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Is it possible to slow the rate of ageing, or do biological constraints limit its plasticity? We test the ‘invariant rate of ageing’ hypothesis, which posits that the rate of ageing is relatively fixed within species, with a collection of 39 human and nonhuman primate datasets across seven genera. We first recapitulate, in nonhuman primates, the highly regular relationship between life expectancy and lifespan equality seen in humans. We next demonstrate that variation in the rate of ageing within genera is orders of magnitude smaller than variation in pre-adult and age-independent mortality. Finally, we demonstrate that changes in the rate of ageing, but not other mortality parameters, produce striking, species-atypical changes in mortality patterns. Our results support the invariant rate of ageing hypothesis, implying biological constraints on how much the human rate of ageing can be slowed., The ‘invariant rate of ageing’ hypothesis suggests that the rate of ageing tends to be constant within species. Here, Colchero et al. find support for the hypothesis across primates, including humans, suggesting biological constraints on the rate of ageing.

Around the world, people are increasingly living to retirement and old age. This poses a threat to the financing of pension systems. In Denmark, statutory retirement age increases gradually to account for changes in life expectancy. However, the chances of reaching retirement age are not equal across the Danish population, and raising the retirement age could disproportionally affect those of lower socioeconomic status. In this paper, we investigate socioeconomic inequalities in mortality before and within five years of retirement age in Denmark. We use Danish registry data over a 30-year period, focusing on individuals aged 50 to 70. We conduct sex-specific survival analyses across socioeconomic groups using three dimensions of socioeconomic status: education, income, and occupation. We find that the gap in survival inequalities has widened over time between the lowest and highest socioeconomic groups for each measure, caused primarily by little or no mortality improvements in the lowest socioeconomic groups. These results are complemented by lifespan inequality measures, which also show increasing inequalities over time between socioeconomic groups. Individual level variability in socioeconomic characteristics therefore play a crucial role in defining the survival chances just before and shortly after retirement and thus should be considered when designing retirement policies.

Background Mortality rates have been reduced by half over the last 60 years for nonagenarians, and the progress is continuing. The greater survival might be due to overtreatment of severely physically and cognitively disabled individuals, which is a big concern for societies and individuals. Methods The study population comprised two Danish birth cohorts: the 1905 Cohort and the 1915 Cohort. At age 95, all from the two cohorts who were still alive and living in Denmark were invited to participate in a health survey that used the same assessment instrument. A total of 2,670 (56.8%) persons participated in the two surveys and survival was assessed through a 7.3-year follow-up period during which 2,497 (93.5%) had died, and with virtually no loss to follow-up. Results Despite the increasing chance of surviving to age 95, the 1915 Cohort had significantly better health and functioning than the 1905 Cohort. The survival advantage in the 1915 Cohort continued in the follow-up period after age 95: Median survival length was 2.4 months longer, p = .011. This advantage was not statistically associated with different levels of activities of daily living, physical performance, cognitive functioning, self-rated health and life satisfaction. However, the advantage tended to be more pronounced among people with better health. Conclusions Life span and health increases among the oldest old. The improvement in survival for 95-year olds born in 1915 compared with 1905 was seen across the whole spectrum of health and functioning, with a tendency towards bigger improvement among those in good health.

NOTE: this is an early registration of the research idea and findings in form of slides for a talk presented at EAPS Mort workshop on 2021-09-22 (video: https://youtu.be/rOndHnuajH4?t=2370)Period Life Expectancy is the key summary measure of current mortality. Elimination of the direct influence of population age structure allows to meaningfully compare mortality levels and changes across the populations and over time. Calculation of life expectancy demands high quality detailed data on death and population counts disaggregated by sex and age. Such data is only available for the more developed countries. Moreover, even in the most developed countries, it becomes available with a considerable time lag. And for the majority of countries across the world timely and high quality deaths statistics is not available. In situations of mortality shocks such as the COVID–19 pandemic near real time mortality level comparisons are crucial.Building on the studied regularities of human mortality, we offer a method of reliable life expectancy short-casting based only on the time series of its previous values and the time series of total deaths counts observed in the population, not disaggregated by sex and age. The radical simplicity of the method allows to monitor changes in life expectancy in near real time, if time disaggregated (daily, weekly, or monthly) total death counts are available.

