GM01 GM07 0.36

1
MadMapper And CheckMatrix Python Scripts To
Infer Orders Of Genetic Markers And For
Visualization And Validation Of Genetic Maps And
Haplotypes. Alexander Kozik and Richard
Michelmore. The Genome Center, University of
California Davis, CA 95616. Contemporary
molecular marker techniques can generate mapping
data for thousands molecular markers
simultaneously. Construction and validation of
high density genetic maps is a challenge and
requires robust, high-throughput approaches. As
part of the Compositae Genome Project, we
developed a suite of Python scripts for quality
control of genetic markers, grouping and
inference of linear order of markers in linkage
groups. These scripts can be used in conjunction
with other mapping programs or can be used as a
stand-alone package. The suite consists of three
programs MadMapper_RECBIT, MadMapper_XDELTA and
CheckMatrix. MadMapper_RECBIT analyses raw marker
scores for recombinant inbred lines.
MadMapper_RECBIT generates pairwise distance
scores for all markers, clusters based on
pairwise distances, identifies genetic bins,
assigns new markers to known linkage groups,
validates allele calls, and assigns quality
classes to each marker based on several criteria
and cutoff values. MadMapper_XDELTA utilizes a
new algorithm, Minimum Entropy Approach and
Best-Fit Extension, to infer linear order of
markers. MadMapper_XDELTA analyzes
two-dimensional matrices of all pairwise scores
and finds best map that has minimal total sum of
differences between adjacent cells (map with
lowest entropy). This approach scales well and
can accommodate large numbers of markers, unlike
some commonly used mapping programs. CheckMatrix
serves as a visualization tool to validate
constructed genetic maps. CheckMatrix generates
graphical genotypes and two-dimensional heat
plots of pairwise scores. Visualization of
regions with positive and negative linkage as
well as of allele fraction per marker simplifies
genetic map validation without applying
statistical approaches. Scripts are freely
available at http//cgpdb.ucdavis.edu/XLinkage/Mad
Mapper/
BRIEF DESCRIPTION OF RIL MAPPING
PIPELINE 1. Processing of raw markers scores
and grouping MadMapper_RECBIT generates
multiple text files for further analysis 2.
Construction of genetic map (ordering of markers)
per linkage group MadMapper_XDELTA (or any
other mapping program) 3. Visualization and
validation of genetic maps CheckMatrix
generates heat plots of recombination scores and
graphical genotyping MadMapper and
CheckMatrix are Python scripts and can be used on
any computer platform UNIX, Windows, Mac OS-X.
Grouping can be done on a set of 2,000 markers
map construction works in reasonable timeframe
with up to 500 markers
grouping cutoff stringency
distinct linkage group 4
Example of group analysis by MadMapper_RECBIT
MINIMUM ENTROPY APPROACH TO INFER LINEAR ORDER
OF MARKERS
CheckMatrix 2D plot
MadMapper_XDELTA analyzes two-dimensional
matrices of all pairwise scores and finds best
map that has minimal total sum of differences
between adjacent cells (map with lowest
entropy).
