Genetic predisposition, modifiable lifestyles, and their joint effects on human lifespan: evidence from multiple cohort studies.

OBJECTIVE: To investigate the associations across genetic and lifestyle factors with lifespan. DESIGN: A longitudinal cohort study. SETTING: UK Biobank. PARTICIPANTS: 353 742 adults of European ancestry, who were recruited from 2006 to 2010 and were followed up until 2021. EXPOSURES: A polygenic risk score for lifespan with long (highest quintile) risk categories and a weighted healthy lifestyle score, including no current smoking, moderate alcohol consumption, regular physical activity, healthy body shape, adequate sleep duration, and a healthy diet, categorised into favourable, intermediate, and unfavourable lifestyles. MAIN OUTCOME MEASURES: Lifespan defined as the date of death or the censor date minus the date of birth. RESULTS: Of the included 353 742 participants of European ancestry with a median follow-up of 12.86 years, 24 239 death cases were identified. Participants were grouped into three genetically determined lifespan categories including long (20.1%), intermediate (60.1%), and short (19.8%), and into three lifestyle score categories including favourable (23.1%), intermediate (55.6%), and unfavourable (21.3%). The hazard ratio (HR) of death for individuals with a genetic predisposition to a short lifespan was 1.21 (95% CI 1.16 to 1.26) compared to those with a genetic predisposition to a long lifespan. The HR of death for individuals in the unfavourable lifestyle category was 1.78 (95% CI 1.71 to 1.85), compared with those in the favourable lifestyle category. Participants with a genetic predisposition to a short lifespan and an unfavourable lifestyle had 2.04 times (95% CI 1.87 to 2.22) higher rates of death compared with those with a genetic predisposition to a long lifespan and a favourable lifestyle. No multiplicative interaction was detected between the polygenic risk score of lifespan and the weighted healthy lifestyle score (p=0.10). The optimal combination of healthy lifestyles, including never smoking, regular physical activity, adequate sleep duration, and a healthy diet, was derived to decrease risk of premature death (death before 75 years). CONCLUSION: Genetic and lifestyle factors were independently associated with lifespan. Adherence to healthy lifestyles could largely attenuate the genetic risk of a shorter lifespan or premature death. The optimal combination of healthy lifestyles could convey better benefits for a longer lifespan, regardless of genetic background.

Unraveling Neuro-Proteogenomic Landscape and Therapeutic Implications for Human Behaviors and Psychiatric Disorders.

Understanding the genetic basis of neuro-related proteins is essential for dissecting the molecular basis of human behavioral traits and the disease etiology of neuropsychiatric disorders. Here, the SCALLOP Consortium conducted a genome-wide association meta-analysis of over 12,500 individuals for 184 neuro-related proteins in human plasma. The analysis identified 117 cis-regulatory protein quantitative trait loci (cis-pQTL) and 166 trans-pQTL. The mapped pQTL capture on average 50% of each protein's heritability. Mendelian randomization analyses revealed multiple proteins showing potential causal effects on neuro-related traits such as sleeping, smoking, feelings, alcohol intake, mental health, and psychiatric disorders. Integrating with established drug information, we validated 13 out of 13 matched combinations of protein targets and diseases or side effects with available drugs, while suggesting hundreds of re-purposing and new therapeutic targets. This consortium effort provides a large-scale proteogenomic resource for biomedical research on human behaviors and other neuro-related phenotypes.

An atlas of genetic scores to predict multi-omic traits.

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics1. Here we examine a large cohort (the INTERVAL study2; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank3 to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK-STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.

Genetic mechanisms of 184 neuro-related proteins in human plasma.

