Research ArticleBREAKTHROUGH TECHNOLOGIES
Open Access

Production of a High-Efficiency TILLING Population through Polyploidization

Helen Tsai, Victor Missirian, Kathie J. Ngo, Robert K. Tran, Simon R. Chan, Venkatesan Sundaresan, Luca Comai

Published April 2013. DOI: https://doi.org/10.1104/pp.112.213256

Abstract

Targeting Induced Local Lesions in Genomes (TILLING) provides a nontransgenic method for reverse genetics that is widely applicable, even in species where other functional resources are missing or expensive to build. The efficiency of TILLING, however, is greatly facilitated by high mutation density. Species vary in the number of mutations induced by comparable mutagenic treatments, suggesting that genetic background may affect the response. Allopolyploid species have often yielded higher mutation density than diploids. To examine the effect of ploidy, we autotetraploidized the Arabidopsis (Arabidopsis thaliana) ecotype Columbia, whose diploid has been used for TILLING extensively, and mutagenized it with 50 mm ethylmethane sulfonate. While the same treatment sterilized diploid Columbia, the tetraploid M1 plants produced good seed. To determine the mutation density, we searched 528 individuals for induced mutations in 15 genes for which few or no knockout alleles were previously available. We constructed tridimensional pools from the genomic DNA of M2 plants, amplified target DNA, and subjected them to Illumina sequencing. The results were analyzed with an improved version of the mutation detection software CAMBa that accepts any pooling scheme. This small population provided a rich resource with approximately 25 mutations per queried 1.5-kb fragment, including on average four severe missense and 1.3 truncation mutations. The overall mutation density of 19.4 mutations Mb–1 is 4 times that achieved in the corresponding diploid accession, indicating that genomic redundancy engenders tolerance to high mutation density. Polyploidization of diploids will allow the production of small populations, such as less than 2,000, that provide allelic series from knockout to mild loss of function for virtually all genes.

The growing availability of whole-genome sequences is spurring functional gene studies in species where specific tools for reverse genetics are not available. However, developing suitable functional genetic resources is often challenging and expensive. For example, targeted gene inactivation through transfer DNA (T-DNA; Alonso et al., 2003) or transposable elements (Krishnan et al., 2009) typically requires 300,000 to 500,000 tagged individuals to approach saturation, even for a small genome like that of Arabidopsis (Arabidopsis thaliana), and both tools may be needed to avoid intrinsic insertional bias (Pan et al., 2005). Two other tools, RNA interference and targeted endonucleases, require transformation and cannot be scaled easily. Furthermore, transgenic plants require regulatory clearance, hindering phenotypic characterization. Targeting Induced Local Lesions in Genomes (TILLING) is a functional genomics method that discovers chemically induced mutations in populations and presents considerable advantages: it is applicable to many sexual species, it requires relatively small populations, it is not transgenic, and it can target potentially any gene (Comai and Henikoff, 2006; Wang et al., 2012). TILLING consists of mutagenesis, DNA isolation and pooling, and high-throughput mutation discovery in targeted genes. First described in Arabidopsis (McCallum et al., 2000) and Drosophila spp. (Bentley et al., 2000), it has been successfully extended to multiple model and economic species, thus becoming an important tool for functional genomics. Originally, TILLING discovered mutations through the detection of mismatched sites in PCR products (Oleykowski et al., 1998; Till et al., 2004a; Dong et al., 2009). The advent of low-cost high-throughput sequencing has added another powerful method (Rigola et al., 2009; Tsai et al., 2011). Tsai et al. (2011) recently described the use of Illumina sequencing and single-nucleotide polymorphism analysis in multidimensional pools as a method for efficient mutation discovery.

TILLING efficiency (i.e. the cost of obtaining informative mutations per gene) depends on the characteristics of the population used and particularly on the mutation density (Comai and Henikoff, 2006). This, in turn, depends at least in part on the intensity used to mutagenize the target species and on its response. Mutagenesis is usually applied by the treatment of seed with a chemical mutagen in a manner that produces tolerable lethality and sterility of the treated individuals, the M1 plants, while allowing sufficient production of fertile M2 plants. M2 DNA and M3 seed are typically inventoried to preserve the resource. Optimal conditions for mutagenesis vary from species to species, and mutation densities determined by TILLING differ as much as 100-fold. Since similar treatments yield different mutation densities in different species, cellular or developmental characteristics depending on the genetic background must play a role in the outcome. The highest mutation densities were obtained in polyploids (Fig. 1), hexaploid wheat (Triticum aestivum; Slade et al., 2005; Uauy et al., 2009), and tetraploid canola (Brassica napus; Wang et al., 2008; Harloff et al., 2012), displaying the highest number at approximately 40 mutations Mb–1 of diploid DNA (i.e. for each of the ancestral genomes of the polyploid). Allotetraploid peanut (Arachis hypogaea), on the other hand, is an exception, with about 1 mutation Mb–1 (Knoll et al., 2011). Paleopolyploids have yielded diverse mutation rates: Brassica rapa yielded a near-polyploid-like density at 16.6 mutations Mb–1 (Stephenson et al., 2010), while soybean (Glycine max) and maize (Zea mays) displayed lower mutation rates (Till et al., 2004b; Cooper et al., 2008) within the range of diploid responses. Arabidopsis accession Landsberg erecta (Ler) yielded about 11 mutations Mb–1 (Martín et al., 2009), one of the highest densities described for a diploid. Comparable mutagenic treatments in Arabidopsis accession Columbia (Col-0), however, yielded about 4 mutations Mb–1 (Greene et al., 2003).

Figure 1.
Figure 1.

Effect of mutation rate and ploidy on functional discovery through TILLING. A, The probability of getting at least one severe missense mutation (assumed to be 15% of all mutations) and one knockout (KO; 5% of all mutations) is plotted versus the total mutation number identified in the coding region of a gene (redrawn from Henikoff et al. [2004]). B, The relationship between mutant yield and mutation rate is illustrated by the number of individuals in a population required to yield a given number of mutations (in A) in a 1-kb fragment. For example, considering mutations in a 1-kb coding region, the hatched blue stripe highlights how a population with a mutation rate of 2 mutations Mb–1 requires screening more than 15,000 individuals for a 0.8 confidence of obtaining at least a single knockout. The same result can be obtained with 768 individuals of a population that has a mutation rate of 40 mutations Mb–1, such as hexaploid wheat. The number of mutations expected for a given population and mutation density should be scaled according to the gene size. For example, for a 2-kb coding region and a mutation density of 2 mutations Mb–1, 60 mutations are expected. A population of 15,000 individuals would yield an approximately 95% chance of at least one knockout. C, Published mutation rates in TILLING populations of different species organized according to ploidy. The vertical bar connects instances of the same species. Ai, Peanut (Knoll et al., 2011); AtC, Arabidopsis Col-0 (Greene et al., 2003); AtL, Arabidopsis Ler (Martín et al., 2009), Bn, canola (Harloff et al., 2012); Br, B. rapa (Stephenson et al., 2010); Gm, soybean (Cooper et al., 2008); Hv, barley (Hordeum vulgare; Talamè et al., 2008); Mt, Medicago truncatula (Le Signor et al., 2009); Os, rice (Oryza sativa; Till et al., 2007); Ps, pea (Pisum sativum; Dalmais et al., 2008); Ta, wheat (6x; Slade et al., 2005; Uauy et al., 2009); Td, durum wheat (Triticum durum [4x]; Slade et al., 2005; Uauy et al., 2009); Zm, maize (Till et al., 2004b). [See online article for color version of this figure.]

