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QTL lecture for Bio4025

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This is a lecture for Bio4025, a graduate class at Washington University in St. Louis. Some slides are derived from Julin Maloof (University of California, Davis), some of which were altered.

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QTL lecture for Bio4025

  1. 1. An introduction to quantitative genetics Dan Chitwood (with slides from Julin Maloof) March 16, 2015
  2. 2. What is a QTL? • QTL – Quantitative Trait Locus – A genetic locus that contributes to quantitative variation in a trait • What is a quantitative trait? What contributes to the concept of “trait?” – Genes? – Environment? – Cross/allele/species background? – The researcher?
  3. 3. Is hypocotyl length a quantitative trait? No? Qualitative: can classify as tall or shortWT phyB
  4. 4. Is hypocotyl length a quantitative trait? Yes? Segregation Ler x Cvi RIL Quantitative: must measure (quantify) differences
  5. 5. What causes quantitative segregation? • Signal to noise (allelic effect versus unexplained variance, environment, error) • Multiple genes segregating, smaller effects (polygenic traits)
  6. 6. What causes quantitative segregation? • Signal to noise (allelic effect versus unexplained variance, environment, error) • Multiple genes segregating, smaller effects (polygenic traits) allele effect = 3 allele effect = 2
  7. 7. Two Loci +
  8. 8. Why study development with QTL? • Micro-evolution – what makes two strains/populations/species different? – (Teosinte/Maize; Mimmulus; etc) • Plant Breeding – Fruit shattering – Flowering – etc. • Human Disease • Different spectrum of loci than available through forward genetics
  9. 9. Single marker regression
  10. 10. • Simplified version: – Phenotype all individuals QTL mapping: Single marker regression
  11. 11. Marker “A” is linked to a hypocotyl QTL Marker “C” is unlinked • Simplified version: – Phenotype all individuals – Genotype all individuals – Look for correlation QTL mapping: Single marker regression
  12. 12. -Repeat, analyzing correlation to trait for 100s of makers -Limitations: -Confounding: can not separate QTL effect (size) and location of the QTL (relative to the marker). -Does not account for effect of other contributing loci/markers QTL mapping: Single marker regression
  13. 13. y = m*x + b hyp = m*gtB + mean + error is m not equal to 0? If so, then we have a QTL QTL mapping: Single marker regression
  14. 14. y = m*x + b hyp = m*gtB + mean + error is m not equal to 0? If so, then we have a QTL QTL mapping: Single marker regression
  15. 15. Simple interval mapping
  16. 16. QTL mapping: recombination increases variance • The QTL “Q” may be some distance from marker “B” • Parent genotypes: B-Q and b-q • Progeny genotypes: B-Q, b-q, B-q, b-Q • genotype at QTL: Q or q • Solution: – Interval mapping
  17. 17. Simple Interval Mapping (SIM; Lander and Botstein) • Evaluate intervals between markers rather than markers themselves • Conceptually: – Parents: B-Q-D and b-q-d – Progeny: • B-Q-D and b-q-d • b-q-D b-Q-D B-Q-d B-q-d • very rare: B-q-D b-Q-d – Use B-D and b-d to estimate allelic effect size of QTL – Use recombinants to estimate whether QTL is closer to B or D – LOD score: Likelihood of linkage. Log10 of ratio of likelihood of linkage / likelihood unlinked
  18. 18. Simple Interval Mapping (SIM; Lander and Botstein) • Evaluate intervals between markers rather than markers themselves • In reality – The position of the QTL in the interval is evaluated by maximum likelihood. – At each position in the interval an iterative algorithm is used to determine the most likely model given the data. – The likelihood of a model with the QTL is compared to the null model (no QTL). – These two likelihoods are compared to give a LOD score • LOD score = log10(Likelihood with QTL/Likelihood no QTL) – what does a LOD score of 2 indicate?
