Methods for Genetic Analysis in the Triticeae

Korol, Abraham B.; Mester, David; Frenkel, Zeev; Ronin, Yefim

doi:10.1007/978-0-387-77489-3_6

Cited by 29 publications

(36 citation statements)

References 62 publications

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“…QTL analysis was performed with MultiQTL software package (http://www.multiqtl.com) using the general interval mapping (IM) procedure. Single-QTL and two-linked-QTL models were used for detection of genetic linkage for each trait in each environment, separately (Korol et al , 2009). Multi-environment analysis (MEA) was conducted by joint analysis of trait values scored in three environments (WL05, WW05 and WW07).…”

Section: Methodsmentioning

confidence: 99%

Variation in phosphorus and sulfur content shapes the genetic architecture and phenotypic associations within wheat grain ionome

Fatiukha

Klymiuk

Peleg

et al. 2019

Preprint

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Summary Dissection of the genetic basis of wheat ionome is crucial for understanding the physiological and biochemical processes underlying mineral accumulation in seeds, as well as for efficient crop breeding. Most of the elements essential for plants are metals stored in seeds as chelate complexes with phytic acid or sulfur‐containing compounds. We assume that the involvement of phosphorus and sulfur in metal chelation is the reason for strong phenotypic correlations within ionome. Adjustment of element concentrations for the effect of variation in phosphorus and sulfur seed content resulted in drastic change of phenotypic correlations between the elements. The genetic architecture of wheat grain ionome was characterized by quantitative trait loci (QTL) analysis using a cross between durum and wild emmer wheat. QTL analysis of the adjusted traits and two‐trait analysis of the initial traits paired with either P or S considerably improved QTL detection power and accuracy, resulting in the identification of 105 QTLs and 617 QTL effects for 11 elements. Candidate gene search revealed some potential functional associations between QTLs and corresponding genes within their intervals. Thus, we have shown that accounting for variation in P and S is crucial for understanding of the physiological and genetic regulation of mineral composition of wheat grain ionome and can be implemented for other plants.

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Section: Methodsmentioning

confidence: 99%

Variation in phosphorus and sulfur content shapes the genetic architecture and phenotypic associations within wheat grain ionome

Fatiukha

Klymiuk

Peleg

et al. 2019

Preprint

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“…1), we developed two different algorithms for the second phase, i.e., searching for the consensus solution to MCGM. The first one was named Full Frame (FF), which uses special heuristics for global discrete optimization of synchronized-TSP for all markers (unique, shared conflicting, and shared nonconflicting) [16,18]. Our numerous simulation tests show that the FF algorithm is effective up to k=10-15 populations (data sets) with a total number of shared markers N<50.…”

Section: B Algorithmsmentioning

confidence: 98%

“…2) Building consensus maps by re-analyzing the initial (raw) data: This approach is based on searching the best multilocus order corresponding to minimum sum of map lengths among all possible non-conflicting orders, i.e., testing only those orders that fit the condition that shared markers must be in shared order for any subgroup of mapping populations [16]. With this approach, consensus mapping is reduced to a specific version of TSP that can be referred to as synchronized TSP [17]).…”

Section: Two Approaches In Consensus Mappingmentioning

confidence: 99%

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Synchronized-TSP as a model for multilocus genetic consensus mapping

Mester

Ronin

Minkov

et al. 2010

2010 Sixth International Conference on Natural Computation

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Numerous mapping projects conducted on different organisms have generated an abundance of mapping data. Consequently, many multilocus maps were constructed using diverse mapping populations and marker sets for the same species. The quality of maps varied broadly between populations, marker sets, and applied software. There might be some inconsistencies between different versions of the maps for the same organism, calling for the integration of mapping information and building of consensus maps. The problem of multilocus consensus genetic mapping (MCGM) is even more challenging, compared to multilocus mapping based on one data set, due to several complications: differences in recombination rate and distribution along chromosomes, and different subsets of markers used by different labs. We developed an approach to solve MCGM problems, by searching multilocus orders with the maximum number of shared markers yielding maps with minimum total length. The approach is based on re-analysis of raw data and is implemented in a two-phase algorithm. In Phase 1, for each data set, multilocus ordering is performed combined with iterative re-sampling to evaluate the stability of marker orders. In this phase, the ordering problem is reduced to the well known Traveling Salesperson Problem (TSP). In Phase 2, consensus mapping is conducted by reducing the problem to a specific version of TSP that can be referred to as synchronized TSP. The optimal consensus order of shared markers is defined by the minimal total length of non-conflicting maps of the chromosome. This criterion includes various modifications that take into account the variation in the quality of the original data (e.g., population size, marker quality, etc.). We use our powerful Guided Evolution Strategy algorithm for discrete optimization of constrained problems that was adapted to solve MCGM problems. The developed approach was tested on a wide range of simulated data.

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“…However, with very large numbers of markers available for a mapping population, most of the markers will remain inseparable by recombination and will represent groups of co-segregating, or absolutely linked markers (AL markers). In such cases, only one marker from each group could be placed on the map that can be referred to as a framework, skeleton, or bin map; the remaining markers can then be attached to the skeleton map (Mester et al 2003 ;Korol et al 2009 ;Ronin et al 2010 ). The real situation is signifi cantly complicated by genotyping errors, which "diversify" a certain part of markers that would be identical in the ideal situation of no errors.…”

Section: Introductionmentioning

confidence: 99%

Building Ultra-Dense Genetic Maps in the Presence of Genotyping Errors and Missing Data

Ronin

Minkov

Mester

et al. 2015

Advances in Wheat Genetics: From Genome to Field

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Recent advances of genomic technologies have opened unprecedented possibilities in building high-quality ultra-dense genetic maps. However, with very large numbers of markers available for a mapping population, most of the markers will remain inseparable by recombination. Real situations are also complicated by genotyping errors, which "diversify" a certain part of the markers that would be identical in error-free situations. The higher the error rate the more diffi cult is the problem of building a reliable map. In our algorithm, we assume that error-free markers can be selected based on the presence of "twins". There is also a probability of an opposite effect, when non-identical markers may become "twins" because of genotyping errors. Thus, a certain threshold is introduced for the selection of markers with a suffi cient number of twins. The developed algorithm (implemented in MultiPoint software) enables mapping big sets of markers (~10 5 -10 6 ). Unlike some other algorithms used in building ultra-dense genetic maps, the proposed "twins" approach does not need any prior information (e.g., anchor markers), and hence can be applied to genetically poorly studied organisms.

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Methods for Genetic Analysis in the Triticeae

Cited by 29 publications

References 62 publications

Variation in phosphorus and sulfur content shapes the genetic architecture and phenotypic associations within wheat grain ionome

Variation in phosphorus and sulfur content shapes the genetic architecture and phenotypic associations within wheat grain ionome

Synchronized-TSP as a model for multilocus genetic consensus mapping

Building Ultra-Dense Genetic Maps in the Presence of Genotyping Errors and Missing Data

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