减少等位基因发现中假阳性的分析策略

Significance

Genetic analyses require allelic markers, which are often DNA polymorphisms and can be analyzed by using short reads from high-throughput sequencing. Therefore, accuracy in genetic studies depends on correct identification of DNA polymorphic markers, but genomic structural variants increase the complexity of allelic detection and must be carefully accounted for to avoid errors. Here, we examine potential mistakes in single-nucleotide polymorphism calling caused by structural variants and their impact on detecting meiotic recombination events. Our results demonstrate that it is crucial to examine structural variants in genetic analysis with DNA marker detection by using short reads, with implications for a wide range of genetic analyses.

Abstract

DNA polymorphisms are important markers in genetic analyses and are increasingly detected by using genome resequencing. However, the presence of repetitive sequences and structural variants can lead to false positives in the identification of polymorphic alleles. Here, we describe an analysis strategy that minimizes false positives in allelic detection and present analyses of recently published resequencing data from Arabidopsis meiotic products and individual humans. Our analysis enables the accurate detection of sequencing errors, small insertions and deletions (indels), and structural variants, including large reciprocal indels and copy number variants, from comparisons between the resequenced and reference genomes. We offer an alternative interpretation of the sequencing data of meiotic products, including the number and type of recombination events, to illustrate the potential for mistakes in single-nucleotide polymorphism calling. Using these examples, we propose that the detection of DNA polymorphisms using resequencing data needs to account for nonallelic homologous sequences.

Ji Qi, Yamao Chen, Gregory P. Copenhaver, and Hong Ma, Detection of genomic variations and DNA polymorphisms and impact on analysis of meiotic recombination and genetic mapping