attainment, and self-rated health. Analysis was through parallel process latent growth models.

RESULTS

Women's and men's genetic scores for education had transpersonal linkages with their partner's health. Such associations were solely with life-course variations and not late-life change in outcomes. Moreover, they were indirect, mediated by educational attainment itself. Evidence also emerged for individual-level genetic effects mediated by the partner's education.

DISCUSSION

In addition to the subject-specific linkages emphasized in extant genetics literature, relational contexts involve multiple transpersonal genetic associations. These appear to have consequences for a partner's and one's own health. Life-course theory indicates that a person is never not embedded in such contexts, suggesting that these patterns may be widespread. Research is needed on their implications for the life-course and gene-environment correlation literature.

Genetic and non-genetic factors associated with the phenotype of exceptional longevity & normal cognition

In this study, we split 2156 individuals from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) data into two groups, establishing a phenotype of exceptional longevity & normal cognition versus cognitive impairment. We conducted a genome-wide association study (GWAS) to identify significant genetic variants and biological pathways that are associated with cognitive impairment and used these results to construct polygenic risk scores. We elucidated the important and robust factors, both genetic and non-genetic, in predicting the phenotype, using several machine learning models. The GWAS identified 28 significant SNPs at p-value [Formula: see text] significance level and we pinpointed four genes, ESR1, PHB, RYR3, GRIK2, that are associated with the phenotype though immunological systems, brain function, metabolic pathways, inflammation and diet in the CLHLS cohort. Using both genetic and non-genetic factors, four machine learning models have close prediction results for the phenotype measured in Area Under the Curve: random forest (0.782), XGBoost (0.781), support vector machine with linear kernel (0.780), and [Formula: see text] penalized logistic regression (0.780). The top four important and congruent features in predicting the phenotype identified by these four models are: polygenic risk score, sex, age, and education.

Polygenic predictors of age-related decline in cognitive ability

Polygenic scores can be used to distil the knowledge gained in genome-wide association studies for prediction of health, lifestyle, and psychological factors in independent samples. In this preregistered study, we used fourteen polygenic scores to predict variation in cognitive ability level at age 70, and cognitive change from age 70 to age 79, in the longitudinal Lothian Birth Cohort 1936 study. The polygenic scores were created for phenotypes that have been suggested as risk or protective factors for cognitive ageing. Cognitive abilities within older age were indexed using a latent general factor estimated from thirteen varied cognitive tests taken at four waves, each three years apart (initial n = 1091 age 70; final n = 550 age 79). The general factor indexed over two-thirds of the variance in longitudinal cognitive change. We ran additional analyses using an age-11 intelligence test to index cognitive change from age 11 to age 70. Several polygenic scores were associated with the level of cognitive ability at age-70 baseline (range of standardized β-values = -0.178 to 0.302), and the polygenic score for education was associated with cognitive change from childhood to age 70 (standardized β = 0.100). No polygenic scores were statistically significantly associated with variation in cognitive change between ages 70 and 79, and effect sizes were small. However, APOE e4 status made a significant prediction of the rate of cognitive decline from age 70 to 79 (standardized β = -0.319 for carriers vs. non-carriers). The results suggest that the predictive validity for cognitive ageing of polygenic scores derived from genome-wide association study summary statistics is not yet on a par with APOE e4, a better-established predictor.

Combined Utility of 25 Disease and Risk Factor Polygenic Risk Scores for Stratifying Risk of All-Cause Mortality

While genome-wide association studies have identified susceptibility variants for numerous traits, their combined utility for predicting broad measures of health, such as mortality, remains poorly understood. We used data from the UK Biobank to combine polygenic risk scores (PRS) for 13 diseases and 12 mortality risk factors into sex-specific composite PRS (cPRS). These cPRS were moderately associated with all-cause mortality in independent data within the UK Biobank: the estimated hazard ratios per standard deviation were 1.10 (95% confidence interval: 1.05, 1.16) and 1.15 (1.10, 1.19) for women and men, respectively. Differences in life expectancy between the top and bottom 5% of the cPRS were estimated to be 4.79 (1.76, 7.81) years and 6.75 (4.16, 9.35) years for women and men, respectively. These associations were substantially attenuated after adjusting for non-genetic mortality risk factors measured at study entry (i.e., middle age for most participants). The cPRS may be useful in counseling younger individuals at higher genetic risk of mortality on modification of non-genetic factors.

