Linkage, QTL, Mapping Populations and Marker-Assisted-Selection. Dave Douches, Allen Van Deynze and Dave Francis PAA SolCAP Workshop August 2009

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1 Linkage, QTL, Mapping Populations and Marker-Assisted-Selection Dave Douches, Allen Van Deynze and Dave Francis PAA SolCAP Workshop August

2 Detecting QTLs using Marker-based analysis (MBA) requires genotyping of individuals Steps toward QTL analysis using MBA: 1- Segregating populations 2- Measure phenotypic variation The more accurate measurement the more precise QTL identification. 3- Measure genotypic variation Genotype individuals Genetic map construction 4- Statistical analysis to find QTLs and QTL validation

3 Common Population Structures F2; F3; F4; Recombinant Inbred (RI); F1-derived, intermated recombinant inbred (IRI); Doubled haploids (DH); Backcross (BC1); Association study in the base germplasm; Near Isogenic Lines (NIL). 3

4 QTL mapping in an F from Parent 1 is an association study in a population where the confounding effect of pedigree has been removed. + from Parent 2 4

5 Mapping Populations in Potato 4x crosses (between heterozygous parents) Mixed segregation patterns Simplex and duplex segregation patterns 2x crosses Testcross, F2, tri-allelic ratios Doubled monoploid cross Segregation in one parent - testcross

6 Mapped disease resistance genes and QTL

7 Potato: Ultra-high density (UHD) genetic map 70,344 unigenes 150 SSRs (1000 more in ESTs) 10,000+ AFLP markers (from 381 primer combinations) ordered on 136 progeny of diploid RH x SH cross most dense genetic map in any organism Walter De Jong, Cornell University

8 The level of resolution needed depends on intended application Increased need for resolution Combining QTLs between lines within a segregating population. Moving chromosome segments within heterotic pools Make germplasm wide inferences/claims about a particular chromosome segment (assuming IBD). Moving chromosome segments across heterotic pools of elite germplasm. Introgression of special trait (e.g. disease resistance) from exotic germplasm. QTL cloning 8

9 Type of population depends on resolution needed Comparison of resolution and research time for various approaches to dissect quantitative variation. The research times assume the target species has only two generations per year. NIL, nearisogenic line; RIL, recombinant inbred line Buckler and Thornsberry (2002) 9

10 Linkage Linkage Association of two or more loci on a chromosome with limited recombination Linkage Disequilibrium or Gametic Phase Disequilibrium Non random association of alleles at two or more loci not necessarily on the same chromosome Measures co-segregation of alleles in a population Mendel s pea traits showed complete linkage equilibrium and hence independent assortment Can arise from intermixture of populations with different gene frequencies Can also be produced or maintained by selection favoring one combination of alleles over the other e.g. selection for yield in a breeding population Falconer and McKay (1996) 10

11 Linkage disequilibrium 11

12 Linkage Disequilibrium Decay r = Recombination Rate between two loci r = 0.5 = Two loci are unlinked r = 0 = Two loci are completely linked and do not independently assort Falconer and Mackay (1996) 12

13 What are QTL? Genes are regions of DNA controlling a trait or phenotype Quantitative Trait Loci (QTL) are regions of DNA associated with quantitative traits They can be defined using molecular markers that highlight specific DNA segments or genes This can change how we manipulate these traits in breeding

14 A trait is quantitative/polygenic if its inheritance is controlled by two or more genes and their interaction with environment 1- A trait having a continuous distribution 2- Controlled by the interaction of many genes Cont

15 A trait is quantitative/polygenic if its inheritance is controlled by two or more genes and the their interaction with environment 3- Influenced by the environment Examples Yield Lycopene content in tomato Early Blight (EB) resistance

16 Trait Distribution in the Progeny Trait distribution in the F2 progeny If we could observe directly the QTL we could see the 3 underlying trait distributions

17 Modeling a Marker Locus and a Linked QTL Single Marker Analysis modeling, assume: One QTL locus A, One Marker locus M, Measure of association between A and M Self pollinated crops: compare AA to aa (no dominance effect) Cross Pollinated crop: compare AA T to aa T (dominance and additive effects are confounded) 17

