Statistical considerations for genomic selection
Genomic selection is becoming increasingly important in animal and plant breeding, and is attracting greater attention for human disease risk prediction. This review covers the most commonly used statistical methods and some extensions of them, i.e., ridge regression and genomic best linear unbiased...
Main Author: | Huimin KANG, Lei ZHOU, Jianfeng LIU |
---|---|
Format: | Article |
Language: | English |
Published: |
Higher Education Press
2017-09-01
|
Series: | Frontiers of Agricultural Science and Engineering |
Subjects: | |
Online Access: | http://academic.hep.com.cn/fase/fileup/2095-7505/PDF/1498800634634-1990637865.pdf |
Similar Items
-
Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers
by: Dennis N. Lozada, et al.
Published: (2023-12-01) -
A Bioinformatics Pipeline to Identify a Subset of SNPs for Genomics-Assisted Potato Breeding
by: Catja Selga, et al.
Published: (2020-12-01) -
Analysis of selection signatures in the beef cattle genome
by: Nina Moravčíková, et al.
Published: (2019-12-01) -
Genome selection in fruit breeding: application to table grapes
by: Alexandre Pio Viana, et al.
Published: (2016-04-01) -
A Comprehensive Comparison of Haplotype-Based Single-Step Genomic Predictions in Livestock Populations With Different Genetic Diversity Levels: A Simulation Study
by: Andre C. Araujo, et al.
Published: (2021-10-01)