There is no information on the molecular mechanisms underlying variable mutation yields in plants. Such knowledge should be useful for improving TILLING populations. Available data suggest a possible role for polyploidy, but not genome size, in conferring tolerance to high mutation density, a finding consistent with an early analysis (Stadler, 1929). There are at least two possible explanations for such an effect. First, genetic redundancy may shield polyploids from the deleterious consequences of mutation, making them more tolerant to mutagenic agents. Second, polyploids may be physiologically more tolerant of genotoxic treatments or more susceptible to mutagenesis. Such changes, for example, could result from adaptive changes affecting DNA repair and genome maintenance taking place after polyploidization.

Here, we show that an autotetraploid derivative of Arabidopsis accession Col-0 resists an ethylmethane sulfonate (EMS) concentration that sterilizes its diploid progenitor while at the same time accumulating mutations at a density four times that previously achieved with this ecotype. We discuss the implications of this finding in the context of whole-genome duplication and functional genomics.

RESULTS

Mutagenesis

To investigate the effect of ploidy on the response to EMS mutagenesis, we treated wild-type Col-0 (2x = 2n = 10) and its autotetraploid derivative Col-4X (4x = 2n = 20) with 30 and 50 mm EMS using our standard mutagenesis protocol (Fig. 2). The M1 seed (i.e. treated with EMS) was germinated and grown. During seed development in the M1 plants, immature siliques (seed pods) were dissected and the number of healthy versus dead or dying seed (presumed to harbor embryo-lethal mutations) was scored. Treatment of the diploid line with 30 mm EMS resulted in a high frequency (75%) of individuals segregating embryo-lethal mutations (i.e. displaying siliques with seed counts consistent with a 3:1 ratio of live to dead seed). By contrast, we observed no tetraploid silique consistent with a 3:1 ratio. Instead, one to four dead embryos occurred in 35% of M1 plants (see “Materials and Methods”). At 50 mm, most diploid siliques were empty or had a few shrunken dead seed. At 50 mm, one to four dead seed could be observed in 40% of the tetraploid M1 plants. Control untreated plants of either ploidy displayed negligible counts of embryo-lethal mutations. M2 seed from the 50 mm treatment germinated with good efficiency and produced plants whose phenotype was moderately variable in size and growth habit. Most produced seed. In contrast, diploid M2 plants resulting from treatment with 30 mm EMS display 20% to 40% sterility. In conclusion, autotetraploid Col-0 demonstrated higher tolerance to EMS treatment than its isogenic diploid.

Figure 2.
Figure 2.

Embryo lethality in mutagenized diploid and autotetraploid Arabidopsis. Two genetically identical strains of Col-0, but differing in ploidy, were treated as seed with the chemical mutagen EMS. The effect of the treatment was monitored by observing the development of M2 seed in the siliques of the M1 plants. Mutagenesis-induced changes affect one allele in any target gene: the corresponding genotype is Aa in the diploid and AAAa (simplex) in the tetraploid. M1 individuals are chimerae because the mature embryo shoot meristem is a multicellular structure and each cell is mutagenized independently. Genetic evidence indicates that the same cell lineage forms male and female gametes in a single flower. The occurrence of failed seed in a 1:3 (failed:live) ratio in the 30 mm EMS-treated diploid can be attributed to homozygous recessive mutants in genes required for embryo development and formed by selfed Aa lineages of the M1 plant. Diploid Arabidopsis was effectively sterilized by 50 mm EMS, which only marginally affected seed set in the autotetraploid. In tetraploids, the probability of a homozygous recessive embryo (aaaa) produced from an AAAa flower lineage is minuscule (1:575; Table III). Therefore, the dead seed progeny of the polyploid M2 is not readily explained by mutations in genes where the wild-type allele has a dominant action. More likely, the dead seed in polyploids are the result of mutations, or perhaps chromosomal aberrations, that have a lethal dosage effect. [See online article for color version of this figure.]

Tridimensional Pooling, Sequencing, and Identification of Mutations

To identify the mutation rate resulting from 50 mm EMS treatment, we grew a single M2 plant from each of about 600 M1 families. Genomic DNA and seed were obtained from 528 individuals. Twenty-four overlapping DNA pools were constructed from 528 M2 individuals by mixing equal amounts of 33 individual genomic DNA samples (see “Materials and Methods”). Autotetraploidy requires decreasing the pooling ratio because each individual has four alleles. In a pool made from the DNA of 33 tetraploids, a mutant allele in an individual with a simplex genotype (AAAa) is diluted by a factor of 1:132. The same dilution factor is achieved in a pool of 66 diploids. To test the suitability of such a population for mutation discovery, we chose genes for which knockouts were not available at the time when this study initiated (2007) in the Signal T-DNA insertion collection and searched mutations in an optimally positioned amplicon approximately 1.5 kb in size. From each pool, genomic DNA fragments were amplified for each of 15 target genes, processed for Illumina sequencing (see “Materials and Methods”), marked with 48 different barcoded adapters, and sequenced in two Illumina GAIIx lanes using 100-bp paired end reads. The sequence was processed to divide the reads according to barcodes and filter them for quality (see “Materials and Methods”). The reads were then aligned to the reference (i.e. the expected sequence for the queried amplicons). The alignment was processed with CAMBa2, a program that compares the frequency of changes at each nucleotide position across the 24 libraries, identifying putative mutations. The analysis pipeline plots the frequency of changes versus their positions on the amplicon (Fig. 3). In positions where no mutations are found, all 24 pools display background noise. In positions were candidate mutations are present, three pools are outliers. CAMBa2 compares these outlier patterns to call mutations.

Figure 3.
Figure 3.

