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

Anatoliy I. Yashin,1,2 Deqing Wu,1 Konstantin G. Arbeev,1 and Svetlana V. Ukraintseva1,2

Abstract

Recently we have shown that the human life span is influenced jointly by many common single-nucleotide polymorphisms (SNPs), each with a small individual effect. Here we investigate further the polygenic influence on life span 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 joint effects of these SNPs on human survival. We found that alleles in each set show significant additive influence on life span. Twenty-seven SNPs comprised the overlapping set of SNPs that influenced life span, 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 life span. 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. Functional relevance of respective genes to aging and major diseases supports causal relationships between the identified SNPs and life span. 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

fects. The criteria for associating a single genetic variant with a trait in GWAS are based on comparison of p values resulting from testing the null hypotheses regarding the absence of the variant effect on life span, 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 life span and other complex traits1–3 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 pre- dictive 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 bio- logical 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 of the use of genome-wide SNP data and to explain a substantial part of so called ‘‘missing heritabili- ty’’ of complex traits,8 including the aging-related ones. Re- cent GWAS1–3,9,10 strongly support this idea demonstrating that life span 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 life span4–7; 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 ef-

of the study results, because different statistical models may potentially yield different numbers of SNPs influencing the trait of interest. This 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 life span 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 life span, 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.