1
|
Li H, Singh S, Bhavani S, Singh RP, Sehgal D, Basnet BR, Vikram P, Burgueno-Ferreira J, Huerta-Espino J. Identification of Genomic Associations for Adult Plant Resistance in the Background of Popular South Asian Wheat Cultivar, PBW343. FRONTIERS IN PLANT SCIENCE 2016; 7:1674. [PMID: 27877188 PMCID: PMC5099247 DOI: 10.3389/fpls.2016.01674] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 10/24/2016] [Indexed: 06/01/2023]
Abstract
Rusts, a fungal disease as old as its host plant wheat, has caused havoc for over 8000 years. As the rust pathogens can evolve into new virulent races which quickly defeat the resistance that primarily rely on race specificity, adult plant resistance (APR) has often been found to be race non-specific and hence is considered to be a more reliable and durable strategy to combat this malady. Over decades sets of donor lines have been identified at International Maize and Wheat Improvement Center (CIMMYT) representing a wide range of APR sources in wheat. In this study, using nine donors and a common parent "PBW343," a popular Green Revolution variety at CIMMYT, the nested association mapping (NAM) population of 1122 lines was constructed to understand the APR genetics underlying these founder lines. Thirty-four QTL were associated with APR to rusts, and 20 of 34 QTL had pleiotropic effects on SR, YR and LR resistance. Three chromosomal regions, associated with known APR genes (Sr58/Yr29/Lr46, Sr2/Yr30/Lr27, and Sr57/Yr18/Lr34), were also identified, and 13 previously reported QTL regions were validated. Of the 18 QTL first detected in this study, 7 were pleiotropic QTL, distributing on chromosomes 3A, 3B, 6B, 3D, and 6D. The present investigation revealed the genetic relationship of historical APR donor lines, the novel knowledge on APR, as well as the new analytical methodologies to facilitate the applications of NAM design in crop genetics. Results shown in this study will aid the parental selection for hybridization in wheat breeding, and envision the future rust management breeding for addressing potential threat to wheat production and food security.
Collapse
Affiliation(s)
- Huihui Li
- International Maize and Wheat Improvement Center (CIMMYT)Texcoco, Mexico
- Institute of Crop Science, Chinese Academy of Agricultural SciencesBeijing, China
| | - Sukhwinder Singh
- International Maize and Wheat Improvement Center (CIMMYT)Texcoco, Mexico
| | - Sridhar Bhavani
- International Maize and Wheat Improvement Center (CIMMYT)Texcoco, Mexico
| | - Ravi P. Singh
- International Maize and Wheat Improvement Center (CIMMYT)Texcoco, Mexico
| | - Deepmala Sehgal
- International Maize and Wheat Improvement Center (CIMMYT)Texcoco, Mexico
| | - Bhoja R. Basnet
- International Maize and Wheat Improvement Center (CIMMYT)Texcoco, Mexico
| | - Prashant Vikram
- International Maize and Wheat Improvement Center (CIMMYT)Texcoco, Mexico
| | | | - Julio Huerta-Espino
- Campo Experimental Valle de México, Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, Universidad Autónoma ChapingoTexcoco, Mexico
| |
Collapse
|
2
|
Guo NL, Wan YW. Network-based identification of biomarkers coexpressed with multiple pathways. Cancer Inform 2014; 13:37-47. [PMID: 25392692 PMCID: PMC4218687 DOI: 10.4137/cin.s14054] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2014] [Revised: 06/25/2014] [Accepted: 06/29/2014] [Indexed: 02/07/2023] Open
Abstract
Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson’s correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson’s correlation networks when evaluated with MSigDB database.