Understanding the genetic basis of neuro-related proteins is essential for dissecting the disease etiology of neuropsychiatric disorders and other complex traits and diseases. Here, the SCALLOP Consortium conducted a genome-wide association meta-analysis of over 12,500 individuals for 184 neuro-reiated proteins in human plasma. The analysis identified 117 cis-regulatory protein quantitative trait loci (cis-pQTL) and 166 trans-pQTL. The mapped pQTL capture on average 50% of each protein's heritability. Mendelian randomization analyses revealed multiple proteins showing potential causal effects on neuro-reiated traits as well as complex diseases such as hypertension, high cholesterol, immune-related disorders, and psychiatric disorders. Integrating with established drug information, we validated 13 combinations of protein targets and diseases or side effects with available drugs, while suggesting hundreds of re-purposing and new therapeutic targets for diseases and comorbidities. This consortium effort provides a large-scale proteogenomic resource for biomedical research.

A Novel Framework for Analysis of the Shared Genetic Background of Correlated Traits.

We propose a novel effective framework for the analysis of the shared genetic background for a set of genetically correlated traits using SNP-level GWAS summary statistics. This framework called SHAHER is based on the construction of a linear combination of traits by maximizing the proportion of its genetic variance explained by the shared genetic factors. SHAHER requires only full GWAS summary statistics and matrices of genetic and phenotypic correlations between traits as inputs. Our framework allows both shared and unshared genetic factors to be effectively analyzed. We tested our framework using simulation studies, compared it with previous developments, and assessed its performance using three real datasets: anthropometric traits, psychiatric conditions and lipid concentrations. SHAHER is versatile and applicable to summary statistics from GWASs with arbitrary sample sizes and sample overlaps, allows for the incorporation of different GWAS models (Cox, linear and logistic), and is computationally fast.

Cystatin C is associated with adverse COVID-19 outcomes in diverse populations.

COVID-19 has highly variable clinical courses. The search for prognostic host factors for COVID-19 outcome is a priority. We performed logistic regression for ICU admission against a polygenic score (PGS) for Cystatin C (CyC) production in patients with COVID-19. We analyzed the predictive value of longitudinal plasma CyC levels in an independent cohort of patients hospitalized with COVID-19. In four cohorts spanning European and African ancestry populations, we identified a significant association between CyC-production PGS and odds of critical illness (n cases=2,319), with the strongest association captured in the UKB cohort (OR 2.13, 95% CI 1.58-2.87, p=7.12e-7). Plasma proteomics from an independent cohort of hospitalized COVID-19 patients (n cases = 131) demonstrated that CyC production was associated with COVID-specific mortality (p=0.0007). Our findings suggest that CyC may be useful for stratification of patients and it has functional role in the host response to COVID-19.

Limited Effect of Y Chromosome Variation on Coronary Artery Disease and Mortality in UK Biobank-Brief Report.

BACKGROUND: The effect of genetic variation in the male-specific region of the Y chromosome (MSY) on coronary artery disease and cardiovascular risk factors has been disputed. In this study, we systematically assessed the association of MSY genetic variation on these traits using a kin-cohort analysis of family disease history in the largest sample to date. METHODS: We tested 90 MSY haplogroups against coronary artery disease, hypertension, blood pressure, classical lipid levels, and all-cause mortality in up to 152 186 unrelated, genomically British individuals from UK Biobank. Unlike previous studies, we did not adjust for heritable lifestyle factors (to avoid collider bias) and instead adjusted for geographic variables and socioeconomic deprivation, given the link between MSY haplogroups and geography. For family history traits, subject MSY haplogroups were tested against father and mother disease as validation and negative control, respectively. RESULTS: Our models find little evidence for an effect of any MSY haplogroup on cardiovascular risk in participants. Parental models confirm these findings. CONCLUSIONS: Kin-cohort analysis of the Y chromosome uniquely allows for discoveries in subjects to be validated using family history data. Despite our large sample size, improved models, and parental validation, there is little evidence to suggest cardiovascular risk in UK Biobank is influenced by genetic variation in MSY.

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

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

Genome-wide analysis identifies gallstone-susceptibility loci including genes regulating gastrointestinal motility.

