1. Finding “the gene” for cystic fibrosis

2. Why is this in quotes? A.CF is not caused by a gene, it’s caused by multiple genes. B.CF is not caused by genetic factors. C.CF is not caused by a gene, it’s caused by a mutation.

4. How to find genetic determinants of naturally varying traits?

5. Genetic markers Fig. 10.3 (microsatellite)

6. Genetic markers Fig. 10.3 (microsatellite) Table 11.1

7. Lots of benign variation between us.

8. How do you find polymorphisms? Fig. 11.6

9. How do you find polymorphisms? Fig. 11.6 Introduced in lecture 9/15.

10. How do you find polymorphisms? Fig. 11.6

11. How do you find polymorphisms? Fig. 11.6

12. How do you find polymorphisms? Fig. 11.6

13. How do you find polymorphisms? Fig. 11.6

14. How do you find polymorphisms? Fig. 11.6

15. Hybrid mapping: location of probe www3.mdanderson.org/depts/cellab/fish1.htm mousehuman/mouse hybrid

16. Hybrid mapping: location of probe Back then, no technique to see 6kb at cytological resolution.

17. Who cares about benign polymorphisms? Remember Sturtevant? Fig. 5.10

18. Who cares about benign polymorphisms? We are going to do a two- point cross. One of our genetic loci is represented by phenotype; the other is a DNA marker.

19. Mapping a disease locus Fig. 11.A (Autosomal dom) A1 A2

20. Mapping a disease locus Fig. 11.A (Autosomal dom) phenotype (variation in locus 1) A1 A2

21. Mapping a disease locus Fig. 11.A (Autosomal dom) phenotype (variation in locus 1) marker genotype (variation in locus 2) A1 A2

22. Mapping a disease locus Fig. 11.A (Autosomal dom) phenotype (variation in locus 1) marker genotype (variation in locus 2) How close are they in genetic distance? A1 A2

23. Mapping a disease locus Fig. 11.A A1D A2d (Autosomal dom) A1 A2

24. Mapping a disease locus Fig. 11.A A1D A2d (Autosomal dom) A1 A2 (assume phase)

25. Mapping a disease locus Fig. 11.A A1D A2d A1d d A2

26. Mapping a disease locus Fig. 11.A A1D A2d A1d d A2d A1 A2

27. Mapping a disease locus Fig. 11.A A1D A2d A1d d D A2

28. Mapping a disease locus Fig. 11.A A1D A2d A1d d A2d A1 A2

29. Mapping a disease locus Fig. 11.A A1D A2d A1d d ? A2

30. Mapping a disease locus Fig. 11.A A1d d ? D A2d (sperm) A1 A2

31. Mapping a disease locus Fig. 11.A A1d d A2D A1D A2d (sperm) A1 A2

32. Mapping a disease locus Fig. 11.A A1d d D A2d A1 A2 In total, 7 of the kids are non-recombinants and 1 is a recombinant.

33. Mapping a disease locus Fig. 11.A What is the apparent RF between the DNA marker and the disease mutation? A.1/10 B.1/8 C.1/20 A1 A2 In total, 7 of the kids are non-recombinants and 1 is a recombinant.

34. Mapping a disease locus Fig. 11.A What is the apparent RF between the DNA marker and the disease mutation? 1/8 = 12.5 m.u. A1 A2 A.1/10 B.1/8 C.1/20 In total, 7 of the kids are non-recombinants and 1 is a recombinant.

35. Why do I say “apparent RF?”

36. What if… observed recombination fraction = 1/8 = 12.5 cM Disease- causing mutation Restriction fragment length polymorphism True distance 30 cM

37. What if… observed recombination fraction = 1/8 = 12.5 cM Disease- causing mutation Restriction fragment length polymorphism True distance 30 cM You could say this will never happen. But…

38. What if… observed recombination fraction = 1/8 = 12.5 cM Disease- causing mutation Restriction fragment length polymorphism True distance 30 cM this is our observation

39. What if… observed recombination fraction = 1/8 = 12.5 cM Disease- causing mutation Restriction fragment length polymorphism True distance 30 cM The observed number of recombinants is just a point estimate, with some error associated. this is our observation

40. 12 cM, 18 cM…who cares? Further experiments need to find the causal variant, not just a marker. If distances are wrong, could be hunting for years.

41. Mapping a disease locus Fig. 11.A We now know the mutation is near (linked to) the marker. 1/8 = 12.5 m.u. A1 A2

42. Mapping a disease locus We now know the mutation is near (linked to) the marker. marker (known) 1/8 = 12.5 m.u. A1 A2

43. Mapping a disease locus We now know the mutation is near (linked to) the marker. window containing causative mutation 1/8 = 12.5 m.u. marker (known) A1 A2

44. Mapping a disease locus 1/8 = 12.5 m.u. How significant? A1 A2

45. Mapping a disease locus 1/8 = 12.5 m.u. How significant? If RF = 0.5 (unlinked), would be like flipping a coin 8 times. How likely would you be to get 7 heads and 1 tail? A1 A2

46. If RF = 0.5 (unlinked), would be like flipping a coin 8 times. How likely would you be to get 7 heads and 1 tail? How much MORE likely is a model of RF < 0.5?