Collapse
Affiliation(s)
- Nancy Lan Guo
- Mary Babb Randolph Cancer Center/School of Public Health, West Virginia University, Morgantown, WV, USA
| | - Ying-Wooi Wan
- Mary Babb Randolph Cancer Center/School of Public Health, West Virginia University, Morgantown, WV, USA
| |
Collapse
|
3
|
Two-phase analysis in consensus genetic mapping. G3-GENES GENOMES GENETICS 2012; 2:537-49. [PMID: 22670224 PMCID: PMC3362937 DOI: 10.1534/g3.112.002428] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2012] [Accepted: 03/01/2012] [Indexed: 01/25/2023]
Abstract
Numerous mapping projects conducted on different species have generated an abundance of mapping data. Consequently, many multilocus maps have been constructed using diverse mapping populations and marker sets for the same organism. The quality of maps varies broadly among populations, marker sets, and software used, necessitating efforts to integrate the mapping information and generate consensus maps. The problem of consensus genetic mapping (MCGM) is by far more challenging compared with genetic mapping based on a single dataset, which by itself is also cumbersome. The additional complications introduced by consensus analysis include inter-population differences in recombination rate and exchange distribution along chromosomes; variations in dominance of the employed markers; and use of different subsets of markers in different labs. Hence, it is necessary to handle arbitrary patterns of shared sets of markers and different level of mapping data quality. In this article, we introduce a two-phase approach for solving MCGM. In phase 1, for each dataset, multilocus ordering is performed combined with iterative jackknife resampling to evaluate the stability of marker orders. In this phase, the ordering problem is reduced to the well-known traveling salesperson problem (TSP). Namely, for each dataset, we look for order that gives minimum sum of recombination distances between adjacent markers. In phase 2, the optimal consensus order of shared markers is selected from the set of allowed orders and gives the minimal sum of total lengths of nonconflicting maps of the chromosome. This criterion may be used in different modifications to take into account the variation in quality of the original data (population size, marker quality, etc.). In the foregoing formulation, consensus mapping is considered as a specific version of TSP that can be referred to as “synchronized TSP.” The conflicts detected after phase 1 are resolved using either a heuristic algorithm over the entire chromosome or an exact/heuristic algorithm applied subsequently to the revealed small non-overlapping regions with conflicts separated by non-conflicting regions. The proposed approach was tested on a wide range of simulated data and real datasets from maize.
Collapse
|
4
|
Affiliation(s)
- Nancy Lan Guo
- Mary Babb Randolph Cancer Center/Department of Community Medicine, School of Medicine, West Virginia University, Morgantown, WV 26506-9300
| |
Collapse
|
5
|
Wu J, Jenkins JN, McCarty JC, Lou XY. Comparisons of four approximation algorithms for large-scale linkage map construction. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2011; 123:649-655. [PMID: 21611760 PMCID: PMC3172867 DOI: 10.1007/s00122-011-1614-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2010] [Accepted: 05/09/2011] [Indexed: 05/30/2023]
Abstract
Efficient construction of large-scale linkage maps is highly desired in current gene mapping projects. To evaluate the performance of available approaches in the literature, four published methods, the insertion (IN), seriation (SER), neighbor mapping (NM), and unidirectional growth (UG) were compared on the basis of simulated F(2) data with various population sizes, interferences, missing genotype rates, and mis-genotyping rates. Simulation results showed that the IN method outperformed, or at least was comparable to, the other three methods. These algorithms were also applied to a real data set and results showed that the linkage order obtained by the IN algorithm was superior to the other methods. Thus, this study suggests that the IN method should be used when constructing large-scale linkage maps.
Collapse
Affiliation(s)
- Jixiang Wu
- Department of Plant Sciences, Mississippi State University, Mississippi State, MS 39762, USA.
| | | | | | | |
Collapse
|
6
|
A rearrangement of the Z chromosome topology influences the sex-linked gene display in the European corn borer, Ostrinia nubilalis. Mol Genet Genomics 2011; 286:37-56. [DOI: 10.1007/s00438-011-0624-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2010] [Accepted: 04/16/2011] [Indexed: 12/22/2022]
|
7
|
Nascimento M, Cruz CD, Peternelli LA, Campana ACM. Comparison between simulated annealing algorithms and rapid chain delineation in the construction of genetic maps. Genet Mol Biol 2010; 33:398-407. [PMID: 21637501 PMCID: PMC3036851 DOI: 10.1590/s1415-47572010005000033] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2009] [Accepted: 11/17/2009] [Indexed: 11/21/2022] Open
Abstract
The efficiency of simulated annealing algorithms and rapid chain delineation in establishing the best linkage order, when constructing genetic maps, was evaluated. Linkage refers to the phenomenon by which two or more genes, or even more molecular markers, can be present in the same chromosome or linkage group. In order to evaluate the capacity of algorithms, four F2 co-dominant populations, 50, 100, 200 and 1000 in size, were simulated. For each population, a genome with four linkage groups (100 cM) was generated. The linkage groups possessed 51, 21, 11 and 6 marks, respectively, and a corresponding distance of 2, 5, 10 and 20 cM between adjacent marks, thereby causing various degrees of saturation. For very saturated groups, with an adjacent distance between marks of 2 cM and in greater number, i.e., 51, the method based upon stochastic simulation by simulated annealing presented orders with distances equivalent to or lower than rapid chain delineation. Otherwise, the two methods were commensurate through presenting the same SARF distance.
