1. Office hours Wednesday 3-4pm 304A Stanley Hall Review session 5pm Thursday, Dec. 11 GPB100

2. Association vs. linkage Strong, easy to detect, but rare in population; may not be reflective of common disease. Also, hard to collect family data. Common but weak effects; need 1000’s of samples to detect. If no common cause, can fail. Unrelated individuals Related individuals

3. Association vs. linkage small number of generations; individuals share big chunks of genome; can get co- inheritance between distant markers many recombinations have happened since common ancestor; shared region is small; no co- inheritance between distant markers So you need very high density of markers to get signal in an association study, but you get very high spatial resolution.

4. Association and admixture Cases Controls = = At any one of these loci, Caucasian-like allele will be enriched in control samples.

5. Genotyping by array Fig. 11.8

6. Expression microarrays Fig. 1.13

7. Labeled DNA sample vs. labeled mRNA (cDNA) sample

8. (reverse transcription in the test tube)

9. Coding sequence array Fig. 1.13

10. Coding sequence array Fig. 1.13 say, with chemotherapeutic

11. Coding sequence array Fig. 1.13

12. Coding sequence array Fig. 1.13

13. Coding sequence array Fig. 1.13 expression expressed not expressed

14. Why measure expression of all genes at once?

15. “Regulon” expression response http://www.mcb.mcgill.ca/~hallett/GEP/Lecture2/Image17.gif (e.g. cortisol)

16. “Regulon” expression response http://www2.kenyon.edu/Depts/BioEllipse/courses/biol114/Chap06/week08a_files/regulon.gif

17. Finding regulatory response on a large scale

18. Oligo expression array transcripts (soybean component)

19. Expression profiles Time since drug administered

20. Expression profiles Time since drug administered

21. Expression profiles Time since drug administered Each color is a regulon, or “cluster,” of co- regulated genes

22. Expression effects of cancer

23. Cancer classification

25. Histological classification is finicky: can we do better?

26. Diagnosis via transcriptional profile

27. transcripts patient samples

28. Diagnosis via transcriptional profile

30. Natural variation among “normals” Two human chromosomes differ at ~1/1000 bases. 96% of these differences are not in protein-coding sequence. Why?

31. Two human chromosomes differ at ~1/1000 bases. 96% of these differences are not in protein-coding sequence. Most protein coding mutations are deleterious; appear but are culled by natural selection. Natural variation among “normals”

34. Genetic variation in mRNA levels ORF TF G kinase TF

35. Genetic variation in mRNA levels ORF TF G kinase TF Likely to be a complex trait.

36. Many mRNA differences at once

37. Linkage mapping of mRNA levels “Black 6” mousex“DBA” mouse

38. Linkage mapping of mRNA levels “Black 6” mousex“DBA” mouse ~10% mRNA levels significantly different

39. Linkage mapping of mRNA levels “Black 6” mousex“DBA” mouse 111 F 2 progeny …

40. Linkage mapping of mRNA levels “Black 6” mousex“DBA” mouse 111 F 2 progeny Microarray each F 2 liver …

41. Linkage mapping of mRNA levels “Black 6” mousex“DBA” mouse 111 F 2 progeny Microarray each F 2 liver … Genotype each F 2

42. Linkage mapping of mRNA levels “Black 6” mousex“DBA” mouse 111 F 2 progeny Microarray each F 2 liver … Genotype each F 2 Looking for linkage (coinheritance) between marker and mRNA level.

43. Marker is linked to polymorphism in expression regulation cascade ORF TF G kinase TF

44. Marker is linked to polymorphism in expression regulation cascade ORF TF G kinase TF G G

45. Marker is linked to polymorphism in expression regulation cascade ORF TF G kinase TF G G

46. Marker is linked to polymorphism in expression regulation cascade ORF TF G kinase TF G G One allele = high mRNA, the other = low mRNA

47. Marker is linked to polymorphism in expression regulation cascade ORF TF G kinase TF

48. Marker is linked to polymorphism in expression regulation cascade ORF TF G kinase TF

49. Marker is linked to polymorphism in expression regulation cascade ORF TF G kinase TF mRNA level shows linkage to locus of polymorphic regulator(s).

50. Marker is linked to polymorphism in expression regulation cascade ORF TF G kinase TF mRNA level shows linkage to locus of polymorphic regulator(s).

51. Locally acting polymorphisms

53. Polymorphism responsible for mRNA difference is at the locus of the gene itself

54. Locally acting polymorphisms ORF TF G kinase TF

55. Locally acting polymorphisms ORF TF G kinase TF

56. Locally acting polymorphisms Polymorphism responsible for mRNA difference is at the locus of the gene itself

57. Locally acting polymorphisms Polymorphism responsible for mRNA difference is at the locus of the gene itself ~25% of varying mRNAs are caused by locally acting polymorphism

58. Nonlocal polymorphisms

59. One polymorphism in a key regulator can affect a regulon: 100’s of related mRNAs.

60. Clinical applications “Black 6” mousex“DBA” mouse 111 F 2 progeny Microarray each F 2 liver … Genotype each F 2 Measure fat pad each F 2

61. Clinical applications

62. Colored curves = fat mass at different body locations

63. Clinical applications Finding polymorphism responsible for difference in macroscopic phenotype is hard

64. Clinical applications Finding polymorphism responsible for difference in macroscopic phenotype is hard If mRNAs change too, can learn mechanism from known function of encoded proteins

65. Clinical applications

68. Counts allele 1/allele 2, cases Counts allele 1/allele 2, controls = 1.5

69. Clinical applications

70. Marker predicts quantitative expression level = association

71. Can we map expression traits first, disease afterward?

72. Linkage of human transcripts

74. Association of human transcripts

75. linkage (families) assoc (unrelated)

76. Association in multiple populations Han Chinese and Japanese European-Americans in Utah

77. Association in multiple populations