  19. 19. Composite interval mapping
  20. 20. QTL mapping: other loci increase variance
  21. 21. QTL mapping: other loci increase variance Problem with SIM: Linked and unlinked QTL affect the analysis
  22. 22. QTL mapping: composite interval mapping (Zeng; Jansen and Stam) Simplifying and ignoring the “interval” issue: hyp = mean + m1*gtB + m2*gtA* + error
  23. 23. Composite Interval Partial CIM Simple Interval Mapping Zeng, Genetics, 1994 Comparison of QTL methods
  24. 24. Practicalities of QTL experiments
  25. 25. Practicalities—Backcross Population
  26. 26. Practicalities—F2 population
  27. 27. Practicalities: RIL population
  28. 28. Practicalities: experimental design Design: Randomization, Replication, Measurer effects, Positional effects, Environmental effects
  29. 29. y = m*x + b hyp = m*gtB +mean + error is m not equal to 0? If so, then we have a QTL QTL mapping: Single marker regression J. MaloofPhotos: Charlie Rick, TGRC Solanum pennellii (Peruvian desert) Solanum lycopersicum (cultivated) Desert tomato Cactus Single marker regression, sort of: Tomato introgression lines
  30. 30. Single marker regression, sort of: Tomato introgression lines X Backcross, marker-assisted selection Self … Look for phenotypic differences S. lycopersicum (domesticated) S. pennellii (desert)
  31. 31. Single marker regression, sort of: Tomato introgression lines
  32. 32. Ravi Kumar Aashish Ranjan Mike Covington RNA-Seq (genic polymorphisms) RESCAN (genic/non-genic polymorphisms) Genotyping using next-generation sequencing Kumar et al., Front. Plant Sci. 2012
  33. 33. A precise genetic map of the tomato introgression lines Chitwood et al., Plant Cell (2013)
  34. 34. A precise genetic map of the tomato introgression lines Chitwood et al., Plant Cell (2013)
  35. 35. Detecting subtle change takes field space and generates large phenomic datasets . . .
  36. 36. Field Aggie Stadium Medical Center Detecting subtle change takes field space and generates large phenomic datasets . . .
  37. 37. Detecting subtle change takes field space and generates large phenomic datasets . . .
  38. 38. Detecting subtle change takes field space and generates large phenomic datasets . . .
  39. 39. A QTL Network . . . Introgression Lines
  40. 40. Classic examples of QTL experiments
  41. 41. Doebley et al. Genetics 1995 Crop Domestication
  42. 42. Crop Domestication tb1-ref tb1-refA158 A158 Doebley et al. The evolution of apical dominance in maize. Nature 1997
  43. 43. A. Mimulus lewisii--Bee Pollinated C. Mimulus cardinalis--Humingbird Pollinated Schemske and Bradshaw, PNAS 1999 Pollination Syndromes
  44. 44. Variation in F2 Schemske and Bradshaw PNAS 1999 M. lewisii M. cardinalisF1 Hybrid F2
  45. 45. • Measure visitation rates in F2 population • Look for correlation between floral QTL and visitation • One QTL increases carotenoids -> decreases bee visitation 80% • Another QTL increases nectar 3-fold, double hummingbird visits (indpendent of color) Which Traits affect pollinator visitation?
  46. 46. Genetics of Reproductive Isolation • 12 Traits…47 QTL • 9/12 Traits had “major” QTL • Therefore, major QTL can play a role in speciation. – Contrasts with a very polygenic, additive small effect loci view of evolution
  47. 47. Genetics of Reproductive Isolation Kuhlemeier Lab
  48. 48. Genetics of Reproductive Isolation Hoballah et al. Plant Cell 2007
  49. 49. Genetics of Reproductive Isolation Hoballah et al. Plant Cell 2007
  50. 50. Frary et al. Science 2000 Crop Breeding S. pimpinellifolium S. lycopersicum Transgenic for S. pennellii fw2.2 candidate
  51. 51. Fruit Weight • Cross wild to domesticated • 11 fruit mass QTL • fw2.2 largest effect, modifying fruit size up to 30% • Create NIL and backcross • Large allele in domesticated partially recessive • Transgenics with wild allele have smaller fruit • Structural homology to ras oncogene
  52. 52. Developmental effect of fw2.2? • What makes 2 alleles different? • Expression: temporal expression different • Phenotypic effect: Reduced cell division in carpels
  53. 53. Real QTL effects are often “wimpy” --ie, polygenic, small effects --Contrasts with tb1 and pollinator shifts
  54. 54. Drastic differences in fruit and leaf phenotypes between wild and domesticated tomato species
  55. 55. How do we measure leaf shape? Elliptical Fourier Shape Descriptors
  56. 56. How do we measure leaf shape? Elliptical Fourier Shape Descriptors -2 SD +2 SD Overlay PC1 44.4% S.penn (desert) S.lyco (dom.)