random order high entropy
CheckMatrix Color Scheme
partially wrong order
Numerical data generated by MadMapper
right order low entropy
Visualization of numerical data using ChekMatrix
Two-dimensional matrix of recombination pairwise
scores
adjacent cells (values)
Haplotypes per RIL (inbred line) red
Columbia blue L.erecta
VISUALIZATION OF ARABIDOPSIS GENETIC MAP (DEAN
AND LISTER, http//www.arabidopsis.info/ ) USING
CHECKMATRIX MAP WAS RE-CONSTRUCTED USING
MADMAPPER
CHECKMATRIX USAGE
Three input files are required
1 10 20
25
GM01 A A A A A A A A A A A A A
A A A B B B B B B B B B GM02 A A A A A A A A A
A A A A A A B B B B B B B B B B GM03 A A A A A
A A A A A A A A B B B B B B B B B B B B GM04 A
A A A A A A A A A A B B B B B B B B B B B B B B
GM05 A A A A A A A A A A B B B B B B B B B B B
B B B B GM06 A A A A A A A A A B B B B B B B B
B B B B B B B B GM07 A A A A A A A A A B B B B
B B B B B B B B B B A A GM08 A A A A A A A A A
B B B B B B B B B B B B B A A A GM09 A A A A A
A A A A B B B B B B B B B B B A A A A A GM10 B
A A A A A A A A A B B B B B B B B B A A A A A A
GM11 B B A A A A A A A A B B B B B B B B A A A
A A A A GM12 B B B A A A A A A A B B B B B B B
A A A A A A A A
Locus file
Linkage group I
Linkage group I
regions with negative linkage
................... GM01 GM07 0.36 GM01
GM08 0.40 GM01 GM09 0.48 GM01 GM10
0.52 GM01 GM11 0.60 GM01 GM12 0.68
GM02 GM01 0.04 GM02 GM02 0.00 GM02
GM03 0.08 GM02 GM04 0.16 GM02 GM05
0.20 GM02 GM06 0.24 ...................
LG GM01 0 LG GM02 1 LG GM03 2
LG GM04 3 LG GM05 4 LG GM06 5
LG GM07 6 LG GM08 7 LG GM09 8
LG GM10 9 LG GM11 10 LG GM12 11
main diagonal with linked markers
Linkage group II
Linkage group II
CheckMatrix
Map file
Matrix file
Upon program execution three output files will be
generated
1
HEAT PLOT it assists to validate the quality of
constructed genetic map and identify markers with
wrong position
Linkage group III
Linkage group III
GRAPHICAL GENOTYPING visualization of haplotypes
per recombinant line (suspicious double
crossovers are highlighted)
high density of markers
Linkage group IV
Linkage group IV
2
low density of markers
regions with quasi linkage
CIRCULAR GRAPH it assists to validate genetic
map and identify markers with spurious linkage
Linkage group V
Linkage group V
allele composition per markers
3
Linkage group I
Linkage group II
Linkage group III
Linkage group IV
Linkage group V
CheckMatrix graphical genotyping
2-D diagonal ChekMatrix heat-plot all markers
versus all markers color gradient reflects
linkage scores between markers
LINEAR ORDER OF MARKERS INFERRED BY THREE
DIFFERENT METHODS
REFERENCES AND DATA SOURCES 1. Dean and Lister
Arabidopsis Genetic Map and Raw Data
http//www.arabidopsis.info/new_ri_map.html 2.
MadMapper http//cgpdb.ucdavis.edu/XLinkage/M
adMapper/ 3. JoinMap http//www.kyazma.nl/ind
ex.php/mc.JoinMap 4. RECORD
http//www.dpw.wau.nl/pv/pub/recORD/index.htm 5.
GenoPix_2D_Plotter http//www.atgc.org/GenoPix
_2D_Plotter/ CREDITS This work was funded
by NSF grant 0421630 to Compositae Genome
Consortium http//compgenomics.ucdavis.edu/ P
AG-14 POSTERS WITH EXAMPLES OF MADMAPPER
USAGE P751 High-Density Haplotyping With
Microarray-Based Single Feature Polymorphism
Markers In Arabidopsis P761 Gene Expression
Markers Using Transcript Levels Obtained From
Microarrays To Genotype A Segregating Population
physical coordinates of markers on Arabidopsis
genome
inferred order of markers by three different
approaches (mapping programs)
Side-by-side comparison of linear order of
markers on Arabidopsis genome inferred by three
different approaches (mapping programs) and
comparison with physical order of markers (Col- 0
genomic sequence) MadMapper_XDELTA (minimum
entropy approach), JoinMap (maximum likelihood)
and RECORD (minimum number of recombination
events) Diagonal dot-plot was created using
GenoPix_2D_Plotter
MadMapper
JoinMap
RECORD