Dataset Information

Mechanisms and control of rapid genomic changes in flax.

ABSTRACT: BACKGROUND AND AIMS: The nuclear DNA of certain varieties of flax (Linum usitatissimum) can vary within a single generation when the plants are grown under specific environmental conditions. This review details the genomic variations that have been identified and associated with this environmental response. CONCLUSIONS: The variation occurs across the whole spectrum of sequence repetition and has been shown to occur in the highly repeated, middle repetitive and low copy number sequences. Although the variation has been shown to be spread throughout the genome it does not occur at random, as similar molecular events have been shown to occur repeatedly. The changes in two labile regions in the nucleus, the ribosomal RNA genes and a site-specific insertion event, have been shown to occur within the period of vegetative growth and over a relatively short period of that growth. The gradual change in total nuclear DNA that has been described would then need to have arisen through an accumulation of changes occurring over the whole, or most of the, period of growth prior to flowering. The polymorphisms that result from these rapidly occurring genomic events have also been observed in many other flax and linseed varieties as well as in the wild progenitors of flax.

SUBMITTER: Cullis CA

PROVIDER: S-EPMC4246718 | biostudies-literature | 2005 Jan

REPOSITORIES: biostudies-literature

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Mechanisms and control of rapid genomic changes in flax.

Cullis Christopher A CA

Annals of botany 20050101 1

<h4>Background and aims</h4>The nuclear DNA of certain varieties of flax (Linum usitatissimum) can vary within a single generation when the plants are grown under specific environmental conditions. This review details the genomic variations that have been identified and associated with this environmental response.<h4>Conclusions</h4>The variation occurs across the whole spectrum of sequence repetition and has been shown to occur in the highly repeated, middle repetitive and low copy number seque ...[more]

PMID: 15596467

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Project description:BackgroundFlax (Linum usitatissimum. L) is an ancient oilseed and natural fiber crop. It could be divided into three categories by use, namely oil flax, fiber flax and oil-fiber dual purpose (OF). Cultivated flax is widely used in the food and textile industry. It is of great significance to elucidate the genetic characteristics of flax collections for accelerating the process of breeding improvement in this dual purpose crop. With the development of next-generation sequencing, we can use new methods, such as SLAF-seq (specific-locus amplified fragment sequencing), to decode unknown genomes of species. In this study, a high-through sequencing of flax collections using SLAF-seq was conducted. The evolutionary tendency was defined and candidate genes associated with agronomic traits of flax species were identified by Genome-Wide Association Studying (GWAS).ResultsA flax collection consisting of 224 varieties were sequenced by SLAF-seq. In total, 346,639 SLAF tags were developed from all accessions, with an average sequencing depth of 7.19 for each accession. A total of 584,987 SNPs (single nucleotide polymorphism) with an MAF > 0.05 were identified from these SLAFs. The population structure division and phylogenetic analysis indicated a strong divergence among three kinds of flax groups. The genome-wide variation uncovered that oil flax had the highest genetic diversity and was considered to be the ancestor of fiber flax and oil-fiber flax. Sixteen associated peak SNPs for six traits were obtained by GWAS of oil-related traits using EMMAX (efficient mixed-model association eXpedited). Candidate genes and their related pathway were evaluated. A new GWAS was developed for fiber properties using the GLM (General linear model) model and a number of loci were identified.ConclusionsTo our knowledge, this is the first study on discovery multiple loci for important agronomic traits of flax species using GWAS strategy. These results will provide the highest possibility of incorporating both high fiber and good oil traits in a single variety.

Project description:Molecular markers are one of the major factors affecting genomic prediction accuracy and the cost of genomic selection (GS). Previous studies have indicated that the use of quantitative trait loci (QTL) as markers in GS significantly increases prediction accuracy compared with genome-wide random single nucleotide polymorphism (SNP) markers. To optimize the selection of QTL markers in GS, a set of 260 lines from bi-parental populations with 17,277 genome-wide SNPs were used to evaluate the prediction accuracy for seed yield (YLD), days to maturity (DTM), iodine value (IOD), protein (PRO), oil (OIL), linoleic acid (LIO), and linolenic acid (LIN) contents. These seven traits were phenotyped over four years at two locations. Identification of quantitative trait nucleotides (QTNs) for the seven traits was performed using three types of statistical models for genome-wide association study: two SNP-based single-locus (SS), seven SNP-based multi-locus (SM), and one haplotype-block-based multi-locus (BM) models. The identified QTNs were then grouped into QTL based on haplotype blocks. For all seven traits, 133, 355, and 1,208 unique QTL were identified by SS, SM, and BM, respectively. A total of 1420 unique QTL were obtained by SS+SM+BM, ranging from 254 (OIL, LIO) to 361 (YLD) for individual traits, whereas a total of 427 unique QTL were achieved by SS+SM, ranging from 56 (YLD) to 128 (LIO). SS models alone did not identify sufficient QTL for GS. The highest prediction accuracies were obtained using single-trait QTL identified by SS+SM+BM for OIL (0.929 ± 0.016), PRO (0.893 ± 0.023), YLD (0.892 ± 0.030), and DTM (0.730 ± 0.062), and by SS+SM for LIN (0.837 ± 0.053), LIO (0.835 ± 0.049), and IOD (0.835 ± 0.041). In terms of the number of QTL markers and prediction accuracy, SS+SM outperformed other models or combinations thereof. The use of all SNPs or QTL of all seven traits significantly reduced the prediction accuracy of traits. The results further validated that QTL outperformed high-density genome-wide random markers, and demonstrated that the combined use of single and multi-locus models can effectively identify a comprehensive set of QTL that improve prediction accuracy, but further studies on detection and removal of redundant or false-positive QTL to maximize prediction accuracy and minimize the number of QTL markers in GS are warranted.

Dataset Information

Mechanisms and control of rapid genomic changes in flax.

Publications

Mechanisms and control of rapid genomic changes in flax.

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