Class GWAS Odds Ratio, Increased Risk P-valueORIR Lactose Intolerance rs4988235.092.71.2 Eye...

Class GWASOdds Ratio, Increased Risk

P-value OR IR

Lactose Intolerance

rs4988235 .09 2.7 1.2

Eye Color rs7495174 .0093 0 inf

Asparagus rs4481887 .084 2.35 1.18

Bitter Taste rs713598 .000498 0.22 0.519

Earwax rs17822931 .004 4.6 2.6

Strong geneticsNot disease related

Lactose Intolerance

Rs4988235

Lactase GeneA/G

A – lactase expressed in adulthoodG – lactase expression turns off in adulthood

Lactose Intolerance

Eye Color

Rs7495174In 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

AsparagusCertain 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 disulfidebis(methylthio)methanedimethyl sulfoxide (sweet aroma)dimethyl sulfone (sweet aroma)

rs4481887 is in a region containing 39 olfactory receptors

Bitter Taste (phenylthiocarbamide)

TAS2R38: taste receptorThe rs713598(G) allele is the "tasting" allele, and it is dominant to the "non-tasting" allele rs713598(C).

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

Bitter Taste

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

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).

Ear Wax

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

Genetic principles are universal

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

Different genetics for different traits

Simple: Lactose tolerance, asparagus smell, photic sneezeComplex: T2D, CVDSame allele: CFTR, Different alleles: BRCA1, hypertrophic cardiomyopathy

Genotation project updateGo to genotation.stanford.eduClick clinical/diseaseClick show my snps.Do this for CEU and Chinese.

~5200 SNPs ~19000 SNPs in newest update502 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.

Ancestry

Go to Genotation, Ancestry, PCA (principle components analysis)Load in genome.Start with HGDP worldResolution 10,000PC1 and PC2

To recapitulate the results of Novembre et al., for the POPRES dataset, use PC1 vs. PC4.

Then go to Ancestry, painting

Ancestry Analysispeople1 10,000

AACCetc

GGTTetc

AGCTetc

We want to simplify this 10,000 people x 1M SNP matrix using a method called Principle Component Analysis.

PCA examplestudents1 30

Eye colorLactose intolerant

AsparagusEar Wax

Bitter tasteSex

HeightWeight

Hair colorShirt Color

Favorite ColorEtc.100

Kinds of students

Body types

simplify

Informative traitsSkin coloreye color

heightweight

sexhair length

Uninformative traitsshirt colorPants color

favorite toothpastefavorite color

~SNPs informative for ancestry

~SNPs not informative for ancestry

PCA example

Skin ColorEye color

Lactose intolerantAsparagus

Ear WaxBitter taste

SexHeight

WeightPant sizeShirt size

Favorite ColorEtc.

Skin colorEye color

Hair colorLactose intolerant

Ear WaxBitter taste

SexHeight

AsparagusShirt Color

Favorite ColorEtc.

Bitter taste

Favorite ColorEtc.

PCA example

Skin colorEye color

Ear WaxBitter taste

SexHeight

Favorite ColorEtc.

Bitter taste

Favorite ColorEtc.