18 Assigned values of the genotypes at A and M Genotyping Individual Genotyped Cloned Progeny Phenotyping Selfed Progeny Testcrossed Progeny AA AA => a AA => a AT => a Aa Aa => d 1AA:2Aa:1aa => d/2 ½ AT+ ½ at => 0 aa aa => -a aa => -a at => -a Let δ=d, d/2, and 0 for cloned, selfed, and tescrossed progenies, respectively. 18

19 Need to collect good data: Good phenotypes are important infection ripening Ann Powell UC Davis

20 What information do we need? Information pertinent to the association between marker genotypes and phenotypic values. Total number of individuals genotyped, N (and number in each class, n MM, n Mm, and n mm ) Values of a and δ. Values of residual genetic variance and error variance 20

21 Detection of a QTL Detection of linkage between A and M, will occur only if both are segregating in the population and if they are physically linked; We cannot extrapolate results from one mapping population to the rest of our germplasm without considering disequilibrium. 21

22 Do we have disequilibrium between a linked marker and a QTL? We cannot really assess very well the extent of gametic phase disequilibrium between loci in the population without very extensive mapping studies. With SNPs we can look at marker-marker associations for a much smaller cost: we just need to genotype our germplasm. 22

23 The marker density needed depends on the population Power of (MM-mm)/a detecting a * QTL ( ) cm 34.7 cm Distance of Marker from QTL (in cm) DH or F2 or 2xBC1 F3 F4 IRI (n# gen RM=0) IRI (n# gen RM=2) IRI (n# gen RM=5) IRI (n# gen RM=10) IRI (n# gen RM=25) 23

24 TG67 0 SSR CT62 13 SSR SSR192 TG SSR95 CT SSR CT CT20134I CT10725I 41 CT20268I 42 SSR CT10975I 51 TG CT10030I.2 58 CT LEOH106 TG59 60 CT10629 CT10811 CT10945 LEVCOH12 65 LEVCOH11 66 CT SSR9 73 SSR308 TG LEOH SSR42 TG260 TG SSR37 96 CT TG SSR65 SSR582 SSR288 TG CT10126I 115 Chr.1 CT LEOH342 3 LEOH342n 7 TG CT CT10682I 29 CT TG SSR66 33 SSR96 40 CT CT TG14 46 SSR5 SSR CT10771 CT CT CT10279I 55 CT SSR32 59 LEOH SSR SSR26 64 TG LEOH TG TG TG TG LEOH319 LEOH TG TG Chr.2 TG214 SSR601 0 SSR14 LEOH127 1 SSR320 2 CT10690I 3 CT20050 CT10772I 4 CT CT10042I 13 CT CT85 19 CT10437I 22 TG LEOH LEOH TG CT10689I 36 CT10480I 38 SSR CD51 45 CT20195 CT10402I 46 CT20037 CT CT10736 CT82 50 CT LEOH CT TG CT TG13B 77 TG TG Chr.3 CT SSR TG15 22 CT10255I SSR43 25 LEOH SSR TG CT CT CT SSR SSR450 CT10485 CT157 SSR CT CT LEOH101 CT CT CT TG CT CT10184I 79 CT CT TG CT50 92 CT Chr.4 CT101 0 CT TG441 9 CT167 SSR CT CT93 30 TG96 37 CT CT10373I TG619 CT10151I 42 CT10765I 44 CT20210I 45 CT10526 LEOH63 46 TG100A 47 CT LEOH LEOH CT SSR SSR109 TG Chr.5 CT216 0 CT CT10242I 7 CT TG CT10328I 27 SSR TG LEOH TG TG LEOH LEOH LEOH LEOH CT TG Chr.6 TG342 0 CT CT52 10 SSR L21J7a 18 LEOH SSR CD57 28 TG TG CT CD54 46 CT SSR45 64 TG20 70 CT LEOH TG Chr.7 CT10152I 0 CT LEOH70 4 TG LEOH LEOH147 CT47 CT CT CT92 SSR CT TG TG SSR SSR63 50 TG SSR38 53 CT10367I 62 CT CT68 82 Chr.8 GP39 0 TG18 9 CT SSR68 SSR70 30 CT CT TG CT SSR LEOH TG CT74 71 LEOH LEOH TG SSR333 TG Chr.9 CT CT10082I 1 TG122 CT16 6 CT SSR34 16 SSR CT CT10105I 30 CT10464I 31 SSR CT11 36 CT CT CT10419I 43 CT10078I CT CT10386I CT TG TG LEOH336 TG63 89 SSR Chr.10 CT10683I 0 TG497 4 LEOH176 5 SSR80 17 TG SSR76 27 CT10120 CT20244I 34 TG147 CT10781 CT TG CT10737I 53 CT10615I 59 CT TG TG36 78 TG CT Chr.11 TG180 0 TG68 9 CT CT10953I 29 TG SSR20 41 TG TG LEOH66 64 LEOH CT156 CT10329I 78 CT CT10796I LEOH LEOH CT Chr.12 Tomato SNP and Indel Map Matthew Robbins, Ohio State University

