scholarly journals Snp_blup_rel: software for calculating individual animal SNP-BLUP model reliabilities

2020 ◽  
Vol 29 (4) ◽  
Author(s):  
Hafedh Ben Zaabza ◽  
Esa A. Mäntysaari ◽  
Ismo Strandén

The snp_blup_rel program computes model reliabilities for genomic breeding values. The program assumes a single trait SNP-BLUP model where the breeding value can include a residual polygenic (RPG) effect. The reliability calculation requires elements of the inverse of the mixed model equations (MME). The calculation has three steps: 1) MME calculation, 2) MME coefficient matrix inversion, and 3) reliability computation. When needed, the inverted matrix can be saved after step 2. Step 3 can be used separately to new genotypes which do not contribute information to Step 2. When an RPG effect is included, an approximate method based on Monte Carlo sampling is applied. This reduces the MME matrix size and allows including many genotyped individuals. The program is written in Fortran 90/95, and uses LAPACK subroutines which enable multi-threaded parallel computing. The program is efficient in terms of computing time and memory requirements, and can be used to analyze even large genomic data. Thus, the program can be used in calculating model reliabilities for large national genomic evaluations.

2014 ◽  
Vol 97 (1) ◽  
pp. 537-542 ◽  
Author(s):  
J.E. Pryce ◽  
O. Gonzalez-Recio ◽  
J.B. Thornhill ◽  
L.C. Marett ◽  
W.J. Wales ◽  
...  

2013 ◽  
Vol 55 (1) ◽  
pp. 13-18 ◽  
Author(s):  
Seung Soo Lee ◽  
Seung Hwan Lee ◽  
Tae Jeong Choi ◽  
Yun Ho Choy ◽  
Kwang Hyun Cho ◽  
...  

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Yu-Ye Feng ◽  
Qing-Biao Wu

For solving the large sparse linear systems with 2 × 2 block structure, the generalized successive overrelaxation (GSOR) iteration method is an efficient iteration method. Based on the GSOR method, the PGSOR method introduces a preconditioned matrix with a new parameter for the coefficient matrix which can enhance the efficiency. To solve the nonlinear systems in which the Jacobian matrices are complex and symmetric with the block two-by-two form, we try to use the PGSOR method as an inner iteration, with the help of the modified Newton method as an efficient outer iteration method. This new method is called the modified Newton-PGSOR (MN-PGSOR) method. Local convergence properties of the MN-PGSOR are analyzed under the Hölder condition. Finally, we give the comparison of our new method with some previous methods in the numerical results. The MN-PGSOR method is superior in both iteration steps and computing time.


2021 ◽  
Vol 12 ◽  
Author(s):  
Mohammad Ali Nilforooshan ◽  
Dorian Garrick

Reduced models are equivalent models to the full model that enable reduction in the computational demand for solving the problem, here, mixed model equations for estimating breeding values of selection candidates. Since phenotyped animals provide data to the model, the aim of this study was to reduce animal models to those equations corresponding to phenotyped animals. Non-phenotyped ancestral animals have normally been included in analyses as they facilitate formation of the inverse numerator relationship matrix. However, a reduced model can exclude those animals and obtain identical solutions for the breeding values of the animals of interest. Solutions corresponding to non-phenotyped animals can be back-solved from the solutions of phenotyped animals and specific blocks of the inverted relationship matrix. This idea was extended to other forms of animal model and the results from each reduced model (and back-solving) were identical to the results from the corresponding full model. Previous studies have been mainly focused on reduced animal models that absorb equations corresponding to non-parents and solve equations only for parents of phenotyped animals. These two types of reduced animal model can be combined to formulate only equations corresponding to phenotyped parents of phenotyped progeny.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Marie Lillehammer ◽  
Rama Bangera ◽  
Marcela Salazar ◽  
Sergio Vela ◽  
Edna C. Erazo ◽  
...  

AbstractWhite spot syndrome virus (WSSV) causes major worldwide losses in shrimp aquaculture. The development of resistant shrimp populations is an attractive option for management of the disease. However, heritability for WSSV resistance is generally low and genetic improvement by conventional selection has been slow. This study was designed to determine the power and accuracy of genomic selection to improve WSSV resistance in Litopenaeus vannamei. Shrimp were experimentally challenged with WSSV and resistance was evaluated as dead or alive (DOA) 23 days after infestation. All shrimp in the challenge test were genotyped for 18,643 single nucleotide polymorphisms. Breeding candidates (G0) were ranked on genomic breeding values for WSSV resistance. Two G1 populations were produced, one from G0 breeders with high and the other with low estimated breeding values. A third population was produced from “random” mating of parent stock. The average survival was 25% in the low, 38% in the random and 51% in the high-genomic breeding value groups. Genomic heritability for DOA (0.41 in G1) was high for this type of trait. The realised genetic gain and high heritability clearly demonstrates large potential for further genetic improvement of WSSV resistance in the evaluated L. vannamei population using genomic selection.


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