Gene-environment interaction correlation analysis method based on large sample

Abstract

The invention discloses a gene-environment interaction correlation analysis method based on a large sample, and the method comprises the steps: building a gene-environment detection linear hybrid model through collecting the gene sequence, environment information and phenotype information of a target individual, and solving the gene-environment detection linear hybrid model based on a preprocessing conjugate gradient method and a moment estimation method, the method comprises the following steps: constructing a gene-environment interaction score test statistical magnitude obeying chi-square distribution, and then applying the gene-environment interaction score test statistical magnitude to judge whether an interaction effect exists in a gene sequence and environment information, so that the method can be used for quickly identifying the influence of multiple pieces of environment information on the gene of the gene-environment interaction site, and can be used for quickly identifying the gene of the gene-environment interaction site. The method is suitable for gene-environment interaction correlation rapid detection of large samples.

Description

technical field [0001] The invention relates to the field of genetic algorithms, in particular to a gene-environment interaction correlation analysis method based on large samples. Background technique [0002] Many human genes are regulated by environmental factors, including diet, physical activity and other lifestyle covariates, of which gene-environment interaction (GEI) is worth studying. Genome datasets with millions of participants are currently being collected on an unprecedented scale, including the All of Us Research Program (~1 million participants), UK BioBank (~500,000 participants) and the Million Veteran Program (more than 1 million participants). While such large amounts of genomic data provide excellent opportunities for gene-environment interaction (GEI) studies, the lag in the development of GEI analysis tools has remained constant and is completely ill-suited to the needs of gene-environment interaction investigations. Most existing methods are not opti...

AI Technical Summary

Problems solved by technology

Although such large genomic data provide excellent opportunities for gene-environment interaction (GEI) research, the lag in the development of GEI analysis tools has remained constant, completely unable to adapt to the needs of gene-environment interaction investigations

Most existing methods are not optimized for analysis of hundreds of thousands or millions of individuals, are computationally resource prohibitive, and lack multithreading capabilities to reduce runtime

Although the QUICKTEST method, PLINK2 method, CGEN method and GxEScan method can perform effective analysis in large samples, they cannot perform robust reasoning and calculation of multiple interaction items; the SPAGE method currently only supports the analysis of binary traits and lacks multi-threaded computing capabilities

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