Bredberg and Bredberg (1) suggest that some individuals age more slowly than others and that this accounts for the leveling off of death rates after age 100 y. They make this claim in a letter responding to Vaupel et al. (2). Bredberg and Bredberg (1) vaguely describe their mathematical model without specifying formulas. Apparently, their model is based on q ( x − 70 , b ) = a e b ( x − 70 ) , where x ≥ 70 is age and q ( x − 70 , b ) is the annual probability of death at ages 70+ y for an individual with aging rate b … [↵][1]1To whom correspondence may be addressed. Email: jamesw.vaupel{at}gmail.com. [1]: #xref-corresp-1-1

Aims: During the first wave of the COVID-19 pandemic, Sweden registered a high level of excess deaths. Non-pharmaceutical interventions adopted by Sweden have been milder compared to those implemented in Denmark. Moreover, Sweden might have started the pandemic with a large proportion of vulnerable elderly with a high mortality risk. This study aimed to clarify whether excess mortality in Sweden can be explained by a large stock of ‘dry tinder’ instead of being attributed to faulty lockdown policies. Methods: We analysed weekly death counts in Sweden and Denmark from July 2007 to June 2020. We used a novel method for short-term mortality forecasting to estimate expected and excess deaths during the first COVID-19 wave in Sweden and Denmark. Results: In the first part of the epiyear 2019–2020, deaths were low in both Sweden and Denmark. In the absence of COVID-19, a relatively low level of death would be expected for the later part of the epiyear. The registered deaths were, however, way above the upper bound of the prediction interval in Sweden and within the range in Denmark. Conclusions: ‘Dry tinder’ can only account for a modest fraction of excess Swedish mortality. The risk of death during the first COVID-19 wave rose significantly for Swedish women aged >85 but only slightly for Danish women aged >85. The risk discrepancy seems more likely to result from differences between Sweden and Denmark in how care and housing for the elderly are organised, coupled with a less successful Swedish strategy of shielding the elderly.

Significance Female life expectancy exceeds male life expectancy. Males at ages 15 to 40 die at rates that are often three times female levels, but this excess mortality is not the main cause of the life expectancy gap. Few deaths occur at younger adult ages compared with mortality after age 60 or, historically, among newborns. Our demographic analysis shows that, up through the early decades of the 20th century, the life expectancy gap largely resulted from excess deaths of infant boys. Afterward, higher mortality among men 60+ became crucial. The higher mortality of males at ages 15 to 40 has played a modest role., Why do women live longer than men? Here, we mine rich lodes of demographic data to reveal that lower female mortality at particular ages is decisive—and that the important ages changed around 1950. Earlier, excess mortality among baby boys was crucial; afterward, the gap largely resulted from elevated mortality among men 60+. Young males bear modest responsibility for the sex gap in life expectancy: Depending on the country and time, their mortality accounts for less than a quarter and often less than a 10th of the gap. Understanding the impact on life expectancy of differences between male and female risks of death by age, over time, and across populations yields insights for research on how the lives of men and women differ.

Demographers have always held great interest in extremal phenomena. Extreme value distributions are tailor made to model extremes but demographers do not often take advantage of them. We argue that demographers would benefit by using these models more often and present one potential usage: the Extreme Value Distribution as a candidate for modelling the error distribution in time series models. As an example, we use the so called best practice life expectancy. The residuals from the fitted models are tested for Normality. They are also fitted with Gaussian and Generalized Extreme Value distributions and the fit of these two distributions is compared. The results suggest that demographers ought to further explore and take greater advantage of extreme value models.

Although Denmark and Sweden have close cultural and historical ties, lifespans for Danes have generally been lower than those of Swedes. Recent improvements in Danish mortality after a period of stagnation have led to the suspicion that there may be positive trends at the very high ages at death within that population and that these trends could be quite different from those observed in Sweden. Although the mean ages at death for Danish and Swedish centenarians have been relatively constant at about 102 years for the cohorts born 1870–1904, the oldest-old in Denmark have been getting older, but no evidence has suggested any increase in lifespan for Swedes. Using quantile regression, we show that Danish centenarian lifespans in the 90th percentile have been lengthening, with those in 94th percentile (6 % longest-lived individuals) having a trend that is statistically significant at the 5 % level. We demonstrate that the increase observed is not due to the increasing sizes of birth cohorts and thus must be due to improving survival among this select top tier. We postulate that this super-select group in Denmark is best able to take advantage of the factors driving mortality reduction, whereas the majority of centenarians are not. Electronic supplementary material The online version of this article (10.1007/s13524-018-0755-7) contains supplementary material, which is available to authorized users.