Separating Measured Genetic and Environmental Effects: Evidence Linking Parental Genotype and Adopted Child Outcomes

There has been widespread adoption of genome wide summary scores (polygenic scores) as tools for studying the importance of genetics and associated life course mechanisms across a range of demographic and socioeconomic outcomes. However, an often unacknowledged issue with these studies is that parental genetics impact both child environments and child genetics, leaving the effects of polygenic scores difficult to interpret. This paper uses multi-generational data containing polygenic scores for parents (n = 7193) and educational outcomes for adopted (n = 855) and biological (n = 20,939) children, many raised in the same families, which allows us to separate the influence of parental polygenic scores on children outcomes between environmental (adopted children) and environmental and genetic (biological children) effects. Our results complement recent work on "genetic nurture" by showing associations of parental polygenic scores with adopted children's schooling, providing additional evidence that polygenic scores combine genetic and environmental influences and that research designs are needed to separate these estimated impacts.

Separating Measured Genetic and Environmental Effects: Evidence Linking Parental Genotype and Adopted Child Outcomes

There has been widespread adoption of genome wide summary scores (polygenic scores) as tools for studying the importance of genetics and associated life course mechanisms across a range of demographic and socioeconomic outcomes. However, an often unacknowledged issue with these studies is that parental genetics impact both child environments and child genetics, leaving the effects of polygenic scores difficult to interpret. This paper uses multi-generational data containing polygenic scores for parents (n = 7193) and educational outcomes for adopted (n = 855) and biological (n = 20,939) children, many raised in the same families, which allows us to separate the influence of parental polygenic scores on children outcomes between environmental (adopted children) and environmental and genetic (biological children) effects. Our results complement recent work on "genetic nurture" by showing associations of parental polygenic scores with adopted children's schooling, providing additional evidence that polygenic scores combine genetic and environmental influences and that research designs are needed to separate these estimated impacts.

Genetics of frailty: A longevity perspective

Frailty is a complex late life phenotype characterized by cumulative declines in multiple physiological systems that increases the risk for disability and mortality. The biological changes associated with aging are risk factors for frailty as well as for complex diseases; whereas longevity is assumed to be an outcome of protective biological mechanisms. Understanding the interplay between biological alterations associated with aging and protective mechanisms associated with longevity in the context of frailty may help guide development of interventions to increase healthspan and promote successful aging. The complexity of these phenotypes and relatively low heritability in studies are the main roadblocks in deciphering genetic mechanisms of these age associated conditions. We review genetic research related to frailty, and discuss the possible intertwined biology of frailty and longevity.

Desarrollo evolutivo humano y longevidad. Un análisis bio-psicosocial

En este trabajo se hace una revisión bibliográfica sobre el desarrollo evolutivo humano y longevidad, desde un enfoque biopsicosocial (Engel, 1977; Gliedt et al., 2017; Lehman et al., 2017). Tras aplicar el método de análisis PRISMA, se obtuvieron diversos resultados relacionados con un desarrollo evolutivo más longevo; así, en el área biológica, 3 factores: los SNPs, los telómeros y la química del estrés; en el área psicológica, 5 factores: la metacognición, la resiliencia, la espiritualidad, las relaciones personales y la depresión; y en el área social, 8 factores: la pseudo-heredabilidad, las relaciones conyugales, la maternidad, el nivel educativo, estilos de vida, dieta y restricción calórica, actividad física y mental y tecnología sanitaria. Ante los datos obtenidos en las tres áreas, de este enfoque biopsicosocial, y el repetido solapamiento entre factores del área psicológica y del área social, se plantea que pudieran considerarse estas dos como una conjunta, proponiéndose un enfoque explicativo con dos áreas: bio-psicosocial que, por factores encontrados en este trabajo, quedarían un 18,7% de biológica y un 81,3% psicosocial. Actualmente, hay suficiente información sobre desarrollo evolutivo humano y longevidad, pero una ausencia de investigaciones que estudien esos factores desde una perspectiva integrada. Mucha de esa información privilegiada se podría aplicar ya, psicológica y socialmente, a la población en general, para una mejora de su salud, en cualquier fase del desarrollo evolutivo humano.

