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ABSTRACT While previous studies identified common genetic variants associated with longevity in centenarians, the role of the rare loss-of-function (LOF) mutation burden remains largely

unexplored. Here, we investigated the burden of rare LOF mutations in Ashkenazi Jewish individuals from the Longevity Genes Project and LonGenity study cohorts using whole-exome sequencing

data. We found that centenarians had a significantly lower burden (11-22%) of LOF mutations compared to controls. Similar effects were also observed in their offspring. Gene-level burden

analysis identified 35 genes with depleted LOF mutations in centenarians, with 14 of these validated in the UK Biobank. Mendelian randomization and multi-omic analyses on these genes

identified _RGP1_, _PCNX2_, and _ANO9_ as longevity genes with consistent causal effects on multiple aging-related traits and altered expression during aging. Our findings suggest that a

protective genetic background, characterized by a reduced burden of damaging variants, contributes to exceptional longevity, likely acting in concert with specific protective variants to

promote healthy aging. SIMILAR CONTENT BEING VIEWED BY OTHERS RARE GENETIC CODING VARIANTS ASSOCIATED WITH HUMAN LONGEVITY AND PROTECTION AGAINST AGE-RELATED DISEASES Article 13 September

2021 THE 90 PLUS: LONGEVITY AND COVID-19 SURVIVAL Article 08 February 2022 THE BURDEN OF RARE PROTEIN-TRUNCATING GENETIC VARIANTS ON HUMAN LIFESPAN Article Open access 03 March 2022

INTRODUCTION Aging is a complex process characterized by an accumulation of molecular damage, progressive decline in physiological function, increased susceptibility to disease, and,

ultimately, higher risk of mortality1. While chronological age is a major risk factor, there is remarkable variability in how individuals age, with some experiencing severe disability and

premature death while others maintain good health well into old age2,3. This heterogeneity suggests that aging is a multifactorial process shaped by both genetic and environmental factors4.

At the extreme end of the lifespan spectrum are centenarians, individuals with exceptional longevity who have reached the age of 100 years or more. Centenarians represent a rare and valuable

model of successful aging, often displaying delayed onset or escape from major age-related diseases such as cardiovascular disease, diabetes, and dementia5,6. Furthermore, many maintain

physical and cognitive function, as well as independence, well into old age7. Understanding the factors that contribute to their exceptional longevity could provide valuable insights into

the biology of healthy aging and lifespan determination. Studies in model organisms have firmly established that lifespan has a significant genetic component. Single gene mutations in

pathways related to insulin/insulin-like growth factor-1 (IGF-1) signaling, mechanistic target of rapamycin (mTOR) signaling, and AMP-activated protein kinase (AMPK) signaling have been

shown to dramatically extend lifespan in yeast, worms, flies, and mice8. Many of these pathways are evolutionarily conserved, suggesting they may play a role in human aging as well. Indeed,

functional variants in the IGF-1 receptor have been identified in centenarians, supporting a role for this pathway in exceptional longevity9. In humans, genome-wide association studies

(GWAS) have identified numerous common genetic variants associated with longevity, defined as attaining exceptional old age or having long-lived parents10,11. However, these variants explain

only a small portion of the heritability (12%)11, suggesting that rare variants may also play an important role12. Rare variants, particularly those that lead to loss of gene function

(LOF), are of great interest in studying human lifespan. LOF variants, including nonsense, splice-site, and frameshift mutations, are generally deleterious and subject to strong purifying

selection13. An increased burden of LOF mutations has been observed in individuals with shorter lifespans and shorter period of life people spent free of disease (or healthspan), suggesting

this may significantly impact human health14. However, LOF variants that confer protective effects, such as those in the _APOC3_ and _PCSK9_ genes associated with a lower risk of

cardiovascular disease have also been identified15,16. Despite the growing evidence for the importance of rare variants in aging, the overall burden of LOF mutations in exceptionally

long-lived individuals compared to controls has not been systematically examined. A previous study observed no difference in the burden of pathogenic variants between centenarians, their

offspring, and controls17. However, this study did not specifically focus on LOF variants or incorporate key covariates that may introduce batch effects confounding the results. Furthermore,

the sample size was smaller than in the present study, limiting the power to detect significant differences. Another study found that the burden of rarest protein-truncating variants (PTVs)

in two large cohorts was negatively associated with human healthspan and lifespan, accounting for 0.4 and 1.3 years of their variability, respectively14. In this study, we leveraged

whole-exome sequencing data from a large cohort of Ashkenazi Jewish centenarians and controls to comprehensively compare the burden of rare LOF variants (Fig. 1a). By focusing on a

