Marker Assisted Selection in Tomato Pathway approach for

Marker Assisted Selection in Tomato

Pathway approach for candidate gene identification and introduction to metabolic pathway databases.

Identification of polymorphisms in data-based sequences

MAS – forward selection, background selection, combining traits, relative efficiency of selection

Why (population) size matters

Dist MarkercM Name

CT233

17.1

TG672.1LEOH36

20.0

TG1252.0CT62

24.7

CT1497.2

LEOH17*

12.8

TG2739.3

TG599.9

CT191

10.7TG465

5.8TG2601.1LEOH7

15.1

TG25510.1

TG580

Chr 1

Dist MarkercM Name

TG608

13.9

CT205

10.7TG1653.8 LEOH23

10.9TG14

18.4

CT2446.1

TG4695.2

TG645

10.6TG537

7.0TG1673.1TG151

10.2TG154

Chr 2

Dist MarkercM Name

TG15

12.4

TG483

23.6

CT157

18.5

LEOH377.7

CT1786.5

CT194

16.5

CT501.6TG500

13.6

TG1630.0LEOH10

Chr 4

Dist MarkercM Name

CT1017.9

TG4413.6CT167

18.5

CT933.0LEOH163.0TG96

12.5

TG100A

19.5

CT118

28.3

TG185

Chr 5

IL1-

1

IL4-

3IL

4-4

IL1-

2IL

1-3

Chr 3

IL2-

4

LEOH15*

IL4-

1

LEOH17*

IL5-

2

LEOH17*

Dist MarkercM Name

TG1145.2

TG130

18.5

CT141

13.4

TG520

23.5

CT829.1

CD5110.1

TG1295.7

TG2468.2

CT85

18.2

TG214

IL3-

2

LEOH17*

IL3-

1

LEOH15*

IL3-

3

LEOH17*

LEOH17*

LEOH36

LEOH10

LEOH37

Example: QTL for color uniformity in elite crosses

QTL Trait Origin2 L, YSD S. lyc.4 YSD S. lyc.6 L, Hue ogc

7 L, Hue S. hab.11 L, Hue S. lyc.Audrey Darrigues, Eileen Kabelka

Carotenoid Biosynthesis: Candidate pathway for genes that affect color and color uniformity.

Disclaimer: this is not the only candidate pathway…

http://www.arabidopsis.org/help/tutorials/aracyc_intro.jsp

Databases that link pathways to genes

http://metacyc.org/

http://www.plantcyc.org/

http://sgn.cornell.edu/tools/solcyc/

http://www.arabidopsis.org/biocyc/index.jsp

http://www.arabidopsis.org/help/tutorials/aracyc_intro.jsp

External Plant Metabolic databasesCapCyc (Pepper) (C. anuum) CoffeaCyc (Coffee) (C. canephora) SolCyc (Tomato) (S. lycopersicum) NicotianaCyc (Tobacco) (N. tabacum) PetuniaCyc (Petunia) (P. hybrida) PotatoCyc (Potato) (S. tuberosum) SolaCyc (Eggplant) (S. melongena)

Databases that link pathways to genes

http://www.plantcyc.org:1555/

Note: missing step (lycopene isomerase, tangerine)

Check boxes (Note: MetaCyc has many more choices, but no plants)

Scroll down page

Capsicum annum sequence retrieved

http://www.ncbi.nlm.nih.gov/

Select database

Query CCACCACCATCCTCACTTTAACCCACAAATCCCACTTTCTTTGGCCTAATTAACAATTTT|||||||||||||||||||||||||||||||||| |||||||||||||||||||||||||

Sbjct CCACCACCATCCTCACTTTAACCCACAAATCCCATTTTCTTTGGCCTAATTAACAATTTT

Zeaxanthin epoxidase

Probable location on Chromosome 2

Alignment of Z83835 and EF581828 reveals 5 SNPs over ~2000 bp

51 annotated loci

Information missing from other databases is here…

Candidates identified in other databases are here

Comment on the databases:

Information is not always complete/up to date.

Display is not always optimal, and several steps may be needed to go from pathway > gene > potential marker.

Sequence data has error associated with it. eSNPs are not the same as validated markers.

There is a wealth of information organized and available.

We will be asking for feed-back RE how best to improve the SGN database and access via the Breeders Portal

The previous example detailed how we might identify sequence based markers for trait selection.

Query CCACCACCATCCTCACTTTAACCCACAAATCCCACTTTCTTTGGCCTAATTAACAATTTT|||||||||||||||||||||||||||||||||| |||||||||||||||||||||||||

Sbjct CCACCACCATCCTCACTTTAACCCACAAATCCCATTTTCTTTGGCCTAATTAACAATTTT

Improving efficiency of selection in terms of 1) relative efficiency of selection, 2) time, 3) gain under selection and 4) cost will benefit from markers for both forward and background selection.

Remainder of Presentation will focus onWhere to apply markers in a programForward and background selectionMarker resourcesAlternative population structures and size

Relative efficiency of selection:r(gen) x {Hi/Hd}

Line performance over locations > MAS > Single plant

Comparison of direct selection with indirect selection (MAS).

F1 50:50

BC1 75:25

BC2 87.5:12.5

BC3 93.75:6.25

BC4 96.875:3.125

Expected proportion of Recurrent Parent (RP) genome in BC progeny

Accelerating Backcross Selection

Select for target allele

Select for RP genome at unlinked markers


Select for RP recombinants at flanking markers



Select for RP recombinants at flanking markers

Select for RP genome on carrier chromosome


Four-stage selection

Two-stage selection

Three-stage selection

References:

Frisch, M., M. Bohn, and A.E. Melchinger. 1999. Comparison of Selection Strategies for Marker-Assisted Backcrossing of a Gene. Crop Science 39: 1295-1301.

