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. 2017 Jul 27:8:15842.
doi: 10.1038/ncomms15842.

Bayesian association scan reveals loci associated with human lifespan and linked biomarkers

Aaron F McDaid  1   2 , Peter K Joshi  3 , Eleonora Porcu  2   4 , Andrea Komljenovic  2   5 , Hao Li  6 , Vincenzo Sorrentino  6 , Maria Litovchenko  2   7 , Roel P J Bevers  2   7 , Sina Rüeger  1   2 , Alexandre Reymond  4 , Murielle Bochud  1 , Bart Deplancke  2   7 , Robert W Williams  8 , Marc Robinson-Rechavi  2   5 , Fred Paccaud  1 , Valentin Rousson  1 , Johan Auwerx  6 , James F Wilson  3   9 , Zoltán Kutalik  1   2
Affiliations

Bayesian association scan reveals loci associated with human lifespan and linked biomarkers

Aaron F McDaid et al. Nat Commun. .

Abstract

The enormous variation in human lifespan is in part due to a myriad of sequence variants, only a few of which have been revealed to date. Since many life-shortening events are related to diseases, we developed a Mendelian randomization-based method combining 58 disease-related GWA studies to derive longevity priors for all HapMap SNPs. A Bayesian association scan, informed by these priors, for parental age of death in the UK Biobank study (n=116,279) revealed 16 independent SNPs with significant Bayes factor at a 5% false discovery rate (FDR). Eleven of them replicate (5% FDR) in five independent longevity studies combined; all but three are depleted of the life-shortening alleles in older Biobank participants. Further analysis revealed that brain expression levels of nearby genes (RBM6, SULT1A1 and CHRNA5) might be causally implicated in longevity. Gene expression and caloric restriction experiments in model organisms confirm the conserved role for RBM6 and SULT1A1 in modulating lifespan.

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Conflict of interest statement

The authors declare no competing financial interests.

Figures

Figure 1
Figure 1. Analysis steps to obtain longevity prior effects for Bayesian analysis.
For each SNP its prior effect on lifespan is calculated as the product of the effect of the SNP i on GWAS traits (risk factors) [Image: see text] and the causal effect of the trait t on lifespan [[Image: see text]], summed over all available T GWAS traits. The causal effects of the traits on lifespan were calculated via a leave-one-chromosome out multivariate Mendelian randomization.
Figure 2
Figure 2. Multivariate MR causal effect estimates and 95% confidence intervals of the 11 significant traits on lifespan.
Effects are standardized such that they correspond to the square-root of the variance explained. In other terms, for example, 1 SD increase in BMI leads to 0.09 SD reduction in lifespan. Each black vertical bar represents the causal effect estimates obtained when leaving one chromosome in the estimation (Methods section).
Figure 3
Figure 3. Manhattan plot of the permutation P values of the BF.
The nearest genes to the 16 significant loci are indicated next to the lead SNP. Regions implicated in a recent longevity study are highlighted in green. X-axis represents the chromosome number and the physical position within each chromosome.
Figure 4
Figure 4. The frequency of life-shortening alleles decreases with the increasing age of study participants for 13 out of the 16 SNPs.
Each dot represents a SNP, with x-coordinate marking the Z-statistic in the lifespan association study and y-coordinate the age difference per allele (in month) with 95% confidence interval. SNPs whose effect direction agrees in the two studies are in red, others in blue. The nearest genes of the five SNPs with age-association P<0.05 are indicated.
Figure 5
Figure 5. Heatmap of the standardized effects of the 16 lifespan-associated SNPs on the 11 lifespan-impacting traits and lifespan.
We plotted the standardized effects of the 16 lifespan-associated SNPs on the 11 traits altering lifespan. Trait-increasing (decreasing) effects are shown in red (blue). Most of these SNPs show extensive pleiotropic effects.
See this image and copyright information in PMC

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