Collapse
Affiliation(s)
- Moysés Nascimento
- Departamento de Estatística, Universidade Federal de Viçosa, Viçosa, MG Brazil
| | | | | | | |
Collapse
|
8
|
Wu Y, Bhat PR, Close TJ, Lonardi S. Efficient and accurate construction of genetic linkage maps from the minimum spanning tree of a graph. PLoS Genet 2008; 4:e1000212. [PMID: 18846212 PMCID: PMC2556103 DOI: 10.1371/journal.pgen.1000212] [Citation(s) in RCA: 371] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2008] [Accepted: 09/02/2008] [Indexed: 11/18/2022] Open
Abstract
Genetic linkage maps are cornerstones of a wide spectrum of biotechnology applications, including map-assisted breeding, association genetics, and map-assisted gene cloning. During the past several years, the adoption of high-throughput genotyping technologies has been paralleled by a substantial increase in the density and diversity of genetic markers. New genetic mapping algorithms are needed in order to efficiently process these large datasets and accurately construct high-density genetic maps. In this paper, we introduce a novel algorithm to order markers on a genetic linkage map. Our method is based on a simple yet fundamental mathematical property that we prove under rather general assumptions. The validity of this property allows one to determine efficiently the correct order of markers by computing the minimum spanning tree of an associated graph. Our empirical studies obtained on genotyping data for three mapping populations of barley (Hordeum vulgare), as well as extensive simulations on synthetic data, show that our algorithm consistently outperforms the best available methods in the literature, particularly when the input data are noisy or incomplete. The software implementing our algorithm is available in the public domain as a web tool under the name MSTmap. Genetic linkage maps are cornerstones of a wide spectrum of biotechnology applications. In recent years, new high-throughput genotyping technologies have substantially increased the density and diversity of genetic markers, creating new algorithmic challenges for computational biologists. In this paper, we present a novel algorithmic method to construct genetic maps based on a new theoretical insight. Our approach outperforms the best methods available in the scientific literature, particularly when the input data are noisy or incomplete.
Collapse
Affiliation(s)
- Yonghui Wu
- Department of Computer Science and Engineering, University of California Riverside, Riverside, California, United States of America
| | - Prasanna R. Bhat
- Department of Botany and Plant Sciences, University of California Riverside, Riverside, California, United States of America
| | - Timothy J. Close
- Department of Botany and Plant Sciences, University of California Riverside, Riverside, California, United States of America
| | - Stefano Lonardi
- Department of Computer Science and Engineering, University of California Riverside, Riverside, California, United States of America
- * E-mail:
| |
Collapse
|
9
|
Jackson BN, Schnable PS, Aluru S. Consensus genetic maps as median orders from inconsistent sources. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2008; 5:161-171. [PMID: 18451426 DOI: 10.1109/tcbb.2007.70221] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
A genetic map is an ordering of genetic markers calculated from a population of known lineage. While traditionally a map has been generated from a single population for each species, recently researchers have created maps from multiple populations. In the face of these new data, we address the need to find a consensus map--a map that combines the information from multiple partial and possibly inconsistent input maps. We model each input map as a partial order and formulate the consensus problem as finding a median partial order. Finding the median of multiple total orders (preferences or rankings)is a well studied problem in social choice. We choose to find the median using the weighted symmetric difference distance, a more general version of both the symmetric difference distance and the Kemeny distance. Finding a median order using this distance is NP-hard. We show that for our chosen weight assignment, a median order satisfies the positive responsiveness, extended Condorcet,and unanimity criteria. Our solution involves finding the maximum acyclic subgraph of a weighted directed graph. We present a method that dynamically switches between an exact branch and bound algorithm and a heuristic algorithm, and show that for real data from closely related organisms, an exact median can often be found. We present experimental results using seven populations of the crop plant Zea mays.