genetically homogeneous population, we minimized the potential confounding effects of population stratification. Importantly, we incorporated the dates of recruitment and birth as

coefficients in our analysis to control for cohort effects and potential secular trends in environmental and lifestyle factors that may impact lifespan. Our results suggest that centenarians

have a lower burden of LOF mutations compared to controls. This depletion was observed across multiple categories of predicted deleterious variants. Furthermore, we performed a genome-wide

association study to identify specific genes and pathways that were enriched for protective variants in centenarians. Several genes reached suggestive significance levels, and pathway

analysis revealed a depletion of variants in pathways related to hyaluronan metabolism, G-protein receptors, post-translational protein modification, and mitochondrial translation. Notably,

14 out of 35 of these gene associations were validated in an independent cohort from the UK Biobank based on parental lifespan-related traits, supporting the reproducibility of our findings.

Together, these results provide new insights into the genetic architecture of human exceptional longevity and highlight potential molecular mechanisms that may contribute to healthy aging.

Further studies will be necessary to validate and functionally characterize the roles of these genes and pathways in promoting longevity. RESULTS The whole-exome sequencing data was obtained

from 637 centenarians, 917 offspring of centenarians, and 595 controls from the Longevity Genes Project (LGP) and LonGenity study cohorts of Ashkenazi Jewish individuals (Table 1, Fig. 1a,

Methods)18. Based on the demographic characteristics, participants were recruited continuously over a period of 20 years (2000–2020). However, the recruited centenarians were mostly born

between 1900 and 1920, while most of the offspring and controls were born between 1920 and 1960 (Fig. 1b). This suggests that the direct comparison of mutation burden between centenarians

and controls may potentially be confounded by the date of recruitment and date of birth. We identified loss-of-function (LOF) mutations based on the following criteria: alternate allele

frequency (AAF) < 1%, Hardy–Weinberg equilibrium (HWE) threshold of 10−15, and variant missingness <10%. We classified the variants into different categories based on their predicted

deleteriousness: pLOF only, pLOF and missense, pLOF and predicted deleterious missense (5/5 algorithms predict a deleterious variant), and pLOF and predicted deleterious missense (at least

1/5 algorithms predict a deleterious variant). The deleteriousness of missense variants was assessed using five different computational methods (Method). We counted the cumulative mutation

burden in centenarians, their offspring, and controls across different categories of predicted deleterious variants. Consistent with the potential confounding effects of dates of recruitment

and birth, we initially observed a similar distribution of LOF mutations across the different categories in centenarians and controls (Fig. 1c). We performed quality control and filtering,

retaining 338 centenarians with recorded age over 100 years old and 420 controls with age less than 90 years old (Table 1, Methods). We observed a similar distribution of raw mutation count

after filtering (Supplementary Fig. 1) We then performed the count-based burden test using linear regression models. Furthermore, we found that even without adjusting for potential

confounders, offspring but not centenarians showed a significantly lower mutation burden in all pLOF categories (Supplementary Fig. 2). This is likely due to the smaller batch effect between

offspring group and control, compared to the centenarian group. We also showed that there is no significant difference observed between centenarians and their offspring (Supplementary Fig. 

3). To account for these potential confounders, we binned the dates of recruitment and birth and added them as coefficients in the burden test model. After adjusting for these covariates, we

found a consistent and significant trend of lower burden of LOF mutations in centenarians and their offspring compared to controls across all categories of predicted deleterious variants

(Fig. 2). Notably, the depletion of LOF variants was statistically significant for centenarians in all categories, including the pLOF-only category (_b_ = −5.5, _p_ = 0.0453). The effect

sizes for centenarians ranged from −5.5 to −39.6, indicating a 11% to 22% reduction in mutation burden compared to controls. Furthermore, the offspring of centenarians also exhibited a

significantly lower mutation burden compared to controls in both the LGP (related to LGP centenarians) and LonGenity (unrelated to LGP centenarians) cohorts (Fig. 2). The effect sizes for

offspring were smaller than those observed for centenarians, but still significant, with _p_-values ranging from 1.17e-07 to 4.99e-4 in the LGP cohort and from 4.52e-4 to 0.021 in the

LonGenity cohort. These results suggest that the protective effect of a lower LOF mutation burden may be inherited by the offspring of centenarians, contributing to their increased

likelihood of exceptional longevity. We also performed a sensitivity analysis by using different covariates, including age at recruitment, top 10 genetic principal components, numerical date

of birth, and date of recruitment, and found consistent results for centenarian offspring in the LGP cohort (Supplementary Fig. 4). Statistical significance for centenarians and the