Size = Sex + Height + Weight +Pant size + Shirt size …

Ancestry Analysis1 2 3 4 5 6 7

Snp1 A A A A A A T

Snp2 G G G G G G G

Snp3 A A A A A A T

Snp4 C C C T T T T

Snp5 A A A A A A G

Snp6 G G G A A A A

Snp7 C C C C C C A

Snp8 T T T G G G G

Snp9 G G G G G G T

Snp10 A G C T A G C

Snp11 T T T T T T C

Snp12 G C T A A G C

Reorder the SNPs1 2 3 4 5 6 7

Snp1 A A A A A A T

Snp3 A A A A A A T

Snp5 A A A A A A G

Snp7 C C C C C C A

Snp9 G G G G G G T

Snp11 T T T T T T C

Snp2 G G G G G G G

Snp4 C C C T T T T

Snp6 G G G A A A A

Snp8 T T T G G G G

Snp10 A G C T A G C

Snp12 G C T A A G C

Snp1 A A A A A A T

Snp3 A A A A A A T

Snp5 A A A A A A G

Snp7 C C C C C C A

Snp9 G G G G G G T

Snp11 T T T T T T C

Snp4 C C C T T T T

Snp6 G G G A A A A

Snp8 T T T G G G G

Snp2 G G G G G G G

Snp10 A G C T A G C

Snp12 G C T A A G C

Snp1 A A A A A A T

Snp3 A A A A A A T

Snp5 A A A A A A G

Snp7 C C C C C C A

Snp9 G G G G G G T

Snp11 T T T T T T C

Snp1 A T

Snp3 A T

Snp5 A G

Snp7 C A

Snp9 G T

Snp11 T C

Snp1 A

Snp3 A

Snp5 A

Snp7 C

Snp9 G

Snp11 T

Snp1 T

Snp3 T

Snp5 G

Snp7 A

Snp9 T

Snp11 C

Snp1 A A A A A A T

Snp3 A A A A A A T

Snp5 A A A A A A G

Snp7 C C C C C C A

Snp9 G G G G G G T

Snp11 T T T T T T C

PC1 X x

Snp4 C C C T T T T

Snp6 G G G A A A A

Snp8 T T T G G G G

1-3 4-7

Snp4 C T

Snp6 G A

Snp8 T G

Snp4 C

Snp6 G

Snp8 T

Snp4 T

Snp6 A

Snp8 G

1-3 4-7

PC2 Y y

PC1 X X X X X X x

PC2 Y Y Y y y y y

Snp2 G G G G G G G

Snp10 A G C T A G C

Snp12 G C T A A G C

1-3 4-6 7

PC1 X X x

PC2 Y y y

PC1 and PC2 inform about ancestry

1-3 4-6 7

PC1 X X x

PC2 Y y y

Snp2 G G G

Snp10 A T C

Snp12 G A C

Ancestry PCA

NATURE GENETICS VOLUME 40 [ NUMBER 5 [ MAY 2008Nature Genetics VOLUME 42 | NUMBER 11 | NOVEMBER 2010

63K people54 loci~5% variance explained.

832 | NATURE | VOL 467 | 14 OCTOBER 2010

183K people180 loci~10% variance explained

Complex traits: heightheritability is 80%

NATURE GENETICS | VOLUME 40 | NUMBER 5 | MAY 2008

Missing Heritability

Where is the missing heritability?

Lots of minor lociRare alleles in a small number of lociGene-gene interactionsGene-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 sequence2. Each allele appears once, so need to aggregate alleles in the

same gene in order to do statistics.

Cases Controls

Gene-Gene

D E Fdiabetes

A- not affectedD- not affected

A- D- affectedA- E- affectedA- F- affected

A- B- not affectedD- E- not affected

Gene-environment

1. Height gene that requires eating meat2. Lactase gene that requires drinking milk

These are SNPs that have effects only under certain environmental conditions

SNPedia

The SNPedia websitehttp://www.snpedia.com/index.php/SNPedia

A thank you from SNPediahttp://snpedia.blogspot.com/2012/12/o-come-all-ye-faithful.html

Class website for SNPediahttp://stanford.edu/class/gene210/web/html/projects.html

List of last years write-upshttp://stanford.edu/class/gene210/archive/2012/projects_2012.html

How to write up a SNPedia entryhttp://stanford.edu/class/gene210/web/html/snpedia.html

Ancestry/HeightGo to Genotation, Ancestry, PCA (principle components analysis)Load in genome.Start with HGDP worldResolution 10,000PC1 and PC2

Then go to Ancestry, painting

Ancestry Analysispeople1 10,000

AACCetc

GGTTetc

AGCTetc

We want to simplify this 10,000 people x 1M SNP matrix using a method called Principle Component Analysis.

PCA examplestudents1 30

Eye colorLactose intolerant

AsparagusEar Wax

Bitter tasteSex

HeightWeight

Favorite ColorEtc.100

Kinds of students

Body types

simplify

Informative traitsSkin coloreye color

heightweight

sexhair length

Uninformative traitsshirt colorPants color

favorite toothpastefavorite color

~SNPs informative for ancestry

~SNPs not informative for ancestry

PCA example

Skin ColorEye color

Lactose intolerantAsparagus

Ear WaxBitter taste

SexHeight

Favorite ColorEtc.

Skin colorEye color

Ear WaxBitter taste

SexHeight

Favorite ColorEtc.

Bitter taste

Favorite ColorEtc.

