Class GWAS Odds Ratio, Increased Risk

Class GWAS Odds Ratio, Increased Risk
P-value OR IR Lactose Intolerance rs .09 2.7 1.2 Eye Color rs .0093 inf Asparagus rs .084 2.35 1.18 Bitter Taste rs713598 0.22 0.519 Earwax rs .004 4.6 2.6 Strong genetics Not disease related

Lactose Intolerance Lactase Gene Rs4988235 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

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

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).

Bitter Taste 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.

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 body odor and apocrine colostrum (breast milk).

Ear Wax Rs “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):

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

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) anyone: suggest to me studies you want me to write-up. Anyone: write-up a disease for genotation. Meet with Stuart to discuss and plan. 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 world Resolution 10,000 PC1 and PC2 To recapitulate the results of Novembre et al., for the POPRES dataset, use PC1 vs. PC4. Then go to Ancestry, painting

Ancestry Analysis people We want to simplify this SNPs
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

PCA example students simplify Kinds of 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

Uninformative traits shirt color
Pants color favorite toothpaste favorite color etc. Informative traits Skin color eye color height weight sex hair length etc. ~SNPs informative for ancestry ~SNPs not informative for ancestry

PCA example 100 100 100 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. 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. 100 100 100

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

Ancestry Analysis 1 2 3 4 5 6 7 Snp1 A T Snp2 G Snp3 Snp4 C Snp5 Snp6

Reorder the SNPs 1 2 3 4 5 6 7 Snp1 A T Snp3 Snp5 G Snp7 C Snp9 Snp11

Ancestry Analysis 1 2 3 4 5 6 7 Snp1 A T Snp3 Snp5 G Snp7 C Snp9 Snp11

Ancestry Analysis =X =x 1 2 3 4 5 6 7 Snp1 A T Snp3 Snp5 G Snp7 C Snp9
1-6 7 Snp1 A T Snp3 Snp5 G Snp7 C Snp9 Snp11 1 Snp1 A Snp3 Snp5 Snp7 C Snp9 G Snp11 T 7 Snp1 T Snp3 Snp5 G Snp7 A Snp9 Snp11 C =X =x

M N PC1 X x

Ancestry Analysis =Y =y 1 2 3 4 5 6 7 Snp4 C T Snp6 G A Snp8 1-3 4-7

Ancestry Analysis 1 2 3 4 5 6 7 PC1 X x PC2 Y y Snp2 G Snp10 A C T
1-3 4-6 7 PC1 X x PC2 Y y Snp2 Snp10 Snp12

PC1 and PC2 inform about ancestry
1-3 4-6 7 PC1 X x PC2 Y y Snp2 G Snp10 A T C Snp12

Ancestry PCA

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

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

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

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

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

Rare alleles Cases Controls
You wont see the rare alleles unless you sequence Each allele appears once, so need to aggregate alleles in the same gene in order to do statistics.

Gene-Gene A B C diabetes D E F 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 Height gene that requires eating meat
Lactase gene that requires drinking milk These are SNPs that have effects only under certain environmental conditions

SNPedia The SNPedia website http://www.snpedia.com/index.php/SNPedia
A thank you from SNPedia Class website for SNPedia List of last years write-ups How to write up a SNPedia entry

Ancestry/Height 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