Method for estimating breeding value by fitting genome with non-additive effect

A technology of additive effect and breeding value, applied in the fields of genomics, proteomics, instruments, etc., can solve the problems of complex genetic composition, and achieve the effect of good flexibility, improved prediction accuracy, and improved estimation accuracy.

Pending Publication Date: 2020-11-03
JIMEI UNIV
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Problems solved by technology

But in hybrids and pure-line breeds, the actual genetic makeup is complex

Method used

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  • Method for estimating breeding value by fitting genome with non-additive effect
  • Method for estimating breeding value by fitting genome with non-additive effect
  • Method for estimating breeding value by fitting genome with non-additive effect

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Embodiment 1

[0047] The experimental data is the wheat data set provided by CIMMYT Global Wheat Project, which contains 599 wheat lines. The wheat dataset includes the average yield of wheat in four environments, and the dataset has four objects: wheat.Y, wheat.A, wheat.X, and wheat.set. wheat.Y is the two-year average yield of wheat lines; wheat.A is the molecular relationship matrix of the same family; wheat.X is the DArT marker genotype, and the data comes from pure breeds. For the DArT marker, an allele is coded by 1 or 0, indicating its presence or absence, respectively. The wheat set represents 10 sets of observations that are disjoint from each other.

[0048] The specific method of calculating the genome estimated breeding value for wheat in each observation set is as follows:

[0049] 1) Data collection: sampling the wheat individuals to be judged, and measuring the phenotype value and genotype data of each individual;

[0050] 2) data grouping: the wheat individuals in step 1)...

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Abstract

The invention discloses a new method for estimating breeding value by fitting genome with non-additive effect. According to the method, an additive effect prediction model and a non-additive effect prediction model in genome selection are combined into a meta-algorithm of a prediction model,. Compared with a prediction model only fitting an additive effect, the method disclosed by the invention can generally obtain a better prediction effect. The method comprises the following specific steps of acquiring complete genotype information and phenotype information of a single group; randomly dividing a training group and a test group, and performing iterative training on the training group through a hybrid algorithm MixPGV; obtaining an expected additive effect value and an expected non-additive effect value of each SNP site, and performing accumulating to obtain an additive genome estimated breeding value GEBVAdd and a non-additive genome estimated breeding value GEBVNon-Add of an MixPGV prediction model; and accumulating the additive genome estimated breeding value GEBVAdd and the non-additive genome estimated breeding value GEBVNon-Add to obtain a genome estimated breeding value GEBVof the group, and finally calculating a correlation coefficient of the genome estimated breeding value GEBV and a real breeding value to obtain estimation accuracy.

Description

technical field [0001] The invention relates to the field of genome breeding, in particular to a method for estimating the breeding value of a genome fitting non-additive effects. Background technique [0002] Genomic selection (GS) is the better prediction of plant or animal phenotypes for polygenic traits through the use of genome-wide markers. This method exploits linkage disequilibrium (LD) between markers and QTLs to capture small QTL effects. Unlike pedigree-based prediction methods and marker-assisted selection prediction methods based on a small number of markers, genomic selection can predict the breeding value of individuals with unknown phenotypes, and this property allows it to be applied to early selection, which can be greatly improved. Reduce breeding cycles and speed up selection progress. At present, a variety of prediction models have been used to predict the genomic estimated breeding value (GEBV) of traits, such as based on BLUP algorithm (best linear u...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16B20/20G16B20/30
CPCG16B20/20G16B20/30
Inventor 顾林林姜丹方铭王志勇
Owner JIMEI UNIV
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