BACKGROUND AND AIMS: Genome-wide association studies (GWAS) have identified several risk loci for gallstone disease. As with most polygenic traits, it is likely that many genetic determinants are undiscovered. The aim of this study was to identify genetic variants that represent new targets for gallstone research and treatment. APPROACH AND RESULTS: We performed a GWAS of 28,627 gallstone cases and 348,373 controls in the UK Biobank, replicated findings in a Scottish cohort (1089 cases, 5228 controls), and conducted a GWA meta-analysis (43,639 cases, 506,798 controls) with the FinnGen cohort. We assessed pathway enrichment using gene-based then gene-set analysis and tissue expression of identified genes in Genotype-Tissue Expression project data. We constructed a polygenic risk score (PRS) and evaluated phenotypic traits associated with the score. Seventy-five risk loci were identified (p < 5 × 10-8 ), of which 46 were new. Pathway enrichment revealed associations with lipid homeostasis, glucuronidation, phospholipid metabolism, and gastrointestinal motility. Anoctamin 1 (ANO1) and transmembrane Protein 147 (TMEM147), both in novel, replicated loci, are expressed in the gallbladder and gastrointestinal tract. Both regulate gastrointestinal motility. The gallstone risk allele rs7599-A leads to suppression of hepatic TMEM147 expression, suggesting that the protein protects against gallstone formation. The highest decile of the PRS demonstrated a 6-fold increased odds of gallstones compared with the lowest decile. The PRS was strongly associated with increased body mass index, serum liver enzymes, and C-reactive protein concentrations, and decreased lipoprotein cholesterol concentrations. CONCLUSIONS: This GWAS demonstrates the polygenic nature of gallstone risk and identifies 46 novel susceptibility loci. We implicate genes influencing gastrointestinal motility in the pathogenesis of gallstones.

Genome-Wide Association Study of NAFLD Using Electronic Health Records.

Genome-wide association studies (GWAS) have identified several risk loci for nonalcoholic fatty liver disease (NAFLD). Previous studies have largely relied on small sample sizes and have assessed quantitative traits. We performed a case-control GWAS in the UK Biobank using recorded diagnosis of NAFLD based on diagnostic codes recommended in recent consensus guidelines. We performed a GWAS of 4,761 cases of NAFLD and 373,227 healthy controls without evidence of NAFLD. Sensitivity analyses were performed excluding other co-existing hepatic pathology, adjusting for body mass index (BMI) and adjusting for alcohol intake. A total of 9,723,654 variants were assessed by logistic regression adjusted for age, sex, genetic principal components, and genotyping batch. We performed a GWAS meta-analysis using available summary association statistics. Six risk loci were identified (P < 5*10-8 ) (apolipoprotein E [APOE], patatin-like phospholipase domain containing 3 [PNPLA3, transmembrane 6 superfamily member 2 [TM6SF2], glucokinase regulator [GCKR], mitochondrial amidoxime reducing component 1 [MARC1], and tribbles pseudokinase 1 [TRIB1]). All loci retained significance in sensitivity analyses without co-existent hepatic pathology and after adjustment for BMI. PNPLA3 and TM6SF2 remained significant after adjustment for alcohol (alcohol intake was known in only 158,388 individuals), with others demonstrating consistent direction and magnitude of effect. All six loci were significant on meta-analysis. Rs429358 (P = 2.17*10-11 ) is a missense variant within the APOE gene determining ϵ4 versus ϵ2/ϵ3 alleles. The ϵ4 allele of APOE offered protection against NAFLD (odds ratio for heterozygotes 0.84 [95% confidence interval 0.78-0.90] and homozygotes 0.64 [0.50-0.79]). Conclusion: This GWAS replicates six known NAFLD-susceptibility loci and confirms that the ϵ4 allele of APOE is associated with protection against NAFLD. The results are consistent with published GWAS using histological and radiological measures of NAFLD, confirming that NAFLD identified through diagnostic codes from consensus guidelines is a valid alternative to more invasive and costly approaches.

Mendelian randomization of genetically independent aging phenotypes identifies LPA and VCAM1 as biological targets for human aging.