LonGenity cohort was sensitive to the choice of covariates, suggesting that the genetic associations with longevity are complex and possibly influenced by unmeasured factors. To identify

specific genes and pathways that carry a lower mutation burden in centenarians, we performed a gene-level and pathway-level burden test. The gene-level analysis identified 35 genes that

reached the significance level at FDR < 0.05 (Fig. 3a). Remarkably, 14 out of these 35 genes were validated in an independent study from the UK Biobank using parental lifespan-related

traits (Fig. 3a)19. Note that this is an indirect validation as the genetics of exceptional longevity and parental lifespan, while having similarities, may still obtain different

characteristics. Pathway-level analysis revealed processes related to hyaluronan metabolism, Class A/1 (Rhodopsin-like receptors), post-translational protein modification, and mitochondrial

translation reached the significance level at FDR < 0.05 (Fig. 3b). We observed a mild inflation in our test statistics, with a genomic inflation factor (λ) of 1.57. After adjusting for

the inflation, the top three pathways still reached the suggestive FDR threshold of 0.2. These results suggest that the depletion of mutations in these pathways may contribute to exceptional

longevity. To further investigate the potential causal effects of the identified longevity-associated genes on lifespan-related traits, we performed Mendelian Randomization (MR) analyzes

using public blood gene expression QTL data from eQTLgen and GWAS summary statistics of multiple lifespan-related traits (Fig. 4a)20. It is important to note that while the MR analysis uses

common variants (eQTLs) rather than rare coding variants, it can provide complementary evidence about a gene’s role in longevity through different mechanisms. MR analysis revealed that seven

genes had significant causal effects on multiple lifespan-related traits, such as frailty index, healthspan, lifespan, and extreme longevity (90th and 99th percentiles), and lifespan-GIP1

(the genetic principal component of healthy longevity, Methods). Among them, three genes (_RGP1_, _PCNX2_, and _ANO9_) showed consistent pro-longevity effects across the multiple traits

tested, supporting their potential roles in promoting longevity as suggested by burden analysis, while the other four genes showed anti-longevity effects. On the other hand, two of the genes

(_DYNC1H1_ and _GALNT12_) only show a significant protective effect on one trait (lifespan and extreme longevity at 99th percentile), while _PKP4_ only shows a significant positive effect

on healthspan but not in other traits. The other four genes (_ZNF446_, _PLA2G4B_, _EFNA3_, and _ABCF3_) show inconsistent effects on lifespan-related traits. We then profiled the multi-omic

associations of the identified longevity-associated genes to provide a systematic evaluation of their expression and regulation during aging (Fig. 4b–e). Comparison with exome-wide

gene-level associations with parental lifespan obtained from GeneBass (Fig. 4b)19, showed that six out of seven causal genes were significantly associated with parental lifespan, three genes

(_MLXIP_, _PCNX2_, and _DYNC1H1_) remain significant after corrected with multiple-testing with FDR. Analysis of age-related changes in promoter DNA methylation using data from 500

individuals in the Massachusetts General Brigham (MGB) biobank (Fig. 4c) revealed significant changes for most longevity-associated genes, except two (_RGP1_ and _BCLAF1_). Similarly,

age-related changes in blood gene expression obtained from the transcriptome-wide association study (TWAS) for aging by Peters et al. (Fig. 4d) showed significant changes for genes such as

_OPN3_, _PCNX2_, _GALNT12_, and _RGP1_21. Furthermore, age-related changes in plasma protein levels using Olink data from 53,015 UK Biobank participants (Fig. 4e) revealed significant

changes for proteins encoded by _DYNC1H1_ and _FLT4_ genes. The results suggest that the expression and regulation of these longevity-associated genes are altered during the aging process.

To gain further insights into the potential relevance of the identified longevity-associated genes in aging and interventions, we further compared their significance scores across different

signatures of aging and longevity interventions (Fig. 4f)22,23. The signature analysis results in 69 significant associations after adjusting for multiple testing of 266 tests using FDR

(Fig. 4f). It revealed that many of these genes (18 out of 21 tested) were also significantly associated with aging in humans and rodents, as well as with interventions known to extend

lifespan, such as caloric restriction (_ABCF3_, _CKAP2L_, and _CEP68_), rapamycin treatment (_PKP4_, _CTNND1_, and _RTRAF_), growth hormone deficiency (_HOGA1_, _ANKRD33_, and _MLXIP_), as

well as overall lifespan after intervention (_HOGA1_). Together, this multi-layered evidence supports the potential roles of these genes in regulating healthy aging and longevity. DISCUSSION

In this study, we have discovered that centenarians, within the large cohort we examined, possess a significantly lower burden of predicted deleterious LOF variants compared to controls.

