عنوان

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

Number

TL34q919vf

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

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

General Material Designation

[Thesis]

First Statement of Responsibility

Lin, Chen

Subsequent Statement of Responsibility

Xu, Shizhong

Date of Publication, Distribution, etc.

2019

Body granting the degree

Xu, Shizhong

Text preceding or following the note

2019

Text of Note

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.

Zareie, Hosein

Lin, Chen

UC Riverside

Electronic name

p

[Thesis]

276903

a

Y