Heterogeneous associations of polygenic indices of 35 traits with mortality: a register-linked population-based follow-up study

The genome-wide association study (GWAS) literature has identified a vast number of polygenic indices (PGIs) on almost every widely-measured human phenotype (Burt, 2024; Becker et al., 2021; Choi et al., 2020). One motivation for this endeavour has been to construct tools that may help clinical practice in disease risk prediction (Torkamani et al., 2018; Lambert et al., 2019; Lewis and Vassos, 2020). In addition, PGIs can have possible utility for health risk assessment in a more indirect manner, since they may have downstream importance in predicting health and functioning more widely beyond their immediate phenotypes, such as regarding their ability to predict all-cause mortality. Previous studies have sought genetic variants and their composites to explain mortality (Ganna et al., 2013) or closely related concepts such as (disease-free) life span (Jukarainen et al., 2022; Sakaue et al., 2020) and biological ageing (Argentieri et al., 2025). Some previous studies have also assessed associations between PGIs of different traits and mortality (Argentieri et al., 2025; Karlsson Linnér and Koellinger, 2022; Meisner et al., 2020). However, the existing literature covers mostly disease- or biomarker-related PGIs, and our knowledge is still limited on to what extent PGIs for social, psychological, and behavioural phenotypes or PGIs for typically non-fatal health conditions can help in mortality prediction.

Additionally, the state-of-the-art knowledge is scarce regarding to what extent the PGI–mortality associations stem from direct genetic effects. When the interest lies beyond merely predictive uses, the research has increasingly shown the limitations of PGIs as black-box predictors, which may include – in addition to the usually desired direct genetic signals – population-related phenomena due to geographical stratification of ancestries, dynastic or shared environmental effects in families and kins, as well as assortative mating. Within-sibship analysis designs may alleviate such limitations, taking advantage of the fact that the genetic differences between siblings originate from the random segregation at meiosis (Burt, 2024; Howe et al., 2022).

Another area where the knowledge on the PGI–mortality relationship is still relatively limited includes heterogenous associations with socio-demographic factors, different contributions to causes of death, as well as how the associations interplay with corresponding phenotypes. The mortality risks between individuals differ substantially by their sex, age, and education (Hoffmann et al., 2019; Rogers et al., 2010), warranting an assessment of the potential heterogeneous effects regarding them. It is also possible that social, psychological, and behaviour-related PGIs may matter disproportionately more for certain causes of death, including accidents, suicides, and violent and alcohol-related deaths. Such ‘external’ mortality is conceptually closely connected to risky behaviour and substance use. A relevant question for their practical utility is also whether PGIs can bring additional information to predicting the risk of death in cases when information of the measured phenotype is available.

In this study, we address these gaps in knowledge by assessing the association between 35 different PGIs – mostly related to social, psychological, and behavioural traits or typically non-fatal health conditions – and mortality using a population-representative sample of over 40,000 Finnish individuals with up to 25 years of register-based mortality follow-up. We also assess the extent of potential population stratification and related biases by within-sibship analysis of over 10,000 siblings. Furthermore, we examine potential heterogeneous PGI–mortality associations by sex and education, as well as for mortality occurring at different ages and separately for external and natural causes of death. Finally, we compare six PGIs that show the strongest associations with mortality (PGIs of ever smoking, body mass index [BMI], depressive symptoms, alcoholic drinks per week, educational attainment and self-rated health) and their phenotypes when mutually adjusting for each other.

Figure 1 displays the associations between 35 PGIs and all-cause mortality for the population analysis and within-sibship analysis samples. In the population analysis, the PGIs that showed the strongest associations with mortality were ever smoking (hazard ratio [HR] per 1 SD larger PGI = 1.12, 95% confidence interval [95% CI] 1.09; 1.15), self-rated health (HR = 0.90, 95% CI 0.88; 0.93), BMI (HR = 1.10, 95% CI 1.07; 1.13), educational attainment (HR = 0.91, 95% CI 0.89; 0.94), depressive symptoms (HR = 1.07, 95% CI 1.04; 1.10), and drinks per week (HR = 1.06, 95% CI 1.04; 1.09). Most of the studied PGIs had negligible associations with mortality, as 18 PGIs had HRs between 0.98 and 1.02.

