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Genetic variants specific to aging-related verbal memory: Insights from GWASs in a population-based cohort

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  • Thalida E Arpawong
  • Neil Pendleton
  • Krisztina Mekli
  • John J McArdle
  • Margaret Gatz
  • Chris Armoskus
  • James A Knowles
  • Carol A Prescott

Abstract

Verbal memory is typically studied using immediate recall (IR) and delayed recall (DR) scores, although DR is dependent on IR capability. Separating these components may be useful for deciphering the genetic variation in age-related memory abilities. This study was conducted to (a) construct individual trajectories in IR and independent aspects of delayed recall, or residualized-DR (rDR), across older adulthood; and (b) identify genetic markers that contribute to four estimated phenotypes: IR and rDR levels and changes after age 60. A cognitively intact sample (N = 20,650 with 125,164 observations) was drawn from the U.S. Health and Retirement Study, a nationally representative study of adults aged 50 and older. Mixed effects regression models were constructed using repeated measures from data collected every two years (1996–2012) to estimate level at age 60 and change in memory post-60 in IR and rDR. Genome-wide association scans (GWAS) were conducted in the genotypic subsample (N = 7,486) using ~1.2 million single nucleotide polymorphisms (SNPs). One SNP (rs2075650) in TOMM40 associated with rDR level at the genome-wide level (p = 5.0x10-08), an effect that replicated in an independent sample from the English Longitudinal Study on Ageing (N = 6,898 with 41,328 observations). Meta-analysis of rDR level confirmed the association (p = 5.0x10-11) and identified two others in TOMM40 (rs71352238 p = 1.0x10-10; rs157582 p = 7.0x10-09), and one in APOE (rs769449 p = 3.1 x10-12). Meta-analysis of IR change identified associations with three of the same SNPs in TOMM40 (rs157582 p = 8.3x10-10; rs71352238 p = 1.9x10-09) and APOE (rs769449 p = 2.2x10-08). Conditional analyses indicate GWAS signals on rDR level were driven by APOE, whereas signals on IR change were driven by TOMM40. Additionally, we found that TOMM40 had effects independent of APOE e4 on both phenotypes. Findings from this first U.S. population-based GWAS study conducted on both age-related immediate and delayed verbal memory merit continued examination in other samples and additional measures of verbal memory.

Suggested Citation

  • Thalida E Arpawong & Neil Pendleton & Krisztina Mekli & John J McArdle & Margaret Gatz & Chris Armoskus & James A Knowles & Carol A Prescott, 2017. "Genetic variants specific to aging-related verbal memory: Insights from GWASs in a population-based cohort," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-27, August.
    • Handle: RePEc:plo:pone00:0182448
    DOI: 10.1371/journal.pone.0182448
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    References listed on IDEAS

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    1. Nick Patterson & Alkes L Price & David Reich, 2006. "Population Structure and Eigenanalysis," PLOS Genetics, Public Library of Science, vol. 2(12), pages 1-20, December.
    2. John McArdle, 2011. "Longitudinal dynamic analyses of cognition in the health and retirement study panel," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 453-480, December.
    3. John Novembre & Toby Johnson & Katarzyna Bryc & Zoltán Kutalik & Adam R. Boyko & Adam Auton & Amit Indap & Karen S. King & Sven Bergmann & Matthew R. Nelson & Matthew Stephens & Carlos D. Bustamante, 2008. "Genes mirror geography within Europe," Nature, Nature, vol. 456(7219), pages 274-274, November.
    4. Eileen M. Crimmins & Jung Ki Kim & Kenneth M. Langa & David R. Weir, 2011. "Assessment of Cognition Using Surveys and Neuropsychological Assessment: The Health and Retirement Study and the Aging, Demographics, and Memory Study," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 66(suppl_1), pages 162-171.
    5. John Novembre & Toby Johnson & Katarzyna Bryc & Zoltán Kutalik & Adam R. Boyko & Adam Auton & Amit Indap & Karen S. King & Sven Bergmann & Matthew R. Nelson & Matthew Stephens & Carlos D. Bustamante, 2008. "Genes mirror geography within Europe," Nature, Nature, vol. 456(7218), pages 98-101, November.
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