Genetic Endowments and Wealth Inequality

We show that genetic endowments linked to educational attainment strongly and robustly predict wealth at retirement. The estimated relationship is not fully explained by flexibly controlling for education and labor income. We therefore investigate a host of additional mechanisms that could account for the gene-wealth gradient, including inheritances, mortality, risk preferences, portfolio decisions, beliefs about the probabilities of macroeconomic events, and planning horizons. We provide evidence that genetic endowments related to human capital accumulation are associated with wealth not only through educational attainment and labor income, but also through a facility with complex financial decision-making.

Education leads to a more physically active lifestyle: Evidence based on Mendelian randomization

Physical inactivity is a major health risk worldwide. Observational studies suggest that higher education is positively related to physical activity, but it is not clear whether this relationship constitutes a causal effect. Using participants (N = 1651) drawn from the Cardiovascular Risk in Young Finns Study linked to nationwide administrative data from Statistics Finland, this study examined whether educational attainment, measured by years of education, is related to adulthood physical activity in terms of overall physical activity, weekly hours of intensive activity, total steps per day, and aerobic steps per day. We employed ordinary least squares (OLS) models and extended the analysis using an instrumental variables approach (Mendelian randomization, MR) with a genetic risk score as an instrument for years of education. Based on the MR results, it was found that years of education is positively related to physical activity. On average, one additional year of education leads to a 0.62-unit higher overall physical activity (P < .01), 0.26 more hours of weekly intensive activity (P < .05), 560 more steps per day (P < .10), and 390 more aerobic steps per day (P < .09). The findings indicate that education may be a factor leading to higher leisure-time physical activity and thus promoting global health.

Genomic prediction of alcohol-related morbidity and mortality

While polygenic risk scores (PRS) have been shown to predict many diseases and risk factors, the potential of genomic prediction in harm caused by alcohol use has not yet been extensively studied. Here, we built a novel polygenic risk score of 1.1 million variants for alcohol consumption and studied its predictive capacity in 96,499 participants from the FinnGen study and 39,695 participants from prospective cohorts with detailed baseline data and up to 25 years of follow-up time. A 1 SD increase in the PRS was associated with 11.2 g (=0.93 drinks) higher weekly alcohol consumption (CI = 9.85-12.58 g, p = 2.3 × 10-58). The PRS was associated with alcohol-related morbidity (4785 incident events) and the risk estimate between the highest and lowest quintiles of the PRS was 1.83 (95% CI = 1.66-2.01, p = 1.6 × 10-36). When adjusted for self-reported alcohol consumption, education, marital status, and gamma-glutamyl transferase blood levels in 28,639 participants with comprehensive baseline data from prospective cohorts, the risk estimate between the highest and lowest quintiles of the PRS was 1.58 (CI = 1.26-1.99, p = 8.2 × 10-5). The PRS was also associated with all-cause mortality with a risk estimate of 1.33 between the highest and lowest quintiles (CI = 1.20-1.47, p = 4.5 × 10-8) in the adjusted model. In conclusion, the PRS for alcohol consumption independently associates for both alcohol-related morbidity and all-cause mortality. Together, these findings underline the importance of heritable factors in alcohol-related health burden while highlighting how measured genetic risk for an important behavioral risk factor can be used to predict related health outcomes.

Genetics of Human Longevity Within an Eco-Evolutionary Nature-Nurture Framework

Human longevity is a complex trait, and to disentangle its basis has a great theoretical and practical consequences for biomedicine. The genetics of human longevity is still poorly understood despite several investigations that used different strategies and protocols. Here, we argue that such rather disappointing harvest is largely because of the extraordinary complexity of the longevity phenotype in humans. The capability to reach the extreme decades of human lifespan seems to be the result of an intriguing mixture of gene-environment interactions. Accordingly, the genetics of human longevity is here described as a highly context-dependent phenomenon, within a new integrated, ecological, and evolutionary perspective, and is presented as a dynamic process, both historically and individually. The available literature has been scrutinized within this perspective, paying particular attention to factors (sex, individual biography, family, population ancestry, social structure, economic status, and education, among others) that have been relatively neglected. The strength and limitations of the most powerful and used tools, such as genome-wide association study and whole-genome sequencing, have been discussed, focusing on prominently emerged genes and regions, such as apolipoprotein E, Forkhead box O3, interleukin 6, insulin-like growth factor-1, chromosome 9p21, 5q33.3, and somatic mutations among others. The major results of this approach suggest that (1) the genetics of longevity is highly population specific; (2) small-effect alleles, pleiotropy, and the complex allele timing likely play a major role; (3) genetic risk factors are age specific and need to be integrated in the light of the geroscience perspective; (4) a close relationship between genetics of longevity and genetics of age-related diseases (especially cardiovascular diseases) do exist. Finally, the urgent need of a global approach to the largely unexplored interactions between the 3 genetics of human body, that is, nuclear, mitochondrial, and microbiomes, is stressed. We surmise that the comprehensive approach here presented will help in increasing the above-mentioned harvest.

