1. Class GWAS Odds Ratio, Increased Risk P-valueORIR Lactose Intolerance rs4988235.092.71.2 Eye Colorrs7495174.00930inf Asparagusrs4481887.0842.351.18 Bitter Tasters713598.0004980.220.519 Earwaxrs17822931.0044.62.6 Strong genetics Not disease related

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

3. Lactose Intolerance

4. 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

5. 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

6. Bitter Taste (phenylthiocarbamide) TAS2R38: taste receptor The rs713598(G) allele is the "tasting" allele, and it is dominant to the "non-tasting" allele rs713598(C).

7. TAS2R38: taste receptor used for similar molecules in foods (like cabbage and raw broccoli) or drinks (like coffee and dark beers).coffee Bitter Taste

8. Plants produce a variety of toxic compounds in order to protect themselves from being eaten. The ability to discern bitter tastes evolved as a mechanism to prevent early humans from eating poisonous plants. Humans have about 30 genes that code for bitter taste receptors. Each receptor can interact with several compounds, allowing people to taste a wide variety of bitter substances. Bitter Taste

9. 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 body odor and apocrine colostrum (breast milk).

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

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

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

13. 832 | NATURE | VOL 467 | 14 OCTOBER 2010 183K people 180 loci ~10% variance explained

15. Family vs Genetic Height 2015 students Mom/Dad Genes

16. Family vs Genetic Height 2011-2015 students Mom/Dad Genes

17. Missing Heritability

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

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

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

23. 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

24. Gene-Gene ABC DEF diabetes A - weak D - weak A - D - strong A - E - strong A - F - strong A - B - weak D - E - weak

25. 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

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

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

28. Genotation project update Go to genotation.stanford.edu Click clinical/disease Click show my snps. Do this for CEU and Chinese. ~5200 SNPs  ~19000 SNPs in newest update 502 GWAS studies  ~1200 GWAS studies in newest update I will create buttons for some of the important studies for class to use. (clinically important, scientific strength, # SNPs) 1.anyone: suggest to me studies you want me to write-up. 2.Anyone: write-up a disease for genotation. Meet with Stuart to discuss and plan. 3.BMI/bioinformatics students: add new functionality to genotation. Create script to calculate running score or riskogram that computes final genetic influence from all SNPs combined. See diabetes for example. Meet with Stuart to discuss and plan.

30. Ancestry Go to Genotation, Ancestry, PCA (principle components analysis) Load in genome. Start with HGDP world Resolution 10,000 PC1 and PC2 To recapitulate the results of Novembre et al., for the POPRES dataset, use PC1 vs. PC4. Novembre et al. Then go to Ancestry, painting

31. 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.

32. 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

33. 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

34. 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

35. 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 …

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

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

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

39. 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

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

41. 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

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

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

44. Ancestry PCA

46. Chromosome painting

47. Jpn x CEUCEU x CEU fathermother Stephanie Zimmerman x