Modified whole genome correlation analysis algorithm based on channel

An association analysis and genome-wide technology, applied in the field of pathway-based genome-wide association analysis algorithms, can solve problems such as SNP linkage disequilibrium, reduced accuracy of association analysis, SNP or genes cannot explain genetic variation, etc., to achieve SNP linkage, The effect of removing interaction effects

Inactive Publication Date: 2015-05-13
INST OF ANIMAL SCI OF CHINESE ACAD OF AGRI SCI
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Problems solved by technology

[0005] (1) For some traits, no SNP can pass multiple tests, so that gene mapping cannot be performed, or even if some SNPs pass the test, it is found that they do not show any biological significance
[0006] (2) Studies have shown that the phenotypic variation of complex quantitative traits is often not determined by a few SNPs or genes, so that the SNPs or genes found by the single-point regression research algorithm cannot explain all genetic variation
[0008] (1) Use the most significant SNP effect to construct gene statistics. This algorithm may not be able to detect those SNP sites with small indivi

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  • Modified whole genome correlation analysis algorithm based on channel
  • Modified whole genome correlation analysis algorithm based on channel
  • Modified whole genome correlation analysis algorithm based on channel

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Embodiment Construction

[0062]We use the idea of ​​principal component analysis to improve the existing pathway-based genome-wide association analysis algorithm, and use a new formula to construct gene statistics. The improved algorithm takes the SNP interaction effect into the pathway-based GWAS analysis, which can effectively reduce the impact of SNP linkage on the results.

[0063] The present invention will be specifically introduced below in conjunction with the accompanying drawings and specific embodiments.

[0064] We selected 807 beef cattle in the Ulagai area of ​​Inner Mongolia as a reference group, and collected the phenotypic data of the two traits of live weight and eye muscle area before slaughter. The calculation results of the data of the two traits are shown in Table 1.

[0065] Table 1 Basic information of the phenotype data of two meat quality traits

[0066] Phenotype

average

standard deviation

standard error

maximum value

minimum value

live ...

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Abstract

The invention discloses a modified whole genome correlation analysis algorithm based on a channel. The correlation analysis algorithm adopts a principal component analysis method and a maximal mean value method to build gene statistic for first time, removes interaction effect among SNP, and effectively solves the problem of SNP chain in genes; the strategy is applied to Simon Tal beef cattle GWAS data; remarkable correlation between two passages (a gamma propalanine channel and an NAFLD channel) and two characters (live weight before slaughter and area of eye muscle) is founded; reliable reference is provided for beef cattle modification breeding; reliable theoretical basis is provided for further molecular verification.

Description

technical field [0001] The invention relates to a pathway-based whole-genome association analysis algorithm, in particular to an improved pathway-based whole-genome association analysis algorithm, belonging to the field of biotechnology. Background technique [0002] With the development of sequencing technology and the popularity of high-density SNP chips, genome-wide association analysis (GWAS) has increasingly become a powerful tool for human disease research and animal breeding. [0003] Traditional genome-wide association analysis only focuses on a very small number of loci that strictly meet the statistical significance level of "genome-wide" in whole-genome data. However, these very few loci usually only explain a small part of genetic variation. There is a vast amount of remaining genetic information that remains to be unearthed. [0004] With the in-depth research on GWAS, it has gradually exposed the following defects, specifically: [0005] (1) For some traits, ...

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Application Information

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IPC IPC(8): G06F19/18
Inventor 高会江樊惠中李俊雅夏江威吴洋张路培高雪陈燕郭鹏
Owner INST OF ANIMAL SCI OF CHINESE ACAD OF AGRI SCI
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