Figure 1

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Although CIs overlapped with regards to every individual PGI except extraversion, on average, within-sibship analysis had marginally larger associations than the population analysis (inverse-variance-weighted mean absolute log HR was 0.023, 95% CI 0.005; 0.042 larger in within-sibship than population analyses). In within-sibship analysis, the PGI of BMI had the strongest association with mortality (HR = 1.22, 95% CI 1.10; 1.36).

Figure 2 presents PGI–mortality associations by sex, education, age, and cause of death. Overall, men had slightly stronger PGI–mortality associations (Panel A; inverse-variance-weighted mean absolute log HR was 0.013, 95% CI 0.005; 0.022 larger among men). The largest sex differences were observed for the PGI for educational attainment (HR = 0.89, 95% CI 0.86; 0.92 among men; HR = 0.95, 95% CI 0.91; 0.99 among women; p=0.010 for difference, p=0.21 after multiple-testing adjustment with Benjamini–Hochberg method for 35 joint tests) and PGI for attention deficit hyperactivity disorder (ADHD; HR = 1.08, 95% CI 1.04; 1.11 among men; HR = 1.01, 95% CI 0.97; 1.05 among women; p=0.012 for difference, p=0.21 after multiple-testing adjustment). The HRs in Panel B indicate no evidence for substantial heterogeneous associations by education level. Panel C analyses mortality in three age-specific follow-up periods. The PGIs were more predictive of death in younger age groups, although the difference between the 25–64 and 65–79 age groups was small, except for the PGI of ADHD (HR = 1.14, 95% CI 1.08; 1.21 for 25–64 year-olds; HR = 1.04, 95% CI 1.00; 1.08 for 65–79 year-olds; p=0.008 for difference, p=0.27 after multiple-testing adjustment). PGIs predicted death only negligibly among those aged 80+, and the largest differences between the age groups 25–64 and 80+were for PGIs of self-rated health (HR = 0.87, 95% CI 0.82; 0.93 for 25–64 year-olds, HR = 1.00, 95% CI 0.94; 1.04 for 80+ year olds, p=2*10^–4 for difference, p=0.006 after multiple-testing adjustment), ADHD (HR = 1.14, 95% CI 1.08; 1.21 for 25–64 year-olds, HR = 0.99, 95% CI 0.95; 1.03 for 80+ year olds, p=7*10^–4 for difference, p=0.012 after multiple-testing adjustment) and depressive symptoms (HR = 1.12, 95% CI 1.06; 1.18 for 25–64 year-olds, HR = 1.00, 95% CI 0.96; 1.04 for 80+ year olds, p=0.002 for difference, p=0.032 after multiple-testing adjustment). Additionally, the difference in HRs between these age groups achieved significance after multiple-testing adjustment at the conventional 5% level for PGIs of cigarettes per day, educational attainment, and ever smoking.

Figure 2

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Panel D displays that most PGIs had stronger associations with external (accidents, suicides, violence, and alcohol-related causes of death) than natural causes of death. An exception was the PGI of BMI that had a larger HR for natural (HR = 1.11, 95% CI 1.08; 1.14) than external causes of death (HR = 1.01, 95% CI 0.93; 1.10). The HR differences between external and natural causes of death were nominally significant at the conventional 5% level for cannabis use (p=0.016), drinks per week (p=0.028), left out of social activity (p=0.029), ADHD (p=0.031), BMI (p=0.035), and height (p=0.049), but none of these differences remained significant after adjusting for 35 multiple tests. For external causes, the strongest associations were observed for the PGI for drinks per week (HR = 1.16, 95% CI 1.07; 1.26), depressive symptoms (HR = 1.15, 95% CI 1.06; 1.25), educational attainment (HR = 0.88, 95% CI 0.81; 0.95), and ADHD (HR = 1.14, 95% CI 1.05; 1.23). Twelve PGIs had HRs ≥1.1 (drinks per week, depressive symptoms, cigarettes per day, ADHD, alcohol misuse, ever smoking, risk tolerance) or ≤0.9 (cannabis use, self-rated health, religious attendance, age at first birth, and educational attainment) per 1 SD difference in PGI, whereas only three PGIs had HRs ≥1.1 or≤0.9 for natural causes of death (ever smoking, BMI, and self-rated health). HRs of natural causes of death followed similar patterns as all-cause mortality, which was expected, as they constituted 90% of all observed deaths.