Collapse
Affiliation(s)
- Benjamin N Jackson
- Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50014, USA.
| | | | | |
Collapse
|
10
|
Abstract
The goal of linkage mapping is to find the true order of loci from a chromosome. Since the number of possible orders is large even for a modest number of loci, the problem of finding the optimal solution is known as a NP-hard problem or traveling salesman problem (TSP). Although a number of algorithms are available, many either are low in the accuracy of recovering the true order of loci or require tremendous amounts of computational resources, thus making them difficult to use for reconstructing a large-scale map. We developed in this article a novel method called unidirectional growth (UG) to help solve this problem. The UG algorithm sequentially constructs the linkage map on the basis of novel results about additive distance. It not only is fast but also has a very high accuracy in recovering the true order of loci according to our simulation studies. Since the UG method requires n-1 cycles to estimate the ordering of n loci, it is particularly useful for estimating linkage maps consisting of hundreds or even thousands of linked codominant loci on a chromosome.
Collapse
Affiliation(s)
- Yuan-De Tan
- Human Genetics Center, School of Public Health, University of Texas, Houston 77030, USA
| | | |
Collapse
|
11
|
Jackson BN, Aluru S, Schnable PS. Consensus genetic maps: a graph theoretic approach. PROCEEDINGS. IEEE COMPUTATIONAL SYSTEMS BIOINFORMATICS CONFERENCE 2006:35-43. [PMID: 16447960 DOI: 10.1109/csb.2005.26] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
A genetic map is an ordering of genetic markers constructed from genetic linkage data for use in linkage studies and experimental design. While traditional methods have focused on constructing maps from a single population study, increasingly maps are generated for multiple lines and populations of the same organism. For example, in crop plants, where the genetic variability is high, researchers have created maps for many populations. In the face of these new data, we address the increasingly important problem of generating a consensus map - an ordering of all markers in the various population studies. In our method, each input map is treated as a partial order on a set of markers. To find the most consistent order shared between maps, we model the partial orders as directed graphs. We create an aggregate by merginging the transitive closure of the input graphs and taking the transitive reduction of the result. In this process, cycles may need to be broken to resolve inconsistencies between the inputs. The cycle breaking problem is NP-hard, but the problem size depends upon the scope of the inconsistency between the input graphs, which will be local if the input graphs are from closely related organisms. We present results of running the resulting software on maps generated from seven populations of the crop plant Zea Mays.
Collapse
Affiliation(s)
- Benjamin N Jackson
- Dept. of Electrical and Computer Engineering, Iowa State University, Ames, IA 50010, USA.
| | | | | |
Collapse
|
12
|
Mester DI, Ronin YI, Korostishevsky MA, Pikus VL, Glazman AE, Korol AB. Multilocus consensus genetic maps (MCGM): formulation, algorithms, and results. Comput Biol Chem 2005; 30:12-20. [PMID: 16301000 DOI: 10.1016/j.compbiolchem.2005.09.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2005] [Revised: 09/30/2005] [Accepted: 09/30/2005] [Indexed: 11/18/2022]
Abstract
In process of creating genetic maps different labs/research groups obtain overlapping parts of the map. Merging these parts into one integrative map is based on looking for maximum shared marker orders among the maps. Really, not all shared markers of such maps have consensus order that obstructs building of the integrative maps. In this paper we propose a new approach to build verified multilocus consensus genetic maps in which shared markers always are integrated in stable consensus order. The approach is based on combined analysis of initial mapping data rather than manipulating with previously constructed maps. We show that more effective and reliable solutions may be obtained based on "synchronized ordering" facilitated by cycles of "re-sampling-->ordering-->removing unstable markers". The proposed formulation of consensus genetic mapping can be considered as a version of traveling salesperson problem (TSP) that we refer to as synchronized-TSP. From the viewpoint of optimization, synchronized-TSP belongs to discrete constrained optimization problems. Earlier we developed new powerful and fast guided evolution strategy algorithms for some types of discrete constrained optimization. These algorithms were used here as a basis for solving more challenging problems of consensual marker ordering.
