Height Do “height” exercise in Genotation/traits/height Fill out form. Submit SNPs.

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1 Height Do “height” exercise in Genotation/traits/height Fill out form. Submit SNPs

2 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_2014.html How to write up a SNPedia entry http://stanford.edu/class/gene210/web/html/snpedia.html

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4 What should be in your SNPedia write-up? 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?

5 Summarize the trait “BD is characterized by a fluctuation between manic episodes and severe depression. Schizophrenia is characterized by hallucinations, both visual and auditory, paranoia, disorganized thinking and lack of normal social skills.”

6 Summarize the study How large was the cohort? How strong was the p-value? What was the OR, likelihood ratio or increased risk? “This study was done by analyzing around 500,000 autosomal SNPs and 12,000 X-chromosomal SNPS in 682 patients with BD and 1300 controls.” “The rs1064395 was highly significant with a p-value of 3.02X10-8 and an odds ratio of 1.31, with A being the risk allele. ”

7 What is known about the SNP? Associated genes? What was the OR, likelihood ratio or increased risk? “rs1064395 is a single nucleotide variant (SNV) found in the neurocan gene (NCAN) that has been implicated as a predictor of both bipolar disorder (BD) and schizophrenia. ”

8 Class GWAS

9 Class GWAS http://web.stanford.edu/class/gene210/web/html/exercises.html eye color data for rs4988235

10 Class GWAS http://web.stanford.edu/class/gene210/web/html/exercises.html eye color data for rs7495174

11 eye color data for rs7495174 How do we calculate whether rs7495174 is associated with eye color? What is the threshold for significance? Later: odds ratio, increased likelihood

12 Is rs7495174 is associated with eye color??

13 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

14 Class GWAS (n=98) Allele p-values rs4988235rs7495174rs713598rs17822931rs4481887 Earwax Eyes.12 Asparagus Bitter Lactose

15 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.

16 Recessive model is best for earwax rs17822931 Allelic p value =.0014 Genotype p value, T is dominant = 0.34 Genotype p value, T is recessive =.0001

17 3 genetic models allelicDominantRecessive Earwax rs17822931 P =.0014(T) P=.34 (T) P =.0001 Eyes Asparagus Bitter Lactose

18 Class GWAS results Lactose intolerance: rs4988235, GG associated with lactose intolerance Eye color: rs7495174, AA associated with blue/green eyes Bitter taste: rs713598, CC associated with inability to taste bitterness Earwax: rs17822931, TT associated with dry earwax Asparagus smell: rs4481887, A more likely to be able to smell asparagus than G

19 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 = 48/22 = 2.18 Allelic odds ratio = 2.18/.47 = 4.6 In 2014, OR was 10.9 Dry wax C/T = 9/19 =.47

20 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/49 total students =.286 Increased risk for dry earwax for TT compared to prior:.75/.286 = 2.6 For TT genotype, chance is 9 Dry/12 students =.75

21 Class GWAS Odds Ratio, Increased Risk P-valueORIR Lactose Intolerance rs4988235 Eye Colorrs7495174 Asparagusrs4481887 Bitter Tasters713598 Earwaxrs178229314.62.6

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

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

24 Lactose Intolerance

25 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

26 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.

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

28 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

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

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

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34 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

35 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.

36 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

37 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

38 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

39 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 …

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

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

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

43 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

44 Ancestry Analysis 1234567 Snp1AAAAAAT Snp3AAAAAAT Snp5AAAAAAG Snp7CCCCCCA Snp9GGGGGGT Snp11TTTTTTC MN PC1Xx

45 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

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

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

48 Ancestry PCA

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

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

51 832 | NATURE | VOL 467 | 14 OCTOBER 2010 183K people 180 loci ~10% variance explained

52 Missing Heritability

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

54 Nature Genetics VOLUME 42 | NUMBER 7 | JULY 2010

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

57 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

58 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

59 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