PCA example

Skin colorEye color

Ear WaxBitter taste

SexHeight

Favorite ColorEtc.

Bitter taste

Favorite ColorEtc.

Size = Sex + Height + Weight +Pant size + Shirt size …

Snp1 A A A A A A T

Snp2 G G G G G G G

Snp3 A A A A A A T

Snp4 C C C T T T T

Snp5 A A A A A A G

Snp6 G G G A A A A

Snp7 C C C C C C A

Snp8 T T T G G G G

Snp9 G G G G G G T

Snp10 A G C T A G C

Snp11 T T T T T T C

Snp12 G C T A A G C

Reorder the SNPs1 2 3 4 5 6 7

Snp1 A A A A A A T

Snp3 A A A A A A T

Snp5 A A A A A A G

Snp7 C C C C C C A

Snp9 G G G G G G T

Snp11 T T T T T T C

Snp2 G G G G G G G

Snp4 C C C T T T T

Snp6 G G G A A A A

Snp8 T T T G G G G

Snp10 A G C T A G C

Snp12 G C T A A G C

Snp1 A A A A A A T

Snp3 A A A A A A T

Snp5 A A A A A A G

Snp7 C C C C C C A

Snp9 G G G G G G T

Snp11 T T T T T T C

Snp4 C C C T T T T

Snp6 G G G A A A A

Snp8 T T T G G G G

Snp2 G G G G G G G

Snp10 A G C T A G C

Snp12 G C T A A G C

Snp1 A A A A A A T

Snp3 A A A A A A T

Snp5 A A A A A A G

Snp7 C C C C C C A

Snp9 G G G G G G T

Snp11 T T T T T T C

Snp1 A T

Snp3 A T

Snp5 A G

Snp7 C A

Snp9 G T

Snp11 T C

Snp1 A

Snp3 A

Snp5 A

Snp7 C

Snp9 G

Snp11 T

Snp1 T

Snp3 T

Snp5 G

Snp7 A

Snp9 T

Snp11 C

Snp1 A A A A A A T

Snp3 A A A A A A T

Snp5 A A A A A A G

Snp7 C C C C C C A

Snp9 G G G G G G T

Snp11 T T T T T T C

PC1 X x

Snp4 C C C T T T T

Snp6 G G G A A A A

Snp8 T T T G G G G

1-3 4-7

Snp4 C T

Snp6 G A

Snp8 T G

Snp4 C

Snp6 G

Snp8 T

Snp4 T

Snp6 A

Snp8 G

1-3 4-7

PC2 Y y

PC1 X X X X X X x

PC2 Y Y Y y y y y

Snp2 G G G G G G G

Snp10 A G C T A G C

Snp12 G C T A A G C

1-3 4-6 7

PC1 X X x

PC2 Y y y

PC1 and PC2 inform about ancestry

1-3 4-6 7

PC1 X X x

PC2 Y y y

Snp2 G G G

Snp10 A T C

Snp12 G A C

Chromosome painting

Jpn x CEU CEU x CEU

father mother

Stephanie Zimmerman

Complex traits: heightheritability is 80%

NATURE GENETICS | VOLUME 40 | NUMBER 5 | MAY 2008

NATURE GENETICS VOLUME 40 [ NUMBER 5 [ MAY 2008Nature Genetics VOLUME 42 | NUMBER 11 | NOVEMBER 2010

63K people54 loci~5% variance explained.

Slide by Rob Tirrell, 2010

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.

832 | NATURE | VOL 467 | 14 OCTOBER 2010

183K people180 loci~10% variance explained

Missing Heritability

Where is the missing heritability?

Lots of minor lociRare alleles in a small number of lociGene-gene interactionsGene-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 sequence2. Each allele appears once, so need to aggregate alleles in the

same gene in order to do statistics.

Cases Controls

Gene-Gene

D E Fdiabetes

A- not affectedD- not affected

A- D- affectedA- E- affectedA- F- affected

A- B- not affectedD- E- not affected

Gene-environment

1. Height gene that requires eating meat2. Lactase gene that requires drinking milk

These are SNPs that have effects only under certain environmental conditions

GWAS guides on genotation

http://www.stanford.edu/class/gene210/web/html/exercises.html

Class GWAS Odds Ratio, Increased Risk P-valueORIR Lactose Intolerance rs4988235.092.71.2 Eye...

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