Collapse
Affiliation(s)
- D I Mester
- Institute of Evolution, University of Haifa, Haifa 31905, Israel
| | | | | | | | | | | |
Collapse
|
13
|
Abstract
Mapping markers from linkage data continues to be a task performed in many genetic epidemiological studies. Data collected in a study may be used to refine published map estimates and a study may use markers that do not appear in any published map. Furthermore, inaccuracies in meiotic maps can seriously bias linkage findings. To make best use of the available marker information, multilocus linkage analyses are performed. However, two computational issues greatly limit the number of markers currently mapped jointly; the number of candidate marker orders increases exponentially with marker number and computing exact multilocus likelihoods on general pedigrees is computationally demanding. In this article, a new Markov chain Monte Carlo (MCMC) approach that solves both these computational problems is presented. The MCMC approach allows many markers to be mapped jointly, using data observed on general pedigrees with unobserved individuals. The performance of the new mapping procedure is demonstrated through the analysis of simulated and real data. The MCMC procedure performs extremely well, even when there are millions of candidate orders, and gives results superior to those of CRI-MAP.
Collapse
Affiliation(s)
- Andrew W George
- Program in Public Health Genetics, University of Iowa, Iowa City, 52242, USA.
| |
Collapse
|
14
|
Mester DI, Ronin YI, Nevo E, Korol AB. Fast and high precision algorithms for optimization in large-scale genomic problems. Comput Biol Chem 2005; 28:281-90. [PMID: 15548455 DOI: 10.1016/j.compbiolchem.2004.08.003] [Citation(s) in RCA: 36] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2004] [Revised: 08/16/2004] [Accepted: 08/16/2004] [Indexed: 11/19/2022]
Abstract
There are several very difficult problems related to genetic or genomic analysis that belong to the field of discrete optimization in a set of all possible orders. With n elements (points, markers, clones, sequences, etc.), the number of all possible orders is n!/2 and only one of these is considered to be the true order. A classical formulation of a similar mathematical problem is the well-known traveling salesperson problem model (TSP). Genetic analogues of this problem include: ordering in multilocus genetic mapping, evolutionary tree reconstruction, building physical maps (contig assembling for overlapping clones and radiation hybrid mapping), and others. A novel, fast and reliable hybrid algorithm based on evolution strategy and guided local search discrete optimization was developed for TSP formulation of the multilocus mapping problems. High performance and high precision of the employed algorithm named guided evolution strategy (GES) allows verification of the obtained multilocus orders based on different computing-intensive approaches (e.g., bootstrap or jackknife) for detection and removing unreliable marker loci, hence, stabilizing the resulting paths. The efficiency of the proposed algorithm is demonstrated on standard TSP problems and on simulated data of multilocus genetic maps up to 1000 points per linkage group.
Collapse
Affiliation(s)
- D I Mester
- Institute of Evolution, University of Haifa, Haifa 31905, Israel
| | | | | | | |
Collapse
|
15
|
Mester D, Ronin Y, Minkov D, Nevo E, Korol A. Constructing large-scale genetic maps using an evolutionary strategy algorithm. Genetics 2004; 165:2269-82. [PMID: 14704202 PMCID: PMC1462914 DOI: 10.1093/genetics/165.4.2269] [Citation(s) in RCA: 87] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
This article is devoted to the problem of ordering in linkage groups with many dozens or even hundreds of markers. The ordering problem belongs to the field of discrete optimization on a set of all possible orders, amounting to n!/2 for n loci; hence it is considered an NP-hard problem. Several authors attempted to employ the methods developed in the well-known traveling salesman problem (TSP) for multilocus ordering, using the assumption that for a set of linked loci the true order will be the one that minimizes the total length of the linkage group. A novel, fast, and reliable algorithm developed for the TSP and based on evolution-strategy discrete optimization was applied in this study for multilocus ordering on the basis of pairwise recombination frequencies. The quality of derived maps under various complications (dominant vs. codominant markers, marker misclassification, negative and positive interference, and missing data) was analyzed using simulated data with approximately 50-400 markers. High performance of the employed algorithm allows systematic treatment of the problem of verification of the obtained multilocus orders on the basis of computing-intensive bootstrap and/or jackknife approaches for detecting and removing questionable marker scores, thereby stabilizing the resulting maps. Parallel calculation technology can easily be adopted for further acceleration of the proposed algorithm. Real data analysis (on maize chromosome 1 with 230 markers) is provided to illustrate the proposed methodology.
