1. Genetic Linkage 1 rs12426597 rs6447271 Chr. 4 Chr. 12

2. Genetic Linkage 2 rs10757274 rs1333049 Chr. 9 29 kb R 2 = 1.0

3. Genetic Linkage 3 Chr. 2 Chr. 26 rs17822931 rs4988235 Ear wax, TT-> dry earwax Lactase, GG -> lactose intolerance

4. Colorectal cancer 1057 cases 960 controls 550K SNPs

5. Cancer: 0.57G 0.43T controls: 0.49G 0.51T Are these different? Chi squared

6. Chi squared http://www.graphpad.com/quickcalcs/chisquared1.cfm Chi squared = 65 P value << 10 -7

7. Multiple hypothesis testing P =.05 means that there is a 5% chance for this to occur randomly. If you try 100 times, you will get about 5 hits. If you try 547,647 times, you should expect 547,647 x.05 = 27,382 hits. So 27,673 (observed) is about the same as one would randomly expect. “Of the 547,647 polymorphic tag SNPs, 27,673 showed an association with disease at P <.05.”

8. Multiple hypothesis testing Here, have 547,647 SNPs = # hypotheses False discover rate = q = p x # hypotheses. This is called the Bonferroni correction. Want q =.05. This means a positive SNP has a.05 likelihood of rising by chance. At q =.05, p =.05 / 547,647 =.91 x 10 -7 This is the p value cutoff used in the paper. “Of the 547,647 polymorphic tag SNPs, 27,673 showed an association with disease at P <.05.”

9. Class GWAS Go to genotation.stanford.edu Go to “traits”, then “GWAS” Look up your SNPs Fill out the table Submit information

10. Class GWAS

11. Class GWAS (n=55) 1. Allele counts

12. Is rs17822931 associated with earwax?

13. GWAS guides on genotation http://www.stanford.edu/class/gene210/web/html/exercises.html

14. Class GWAS Calculate chi-squared for allelic differences in all five SNPs for one of these traits: Earwax Lactose intolerance Eye color Bitter taste Asparagus smell

15. Class GWAS (n=79) 2. Allele p-values rs4988235rs7495174rs713598rs17822931rs4481887 Earwax Eyes Asparagus Bitter Lactose

16. Class GWAS (n=79) 2. Allele p-values

17. Class GWAS 3. genotype counts T is a null allele in ABC11 T/T has dry wax. T/C and C/C have wet earwax usually.

18. Class GWAS 3. genotype counts rs17822931 Allelic p value =5 x 10 -6 Genotype p value, T is dominant = 0.15 Genotype p value, T is recessive = 3 x 10 -9

19. Class GWAS results

20. Allelic odds ratio: ratio of the allele ratios in the cases divided by the allele ratios in the controls How different is this SNP in the cases versus the controls? Wet waxC/T = 53/29 = 1.82 Allelic odds ratio = 1.82/0..167 = 10.9 Dry wax C/T = 4/24 =.167

21. Class GWAS Odds Ratio, Likelihood Ratio, Relative Risk P-valueORRR Lactose Intolerance rs49882357x10 -6 Eye Colorrs74951740.4 Asparagusrs4481887.01 Bitter Tasters7135983x10 -4 Earwaxrs178229313x10 -9 10.9

22. Increased Risk: What is the likelihood of seeing a trait given a genotype compared to overall likelihood of seeing the trait in the population? Prior chance to have dry earwax 14 Dry/55 total students =.25 Increased risk for dry earwax for TT compared to prior: 1.0/0.25 = 4.0 For TT genotype, chance is 11 Dry/11 students = 1.0

23. Class GWAS Odds Ratio, Relative Risk P-valueORRR Lactose Intolerance rs49882357x10 -6 Eye Colorrs74951740.4 Asparagusrs4481887.01 Bitter Tasters7135983x10 -4 Earwaxrs178229313x10 -9 10.74.0

24. Class GWAS Odds Ratio, Likelihood Ratio, Relative Risk P-valueORRR Lactose Intolerance rs49882357x10 -6 high Eye Colorrs74951740.43.62.9 Asparagusrs4481887.014.41.9 Bitter Tasters7135983x10 -4 7.12.1 Earwaxrs178229313x10 -9 10.714.7

