Proceedings of the 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)

Analysis and Study of the Correlation of Genetic Loci

Authors
Yang Liu, Xiaojun Bai, Xiaofeng Kang
Corresponding Author
Yang Liu
Available Online December 2016.
DOI
https://doi.org/10.2991/mcei-16.2016.237How to use a DOI?
Keywords
SNP loci; Linear mixture model; Genome-wide association analysis
Abstract
Based on information of 1000 samples provided by single nucleotide polymorphisms, analyzing the existing SNP data, using the optimized linear mixed model for data screening of correlation analysis, the coding mode of each given locus bases (A, T, C, G) are encoded into numerical encoding, with correlation analysis of collection of evolutionary algorithms, the corresponding figure in Manhattan and risk point and the position of the gene are obtained for further operations of the transferred numerical language, and finally got the image of the corresponding computing program operation and results. By carrying out statistical analysis and inspection for results, the score genotype data are more reasonable and accurate to explain theoretically the rationality of the discovered disease locus and gene.
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Proceedings
2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)
Part of series
Advances in Intelligent Systems Research
Publication Date
December 2016
ISBN
978-94-6252-282-4
ISSN
1951-6851
DOI
https://doi.org/10.2991/mcei-16.2016.237How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yang Liu
AU  - Xiaojun Bai
AU  - Xiaofeng Kang
PY  - 2016/12
DA  - 2016/12
TI  - Analysis and Study of the Correlation of Genetic Loci
BT  - 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)
PB  - Atlantis Press
SP  - 1132
EP  - 1137
SN  - 1951-6851
UR  - https://doi.org/10.2991/mcei-16.2016.237
DO  - https://doi.org/10.2991/mcei-16.2016.237
ID  - Liu2016/12
ER  -