Abstract
AbstractLarge-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present , a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.
Publisher
Springer Science and Business Media LLC
Reference80 articles.
1. Araus JL, Kefauver SC, Zaman-Allah M, Olsen MS, Cairns JE. Translating High-Throughput Phenotyping into Genetic Gain. Trends Plant Sci. 2018; 23(5):451–66.
2. Koltes JE, Cole JB, Clemmens R, Dilger RN, Kramer LM, Lunney JK, McCue ME, McKay SD, Mateescu RG, Murdoch BM, Reuter R, Rexroad CE, Rosa GJM, Serão NVL, White SN, Woodward-Greene MJ, Worku M, Zhang H, Reecy JM. A vision for development and utilization of high-throughput phenotyping and big data analytics in livestock. Front Genet. 2019; 10:1197. https://doi.org/10.3389/fgene.2019.01197.
3. Rutkoski J, Poland J, Mondal S, Autrique E, Pérez LG, Crossa J, Reynolds M, Singh R. Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat. G3 Genes Genomes Genetics. 2016; 6(9):2799–808.
4. Neethirajan S. Recent advances in wearable sensors for animal health management. Sens and Bio-Sens Res. 2017; 12:15–29.
5. Schrag TA, Westhues M, Schipprack W, Seifert F, Thiemann A, Scholten S, Melchinger AE. Beyond genomic prediction: combining different types of omics data can improve prediction of hybrid performance in maize. Genetics. 2018; 208(4):1373–85.
Cited by
33 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献