Mutations detected in the Ala-tRNA ligase gene (At1G50200). The change frequencies for the 24 sequenced pools are plotted versus the base position on the queried DNA fragment. The triplet pattern formed by three outliers, corresponding to a unique mutant individual shared by three pools, is evident in the G→C and C→T frequency tracks. No triplets (i.e. candidate mutations) are visible in the A→C and A→G tracks. This is consistent with the observation that EMS in Arabidopsis is specific for GC→AT base pair changes (Greene et al., 2003) and with the notion that the triplet pattern is not generated by random sequencing errors. Frequency plots for the remaining base changes were comparable to the latter two in pattern and in range (data not shown). Background noise consists of high-quality sequence changes and is characteristic of each change type: for example, higher in A→G than in A→C. SNP, Single-nucleotide polymorphism. [See online article for color version of this figure.]

The three-dimensional pooling scheme employed here differed from that described previously. The original CAMBa was written for one bidimensional and one tridimensional pooling scheme (Tsai et al., 2011), both involving 768 individuals (Missirian et al., 2011). To fit our study and to prepare CAMBa for any possible population size and dimensional pooling, parts of the program were extensively rewritten. Gridded and nongridded pooling schemes are now supported to an arbitrary number of dimensions, multiple individuals can be placed in the same combination of libraries, and the number of individuals can vary between different library combinations. One important restriction on pooling scheme designs is that all individuals must be placed in the same number of sequencing libraries. Using this improved version of the program, we identified 413 candidate mutations (Supplemental Table S1).

Statistical Analysis

For each candidate mutation, CAMBa2 calculates the probability of the null hypothesis (i.e. that the mutation is false). This value, F(t), is expressed as the mean-centered log of the posterior probability “t”. Higher F(t) values correspond to higher confidence in a specific mutation. The distribution of F(t) scores (Fig. 4A) forms a prominent peak centered around F(t) = 10 and displaying a long left tail. The F(t) score was essentially independent of the depth of sequence coverage (Fig. 4B), indicating that CAMBa2 was not biased by sequence read depth. The false discovery rate (FDR) can be derived from F(t), but to strengthen our calls, we investigated what score distribution could be expected from noise. In a second analysis, we applied CAMBa2 both on nonoverlapping pools (i.e. that do not share any individuals) and on correct pools. Mutation calls derived from nonoverlapping pool sets are false (implausible) unless the same mutation occurred independently in different individuals. We then compared the distribution of F(t) scores for plausible and implausible calls (Fig. 4C). The two distributions are clearly distinct. Their overlap suggests the optimal placement of a threshold separating high-confidence calls from rejected ones. Of the 413 mutations, 384 had a score of 2 or higher, which is connected with an empirically determined FDR of 0.02 [using the number of implausible mutations with F(t) ≥ 2; Fig. 4C] and a statistical FDR of 0.005 (Missirian et al., 2011; Tsai et al., 2011). A fraction of the 29 mutations below the threshold are also expected to be true, but they are associated with higher FDR. Considering the 384 high-probability mutations (Table I), our screen of 528 individuals in 15 1.5-kb amplicons (one per gene) discovered on average a total of 25 mutations per gene. All changes were GC-to-AT transitions, and their effect ranged from predicted knockouts (19 in 10 targets, 1.3 per target) to missense changes of predicted varying severity (Kumar et al., 2009; four severe per target) to silent or intronic changes. Four of these mutations were tested independently by PCR amplification and Sanger sequencing and confirmed (data not shown). In conclusion, TILLING of the 528 lines yielded extensive allelic series for each target gene. The 384 mutations in an adjusted query space of 9.9 Mb of tetraploid DNA (19 kb of queried DNA × 528 individuals) is equal to one mutation per 25.8 kb or 38.8 mutations Mb–1 of the tested tetraploid DNA. This correspond to a mutation density of 19.4 mutations Mb–1 of diploid DNA.

Figure 4.
Figure 4.

Discovery of mutations through Illumina sequencing of overlapping pools. A, The probability score F(t) is the mean centered log of the posterior probability “t” of the null hypothesis. Higher F(t) scores are desirable, as they correspond to lower probability of false discovery. The distribution of 413 mutation candidates is shown. The mutations were detected by TILLING 15 amplicons corresponding to 15 Arabidopsis genes in a population of 528 autotetraploid individuals. Changes detected by aligning Illumina reads to the queried sequence reference were subjected to CAMBa2 analysis to discover mutation candidates. The vertical gray line indicates the threshold of F(t) = 2, corresponding to an empirically determined FDR of 0.02 (see C) and to a statistical FDR of 0.005. B, Probability is independent of sequencing coverage. Each point corresponds to a putative mutation and displays F(t) versus mean coverage. The lack of correlation between F(t) and coverage (r2 = 0.02) indicates that once sequencing coverage is above a critical threshold, discovery is reliable. C, Setting the threshold probability for mutation calling. To shorten the time required by the computationally complex step, half the data set (264 individuals) used in A was subjected to mutation detection using CAMBa2 allowing all potential pool overlaps, both true and false. Mutations were then divided according to the overlap (i.e. the shared individuals from the population) type and are displayed both as frequency histograms and as dithered dots. Those detected in true overlaps (had common individuals) were defined as plausible (i.e. true). Those detected in false overlaps (did not share individuals) were defined as implausible (i.e. false). “Double mutations” are consistent with two identical changes occurring in independent individuals and thus found in two sets of three correctly overlapping pools. The threshold at F(t) = 2 was arbitrarily chosen to provide a predicted FDR of 2% and corresponds to the F(t) value above most implausible mutations and below most plausible ones. Using that threshold, 384 mutations are called in the whole data set. [See online article for color version of this figure.]

View this table:
Table I. Summary of induced mutations discovered in a 528-tetraploid Arabidopsis population

DISCUSSION

A Total of 528 Tetraploid Individuals Provide Functional Discovery for a Majority of Genes

Arabidopsis accession Col-0 has been employed in its natural diploid state for the development of an extensive and well-characterized population from which 9,600 mutations have been discovered. The mutation rate in the diploid Col-0 population was calculated at 6 mutations Mb–1 (Greene et al., 2003), but the estimate of 4 mutations Mb–1 was also obtained using a slightly different calculation approach used commonly in most TILLING publications (see above). A study in accession Ler used M2 mutagenized at different concentrations of EMS (20–40 mm) and yielded an average mutation rate of about 10 mutations Mb–1, one of the highest rates reported for a diploid. In comparison, the high mutation rate of 19.4 mutations Mb–1 achieved here approaches that found in tetraploid wheat and canola. Given the low toxicity and sterility displayed by autotetraploid Col-0, higher EMS concentrations could be used, and one would expect that an even higher mutation rate could be achieved. Truncations, which are most likely to result in knockouts, were found for two-thirds of the tested genes and predicted deleterious mutations for all. One knockout, for example, affected the Centromeric Histone3 (CENH3) gene, for which knockout alleles were not present in other functional resources at the time of this study (Table II). This cenh3-1 allele was used to investigate the function of the encoded histone variant, demonstrating its role in chromosomal inheritance and genome maintenance as well as its utility for haploid induction, artificial apomixis, and genetic analysis (Ravi and Chan, 2010; Ravi et al., 2010, 2011; Marimuthu et al., 2011; Seymour et al., 2012; Wijnker et al., 2012). The potential yield of nullimorphic and hypomorphic mutations from this easily assembled population compares favorably with the one provided by all the tagging populations available worldwide for Arabidopsis. A moderate expansion to 1,500 M2 individuals would ensure virtual saturation of the Arabidopsis genes (Fig. 1). The TILLING efficiency displayed when using polyploids makes polyploidization an attractive strategy for the development of populations in new species.