Human longevity is heritable, but genome-wide association (GWA) studies have had limited success. Here, we perform two meta-analyses of GWA studies of a rigorous longevity phenotype definition including 11,262/3484 cases surviving at or beyond the age corresponding to the 90th/99th survival percentile, respectively, and 25,483 controls whose age at death or at last contact was at or below the age corresponding to the 60th survival percentile. Consistent with previous reports, rs429358 (apolipoprotein E (ApoE) ε4) is associated with lower odds of surviving to the 90th and 99th percentile age, while rs7412 (ApoE ε2) shows the opposite. Moreover, rs7676745, located near GPR78, associates with lower odds of surviving to the 90th percentile age. Gene-level association analysis reveals a role for tissue-specific expression of multiple genes in longevity. Finally, genetic correlation of the longevity GWA results with that of several disease-related phenotypes points to a shared genetic architecture between health and longevity.

Significance We introduce a simple but powerful method for analyzing mortality after a major shock. We apply the method to show, more conclusively than up to now, that Denmark, which imposed a lockdown during the first wave of the coronavirus pandemic, suffered considerably lower risks of death than Sweden, which did not impose a lockdown. Our method makes short-term forecasts of the number of deaths that would have occurred if the coronavirus pandemic or other health catastrophe had not occurred. By subtracting the forecast counts from actual death counts, excess mortality can be estimated. This can be done by age, sex, and other characteristics. The method can also be used for other kinds of short-term forecasting., We introduce a method for making short-term mortality forecasts of a few months, illustrating it by estimating how many deaths might have happened if some major shock had not occurred. We apply the method to assess excess mortality from March to June 2020 in Denmark and Sweden as a result of the first wave of the coronavirus pandemic; associated policy interventions; and behavioral, healthcare, social, and economic changes. We chose to compare Denmark and Sweden because reliable data were available and because the two countries are similar but chose different responses to COVID-19: Denmark imposed a rather severe lockdown; Sweden did not. We make forecasts by age and sex to predict expected deaths if COVID-19 had not struck. Subtracting these forecasts from observed deaths gives the excess death count. Excess deaths were lower in Denmark than Sweden during the first wave of the pandemic. The later/earlier ratio we propose for shortcasting is easy to understand, requires less data than more elaborate approaches, and may be useful in many countries in making both predictions about the future and the past to study the impact on mortality of coronavirus and other epidemics. In the application to Denmark and Sweden, prediction intervals are narrower and bias is less than when forecasts are based on averages of the last 5 y, as is often done. More generally, later/earlier ratios may prove useful in short-term forecasting of illnesses and births as well as economic and other activity that varies seasonally or periodically.

We introduce a new method for making short-term mortality forecasts of a few months, illustrating it by estimating how many deaths might have happened if some major shock had not occurred. We apply the method to assess excess mortality from March to June 2020 in Denmark and Sweden as a result of the first wave of the coronavirus pandemic, associated policy interventions and behavioral, healthcare, social and economic changes. We chose to compare Denmark and Sweden because reliable data were available and because the two countries are similar but chose different responses to covid-19: Denmark imposed a rather severe lockdown; Sweden did not. We make forecasts by age and sex to predict expected deaths if covid-19 had not struck. Subtracting these forecasts from observed deaths gives the excess death count. Excess deaths were lower in Denmark than Sweden during the first wave of the pandemic. The later/earlier ratio we propose for shortcasting is easy to understand, requires less data than more elaborate approaches, and may be useful in many countries in making both predictions about the future and the past to study the impact on mortality of coronavirus and other epidemics. In the application to Denmark and Sweden, prediction intervals are narrower and bias is less than when forecasts are based on averages of the last five years, as is often done. More generally, later/earlier ratios may prove useful in short-term forecasting of illnesses and births as well as economic and other activity that varies seasonally or periodically.

