<scp>RNA</scp>‐<scp>S</scp>eq bulked segregant analysis enables the identification of high‐resolution genetic markers for breeding in hexaploid wheat

Comprehensive reverse genetic resources, which have been key to understanding gene function in diploid model organisms, are missing in many polyploid crops. Young polyploid species such as wheat, which was domesticated less than 10,000 y ago, have high levels of sequence identity among subgenomes that mask the effects of recessive alleles. Such redundancy reduces the probability of selection of favorable mutations during natural or human selection, but also allows wheat to tolerate high densities of induced mutations. Here we exploited this property to sequence and catalog more than 10 million mutations in the protein-coding regions of 2,735 mutant lines of tetraploid and hexaploid wheat. We detected, on average, 2,705 and 5,351 mutations per tetraploid and hexaploid line, respectively, which resulted in 35-40 mutations per kb in each population. With these mutation densities, we identified an average of 23-24 missense and truncation alleles per gene, with at least one truncation or deleterious missense mutation in more than 90% of the captured wheat genes per population. This public collection of mutant seed stocks and sequence data enables rapid identification of mutations in the different copies of the wheat genes, which can be combined to uncover previously hidden variation. Polyploidy is a central phenomenon in plant evolution, and many crop species have undergone recent genome duplication events. Therefore, the general strategy and methods developed herein can benefit other polyploid crops.wheat | polyploidy | mutations | reverse genetics | exome capture S ince the dawn of agriculture, wheat has been a major dietary source of calories and protein for humans. The cultivated wheat species Triticum turgidum (tetraploid, AABB genome) and Triticum aestivum (hexaploid, AABBDD genome) originated via recent polyploidization events followed by domestication.

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“…For the allele-specific primers, fluorophore-compatible tails need to be added to the 5′ end before oligo synthesis (45).…”

Section: Maps Parameter Optimizationmentioning

confidence: 99%

Uncovering hidden variation in polyploid wheat

Krasileva

Vasquez-Gross

Howell

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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“…Merr. ] and wheat (Jiao et al, 2009; Filichkin et al, 2010; Li et al, 2010; Severin et al, 2010; Ramirez-Gonzalez et al, 2015). These applications have demonstrated its tremendous power in characterizing transcriptomes, as it can detect low-expressed transcripts, splice variants, and novel transcripts (Socquet-Juglard et al, 2013).…”

Section: Rna-seq and Snp Markers For Specific Traitsmentioning

confidence: 99%

“…Chopra et al (2015) applied an RNA-Seq to develop and validate a set of gene-based SNPs in sorghum ( Sorghum bicolor ) genotypes with contrasting responses to cold stress. Ramirez-Gonzalez et al (2015) implemented an RNA-Seq analysis of bulked pools sampled from a F2 population to identify 175 putative SNP markers associated with Yr15 , the yellow rust ( Puccinia striiformis f.sp. tritici ) resistance in wheat germplasm.…”

Section: Rna-seq and Snp Markers For Specific Traitsmentioning

confidence: 99%

Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding

Yang

Zeng

et al. 2017

Front. Plant Sci.

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Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST) SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding.

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“…It provides not only the position of a gene underlying a mutant phenotype but also patterns of gene expression [46, 47]. In this study, the intervals of two significant QTL for the KRN on chromosome 8 were 15 Mb and 90 Mb.…”

Section: Discussionmentioning

confidence: 68%

Genetic architecture of the maize kernel row number revealed by combining QTL mapping using a high-density genetic map and bulked segregant RNA sequencing

et al. 2016

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BackgroundThe maize kernel row number (KRN) is a key component that contributes to grain yield and has high broad-sense heritability (H 2). Quantitative trait locus/loci (QTL) mapping using a high-density genetic map is a powerful approach to detecting loci that are responsible for traits of interest. Bulked segregant ribonucleic acid (RNA) sequencing (BSR-seq) is another rapid and cost-effective strategy to identify QTL. Combining QTL mapping using a high-density genetic map and BSR-seq may dissect comprehensively the genetic architecture underlying the maize KRN.ResultsA panel of 300 F2 individuals derived from inbred lines abe2 and B73 were genotyped using the specific-locus amplified fragment sequencing (SLAF-seq) method. A total of 4,579 high-quality polymorphic SLAF markers were obtained and used to construct a high-density genetic map with a total length of 2,123 centimorgan (cM) and an average distance between adjacent markers of 0.46 cM. Combining the genetic map and KRN of F2 individuals, four QTL (qKRN1, qKRN2, qKRN5, and qKRN8-1) were identified on chromosomes 1, 2, 5, and 8, respectively. The physical intervals of these four QTL ranged from 4.36 Mb for qKRN8-1 to 7.11 Mb for qKRN1 with an average value of 6.08 Mb. Based on high-throughput sequencing of two RNA pools bulked from leaves of plants with extremely high and low KRNs, two QTL were detected on chromosome 8 in the 10–25 Mb (BSR_QTL1) and 60–150 Mb (BSR_QTL2) intervals. According to the physical positions of these QTL, qKRN8-1 was included by BSR_QTL2. In addition, qKRN8-1 was validated using QTL mapping with a recombinant inbred lines population that was derived from inbred lines abe2 and B73.ConclusionsIn this study, we proved that combining QTL mapping using a high-density genetic map and BSR-seq is a powerful and cost-effective approach to comprehensively revealing genetic architecture underlying traits of interest. The QTL for the KRN detected in this study, especially qKRN8-1, can be used for performing fine mapping experiments and marker-assisted selection in maize breeding.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3240-y) contains supplementary material, which is available to authorized users.

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RNA‐Seq bulked segregant analysis enables the identification of high‐resolution genetic markers for breeding in hexaploid wheat

Cited by 196 publications

References 50 publications

Uncovering hidden variation in polyploid wheat

Uncovering hidden variation in polyploid wheat

Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding

Genetic architecture of the maize kernel row number revealed by combining QTL mapping using a high-density genetic map and bulked segregant RNA sequencing

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