Physical mapping of 38 highly informative genetic markers to 10 intervals of chromosome 11q: integration of the physical and genetic maps

The laboratory rat (Rattus norvegicus) is a key animal model for biomedical research. However, the genetic infrastructure required for connecting phenotype and genotype in the rat is currently incomplete. Here, we report the construction and integration of two genomic maps: a dense genetic linkage map of the rat and the first radiation hybrid (RH) map of the rat. The genetic map was constructed in two F2 intercrosses (SHRSP × BN and FHH × ACI), containing a total of 4736 simple sequence length polymorphism (SSLP) markers. Allele sizes for 4328 of the genetic markers were characterized in 48 of the most commonly used inbred strains. The RH map is a lod ≥ 3 framework map, including 983 SSLPs, thereby allowing integration with markers on various genetic maps and with markers mapped on the RH panel. Together, the maps provide an integrated reference to >3000 genes and ESTs and >8500 genetic markers (5211 of our SSLPs and >3500 SSLPs developed by other groups). [Bihoreau et al. (1997); James and Tanigami, RHdb (http://www.ebi.ac.uk/RHdb/index.html); Wilder (http://www.nih.gov/niams/scientific/ratgbase); Serikawa et al. (1992); RATMAP server (http://ratmap.gen.gu.se)] RH maps (v. 2.0) have been posted on our web sites at http://goliath.ifrc.mcw.edu/LGR/index.htmlor http://curatools.curagen.com/ratmap. Both web sites provide an RH mapping server where investigators can localize their own RH vectors relative to this map. The raw data have been deposited in the RHdb database. Taken together, these maps provide the basic tools for rat genomics. The RH map provides the means to rapidly localize genetic markers, genes, and ESTs within the rat genome. These maps provide the basic tools for rat genomics. They will facilitate studies of multifactorial disease and functional genomics, allow construction of physical maps, and provide a scaffold for both directed and large-scale sequencing efforts and comparative genomics in this important experimental organism.

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Mapping the Brassica Genome

Outlook on Agriculture ◽

10.1177/003072709302200204 ◽

1993 ◽

Vol 22 (2) ◽

pp. 85-92 ◽

Cited By ~ 31

Author(s):

Derek Lydiate ◽

Andrew Sharpe ◽

Ulf Lagercrantz ◽

Isobel Parkin

Keyword(s):

Genetic Markers ◽

General Structure ◽

General Rule ◽

Brassica Species ◽

Genetic Maps ◽

Wild Plants ◽

Brassica Genome ◽

Cereal Species ◽

Wide Range ◽

Cultivated Species

The six cultivated species of Brassica furnish a wide range of crop types (including oilseed, vegetable and fodder crops) which seem quite different when observed under normal cultivation (Figure 1). However, Brassica species and a large number of other wild and cultivated species are all closely related (Figure 2) and genetic exchange through sexual crosses is possible across most of this very extensive gene pool. Traditionally, the investigation of genome organization in plants has employed cytology to study chromosomes and genetic markers to define linkage groups. Cytology is difficult in Brassica because the chromosomes are small, but the genus is very amenable to investigations using molecular-genetic markers because of the high degree of natural polymorphism. Gene homology and the general structure of the genome seems to be conserved between Brassica and related genera and modern marker technologies are freely interchangeable across this group. However, the collinearity of related chromosomes in different Brassica species has been disrupted frequently by chromosomal translocations. Thus Brassica species have quite distinct genetic maps, in contrast to cereal species where collinear homoeologous chromosomes are the general rule. The mapping of the Brassica genome will have a considerable impact on the breeding of Brassica crops. In particular, it will facilitate the transfer of beneficial genes between species and the rapid introgression of genes from wild plants into useful cultivars. These improvements in breeding should be translated into crops which are more easily adapted to suit the needs of new agronomic practices and the demands of a changing environment.