Length and quality of life are important to us all, yet identification of promising drug targets for human aging using genetics has had limited success. In the present study, we combine six European-ancestry genome-wide association studies of human aging traits-healthspan, father and mother lifespan, exceptional longevity, frailty index and self-rated health-in a principal component framework that maximizes their shared genetic architecture. The first principal component (aging-GIP1) captures both length of life and indices of mental and physical wellbeing. We identify 27 genomic regions associated with aging-GIP1, and provide additional, independent evidence for an effect on human aging for loci near HTT and MAML3 using a study of Finnish and Japanese survival. Using proteome-wide, two-sample, Mendelian randomization and colocalization, we provide robust evidence for a detrimental effect of blood levels of apolipoprotein(a) and vascular cell adhesion molecule 1 on aging-GIP1. Together, our results demonstrate that combining multiple aging traits using genetic principal components enhances the power to detect biological targets for human aging.

Genetic insights into biological mechanisms governing human ovarian ageing.

Reproductive longevity is essential for fertility and influences healthy ageing in women1,2, but insights into its underlying biological mechanisms and treatments to preserve it are limited. Here we identify 290 genetic determinants of ovarian ageing, assessed using normal variation in age at natural menopause (ANM) in about 200,000 women of European ancestry. These common alleles were associated with clinical extremes of ANM; women in the top 1% of genetic susceptibility have an equivalent risk of premature ovarian insufficiency to those carrying monogenic FMR1 premutations3. The identified loci implicate a broad range of DNA damage response (DDR) processes and include loss-of-function variants in key DDR-associated genes. Integration with experimental models demonstrates that these DDR processes act across the life-course to shape the ovarian reserve and its rate of depletion. Furthermore, we demonstrate that experimental manipulation of DDR pathways highlighted by human genetics increases fertility and extends reproductive life in mice. Causal inference analyses using the identified genetic variants indicate that extending reproductive life in women improves bone health and reduces risk of type 2 diabetes, but increases the risk of hormone-sensitive cancers. These findings provide insight into the mechanisms that govern ovarian ageing, when they act, and how they might be targeted by therapeutic approaches to extend fertility and prevent disease.

Comparative Analysis of Mammal Genomes Unveils Key Genomic Variability for Human Life Span.

The enormous mammal's lifespan variation is the result of each species' adaptations to their own biological trade-offs and ecological conditions. Comparative genomics have demonstrated that genomic factors underlying both, species lifespans and longevity of individuals, are in part shared across the tree of life. Here, we compared protein-coding regions across the mammalian phylogeny to detect individual amino acid (AA) changes shared by the most long-lived mammals and genes whose rates of protein evolution correlate with longevity. We discovered a total of 2,737 AA in 2,004 genes that distinguish long- and short-lived mammals, significantly more than expected by chance (P = 0.003). These genes belong to pathways involved in regulating lifespan, such as inflammatory response and hemostasis. Among them, a total 1,157 AA showed a significant association with maximum lifespan in a phylogenetic test. Interestingly, most of the detected AA positions do not vary in extant human populations (81.2%) or have allele frequencies below 1% (99.78%). Consequently, almost none of these putatively important variants could have been detected by genome-wide association studies. Additionally, we identified four more genes whose rate of protein evolution correlated with longevity in mammals. Crucially, SNPs located in the detected genes explain a larger fraction of human lifespan heritability than expected, successfully demonstrating for the first time that comparative genomics can be used to enhance interpretation of human genome-wide association studies. Finally, we show that the human longevity-associated proteins are significantly more stable than the orthologous proteins from short-lived mammals, strongly suggesting that general protein stability is linked to increased lifespan.

Mendelian randomisation identifies alternative splicing of the FAS death receptor as a mediator of severe COVID-19.

