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Beyond Missing Heritability: Prediction of Complex Traits

Author

Listed:
  • Robert Makowsky
  • Nicholas M Pajewski
  • Yann C Klimentidis
  • Ana I Vazquez
  • Christine W Duarte
  • David B Allison
  • Gustavo de los Campos

Abstract

Despite rapid advances in genomic technology, our ability to account for phenotypic variation using genetic information remains limited for many traits. This has unfortunately resulted in limited application of genetic data towards preventive and personalized medicine, one of the primary impetuses of genome-wide association studies. Recently, a large proportion of the “missing heritability” for human height was statistically explained by modeling thousands of single nucleotide polymorphisms concurrently. However, it is currently unclear how gains in explained genetic variance will translate to the prediction of yet-to-be observed phenotypes. Using data from the Framingham Heart Study, we explore the genomic prediction of human height in training and validation samples while varying the statistical approach used, the number of SNPs included in the model, the validation scheme, and the number of subjects used to train the model. In our training datasets, we are able to explain a large proportion of the variation in height (h2 up to 0.83, R2 up to 0.96). However, the proportion of variance accounted for in validation samples is much smaller (ranging from 0.15 to 0.36 depending on the degree of familial information used in the training dataset). While such R2 values vastly exceed what has been previously reported using a reduced number of pre-selected markers (

Suggested Citation

  • Robert Makowsky & Nicholas M Pajewski & Yann C Klimentidis & Ana I Vazquez & Christine W Duarte & David B Allison & Gustavo de los Campos, 2011. "Beyond Missing Heritability: Prediction of Complex Traits," PLOS Genetics, Public Library of Science, vol. 7(4), pages 1-9, April.
  • Handle: RePEc:plo:pgen00:1002051
    DOI: 10.1371/journal.pgen.1002051
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    Cited by:

    1. Yunpeng Wang & Arne B Gjuvsland & Jon Olav Vik & Nicolas P Smith & Peter J Hunter & Stig W Omholt, 2012. "Parameters in Dynamic Models of Complex Traits are Containers of Missing Heritability," PLOS Computational Biology, Public Library of Science, vol. 8(4), pages 1-9, April.
    2. Jesse D. Raffa & Elizabeth A. Thompson, 2016. "Power and Effective Study Size in Heritability Studies," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(2), pages 264-283, October.
    3. Ulrike Ober & Wen Huang & Michael Magwire & Martin Schlather & Henner Simianer & Trudy F C Mackay, 2015. "Accounting for Genetic Architecture Improves Sequence Based Genomic Prediction for a Drosophila Fitness Trait," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-17, May.
    4. Lun Li & Yan Long & Libin Zhang & Jessica Dalton-Morgan & Jacqueline Batley & Longjiang Yu & Jinling Meng & Maoteng Li, 2015. "Genome Wide Analysis of Flowering Time Trait in Multiple Environments via High-Throughput Genotyping Technique in Brassica napus L," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-18, March.
    5. Gustavo de los Campos & Yann C Klimentidis & Ana I Vazquez & David B Allison, 2012. "Prediction of Expected Years of Life Using Whole-Genome Markers," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-7, July.
    6. Sverdlov, Serge & Thompson, Elizabeth A., 2013. "Correlation between relatives given complete genotypes: From identity by descent to identity by function," Theoretical Population Biology, Elsevier, vol. 88(C), pages 57-67.

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