Evidence map›Paper›PMID 23265623›Full record

ArticleBMC genomics2012

Development of high-throughput SNP-based genotyping in Acacia auriculiformis x A. mangium hybrids using short-read transcriptome data.

Melissa M L Wong, Charles H Cannon, Ratnam Wickneswari

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Article in BMC genomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 34% of its field
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 14 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors at 2 institutions in 3 countries.

Melissa M L WongSchool of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM Bangi 43600, Selangor, Malaysia.
Charles H Cannon
Ratnam Wickneswari
National University of Malaysia · MYTexas Tech University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNext Generation Sequencing has provided comprehensive, affordable and high-throughput DNA sequences for Single Nucleotide Polymorphism (SNP) discovery in Acacia auriculiformis and Acacia mangium. Like other non-model species, SNP detection and genotyping in Acacia are challenging due to lack of genome sequences. The main objective of this study is to develop the first high-throughput SNP genotyping assay for linkage map construction of A. auriculiformis x A. mangium hybrids.

resultsWe identified a total of 37,786 putative SNPs by aligning short read transcriptome data from four parents of two Acacia hybrid mapping populations using Bowtie against 7,839 de novo transcriptome contigs. Given a set of 10 validated SNPs from two lignin genes, our in silico SNP detection approach is highly accurate (100%) compared to the traditional in vitro approach (44%). Further validation of 96 SNPs using Illumina GoldenGate Assay gave an overall assay success rate of 89.6% and conversion rate of 37.5%. We explored possible factors lowering assay success rate by predicting exon-intron boundaries and paralogous genes of Acacia contigs using Medicago truncatula genome as reference. This assessment revealed that presence of exon-intron boundary is the main cause (50%) of assay failure. Subsequent SNPs filtering and improved assay design resulted in assay success and conversion rate of 92.4% and 57.4%, respectively based on 768 SNPs genotyping. Analysis of clustering patterns revealed that 27.6% of the assays were not reproducible and flanking sequence might play a role in determining cluster compression. In addition, we identified a total of 258 and 319 polymorphic SNPs in A. auriculiformis and A. mangium natural germplasms, respectively.

conclusionWe have successfully discovered a large number of SNP markers in A. auriculiformis x A. mangium hybrids using next generation transcriptome sequencing. By using a reference genome from the most closely related species, we converted most SNPs to successful assays. We also demonstrated that Illumina GoldenGate genotyping together with manual clustering can provide high quality genotypes for a non-model species like Acacia. These SNPs markers are not only important for linkage map construction, but will be very useful for hybrid discrimination and genetic diversity assessment of natural germplasms in the future.

Indexed as

Hybridization, GeneticTranscriptomeAcaciaChromosome MappingComputational BiologyGenotyping TechniquesLigninPolymorphism, Single NucleotideReproducibility of ResultsLignin

Identifiers

PMID23265623
PMCPMC3556151
OpenAlexW2096448455

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.