Severe COVID-19 is characterised by immunopathology and epithelial injury. Proteomic studies have identified circulating proteins that are biomarkers of severe COVID-19, but cannot distinguish correlation from causation. To address this, we performed Mendelian randomisation (MR) to identify proteins that mediate severe COVID-19. Using protein quantitative trait loci (pQTL) data from the SCALLOP consortium, involving meta-analysis of up to 26,494 individuals, and COVID-19 genome-wide association data from the Host Genetics Initiative, we performed MR for 157 COVID-19 severity protein biomarkers. We identified significant MR results for five proteins: FAS, TNFRSF10A, CCL2, EPHB4 and LGALS9. Further evaluation of these candidates using sensitivity analyses and colocalization testing provided strong evidence to implicate the apoptosis-associated cytokine receptor FAS as a causal mediator of severe COVID-19. This effect was specific to severe disease. Using RNA-seq data from 4,778 individuals, we demonstrate that the pQTL at the FAS locus results from genetically influenced alternate splicing causing skipping of exon 6. We show that the risk allele for very severe COVID-19 increases the proportion of transcripts lacking exon 6, and thereby increases soluble FAS. Soluble FAS acts as a decoy receptor for FAS-ligand, inhibiting apoptosis induced through membrane-bound FAS. In summary, we demonstrate a novel genetic mechanism that contributes to risk of severe of COVID-19, highlighting a pathway that may be a promising therapeutic target.

Multivariate genomic scan implicates novel loci and haem metabolism in human ageing.

Ageing phenotypes, such as years lived in good health (healthspan), total years lived (lifespan), and survival until an exceptional old age (longevity), are of interest to us all but require exceptionally large sample sizes to study genetically. Here we combine existing genome-wide association summary statistics for healthspan, parental lifespan, and longevity in a multivariate framework, increasing statistical power, and identify 10 genomic loci which influence all three phenotypes, of which five (near FOXO3, SLC4A7, LINC02513, ZW10, and FGD6) have not been reported previously at genome-wide significance. The majority of these 10 loci are associated with cardiovascular disease and some affect the expression of genes known to change their activity with age. In total, we implicate 78 genes, and find these to be enriched for ageing pathways previously highlighted in model organisms, such as the response to DNA damage, apoptosis, and homeostasis. Finally, we identify a pathway worthy of further study: haem metabolism.

Genomic analysis of male puberty timing highlights shared genetic basis with hair colour and lifespan.

The timing of puberty is highly variable and is associated with long-term health outcomes. To date, understanding of the genetic control of puberty timing is based largely on studies in women. Here, we report a multi-trait genome-wide association study for male puberty timing with an effective sample size of 205,354 men. We find moderately strong genomic correlation in puberty timing between sexes (rg = 0.68) and identify 76 independent signals for male puberty timing. Implicated mechanisms include an unexpected link between puberty timing and natural hair colour, possibly reflecting common effects of pituitary hormones on puberty and pigmentation. Earlier male puberty timing is genetically correlated with several adverse health outcomes and Mendelian randomization analyses show a genetic association between male puberty timing and shorter lifespan. These findings highlight the relationships between puberty timing and health outcomes, and demonstrate the value of genetic studies of puberty timing in both sexes.

Trends in disease incidence and survival and their effect on mortality in Scotland: nationwide cohort study of linked hospital admission and death records 2001-2016.