The E Is in the G: Gene-Environment-Trait Correlations and Findings From Genome-Wide Association Studies

Genome-wide association studies (GWASs) have shown that pleiotropy is widespread (i.e., the same genetic variants affect multiple traits) and that complex traits are polygenic (i.e., affected by many genetic variants with very small effect sizes). However, despite the growing number of GWASs, the possible contribution of gene-environment correlations (rGEs) to pleiotropy and polygenicity has been mostly ignored. rGEs can lead to environmentally mediated pleiotropy or gene-environment-trait correlations (rGETs), given that an environment that is affected by one genetically influenced phenotype, can in turn affect a different phenotype. By adding correlations with environmentally mediated genetic variants, rGETs can contribute to polygenicity. Socioeconomic status (SES) and the experience of stressful life events may, for example, be involved in rGETs. Both are genetically influenced and have been associated with a myriad of physical and mental disorders. As a result, GWASs of these disorders may find the genetic correlates of SES and stressful life events. Consequently, some of the genetic correlates of physical and mental disorders may be modified by public policy that affects environments such as SES and stressful life events. Thus, identifying rGETs can shed light on findings from GWASs and have important implications for public health.

Genetically modulated educational attainment and coronary disease risk

AIMS

Genetic disposition and lifestyle factors are understood as independent components underlying the risk of multiple diseases. In this study, we aim to investigate the interplay between genetics, educational attainment-an important denominator of lifestyle-and coronary artery disease (CAD) risk.

METHODS AND RESULTS

Based on the effect sizes of 74 genetic variants associated with educational attainment, we calculated a 'genetic education score' in 13 080 cases and 14 471 controls and observed an inverse correlation between the score and risk of CAD [P = 1.52 × 10-8; odds ratio (OR) 0.79, 95% confidence interval (CI) 0.73-0.85 for the higher compared with the lowest score quintile]. We replicated in 146 514 individuals from UK Biobank (P = 1.85 × 10-6) and also found strong associations between the 'genetic education score' with 'modifiable' risk factors including smoking (P = 5.36 × 10-23), body mass index (BMI) (P = 1.66 × 10-30), and hypertension (P = 3.86 × 10-8). Interestingly, these associations were only modestly attenuated by adjustment for years spent in school. In contrast, a model adjusting for BMI and smoking abolished the association signal between the 'genetic education score' and CAD risk suggesting an intermediary role of these two risk factors. Mendelian randomization analyses performed with summary statistics from large genome-wide meta-analyses and sensitivity analysis using 1271 variants affecting educational attainment (OR 0.68 for the higher compared with the lowest score quintile; 95% CI 0.63-0.74; P = 3.99 × 10-21) further strengthened these findings.

CONCLUSION

Genetic variants known to affect educational attainment may have implications for a health-conscious lifestyle later in life and subsequently affect the risk of CAD.

Genetic and epigenetic regulation of human aging and longevity

Here we summarize the latest data on genetic and epigenetic contributions to human aging and longevity. Whereas environmental and lifestyle factors are important at younger ages, the contribution of genetics appears more important in reaching extreme old age. Genome-wide studies have implicated ~57 gene loci in lifespan. Epigenomic changes during aging profoundly affect cellular function and stress resistance. Dysregulation of transcriptional and chromatin networks is likely a crucial component of aging. Large-scale bioinformatic analyses have revealed involvement of numerous interaction networks. As the young well-differentiated cell replicates into eventual senescence there is drift in the highly regulated chromatin marks towards an entropic middle-ground between repressed and active, such that genes that were previously inactive "leak". There is a breakdown in chromatin connectivity such that topologically associated domains and their insulators weaken, and well-defined blocks of constitutive heterochromatin give way to generalized, senescence-associated heterochromatin, foci. Together, these phenomena contribute to aging.