This finding suggests that a protective genetic background, characterized by the depletion of damaging coding mutations, contributes to the exceptional longevity of centenarians. Notably, we

also observed a lower mutation burden in centenarian offspring, although the effect was less pronounced. These findings support the notion of a heritable component to longevity outside of

protective and common variants and suggest that the combined genetic background, including protective variants and depletion of damaging variants, may be transmitted across generations to

support exceptional longevity. Our results are consistent with previous studies that reported an increased burden of LOF variants in individuals with shorter lifespans and age-related

diseases14,24, and provide further evidence for the role of rare coding variants in extreme human longevity. Our study extends these findings by demonstrating that the depletion of LOF

variants in centenarians is not limited to the rarest variants but is observed across multiple categories of predicted deleterious variants. However, our findings contrast with those of

another study that observed no difference in the burden of pathogenic variants between centenarians, their offspring, and controls17. This discrepancy may be due to differences in study

design, such as the focus on LOF variants specifically, the larger sample size of our study, and the adjustment for potential confounding factors such as date of recruitment, age at

recruitment, and date of birth. Besides, due to the retrospective nature of the centenarian study, the centenarians usually have different demographic properties (age, date of birth, and

potentially other early life exposures) compared to the control group. While this can be addressed by including these features as covariates, this demographic disparity between centenarians

and controls emerges as a critical factor limiting the statistical power of centenarian studies (Fig. 1). In contrast, centenarian offspring, demographically more similar to controls, yield

stronger statistical evidence, corroborating our findings in centenarians. Future prospective studies with improved demographic matching are essential to elucidate the role of LOF variants

in exceptional longevity. Our pathway analysis revealed that centenarian exomes are depleted of LOF variants in several pathways related to aging and disease, including Class A/1

(Rhodopsin-like receptors), hyaluronan metabolism, post-translational protein modification, and mitochondrial translation. Class A/1 (Rhodopsin-like) receptors are involved in various

physiological processes and have been implicated in age-related diseases, suggesting their potential role in longevity25. Hyaluronan is a key component of the extracellular matrix that has

been shown to decline with age, and its increase contributes to the extension of lifespan26. Variants that maintain hyaluronan homeostasis may, therefore, promote healthy aging in humans.

Post-translational protein modifications play crucial roles in protein function and stability, and their dysregulation has been associated with various age-related diseases1. Mitochondrial

translation has also been linked to lifespan extension in model organisms27. To complement our analysis of rare LOF variants, we also investigated the causal role of identified longevity

genes in aging-related traits using MR analyzes. This approach allows us to infer potential causal relationships between gene expression and phenotypes of interest by using eQTLs (common

variants that are associated with gene expression) as instrumental variables. Our MR analyzes provided evidence for the causal effects of several longevity-associated genes, including

_RGP1_, _PCNX2_, and _ANO9_, on multiple aging-related traits. _PCNX2_ was identified to be associated with longevity in an independent GWAS study28, while _ANO9_ was associated with various

cancers29. These findings suggest that these genes may directly influence the aging process and contribute to the extended healthspan and lifespan. The consistent causal effect estimates

across different aging-related traits further support the robustness of these associations. Interestingly, our analyzes also revealed genes with more nuanced effects on longevity. For

instance, _DYNC1H1_ and _GALNT12_ showed significant deleterious effects on only one trait each (lifespan and extreme longevity at the 99th percentile, respectively), while _PKP4_

demonstrated a significant positive effect solely on healthspan. This suggests that these genes may influence particular aspects of the aging process rather than having a broad impact on all

longevity-related traits. Moreover, the inconsistent effects observed for genes such as _ZNF446_, _PLA2G4B_, _EFNA3_, and _ABCF3_ across different lifespan-related traits underscore the

complexity of genetic influences on aging and longevity. The multi-omic analyzes revealed that the expression and regulation of many longevity-associated genes are altered during aging,

specifically, 29 out of 31 for DNA methylation, 4 out of 11 for gene expression, and 2 out of 2 for plasma protein (Fig. 4). Follow-up studies are needed to elucidate the specific mechanisms

by which these genes and their encoded proteins contribute to healthy aging and longevity. Future studies could also explore the relationship between the burden of deleterious germline

