A Stochastic Restricted Maximum Likelihood Method for Genomic Selection and Genome-Wide Association ...

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" A Stochastic Restricted Maximum Likelihood Method for Genomic Selection and Genome-Wide Association Studies "
Lin, Chen Xu, Shizhong

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	897832
Doc. No	:	TL34q919vf
Main Entry	:	Zareie, Hosein
Title & Author	:	A Stochastic Restricted Maximum Likelihood Method for Genomic Selection and Genome-Wide Association Studies\ Lin, ChenXu, Shizhong
Date	:	2019
student score	:	2019
Abstract	:	Genomic selection is a marker-assisted methodology that dramatically decreases the cost of measuring phenotypes by using the whole-genome information to predict and select desirable individuals. In plant breeding, it plays an important role to speed up the breeding cycles. Modern techniques make obtaining marker information from the entire genome feasible. However, it results in high dimensionality of predictors when we implement a mathematical model to estimate the parameters and predict future crosses. Many statistical models including variable selection models can address this problem and have been applied in genomic selection. Variable selection models can also be applied in GWAS which is a powerful tool to discover the association between genetic variation and variation in quantitative traits. A novel statistical approach based on BLUP was proposed to be implemented in both genomic selection and GWAS. The general idea of the proposed approach is using an algorithm to divide markers into the small effect group and the large effect group. Markers within the large effect group can be potentially significant markers associated with the analyzed phenotypic trait. In Chapter 3, we used simulated data and two real-world data sets to demonstrate the distinctions among six statistical methods for genomic selection. In addition, the proposed model was applied in GWAS based on another simulated data, and the proposed model is superior to the other two variable selection models.
Added Entry	:	Lin, Chen
Added Entry	:	UC Riverside
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