What genome-wide association studies reveal about the association between intelligence and physical health, illness, and mortality

The associations between higher intelligence test scores from early life and later good health, fewer illnesses, and longer life are recent discoveries. Researchers are mapping the extent of these associations and trying to understanding them. Part of the intelligence-health association has genetic origins. Recent advances in molecular genetic technology and statistical analyses have revealed that: intelligence and many health outcomes are highly polygenic; and that modest but widespread genetic correlations exist between intelligence and health, illness and mortality. Causal accounts of intelligence-health associations are still poorly understood. The contribution of education and socio-economic status - both of which are partly genetic in origin - to the intelligence-health associations are being explored.

Major Discrimination Experiences, Education, and Genes

Objectives: Rather than acting as a buffer, educational attainment has a known positive linkage with major experiences of lifetime discrimination. Recently established genetic roots of education, then, may also influence such reports. The current study examined these patterns. Methods: Data were from the 2010 wave of the Health and Retirement Study. Polygenic scores indexed one's genetic propensity for more education. Mediation analysis was through counterfactual methods. Results: Among Whites as well as Blacks, genetic antecedents of education also elevated discrimination reports. Part of this influence was channeled through education. At least among Whites, direct effects were also found. Discussion: Major discrimination experiences seem partly rooted in genes. Mechanisms are tentatively suggested. Direct genetic influences, in particular, indicate potential confounding of previously estimated linkages between discrimination and health or life course factors. Given the range of these prior results, and their implications for healthy aging, investigation of these possibilities is needed.

Schizophrenia risk and reproductive success: a Mendelian randomization study

Schizophrenia is a debilitating and heritable mental disorder associated with lower reproductive success. However, the prevalence of schizophrenia is stable over populations and time, resulting in an evolutionary puzzle: how is schizophrenia maintained in the population, given its apparent fitness costs? One possibility is that increased genetic liability for schizophrenia, in the absence of the disorder itself, may confer some reproductive advantage. We assessed the correlation and causal effect of genetic liability for schizophrenia with number of children, age at first birth and number of sexual partners using data from the Psychiatric Genomics Consortium and UK Biobank. Linkage disequilibrium score regression showed little evidence of genetic correlation between genetic liability for schizophrenia and number of children (r g = 0.002, p = 0.84), age at first birth (r g = -0.007, p = 0.45) or number of sexual partners (r g = 0.007, p = 0.42). Mendelian randomization indicated no robust evidence of a causal effect of genetic liability for schizophrenia on number of children (mean difference: 0.003 increase in number of children per doubling in the natural log odds ratio of schizophrenia risk, 95% confidence interval (CI): -0.003 to 0.009, p = 0.39) or age at first birth (-0.004 years lower age at first birth, 95% CI: -0.043 to 0.034, p = 0.82). We find some evidence of a positive effect of genetic liability for schizophrenia on number of sexual partners (0.165 increase in the number of sexual partners, 95% CI: 0.117-0.212, p = 5.30×10-10). These results suggest that increased genetic liability for schizophrenia does not confer a fitness advantage but does increase mating success.

Gene-environment correlations in parental emotional warmth and intolerance: genome-wide analysis over two generations of the Young Finns Study

BACKGROUND

Genomic analysis of the child might offer new potential to illuminate human parenting. We examined whether offspring (G2) genome-wide genotype variation (SNPs) is associated with their mother's (G1) emotional warmth and intolerance, indicating a gene-environment correlation. If this association is stronger than between G2's genes and their emotional warmth and intolerance toward their own children, then this would indicate the presence of an evocative gene-environment correlation. To further understand how G1 mother's parenting has been evoked by genetically influenced characteristics of the child (G2), we examined whether child (G2) temperament partially accounted for the association between offspring genes and parental responses.

METHODS

Participants were from the Young Finns Study. G1 mothers (N = 2,349; mean age 39 years) self-reported the emotional warmth and intolerance toward G2 in 1980 when the participants were from 3 to 18 years old. G2 participants answered the same parenting scales in 2007/2012 (N = 1,378; mean age = 38 years in 2007; 59% female) when their children were on average 11 years old. Offspring temperament traits were self-reported in 1992 (G2 age range 15-30 years). Estimation of the phenotypic variance explained by the SNPs of G2 was done by genome-wide complex trait analysis with restricted maximum likelihood (GCTA-GREML).