Background: This article reviews findings about the rise of life expectancy, current levels of life expectancy in countries with high life expectancies, and possible future trends in life expectancy. Maximum lifespans and the equality of lifespans are also considered. Methods: Demographic data on age-specific mortality are used to estimate life expectancy. Validated data on exceptional lifespans are used to study the maximum length of life. Findings of the most significant publications are critically summarized.Results: In the countries doing best, life expectancy started to increase around 1840 at a pace of almost 2.5 years per decade. This trend has continued until the present. Contrary to classical evolutionary theories of senescence and contrary to the predictions of many experts, the frontier of survival is advancing to higher ages. Furthermore, lifespans are becoming more equal, with octogenarians and nonagenarians accounting for most deaths in countries with high life expectancy. Extrapolation of the trend indicates that most children born this millennium will celebrate their 100th birthdays. Considerable uncertainty, however, clouds forecasts of life expectancy and maximum lifespans: life expectancy and maximum lifespan might increase very little if at all or longevity might rise much faster than in the past. Conclusions: Substantial progress has been made over the past three decades in deepening understanding of how long humans have lived and have long they might live. The social, economic, health, cultural and political consequences of further increases in longevity are so significant that the development of more powerful methods of forecasting is a priority.

Since 1990 Jeanne Louise Calment has held the record for human longevity. She was born on 21 February 1875, became the longest-lived human on 12 May 1990 when she was 115.21 and died on 4 August 1997 at age 122.45 years. In this chapter, we use data available on 25 September 2017 on people who reached age 110, supercentenarians, to address the following questions: How likely is it that a person has reached age 122.45? How unlikely is it that Calment’s record has not yet been broken? How soon might it be broken? Assuming a constant annual probability of death of 50% after age 110, we found that the probability that a person who survived to age 110 would have lived to 122.45 by 25 September 2017 is 17.1%. Furthermore, we calculated that there was only a 20.3% chance that Calment’s record would have been broken after 1997 but before 2017. Finally, we estimated that there is less than a 50% chance that someone will surpass Calment’s lifespan before 2045. Jeanne Louise Calment’s record is exceptional but not impossible. It does not provide evidence that her lifespan is an upper limit to human lifespans.

Permanyer et al. (1) extend the analysis of human longevity by Vaupel et al. (2) to analyze trends in best-practice healthy life expectancy (BPHLE) in addition to best-practice life expectancy (BPLE) at birth. They estimate that, in the period 1990–2019, BPHLE increased at a slower pace than BPLE. We concur with Permanyer et al. (1) about the importance of healthy life expectancy. Differences in trends between BPHLE and BPLE, however, are not obvious when it is recognized that BPHLE estimates are uncertain. As life expectancy rises, it is unclear whether the extra years of life are being lived in good health. Mixed results have been found, depending on the age, population, and measure used (3). Since the seminal work by Sanders (4 … [↵][1]1To whom correspondence may be addressed. Email: jamesw.vaupel{at}gmail.com. [1]: #xref-corresp-1-1

Is it possible to slow the rate of aging, or do biological constraints limit its plasticity? We test this ‘invariant rate of aging’ hypothesis with an unprecedented collection of 39 human and nonhuman primate datasets across seven genera. We first recapitulate, in nonhuman primates, the highly regular relationship between life expectancy and lifespan equality seen in humans. We next demonstrate that variation in the rate of aging within genera is orders of magnitude smaller than variation in pre-adult and age-independent mortality. Finally, we demonstrate that changes in the rate of aging, but not other mortality parameters, produce striking, species-atypical changes in mortality patterns. Our results support the invariant rate of aging hypothesis, implying biological constraints on how much the human rate of aging can be slowed.

Differences in lifespan between populations, e.g. between females and males, are often measured by differences in summary statistics, such as life expectancy, which generally show an advantage of females over males across the whole age span. However, such statistics ignore the fact that two lifespan distributions are generally not mutually exclusive and that not all females outlive all males. Here we use a novel measure of inequality in lifespans: the outsurvival probability, which is interpreted as the probability of males to outlive females. The measure accounts for the similarities in lifespan between populations. It also considers the interaction between the mean and variance of two lifespan distributions and their combined effect on between-populations inequalities. Our results show that the probability of males outliving females varied between 25% and 50%, across 44 countries and regions since the middle of the 18th century. Thus, despite the usually male lower life expectancy and higher death rates at all ages, males have a substantial chance of outliving females. Our suggested approach is generalizable to any pair of populations.

