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Meta-Analysis
.2017 Oct 13;8(1):910.
doi: 10.1038/s41467-017-00934-5.

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

Peter K Joshi  1Nicola Pirastu  2Katherine A Kentistou  2  3Krista Fischer  4Edith Hofer  5  6Katharina E Schraut  2  3David W Clark  2Teresa Nutile  7Catriona L K Barnes  2Paul R H J Timmers  2Xia Shen  2  8Ilaria Gandin  9  10Aaron F McDaid  11  12Thomas Folkmann Hansen  13  14Scott D Gordon  15Franco Giulianini  16Thibaud S Boutin  17Abdel Abdellaoui  18Wei Zhao  19Carolina Medina-Gomez  20  21Traci M Bartz  22Stella Trompet  23  24Leslie A Lange  25Laura Raffield  26Ashley van der Spek  21Tessel E Galesloot  27Petroula Proitsi  28Lisa R Yanek  29Lawrence F Bielak  19Antony Payton  30Federico Murgia  31Maria Pina Concas  32Ginevra Biino  33Salman M Tajuddin  34Ilkka Seppälä  35Najaf Amin  21Eric Boerwinkle  36Anders D Børglum  14  37  38Archie Campbell  39Ellen W Demerath  40Ilja Demuth  41  42  43Jessica D Faul  44Ian Ford  45Alessandro Gialluisi  46Martin Gögele  31MariaElisa Graff  47Aroon Hingorani  48Jouke-Jan Hottenga  18David M Hougaard  14  49Mikko A Hurme  50M Arfan Ikram  21Marja Jylhä  51Diana Kuh  28Lannie Ligthart  18Christina M Lill  52Ulman Lindenberger  53  54Thomas Lumley  55Reedik Mägi  4Pedro Marques-Vidal  56Sarah E Medland  15Lili Milani  4Reka Nagy  17William E R Ollier  57Patricia A Peyser  19Peter P Pramstaller  31Paul M Ridker  16  58Fernando Rivadeneira  20  21Daniela Ruggiero  7Yasaman Saba  59Reinhold Schmidt  5Helena Schmidt  59P Eline Slagboom  60Blair H Smith  61Jennifer A Smith  19  44Nona Sotoodehnia  62Elisabeth Steinhagen-Thiessen  41Frank J A van Rooij  21André L Verbeek  27Sita H Vermeulen  27Peter Vollenweider  56Yunpeng Wang  14  63Thomas Werge  13  14John B Whitfield  15Alan B Zonderman  34Terho Lehtimäki  35Michele K Evans  34Mario Pirastu  32Christian Fuchsberger  31Lars Bertram  64  65Neil Pendleton  66Sharon L R Kardia  19Marina Ciullo  7  46Diane M Becker  29Andrew Wong  28Bruce M Psaty  67  68Cornelia M van Duijn  21James G Wilson  69J Wouter Jukema  24Lambertus Kiemeney  27André G Uitterlinden  20  21Nora Franceschini  47Kari E North  47David R Weir  44Andres Metspalu  4Dorret I Boomsma  18Caroline Hayward  17Daniel Chasman  16  58Nicholas G Martin  15Naveed Sattar  70Harry Campbell  2Tōnu Esko  4  71Zoltán Kutalik  11  12James F Wilson  2  17
Affiliations
Meta-Analysis

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

Peter K Joshi et al. Nat Commun..

Abstract

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.

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Conflict of interest statement

The authors declare no competing financial interests.

Figures

Fig. 1
Fig. 1
Genome-wide associations with parental lifespan. Association analysis was carried out using imputed allelic dosages.a Manhattan plot for LifeGen European ancestry, with both parents combined;b Q−Q plot comparing the expected (under the null hypothesis) and actual (observed) –log10p-values for results ina;c Manhattan plot of meta-analysis of LifeGen Europeans (both parents combined) with CHARGE-EU 90+ published summary statistics. The meta-analysis usedZ-scores and equal weights, as suggested by the near equality (9.5/9.4, LifeGen, CHARGE) ofZ-test statistics at rs4420638. The additional (just) GW significant SNP lies between the two chromosome 6 hits ina;d Manhattan plot for LifeGen African fathers only. In Manhattan plots, they-axis has been restricted to 15 to aid legibility
Fig. 2
Fig. 2
Locus zoom plots for four genome-wide significant associations with lifespan. Results from the meta-analysis of subjects of European ancestry analysis, for both parents combined. The displayedp-value corresponds to that of a two-sided test of association between the SNP and parent lifespan under the Cox model.a The rs34831921 variant, at the HLA-DQA1/DRB1 locus,P = 4.18E-08.b The rs55730499 variant, at the LPA locus,P = 8.67E-11.c The rs8042849 variant, at the CHRNA3/5 locus,P = 3.75E-14.d The rs429358 variant, at the APOE locus,P = 1.44E-27
Fig. 3
Fig. 3
Validation of associations reported elsewhere by lookup in LifeGen. A search of recent literature suggested the gene regions shown here were most likely to harbour associations with lifespan, beyond the four loci identified in Table 2, which are further explored in the Discussion. The most powerful LifeGen analysis (i.e., European ancestry, father and mother combined) was used for validation. The odds ratio (OR) for extreme long-livedness is presented for the reported life-shortening allele (i.e., the OR for long-livedness < 1) in the original study, but not necessarily in LifeGen. The LifeGen OR of being long-lived was estimated empirically on the assumption that the relationship between the LifeGen observed hazard ratio (HR) and the OR is stable across allelic effects, with APOE results from LifeGen and CHARGE-EU 90+ 6 being used to estimate the ratio of ln HR to ln OR (−4.7). These estimates will only fully align with the published ORs if the shape of the effect on lifespan is similar to APOE, as is true under the proportional hazards assumption, nonetheless the pattern is suggestive. Further details are shown in Supplementary Data 3
Fig. 4
Fig. 4
Age-specific and sex-specific effects of the 4 GWS associations in LifeGen and the validated candidate loci. The four GWS and three suggestive replicated loci were analysed for age-specific and sex-specific effects on lifespan.a The variants at APOE and CHRNA3/5 exhibit sexually dimorphic effects on parental mortality, while all other variants exhibit more modest often non-significant sex-specific differences.b The effects of each gene on male and female lifespan were meta-analysed and studied in the cases that died aged between 40 and 75 or after 75. APOE exerts a much greater effect in the older age group, while most of the other genes exhibit the opposite effect. FOXO3 appears neutral, if not positive, in the earlier age group.c Effects on mortality were studied in both age groups for both sexes. APOE has the strongest effect on females aged 75+, CHRNA3/5 acts on males aged 40−75 and all other genes display more ambiguous trends
Fig. 5
Fig. 5
Genetic correlations between trait clusters that associate with mortality. The upper panel shows whole genetic correlations, the lower panel, partial correlations. T2D, type 2 diabetes; BP, blood pressure; BC, breast cancer; CAD, coronary artery disease; Edu, educational attainment; RA, rheumatoid arthritis; AM, age at menarche; DL/WHR Dyslipidaemia/Waist-Hip ratio; BP, blood pressure
See this image and copyright information in PMC

References

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