Progeny needed for Background Selection During MAS

20 40 60 80 100 125 150 200Two-StageBC1 76.7 78.7 79.7 80.3 80.7 81.3 81.7 82.2BC2 90.3 91.9 92.8 93.3 93.6 93.9 94.0 94.6BC3 95.8 96.2 97.1 97.3 97.4 97.5 97.6 97.8Three-StageBC1 71.2 72.7 73.4 73.6 73.3 73.2 72.8 72.2BC2 86.1 87.2 88.5 89.3 90.2 90.7 91.3 91.8BC3 94.4 95.7 96.5 96.9 97.2 97.3 97.5 97.6

Q10 of RP genome in percentPopulation Size

Q10 indicates a 90% probability of success

From Frisch et al., 1999.

Two-Stage Selection 60 80 100 125BC1 2880 3840 4800 6000BC2 900 1164 1416 1716BC3 228 264 300 348

Total Marker points 4008 5268 6516 8064Cost 0.15 601.2 790.2 977.4 1209.6

0.20 801.6 1053.6 1303.2 1612.80.25 1002.0 1317.0 1629.0 2016.0

Three-Stage SelectionBC1 2880 3840 4800 6000BC2 492 708 960 1308BC3 250 444 504 576

Total Marker point 3622 4992 6264 7884Cost 0.15 543.3 748.8 939.6 1182.6

0.20 724.4 998.4 1252.8 1576.80.25 905.5 1248.0 1566.0 1971.0

Population Size

Marker Data Points required (Modified from Frisch et al., 1999; based on assumption of 12 chromosomes; initial selection with 4 markers/chromosome)

For effective background selection we need:

Markers for our target locus (C > T SNP for Zep)

Markers on the target chromosome (Chrom. 2)

Markers unlinked to the target chromosome

http://www.tomatomap.net

http://sgn.cornell.edu/

Ovate

HBa0104A12

55 polymorphic markers

44 polymorphic markers

Missing data in SGN

Limited ability to generate tables, PCR conditions sometimes incomplete, Enzyme sometimes missing, SNP not described.

Missing data in Tomatomap.net

SNP and sequence context requires BMC genomics supplemental table , ASPE primers, GoldenGate primers.

2007. BMC Genomics 8:465www.biomedcentral.com/content/pdf/1471-2164-8-465.pdf

Where can we expect to be?

n = 1 n = 2 n = 3 n = 4 n = 5-10 n > 10Total 806 596 106 34 22 38 10

n = 1 n = 2 n = 3 n = 4 n = 5-10 n > 10Total 127 not tested 64 22 11 23 7

Proportion 0.16 0.60 0.65 0.50 0.61 0.70

TA496 ESTs with SNPs VS H1706 BAC sequences

Where EST Coverage = Allele Coverage

Data based on estimated ~42% of sequence, therefore expect as many as 300 markers for a cross like E6203 x H1706

analysis by Buell et al., unpublished

QTL’s mapped in a bi-parental cross may not be appropriate for MAS in all populations…

Marker allele and trait may not be linked in all populations.

Genetic background effects may be population specific.

Original association may be spurious.

QTL detection is dependent on magnitude of the difference between alleles and the variance within marker classes.

What about mapping and MAS in unstructured populations?

A brief introduction to “Association Mapping” follows.

Y = μ REPy + Qw + Markerα + Zv + Error

“Association Mapping” statistical model – designed to account for population structure (Q), correct for genetic background effects (Z), and identify marker-trait linkage (Marker)

y = -0.054ln(x) + 0.2583

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 20 40 60 80 100 120 140

LD

mea

sure

(R2 )

Distance between loci (cM)

Processing

y = -0.037ln(x) + 0.1713

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 20 40 60 80 100 120 140

LD

mea

sure

(R2 )

Distance between loci (cM)

Fresh market

1

2

3

4

5

6

7

8

9

10

11

12

1

2

3

4

5

6

7

8

9

10

11

12

1 2 3 4

1) Fresh Market (FM) ; 2) Landrace; 3) Heirloom; 4) Processing

K=4

1,6,7) Processing; 2) Landrace: 3,5) FM; 4) FM & Processing; 8) Heirloom

K=8

1 2 3 4 5 6 7 8

Output from Pritchard’s “STRUCTURE”

Tomato populations will have sub-structure

Association mapping

Incorporates population structure and coefficient of relatedness

The number of markers needed depends on the rate of LD decay (reflects recombination history)

Highly specific to “inference population”

wild species vs breeding program

Sensitive to marker coverage

LD decay and number of alleles (Nor, gf, and others all have multiple alleles within populations used by breeders)

Will not be able to “map” traits where trait variation overlaps with population structure.

Even without sequence or marker data, there are lessons for practical breeding:

Use pedigree data, knowledge of population structure, and objective data to increase precision of estimates of breeding value.

Take home messages:

Marker resources exist for forward and background selection in elite x elite crosses in tomato.

Marker resources are currently not sufficient for QTL discovery in bi-parental or AM populations; they will soon be.

The best time to use genetic markers : early generation selection

Restructuring of breeding program to integrate markers may include:

1) Increasing genotypic replication (population size) at the expense of replication (consider augmented designs).

2) Collecting objective data.

Further discussion of AM approach in session VI “Unstructured mapping of bacterial spot resistance”

References:

Kaepler, 1997. TAG 95:618-621.

Frisch, et al., 1999. Crop Science 39: 1295-1301.

Knapp and Bridges, 1990. Genetics 126: 769-777.

Yu et al., 2006. Nature Genetics 38:203-308.

Van Deynze et al., 2007. BMC Genomics 8:465www.biomedcentral.com/content/pdf/1471-2164-8-465.pdf

Marker Assisted Selection in Tomato Pathway approach for

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