A High-Resolution Method for the Systematic Detection of EMS-Induced Mutations in a Sequenced Population

Abstract

AbstractThe precise detection of causal DNA mutations is very crucial for forward genetic studies. Several sources of errors contribute to false-positive detections by current variant-calling algorithms, and these impact associating phenotypes with genotypes. To improve the accuracy of mutation detection we propose and implemented a high-resolution binning method for the accurate detection of likely EMS-induced mutations in a sequenced mutant population. The approach also incorporates a novel clustering algorithm for detecting likely false negatives with high accuracy. Sorghum bicolor is a very valuable crop species with tremendous potential for uncovering novel gene functions associated with highly desirable agronomical traits. We demonstrate the precision of the proposed method in the detection of likely EMS-induced mutations in the publicly available low-cost sequencing of the M3 generation from 600 sorghum BTx623 mutants. The method detected 3,274,606 single nucleotide polymorphisms (SNPs) of which 96% (3,141,908) were G/C to A/T DNA substitutions, as expected by EMS-mutagenesis action. We demonstrated the general applicability of the method, and showed a high concordance, 94% (3,074,759) SNPs overlap between SAMtools-based and GATK-based variant-calling algorithms. We also implemented a novel clustering algorithm which uncovered evidence for an additional 223,048 likely false-negative shared EMS-induced mutations. The final 3,497,654 SNPs represents an 87% increase in SNPs detected in the previous analysis of the sorghum mutant population. Annotation of the final SNPs revealed 10,263 high impact and 136,639 moderate impact SNPs, including 7,217 stop-gained mutations, and an average of 12 stop-gained mutations per mutant. We have implemented a public search database for this new genetic resource of 30,285 distinct sorghum genes containing medium or high impact EMS-induced mutations. Seedstock for a select 486 of the 600 described mutants are publicly available in the Germplasm Resources Information Network (GRIN) database.