Dataset Information

Efficient molecular marker design using the MaizeGDB Mo17 SNPs and Indels track.

ABSTRACT: Positional cloning in maize (Zea mays) requires development of markers in the region of interest. We found that primers designed to amplify annotated insertion-deletion polymorphisms of seven base pairs or greater between B73 and Mo17 produce polymorphic markers at a 97% frequency with 49% of the products showing co-dominant fragment length polymorphisms. When the same polymorphisms are used to develop markers for B73 and W22 or Mo17 and W22 mapping populations, 22% and 31% of markers are co-dominant, respectively. There are 38,223 Indel polymorphisms that can be converted to markers providing high-density coverage throughout the maize genome. This strategy significantly increases the efficiency of marker development for fine-mapping in maize.

SUBMITTER: Settles AM

PROVIDER: S-EPMC4065257 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Efficient molecular marker design using the MaizeGDB Mo17 SNPs and Indels track.

Settles A Mark AM Bagadion Alyssa M AM Bai Fang F Zhang Junya J Barron Brady B Leach Kristen K Mudunkothge Janaki S JS Hoffner Cassandra C Bihmidine Saadia S Finefield Erin E Hibbard Jaime J Dieter Emily E Malidelis I Alex IA Gustin Jeffery L JL Karoblyte Vita V Tseung Chi-Wah CW Braun David M DM

G3 (Bethesda, Md.) 20140417 6

Positional cloning in maize (Zea mays) requires development of markers in the region of interest. We found that primers designed to amplify annotated insertion-deletion polymorphisms of seven base pairs or greater between B73 and Mo17 produce polymorphic markers at a 97% frequency with 49% of the products showing co-dominant fragment length polymorphisms. When the same polymorphisms are used to develop markers for B73 and W22 or Mo17 and W22 mapping populations, 22% and 31% of markers are co-dom ...[more]

PMID: 24747759

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Project description:BACKGROUND: Genome-wide detection of single feature polymorphisms (SFP) in swine using transcriptome profiling of day 25 placental RNA by contrasting probe intensities from either Meishan or an occidental composite breed with Affymetrix porcine microarrays is presented. A linear mixed model analysis was used to identify significant breed-by-probe interactions. RESULTS: Gene specific linear mixed models were fit to each of the log2 transformed probe intensities on these arrays, using fixed effects for breed, probe, breed-by-probe interaction, and a random effect for array. After surveying the day 25 placental transcriptome, 857 probes with a q-value < or = 0.05 and |fold change| > or = 2 for the breed-by-probe interaction were identified as candidates containing SFP. To address the quality of the bioinformatics approach, universal pyrosequencing assays were designed from Affymetrix exemplar sequences to independently assess polymorphisms within a subset of probes for validation. Additionally probes were randomly selected for sequencing to determine an unbiased confirmation rate. In most cases, the 25-mer probe sequence printed on the microarray diverged from Meishan, not occidental crosses. This analysis was used to define a set of highly reliable predicted SFPs according to their probability scores. CONCLUSION: By applying a SFP detection method to two mammalian breeds for the first time, we detected transition and transversion single nucleotide polymorphisms, as well as insertions/deletions which can be used to rapidly develop markers for genetic mapping and association analysis in species where high density genotyping platforms are otherwise unavailable.SNPs and INDELS discovered by this approach have been publicly deposited in NCBI's SNP repository dbSNP. This method is an attractive bioinformatics tool for uncovering breed-by-probe interactions, for rapidly identifying expressed SNPs, for investigating potential functional correlations between gene expression and breed polymorphisms, and is robust enough to be used on any Affymetrix gene expression platform.

Dataset Information

Efficient molecular marker design using the MaizeGDB Mo17 SNPs and Indels track.

Publications

Efficient molecular marker design using the MaizeGDB Mo17 SNPs and Indels track.

Similar Datasets

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