UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Using Genetic Distance to Infer the Accuracy of Genomic Prediction

Scutari, M; Mackay, I; Balding, D; (2016) Using Genetic Distance to Infer the Accuracy of Genomic Prediction. PLOS GENETICS , 12 (9) 10.1371/journal.pgen.1006288. Green open access

[thumbnail of journal.pgen.1006288.PDF]
Preview
Text
journal.pgen.1006288.PDF

Download (3MB) | Preview

Abstract

The prediction of phenotypic traits using high-density genomic data has many applications such as the selection of plants and animals of commercial interest; and it is expected to play an increasing role in medical diagnostics. Statistical models used for this task are usually tested using cross-validation, which implicitly assumes that new individuals (whose phenotypes we would like to predict) originate from the same population the genomic prediction model is trained on. In this paper we propose an approach based on clustering and resampling to investigate the effect of increasing genetic distance between training and target populations when predicting quantitative traits. This is important for plant and animal genetics, where genomic selection programs rely on the precision of predictions in future rounds of breeding. Therefore, estimating how quickly predictive accuracy decays is important in deciding which training population to use and how often the model has to be recalibrated. We find that the correlation between true and predicted values decays approximately linearly with respect to either FST or mean kinship between the training and the target populations. We illustrate this relationship using simulations and a collection of data sets from mice, wheat and human genetics.

Type: Article
Title: Using Genetic Distance to Infer the Accuracy of Genomic Prediction
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pgen.1006288
Publisher version: http://dx.doi.org/10.1371/journal.pgen.1006288
Language: English
Additional information: © 2016 Scutari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Keywords: Science & Technology, Life Sciences & Biomedicine, Genetics & Heredity, BREEDING VALUES, COMPLEX TRAITS, CORRELATION-COEFFICIENTS, RELATIONSHIP INFORMATION, RIDGE-REGRESSION, CROSS-VALIDATION, SELECTION, ASSOCIATION, ANIMALS, IMPACT
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Genetics, Evolution and Environment
URI: https://discovery.ucl.ac.uk/id/eprint/1504348
Downloads since deposit
81Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item