View this table:
Table II. Comparison of tetraploid TILLING with available tag resources

Effect of Polyploidy

Our study differs from previously published work on polyploids because we used a newly made (synthetic) autopolyploid instead of a natural allopolyploid species. We can thus rule out evolved mechanisms that may stabilize polyploidy genomes and affect responses to mutagenic treatments. Because the response reported in this study was measured in the immediate generations after polyploidization, it is a direct effect of ploidy. We believe that the best explanation involves the masking of recessive deleterious mutations by redundant copies. Masking occurs in sporophytes because individuals homozygous for a given mutation are rare (see below). Masking also occurs in the diploid gametophytes, buffering selection against gametophyte-lethal mutations and boosting fertility. This is potentially an important feature of the autotetraploid approach, as such mutations may not be transmitted through the germline of diploids and therefore never recovered. Additionally, DNA damage from mutagenesis may cause chromosomal breakage and segmental aneuploidy, compromising the health of the M1 plant. The chimeric nature of the M1 meristems after seed mutagenesis (Comai and Henikoff, 2006) could result in clonal selection (Fagerström, 1992), but no investigation addressing this possibility is available in the literature. Aneuploidy and chromosomal deletions would complicate the transmission of the mutagenized genome, because unbalanced parental gametes can result in failure of the endosperm and seed death. Polyploidy would lessen the impact of this hypothetical occurrence by reducing imbalance and providing intact copies of each chromosome (Henry et al., 2010).

Analysis of Mutations Discovered in Autotetraploids

Autotetraploid inheritance has important consequences for the analysis of mutant alleles. In the M1 generation of an autotetraploid, any induced mutation (represented as A→a) will affect one of four alleles resulting in the simplex genotype AAAa. Inheritance in an autopolyploid is affected by linkage to the centromere and the number of homologous chromosomes in a synaptic group (disomic versus multisomic pairing). When the mutant locus is not involved in crossing over or the pairing is disomic, a situation defined as chromosome segregation (Burnham, 1962), an AAAa plant can only form two types of gametes each with equal probability: AA and Aa. If meiotic pairing involves multivalents and crossing over can occur between the A locus and the centromere, a scenario defined as maximum equational segregation can be applied (Burnham, 1962). Following recombination events in which the “a” allele is moved to a different chromosome, both mutant alleles can move to the same pole and at anaphase II can enter the same gamete, resulting in an “aa” genotype, a process called double reduction (theoretical frequency = 1/24). Thus, individuals with the AAAa genotype can produce “aaaa” recessive zygotes, albeit at a frequency (1/576) much lower than the 3:1 at which diploid Aa can form an “aa” zygote. Therefore, the “a” mutant allele will be present in approximately 70% of the M2 individuals and will be arranged in simplex (AAAa), duplex (AAaa), and, sometimes, triplex and quadruplex genotypes according to the ratios presented in Table III. The two most abundant classes are simplex and duplex, present at an approximately 2:1 ratio. TILLING populations are usually stored as M3 seed families derived by the selfing of a single M2 plant. Since most M2 plants are simplex or duplex, the ratios expected can be derived from Table III. In conclusion, a minimum of 70% of the M3 individuals in a family should carry at least one allele, and production of a homozygous recessive individual is laborious, as it requires either double reduction associated with a lack of centromeric linkage or a duplex parental genotype. For these reasons, analysis of a mutation is best carried out through conversion to diploidy.

View this table:
Table III. Inheritance ratios for simplex and duplex autotetraploid genotypes

Conversion to diploidy can be achieved using the triploid bridge (two crosses; Fig. 5A) or using a haploid inducer (one cross; Fig. 5B). Using the triploid bridge, an AAAa tetraploid mutant is crossed to the diploid wild type, producing a triploid progeny, approximately 50% of which carry the mutant allele. Sometimes, a strong interploidy mating barrier exists and may complicate the use of polyploids for TILLING. In certain cases, these barriers are accession specific. In the Col-0 accession, for example, a lethal male-excess barrier (Dilkes et al., 2008) can be avoided by using the tetraploid Col-0 as a female or by employing a tetraploid male from a different accession. Once a triploid carrying the mutation has been obtained, it is crossed to the diploid wild type. In Arabidopsis, this type of cross produces about one-third diploids. The frequency of Aa heterozygotes in the diploid progeny will be 2/3 for the Aaa parent and 1/3 for the AAa parent. The presence of a gametophyte-lethal mutation can be tested at the same time by observing transmission through diploid (or disomic) gametes but failure in haploid ones. This strategy was used to obtain diploid plants carrying a null mutation generated by autotetraploid TILLING for CENH3, a gene for which insertional alleles were unavailable (Ravi and Chan, 2010). Manipulation of this mutant enabled the production of a haploid inducer. Its use to diploidize a tetraploid involves crossing the tetraploid mutant to a line expressing a modified CENH3. The modified CENH3 causes genome elimination during hybridization to an individual with normal centromeric histone (Fig. 5B). About one-fourth of the progeny will lose the genome marked by the modified histone, resulting in diploids, one-half of which will be Aa. Selfing of the Aa heterozygote will produce a segregating progeny where the effect of the mutation can be determined by comparing homozygous mutants with individuals carrying wild-type alleles. Haploid induction through natural crosses is available in some species and may be extended soon to others via the engineering of CENH3.

Figure 5.
Figure 5.

