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

  2. Genetic Linkage 2 rs1333049 rs10757274 Chr. 9 29 kb R2 = 1.0

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

  4. Colorectal cancer 1057 cases 960 controls 550K SNPs

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

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

  7. Multiple hypothesis testing “Of the 547,647 polymorphic tag SNPs, 27,673 showed an association with disease at P < .05.” • 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.

  8. Multiple hypothesis testing “Of the 547,647 polymorphic tag SNPs, 27,673 showed an association with disease at P < .05.” • 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.

  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

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

  17. Class GWAS3. 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 GWAS3. 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 GWASresults

  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 wax C/T = 53/29 = 1.82 Dry wax C/T = 4/24 = .167 Allelic odds ratio = 1.82/0..167 = 10.9

  21. Class GWASOdds Ratio, Likelihood Ratio, Relative Risk

  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 For TT genotype, chance is 11 Dry/11 students = 1.0 Increased risk for dry earwax for TT compared to prior: 1.0/0.25 = 4.0

  23. Class GWASOdds Ratio, Relative Risk

  24. Class GWASOdds Ratio, Likelihood Ratio, Relative Risk

  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 dimethylsulfoxide (sweet aroma) dimethylsulfone (sweet aroma) rs4481887 is in a region containing 39 olfactory receptors

  32. Genetic principles are universal 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 10,000 1 1 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. SNPs 1M

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

  39. Informative traitsSkin coloreye colorheightweightsexhair lengthetc. Uninformative traitsshirt color Pants color favorite toothpaste favorite color etc. ~SNPs not informative for ancestry ~SNPs informative for ancestry

  40. 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. RACE Bitter taste SIZE Asparagus Shirt Color Favorite Color Etc. 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. 100 100 100

  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. RACE Bitter taste SIZE Asparagus Shirt Color Favorite Color Etc. Size = Sex + Height + Weight + Pant size + Shirt size … 100 100

  42. Ancestry Analysis

  43. Reorder the SNPs

  44. Ancestry Analysis

  45. Ancestry Analysis =X =x

  46. Ancestry Analysis

  47. Ancestry Analysis =Y =y

  48. Ancestry Analysis

  49. PC1 and PC2 inform about ancestry

  50. Ancestry PCA