  57. 57. How do we measure shape? Elliptical Fourier Shape Descriptors -2 SD +2 SD Overlay PC1 44.4% PC2 13.0%
  58. 58. How do we measure shape? Elliptical Fourier Shape Descriptors -2 SD +2 SD Overlay PC1 44.4% PC2 13.0% PC3 6.9% PC4 6.6% PC5 4.1%
  59. 59. -5.00E-02 -4.00E-02 -3.00E-02 -2.00E-02 -1.00E-02 0.00E+00 1.00E-02 2.00E-02 3.00E-02 4.00E-02 5.00E-02 -0.55 -0.45 -0.35 -0.25 -0.15 -0.05 0.05 S.lyco (dom.) The genetic basis of natural variation in leaflet morphology: an example PC1 PC2
  60. 60. -5.00E-02 -4.00E-02 -3.00E-02 -2.00E-02 -1.00E-02 0.00E+00 1.00E-02 2.00E-02 3.00E-02 4.00E-02 5.00E-02 -0.55 -0.45 -0.35 -0.25 -0.15 -0.05 0.05 S.penn (desert) IL4-3 S.lyco (dom.) PC1 PC2 The genetic basis of natural variation in leaflet morphology: an example
  61. 61. -5.00E-02 -4.00E-02 -3.00E-02 -2.00E-02 -1.00E-02 0.00E+00 1.00E-02 2.00E-02 3.00E-02 4.00E-02 5.00E-02 -0.55 -0.45 -0.35 -0.25 -0.15 -0.05 0.05 S.penn (desert) IL4-3 S.lyco (dom.) PC1 PC2 The genetic basis of natural variation in leaflet morphology: an example How to explain shape differences between tomatoes? --Polygenic trait or epistasis
  62. 62. -5.00E-02 -4.00E-02 -3.00E-02 -2.00E-02 -1.00E-02 0.00E+00 1.00E-02 2.00E-02 3.00E-02 4.00E-02 5.00E-02 -0.55 -0.45 -0.35 -0.25 -0.15 -0.05 0.05 S.penn (desert) IL4-3 S.lyco (dom.) PC1 PC2 The genetic basis of natural variation in leaflet morphology: an example How to explain shape differences between tomatoes? --Polygenic trait or epistasis --Additive effects?
  63. 63. QTL Advances
  64. 64. The Punctate phenotype: An example of bulk-segregant approaches with next-generation sequencing S. pennellii, low magnification S. penn., high magnification
  65. 65. The Punctate locus lies on chromosome 10 S. lycopersicumIL10-3, chrom. 10
  66. 66. The Punctate locus lies on chromosome 10 IL10-3, chrom. 10
  67. 67. The Punctate locus lies on chromosome 10 IL10-3, chrom. 10 Chromosomes 1 2 3 4 5 6 7 8 9 10 11 12 Chromosome 10
  68. 68. The Punctate locus lies on chromosome 10 IL10-3, chrom. 10 Chromosomes 1 2 3 4 5 6 7 8 9 10 11 12 Chromosome 10 2.65 Mbp; ~300 genes
  69. 69. X … S. lycopersicum (domesticated) S. pennellii (desert) Backcrossed Introgression Lines (BILs) Backcrosses Self
  70. 70. BIL-460 BIL-430 BIL-263 BIL-274 BIL-202 BIL-040 BIL-218 BIL-176 BIL-466 BIL-405 BIL-057 BIL-194 BIL-376 BIL-232 BIL-127 BIL-347 1 2 3 4 5 6 7 8 9 10 11 12Chromosome Genotypes of Punctate BILs share chrom. 10 region Aashish Ranjan
  71. 71. BIL-224 BIL-067 BIL-039 BIL-215 BIL-176 BIL-031 BIL-427 BIL-007 BIL-003 BIL-289 BIL-422 BIL-433 BIL-066 BIL-439 BIL-275 BIL-360 BIL-046 Genotypes of Punctate BILs share chrom. 10 region 1 2 3 4 5 6 7 8 9 10 11 12Chromosome Aashish Ranjan
  72. 72. On the Pn interval are four related MYBs, one of which is is Anthocyanin 1 (ANT1) “MYB250” ANT1 “MYB270” Heavy metal-associated domain gene “MYB290” S. lycopersicum: Aashish Ranjan