Diploidization strategies for mutations discovered in autotetraploids. Two alternatives are illustrated for a simplex autotetraploid mutant with genotype CCCc. A, Diploidization via triploid bridge (Henry et al., 2009). All progeny of cross 1 are expected to be triploid. Triploids transmit either one or two copies of each chromosome type (Table III). The progeny of cross 2 will fit in three classes: diploids (approximately 30%; Henry et al., 2009), aneuploids, and some triploids. B, Diploidization via genomic elimination achieved through the use of a haploid inducer variety, such as strain “Col-0 cenh3-1, GFP-tailswap” (Ravi and Chan, 2010). Cross 3 will produce tetraploids, diploids, and aneuploids. The direction of the cross influences the percentage of diploids produced. Furthermore, some plant varieties, such as Col-0, carry male-specific interploidy lethality factors (Dilkes et al., 2008). Reciprocal crosses should be tested for optimal results. c, Mutant allele; C, wild-type allele; 2X, diploid; 4X, autotetraploid. Brackets indicate gametic genotypes; thick-lettered genotypes in haploid inducers refer to alleles on genomes targeted for elimination. [See online article for color version of this figure.]

Given the high density of mutations, will linkage to deleterious mutations complicate the use of these resources? The experimental design described above uses two outcrosses to transfer a mutation into a diploid genome followed by selfing to generate a homozygote required to investigate the phenotype of a recessive mutation. We consider only mutations in the exome (including splice sites), as those in intergenic and intronic regions are very unlikely to be deleterious. With a density of 19.4 mutations Mb–1 calculated for a diploid genome equivalent (see above), and assuming that most of the mutations are in a heterozygous state, an outcross will transfer about one-half of the mutations, or 19.4/2 × 40-Mb exome in the Arabidopsis genome, yielding 388 mutations per gamete. Assuming that 15% of these are highly to moderately deleterious (Greene et al., 2003; Comai and Henikoff, 2006) results in 58 mutations, or 0.11 mutations per centimorgan (cM) in the total 520 cM of the genetic map (Hauge et al., 1993; Arabidopsis Genome Initiative, 2000). Modeling the occurrence of mutations according to the Poisson distribution using P = 0.11, the probability of one or more deleterious mutations occurring within 1, 2, 4, and 8 cM of a mutation of interest is, respectively, 0.11, 0.2, 0.36, and 0.59. Only 2,400 Arabidopsis genes yield a detectable phenotype when mutated, which is a fraction of all that were tested (Lloyd and Meinke, 2012). Therefore, the probability of a phenotype-perturbing, tightly linked mutation is manageably small, but it still requires an appropriate genetic design in the analysis. A conclusion on a given gene will be both facilitated and greatly strengthened by the availability of two independent alleles, a common-sense requirement. These could be characterized independently or could be combined in a transheterozygous arrangement to cancel the effect of most other genetic lesions.

CONCLUSION

The use of polyploid lines would be effective for TILLING in species recalcitrant to mutagenesis and to increase mutation density in any species. Populations with a high density of mutations lower the cost of discovery and are ideally suited for genomic approaches that, instead of TILLING one gene at time, sequence the whole genome or exome to collect all significant mutations. In conclusion, this work indicates that the genomic redundancy of polyploids confers tolerance to the alkylating mutagen EMS, demonstrating the production and exploitation of populations with mutation rates considerably higher than those possible in the isogenic diploids.

MATERIALS AND METHODS

Mutagenesis and Plant Growth

The autotetraploid Arabidopsis (Arabidopsis thaliana) Col-4X line was described previously (Dilkes et al., 2008). A seed batch produced from a single plant that was either two or three generations from the primary colchicine-induced tetraploid was washed in water for 1 h, transferred to a solution of EMS in distilled water, and shaken gently in glass drum vials (approximately 3 cm in diameter) for 17 h at about room temperature (approximately 23°C). Seed were rinsed five times in distilled water and sown on potting mix. They were grown to maturity in a growth chamber at 21°C and 16 h of daylight. Seed was harvested separately from each M1 individual. A single M2 plant was grown for DNA isolation and M3 banking. Hypothetical colchicine-induced point mutations (AAAa in the original tetraploid) would most likely be shared by many of the pooled individuals and would have violated a requirement imposed by CAMBa2: for any mutation to be considered, it should be carried by a single individual in the population or, exceptionally, by two. Therefore, these mutations would have been ignored in the analysis.

Phenotyping

Siliques of M1 plants were dissected about 2 weeks after pollination, and seed health was evaluated by color and shape. Dead seed were dark or transparent instead of displaying a well-shaped green embryo at the expected stage of development. The occurrence of embryo-lethal mutations in the diploid was estimated by counting 10 siliques in 10 individuals and scoring as positive any silique that displayed six to 12 dead seed, which in the average silique of 40 seed approximates a 1:3 ratio of dead to live seed, thus fulfilling the expected behavior for a recessive mutation present in the heterozygous state in the germ cells of the flower. The same expectation could not be applied to tetraploid plants because, under the assumption of a simple dominant-recessive pair of alleles, a simplex AAAa flower would yield a maximum 1:575 dead:live seed ratio (Table III). Therefore, in the tetraploid M1 siliques, embryo-lethal mutations could not be estimated. Other factors, such as haploinsufficiency and chromosomal aberrations, may contribute to death.

DNA Isolation

Genomic DNA was isolated as described (Tsai et al., 2011). DNA was quantified using SYBR Green1 dye fluorescence and standardized.

DNA-Pooling Strategy

Our method is based on overlapping genomic DNA pools (Tsai et al., 2011). We wanted the ability to sequence all pools in a minimal number of Illumina lanes (unit of sequencing). Each pool was prepared by combining equal amounts of genomic DNAs from 33 individuals, providing a mix of 132 alleles (33 tetraploid individuals × 4 alleles). Different pools shared a given set of individuals in such a way that each individual should be uniquely identified by three intersecting pools. To fit the 528 individuals of this library in 24 pools, we chose a mixed pooling scheme in which 480 individuals were uniquely identified and 48 were not, but these could be traced to a trio of individuals and subsequently deconvolved. This scheme was an acceptable compromise, but for future work we designed a custom python program (pooling_capacity.py) that takes a range of pool numbers (number of available barcodes), maximum pooling depth (individuals per pool), and the total number of individuals in order to elaborate possible pooling solutions. The program outputs a table of individuals and pools that can be used by robotic pipettors to prepare the pool from plates containing individual DNAs. A Biomek 2000 robotic pipettor was used to prepare the 24 template pools. TILLING targets amplified by PCR (Supplemental Table S2) from the same pool were processed to make an Illumina library.

PCR Amplification of Targets and Library Preparation

We followed the methods described by Tsai et al. (2011). We used five-base barcoded adapters so that 24 libraries with different barcodes could be pooled into one 100-base paired-read sequencing lane. The names and sequences of the adapters were as follows: adA2_nnnn, P-nnnnnAGATCGGAAGAGCGGTTCAGCAGGAATGCCGAG, and adB2_nnnnn, ACACTCTTTCCCTACACGACGCTCTTCCGATCTuuuuuT, where nnnnn is the five-nucleotide barcode, u is the complementary base, and P represents 5′ phosphorylation. The expected coverage per individual was calculated as follows: lane yield/(library ratio × input DNA × pooling ratio).