25. GWAS guides on genotation http://www.stanford.edu/class/gene210/web/html/exercises.html

26. Lactose Intolerance Rs4988235 Lactase Gene A/G A – lactase expressed in adulthood G – lactase expression turns off in adulthood

27. Lactose Intolerance

28. Eye Color Rs7495174 In OCA2, the oculocutaneous albinism gene (also known as the human P protein gene). Involved in making pigment for eyes, skin, hair. accounts for 74% of variation in human eye color. Rs7495174 leads to reduced expression in eye specifically. Null alleles cause albinism

29. Ear Wax Rs17822931 In ABCC11 gene that transports various molecules across extra- and intra-cellular membranes. The T allele is loss of function of the protein. Phenotypic implications of wet earwax: Insect trapping, self-cleaning and prevention of dryness of the external auditory canal. Wet earwax: linked to axillary odor and apocrine colostrum.

30. Ear Wax Rs17822931 “the allele T arose in northeast Asia and thereafter spread through the world.”

31. Asparagus Certain compounds in asparagus are metabolized to yield ammonia and various sulfur-containing degradation products, including various thiols and thioesters, which give urine a characteristic smell. Methanethiol (pungent) dimethyl sulfide (pungent) dimethyl disulfide bis(methylthio)methane dimethyl sulfoxide (sweet aroma) dimethyl sulfone (sweet aroma) rs4481887 is in a region containing 39 olfactory receptors

32. Genetic principles are universal Am J Hum Genet.Am J Hum Genet. 1980 May;32(3):314-31.

33. Different genetics for different traits Simple: Lactose tolerance, asparagus smell, photic sneeze Complex: T2D, CVD Same allele: CFTR, Different alleles: BRCA1, hypertrophic cardiomyopathy

34. SNPedia The SNPedia website http://www.snpedia.com/index.php/SNPedia A thank you from SNPedia http://snpedia.blogspot.com/2012/12/o-come-all-ye-faithful.html Class website for SNPedia http://stanford.edu/class/gene210/web/html/projects.html List of last years write-ups http://stanford.edu/class/gene210/archive/2012/projects_2012.html How to write up a SNPedia entry http://stanford.edu/class/gene210/web/html/snpedia.html

35. SNPedia Summarize the trait Summarize the study How large was the cohort? How strong was the p-value? What was the OR, likelihood ratio or increased risk? Which population? What is known about the SNP? Associated genes? Protein coding? Allele frequency? Does knowledge of the SNP affect diagnosis or treatment?

36. Ancestry Go to Genotation, Ancestry, PCA (principle components analysis) Load in genome. Start with HGDP world Resolution 10,000 PC1 and PC2 Then go to Ancestry, painting

37. Ancestry Analysis people 1 10,000 SNPs 1 1M AA CC etc GG TT etc AG CT etc We want to simplify this 10,000 people x 1M SNP matrix using a method called Principle Component Analysis.

38. PCA example students 1 30 Eye color Lactose intolerant Asparagus Ear Wax Bitter taste Sex Height Weight Hair color Shirt Color Favorite Color Etc. 100 Kinds of students Body types simplify

39. Informative traits Skin color eye color height weight sex hair length etc. Uninformative traits shirt color Pants color favorite toothpaste favorite color etc. ~SNPs informative for ancestry ~SNPs not informative for ancestry

40. PCA example Skin Color Eye color Lactose intolerant Asparagus Ear Wax Bitter taste Sex Height Weight Pant size Shirt size Hair color Shirt Color Favorite Color Etc. 100 Skin color Eye color Hair color Lactose intolerant Ear Wax Bitter taste Sex Height Weight Pant size Shirt size Asparagus Shirt Color Favorite Color Etc. 100 RACE Bitter taste SIZE Asparagus Shirt Color Favorite Color Etc. 100

41. PCA example Skin color Eye color Hair color Lactose intolerant Ear Wax Bitter taste Sex Height Weight Pant size Shirt size Asparagus Shirt Color Favorite Color Etc. 100 RACE Bitter taste SIZE Asparagus Shirt Color Favorite Color Etc. 100 Size = Sex + Height + Weight + Pant size + Shirt size …

