Linkage Mapping
Author
P Mathankumar, Ph.D. Scholar (Plant Breeding and Genetics), Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu
Aim!!!
This blog writeup solely aims to give an overall idea about linkage mapping. Experts in the field may find it boring and can skip reading this blog. I hope after reading this blog one will have an idea about linkage mapping.
What is Linkage???
By definition, Linkage is the tendency of two or more genes to stay together during inheritance. It was proposed by Morgan. If linkage occurs means there must be a deviation from expected ratios in F2 and test cross generations.
What is Linkage Map???
Linkage map is picturized depiction of relative locations of various genetic markerspresent in the chromosomes of an organism.
Steps in the linkage map construction:
There were five steps in linkage map construction,
1. Selection of parents – Diverse parents
2. Production of mapping population – F2, RILS, NILS, DH
3. Identification of polymorphism – (Using RAPD, AFLP, SSR, SNP MARKERS)
4. Genotyping of polymorphic markers in mapping population
5. Linkage map analysis (Using software)
Linkage map analysis
How locations of various genetic markers were determined in chromosomes?
The recombination frequencies between marker pairs are estimated from suitable mapping populations and are converted to map or genetic distances. Based on the genetic distance, the markers are grouped into linkage groups, and their order in the linkage group is depicted as the linkage map. The formula for estimating recombinant frequency is given hereunder,
The map units are centimorgan (cM). One cM is the distance over which, on average, one crossover occurs per meiosis.
Can we use recombinant frequency directly as genetic distance?
Usage of recombinant frequency directly as the genetic distance will not be possible as in the case of double or more crossovers and interferences. For example, in the case of double crossover parental types will be more than recombinant types that will result in less recombinant frequency. In this case, if we use recombinant frequency directly as the genetic distance will result in false estimation. To overcome this error, mapping functions were used to convert the recombinant frequency to genetic distance
Mapping functions
Most widely used mapping functions to convert the recombinant frequency to genetic distance are,
Haldane mapping function (1919)
Kosambi mapping function (1944)
Haldane mapping function
The Haldane mapping function corrects recombinationfrequencies for multiple crossing overevents assuming that occurrence of a crossingover does not affect the likelihood of anothercrossing over in the neighboring regions of thechromosome, i.e., there is no interference.
The simplified the formula for Haldane mapping function is,
Kosambi Mapping function
The assumption of lack of interference in the Haldane function is rectified in the Kosambi mapping function. The formula for Kosambi function is given hereunder,
"where ln = natural logarithm and r = recombinant frequency"
Note:
The above figure depicts the difference in recombinant frequency to genetic distance conversion in three mapping functions [Morgan (
Red line
), Haldane (
Green
) and Kosambi (
Cyan
)].
In general, the value of Kosambi distance for a given value of r is lower than that of Haldane distance and this difference increase with the value of r.
LOD score calculation
This statistical test is performed to estimate the likelihood of a certain recombination fraction (
r
) versus the likelihood of no linkage (
r
=0.5). LOD score is estimated using formula was,
A LOD score above 3 is generally used as a critical value. A LOD score of >3 implies that the null hypothesis (
r
= 0.5) is rejected. This value implies a ratio of likelihoods of 1,000 to 1 (i.e., among the 1,000 analysis, there is a chance of one failure).
"The very thought of doing linkage analysis calculation manually would give
some sort of fear for persons
like me. Luckily,
we do have many softwares, which are available to do the task with precision!
"
List of some software for linkage mapping
QTLIci Mapping
MAPmaker
Joint Map
Map Manager
Mapdisto
G Mendel
Linkage mapping using QTLici Mapping
1. Datasheet preparation
The data for QTLici Mapping software can be prepared in Microsoft excel.
2. In Microsoft excel
Three sheets have to be opened as given in the below figure,
In the first sheet, we have to fill in the details of mapping population type (ie each type of mapping population is assigned with specific number eg; 7 for F2), Mapping function (1 for Kosambi; 2 for Haldane; 3 for Morgan), Marker space type (1 for interval; 2 for positions), number of markers and size of mapping population (refer to the fiigure below).
In the second sheet, genotypic data obtained using polymorphic markers in mapping population is given in 201 format (2-P1,0-H,1-P2). Here, missing values are given as -1 (refer to the figure below). There are different formats available for scoring markers suitable to the software. Please refer to the user guide manual.
In the third sheet, anchoring data (i.e., allocation of markers to chromosomes based on reference studies) of markers were given (refer to the figure below). If prior information is not available, then zero is assigned. Save the file in either 2003 or 2007 excel file format.
Steps in
QTLici Mapping
References
Collard, Bertrand CY, M. Z. Z. Jahufer, J. B. Brouwer, and E. C. K. Pang. "An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: the basic concepts." Euphytica 142, no. 1 (2005): 169-196.
Kosambi, Damodar D. "The estimation of map distances from recombination values." In DD Kosambi, pp. 125-130. Springer, New Delhi, 2016.
Singh, B. D., and Ashok Kumar Singh. Marker-assisted plant breeding: principles and practices. New Delhi: Springer India, 2015.
Vinod, K. K. "Kosambi and the genetic mapping function." Resonance 16, no. 6 (2011): 540-550.
Wang, Jiankang, Huihui Li, Luyan Zhang, Chunhui Li, and Lei Meng. "Users’ manual of QTL IciMapping v3 . 1." Institute of Crop Science, CAAS, Beijing, and Crop Research Informatics Lab, Mexico (2011).