Collapse
Affiliation(s)
- D Mester
- Institute of Evolution, University of Haifa, Haifa 31905, Israel
| | | | | | | | | |
Collapse
|
16
|
Mester DI, Ronin YI, Hu Y, Peng J, Nevo E, Korol AB. Efficient multipoint mapping: making use of dominant repulsion-phase markers. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2003; 107:1102-1112. [PMID: 12928774 DOI: 10.1007/s00122-003-1305-1] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2002] [Accepted: 01/27/2003] [Indexed: 05/24/2023]
Abstract
The paper is devoted to the problem of multipoint gene ordering with a particular focus on "dominance" complication that acts differently in conditions of coupling-phase and repulsion-phase markers. To solve the problem we split the dataset into two complementary subsets each containing shared codominant markers and dominant markers in the coupling-phase only. Multilocus ordering in the proposed algorithm is based on pairwise recombination frequencies and using the well-known travelling salesman problem (TSP) formalization. To obtain accurate results, we developed a multiphase algorithm that includes synchronized-marker ordering of two subsets assisted by re-sampling-based map verification, combining the resulting maps into an integrated map followed by verification of the integrated map. A new synchronized Evolution-Strategy discrete optimization algorithm was developed here for the proposed multilocus ordering approach in which common codominant markers facilitate stabilization of the marker order of the two complementary maps. High performance of the employed algorithm allows systematic treatment for the problem of verification of the obtained multilocus orders, based on computing-intensive bootstrap and jackknife technologies for detection and removing unreliable marker scores. The efficiency of the proposed algorithm was demonstrated on simulated and real data.
Collapse
Affiliation(s)
- D I Mester
- Institute of Evolution, University of Haifa, Mt. Carmel, Haifa 31905, Israel
| | | | | | | | | | | |
Collapse
|
17
|
Hackett CA, Pande B, Bryan GJ. Constructing linkage maps in autotetraploid species using simulated annealing. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2003; 106:1107-1115. [PMID: 12671760 DOI: 10.1007/s00122-002-1164-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2002] [Accepted: 08/26/2002] [Indexed: 05/24/2023]
Abstract
In this paper we demonstrate how molecular markers segregating in a full-sib autotetraploid mapping population can be ordered to form a linkage map using simulated annealing. This approach facilitates the examination of orders close to the optimum to see which marker placings are fixed and identify the markers whose position is less certain. A simulation study investigates the effects of population size, marker spacing, ratio of dominant to codominant markers, typing errors and missing values. The method is applied to map 30 amplified fragment length polymorphism and microsatellite markers on linkage group IV of potato.
Collapse
Affiliation(s)
- C A Hackett
- Biomathematics and Statistics Scotland, Scottish Crop Research Institute, Invergowrie, Dundee, Scotland DD2 5DE.
| | | | | |
Collapse
|
18
|
Doerge RW, Weir BS, Zeng ZB. Statistical issues in the search for genes affecting quantitative traits in experimental populations. Stat Sci 1997. [DOI: 10.1214/ss/1030037909] [Citation(s) in RCA: 71] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
19
|
Abstract
Because of the difficulty of performing full likelihood analysis over multiple loci and the large numbers of possible orders, a number of methods have been proposed for quickly evaluating orders and, to a lesser extent, for generating good orders. A new method is proposed which uses a function which is moderately laborious to compute, the sum of lod scores between all pairs of loci. This function can be smoothly minimized by initially allowing the loci to be placed anywhere in space, and only subsequently constraining them to lie along a one-dimensional map. Application of this approach to sample data suggests that it has promise and might usefully be combined with other methods when loci need to be ordered.
Collapse
Affiliation(s)
- D Curtis
- Academic Department of Psychiatry, St Mary's Hospital Medical School, London
| |
Collapse
|