Background: Inequality in lifespans between two populations, e.g., males and females or people with low and high SES, is a focus of demographic, economic and sociological research and of public policy analysis. Inequality is usually measured by differences in life expectancy.Objective: To devise a cogent measure of how much distributions of lifespans differ between two populations.Results: We propose an outsurvival statistic, φ, that measures the probability that an individual from a population with low life expectancy will live longer than an individual from a population with high life expectancy. This statistic can also be interpreted as an underdog probability—the chance that a random value from a distribution with a low mean will exceed a random value from a distribution with a higher mean.Contribution: Our outsurvival probability complements life expectancy to provide a more nuanced view of the inequality of lifespans between two populations. Our similar underdog probability provides a perspective on the more general question of how different two distributions are.

The familial resemblance in length of adult life is very modest. Studies of parent‐offspring and twins suggest that exceptional health and survival have a stronger genetic component than lifespan generally. To shed light on the underlying mechanisms, we collected information on Danish long‐lived siblings (born 1886–1938) from 659 families, their 5379 offspring (born 1917–1982), and 10,398 grandchildren (born 1950–2010) and matched background population controls through the Danish 1916 Census, the Civil Registration System, the National Patient Register, and the Register of Causes of Death. Comparison with the background, population revealed consistently lower occurrence of almost all disease groups and causes of death in the offspring and the grandchildren. The expected incidence of hospitalization for mental and behavioral disorders was reduced by half in the offspring (hazard ratio 0.53, 95% confidence interval 0.45–0.62) and by one‐third in the grandchildren (0.69, 0.61–0.78), while the numbers for tobacco‐related cancer were 0.60 (0.51–0.70) and 0.71 (0.48–1.05), respectively. Within‐family analyses showed a general, as opposed to specific, lowering of disease risk. Early parenthood and divorce were markedly less frequent in the longevity‐enriched families, while economic and educational differences were small to moderate. The longevity‐enriched families in this study have a general health advantage spanning three generations. The particularly low occurrence of mental and behavioral disorders and tobacco‐related cancers together with indicators of family stability and only modest socioeconomic advantage implicate behavior as a key mechanism underlying familial aggregation of exceptional health and survival., This Danish three‐generation cohort study of more than 15,000 descendants of long‐lived siblings revealed consistently lower occurrence of almost all disease groups and causes of death in the offspring and grandchildren—most pronounced for mental and behavioral disorders and tobacco‐related cancers. The longevity‐enriched families have a general health advantage that transmits across multiple generations, and the disease and demographic characteristics implicate behavior as a key mechanism underlying the familial aggregation of exceptional health and survival.

Based on harmonized census data from 81 countries, we estimate how age and co-residence patterns shape the vulnerability of countries' populations to outbreaks of covid-19. We estimate variation in deaths arising due to a simulated random infection of 10% of the population living in private households and subsequent within-household transmission of the virus. The age-structures of European and North American countries increase their vulnerability to covid-related deaths in general. The co-residence patterns of elderly persons in Africa and parts of Asia increase these countries' vulnerability to deaths induced by within-household transmission of covid-19. Southern European countries, which have aged populations and relatively high levels of intergenerational co-residence are, all else equal, the most vulnerable to outbreaks of covid-19. In a second step, we estimate to what extent avoiding primary infections for specific age-groups would prevent subsequent deaths due to within-household transmission of the virus. Preventing primary infections among the elderly is the most effective in countries with small households and little intergenerational co-residence such as France, whereas confining younger age groups can have a greater impact in countries with large and inter-generational households such as Bangladesh.

Mortality forecasting has crucial implications for insurance and pension policies. A large amount of literature has proposed models to forecast mortality using cross-sectional (period) data instead of longitudinal (cohort) data. As a consequence, decisions are generally based on period life tables and summary measures such as period life expectancy, which reflect hypothetical mortality rather than the mortality actually experienced by a cohort. This study introduces a novel method to forecast cohort mortality and the cohort life expectancy of non-extinct cohorts. The intent is to complete the mortality profile of cohorts born up to 1960. The proposed method is based on the penalized composite link model for ungrouping data. The performance of the method is investigated using cohort mortality data retrieved from the Human Mortality Database for England & Wales, Sweden, and Switzerland for male and female populations.