42. Ancestry Analysis 1234567 Snp1AAAAAAT Snp2GGGGGGG Snp3AAAAAAT Snp4CCCTTTT Snp5AAAAAAG Snp6GGGAAAA Snp7CCCCCCA Snp8TTTGGGG Snp9GGGGGGT Snp10AGCTAGC Snp11TTTTTTC Snp12GCTAAGC

43. Reorder the SNPs 1234567 Snp1AAAAAAT Snp3AAAAAAT Snp5AAAAAAG Snp7CCCCCCA Snp9GGGGGGT Snp11TTTTTTC Snp2GGGGGGG Snp4CCCTTTT Snp6GGGAAAA Snp8TTTGGGG Snp10AGCTAGC Snp12GCTAAGC

44. Ancestry Analysis 1234567 Snp1AAAAAAT Snp3AAAAAAT Snp5AAAAAAG Snp7CCCCCCA Snp9GGGGGGT Snp11TTTTTTC Snp4CCCTTTT Snp6GGGAAAA Snp8TTTGGGG Snp2GGGGGGG Snp10AGCTAGC Snp12GCTAAGC

45. Ancestry Analysis 1234567 Snp1AAAAAAT Snp3AAAAAAT Snp5AAAAAAG Snp7CCCCCCA Snp9GGGGGGT Snp11TTTTTTC 1-67 Snp1AT Snp3AT Snp5AG Snp7CA Snp9GT Snp11TC 1 Snp1A Snp3A Snp5A Snp7C Snp9G Snp11T 7 Snp1T Snp3T Snp5G Snp7A Snp9T Snp11C =X =x

46. Ancestry Analysis 1234567 Snp1AAAAAAT Snp3AAAAAAT Snp5AAAAAAG Snp7CCCCCCA Snp9GGGGGGT Snp11TTTTTTC MN PC1Xx

47. Ancestry Analysis 1234567 Snp4CCCTTTT Snp6GGGAAAA Snp8TTTGGGG 1-34-7 Snp4CT Snp6GA Snp8TG 1-3 Snp4C Snp6G Snp8T 4-7 Snp4T Snp6A Snp8G 1-34-7 PC2Yy =Y =y

48. Ancestry Analysis 1234567 PC1XXXXXXx PC2YYYyyyy Snp2GGGGGGG Snp10AGCTAGC Snp12GCTAAGC 1-34-67 PC1XXx PC2Yyy Snp2 Snp10 Snp12

49. PC1 and PC2 inform about ancestry 1-34-67 PC1XXx PC2Yyy Snp2GGG Snp10ATC Snp12GAC

50. Ancestry PCA

51. Complex traits: height heritability is 80% NATURE GENETICS | VOLUME 40 | NUMBER 5 | MAY 2008

52. NATURE GENETICS VOLUME 40 [ NUMBER 5 [ MAY 2008 Nature Genetics VOLUME 42 | NUMBER 11 | NOVEMBER 2010 63K people 54 loci ~5% variance explained.

53. Calculating RISK for complex traits Start with your population prior for T2D: for CEU men, we use 0.237 (corresponding to LR of 0.237 / (1 – 0.237) = 0.311). Then, each variant has a likelihood ratio which we adjust the odds by. Slide by Rob Tirrell, 2010

54. 832 | NATURE | VOL 467 | 14 OCTOBER 2010 183K people 180 loci ~10% variance explained

55. Missing Heritability

56. Where is the missing heritability? Lots of minor loci Rare alleles in a small number of loci Gene-gene interactions Gene-environment interactions

57. Nature Genetics VOLUME 42 | NUMBER 7 | JULY 2010

59. This approach explains 45% variance in height. Q-Q plot for human height

60. Rare alleles 1.You wont see the rare alleles unless you sequence 2.Each allele appears once, so need to aggregate alleles in the same gene in order to do statistics. CasesControls

61. Gene-Gene ABC DEF diabetes A - not affected D - not affected A - D - affected A - E - affected A - F - affected A - B - not affected D - E - not affected

62. Gene-environment 1.Height gene that requires eating meat 2.Lactase gene that requires drinking milk These are SNPs that have effects only under certain environmental conditions