OBJECTIVES: Identify causes and future trends underpinning Scottish mortality improvements and quantify the relative contributions of disease incidence and survival. DESIGN: Population-based study. SETTING: Linked secondary care and mortality records across Scotland. PARTICIPANTS: 1 967 130 individuals born between 1905 and 1965 and resident in Scotland from 2001 to 2016. MAIN OUTCOME MEASURES: Hospital admission rates and survival within 5 years postadmission for 28 diseases, stratified by sex and socioeconomic status. RESULTS: 'Influenza and pneumonia', 'Symptoms and signs involving circulatory and respiratory systems' and 'Malignant neoplasm of respiratory and intrathoracic organs' were the hospital diagnosis groupings associated with most excess deaths, being both common and linked to high postadmission mortality. Using disease trends, we modelled a mean mortality HR of 0.737 (95% CI 0.730 to 0.745) from one decade of birth to the next, equivalent to a life extension of ~3 years per decade. This improvement was 61% (30%-93%) accounted for by improved disease survival after hospitalisation (principally cancer) with the remainder accounted for by lowered hospitalisation incidence (principally heart disease and cancer). In contrast, deteriorations in infectious disease incidence and survival increased mortality by 9% (~3.3 months per decade). Disease-driven mortality improvements were slightly greater for men than women (due to greater falls in disease incidence), and generally similar across socioeconomic deciles. We project mortality improvements will continue over the next decade but slow by 21% because much progress in disease survival has already been achieved. CONCLUSION: Morbidity improvements broadly explain observed mortality improvements, with progress on prevention and treatment of heart disease and cancer contributing the most. The male-female health gaps are closing, but those between socioeconomic groups are not. Slowing improvements in morbidity may explain recent stalling in improvements of UK period life expectancies. However, these could be offset if we accelerate improvements in the diseases accounting for most deaths and counteract recent deteriorations in infectious disease.

Comparison of Familial, Polygenic and Biochemical Predictors of Mortality.

Mortality risk is known to be associated with many physiological or biochemical risk factors, and polygenic risk scores (PRSs) may offer an additional or alternative approach to risk stratification. We have compared the predictive value of common biochemical tests, PRSs and information on parental survival in a cohort of twins and their families. Common biochemical test results were available for up to 13,365 apparently healthy men and women, aged 17-93 years (mean 49.0, standard deviation [SD] 13.7) at blood collection. PRSs for longevity were available for 14,169 study participants and reported parental survival for 25,784 participants. A search for information on date and cause of death was conducted through the Australian National Death Index, with median follow-up of 11.3 years. Cox regression was used to evaluate associations with mortality from all causes, cancers, cardiovascular diseases and other causes. Linear relationships with all-cause mortality were strongest for C-reactive protein, gamma-glutamyl transferase, glucose and alkaline phosphatase, with hazard ratios (HRs) of 1.16 (95% CI [1.07, 1.24]), 1.15 (95% CI 1.04-1.21), 1.13 (95% CI [1.08, 1.19]) and 1.11 (95% CI [1.05, 1.88]) per SD difference, respectively. Significant nonlinear effects were found for urea, uric acid and butyrylcholinesterase. Lipid risk factors were not statistically significant for mortality in our cohort. Family history and PRS showed weaker but significant associations with survival, with HR in the range 1.05 to 1.09 per SD difference. In conclusion, biochemical tests currently predict long-term mortality more strongly than genetic scores based on genotyping or on reported parental survival.

Low-frequency variation in TP53 has large effects on head circumference and intracranial volume.

Cranial growth and development is a complex process which affects the closely related traits of head circumference (HC) and intracranial volume (ICV). The underlying genetic influences shaping these traits during the transition from childhood to adulthood are little understood, but might include both age-specific genetic factors and low-frequency genetic variation. Here, we model the developmental genetic architecture of HC, showing this is genetically stable and correlated with genetic determinants of ICV. Investigating up to 46,000 children and adults of European descent, we identify association with final HC and/or final ICV + HC at 9 novel common and low-frequency loci, illustrating that genetic variation from a wide allele frequency spectrum contributes to cranial growth. The largest effects are reported for low-frequency variants within TP53, with 0.5 cm wider heads in increaser-allele carriers versus non-carriers during mid-childhood, suggesting a previously unrecognized role of TP53 transcripts in human cranial development.

Genome-wide meta-analysis associates HLA-DQA1/DRB1 and LPA and lifestyle factors with human longevity.

Genomic analysis of longevity offers the potential to illuminate the biology of human aging. Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA). We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity. Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated. We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD. Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.Variability in human longevity is genetically influenced. Using genetic data of parental lifespan, the authors identify associations at HLA-DQA/DRB1 and LPA and find that genetic variants that increase educational attainment have a positive effect on lifespan whereas increasing BMI negatively affects lifespan.