RESULTS

Results showed that the SNPs of a child (G2) explained 22.6% of the phenotypic variance of maternal intolerance (G1; p-value = .039). G2 temperament trait negative emotionality explained only 2.4% points of this association. G2 genes did not explain G1 emotional warmth or G2's own emotional warmth and intolerance. However, further analyses of a combined measure of both G1 parenting scales found genetic effects. Parent or child gender did not moderate the observed associations.

CONCLUSIONS

Presented genome-wide evidence is pointing to the important role a child plays in affecting and shaping his/her family environment, though the underlying mechanisms remain unclear.

Social Integration, Self-Rated Health . . . and Genes?

Objective: This study examined genetic roots of later life social integration, and their confounding of this social factor's health linkages. Method: Data were from the 2010 wave of the Health and Retirement Study. Two dimensions of integration were examined: with one's "stakeholder" network of family and friends and with the community. Genetic measures included polygenic scores for extraversion and educational attainment. Results: Ties to one's stakeholder network had no genetic associations. The extraversion polygenic score was linked to community integration among Blacks as well as Whites. Among the latter, the same was true of one's genetic propensity for educational attainment. Although this score also influenced self-rated health, neither confounded associations of social integration with this indicator. Discussion: Later life social integration seems influenced by genetically rooted propensities for both sociability and human capital accumulation. Health linkages of integration, however, may not reflect mutual dependencies on the same genetic substrates.

"Same but different": Associations between multiple aspects of self-regulation, cognition, and academic abilities

Self-regulation describes the ability to control both behaviors and internal states against a backdrop of conflicting or distracting situations, drives, or impulses. In the cognitive psychology tradition, individual differences in self-regulation are commonly measured with performance-based tests of executive functioning, whereas in the personality psychology tradition, individual differences in self-regulation are typically assessed with report-based measures of impulse control, sustained motivation, and perseverance. The goal of this project was (a) to comprehensively examine the structure of associations between multiple self-regulatory constructs stemming from the cognitive and personality psychology traditions; (b) to estimate how these constructs, individually and collectively, related to mathematics and reading ability beyond psychometric measures of processing speed and fluid intelligence; and (c) to estimate the extent to which genetic and environmental factors mediated the observed associations. Data were available for 1,019 child participants from the Texas Twin Project (M age = 10.79, range = 7.8-15.5). Results highlighted the differentiation among cognitive and personality aspects of self-regulation, both at observed and genetic levels. After accounting for processing speed and fluid intelligence, EF remained a significant predictor of reading and mathematics ability. Educationally relevant measures of personality-particularly an openness factor representing curiosity and intellectual self-concept-incrementally contributed to individual differences in reading ability. Collectively, measures of cognition, self-regulation, and other educationally relevant aspects of personality accounted for the entirety of genetic variance in mathematics and reading ability. The current findings point to the important independent role that each construct plays in academic settings. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Polygenic Risk Scores, School Achievement, and Risk for Schizophrenia: A Danish Population-Based Study

BACKGROUND

Studies have suggested that poor school achievement is associated with increased risk of schizophrenia; however, the possible genetic contribution to this association is unknown. We investigated the possible effect of the polygenic risk score (PRS) for schizophrenia (PRS_SCZ) and for educational attainment (PRS_EDU) on the association between school performance and later schizophrenia.

METHODS

We conducted a case-cohort study on a Danish population-based sample born from 1987 to 1995 comprising 1470 individuals with schizophrenia and 7318 subcohort noncases. Genome-wide data, school performance, and family psychiatric and socioeconomic background information were obtained from national registers and neonatal biobanks. PRS_SCZ and PRS_EDU were calculated using discovery effect size estimates from a meta-analysis of 34,600 cases and 45,968 controls and 293,723 individuals.

RESULTS

Higher PRS_SCZ increased the risk (incidence rate ratio [IRR]: 1.28; 95% confidence interval [CI], 1.19-1.36), whereas higher PRS_EDU decreased the risk of schizophrenia (IRR, 0.87; 95% CI, 0.82-0.92) per standard deviation. Not completing primary school and receiving low school marks were associated with increased risk of schizophrenia (IRR, 2.92; 95% CI, 2.37-3.60; and IRR, 1.58; 95% CI, 1.27-1.97, respectively), which was not confounded by PRS_SCZ or PRS_EDU. Adjusting for social factors and parental psychiatric history, effects of not completing primary school and receiving low school marks were attenuated by up to 25% (IRR, 2.19; 95% CI, 1.75-2.73; and IRR, 1.39; 95% CI, 1.11-1.75, respectively). Increasing PRS_EDU correlated with better school performance (p < .01; R² = 7.6%). PRS_SCZ and PRS_EDU was significantly negatively correlated (r = -.31, p < .01).

