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. 2010 Sep;2(9):612-20.
doi: 10.18632/aging.100191.

Joint influence of small-effect genetic variants on human longevity

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Joint influence of small-effect genetic variants on human longevity

Anatoliy I Yashin et al. Aging (Albany NY). 2010 Sep.

Abstract

The results of genome-wide association studies of complex traits, such as life span or age at onset of chronic disease, suggest that such traits are typically affected by a large number of small-effect alleles. Individually such alleles have little predictive values, therefore they were usually excluded from further analyses. The results of our study strongly suggest that the alleles with small individual effects on longevity may jointly influence life span so that the resulting influence can be both substantial and significant. We show that this joint influence can be described by a relatively simple "genetic dose - phenotypic response" relationship.

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Conflict of interest statement

The authors of this manuscript have no conflict of interests to declare.

Figures

Figure 1.
Figure 1.
The “genetic dose - phenotypic response” relationship between the numbers of selected 169 longevity alleles contained in individuals' genome and mean life span obtained in the analyses of 550K SNP data on participants of the original FHS cohort. Regression analyses were performed using SAS PROC REG (© SAS Institute, Inc.) with correction for heteroscedasticity.
Figure 2.
Figure 2.
The “genetic dose - phenotypic response” relationship between the numbers of selected 39 longevity alleles contained in individuals' genome and mean life span obtained in the analyses of 550K SNP data on participants of the original FHS cohort. Regression analyses were performed using SAS PROC REG (© SAS Institute, Inc.) with correction for heteroscedasticity.
Figure 3.
Figure 3.
The absence of dependence between the numbers of randomly selected 39 genetic variants contained in individuals' genome and life span. These genetic variants were randomly selected from the same pool of SNPs excluding longevity alleles. Regression analyses were performed using SAS PROC REG (© SAS Institute, Inc.) with correction for heteroscedasticity.

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