Genetic Structures of Population Cohorts Change with Increasing Age: Implications for Genetic Analyses of Human aging and Life Span.

Journal Article (Journal Article)

Correcting for the potential effects of population stratification is an important issue in genome wide association studies (GWAS) of complex traits. Principal component analysis (PCA) of the genetic structure of the population under study with subsequent incorporation of the first several principal components (PCs) in the GWAS regression model is often used for this purpose. For longevity related traits such a correction may negatively affect the accuracy of genetic analyses. This is because PCs may capture genetic structure induced by mortality selection processes in genetically heterogeneous populations. We used the Framingham Heart Study data on life span and on individual genetic background to construct two sets of PCs. One was constructed to separate population stratification due to differences in ancestry from that induced by mortality selection. The other was constructed using genetic data on individuals of different ages without attempting to separate the ancestry effects from the mortality selection effects. The GWASs of human life span were performed using the first 20 PCs from each of the selected sets to control for possible population stratification. The results indicated that the GWAS that used the PC set separating population stratification induced by mortality selection from differences in ancestry produced stronger genetic signals than the GWAS that used PCs without such separation. The quality of genetic estimates in GWAS can be improved when changes in genetic structure caused by mortality selection are taken into account in controlling for possible effects of population stratification.

Full Text

Duke Authors

Cited Authors

  • Yashin, AI; Wu, D; Arbeev, KG; Arbeeva, LS; Akushevich, I; Kulminski, A; Culminskaya, I; Stallard, E; Ukraintseva, SV

Published Date

  • January 2014

Published In

  • Annals of Gerontology and Geriatric Research

Volume / Issue

  • 1 / 4

Start / End Page

  • 1020 -

PubMed ID

  • 25893220

Pubmed Central ID

  • PMC4398390


  • eng