CONCLUSIONS

The current PRS did not account for the observed association between primary school performance and risk of schizophrenia.

Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals

Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.

Quantitative analysis of population-scale family trees with millions of relatives

Family trees have vast applications in fields as diverse as genetics, anthropology, and economics. However, the collection of extended family trees is tedious and usually relies on resources with limited geographical scope and complex data usage restrictions. We collected 86 million profiles from publicly available online data shared by genealogy enthusiasts. After extensive cleaning and validation, we obtained population-scale family trees, including a single pedigree of 13 million individuals. We leveraged the data to partition the genetic architecture of human longevity and to provide insights into the geographical dispersion of families. We also report a simple digital procedure to overlay other data sets with our resource.

Leukocyte count, systemic inflammation, and health status in older adults: a narrative review

Epidemiological and clinical studies suggest that elevated leukocyte count within the normal range can predict cardiovascular and total mortality in older adults. These findings are remarkable because this simple and common laboratory test is included in routine medical check-ups. It is well known that chronic systemic inflammation (inflammaging) is one of the hallmarks of aging and an important component of obesity-associated insulin resistance that can lead to type 2 diabetes and other health problems in both overweight individuals and elderly people. To understand the molecular mechanisms linking increased systemic inflammation with aging-associated diseases and elevated leukocyte counts in the elderly is to unravel the multiplicity of molecular factors and mechanisms involved in chronic low-grade systemic inflammation, the gradual accumulation of random molecular damage, age-related diseases, and the process of leukopoiesis. There are several possible mechanisms through which chronic low-grade systemic inflammation is associated with both higher leukocyte count and a greater risk of aging-associated conditions in older adults. For example, the IL-6 centric model predicts that this biomediator is involved in chronic systemic inflammation and leukopoiesis, thereby suggesting that elevated leukocyte count is a signal of poor health in older adults. Alternatively, an increase in neutrophil and monocyte counts can be a direct cause of cardiovascular events in the elderly. Interestingly, some authors assert that the predictive ability of elevated leukocyte counts with regard to cardiovascular and allcause mortality among older adults surpass the predictive value of total cholesterol. This review reports the recent findings on the links between elevated but normal leukocyte counts and the increased risks of all-cause, cardiovascular, and cancer mortality. The possible molecular mechanisms linking higher but normal leukocyte counts with increased risk of aging-associated diseases in the elderly are discussed here.

Identifying genetic variants that affect viability in large cohorts

A number of open questions in human evolutionary genetics would become tractable if we were able to directly measure evolutionary fitness. As a step towards this goal, we developed a method to examine whether individual genetic variants, or sets of genetic variants, currently influence viability. The approach consists in testing whether the frequency of an allele varies across ages, accounting for variation in ancestry. We applied it to the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort and to the parents of participants in the UK Biobank. Across the genome, we found only a few common variants with large effects on age-specific mortality: tagging the APOE ε4 allele and near CHRNA3. These results suggest that when large, even late-onset effects are kept at low frequency by purifying selection. Testing viability effects of sets of genetic variants that jointly influence 1 of 42 traits, we detected a number of strong signals. In participants of the UK Biobank of British ancestry, we found that variants that delay puberty timing are associated with a longer parental life span (P~6.2 × 10⁻⁶ for fathers and P~2.0 × 10⁻³ for mothers), consistent with epidemiological studies. Similarly, variants associated with later age at first birth are associated with a longer maternal life span (P~1.4 × 10⁻³). Signals are also observed for variants influencing cholesterol levels, risk of coronary artery disease (CAD), body mass index, as well as risk of asthma. These signals exhibit consistent effects in the GERA cohort and among participants of the UK Biobank of non-British ancestry. We also found marked differences between males and females, most notably at the CHRNA3 locus, and variants associated with risk of CAD and cholesterol levels. Beyond our findings, the analysis serves as a proof of principle for how upcoming biomedical data sets can be used to learn about selection effects in contemporary humans.

