Polygenic Effects of Common Single-Nucleotide Polymorphisms on Lifespan: When Association Meets Causality

Anatoliy I. Yashin, Deqing Wu, Konstantin G. Arbeev, and Svetlana V. Ukraintseva

Abstract

Recently, we showed that the human lifespan is influenced jointly by many common single-nucleotide polymorphisms (SNPs), each with a small individual effect. Here, we investigate further the polygenic influence on lifespan and discuss its possible biological mechanisms. First, we identified six sets of prolongevity SNP alleles in the Framingham Heart Study 550K SNPs data, using six different statistical procedures (normal linear, Cox and logistic regressions; generalized estimation equation; mixed model; gene frequency method). We then estimated the joint effects of these SNPs on human survival. We found that alleles in each set show a significant additive influence on lifespan. Twenty-seven SNPs comprised the overlapping set of SNPs that influenced lifespan, regardless of the statistical procedure. The majority of these SNPs (74%) were within genes, compared to 40% of SNPs in the original 550K set. We then performed a review of current literature on functions of genes closest to these 27 SNPs. The review showed that the respective genes are largely involved in aging, cancer, and brain disorders. We concluded that polygenic effects can explain a substantial portion of genetic influence on lifespan. The composition of the set of prolongevity alleles depends on the statistical procedure used for the allele selection. At the same time, there is a core set of longevity alleles that are selected with all statistical procedures. The functional relevance of respective genes to aging and major diseases supports causal relationships between the identified SNPs and lifespan. The fact that genes found in our and other genetic association studies of aging/longevity have similar functions indicates high chances of true positive associations for corresponding genetic variants.

Introduction

The criteria for associating a single genetic variant with a trait in GWAS are based on a comparison of p-values resulting from testing the null hypotheses regarding the absence of the variant effect on lifespan, with a threshold that is supposed to take into account the need for multiple comparisons. This typically leads to p-values below 10-7. GWAS of lifespan and other complex traits have shown that such traits can be affected by the joint influence of large numbers of common single-nucleotide polymorphisms (SNPs), each having small individual effects and little predictive value. The results of these studies indicate an urgent need for advancing methods of evaluating the joint effects of SNP alleles on aging and longevity and investigating biological mechanisms of such joint influence.

Addressing the joint influence of alleles, each with a small/low significant effect, could help both to significantly improve efficiency in the use of genome-wide SNP data and to explain a substantial part of so-called missing heritability of complex traits, including aging-related ones. Recent GWAS strongly support this idea demonstrating that lifespan and other complex traits, such as height and body mass index (BMI), are significantly influenced by large numbers of jointly acting, common SNPs, each with a small individual effect. However, the SNP alleles in these studies were typically selected using one or two statistical models, which might not be sufficient for meaningful interpretation.

Individual small-effect, low-significance SNP alleles have been observed in many GWAS of complex traits, including lifespan. However, such variants were typically excluded from further analyses. This is because respective studies considered only individual (and not collective) SNP effects on the traits of interest, which required extremely low p-value thresholds to reach a genome-wide significance level for the individual effects. The fact that different statistical models may potentially yield different numbers of SNPs influencing the trait of interest stresses the importance of advancing statistical approaches to analyses of joint genetic effects on aging and longevity. Indeed, the potential influence of a genetic variant on lifespan can be described by not just one but by many different statistical models. The use of different models might result in different estimates of the effect size and in different p-values for the same SNP allele, so that the results of selection (i.e., composition of the set of longevity alleles), and hence conclusions about genetics of lifespan, may depend substantially on the statistical model used in the allele selection procedure. Controlling for different observed covariates may add variability to the results of analyses.