  73. 73. This et al. TAG 2007 Foumier-Level et al. Genetics 2009 . . . but berry color in grape is also caused by a set of tandemly duplicated MYBs! MYBA2 MYBA1 V. vinifera: MYBA3
  74. 74. Relatedness of Vitis, Solanum, and Arabidopsis MYBs
  75. 75. Relatedness of Vitis, Solanum, and Arabidopsis MYBs V. vinifera (Grape) S. lycopersicum (Tomato) Arabidopsis Quattrocchio et al. Plant Cell 2006
  76. 76. The Arabidopsis MYB homolog also affects trichome pigmentation Dissertation of Antonio Gonzalez 35S:MYB114 MYB113 MYB114 PAP2/MYB90 Arabidopsis
  77. 77. Evolution at work: from berries to trichomes V. vinifera (Grape) S. lycopersicum (Tomato)Arabidopsis
  78. 78. • 4,523 eQTL for 4,066 genes
  79. 79. A QTL Network . . . Introgression Lines IL4-3
  80. 80. Gene expression as phenotype: eQTL, cis- and trans- relationships, and transcriptional networks IL4-3: I II III IV VVI VII VIII IX X XI XII S. pennellii (desert) S. lycopersicum (domesticated)
  81. 81. Gene expression as phenotype: eQTL, cis- and trans- relationships, and transcriptional networks S. pennellii (desert) S. lycopersicum (domesticated) IL4-3: IV Differentially expressed: IL4-3 <-> S. lycopersicum cis- regulation
  82. 82. Gene expression as phenotype: eQTL, cis- and trans- relationships, and transcriptional networks S. pennellii (desert) S. lycopersicum (domesticated) IL4-3: IV Differentially expressed: IL4-3 <-> S. lycopersicum cis- regulation trans-regulation
  83. 83. Adaxial Abaxial S.lyco. (domesticated) S.penn. (desert) Another phenotype of IL4-3: Increased pavement cell size Pavement cell size:
  84. 84. The molecular mechanisms underlying cellular natural variation Histone H3A, Histone H3B, Histone H2B, MCM3, MCM4, MCM5, ssDNA replication binding protein, Cyclin B1, Cyclin B2, CDC20 Up-regulated genes:
  85. 85. The molecular mechanisms underlying cellular natural variation Histone H3A, Histone H3B, Histone H2B, MCM3, MCM4, MCM5, ssDNA replication binding protein, Cyclin B1, Cyclin B2, CDC20 Cell cycle Up-regulated genes: Sig. GO terms, trans-regulated genes:
  86. 86. The molecular mechanisms underlying cellular natural variation Histone H3A, Histone H3B, Histone H2B, MCM3, MCM4, MCM5, ssDNA replication binding protein, Cyclin B1, Cyclin B2, CDC20 Cell cycle E2F binding site Up-regulated genes: Sig. GO terms, trans-regulated genes: Promoter motifs, trans-regulated genes CDS
  87. 87. E2F promotes endoreduplication G1 SG2 M E2F Mitosis Endocycle
  88. 88. E2F promotes endoreduplication 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 SKP2A expression is reduced in S. penn. and IL4-3 G1 SG2 M SKP2A E2F Mitosis Endocycle Expressionlevel Lauren Headland
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This is a lecture for Bio4025, a graduate class at Washington University in St. Louis. Some slides are derived from Julin Maloof (University of California, Davis), some of which were altered.

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