Significance Why life expectancy and life span equality have increased together is a question of scientific interest. Both measures are calculated for a calendar year and might not describe a cohort’s actual life course. Nonetheless, life expectancy provides a useful measure of average life spans, and life span equality gives insights into uncertainty about age at death. We show how patterns of change in life expectancy and life span equality are described by trajectories of mortality improvements over age and time. The strength of the relationship between life expectancy and life span equality is not coincidental but rather a result of progress in saving lives at specific ages: the more lives saved at the youngest ages, the stronger the relationship is., As people live longer, ages at death are becoming more similar. This dual advance over the last two centuries, a central aim of public health policies, is a major achievement of modern civilization. Some recent exceptions to the joint rise of life expectancy and life span equality, however, make it difficult to determine the underlying causes of this relationship. Here, we develop a unifying framework to study life expectancy and life span equality over time, relying on concepts about the pace and shape of aging. We study the dynamic relationship between life expectancy and life span equality with reliable data from the Human Mortality Database for 49 countries and regions with emphasis on the long time series from Sweden. Our results demonstrate that both changes in life expectancy and life span equality are weighted totals of rates of progress in reducing mortality. This finding holds for three different measures of the variability of life spans. The weights evolve over time and indicate the ages at which reductions in mortality increase life expectancy and life span equality: the more progress at the youngest ages, the tighter the relationship. The link between life expectancy and life span equality is especially strong when life expectancy is less than 70 y. In recent decades, life expectancy and life span equality have occasionally moved in opposite directions due to larger improvements in mortality at older ages or a slowdown in declines in midlife mortality. Saving lives at ages below life expectancy is the key to increasing both life expectancy and life span equality.

In the last three decades, considerable progress in mortality forecasting has been achieved, with new and more sophisticated models being introduced. Most of these forecasting models are based on the extrapolation of past trends, often assuming linear (or log-linear) development of mortality indicators, such as death rates or life expectancy. However, this assumption can be problematic in countries where mortality development has not been linear, such as in Denmark. Life expectancy in Denmark experienced stagnation from the 1980s until the mid-1990s. To avoid including the effect of the stagnation, Denmark’s official forecasts are based on data from 1990 only. This chapter is divided into three parts. First, we highlight and discuss some of the key methodological issues for mortality forecasting in Denmark. How many years of data are needed to forecast? Should linear extrapolation be used? Second, we compare the forecast performance of 11 models for Danish females and males and for period and cohort data. Finally, we assess the implications of the various forecasts for Danish society, and, in particular, their implications for future lifespan variability and age at retirement.

Background: The male-female life expectancy gap is increasingly driven by mortality differences at older ages. However, the dynamics of the onset of the old age contribution to the sex gap in survival have not been analyzed yet. Objective: We investigate how much of the gender gap in life expectancy is attributable to older ages and when the old-age contribution to the gap started to increase in different countries. Methods: Using data from the Human Mortality Database, we computed age-specific contributions to the sex gap, investigated their rate of increase over time, and identified breakpoints. Results: We found a heterogenous picture in Eastern Europe, while in the other countries the contributions of those aged 65-80 are declining, while the contributions of those aged 80+ are increasing. The pace of increase accelerated after 1950. At the end of the observation some countries show signs of stagnation, but this is not related to the level of life expectancy. Conclusions: The timing and pace of the increasingly old-age-driven sex difference in life expectancy differ among countries. The complexity of the patterns that emerged calls for country-specific, in-depth investigation. Contribution: We analyzed trends, temporal breakpoints, and differences among countries of the increasing contribution of older ages to the sex gap in life expectancy. This provides additional information about health transitions and offers new evidence for the development of policies targeting specific age groups and aimed at reducing the sex gap in survival.

Publisher's version (útgefin grein)., Human longevity is heritable, but genome-wide association (GWA) studies have had limited success. Here, we perform two meta-analyses of GWA studies of a rigorous longevity phenotype definition including 11,262/3484 cases surviving at or beyond the age corresponding to the 90th/99th survival percentile, respectively, and 25,483 controls whose age at death or at last contact was at or below the age corresponding to the 60th survival percentile. Consistent with previous reports, rs429358 (apolipoprotein E (ApoE) ε4) is associated with lower odds of surviving to the 90th and 99th percentile age, while rs7412 (ApoE ε2) shows the opposite. Moreover, rs7676745, located near GPR78, associates with lower odds of surviving to the 90th percentile age. Gene-level association analysis reveals a role for tissue-specific expression of multiple genes in longevity. Finally, genetic correlation of the longevity GWA results with that of several disease-related phenotypes points to a shared genetic architecture between health and longevity., Alexander von Humboldt-Stiftung