Literature DB >> 17934869

Mapping functions.

Yuan-De Tan1, Myriam Fornage.   

Abstract

Accurate estimation of map distance between markers is important for the construction of large-scale linkage maps because it provides reliable and useful linkage information of markers on chromosomes. How to improve accuracy of estimating map distances depends on an appropriate mapping function. We used the coefficient of coincidence to integrate the Haldane function, in which crossovers are assumed to be independent and the Morgan function, in which crossovers are assumed to be interfered, and produce a new mapping function. The mapping function based on positive interference is referred to as the positive function and that on negative interference as the negative function. In these two mapping functions, map distances between loci are determined by both recombination frequencies and the coefficient of coincidence. We applied our mapping functions to four examples and show that our map estimates have much higher goodness-of-fit to the observed mapping data than the Haldane and Kosambi functions. Therefore, they can provide much more precise estimates of map distances than the two conventional mapping functions. Furthermore, our mapping functions produced almost linear (additive) map distances.

Entities:  

Mesh:

Year:  2007        PMID: 17934869     DOI: 10.1007/s10709-007-9207-9

Source DB:  PubMed          Journal:  Genetica        ISSN: 0016-6707            Impact factor:   1.082


  30 in total

1.  Negative crossover interference in maize translocation heterozygotes.

Authors:  D L Auger; W F Sheridan
Journal:  Genetics       Date:  2001-12       Impact factor: 4.562

2.  Crossing-over and interference in a multiply marked chromosome arm of Neurospora.

Authors:  D D PERKINS
Journal:  Genetics       Date:  1962-09       Impact factor: 4.562

3.  A mapping function for human chromosomes.

Authors:  E Sturt
Journal:  Ann Hum Genet       Date:  1976-11       Impact factor: 1.670

4.  The Theory of Multiple-Strand Crossing over.

Authors:  A Weinstein
Journal:  Genetics       Date:  1936-05       Impact factor: 4.562

5.  Stocks for detecting linkage in the mouse, and the theory of their design.

Authors:  T C CARTER; D S FALCONER
Journal:  J Genet       Date:  1951-01       Impact factor: 1.166

6.  Modeling interference in genetic recombination.

Authors:  M S McPeek; T P Speed
Journal:  Genetics       Date:  1995-02       Impact factor: 4.562

7.  Statistical analysis of crossover interference using the chi-square model.

Authors:  H Zhao; T P Speed; M S McPeek
Journal:  Genetics       Date:  1995-02       Impact factor: 4.562

8.  Chiasma interference as a function of genetic distance.

Authors:  E Foss; R Lande; F W Stahl; C M Steinberg
Journal:  Genetics       Date:  1993-03       Impact factor: 4.562

9.  Evidence for negative interference: clustering of crossovers close to the am locus in Neurospora crassa among am recombinants.

Authors:  F J Bowring; D E Catcheside
Journal:  Genetics       Date:  1999-07       Impact factor: 4.562

10.  Investigation of crossover interference in barley ( Hordeum vulgare L.) using the coefficient of coincidence.

Authors:  E. Esch; E. Weber
Journal:  Theor Appl Genet       Date:  2002-02-22       Impact factor: 5.699

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  2 in total

1.  Recombination mapping using Boolean logic and high-density SNP genotyping for exome sequence filtering.

Authors:  Thomas C Markello; Ted Han; Hannah Carlson-Donohoe; Chidi Ahaghotu; Ursula Harper; MaryPat Jones; Settara Chandrasekharappa; Yair Anikster; David R Adams; William A Gahl; Cornelius F Boerkoel
Journal:  Mol Genet Metab       Date:  2011-12-23       Impact factor: 4.797

2.  The Theory and Applications of Measuring Broad-Range and Chromosome-Wide Recombination Rate from Allele Frequency Decay around a Selected Locus.

Authors:  Kevin H-C Wei; Aditya Mantha; Doris Bachtrog
Journal:  Mol Biol Evol       Date:  2020-12-16       Impact factor: 16.240

  2 in total

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