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

Transcription

1 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

2 Example: QTL for color uniformity in elite crosses Chr 1 Chr 2 Chr 3 Chr 4 Chr 5 Dist cm Marker Name Dist cm Marker Name Dist cm Marker Name Dist cm Marker Name Dist cm Marker Name CT233 TG67 LEOH36 TG125 CT62 CT149 LEOH17* TG273 TG59 CT191 TG465 TG260 LEOH7 TG255 TG LEOH IL1-1 IL LEOH17* IL1-3 LEOH17* TG608 TG114 LEOH15* TG TG LEOH17* 3.6 CT205 TG TG165 CT LEOH CT157 TG14 TG LEOH15* LEOH17* LEOH37 LEOH37 CT CT82 TG469 CT TG645 CD51 CT TG TG TG167 TG CT50 TG TG500 CT TG TG LEOH10 LEOH10 IL2-4 TG214 IL3-1 IL3-2 IL3-3 Audrey Darrigues, Eileen Kabelka IL4-1 IL4-3 IL4-4 CT101 TG441 LEOH17* CT167 CT93 LEOH16 TG96 TG100A CT118 TG185 IL5-2 QTL Trait Origin 2 L, YSD S. lyc. 4 YSD S. lyc. 6 L, Hue og c 7 L, Hue S. hab. 11 L, Hue S. lyc.

3 Carotenoid Biosynthesis: Candidate pathway for genes that affect color and color uniformity. Disclaimer: this is not the only candidate pathway

4 Databases that link pathways to genes

5 Databases that link pathways to genes External Plant Metabolic databases CapCyc (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)

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9 Note: missing step (lycopene isomerase, tangerine)

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

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15 Scroll down page Capsicum annum sequence retrieved

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18 Select database

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20 Query CCACCACCATCCTCACTTTAACCCACAAATCCCACTTTCTTTGGCCTAATTAACAATTTT Sbjct CCACCACCATCCTCACTTTAACCCACAAATCCCATTTTCTTTGGCCTAATTAACAATTTT Zeaxanthin epoxidase Probable location on Chromosome 2 Alignment of Z83835 and EF reveals 5 SNPs over ~2000 bp

21 51 annotated loci

22 Information missing from other databases is here Candidates identified in other databases are here

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24 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

25 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 on Where to apply markers in a program Forward and background selection Marker resources Alternative population structures and size

26 Comparison of direct selection with indirect selection (MAS). Relative efficiency of selection: r (gen) x {H i /H d } Line performance over locations > MAS > Single plant

27 Accelerating Backcross Selection F1 50:50 BC1 75:25 Expected proportion of Recurrent Parent (RP) genome in BC progeny BC2 87.5:12.5 BC :6.25 BC :3.125

28 Two-stage selection Select for RP genome at unlinked Select for target allele markers Three-stage selection Select for RP recombinants at flanking Select for target allele markers Four-stage selection Select for target allele References: Select for RP recombinants at flanking markers Select for RP genome at unlinked markers Select for RP genome on carrier chromosome Select for RP genome at unlinked markers Frisch, M., M. Bohn, and A.E. Melchinger Comparison of Selection Strategies for Marker-Assisted Backcrossing of a Gene. Crop Science 39:

29 Progeny needed for Background Selection During MAS Q10 of RP genome in percent Population Size Two-Stage BC BC BC Three-Stage BC BC BC Q10 indicates a 90% probability of success From Frisch et al., 1999.

30 Marker Data Points required (Modified from Frisch et al., 1999; based on assumption of 12 chromosomes; initial selection with 4 markers/chromosome) Population Size Two-Stage Selection BC BC BC Total Marker points Cost Three-Stage Selection BC BC BC Total Marker point Cost

31 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

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33 Ovate

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35 HBa0104A12

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38 55 polymorphic markers 44 polymorphic markers

39 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 BMC Genomics 8:465

40 Where can we expect to be? TA496 ESTs with SNPs VS H1706 BAC sequences n = 1 n = 2 n = 3 n = 4 n = 5-10 n > 10 Total Where EST Coverage = Allele Coverage n = 1 n = 2 n = 3 n = 4 n = 5-10 n > 10 Total 127 not tested Proportion analysis by Buell et al., unpublished Data based on estimated ~42% of sequence, therefore expect as many as 300 markers for a cross like E6203 x H1706

41 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.

42 Association Mapping statistical model designed to account for population structure (Q), correct for genetic background effects (Z), and identify marker-trait linkage (Marker) Y = μ REPy + Qw + Markerα + Zv + Error

43 LD measure (R 2 ) Fresh market y = ln(x) Distance between loci (cm) LD measure (R 2 ) Processing y = ln(x) Distance between loci (cm)

44 K=4 Tomato populations will have sub-structure ) Fresh Market (FM) ; 2) Landrace; 3) Heirloom; 4) Processing K= ,6,7) Processing; 2) Landrace: 3,5) FM; 4) FM & Processing; 8) Heirloom Output from Pritchard s STRUCTURE

45 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.

46 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.

47 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

48 References: Kaepler, TAG 95: Frisch, et al., Crop Science 39: Knapp and Bridges, Genetics 126: Yu et al., Nature Genetics 38: Van Deynze et al., BMC Genomics 8:465