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A cytogenetically based physical map of chromosome 1B in common wheat

Genome ◽

10.1139/g93-075 ◽

1993 ◽

Vol 36 (3) ◽

pp. 548-554 ◽

Cited By ~ 51

Author(s):

R. S. Kota ◽

K. S. Gill ◽

B. S. Gill ◽

T. R. Endo

Keyword(s):

Genetic Markers ◽

Common Wheat ◽

Hexaploid Wheat ◽

Genetic Linkage ◽

Genetic Linkage Map ◽

Physical Map ◽

Wheat Chromosome ◽

Homozygous Deletion ◽

Genetic Maps ◽

Rflp Markers

We have constructed a cytogenetically based physical map of chromosome 1B in common wheat by utilizing a total of 18 homozygous deletion stocks. It was possible to divide chromosome 1B into 17 subregions. Nineteen genetic markers are physically mapped to nine subregions of chromosome 1B. Comparison of the cytological map of chromosome 1B with an RFLP-based genetic linkage map of Triticum tauschii revealed that the linear order of the genetic markers was maintained between chromosome 1B of hexaploid wheat and 1D of T. tauschii. Striking differences were observed between the physical and genetic maps in relation to the relative distances between the genetic markers. The genetic markers clustered in the middle of the genetic map were physically located in the distal regions of both arms of chromosome 1B. It is unclear whether the increased recombination in the distal regions of chromosome 1B is due to specific regions of increased recombination or a more broadly distributed increase in recombination in the distal regions of Triticeae chromosomes.Key words: common wheat, chromosome 1B, homozygous deletion lines, physical map, RFLP markers.

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GENETIC MAPPING IN SACCHAROMYCES IV. MAPPING OF TEMPERATURE-SENSITIVE GENES AND USE OF DISOMIC STRAINS IN LOCALIZING GENES

Genetics ◽

10.1093/genetics/74.1.33 ◽

1973 ◽

Vol 74 (1) ◽

pp. 33-54

Author(s):

R K Mortimer ◽

D C Hawthorne

Keyword(s):

Saccharomyces Cerevisiae ◽

Chromosome Number ◽

Genetic Mapping ◽

Genetic Markers ◽

Genetic Maps ◽

Temperature Sensitive ◽

Yeast Saccharomyces Cerevisiae ◽

Haploid Chromosome Number ◽

Number Of Genes

ABSTRACT Through use of tetrad, random spore, trisomic, and mitotic analysis procedures a large number of genes, including 48 new genetic markers, were studied for their locations on the genetic maps of the yeast Saccharomyces cerevisiae. Eighteen new centromere linked genes were discovered and all but one was located on various ones of the 16 previously-established chromosomes. Five fragments of linked genes were also assigned to chromosomes; four were located on known chromosomes while the fifth determined one arm of a new chromosome. The experiments indicate that seventeen is likely to be the haploid chromosome number in this yeast. Most chromosomes have been established by genetic means to be metacentric and their genetic lengths vary from 5 cM to approximately 400 cM. Functionally-related sets of genes generally were found to be dispersed over the genome.

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DESIGN AND ANALYSIS OF AN EFFICIENT RECURSIVE LINKING ALGORITHM FOR CONSTRUCTING LIKELIHOOD BASED GENETIC MAPS FOR A LARGE NUMBER OF MARKERS

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720007002758 ◽

2007 ◽

Vol 05 (02a) ◽

pp. 201-250 ◽

Cited By ~ 1

Author(s):

S. TEWARI ◽

S. M. BHANDARKAR ◽

J. ARNOLD

Keyword(s):

Genetic Markers ◽

Time Complexity ◽

Recursive Algorithm ◽

Genetic Maps ◽

Fungal Genome ◽

Worst Case Analysis ◽

Worst Case ◽

Computation Process ◽

Genetic Interval ◽

Using Data

A multi-locus likelihood of a genetic map is computed based on a mathematical model of chromatid exchange in meiosis that accounts for any type of bivalent configuration in a genetic interval in any specified order of genetic markers. The computational problem is to calculate the likelihood (L) and maximize L by choosing an ordering of genetic markers on the map and the recombination distances between markers. This maximum likelihood estimate (MLE) could be found either with a straightforward algorithm or with the proposed recursive linking algorithm that implements the likelihood computation process involving an iterative procedure is called Expectation Maximization (EM). The time complexity of the straightforward algorithm is exponential without bound in the number of genetic markers, and implementation of the model with a straightforward algorithm for more than seven genetic markers is not feasible, thus motivating the critical importance of the proposed recursive linking algorithm. The recursive linking algorithm decomposes the pool of genetic markers into segments and renders the model implementable for hundreds of genetic markers. The recursive algorithm is shown to reduce the order of time complexity from exponential to linear in the number of markers. The improvement in time complexity is shown theoretically by a worst-case analysis of the algorithm and supported by run time results using data on linkage group-II of the fungal genome Neurospora crassa.

