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Multi-character correlation analysis method based on mixed linear model

A technology of association analysis and traits, applied in genomics, special data processing applications, instruments, etc., can solve problems such as inability to analyze shape, large amount of calculation, inability to analyze the interaction between genes, and the interaction effect between genes and the environment. Achieve the effect of accurate QTS position estimation, robust effect estimation and low false positive

Active Publication Date: 2016-07-06
ZHEJIANG UNIV
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However, these methods are either computationally intensive, or cannot analyze three or m

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  • Multi-character correlation analysis method based on mixed linear model
  • Multi-character correlation analysis method based on mixed linear model
  • Multi-character correlation analysis method based on mixed linear model

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Abstract

The invention discloses a multi-character correlation analysis method based on a mixed linear model. The method comprises the following steps: constructing a statistical genetic model; determining a unit point SNP mark with a significant effect; determining two interaction epistasis SNP marks with a significant interaction effect; and estimating a genetic effect. A multi-character whole genome correlation analysis method based on a multivariate mixed linear model provided by the invention comprehensively utilizes variation information of a plurality of genetic correlated characters, and compared with a single-character analysis method, the multi-character whole genome correlation analysis method has a higher analysis effect and lower false positive, the QTS location estimation is more accurate, and the effect estimation is more steady.

Description

technical field [0001] The invention relates to the technical field of multi-character joint positioning, in particular to a multi-character association analysis method based on a mixed linear model. Background technique [0002] Genome-wide association analysis has become a standard effective method for exploring the genetic structure of complex traits and explaining the basis of genetic variation in quantitative traits. The main problem in the association analysis method is to explain the dependence of the data, including the dependence between individuals and the dependence between sites. The mixed linear model contains both fixed effects and random effects, which can effectively explain the group structure existing in big data, including group stratification and kinship. [0003] However, most of the association analysis methods only focus on a single quantitative trait, and cannot comprehensively consider multiple genetically related traits for joint analysis, cannot a...

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Application Information

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IPC IPC(8): G06F19/18
CPCG16B20/00
Inventor 徐海明刘守业祁婷朱智宏朱军
Owner ZHEJIANG UNIV
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