A Bayesian random regression method using mixture priors for genome‐enabled analysis of time‐series high‐throughput phenotyping data
Author:
Affiliation:
1. Dep. of Animal Science Univ. of California Davis Davis CA 95616 USA
2. Dep. of Animal and Poultry Sciences Virginia Polytechnic Institute and State Univ. Blacksburg VA 24061 USA
Publisher
Wiley
Subject
Plant Science,Agronomy and Crop Science,Genetics
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/tpg2.20228
Reference41 articles.
1. Multi-trait random regression models increase genomic prediction accuracy for a temporal physiological trait derived from high-throughput phenotyping
2. Modelling lactation curves of dairy goats by fitting random regression models using legendre polynomials or b‐splines;Brito L. F.;Canadian Journal of Animal Science,2017
3. Leveraging Breeding Values Obtained from Random Regression Models for Genetic Inference of Longitudinal Traits
4. Utilizing random regression models for genomic prediction of a longitudinal trait derived from high‐throughput phenotyping
5. Genome-Wide Association Analyses Based on Broadly Different Specifications for Prior Distributions, Genomic Windows, and Estimation Methods
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