High-throughput single nucleotide polymorphism genotyping for breeding applications in rice using the BeadXpress platform

Thomson, Michael J.; Zhao, Keyan; Wright, Mark H.; McNally, Kenneth L.; Rey, Jessica; Tung, Chih‐Wei; Reynolds, Andy; Scheffler, Brian E.; Eizenga, G. C.; McClung, Anna M.; Kim, Hyun-Jung; Ismail, Abdelbagi M.; Ocampo, Marjorie de; Mojica, Chromewell A.; Reveche, Ma. Ymber; Dilla-Ermita, Christine Jade; Mauleon, Ramil; Leung, Hei; Bustamante, Carlos D.; McCouch, Susan R.

doi:10.1007/s11032-011-9663-x

Cited by 147 publications

(119 citation statements)

References 32 publications

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“…GBS holds the potential to close the genotyping gap between references of broad interest and mapping/breeding populations of local or specific interest. The multiplexing of samples in GBS protocols keeps molecular biology costs low while the resultant next-generation sequencing data has immediate applications to many different research areas, ranging from gene discovery to genomic-assisted breeding (Thomson et al 2012). …”

Section: Introductionmentioning

confidence: 99%

Bridging the genotyping gap: using genotyping by sequencing (GBS) to add high-density SNP markers and new value to traditional bi-parental mapping and breeding populations

et al. 2013

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Genotyping by sequencing (GBS) is the latest application of next-generation sequencing protocols for the purposes of discovering and genotyping SNPs in a variety of crop species and populations. Unlike other high-density genotyping technologies which have mainly been applied to general interest "reference" genomes, the low cost of GBS makes it an attractive means of saturating mapping and breeding populations with a high density of SNP markers. One barrier to the widespread use of GBS has been the difficulty of the bioinformatics analysis as the approach is accompanied by a high number of erroneous SNP calls which are not easily diagnosed or corrected. In this study, we use a 384-plex GBS protocol to add 30,984 markers to an indica (IR64) x japonica (Azucena) mapping population consisting of 176 recombinant inbred lines of rice (Oryza sativa) and we release our imputation and error correction pipeline to address initial GBS data sparcity and error, and streamline the process of adding SNPs to RIL populations. Using the final imputed and corrected dataset of 30,984 markers, we were able to map recombination hot and cold spots and regions of segregation distortion across the genome with a high degree of accuracy, thus identifying regions of the genome containing putative sterility loci. We mapped QTL for leaf width and aluminum tolerance, and were able to identify additional QTL for both phenotypes when using the full set of 30,984 SNPs that were not identified using a subset of only 1,464 SNPs, including a previously unreported QTL for aluminum tolerance located directly within a recombination hotspot on chromosome 1. These results suggest that adding a high density of SNP markers to a mapping or breeding population through GBS has great value for numerous applications in rice breeding and genetics research.

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Section: Introductionmentioning

confidence: 99%

Bridging the genotyping gap: using genotyping by sequencing (GBS) to add high-density SNP markers and new value to traditional bi-parental mapping and breeding populations

et al. 2013

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“…For high density genotyping of the tested lines, the Infinium platform comprised several subsets of markers from the BeadXpress 384-SNP sets and these were used to detect SNP alleles. The 384-plex genotyping on the BeadXpress platform, with the oligo pool assay (OPA) customized for the Indica-Japonica SNP chip (Illumina OPA ID: GS0011862-OPA), is a robust and efficient method for marker genotyping in rice (Thomson et al 2012). The Infinium 6K BeadChip used for the genotyping contains about 6000 bead types and these results were used for the QTL mapping.…”

Section: Genotypingmentioning

confidence: 99%

Identification of QTLs for tolerance to hypoxia during germination in rice

Kim

Reinke

2018

Euphytica

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Direct seeding of rice as a method of crop establishment is increasingly being adopted by farmers as a means of saving labor and reducing costs. However, the method often results in a poor environment for germination as excessive water levels after seeding can cause poor seedling establishment and a concomitant reduction in yield potential, especially in submergence-prone areas. In this study, we discovered QTLs associated with tolerance of anaerobic germination (AG) in new genetic accessions using genotypic data derived from the Illumina 6K SNP chip. The mapping population developed for QTL analysis comprised 285 F 2:3 plants derived from a cross between Tai Nguyen and Anda. In order to evaluate AG tolerance within the mapping population, phenotyping was carried out under anaerobic conditions for 21 days. Three QTLs associated with AG tolerance were identified in the population, qAG1a and qAG1b on chromosome 1 and qAG8 on chromosome 8 using composite interval mapping (CIM). The percentage of variance explained by these QTLs ranged from 5.49 to 14.14%. The lines with three QTLs (qAG1b ? qAG1a ? qAG8) demonstrated an approximate 50% survival rate under anaerobic conditions, while lines with two QTLs including qAG1b demonstrated survival rates of 36 and 32% after the treatment, respectively. The QTLs detected in this study may be used to improve AG tolerance during germination and may be combined with other QTLs for anaerobic germination to enhance adaptation to direct seeding and to broaden the understanding of the genetic control of tolerance of germination under anaerobic conditions.

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“…Where SolCAP retrieved SNPs predominantly from North American varieties for the processing industry, the discovery panel of Uitdewilligen et al (2013) contributed a relatively high number of SNPs typical for wild species introgression segments in progenitors. Although ascertainment bias is an important issue in the development and application of SNP arrays (Moragues et al 2010;Thomson et al 2012), it is difficult to quantify and difficult to avoid. A wider discovery study is in general better for SNP arrays intended for a wide range of applications.…”

Section: Ascertainment Biasmentioning

confidence: 99%

“…Ascertainment bias is defined as the inability to discover QTLs due to differences in genetic variation present in the population where the SNPs were discovered and the population where the SNP assays are applied. Consequences of disregarding ascertainment bias are well illustrated in rice, where different SNP arrays were tested on the rice gene pool (Thomson et al 2012). Arrays were typically developed using SNPs from the only one gene pool (indica, japonica, aromatic, aus) and subsequently applied to other gene pools.…”

Section: Ascertainment Biasmentioning

confidence: 99%

“…Even the most commonly used array in Arabidopsis was based on the sequence of only 19 accessions ). This could result in an ascertainment bias when this array is applied on a much wider or different germplasm (Moragues et al 2010; Thomson et al 2012). In more recent studies larger SNP discovery panels are sequenced, for example in wheat (Wang et al 2014).…”

Section: Introductionmentioning

confidence: 99%

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Development and application of a 20K SNP array in potato

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High-throughput single nucleotide polymorphism genotyping for breeding applications in rice using the BeadXpress platform

Cited by 147 publications

References 32 publications

Bridging the genotyping gap: using genotyping by sequencing (GBS) to add high-density SNP markers and new value to traditional bi-parental mapping and breeding populations

Bridging the genotyping gap: using genotyping by sequencing (GBS) to add high-density SNP markers and new value to traditional bi-parental mapping and breeding populations

Identification of QTLs for tolerance to hypoxia during germination in rice

Development and application of a 20K SNP array in potato

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