Bioinformatics

A Python program was used to convert Illumina sequence reads from Illumina quality to Sanger quality, group sequence reads by barcodes, remove adapter sequences from sequencing reads, filter sequencing reads by a base quality cutoff (Phred 20), and filter sequencing reads by a minimum length (25 bp). We used Burrows-Wheeler Aligner (Li and Durbin, 2009) in conjunction with SAMtools (Li et al., 2009) to align the sequencing reads to the amplicon sequences. Another Python program was used to extract alignment information from the pileup table (output file format from SAMtools) and generate a human readable output, parsed pileup table.

We used an updated version of CAMBa, CAMBa2, for mutation detection. The inputs to CAMBa2 are the parsed pileup table, a pooling scheme file, and the reference sequences (amplicon sequence, genomic sequence trimmed to begin at the start codon, and coding sequence) for each queried fragment. CAMBa2 processes each position along a queried amplicon fragment separately, considering each possible configuration (assignment of mutations to individuals) that satisfies the assumption of at most one mutation per individual. It then computes the probability of obtaining the observed base calls under each candidate configuration, assuming a binomial model of sequencing error. CAMBa2 incorporates the prior probability of each configuration in order to compute the posterior probabilities of those configurations using Bayes’s theorem. We used CAMBa2 to assign a probability score and identify the mutant individual or mutation-containing subpool and finally produced a list of candidates and predicted effects. Plausibility analysis (the search of mutations in pools that do not overlap) was run separately by rerunning the CAMBa2 program while allowing any library to be considered overlapping with any other one. The effect of the mutation was predicted by the CAMBa2 software. The severity of amino acid substitutions was predicted using SIFT (Kumar et al., 2009).

Mutation density was calculated by dividing the total number of high-confidence mutations by the total sampled DNA sequence, followed by further division by 2 to account for tetraploidy. The sampled DNA sequence was calculated by multiplying the number of sampled individuals by the sum of the well-covered sequence in each queried fragment. The two terminal 50 bases of each queried fragment were subtracted from the fragment size because the termini correspond to the PCR primers and the rest is underrepresented in the sequencing reads.

Sequence reads have been deposited in the National Center for Biotechnology Information Sequence Read Archive with identification number SRS387455. All software used here is freely available on the Comai laboratory methods Web page (http://comailab.genomecenter.ucdavis.edu/index.php/Data_methods).

Supplemental Data

The following materials are available in the online version of this article.

Acknowledgments

We thank Jong-A Park at the Department of Plant Biology, University of California, Davis, for assistance with the EMS mutagenesis and seed collection from the tetraploid plants. We thank Charlie Nicolet, Vanessa Rashbrooks, and Heather Witt at the University of California, Davis, Genome Center DNA Technology Core for their assistance with Illumina sequencing and Smit Shah and Wasinee Pongprayoon at the Genome Center, University of California, Davis, for technical help in DNA and Illumina library preparation.

Footnotes

  • The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Luca Comai (lcomai{at}ucdavis.edu).

  • www.plantphysiol.org/cgi/doi/10.1104/pp.112.213256

  • ↵1 This work was supported by the National Science Foundation (Plant Genome award no. DBI–0822383).

  • ↵2 Present address: Department of Plant Biology, University of California, Davis, CA 95616.

  • ↵3 Present address: Los Angeles County Public Health Laboratory, 12750 Erickson Ave, Downey, CA 90241.

  • ↵[C] Some figures in this article are displayed in color online but in black and white in the print edition.

  • ↵[OA] Open Access articles can be viewed online without a subscription.

  • ↵[W] The online version of this article contains Web-only data.

Glossary

EMS
ethylmethane sulfonate
Col-0
Columbia
TILLING
Targeting Induced Local Lesions in Genomes
Ler
Landsberg erecta
FDR
false discovery rate
cM
centimorgan
T-DNA
transfer DNA
F(t)
mean-centered log of the posterior probability t
  • Received December 22, 2012.
  • Accepted February 8, 2013.
  • Published February 15, 2013.