Table 1 shows HRs of the six most predictive PGIs (ever smoking, BMI, depressive symptoms, drinks per week, educational attainment, and self-rated health) and their corresponding phenotypes (smoking, self-rated health, years of education, BMI, number of depression indicators, and alcohol intake per week) for all-cause mortality. Models 1 a–1 l present HRs of each variable adjusted only for baseline covariates. All phenotypes except BMI had stronger associations with mortality than their corresponding PGIs. Models 2 a–2 f adjust for each phenotype and its corresponding PGI simultaneously. HRs of phenotypes were only slightly attenuated, whereas for PGIs, this attenuation was around one-third on average. Nevertheless, each PGI was clearly associated with mortality even after adjusting for its phenotype, and all estimated 95% CIs excluded one in Models 2 a–2 f. Model 3 a adjusted for all six phenotypes and 3b for all six PGIs simultaneously. The most substantial attenuation was observed for depression indicators and the PGI of depressive symptoms. Finally, Model 4 adjusted for all phenotypes and PGIs simultaneously. In Model 4, PGIs had modest independent associations, the strongest observed being for the PGI of smoking (HR = 1.04, 95% CI 1.01; 1.08), PGI of BMI (HR = 1.03, 95% CI 1.00; 1.06), and PGI of drinks per week (HR = 1.03, 95% CI 1.00; 1.06). Supplementary file 1G presents corresponding analyses with categorised phenotypes, indicating curvilinear mortality patterns for BMI and alcohol intake. Although HRs of these phenotypes in Table 1 should thus be interpreted with caution, HRs of PGIs were consistent between the analyses presented in Table 1 and Supplementary file 1G.

Supplementary file 1H presents information criteria and significance tests on corresponding models. Models with PGI +phenotype (Models 2 a–f) showed improvement over models with the phenotype only (Models 1 a, 1 c, 1e, 1 g, 1i, 1k, with a p=0.0006 or lower) in terms of both Akaike information criterion (AIC) as well as Bayesian (Schwarz) information criterion (BIC) with a p=0.0006 or lower in all comparisons. The full Model 4 again showed improvement over the model with all PGIs jointly (Model 3b, with a p=0.0002 or p=0.00002, depending on continuous/categorical phenotype measurement), which had a lower AIC but not BIC.

Table 2 shows the associations between these six PGIs (ever smoking, BMI, depressive symptoms, drinks per week, educational attainment, and self-rated health) and all-cause mortality stratified by whether an individual was lacking the phenotype risk factor in question. We did not observe evidence for substantial differences in the PGI–mortality HRs between individuals with and without these risk factors in their corresponding phenotype. Furthermore, the only PGI that showed consistent attenuation in their HRs compared to the analysis on the whole sample (presented in Table 1 and Figure 1) was the PGI of ever smoking (HR = 1.07, 95% CI 1.03; 1.11 for never smokers; HR = 1.09, 95% CI 1.05; 1.12 for others; compare HR = 1.12, 95% CI 1.09; 1.15 for unstratified population analysis in Model 1b of Table 1).

We investigated the association between 35 PGIs – mostly related to social, psychological, and behavioural traits, or typically non-fatal health conditions – and mortality using a population-representative register-linked sample from Finland with up to 25-year mortality follow-up. PGIs most strongly associated with mortality were typically related to the best-established phenotypic mortality risk factors, including smoking, body mass index, depression, alcohol use, education and self-rated health (Hummer and Lariscy, 2011; Himes, 2011; Jylhä, 2011). Although the majority of the investigated PGIs had negligible associations with the risk of death, the strongest associations observed were about a 10% difference in the mortality hazard for 1 SD difference in PGI. Given the severity of the outcome, these associations cannot be disregarded as trivial, particularly when considering individuals with particularly high or low PGIs. Our within-sibship analyses showed broadly similar results and thus do not indicate that these PGI–mortality associations were systematically inflated due to population stratification or related biases. Limited previous literature exists overall on PGI–mortality associations, particularly for other than disease- or biomarker-related PGIs. Nevertheless, the associations of PGIs of smoking, alcohol consumption, depression and BMI that we observed for Finland were roughly comparable to or moderately stronger than what was observed in a previous study on the UK Biobank for PGIs unadjusted for each other (Meisner et al., 2020) and in US-based Health and Retirement Study for mutually adjusted PGIs (Karlsson Linnér and Koellinger, 2022).

The investigated PGIs were slightly more predictive of mortality among men than women across the board. This aligns with sex differences for all-cause mortality observed for many social-level mortality risk phenotypes such as socioeconomic position and marital status (Case and Paxson, 2005; Koskinen and Martelin, 1994; Wang et al., 2020), but similar excess risk among men is not consistently observed on more physiologically proximate or behavioural mortality risk phenotypes such as obesity, alcohol use, and smoking (Gellert et al., 2012; Griswold et al., 2018; Hu et al., 2005). We also evaluated but did not observe differences in PGI–mortality associations between education groups. The PGIs were more predictive of death at younger ages, whereas among those who survived to age 80, PGIs made hardly any difference in mortality risk. Such an age-related heterogenous pattern is consistent with a previous study analysing age–PGI associations on common diseases in the UK Biobank (Jiang et al., 2021). In contrast, a previous study found stronger HRs in older age groups when directly identifying SNPs associated with mortality (Ganna et al., 2013). A possible explanation for such an ‘age as a leveller’ pattern (see Hoffmann et al., 2019; Hu et al., 2018) may lie in the increasing importance of acute mortality-risk-enhancing factors towards the end of life, including emerging and progressing illness and biological ageing, which trump more distant and indirect mechanisms (Hoffmann et al., 2019; Hoffmann, 2011). Such age-specific heterogeneity also has methodological implications. Researchers analysing PGI–mortality associations using samples of (disproportionately) older individuals should be cautious in generalising their results to the overall population due to potential survivorship bias (Digitale et al., 2023; Akimova et al., 2021).

In general, PGIs were more strongly predictive of external than natural causes of death, and this was particularly evident for many psychological and behavioural PGIs, including alcohol drinks per week, ADHD, depressive symptoms, religious attendance, and risk tolerance. Only the PGI of BMI showed a clearly stronger association with natural causes. It is worth noting that despite alcohol-related deaths being included in external causes, smoking-related PGIs predicted both external and natural mortality in a roughly consistent manner, and the PGI of cannabis use even had a negative association with external mortality. This suggests that despite a substantial shared genetic aetiology between different addictions (Liu et al., 2019; Prom-Wormley et al., 2017), the genetic architecture between the use of different substances also differs importantly from the perspective of mortality risk.

Among those PGIs that were most predictive of mortality, their associations tended to be roughly one-third of the strength of the respective phenotypes when mutually adjusted, although with substantial variation between phenotypes. Additionally, PGIs were also predictive among those who lack the phenotypic risk factor. These two observations imply that PGIs provide additional information on the risk of death even when the phenotypic measures are available. The potential advantage of PGIs in research and healthcare relative to phenotype is that they need to be measured only once, whereas for many phenotypes the most precise monitoring would require extensive longitudinal measures. This is particularly relevant for phenotypes related to specific points of the life course, for example, on health-related factors typically manifesting at older age. This suggests that the independent association of PGIs might be stronger if we had even longer mortality follow-up. Additionally, PGIs capture liabilities on a continuum, which may offer an advantage in risk assessment compared to phenotypes that are typically measured through binary diagnoses or with limited categories. PGIs also avoid some potential forms of measurement error, such as those related to self-reporting or short-term variations over time.

