Marker-assisted Plant Breeding Principles And Practices Pdf Creator
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- Genomic Selection in Plant Breeding
- Genomic Designing of Climate-Smart Pulse Crops
- Principles of Plant Genetics and Breeding Dedication
Flowering time genes have a strong influence on successful reproduction and life cycle adaptation. However, their regulation is highly complex and only well understood in diploid model systems. For crops with a polyploid background from the genus Brassica , data on flowering time gene variation are scarce, although indispensable for modern breeding techniques like marker-assisted breeding. We have deep-sequenced all paralogs of 35 Arabidopsis thaliana flowering regulators using Sequence Capture followed by Illumina sequencing in two selected accessions of the vegetable species Brassica rapa and Brassica oleracea , respectively. Using these data, we were able to call SNPs, InDels and copy number variations CNVs for genes from the total flowering time network including central flowering regulators, but also genes from the vernalisation pathway, the photoperiod pathway, temperature regulation, the circadian clock and the downstream effectors.
Genomic Selection in Plant Breeding
Flowering time genes have a strong influence on successful reproduction and life cycle adaptation. However, their regulation is highly complex and only well understood in diploid model systems. For crops with a polyploid background from the genus Brassica , data on flowering time gene variation are scarce, although indispensable for modern breeding techniques like marker-assisted breeding.
We have deep-sequenced all paralogs of 35 Arabidopsis thaliana flowering regulators using Sequence Capture followed by Illumina sequencing in two selected accessions of the vegetable species Brassica rapa and Brassica oleracea , respectively.
Using these data, we were able to call SNPs, InDels and copy number variations CNVs for genes from the total flowering time network including central flowering regulators, but also genes from the vernalisation pathway, the photoperiod pathway, temperature regulation, the circadian clock and the downstream effectors.
Comparing the results to a complementary data set from the allotetraploid species Brassica napus , we detected rearrangements in B. Those data are both a valuable resource for flowering time research in those vegetable species, as well as a contribution to speciation genetics. The genus Brassica is highly diverse. It contains many phenotypically extremely different vegetable, turnip and oil crops, among them garden turnip, Chinese cabbage and Pak Choi Brassica rapa and cabbage, broccoli, cauliflower, Brussels sprouts, kale, kohlrabi and savoy Brassica oleracea Paterson et al.
Both species are also diploid progenitors of B. Adequate regulation of flowering and flowering time is crucial for crop production especially for leafy vegetable crops as in B. Early bolting limits vegetable growth and can therefore severely decrease yield. On the other hand, complete inhibition of flowering interferes with seed production.
Knowledge about the impact of flowering time gene variation is therefore crucial for successful vegetable breeding.
All the same, vernalisation and day length have major effects on flowering time. Although B. Whereas A. As in A. Foregoing studies have identified different orthologous copies of FLC and FT as strong candidates for flowering time regulation in B.
Brassica rapa has been found to carry 2 copies of Bra. FT Zhang et al. FLC Schranz et al. CO A01, A03, A In contrast, B. FLC Razi et al. CO C01, C03, and C A07 , often referred to as BrFT2 , was found to underlie a strong QTL for flowering time, possibly due to a transposon insertion in the mapping parent R-o Zhang et al.
Another FLC copy, Bra. A10 , also referred to as BrFLC1, was associated to flowering time due to alternative splicing via variation in intron 6 Yuan et al. Different patterns of functional polymorphisms, including premature stop codons, non-synonymous SNPs and differential promotor structure have been found for Bol.
FLC copies Okazaki et al. C09 Okazaki et al. Most previous research has therefore focused on the central flowering regulators FLC, FT , and CO , whereas other genes which might modulate the flowering response have been largely ignored. In order to provide a more complete description of genetic variation in central flowering time genes, we deep-sequenced representatives of two B.
All those sequence variants are potentially influential on the phenotype and therefore an interesting resource to vegetable breeders. Comparison to previous data from the same genes in the allopolyploid hybrid species B. Two inbred B. The two B. Both had been used as parents for DH populations before Bagheri et al.
Leaf material from 4 week old plants grown in pots in the greenhouse was collected and immediately shock-frozen in liquid nitrogen.
DNA quantity and purity was further checked on 0. The four samples were re-sequenced using targeted deep sequencing along with B. In brief, flowering time genes involving the most important flowering regulation pathways as known from Arabidopsis thaliana were checked for Brassica orthologs. In order to enrich for the respective target regions, a bait pool was constructed based on selected sequences from B.