REFERENCES

  1. ↵
    1. Alonso JM,
    2. Stepanova AN,
    3. Leisse TJ,
    4. Kim CJ,
    5. Chen H,
    6. Shinn P,
    7. Stevenson DK,
    8. Zimmerman J,
    9. Barajas P,
    10. Cheuk R,
    11. et al.
    (2003) Genome-wide insertional mutagenesis of Arabidopsis thaliana. Science 301: 653–657
    OpenUrlAbstract/FREE Full Text
  2. ↵
    1. Arabidopsis Genome Initiative
    (2000) Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408: 796–815
    OpenUrlCrossRefPubMed
  3. ↵
    1. Bentley A,
    2. MacLennan B,
    3. Calvo J,
    4. Dearolf CR
    (2000) Targeted recovery of mutations in Drosophila. Genetics 156: 1169–1173
    OpenUrlAbstract/FREE Full Text
  4. ↵
    Burnham CR (1962) Discussions in Cytogenetics. Burgess Publishing, Minneapolis
  5. ↵
    1. Comai L,
    2. Henikoff S
    (2006) TILLING: practical single-nucleotide mutation discovery. Plant J 45: 684–694
    OpenUrlCrossRefPubMed
  6. ↵
    1. Cooper JL,
    2. Till BJ,
    3. Laport RG,
    4. Darlow MC,
    5. Kleffner JM,
    6. Jamai A,
    7. El-Mellouki T,
    8. Liu S,
    9. Ritchie R,
    10. Nielsen N,
    11. et al.
    (2008) TILLING to detect induced mutations in soybean. BMC Plant Biol 8: 9
    OpenUrlCrossRefPubMed
  7. ↵
    1. Dalmais M,
    2. Schmidt J,
    3. Le Signor C,
    4. Moussy F,
    5. Burstin J,
    6. Savois V,
    7. Aubert G,
    8. Brunaud V,
    9. de Oliveira Y,
    10. Guichard C,
    11. et al.
    (2008) UTILLdb, a Pisum sativum in silico forward and reverse genetics tool. Genome Biol 9: R43
    OpenUrlCrossRefPubMed
  8. ↵
    1. Dilkes BP,
    2. Spielman M,
    3. Weizbauer R,
    4. Watson B,
    5. Burkart-Waco D,
    6. Scott RJ,
    7. Comai L
    (2008) The maternally expressed WRKY transcription factor TTG2 controls lethality in interploidy crosses of Arabidopsis. PLoS Biol 6: 2707–2720
    OpenUrlPubMed
  9. ↵
    1. Dong C,
    2. Vincent K,
    3. Sharp P
    (2009) Simultaneous mutation detection of three homoeologous genes in wheat by High Resolution Melting analysis and Mutation Surveyor. BMC Plant Biol 9: 143
    OpenUrlCrossRefPubMed
  10. ↵
    1. Fagerström T
    (1992) The meristem-meristem cycle as a basis for defining fitness in clonal plants. Oikos 63: 449–453
    OpenUrlCrossRef
  11. ↵
    1. Greene EA,
    2. Codomo CA,
    3. Taylor NE,
    4. Henikoff JG,
    5. Till BJ,
    6. Reynolds SH,
    7. Enns LC,
    8. Burtner C,
    9. Johnson JE,
    10. Odden AR,
    11. et al.
    (2003) Spectrum of chemically induced mutations from a large-scale reverse-genetic screen in Arabidopsis. Genetics 164: 731–740
    OpenUrlAbstract/FREE Full Text
  12. ↵
    1. Harloff HJ,
    2. Lemcke S,
    3. Mittasch J,
    4. Frolov A,
    5. Wu JG,
    6. Dreyer F,
    7. Leckband G,
    8. Jung C
    (2012) A mutation screening platform for rapeseed (Brassica napus L.) and the detection of sinapine biosynthesis mutants. Theor Appl Genet 124: 957–969
    OpenUrlCrossRefPubMed
  13. ↵
    1. Hauge BM,
    2. Hanley SM,
    3. Cartinhour S,
    4. Cherry JM,
    5. Goodman HM,
    6. Koornneef M,
    7. Stam P,
    8. Chang C,
    9. Kempin S,
    10. Medrano L
    (1993) An integrated genetic/RFLP map of the Arabidopsis thaliana genome. Plant J 3: 745–754
    OpenUrlCrossRef
  14. ↵
    1. Henikoff S,
    2. Till BJ,
    3. Comai L
    (2004) TILLING: traditional mutagenesis meets functional genomics. Plant Physiol 135: 630–636
    OpenUrlAbstract/FREE Full Text
  15. ↵
    1. Henry IM,
    2. Dilkes BP,
    3. Miller ES,
    4. Burkart-Waco D,
    5. Comai L
    (2010) Phenotypic consequences of aneuploidy in Arabidopsis thaliana. Genetics 186: 1231–1245
    OpenUrlAbstract/FREE Full Text
  16. ↵
    1. Henry IM,
    2. Dilkes BP,
    3. Tyagi AP,
    4. Lin HY,
    5. Comai L
    (2009) Dosage and parent-of-origin effects shaping aneuploid swarms in A. thaliana. Heredity (Edinb) 103: 458–468
    OpenUrlCrossRefPubMed
  17. ↵
    1. Knoll JE,
    2. Ramos ML,
    3. Zeng Y,
    4. Holbrook CC,
    5. Chow M,
    6. Chen S,
    7. Maleki S,
    8. Bhattacharya A,
    9. Ozias-Akins P
    (2011) TILLING for allergen reduction and improvement of quality traits in peanut (Arachis hypogaea L.). BMC Plant Biol 11: 81
    OpenUrlCrossRefPubMed
  18. ↵
    1. Krishnan A,
    2. Guiderdoni E,
    3. An G,
    4. Hsing YI,
    5. Han CD,
    6. Lee MC,
    7. Yu SM,
    8. Upadhyaya N,
    9. Ramachandran S,
    10. Zhang Q,
    11. et al.
    (2009) Mutant resources in rice for functional genomics of the grasses. Plant Physiol 149: 165–170
    OpenUrlFREE Full Text
  19. ↵
    1. Kumar P,
    2. Henikoff S,
    3. Ng PC
    (2009) Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc 4: 1073–1081
    OpenUrlCrossRefPubMed
  20. ↵
    1. Le Signor C,
    2. Savois V,
    3. Aubert G,
    4. Verdier J,
    5. Nicolas M,
    6. Pagny G,
    7. Moussy F,
    8. Sanchez M,
    9. Baker D,
    10. Clarke J,
    11. et al.
    (2009) Optimizing TILLING populations for reverse genetics in Medicago truncatula. Plant Biotechnol J 7: 430–441
    OpenUrlCrossRefPubMed
  21. ↵
    1. Li H,
    2. Durbin R
    (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25: 1754–1760
    OpenUrlAbstract/FREE Full Text
  22. ↵
    1. Li H,
    2. Handsaker B,
    3. Wysoker A,
    4. Fennell T,
    5. Ruan J,
    6. Homer N,
    7. Marth G,
    8. Abecasis G,
    9. Durbin R
    (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics 25: 2078–2079
    OpenUrlAbstract/FREE Full Text
  23. ↵
    1. Lloyd J,
    2. Meinke D
    (2012) A comprehensive dataset of genes with a loss-of-function mutant phenotype in Arabidopsis. Plant Physiol 158: 1115–1129
    OpenUrlAbstract/FREE Full Text
  24. ↵
    1. Marimuthu MP,
    2. Jolivet S,
    3. Ravi M,
    4. Pereira L,
    5. Davda JN,
    6. Cromer L,
    7. Wang L,
    8. Nogué F,
    9. Chan SW,
    10. Siddiqi I,
    11. et al.
    (2011) Synthetic clonal reproduction through seeds. Science 331: 876
    OpenUrlAbstract/FREE Full Text
  25. ↵
    1. Martín B,
    2. Ramiro M,
    3. Martínez-Zapater JM,
    4. Alonso-Blanco C
