Method for accurately predicting BSA-seq candidate gene in low-density SNP genomic region

Abstract

The invention relates to the genome sequencing technology field and especially relates to a method for accurately predicting a BSA-seq candidate gene in a low-density SNP genomic region. BSA-seq has alow-density SNP region near a candidate interval. Through comparing SNPs between two parents, an SNP list is strictly filtered to find a low-density region, and then, a corresponding candidate interval when a confidence interval is 95% and a low-density candidate interval are used, and a genome annotation website is used to annotate the genes in a candidate region. A candidate region variation point function is annotated so as to obtain a gene with functional variation such as frameshift variation and the like, and the gene is determined to be a candidate gene. The method can be used to makeup for the false positive of the candidate region due to the region with a small difference in a genome, and the real candidate interval is acquired.

Description

【Technical field】 [0001] The invention relates to the technical field of genome sequencing, in particular to a method for accurately predicting BSA-seq candidate genes in a low-density SNP genome region. 【Background technique】 [0002] The bulked segregant analysis (BSA) method was first applied by R.W. MICHELMORE in lettuce in 1991, a method for rapidly locating genes controlling target traits. The method is to take a single plant with an extreme phenotype in the progeny population, pool its DNA in equal amounts to form two DNA pools, and then conduct marker polymorphism screening between the parents and the two pools, and the polymorphism obtained by screening the progeny population The genotype analysis of the markers can complete the positioning of the target gene, without the need for genotype analysis of each marker in the population. With the rise of high-pass sequencing technology, the BSA analysis method based on whole genome resequencing is widely used in plant im...

AI Technical Summary

Problems solved by technology

The basic idea of ​​BSA-seq usually refers to selecting extreme individuals from the mapping population, then mixing equal samples to form two DNA pools, performing high-throughput sequencing on the parents and pools, and identifying common genes in the parents and the two pools SNPs, calculate the genotype frequency of the same variable site in two mixed DNA pools and its difference, and use the difference to reflect the polymorphism of the marker between the pools, so as to realize the positioning of candidate genes. However, BSA-seq is relatively different from the whole Gene mapping technologies such as genome association analysis and genetic map have disadvantages such as low accuracy and low precision. How to accurately predict candidate genes in low-density SNP regions is a difficult problem we face, and more often in regions with small genome differences False positives for candidate regions that are easy to cause

Images