Year: 2016
Type: article
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.
Source: PLoS Genetics
Authors: Marco Scutari, Ian Mackay, David Balding
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,043
Awards: GplusE: Genomic selection and Environment modelling for next generation wheat breeding, Molecular Improvement of Disease Resistance in Barley (MIDRIB), G3073510240, Developing enhanced breeding methodologies for oats for human health and nutrition, IMPROMALT: Improving winter malting barley quality and developing an understanding of the interactions of introgressions with genetic background, Computational Statistical Methods for Population Genomics, G7779422816, Molecular Improvement of Disease Resistance in Barley (MIDRIB), MAGIC Yield: Deploying MAGIC Populations for Rapid Development of Genetic Markers for Yield Improvement in Elite UK Winter Wheat
OpenAlex ID: W2097499820