Construction of a high-throughput rat genetic mapping system with 466 arbitrarily primed-representational difference analysis markers

Yamashita, Satoshi; Yoshida, Yukinari; Kurahashi, Ayako; Sügimura, Takashi

doi:10.1007/s003350010187

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…Further finding of cultivar-specific sequences may enable the identification of a cultivar among all the cultivars grown in Japan. Yamashita et al (2000) have produced 466 markers for dot-blot analysis by representational difference analysis (RDA; Lisitsyn et al 1993) and have constructed a genetic map of the rat. In rice, 13 RFLP markers, including cultivar-specific sequences, have been produced by the RDA method (Kajiya et al 1996).…”

Section: Discussionmentioning

confidence: 99%

“…However, the electrophoresis, which is indispensable for the analysis of these DNA markers, requires much time and labor. Dot-blot analysis has been employed for construction of a rat genetic map (Yamashita et al 2000), identification of S haplotypes in Brassica (Fujimoto and Nishio 2003), and identification of species in Dendrobium . Since this method does not require electrophoresis, nor even PCR in some cases, not only time and labor but also expense can be saved.…”

Section: Introductionmentioning

confidence: 99%

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Dot-blot Analysis for Identification of <i>japonica</i> Rice Cultivars and Genotyping of Recombinant Inbred Lines

2005

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Dot-blot analysis of rice cultivars was performed using 14 cultivar-specific sequences identified previously by AFLP and PCR-RF-SSCP analyses as probes. DNA samples prepared from leaves by a simple method without the use of organic solvent were found to be usable for dot-blot analysis. Although more than a single grain of rice was needed as material for direct dot-blotting of genomic DNA, PCR amplification of DNA enabled detection of cultivar-specific signals in a DNA sample from a single grain by dot-blot analysis. All 31 rice cultivars examined could be distinguished from each other by analysis using ten cultivar-specific probes. Dot-blot analysis was also applied to genotyping of recombinant inbred lines (RILs), the results of which corresponded to those by electrophoretic analysis of PCR products amplified with specific primer sequences. The probe sequences deleted in 'Nipponbare' were mapped on the rice chromosome by BlastN search using all the Kasalath BAC-end sequences and by analysis of RILs and chromosome segment substitution lines (CSSLs). Dot-blot analysis using cultivar-specific sequences was found to be applicable for the identification of cultivars and genotyping of plants in rice.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dot-blot Analysis for Identification of <i>japonica</i> Rice Cultivars and Genotyping of Recombinant Inbred Lines

2005

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“…Genotyping was done using 201 markers (157 microsatellite markers and 44 AP-RDA markers; Supplementary Table S1) and genomic DNA extracted from the tails (27)(28)(29)(30). Linkage maps were constructed by MAPMAKER/EXP (version 3.0b) software (31), and quantitative trait locus (QTL) analysis was done using MAPMAKER/QTL (version 1.1b) software.…”

Section: Introductionmentioning

confidence: 99%

Linkage and Microarray Analyses of Susceptibility Genes in ACI/Seg Rats: A Model for Prostate Cancers in the Aged

Yamashita¹,

Suzuki

Nomoto³

et al. 2005

Cancer Research

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ACI/Seg (ACI) rats develop prostate cancers spontaneously with aging, similar to humans. Here, to identify genes involved in prostate cancer susceptibility, we did linkage analysis and oligonucleotide microarray analysis. Linkage analysis was done using 118 effective rats, and prostate cancer susceptibility 1 (Pcs1), whose ACI allele dominantly induced prostate cancers, was mapped on chromosome 19 [logarithm of odds (LOD) score of 5.0]. PC resistance 1 (Pcr1), whose ACI allele dominantly and paradoxically suppressed the size of prostate cancers, was mapped on chromosome 2 (LOD score of 5.0). When linkage analysis was done in 51 rats with single or no macroscopic testicular tumors, which had larger prostates and higher testosterone levels than those with bilateral testicular tumors, Pcs2 and Pcr2 were mapped on chromosomes 20 and 1, respectively. By oligonucleotide microarray analysis with 8,800 probe sets and confirmation by quantitative reverse transcription-PCR, only two genes within these four loci were found to be differentially expressed >1.8-fold. Membrane metalloendopeptidase (Mme), known to inhibit androgen-independent growth of prostate cancers, on Pcr1 was expressed 2.0-to 5.5-fold higher in the ACI prostate, in accordance with its paradoxical effect. Cdkn1a on Pcs2 was expressed 1.5-to 4.5-fold lower in the ACI prostate. Additionally, genes responsible for testicular tumors and unilateral renal agenesis were mapped on chromosomes 11 and 14, respectively. These results showed that prostate cancer susceptibility of ACI rats involves at least four loci, and suggested Mme and Cdkn1a as candidates for Pcr1 and Pcs2. (Cancer Res 2005; 65(7): 2610-6)

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Mapping of Porcine Genetic Markers Generated by Representational Difference Analysis

Farber

Raney

Kuhlers

et al. 2003

Animal Biotechnology

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Representational difference analysis (RDA) was performed using pig genomic DNA from a Landrace non-selected control population and a Landrace population selected for increased loin muscle area (LMA) for five generations. Pigs used for the analysis differed phenotypically for various carcass traits and were divergent in genotype at the skeletal muscle ryanodine receptor 1 locus. Two RDA experiments were performed using BamHI and BglII. Fourteen BamHI and 37 BglII difference products were cloned and sequenced. Oligonucleotide primers were designed to amplify RDA difference products and sequence-tagged sites (STS) were developed for 16 RDA fragments (two BamHI and 14 BglII). These 16 STS were mapped using the INRA-Minnesota porcine Radiation Hybrid panel. Polymorphisms identified in nine of the STS were used to place these markers on the PiGMaP genetic linkage map. Sequence-tagged sites were localized to 11 different chromosomes including three markers on chromosome 11 and four markers on chromosome 14. Development of RDA markers increases the resolution of the pig genome maps and markers located within putative quantitative trait locus (QTL) regions can be used to refine QTL positions.

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Construction of a high-throughput rat genetic mapping system with 466 arbitrarily primed-representational difference analysis markers

Cited by 4 publications

References 19 publications

Dot-blot Analysis for Identification of <i>japonica</i> Rice Cultivars and Genotyping of Recombinant Inbred Lines

Dot-blot Analysis for Identification of <i>japonica</i> Rice Cultivars and Genotyping of Recombinant Inbred Lines

Linkage and Microarray Analyses of Susceptibility Genes in ACI/Seg Rats: A Model for Prostate Cancers in the Aged

Mapping of Porcine Genetic Markers Generated by Representational Difference Analysis

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