Dataset Information

Comparison of SNP Calling Pipelines and NGS Platforms to Predict the Genomic Regions Harboring Candidate Genes for Nodulation in Cultivated Peanut.

ABSTRACT: Cultivated peanut (Arachis hypogaea L.) forms root nodules to enable a symbiotic relationship with rhizobia for biological nitrogen fixation. To understand the genetic factors of peanut nodulation, it is fundamental to genetically map and clone the genes involved in nodulation. For genetic mapping, high throughput genotyping with a large number of polymorphic markers is critical. In this study, two sets of sister recombinant inbred lines (RILs), each containing a nodulating (Nod+) and non-nodulating (Nod-) line, and their Nod+ parental lines were extensively genotyped. Several next generation sequencing (NGS) methods including target enrichment sequencing (TES), RNA-sequencing (RNA-seq), genotyping by sequencing (GBS), and the 48K Axiom Arachis2 SNP array, and various analysis pipelines were applied to identify single nucleotide polymorphisms (SNP) among the two sets of RILs and their parents. TES revealed the largest number of homozygous SNPs (15,947) between the original parental lines, followed by the Axiom Arachis2 SNP array (1,887), RNA-seq (1,633), and GBS (312). Among the five SNP analysis pipelines applied, the alignment to A/B genome followed by HAPLOSWEEP revealed the largest number of homozygous SNPs and highest concordance rate (79%) with the array. A total of 222 and 1,200 homozygous SNPs were polymorphic between the Nod+ and Nod- sister RILs and between their parents, respectively. A graphical genotype map of the sister RILs was constructed with these SNPs, which demonstrated the candidate genomic regions harboring genes controlling nodulation across the whole genome. Results of this study mainly provide the pros and cons of NGS and SNP genotyping platforms for genetic mapping in peanut, and also provide potential genetic resources to narrow down the genomic regions controlling peanut nodulation, which would lay the foundation for gene cloning and improvement of nitrogen fixation in peanut.

SUBMITTER: Peng Z

PROVIDER: S-EPMC7105825 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Comparison of SNP Calling Pipelines and NGS Platforms to Predict the Genomic Regions Harboring Candidate Genes for Nodulation in Cultivated Peanut.

Peng Ze Z Zhao Zifan Z Clevenger Josh Paul JP Chu Ye Y Paudel Dev D Ozias-Akins Peggy P Wang Jianping J

Frontiers in genetics 20200324

Cultivated peanut (<i>Arachis hypogaea</i> L.) forms root nodules to enable a symbiotic relationship with rhizobia for biological nitrogen fixation. To understand the genetic factors of peanut nodulation, it is fundamental to genetically map and clone the genes involved in nodulation. For genetic mapping, high throughput genotyping with a large number of polymorphic markers is critical. In this study, two sets of sister recombinant inbred lines (RILs), each containing a nodulating (Nod+) and non ...[more]

PMID: 32265983

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Project description:Single nucleotide polymorphisms (SNPs) are widely used in genome-wide association studies and population genetics analyses. Next-generation sequencing (NGS) has become convenient, and many SNP-calling pipelines have been developed for human NGS data. We took advantage of a gap knowledge in selecting the appropriated SNP calling pipeline to handle with high-throughput NGS data. To fill this gap, we studied and compared seven SNP calling pipelines, which include 16GT, genome analysis toolkit (GATK), Bcftools-single (Bcftools single sample mode), Bcftools-multiple (Bcftools multiple sample mode), VarScan2-single (VarScan2 single sample mode), VarScan2-multiple (VarScan2 multiple sample mode) and Freebayes pipelines, using 96 NGS data with the different depth gradients of approximately 5X, 10X, 20X, 30X, 40X, and 50X coverage from 16 Rhode Island Red chickens. The sixteen chickens were also genotyped with a 50K SNP array, and the sensitivity and specificity of each pipeline were assessed by comparison to the results of SNP arrays. For each pipeline, except Freebayes, the number of detected SNPs increased as the input read depth increased. In comparison with other pipelines, 16GT, followed by Bcftools-multiple, obtained the most SNPs when the input coverage exceeded 10X, and Bcftools-multiple obtained the most when the input was 5X and 10X. The sensitivity and specificity of each pipeline increased with increasing input. Bcftools-multiple had the highest sensitivity numerically when the input ranged from 5X to 30X, and 16GT showed the highest sensitivity when the input was 40X and 50X. Bcftools-multiple also had the highest specificity, followed by GATK, at almost all input levels. For most calling pipelines, there were no obvious changes in SNP numbers, sensitivities or specificities beyond 20X. In conclusion, (1) if only SNPs were detected, the sequencing depth did not need to exceed 20X; (2) the Bcftools-multiple may be the best choice for detecting SNPs from chicken NGS data, but for a single sample or sequencing depth greater than 20X, 16GT was recommended. Our findings provide a reference for researchers to select suitable pipelines to obtain SNPs from the NGS data of chickens or nonhuman animals.

Dataset Information

Comparison of SNP Calling Pipelines and NGS Platforms to Predict the Genomic Regions Harboring Candidate Genes for Nodulation in Cultivated Peanut.

Publications

Comparison of SNP Calling Pipelines and NGS Platforms to Predict the Genomic Regions Harboring Candidate Genes for Nodulation in Cultivated Peanut.

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