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Reliability of genomic predictions of complex human phenotypes


Porto, A and Peralta, JM and Blackburn, NB and Blangero, J, Reliability of genomic predictions of complex human phenotypes, BMC Proceedings, 12, (Suppl 9) pp. 157-258. ISSN 1753-6561 (2018) [Refereed Article]

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Copyright Statement

The Author(s) 2018. Open Access This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License, ( which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

DOI: doi:10.1186/s12919-018-0138-5


Genome-wide association studies have helped us identify a wealth of genetic variants associated with complex human phenotypes. Because most variants explain a small portion of the total phenotypic variation, however, marker-based studies remain limited in their ability to predict such phenotypes. Here, we show how modern statistical genetic techniques borrowed from animal breeding can be employed to increase the accuracy of genomic prediction of complex phenotypes and the power of genetic mapping studies. Specifically, using the triglyceride data of the GAW20 data set, we apply genomic-best linear unbiased prediction (G-BLUP) methods to obtain empirical genetic values (EGVs) for each triglyceride phenotype and each individual. We then study 2 different factors that influence the prediction accuracy of G-BLUP for the analysis of human data: (a) the choice of kinship matrix, and (b) the overall level of relatedness. The resulting genetic values represent the total genetic component for the phenotype of interest and can be used to represent a trait without its environmental component. Finally, using empirical data, we demonstrate how this method can be used to increase the power of genetic mapping studies. In sum, our results show that dense genome-wide data can be used in a wider scope than previously anticipated.

Item Details

Item Type:Refereed Article
Keywords:genetic prediction
Research Division:Biological Sciences
Research Group:Bioinformatics and computational biology
Research Field:Statistical and quantitative genetics
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the biological sciences
UTAS Author:Blackburn, NB (Dr Nicholas Blackburn)
ID Code:144995
Year Published:2018
Deposited By:Menzies Institute for Medical Research
Deposited On:2021-06-23
Last Modified:2021-09-08
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