mutations and the rate of biological aging in centenarians and the general population. Epigenetic clocks, which measure biological age based on DNA methylation patterns, have emerged as a

promising tool for assessing the pace of aging30,31. Previous studies have shown that centenarians exhibit slower epigenetic aging rates compared to the general population32. Integrating

rare variant burden data with epigenetic clock measures could provide novel insights into the interplay between genetic and epigenetic factors in shaping the rate of aging and exceptional

longevity, especially with current standardized tools like ClockBase and Biolearn33,34, as well as advanced aging clocks, including GrimAge235, DunedinPace36, and causality-enriched

clocks37. Such studies may uncover whether the reduced burden of harmful mutations observed in centenarians contributes to their slower biological aging rates. Our study also has several

limitations. First, while we adjusted for several important covariates, there may be other confounding factors that were not accounted for, such as environmental exposures and lifestyle

factors. Second, our study focused on a specific population (Ashkenazi Jews), although validation analysis in UK biobank suggests that the result may be generalizable to other ethnic groups.

Future studies in diverse populations will be necessary to confirm the generalizability of our findings. Third, the validation analysis is based on parental lifespan traits in the UK

biobank. Although previous studies on common variants show a substantial similarity between parental lifespan and exceptional longevity (rg = 0.81)38, it is unclear how similar the rare

genetic variants contribute to these two traits. Future validation and meta-analysis with other centenarian cohorts may help strengthen the robustness of our findings. Fourth, our study

relied on computational predictions of variant deleteriousness, which may not always reflect the true biological impact of a variant. Functional studies will be necessary to validate the

causal roles of the identified variants and genes in longevity. It is important to acknowledge that some LOF and missense variants can be protective, as demonstrated by previous

studies39,40,41. However, our hypothesis is that the overall probability of LOF variants being protective is lower than the probability of them being deleterious. This is because damaging a

component in a complex system is more likely to have a detrimental effect than a protective one42. Additionally, there is a selection bias, as highly damaging mutations are under-represented

in the population, while highly protective mutations are preserved43. These factors may explain the small effect sizes observed in our study. It should also be noted that we did not

identify any protective LOF variants (i.e., enrichment of LOF variants in centenarians) as demonstrated in previous study18, because we used a one-tailed test, focusing only on the depletion

of LOF variants. In conclusion, our study provides new insights into the genetic architecture of human exceptional longevity, exemplified by individuals who live to 100 years or beyond,

highlighting the importance of rare LOF variants and identifying novel genes and pathways that may promote healthy aging. We demonstrate that centenarians have a lower burden of predicted

deleterious LOF variants compared to controls and that this protective genetic background may be transmitted across generations. Our findings also underscore the complex interplay between

genetic variation, environmental factors, and age-related diseases in shaping human lifespan. Further studies in diverse populations and integrating multiple omics data will be necessary to

fully elucidate the mechanisms underlying exceptional longevity and develop targeted interventions to promote healthy aging. Nonetheless, our results represent an important step towards

understanding the genetic basis of human longevity and provide a foundation for future studies in this field. METHODS STUDY POPULATION AND DATA COLLECTION The study population was derived

from two ongoing studies of aging and longevity in the Ashkenazi Jewish population: the cross-sectional Longevity Genes Project (LGP) and the longitudinal LonGenity study18. The LGP cohort

consisted of 637 individuals with exceptional longevity, 473 offspring of long-lived individuals, and 224 controls, while the LonGenity cohort included 444 offspring of centenarians and 371

controls. All participants provided written informed consent, and the study was approved by the Institutional Review Board at Albert Einstein College of Medicine. For the analysis, we

applied filtering criteria to ensure the inclusion of appropriate individuals in each group. In the centenarian group, we removed individuals with a  death or dropout record before 100

years, retaining 338 exceptionally long-lived centenarians. Similarly, in the control group, we removed individuals without death or dropout record before 90 years, resulting in 147

individuals from the LGP cohort and 273 individuals from the LonGenity cohort being included in the analysis (Table 1). WHOLE-EXOME SEQUENCING DNA samples from all participants were

subjected to whole-exome sequencing using the Illumina HiSeq 2000 platform at the Regeneron Genetics Center17. The sequencing reads were aligned to the human reference genome (hg38) using

the Burrows-Wheeler Aligner (BWA-mem v0.7.17)44, and duplicate reads were removed using Picard tools (version 1.96, http://broadinstitute.github.io/picard/). Variant calling was performed

using the Genome Analysis Toolkit (GATK v3.7)45. QUALITY CONTROL AND VARIANT ANNOTATION After genomic principal component analysis (PCA), four individuals with non-European ancestry were

excluded from the study. Quality control filtering was applied to remove potentially false-positive variants and genotype calls. Variants were filtered based on the following criteria:

alternate allele frequency (AAF) < 1% in the Ashkenazi Jewish population, Hardy–Weinberg equilibrium (HWE) _P_-value > 10−15, and variant missingness <10%, as suggested by a

previous study46. After QC filtering, autosomal-only variants with a minimum allele count (MAC) of 1 were divided into sets for centenarians, offspring, and controls for downstream analysis.