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LIKELIHOOD OF A PARTICULAR ORDER OF GENETIC MARKERS AND THE CONSTRUCTION OF GENETIC MAPS

Journal of Bioinformatics and Computational Biology ◽

10.1142/s021972000800331x ◽

2008 ◽

Vol 06 (01) ◽

pp. 125-162 ◽

Cited By ~ 1

Author(s):

S. TEWARI ◽

J. ARNOLD ◽

S. M. BHANDARKAR

Keyword(s):

Genetic Markers ◽

Expectation Maximization ◽

Goodness Of Fit ◽

Genomic Sequence ◽

Probability Model ◽

Genetic Maps ◽

Model Framework ◽

Marker Data ◽

Chi Squared ◽

Genetic Interval

We model the recombination process of fungal systems via chromatid exchange in meiosis, which accounts for any type of bivalent configuration in a genetic interval in any specified order of genetic markers, for both random spore and tetrad data. First, a probability model framework is developed for two genes and then generalized for an arbitrary number of genes. Maximum likelihood estimators (MLEs) for both random and tetrad data are developed. It is shown that the MLE of recombination for tetrad data is uniformly more efficient over that from random spore data by a factor of at least 4 usually. The MLE for the generalized probability framework is computed using the expectation-maximization (EM) algorithm. Pearson's chi-squared statistic is computed as a measure of goodness of fit using a product-multinomial setup. We implement our model with genetic marker data on the whole genome of Neurospora crassa. Simulated annealing is used to search for the best order of genetic markers for each chromosome, and the goodness of fit value is evaluated for model assumptions. Inferred map orders are corroborated by genomic sequence, with the exception of linkage groups I, II, and V.

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Mapping of Escherichia coli chromosomal Tn5 and F insertions by pulsed field gel electrophoresis.

Genetics ◽

10.1093/genetics/119.2.227 ◽

1988 ◽

Vol 119 (2) ◽

pp. 227-236

Author(s):

C L Smith ◽

R D Kolodner

Keyword(s):

Escherichia Coli ◽

Physical Mapping ◽

Gel Electrophoresis ◽

Physical Map ◽

Pulsed Field Gel Electrophoresis ◽

Genetic Maps ◽

Low Resolution ◽

Transposon Tn5 ◽

Pulsed Field ◽

E Coli

Abstract A low resolution Not I physical map of Escherichia coli was recently constructed. In this report we demonstrated that this map can be used to map Tn5 and F insertions physically. The transposon, Tn5, contains Not I recognition sequences in its IS50 sequences. F plasmid contains an unmapped Not I site. Hence, the location of Tn5 and F in the chromosome can be mapped by identifying the location of the introduced Not I sites using pulsed field gel electrophoresis. The physical mapping of genetically mapped Tn5 insertions confirm the previously constructed Not I map and helps align the E. coli physical and genetic maps. The use of Tn5 can assist the construction of both physical and genetic maps for microorganisms lacking such maps. Variations on this approach will facilitate physical mapping with a wide variety of organisms, enzymes, and genetic elements.

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Genetic and physical mapping of homoeologous recombination points involving wheat chromosome 2B and rye chromosome 2R

Genome ◽

10.1139/g03-089 ◽

2004 ◽

Vol 47 (1) ◽

pp. 36-45 ◽

Cited By ~ 59

Author(s):

A J Lukaszewski ◽

K Rybka ◽

V Korzun ◽

S V Malyshev ◽

B Lapinski ◽

...

Keyword(s):