Our global understanding of adaptation in humans is limited to indirect statistical inferences from patterns of genetic variation, which are sensitive to past selection pressures. We introduced a method that allowed us to directly observe ongoing selection in humans by identifying genetic variants that affect survival to a given age (i.e., viability selection). We applied our approach to the GERA cohort and parents of the UK Biobank participants. We found viability effects of variants near the APOE and CHRNA3 genes, which are associated with the risk of Alzheimer disease and smoking behavior, respectively. We also tested for the joint effect of sets of genetic variants that influence quantitative traits. We uncovered an association between longer life span and genetic variants that delay puberty timing and age at first birth. We also detected detrimental effects of higher genetically predicted cholesterol levels, body mass index, risk of coronary artery disease (CAD), and risk of asthma on survival. Some of the observed effects differ between males and females, most notably those at the CHRNA3 gene and variants associated with risk of CAD and cholesterol levels. Beyond this application, our analysis shows how large biomedical data sets can be used to study natural selection in humans.

Education and coronary heart disease: mendelian randomisation study

Objective To determine whether educational attainment is a causal risk factor in the development of coronary heart disease.Design Mendelian randomisation study, using genetic data as proxies for education to minimise confounding.Setting The main analysis used genetic data from two large consortia (CARDIoGRAMplusC4D and SSGAC), comprising 112 studies from predominantly high income countries. Findings from mendelian randomisation analyses were then compared against results from traditional observational studies (164 170 participants). Finally, genetic data from six additional consortia were analysed to investigate whether longer education can causally alter the common cardiovascular risk factors.Participants The main analysis was of 543 733 men and women (from CARDIoGRAMplusC4D and SSGAC), predominantly of European origin.Exposure A one standard deviation increase in the genetic predisposition towards higher education (3.6 years of additional schooling), measured by 162 genetic variants that have been previously associated with education.Main outcome measure Combined fatal and non-fatal coronary heart disease (63 746 events in CARDIoGRAMplusC4D).Results Genetic predisposition towards 3.6 years of additional education was associated with a one third lower risk of coronary heart disease (odds ratio 0.67, 95% confidence interval 0.59 to 0.77; P=3×10^-8). This was comparable to findings from traditional observational studies (prevalence odds ratio 0.73, 0.68 to 0.78; incidence odds ratio 0.80, 0.76 to 0.83). Sensitivity analyses were consistent with a causal interpretation in which major bias from genetic pleiotropy was unlikely, although this remains an untestable possibility. Genetic predisposition towards longer education was additionally associated with less smoking, lower body mass index, and a favourable blood lipid profile.Conclusions This mendelian randomisation study found support for the hypothesis that low education is a causal risk factor in the development of coronary heart disease. Potential mechanisms could include smoking, body mass index, and blood lipids. In conjunction with the results from studies with other designs, these findings suggest that increasing education may result in substantial health benefits.

Mortality selection in a genetic sample and implications for association studies

Background

Mortality selection occurs when a non-random subset of a population of interest has died before data collection and is unobserved in the data. Mortality selection is of general concern in the social and health sciences, but has received little attention in genetic epidemiology. We tested the hypothesis that mortality selection may bias genetic association estimates, using data from the US-based Health and Retirement Study (HRS).

Methods

We tested mortality selection into the HRS genetic database by comparing HRS respondents who survive until genetic data collection in 2006 with those who do not. We next modelled mortality selection on demographic, health and social characteristics to calculate mortality selection probability weights. We analysed polygenic score associations with several traits before and after applying inverse-probability weighting to account for mortality selection. We tested simple associations and time-varying genetic associations (i.e. gene-by-cohort interactions).

Results

We observed mortality selection into the HRS genetic database on demographic, health and social characteristics. Correction for mortality selection using inverse probability weighting methods did not change simple association estimates. However, using these methods did change estimates of gene-by-cohort interaction effects. Correction for mortality selection changed gene-by-cohort interaction estimates in the opposite direction from increased mortality selection based on analysis of HRS respondents surviving through 2012.

Conclusions

Mortality selection may bias estimates of gene-by-cohort interaction effects. Analyses of HRS data can adjust for mortality selection associated with observables by including probability weights. Mortality selection is a potential confounder of genetic association studies, but the magnitude of confounding varies by trait.