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An evaluation of the interpretability and predictive performance of the BayesR model for genomic prediction

Fanny Mollandin, Andrea Rau, View ORCID ProfilePascal Croiseau
doi: https://doi.org/10.1101/2020.10.23.351700
Fanny Mollandin
*Université Paris-Saclay, INRAE, AgroParisTech, GABI
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  • For correspondence: fanny.mollandin@inrae.fr
Andrea Rau
*Université Paris-Saclay, INRAE, AgroParisTech, GABI
†BioEcoAgro Joint Research Unit, INRAE, Université de Liège, Université de Lille, Université de Picardie Jules Verne
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Pascal Croiseau
*Université Paris-Saclay, INRAE, AgroParisTech, GABI
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  • ORCID record for Pascal Croiseau
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ABSTRACT

Technological advances and decreasing costs have led to the rise of increasingly dense genotyping data, making feasible the identification of potential causal markers. Custom genotyping chips, which combine medium-density genotypes with a custom genotype panel, can capitalize on these candidates to potentially yield improved accuracy and interpretability in genomic prediction. A particularly promising model to this end is BayesR, which divides markers into four effect size classes. BayesR has been shown to yield accurate predictions and promise for quantitative trait loci (QTL) mapping in real data applications, but an extensive benchmarking in simulated data is currently lacking. Based on a set of real genotypes, we generated simulated data under a variety of genetic architectures, phenotype heritabilities, and we evaluated the impact of excluding or including causal markers among the genotypes. We define several statistical criteria for QTL mapping, including several based on sliding windows to account for linkage disequilibrium. We compare and contrast these statistics and their ability to accurately prioritize known causal markers. Overall, we confirm the strong predictive performance for BayesR in moderately to highly heritable traits, particularly for 50k custom data. In cases of low heritability or weak linkage disequilibrium with the causal marker in 50k genotypes, QTL mapping is a challenge, regardless of the criterion used. BayesR is a promising approach to simultaneously obtain accurate predictions and interpretable classifications of SNPs into effect size classes. We illustrated the performance of BayesR in a variety of simulation scenarios, and compared the advantages and limitations of each.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 23, 2020.
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An evaluation of the interpretability and predictive performance of the BayesR model for genomic prediction
Fanny Mollandin, Andrea Rau, Pascal Croiseau
bioRxiv 2020.10.23.351700; doi: https://doi.org/10.1101/2020.10.23.351700
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An evaluation of the interpretability and predictive performance of the BayesR model for genomic prediction
Fanny Mollandin, Andrea Rau, Pascal Croiseau
bioRxiv 2020.10.23.351700; doi: https://doi.org/10.1101/2020.10.23.351700

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