25 Potato SNP map Potentially 7600 markers Candidate genes and randomly distributed High SNP polymorphism rate Potentially over 600 markers per chromosome We need over 3000 random markers to saturate the 4x genome Bradshaw et al 2008 We need less than 1000 markers to saturate the 2x genome

26 Sample (population) size σ 2 = error σ 2 + residual genetic σ 2 error σ 2 and residual genetic σ 2 are based on overall mean for each entry. Increasing the number of field replications will reduce error σ 2 but not the between-line genetic σ 2. To further reduce the denominator in the t-test. We need to add more genetic entries. Typical split plot: Sub-plot error: plot-level error σ 2 Plot error: genetic σ 2 + (plot-level error σ 2 )/(rep number) 26

27 Hu and Shu 2008 Statistical power h 2 =0.10 h 2 =

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29 Most popular QTL analysis methods for MBA Single-Marker Analysis (SMA) Interval Mapping (IM) Simple Interval Mapping (SIM) MapMaker/QTL QGENE R/QTL MapQTL5 Composite Interval Mapping (CIM) QTL Cartographer R/QTL MapQTL5 QTL Network 2.0 Multiple Interval Mapping (MIM) WinQTL cartographer

30 SMA is being used less nowadays due to its disadvantages and availability of more advanced statistical methods Advantage: Simplest approach for detecting QTLs Quick and less memory intensive Disadvantages: May not detect loosely linked QTLs QTL effects are often underestimated Does not determine the distance between marker and QTL Does not determine the direction of QTL from marker

31 SIM was one the most commonly used methodology in QTL mapping Advantage: The likelihood map represents the position of the QTL at various points of the genome The probable position of the QTL is given by support intervals Requires less progenies than the SMA Disadvantages: The number of QTL cannot be resolved The locations of the QTL are sometimes not well resolved, the exact positions of the QTL cannot be determined The statistical power is still relatively low

32 CIM has at least four advantages over SMA and SIM Advantages: By focusing on one genome region, the multidimensional search is reduced to one-dimensional search The resolution of QTL locations obtained is much higher than SMA and IM There are more variables in the model, making the model more efficient Markers can be used as boundary conditions to narrow down the most likely QTL position

33 Comparison of SMA, SIM and CIM for EB resistance in tomato SMA SIM CIM

34 Trait-based analysis (Selective Genotyping) Solanum lycopersicum X S. habrochaites (EB Susceptible) (EB Resistant) F 1 X S. lycopersicum BC 1 ~850 BC 1 Plants EB Resistant Zhang et al 2003 Mol. Breed. 12: 3-19 Early blight resistance EB Susceptible Foolad et al 1997 Mol. Breed. 3: Salt tolerance

35 QTL mapping step by step: A practical example A raw file is used for the genetic map construction

36 QTL mapping step by step: A practical example A genetic map is constructed

37 QTL mapping step by step: A practical example Phenotypic data is collected and incorporated into the file

38 QTL mapping step by step: A practical example A map file is then created for QTL analysis

39 Key points of QTL mapping 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. Adding more genotypes is more efficient than replicating the same genotypes (provided that a minimum number of environments are sampled) h 2 and size of effect important Results are trait specific 39