Loss-of-function (LOF) variants were defined as nonsense, splice-site, or frameshift mutations. Missense variants were classified as (1) possible deleterious missense mutation if they were

predicted to be damaging by at least 1 out of 5 algorithms (SIFT47, Polyphen2_HDIV48, Polyphen2_HVAR48, LRT49, and MutationTaster50) or (2) deleterious missense mutation if all five

algorithms predicted them to be damaging. SIFT (v6.2.1), Polyphen2_HDIV (v2.2.2), Polyphen2_HVAR (v2.2.2), LRT (v2016), and MutationTaster (v2021) were used in this analysis. BURDEN TEST

ANALYSIS Prior to the burden test, we removed the individual in the extreme longevity group with a lifespan or last reported age less than 100 years old. Therefore, only the 338 centenarians

are kept. Similarly, individuals in the control group with last reported age larger than 90 years old were also removed, with the remaining 147 individuals from LGP and 273 individuals for

lonGenity (Table 1). Descriptive statistics were used to summarize the demographic characteristics of the study population. The cumulative mutation burden for each individual was calculated

as the total number of population-level LOF (pLOF) and predicted deleterious missense variants. Mutation burden is calculated based on different categories of predicted deleterious variants

(pLOF only, pLOF and deleterious missense [5/5 algorithms], and pLOF and possible deleterious missense [≥1/5 algorithms], pLOF and all missense). Count-based burden tests were performed

using linear models with binned covariates to account for potential confounding factors, such as date of recruitment, date of birth, gender, age at visit, and top four genomic principal

components51. The cumulative mutation burden was used as the dependent variable, and the independent variables included centenarian status (or offspring status), binned date of recruitment,

and binned date of birth. Sensitivity analyzes were conducted by including additional covariates, such as age at recruitment, top 10 genetic principal components, numerical date of birth

(i.e., number of days since 1900-01-01), and date of recruitment. GENE-LEVEL AND PATHWAY-LEVEL BURDEN ANALYSIS Gene-level and pathway-level burden tests were performed using linear models,

with the cumulative mutation burden in each gene or pathway as the dependent variable and centenarian status as the independent variable. Only genes containing at least five pLOF variants

across the cohort were included. In total, 4925 unique genes were tested, and the significance threshold for gene-level tests was set at FDR < 0.05. Significant gene-level associations

were replicated using summary statistics from a gene-based association study of paternal or maternal lifespan in the GeneBass from UK biobank19. The significance threshold for replication

was set at _P_ < 0.05. MENDELIAN RANDOMIZATION To investigate the causal relationships between gene expression and aging-related traits, we performed Mendelian Randomization (MR) analyzes

using blood cis-eQTL data from eQTLgen, which includes 31,684 blood samples from 37 studies20. The outcome traits included aging-GIP1, frailty index, healthspan, lifespan, and extreme

longevity (90th and 99th percentiles). The parental lifespan GWAS was used as a proxy for individual lifespan and included 512,047 mothers and 500,193 fathers of European ancestry11. The

extreme longevity GWAS included 11,262 European subjects with a lifespan above the 90th percentile and 25,483 controls below the 60th percentile age10. Healthspan, defined as the age of the

first incidence of major age-related diseases or death, was analyzed using a GWAS of 300,447 UK Biobank participants aged 37–7352. The frailty index GWAS included 164,610 UK Biobank

participants aged 60–70 and 10,616 Swedish TwinGene participants aged 41–8753. Aging-GIP1, the first genetic principal component of six human aging traits, captures both length of life and

well-being indices54. We performed cis-Mendelian Randomization following the approach described by Ying et al37. Genetic variants strongly associated with whole blood gene expression levels

(FDR < 0.05) were selected as instrumental variables for the MR analysis. To minimize pleiotropic effects, only cis-eQTLs (located within 2 MB of target genes) were used, and LD clumping

was applied to remove eQTLs with strong LD (_r_2 > 0.3). We employed three MR methods based on the number of available eQTLs: Wald ratio for a single eQTL, generalized inverse variance

weighted (gIVW) for at least two eQTLs, and generalized MR-Egger regression (gEgger) for at least three eQTLs55. The gEgger method is robust to directional pleiotropy, we therefore reported

the P value from gEgger if pleiotropy is detected by gEgger intercept. MULTI-OMIC ANALYSIS OF THE IDENTIFIED LONGEVITY-ASSOCIATED GENES To systematically evaluate the expression and

regulation of the identified longevity-associated genes during aging, we profiled their multi-omic associations using various datasets. We obtained the exome-wide gene association with

parental lifespan using summary statistics from GeneBass19. Blood gene expression changes with age were obtained from the transcriptome-wide association study (TWAS) for aging by Peters et

al21. Age-related changes in promoter DNA methylation were assessed using data from 500 individuals in the Mass General Brigham (MGB) Biobank, which is also described in this study56. DNA

methylation profiles were generated using the Illumina Infinium MethylationEPIC v2.0 array, which covers over 935,000 CpG sites enriched for regulatory regions56. The cohort comprised

subjects of diverse ages, roughly balanced between male and female, and generally representative of the racial/ethnic distribution of the local area. For each CpG site associated with our

identified longevity-associated genes, we performed a linear regression to predict the methylation beta value using age, where the regression coefficient and _p_-value are calculated. The

CpG with the strongest association with age is used to represent the result. Age-related changes in plasma protein levels were investigated using Olink proteomics data from 53,015 UK Biobank

participants (UK Biobank Record Table 1072). Only two of our identified longevity-associated genes are presented in the Olink panel. We then performed a linear regression to predict the

protein level using age, where the regression coefficient and _p_-value are calculated. FDR was applied to adjust for multiple testing of all 471 sites tested. We performed FDR to adjust for

multiple tests in each omic layer. LONGEVITY SIGNATURE ANALYSIS To further explore the potential relevance of the identified longevity-associated genes in aging and interventions, we

compared their significance scores across different signatures of aging and longevity interventions using the GENtervention database57. For transcriptomic signatures of lifespan-extending

interventions, we selected the ones reflecting the most established longevity interventions that were identified based on gene expression data from at least 3 independent sources, as

described in Tyshkovskiy et al. 201923. The signatures included human aging and rodent aging, and interventions (caloric restriction, rapamycin treatment, and growth hormone deficiency). We

also include signatures of lifespan across interventions based on a larger set of longevity and lifespan-shortening interventions22. The significance scores were calculated as the

-log10(_P_-value) multiplied by the sign of the effect size (beta) for each gene in each signature. Nominal significance was set at _P_ < 0.05. Hierarchical clustering with Euclidean

distance was performed for the genes based on significance score. STATISTICS & REPRODUCIBILITY The study included a total of 2149 participants: 338 centenarians (aged 100 or older), 917

offspring of long-lived individuals, and 894 controls. Detailed age and sex/gender breakdowns for each group are provided in Table 1. Sex and gender were considered in the study design and

determined based on self-report at the time of recruitment. All participants provided written informed consent as stated in the “Study population and data collection” section. Participants

were not compensated for their involvement in the study. No statistical method was used to predetermine the sample size. Data exclusion criteria are detailed in the “Study population and

data collection” section. No other data were excluded from the analyzes. Statistical analyzes primarily employed linear models for burden tests and Mendelian Randomization, with adjustments

for potential confounding factors as described in the “Burden test analysis” and “Mendelian Randomization” sections. Multiple testing corrections were applied using FDR. The experiments were

not randomized, and the investigators were not blinded to allocation during experiments and outcome assessment, as this was an observational genetic study. Reproducibility was addressed

through replication in independent datasets (UK Biobank). REPORTING SUMMARY Further information on research design is available in the Nature Portfolio Reporting Summary linked to this

article. DATA AVAILABILITY All summary statistics for the gene- and pathway-based burden tests in the Ashkenazi Jewish longevity cohort are available in Supplementary Data 1, Supplementary

Data 2, and Source Data files. The individual-level genetic data from the Einstein longevity study are available under restricted access due to privacy concerns of research participants.

Qualified academic investigators (typically faculty members or postdoctoral researchers with relevant expertize) can request access by contacting Dr. Nir Barzilai

([email protected]) and the study’s principal investigator, Dr. Vadim Gladyshev ([email protected]). We aim to respond to all requests within 10 business days.