    (2009) A high-density collection of EMS-induced mutations for TILLING in Landsberg erecta genetic background of Arabidopsis. BMC Plant Biol 9: 147
    OpenUrlCrossRefPubMed
  26. ↵
    1. McCallum CM,
    2. Comai L,
    3. Greene EA,
    4. Henikoff S
    (2000) Targeted screening for induced mutations. Nat Biotechnol 18: 455–457
    OpenUrlCrossRefPubMed
  27. ↵
    1. Missirian V,
    2. Comai L,
    3. Filkov V
    (2011) Statistical mutation calling from sequenced overlapping DNA pools in TILLING experiments. BMC Bioinformatics 12: 287
    OpenUrlCrossRefPubMed
  28. ↵
    1. Oleykowski CA,
    2. Bronson Mullins CR,
    3. Godwin AK,
    4. Yeung AT
    (1998) Mutation detection using a novel plant endonuclease. Nucleic Acids Res 26: 4597–4602
    OpenUrlAbstract/FREE Full Text
  29. ↵
    1. Pan X,
    2. Li Y,
    3. Stein L
    (2005) Site preferences of insertional mutagenesis agents in Arabidopsis. Plant Physiol 137: 168–175
    OpenUrlAbstract/FREE Full Text
  30. ↵
    1. Ravi M,
    2. Chan SW
    (2010) Haploid plants produced by centromere-mediated genome elimination. Nature 464: 615–618
    OpenUrlCrossRefPubMed
  31. ↵
    1. Ravi M,
    2. Kwong PN,
    3. Menorca RM,
    4. Valencia JT,
    5. Ramahi JS,
    6. Stewart JL,
    7. Tran RK,
    8. Sundaresan V,
    9. Comai L,
    10. Chan SW
    (2010) The rapidly evolving centromere-specific histone has stringent functional requirements in Arabidopsis thaliana. Genetics 186: 461–471
    OpenUrlAbstract/FREE Full Text
  32. ↵
    1. Ravi M,
    2. Shibata F,
    3. Ramahi JS,
    4. Nagaki K,
    5. Chen C,
    6. Murata M,
    7. Chan SW
    (2011) Meiosis-specific loading of the centromere-specific histone CENH3 in Arabidopsis thaliana. PLoS Genet 7: e1002121
    OpenUrlCrossRefPubMed
  33. ↵
    1. Rigola D,
    2. van Oeveren J,
    3. Janssen A,
    4. Bonné A,
    5. Schneiders H,
    6. van der Poel HJ,
    7. van Orsouw NJ,
    8. Hogers RC,
    9. de Both MT,
    10. van Eijk MJ
    (2009) High-throughput detection of induced mutations and natural variation using KeyPoint technology. PLoS ONE 4: e4761
    OpenUrlCrossRefPubMed
  34. ↵
    1. Seymour DK,
    2. Filiault DL,
    3. Henry IM,
    4. Monson-Miller J,
    5. Ravi M,
    6. Pang A,
    7. Comai L,
    8. Chan SW,
    9. Maloof JN
    (2012) Rapid creation of Arabidopsis doubled haploid lines for quantitative trait locus mapping. Proc Natl Acad Sci USA 109: 4227–4232
    OpenUrlAbstract/FREE Full Text
  35. ↵
    1. Slade AJ,
    2. Fuerstenberg SI,
    3. Loeffler D,
    4. Steine MN,
    5. Facciotti D
    (2005) A reverse genetic, nontransgenic approach to wheat crop improvement by TILLING. Nat Biotechnol 23: 75–81
    OpenUrlCrossRefPubMed
  36. ↵
    1. Stadler LJ
    (1929) Chromosome number and the mutation rate in Avena and Triticum. Proc Natl Acad Sci USA 15: 876–881
    OpenUrlFREE Full Text
  37. ↵
    1. Stephenson P,
    2. Baker D,
    3. Girin T,
    4. Perez A,
    5. Amoah S,
    6. King GJ,
    7. Østergaard L
    (2010) A rich TILLING resource for studying gene function in Brassica rapa. BMC Plant Biol 10: 62
    OpenUrlCrossRefPubMed
  38. ↵
    1. Talamè V,
    2. Bovina R,
    3. Sanguineti MC,
    4. Tuberosa R,
    5. Lundqvist U,
    6. Salvi S
    (2008) TILLMore, a resource for the discovery of chemically induced mutants in barley. Plant Biotechnol J 6: 477–485
    OpenUrlCrossRefPubMed
  39. ↵
    1. Till BJ,
    2. Burtner C,
    3. Comai L,
    4. Henikoff S
    (2004a) Mismatch cleavage by single-strand specific nucleases. Nucleic Acids Res 32: 2632–2641
    OpenUrlAbstract/FREE Full Text
  40. ↵
    1. Till BJ,
    2. Cooper J,
    3. Tai TH,
    4. Colowit P,
    5. Greene EA,
    6. Henikoff S,
    7. Comai L
    (2007) Discovery of chemically induced mutations in rice by TILLING. BMC Plant Biol 7: 19
    OpenUrlCrossRefPubMed
  41. ↵
    1. Till BJ,
    2. Reynolds SH,
    3. Weil C,
    4. Springer N,
    5. Burtner C,
    6. Young K,
    7. Bowers E,
    8. Codomo CA,
    9. Enns LC,
    10. Odden AR,
    11. et al.
    (2004b) Discovery of induced point mutations in maize genes by TILLING. BMC Plant Biol 4: 12
    OpenUrlCrossRefPubMed
  42. ↵
    1. Tsai H,
    2. Howell T,
    3. Nitcher R,
    4. Missirian V,
    5. Watson B,
    6. Ngo KJ,
    7. Lieberman M,
    8. Fass J,
    9. Uauy C,
    10. Tran RK,
    11. et al.
    (2011) Discovery of rare mutations in populations: TILLING by sequencing. Plant Physiol 156: 1257–1268
    OpenUrlAbstract/FREE Full Text
  43. ↵
    1. Uauy C,
    2. Paraiso F,
    3. Colasuonno P,
    4. Tran RK,
    5. Tsai H,
    6. Berardi S,
    7. Comai L,
    8. Dubcovsky J
    (2009) A modified TILLING approach to detect induced mutations in tetraploid and hexaploid wheat. BMC Plant Biol 9: 115
    OpenUrlCrossRefPubMed
  44. ↵
    1. Wang N,
    2. Wang Y,
    3. Tian F,
    4. King GJ,
    5. Zhang C,
    6. Long Y,
    7. Shi L,
    8. Meng J
    (2008) A functional genomics resource for Brassica napus: development of an EMS mutagenized population and discovery of FAE1 point mutations by TILLING. New Phytol 180: 751–765
    OpenUrlCrossRefPubMed
  45. ↵
    1. Wang TL,
    2. Uauy C,
    3. Robson F,
    4. Till B
    (2012) TILLING in extremis. Plant Biotechnol J 10: 761–772
    OpenUrlCrossRefPubMed
  46. ↵
    1. Wijnker E,
    2. van Dun K,
    3. de Snoo CB,
    4. Lelivelt CL,
    5. Keurentjes JJ,
    6. Naharudin NS,
    7. Ravi M,
    8. Chan SW,
    9. de Jong H,
    10. Dirks R
    (2012) Reverse breeding in Arabidopsis thaliana generates homozygous parental lines from a heterozygous plant. Nat Genet 44: 467–470
    OpenUrlCrossRefPubMed
View Abstract
Back to top

Table of Contents

Print
Download PDF
Article Alerts
Email Article
Citation Tools
Request Permissions
Production of a High-Efficiency TILLING Population through Polyploidization
Helen Tsai, Victor Missirian, Kathie J. Ngo, Robert K. Tran, Simon R. Chan, Venkatesan Sundaresan, Luca Comai
Plant Physiology Apr 2013, 161 (4) 1604-1614; DOI: 10.1104/pp.112.213256
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo

Jump to section

In this issue

View this article with LENS