40 SolCAP Mapping Populations Germplasm panel Over 300 genotypes Over 20 programs represented Important advanced breeding lines Current and Historical varieties Parents of mapping populations Important genetic stocks Phenotyping at NY, WI and OR of core traits Evaluation of specific gravity, glucose and sucrose, chip color, skin type, shape, vine maturity, tuber number, vitamin C, internal defects, etc. Accessible through the SolCAP and Solanaceae Genome Network (SGN) websites. Association mapping Develop linkage hypotheses Phenotyping of other traits

41 SolCAP Mapping Populations 4x population Rio Grande X Premier Russet 200 progeny 7600 SNPs 3 environments North Carolina, Minnesota, Idaho Identify QTL for core traits (CHO, Vit C) Other populations from the community Core trait validation Other traits of regional importance

42 SolCAP populations 2x population DM x 84SD progeny Testcross segregation Map SNPs Anchor scaffolds to the genetic map Link mapping to PGSC Map core traits Other populations from the community

43 Approach of MAS in Tomato Pathway approach for candidate gene identification and introduction to metabolic pathway databases. Identification of polymorphisms in data-based sequences Approach of potato breeding for the future

44 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.

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46 Comments on SGN databases: There is a wealth of information organized and available. 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. SolCAP will be asking for feedback on how best to improve the SGN database and access via the Breeders Portal

47 Developing Breeder Friendly Tools Current SGN interfaces are aimed at the molecular biologist, with searches designed to facilitate molecular discovery Need a portal that is trait and germplasm centered Ability to search by traits relevant to (and defined by) breeders When a marker is identified, a protocol for use in breeding will be provided. Search option that only yields polymorphic marker, phenotype, or QTL results from elite germplasm Ability to search for known parents or offspring of any given genotype Ability to generate a list of markers that are polymorphic between any two parents Detailed tutorial/definitions of terms and traits utilized within the database

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49 Ovate

50 Databases 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

51 Genotyping the core collections will impact breeding. Potential translational approaches: 1) introgression from other populations (domesticated or wild) 2) selection for coupling phase recombinants to establish linkage blocks of favorable alleles (e.g. disease resistance loci) 3) population development designed to maximize variation w/in market classes 4) association approaches 5) whole genome approaches Other translational strategies will emerge under other CAPs or through innovation in public research.

52 Additional Thoughts from Tomato: Marker resources are currently not sufficient for QTL discovery in bi-parental or Association Mapping populations; in the future they should 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.

53 Marker Assisted Selection in Potato Limited number of markers available PVY, RB, GN, VW useful Invertase, sucrose synthase and LB QTL not validated in our germplasm More markers and QTL to validate from Li, et al and Bradshaw et al Some are PCR-based Need for marker conversion Use of high-throughput DNA isolation methods Apply at early generation selection phase Seedling stage After year 1 or 2 selection Need markers linked to core traits (e.g. glucose and starch)

54 Address regional, individual program and emerging needs within the Solanaceae community through a small grants program. Six key areas we intend to allocate resources to are: Genotyping mapping populations in the core facilities requested by the greater breeding community Marker conversion developing SNP markers linked to QTL into easily assayed (e.g. CAPs or dcaps) markers that end-users can readily apply in their own research programs QTL validation and MAS Small Grants Program Population development to address emerging needs Extension or education special projects New directions not envisioned at the time of proposal submission.

55 References Dudley, J.W. and R.J. Lambert Maydica 37: Falconer and McKay Introduction to quantitative genetics. 4 th ed. Longman group LTD. Essex, UK. pp 464. Laurie, C.C. et al Genetics 168: Liu, B.H Statistical Genomics. Linkage, mapping and QTL analysis.crc press LLC. FL, USA. 611p. Schön, C.C. et al Genetics 167: Tanksley S.D. et al Theor. Appl. Genet. 92: Walsh, B. and Lynch, M Genetics and Analysis of Quantitative Traits. Sinauer Associates Inc. MA, USA. 980p. Winkler, C.R. et al Genetics 164: Xiao, J. et al Genetics 150: 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: