The impact of genetic variation on overall disease burden has not been comprehensively evaluated. We introduce an approach to estimate the effect of genetic risk factors on disability-adjusted life years (DALYs; ‘lost healthy life years’). We use genetic information from 735,748 individuals and consider 80 diseases. Rare variants had the highest effect on DALYs at the individual level. Among common variants, rs3798220 (LPA) had the strongest individual-level effect, with 1.18 DALYs from carrying 1 versus 0 copies. Being in the top 10% versus the bottom 90% of a polygenic score for multisite chronic pain had an effect of 3.63 DALYs. Some common variants had a population-level effect comparable to modifiable risk factors such as high sodium intake and low physical activity. Attributable DALYs vary between males and females for some genetic exposures. Genetic risk factors can explain a sizable number of healthy life years lost both at the individual and population level.

Background Obesity is a heritable complex phenotype that can increase the risk of age‐related outcomes. Biological age can be estimated from DNA methylation (DNAm) using various “epigenetic clocks.” Previous work suggests individuals with elevated weight also display accelerated aging, but results vary by epigenetic clock and population. Here, we utilize the new epigenetic clock GrimAge, which closely correlates with mortality. Objectives We aimed to assess the cross‐sectional association of body mass index (BMI) with age acceleration in twins to limit confounding by genetics and shared environment. Methods and results Participants were from the Finnish Twin Cohort (FTC; n = 1424), including monozygotic (MZ) and dizygotic (DZ) twin pairs, and DNAm was measured using the Illumina 450K array. Multivariate linear mixed effects models including MZ and DZ twins showed an accelerated epigenetic age of 1.02 months (p‐value = 6.1 × 10–12) per one‐unit BMI increase. Additionally, heavier twins in a BMI‐discordant MZ twin pair (ΔBMI >3 kg/m2) had an epigenetic age 5.2 months older than their lighter cotwin (p‐value = 0.0074). We also found a positive association between log (homeostatic model assessment of insulin resistance) and age acceleration, confirmed by a meta‐analysis of the FTC and two other Finnish cohorts (overall effect = 0.45 years, p‐value = 4.1 × 10–25) from adjusted models. Conclusion We identified significant associations of BMI and insulin resistance with age acceleration based on GrimAge, which were not due to genetic effects on BMI and aging. Overall, these results support a role of BMI in aging, potentially in part due to the effects of insulin resistance.