A detailed description of the bait pool development can be found in Schiessl et al. The bait pool consisted of bait groups, 63 bait groups for B.
As a short summary, baits were first developed in the program eArrayXD using sequences from B. After a preliminary sequencing test with four diverse B. This improved the specificity of the bait pool Schiessl et al. Custom bait production was carried out by Agilent Technologies Agilent Inc.
Reads were mapped both onto version 4. Removal of duplicates, sorting and indexing was carried out with samtools version 0. Alignments were visualized using the IGV browser version 2. For InDel calling, a separate mapping using Bowtie2 Langmead and Salzberg, was performed, as described in Schmutzer et al.
Removal of duplicates, sorting and indexing was again carried out with samtools version 0. As it turned out that Bol. C02 is likely to be misassembled in the reference genome, we cut out all Bol. FLC copies from the reference genome except Bol. C02 , which we replaced by Bol. The resulting fasta file was used as artificial genome and the mapping was performed accordingly. We first defined regions with sufficient coverage normalized mean coverage at least 10 for B.
A region is defined as being covered by at least two overlapping reads. The coverage was calculated using the bedtools software with multiBamCov Quinlan, and normalized to region length, genotype read number and genome size. Moreover, a bed file with the positions of those regions was compared to the gene positions of the respective reference genomes.
In order to have comparable coverages, we used the gene positions wherever possible, and calculated the normalized coverages on those positions. In a second step, we compared the coverage ratio between both genotypes of a species. For all other cases, we compared the coverage of this region to the respective coverage obtained for the orthologous region in a population of B.
In all other cases, we assumed a duplication for the other genotype. Calling of single nucleotide polymorphisms SNPs was performed with the algorithm mpileup in the samtools toolkit Li H. Calling of InDels was performed based on a separate alignment using Bowtie2. A final InDel calling was then performed as described above. SNPs were filtered for a minimum mapping quality of 50 and a read depth of at least 10, and InDels were filtered for a minimum mapping quality of 30 and a read depth of at least 10 using vcftools Danecek et al.
As InDel calling with this read length and mapping parameters is limited to InDels of a length of 18 bp, we conducted another approach where we searched for regions of zero coverage in a 19 bp window which were strongly covered in the respective other genotype using the SOAP2 mapping, while having a low coverage using the realigned Bowtie2 mapping in the same genotype.
This approach ensures for the detection of larger deletions, which are not due to reference mapping problems. The two accessions of B. In the two B. We therefore analyzed regions for B. For the two B. In total, we analyzed regions for B. When mapping the sequencing reads from B. A02 had a strongly reduced normalized coverage in both B. The B. Coverage Raw read depth for both genotypes of B. A02 BnaA02gD as an example for early rearrangements in the B.
The height of the gray area in each lane marks the raw read depth and is shown from 1 to as read count per base as non-dimensional number. The red bar on top marks the gene extension. Diagram showing gene copies which were lost, gained or rearranged in B. The diagram also shows genetic regions which were considered to be pseudogenes in B.
Gene copies without changes are not displayed. In contrast, BnaCnngD Bna. Cnn showed a much higher coverage in both B. In contrast to B. A03 , BnaA10gD Bna. A10b , BnaAnngD Bna. Ann and BnaAnngD Bna. Annb in B. All the same, when mapping the B. This might either indicate mismapping or missing information in the reference genome. Doing so for B.
Genomic Designing of Climate-Smart Pulse Crops
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Principles of Plant Genetics and Breeding Dedication
English Edition. Meaning Makers. Recursive Macroeconomic Theory. Fachkunde Fahrradtechnik.
Print Send Add Share. Notes Abstract: Genomic selection is increasingly considered vital to accelerate genetic improvement, but prior to the research described here it was unknown how accurate prediction models remained across environments and ages. The work presented here pioneered the application of genomic selection in Pinus taeda, and demonstrated the remarkable gains that can be achieved, as long as prediction models are used at the relevant selection age and within the breeding zone in which they were estimated Chapter 2. However, the application of quantitative genetics in plant and animal breeding, including the work described in Chapter 2, focused on additive models. Thus, the goal of the follow-up study Chapter 3 was to include non-additive effects in genomic selection models, in the form of realized genomic relationships matrices.
Introduction to the principles of plant genetics with an emphasis on the structure and function of economically important plant genomes.