Homologous Recombination ◽

Genetic Map ◽

Physical Mapping ◽

Dna Sequences ◽

Wheat Chromosome ◽

Genetic Maps ◽

Homoeologous Recombination ◽

Chromosome Engineering ◽

Genetic And Physical Mapping ◽

Structural Differences

Wide hybrids have been used in generating genetic maps of many plant species. In this study, genetic and physical mapping was performed on ph1b-induced recombinants of rye chromosome 2R in wheat (Triticum aestivum L.). All recombinants were single breakpoint translocations. Recombination 2RS–2BS was absent from the terminal and the pericentric regions and was distributed randomly along an intercalary segment covering approximately 65% of the arm's length. Such a distribution probably resulted from structural differences at the telomeres of 2RS and wheat 2BS arm that disrupted telomeric initiation of pairing. Recombination 2RL–2BL was confined to the terminal 25% of the arm's length. A genetic map of homoeologous recombination 2R–2B was generated using relative recombination frequencies and aligned with maps of chromosomes 2B and 2R based on homologous recombination. The alignment of the short arms showed a shift of homoeologous recombination toward the centromere. On the long arms, the distribution of homoeologous recombination was the same as that of homologous recombination in the distal halves of the maps, but the absence of multiple crossovers in homoeologous recombination eliminated the proximal half of the map. The results confirm that homoeologous recombination in wheat is based on single exchanges per arm, indicate that the distribution of these single homoeologous exchanges is similar to the distribution of the first (distal) crossovers in homologues, and suggest that successive crossovers in an arm generate specific portions of genetic maps. A difference in the distribution of recombination between the short and long arms indicates that the distal crossover localization in wheat is not dictated by a restricted distribution of DNA sequences capable of recombination but by the pattern of pairing initiation, and that can be affected by structural differences. Restriction of homoeologous recombination to single crossovers in the distal part of the genetic map complicates chromosome engineering efforts targeting genes in the proximal map regions.Key words: homoeologous recombination, genetic mapping, RFLP, RAPD, wheat, rye.

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Combined Genetic and Physical Map of the Complex Genome of Agrobacterium tumefaciens

Journal of Bacteriology ◽

10.1128/jb.181.17.5160-5166.1999 ◽

1999 ◽

Vol 181 (17) ◽

pp. 5160-5166 ◽

Cited By ~ 24

Author(s):

Brad W. Goodner ◽

Brian P. Markelz ◽

M. Casey Flanagan ◽

Chris B. Crowell ◽

Jodi L. Racette ◽

...

Keyword(s):

Agrobacterium Tumefaciens ◽

Physical Mapping ◽

Physical Map ◽

Genomic Structure ◽

Genetic Maps ◽

Mapping Data ◽

Circular Chromosome ◽

Linear Chromosome ◽

Single Chromosome ◽

Complex Genome

ABSTRACT A combined genetic and physical map of the Agrobacterium tumefaciens A348 (derivative of C58) genome was constructed to address the discrepancy between initial single-chromosome genetic maps and more recent physical mapping data supporting the presence of two nonhomologous chromosomes. The combined map confirms the two-chromosome genomic structure and the correspondence of the initial genetic maps to the circular chromosome. The linear chromosome is almost devoid of auxotrophic markers, which probably explains why it was missed by genetic mapping studies.

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A High-Resolution Genetic Map for the Laboratory Rat

10.1101/268227 ◽

2018 ◽

Author(s):

John Littrell ◽

Shirng-Wern Tsaih ◽

Amelie Baud ◽

Pasi Rastas ◽

Leah Solberg-Woods ◽

...

Keyword(s):

High Resolution ◽

Genetic Markers ◽

Genetic Map ◽

Complex Traits ◽

Substantial Improvement ◽

Genetic Maps ◽

Female Rats ◽

Recombination Rates ◽

Male And Female Rats ◽

First Time

ABSTRACTAn accurate and high-resolution genetic map is critical for mapping complex traits, yet the resolution of the current rat genetic map is far lower than human and mouse, and has not been updated since the original ensen-Seaman map in 2004. For the first time, we have refined the rat genetic map to sub-centimorgan (cM) r solution (<0.02 cM) by using 95,769 genetic markers and 870 informative meioses from a cohort of 528 heterogeneous stock (HS) rats. Global recombination rates in the revised sex-averaged map (0.66 cM/Mb) did not difeer compared to the historical map (0.65 cM/Mb); however, substantial refinement was made to the localization of highly recombinant regions within the revised map. Also for the first time, sex-specific rat genetic maps were generated, which revealed both genomewide and fine-scale variation in recombination rates between male and female rats. Reanalysis of multiple quantitative trait loci (QTL) using the historical and refined rat genetic maps demonstrated marked changes to QTL localization, shape, and effect size. As a resource to the rat research community, we have provided revised centimorgan positions for all physical positions within the rat genome and commonly used genetic markers for trait mapping, including 44,828 SSLP markers and the RATDIV genotyping array. Collectively, this study provides a substantial improvement to the rat genetic map and an unprecedented resource for analysis of complex traits and recombination in the rat.

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