47. If RF = 0.5 (unlinked), would be like flipping a coin 8 times. How likely would you be to get 7 heads and 1 tail? How much MORE likely is a model of RF < 0.5? For large cross between known parents, would use  2 to evaluate significance. Here we can’t.

48. LOD scores 1 recomb, 7 non-recomb. r = genetic distance between marker and disease locus A1 A2

49. LOD scores 1 recomb, 7 non-recomb. Odds = P(pedigree | r) P(pedigree | r = 0.5) r = genetic distance between marker and disease locus A1 A2

50. LOD scores 1 recomb, 7 non-recomb. Odds = P(pedigree | r) P(pedigree | r = 0.5) r = genetic distance between marker and disease locus “How likely are the data given our model?” A1 A2

51. LOD scores k = 1 recomb, n = 7 non-recomb. Odds = P(pedigree | r) P(pedigree | r = 0.5) r = genetic distance between marker and disease locus Odds = (1-r) n r k 0.5 n 0.5 k A1 A2

52. LOD scores Odds = P(pedigree | r) P(pedigree | r = 0.5) r = genetic distance between marker and disease locus Odds = (1-r) n r k 0.5 (total # meioses) A1 A2 k = 1 recomb, n = 7 non-recomb.

53. LOD scores Odds = P(pedigree | r) P(pedigree | r = 0.5) r = genetic distance between marker and disease locus Odds = (1-r) n r k 0.5 (total # meioses) We have an idea of true r, but it is imprecise. k = 1 recomb, n = 7 non-recomb. A1 A2

54. Remember? observed recombination fraction = 1/8 = 12.5 cM Disease- causing mutation Restriction fragment length polymorphism True distance 30 cM The observed number of recombinants is just a point estimate, with some error associated. this is our observation

55. LOD scores Odds = P(pedigree | r) P(pedigree | r = 0.5) r = genetic distance between marker and disease locus Odds = (1-r) n r k 0.5 (total # meioses) k = 1 recomb, n = 7 non-recomb. A1 A2 This formalism allows any r value. Let’s guess r = 0.3.

56. LOD scores Odds = P(pedigree | r) P(pedigree | r = 0.5) r = genetic distance between marker and disease locus Odds = (1-r) n r k 0.5 (total # meioses) Odds = 0.7 7 0.3 1 0.5 8 k = 1 recomb, n = 7 non-recomb. A1 A2 This formalism allows any r value. Let’s guess r = 0.3.

57. LOD scores Odds = P(pedigree | r) P(pedigree | r = 0.5) r = genetic distance between marker and disease locus Odds = (1-r) n r k 0.5 (total # meioses) Odds = 0.7 7 0.3 1 0.5 8 = 6.325 k = 1 recomb, n = 7 non-recomb. A1 A2 This formalism allows any r value. Let’s guess r = 0.3.

58. LOD scores Odds = P(pedigree | r) P(pedigree | r = 0.5) r = genetic distance between marker and disease locus Odds = (1-r) n r k 0.5 (total # meioses) Odds = 0.7 7 0.3 1 0.5 8 = 6.325 Data >6 times more likely under LINKED hypothesis than under UNLINKED hypothesis. k = 1 recomb, n = 7 non-recomb. A1 A2

59. LOD scores rodds 0.112.244 0.210.737 0.36.325 0.42.867 0.5?? Odds = P(pedigree | r) P(pedigree | r = 0.5) Odds = (1-r) n r k 0.5 (total # meioses) k = 1 recomb, n = 7 non-recomb.

60. LOD scores Odds = P(pedigree | r) P(pedigree | r = 0.5) Odds = (1-r) n r k 0.5 (total # meioses) Odds at r=0.5? A.2.5 B.0 C.1 D.10 rodds 0.112.244 0.210.737 0.36.325 0.42.867 0.5??

61. LOD scores What’s the best (most likely) value of r? A.0.1 B.0.2 C.0.3 D.0.4 E.0.5 rodds 0.112.244 0.210.737 0.36.325 0.42.867 0.51

62. What problems will look like 1,2 1,11,21,11,21,11,2 1,1 A1 A2

63. What problems will look like 1,2 1,11,21,11,21,11,2 1,1

64. What problems will look like 1,2 1,11,21,11,21,11,2 1,1 Count number of recombinants, calculate odds.

65. Reading and chapter problems on web site.