Research Article: Strong impact of natural-selection–free heterogeneity in genetics of age-related phenotypes

Date Published: March 29, 2018

Publisher: Impact Journals

Author(s): Alexander M. Kulminski, Jian Huang, Yury Loika, Konstantin G. Arbeev, Olivia Bagley, Arseniy Yashkin, Matt Duan, Irina Culminskaya.


A conceptual difficulty in genetics of age-related phenotypes that make individuals vulnerable to disease in post-reproductive life is genetic heterogeneity attributed to an undefined role of evolution in establishing their molecular mechanisms. Here, we performed univariate and pleiotropic genome-wide meta-analyses of 20 age-related phenotypes leveraging longitudinal information in a sample of 33,431 individuals and dealing with the natural-selection–free genetic heterogeneity. We identified 142 non-proxy single nucleotide polymorphisms (SNPs) with phenotype-specific (18 SNPs) and pleiotropic (124 SNPs) associations at genome-wide level. Univariate meta-analysis identified two novel (11.1%) and replicated 16 SNPs whereas pleiotropic meta-analysis identified 115 novel (92.7%) and nine replicated SNPs. Pleiotropic associations for most novel (93.9%) and all replicated SNPs were strongly impacted by the natural-selection–free genetic heterogeneity in its unconventional form of antagonistic heterogeneity, implying antagonistic directions of genetic effects for directly correlated phenotypes. Our results show that the common genome-wide approach is well adapted to handle homogeneous univariate associations within Mendelian framework whereas most associations with age-related phenotypes are more complex and well beyond that framework. Dissecting the natural-selection–free genetic heterogeneity is critical for gaining insights into genetics of age-related phenotypes and has substantial and unexplored yet potential for improving efficiency of genome-wide analysis.

Partial Text

Genome-wide association studies (GWAS) are a powerful tool for hypothesis-free analysis of genetic predisposition to various phenotypes. Historically, GWAS were built within the “common disease – common genetic variant” concept following the framework of medical genetics. The underlying hypothesis in this framework is that there is a “true” or causal genetic effect on a phenotype of interest [1,2]. This approach in GWAS has been supported by successful discovery of causal genetic mutations for Mendelian disorders [3]. Essentially the same approach is pursued in GWAS of complex phenotypes, which do not follow clear pattern of Mendelian inheritance [4]. Extension of the framework of medical genetics of hereditary disorders to the complex non-Mendelian phenotypes relies on the hypothesis that they can have a genetic component. This hypothesis is supported by the concept of heritability, with significant heritability interpreted as indication of “pure” or “true” genetic component in a trait [2]. The concept of heritability, introduced in breeding experiments to improve crop yield, requires, however, controlled and fixed environment [5,6] that is strongly violated in human populations.

Our univariate and pleiotropic meta-analyses of 20 age-related phenotypes dealing with the natural-selection–free genetic heterogeneity identified large number of non-proxy SNPs, 142 SNPs, with GW significance in a relatively modest sample of 33,431 individuals of Caucasian ancestry from five longitudinal studies (Figure 5). Only 18 SNPs (12.7%) were identified in the univariate meta-analysis with most SNPs, 88.9% (16 of 18 SNPs), replicating previously reported associations, primarily with lipids. Two novel SNPs were associated with HC (rs6745983 in TMEM163 gene) and BG (rs10885409 in TCF7L2 gene). Two of 16 replicated SNPs, rs780094 (GCKR gene) and rs261332 (LIPC/LIPC-AS1 genes) associated with BG and TC, respectively, showed evidences for inter-cohort heterogeneity in the effect sizes. Accordingly, smaller p-values were attained by addressing this heterogeneity. The association of rs2155216 (BUD13-ZPR1/APOA5 gene locus) with TG was replicated with substantially higher (4-fold) efficiency in our analysis than in previous study [22] that was achieved, in part, by leveraging information on repeated measurements from longitudinal follow up.




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