Prediction of Complex Human Traits Using the Genomic Best Linear Unbiased Predictor

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Dokumenter

DOI

  • Gustavo de los Campos, Biostatistics Department, University of Alabama at Birmingham, Birmingham, USA
  • Ana I Vazquez, Biostatistics Department, University of Alabama at Birmingham, Birmingham, USA
  • Rohan Fernando, Animal Science Department, Iowa State University, Ames, USA
  • Yann C Klementidis, Animal Science Department, Iowa State University, Ames, USA
  • Daniel Sorensen
Despite important advances from Genome Wide Association Studies (GWAS), for most complex human traits and diseases, a sizable proportion of genetic variance remains unexplained and prediction accuracy (PA) is usually low. Evidence suggests that PA can be improved using Whole-Genome Regression (WGR) models where phenotypes are regressed on hundreds of thousands of variants simultaneously. The Genomic Best Linear Unbiased Prediction G-BLUP, a ridge-regression type method) is a commonly used WGR method and has shown good predictive performance when applied to plant and animal
breeding populations. However, breeding and human populations differ greatly in a number of factors that can affect the predictive performance of G-BLUP. Using theory, simulations, and real data analysis, we study the erformance of G-BLUP when applied to data from related and unrelated human subjects. Under perfect linkage disequilibrium (LD) between
markers and QTL, the prediction R-squared (R2) of G-BLUP reaches trait-heritability, asymptotically. However, under imperfect LD between markers and QTL, prediction R2 based on G-BLUP has a much lower upper bound. We show that the minimum decrease in prediction accuracy caused by imperfect LD between markers and QTL is given by (12b) 2, where b is the regression of marker-derived genomic relationships on those realized at causal loci. For pairs of related individuals, due to within-family disequilibrium, the patterns of realized genomic similarity are similar across the genome; therefore b
is close to one inducing small decrease in R2. However, with distantly related individuals b reaches very low values imposing a very low upper bound on prediction R2. Our simulations suggest that for the analysis of data from unrelated individuals, the asymptotic upper bound on R2 may be of the order of 20% of the trait heritability. We show how PA can be enhanced with use of variable selection or differential shringkage of estimates of marker effects
OriginalsprogEngelsk
Artikelnummere1003608
TidsskriftP L o S Genetics
Vol/bind9
Nummer7
Sider (fra-til)1-15
Antal sider15
ISSN1553-7390
DOI
StatusUdgivet - 13 jul. 2013

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