Access is subject to approval by the Institutional Review Board and requires a material transfer agreement. Upon approval, data use will be restricted by a comprehensive data use agreement

that includes conditions such as using the data solely for the approved research purpose, maintaining participant anonymity, and acknowledging the Einstein longevity study in any resulting

publications. Exact procedures for data transfer will be provided upon approval. The UK Biobank data used for validation is available through application to the UK Biobank

(https://www.ukbiobank.ac.uk/). Summary statistics from the eQTLGen consortium are publicly available at https://www.eqtlgen.org/. The GeneBass exome-wide association results are publicly

accessible at https://genebass.org/. Other publicly available datasets used in this study include: parental lifespan GWAS summary statistics (https://datashare.ed.ac.uk/handle/10283/3209),

healthspan GWAS summary statistics (https://www.gwasarchive.org/), frailty index GWAS summary statistics

(https://figshare.com/articles/dataset/Genome-Wide_Association_Study_of_the_Frailty_Index_-_Atkins_et_al_2019/9204998), longevity GWAS summary statistics

(https://www.longevitygenomics.org/downloads/). Source data are provided with this paper. CODE AVAILABILITY All of the analyzes are done in R 4.1. The custom code used for the burden test

analysis and gene-level and pathway-level burden analysis is available at https://doi.org/10.5281/zenodo.13756349 with a detailed readme file58. Other software used in our analysis was open

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https://doi.org/10.5281/zenodo.13756349 (2024). Download references ACKNOWLEDGEMENTS We thank members of the Gladyshev laboratory for the discussions. This study was supported by NIH R01

AG064223 to V.N.G., and R01AG061155 and P01AG017242 to N.B. K.Y. was supported by NIH F99AG088431. The content is solely the responsibility of the authors and does not necessarily represent

the official views of the National Institutes of Health. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard

Medical School, Boston, USA Kejun Ying, José P. Castro, Anastasia V. Shindyapina, Alexander Tyshkovskiy, Mahdi Moqri, Ludger J. E. Goeminne & Vadim N. Gladyshev * T. H. Chan School of

Public Health, Harvard University, Boston, USA Kejun Ying * i3S, Instituto de Investigação e Inovação em Saúde, Universidade do Porto and Aging and Aneuploidy Laboratory, IBMC, Instituto de

Biologia Molecular e Celular, Universidade do Porto, Porto, Portugal José P. Castro * Retro Biosciences, Redwood City, USA Anastasia V. Shindyapina * Department of Genetics, Albert Einstein

College of Medicine, Bronx, USA Sofiya Milman, Zhengdong D. Zhang & Nir Barzilai * Department of Medicine, Albert Einstein College of Medicine, Bronx, USA Sofiya Milman & Nir

Barzilai Authors * Kejun Ying View author publications You can also search for this author inPubMed Google Scholar * José P. Castro View author publications You can also search for this

author inPubMed Google Scholar * Anastasia V. Shindyapina View author publications You can also search for this author inPubMed Google Scholar * Alexander Tyshkovskiy View author

publications You can also search for this author inPubMed Google Scholar * Mahdi Moqri View author publications You can also search for this author inPubMed Google Scholar * Ludger J. E.

Goeminne View author publications You can also search for this author inPubMed Google Scholar * Sofiya Milman View author publications You can also search for this author inPubMed Google

Scholar * Zhengdong D. Zhang View author publications You can also search for this author inPubMed Google Scholar * Nir Barzilai View author publications You can also search for this author

inPubMed Google Scholar * Vadim N. Gladyshev View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS V.N.G. and K.Y. conceived the project. K.Y.

conducted the main data analysis. J.P.C., A.V.S., A.T., M.M., and L.J.E.G. assisted with data analysis. S.M. and Z.D.Z. provided clinical samples and data. N.B. supervised the clinical

aspects of the study. V.N.G. supervised the project. K.Y. and V.N.G. drafted the manuscript with input from all authors. All authors reviewed and approved the final version of the

manuscript. CORRESPONDING AUTHOR Correspondence to Vadim N. Gladyshev. ETHICS DECLARATIONS COMPETING INTERESTS After the initiation of this project, A.V.S. had a change in employment status

(Retro Biosciences). Analysis work was completed before this employment change. The other authors declare no competing interests. PEER REVIEW PEER REVIEW INFORMATION _Nature Communications_

thanks Harold Bae and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available. ADDITIONAL INFORMATION PUBLISHER’S NOTE

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loss-of-function germline mutations in centenarians reveals longevity genes. _Nat Commun_ 15, 9030 (2024). https://doi.org/10.1038/s41467-024-52967-2 Download citation * Received: 05 April

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