Genetic Linkage 1 rs rs Chr. 4 Chr. 12

Genetic Linkage 2 rs rs Chr kb R 2 = 1.0

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

Colorectal cancer 1057 cases 960 controls 550K SNPs

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

Chi squared Chi squared = 65 P value << 10 -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.”

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 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.”

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

Class GWAS

Class GWAS (n=55) 1. Allele counts

Is rs associated with earwax?

GWAS guides on genotation

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

Class GWAS (n=79) 2. Allele p-values rs rs rs713598rs rs Earwax Eyes Asparagus Bitter Lactose

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

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.

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

Class GWAS results

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/ = 10.9 Dry wax C/T = 4/24 =.167

Class GWAS Odds Ratio, Likelihood Ratio, Relative Risk P-valueORRR Lactose Intolerance rs x10 -6 Eye Colorrs Asparagusrs Bitter Tasters x10 -4 Earwaxrs x

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

Class GWAS Odds Ratio, Relative Risk P-valueORRR Lactose Intolerance rs x10 -6 Eye Colorrs Asparagusrs Bitter Tasters x10 -4 Earwaxrs x

Class GWAS Odds Ratio, Likelihood Ratio, Relative Risk P-valueORRR Lactose Intolerance rs x10 -6 high Eye Colorrs Asparagusrs Bitter Tasters x Earwaxrs x

GWAS guides on genotation

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

Lactose Intolerance

Eye Color Rs 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. Rs leads to reduced expression in eye specifically. Null alleles cause albinism

Ear Wax Rs 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.

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

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) rs is in a region containing 39 olfactory receptors

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

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

SNPedia The SNPedia website A thank you from SNPedia Class website for SNPedia List of last years write-ups How to write up a SNPedia entry

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?

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

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.

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

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

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

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 …

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

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

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

Ancestry Analysis 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

Ancestry Analysis Snp1AAAAAAT Snp3AAAAAAT Snp5AAAAAAG Snp7CCCCCCA Snp9GGGGGGT Snp11TTTTTTC MN PC1Xx

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

Ancestry Analysis PC1XXXXXXx PC2YYYyyyy Snp2GGGGGGG Snp10AGCTAGC Snp12GCTAAGC PC1XXx PC2Yyy Snp2 Snp10 Snp12

PC1 and PC2 inform about ancestry PC1XXx PC2Yyy Snp2GGG Snp10ATC Snp12GAC

Ancestry PCA

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

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

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

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

Missing Heritability

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

Nature Genetics VOLUME 42 | NUMBER 7 | JULY 2010

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

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

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

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