The strengths of this study included population-representative data with high response rates linked to a long register-based follow-up with minimal attrition-related biases. On the other hand, PGIs have known limitations since they incompletely capture the genetic liability, are agnostic regarding the specific biological mechanisms and may also capture environmental signals (Burt, 2024). Within-sibship analysis allowed us to evaluate and mitigate population stratification and other related biases in the analyses; however, at the cost of power and increased sensitivity to measurement error (Gustavson et al., 2024). Overall, these analyses confirmed our main findings. In addition, comparability of the PGIs may be affected by differences in the GWASs that underlie them, for example in their sample sizes and phenotype measurement quality. Finally, despite the Schoenfeld residual correlations suggesting some violation of the proportional hazards assumption, such correlations were present for phenotypes (instead of PGIs) that were not of the primary interest in the analysis.

To conclude, PGIs related to the best-established phenotypic risk factors had the strongest associations with mortality. Particularly for deaths occurring at a younger age, PGIs confer additional information on mortality risk, even when information on the related phenotype is available, and within-sibship analysis did not suggest that such associations were systematically inflated due to population phenomena.

The code used for data preparation and analysis can be found in https://github.com/halahti/eLife26/ (copy archived at Lahtinen, 2026). The statistics used in Figures 1 and 2 can be found in Supplementary file 1E and F. Due to data protection regulations of the biobank and national register-holders, the data are confidential, and we are not allowed to make the individual-level data available to third parties, even as anonymized. Datasets are available from the THL Biobank on written application and following the instructions given on the website of the Biobank (https://thl.fi/en/research-and-development/thl-biobank/for-researchers, contact by email: admin.biobank (at) thl.fi). Register linkage from these data may be applied for from Findata (https://findata.fi/en/permits/, contact by email: info(at)findata.fi.) and Statistics Finland (http://www.stat.fi/tup/mikroaineistot/index_en.html, contact by email: tutkijapalvelut(at)stat.fi). The use of those PGIs that include samples from 23andme data collections requires their own permission to use by the 23andMe, Inc (apply.research (at) 23andme.com).

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Author details

1. Hannu Lahtinen

   1. Helsinki Institute for Demography and Population Health, University of Helsinki, Helsinki, Finland
   2. Max Planck – University of Helsinki Center for Social Inequalities in Population Health, Helsinki, Finland

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Visualization, Methodology, Writing – original draft

   For correspondence

   hannu.lahtinen@helsinki.fi

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0910-823X

2. Jaakko Kaprio

   Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland

   Contribution

   Resources, Data curation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3716-2455

3. Andrea Ganna

   FIMM, University of Helsinki, Helsinki, Finland

   Contribution

   Resources, Data curation, Writing - review and editing

   Competing interests

   No competing interests declared

4. Kaarina Korhonen

   1. Helsinki Institute for Demography and Population Health, University of Helsinki, Helsinki, Finland
   2. Max Planck – University of Helsinki Center for Social Inequalities in Population Health, Helsinki, Finland

   Contribution

   Data curation, Methodology, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8499-2008

5. Stefano Lombardi

   1. FIMM, University of Helsinki, Helsinki, Finland
   2. VATT Institute for Economic Research, Helsinki, Finland
   3. Institute of Labor Economics (IZA), Bonn, Germany

   Contribution

   Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

6. Karri Silventoinen

   Helsinki Institute for Demography and Population Health, University of Helsinki, Helsinki, Finland

   Contribution

   Writing - review and editing

   Competing interests

   No competing interests declared

7. Pekka Martikainen

   1. Helsinki Institute for Demography and Population Health, University of Helsinki, Helsinki, Finland
   2. Max Planck – University of Helsinki Center for Social Inequalities in Population Health, Helsinki, Finland

   Contribution

   Data curation, Supervision, Funding acquisition, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

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