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Reliability of genomic predictions of complex human phenotypes


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Porto, A, Peralta, JM, Blackburn, NB ORCID: 0000-0002-9774-1539 and Blangero, J 2018 , 'Reliability of genomic predictions of complex human phenotypes' , BMC Proceedings, vol. 12, no. Suppl , pp. 157-258 , doi: 10.1186/s12919-018-0138-5.

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Genome-wide association studies have helped us identify a wealth of genetic variants associated with complexhuman 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 modernstatistical genetic techniques borrowed from animal breeding can be employed to increase the accuracy ofgenomic 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 thetotal genetic component for the phenotype of interest and can be used to represent a trait without its environmentalcomponent.Finally, using empirical data, we demonstrate how this method can be used to increase the power of genetic mappingstudies. In sum, our results show that dense genome-wide data can be used in a wider scope than previously anticipated.

Item Type: Article
Authors/Creators:Porto, A and Peralta, JM and Blackburn, NB and Blangero, J
Keywords: genetic prediction
Journal or Publication Title: BMC Proceedings
Publisher: BioMed Central Ltd.
ISSN: 1753-6561
DOI / ID Number: 10.1186/s12919-018-0138-5
Copyright Information:

© 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.

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