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
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 17.1 2.1 20.0 2.0 24.7 7.2 12.8 9.3 9.9 10.7 5.8 1.1 15.1 10.1 CT233 TG67 LEOH36 TG125 CT62 CT149 LEOH17* TG273 TG59 CT191 TG465 TG260 LEOH7 TG255 TG580 13.9 LEOH36 10.7 IL1-1 IL1-2 3.8 10.9 18.4 6.1 5.2 10.6 LEOH17* 7.0 3.1 10.2 IL1-3 LEOH17* TG608 TG114 LEOH15* TG15 5.2 TG130 7.9 12.4 LEOH17* 3.6 CT205 TG483 18.5 18.5 TG165 CT141 23.6 LEOH23 13.4 3.0 CT157 TG14 TG520 3.0 LEOH15* 12.5 18.5 23.5 LEOH17* LEOH37 LEOH37 CT244 7.7 19.5 CT82 TG469 CT178 9.1 6.5 TG645 CD51 CT194 10.1 TG537 16.5 TG129 5.7 28.3 TG167 TG246 1.6 CT50 TG151 8.2 TG500 CT85 13.6 TG154 0.0 TG163 18.2 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.
Carotenoid Biosynthesis: Candidate pathway for genes that affect color and color uniformity. Disclaimer: this is not the only candidate pathway
Databases that link pathways to genes 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 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)
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 on Where to apply markers in a program Forward and background selection Marker resources Alternative population structures and size
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
Accelerating Backcross Selection F1 50:50 BC1 75:25 Expected proportion of Recurrent Parent (RP) genome in BC progeny BC2 87.5:12.5 BC3 93.75:6.25 BC4 96.875:3.125
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. 1999. Comparison of Selection Strategies for Marker-Assisted Backcrossing of a Gene. Crop Science 39: 1295-1301.
Progeny needed for Background Selection During MAS Q10 of RP genome in percent Population Size 20 40 60 80 100 125 150 200 Two-Stage BC1 76.7 78.7 79.7 80.3 80.7 81.3 81.7 82.2 BC2 90.3 91.9 92.8 93.3 93.6 93.9 94.0 94.6 BC3 95.8 96.2 97.1 97.3 97.4 97.5 97.6 97.8 Three-Stage BC1 71.2 72.7 73.4 73.6 73.3 73.2 72.8 72.2 BC2 86.1 87.2 88.5 89.3 90.2 90.7 91.3 91.8 BC3 94.4 95.7 96.5 96.9 97.2 97.3 97.5 97.6 Q10 indicates a 90% probability of success From Frisch et al., 1999.
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 60 80 100 125 BC1 2880 3840 4800 6000 BC2 900 1164 1416 1716 BC3 228 264 300 348 Total Marker points 4008 5268 6516 8064 Cost 0.15 601.2 790.2 977.4 1209.6 0.20 801.6 1053.6 1303.2 1612.8 0.25 1002.0 1317.0 1629.0 2016.0 Three-Stage Selection BC1 2880 3840 4800 6000 BC2 492 708 960 1308 BC3 250 444 504 576 Total Marker point 3622 4992 6264 7884 Cost 0.15 543.3 748.8 939.6 1182.6 0.20 724.4 998.4 1252.8 1576.8 0.25 905.5 1248.0 1566.0 1971.0
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 8:465 www.biomedcentral.com/content/pdf/1471-2164-8-465.pdf
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 806 596 106 34 22 38 10 Where EST Coverage = Allele Coverage n = 1 n = 2 n = 3 n = 4 n = 5-10 n > 10 Total 127 not tested 64 22 11 23 7 Proportion 0.16 0.60 0.65 0.50 0.61 0.70 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
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.
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
LD measure (R 2 ) 1 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 Fresh market y = -0.037ln(x) + 0.1713 0 20 40 60 80 100 120 140 Distance between loci (cm) LD measure (R 2 ) 1 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 Processing y = -0.054ln(x) + 0.2583 0 20 40 60 80 100 120 140 Distance between loci (cm) 2 2 3 3 4 4 5 5 6 6 7 8 9 10 7 8 9 10 11 11 12 12
K=4 Tomato populations will have sub-structure 1 2 3 4 1) Fresh Market (FM) ; 2) Landrace; 3) Heirloom; 4) Processing K=8 1 2 3 4 5 6 7 8 1,6,7) Processing; 2) Landrace: 3,5) FM; 4) FM & Processing; 8) Heirloom Output from Pritchard s 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 8:465 www.biomedcentral.com/content/pdf/1471-2164-8-465.pdf