An improved pathway-based genome-wide association analysis algorithm

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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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 individual SNP effects but large joint effects. Moreover, this algorithm prefers to contain more SNPs Genes and Pathways of More Genes

[0009] (2) Use all SNP effects in the gene to construct statistics. This algorithm not only has too much calculation, but also easily causes false positives.

[0010] (3) After sorting by effect, use the effects of the first K SNPs to construct statistics. This algorithm is based on the assumption that SNPs are independent, but in fact there is linkage disequilibrium between SNPs, and the interaction effect of SNPs will be The accuracy of association analysis is greatly reduced

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