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rs6447271. Chr. 4. Genetic Linkage 1. rs12426597. Chr. 12. Genetic Linkage 2. rs1333049. rs10757274. Chr. 9. 29 kb R 2 = 1.0. Lactase, GG -> lactose intolerance. rs4988235. Chr. 2. Genetic Linkage 3. E ar wax, TT-> dry earwax. rs17822931. Chr. 26. Colorectal cancer. 1057 cases - PowerPoint PPT Presentation
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Genetic Linkage 1
rs12426597
rs6447271
Chr. 4
Chr. 12
Genetic Linkage 2
rs10757274 rs1333049
Chr. 9
29 kbR2 = 1.0
Genetic Linkage 3
Chr. 2
Chr. 26rs17822931
rs4988235
Ear wax, TT-> dry earwax
Lactase, GG -> lactose intolerance
Colorectal cancer
1057 cases960 controls
550K SNPs
Cancer: 0.57G 0.43T
controls: 0.49G 0.51TAre these different?
Chi squared
Chi squaredhttp://www.graphpad.com/quickcalcs/chisquared1.cfm
Chi squared = 65P 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 10-7
• 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.eduGo to “traits”, then “GWAS”Look up your SNPsFill out the tableSubmit information
Class GWAS
Class GWAS (n=55)1. Allele counts
Is rs17822931 associated with earwax?
GWAS guides on genotation
http://www.stanford.edu/class/gene210/web/html/exercises.html
Class GWASCalculate chi-squared for allelic differences in all five SNPs for one of these traits:EarwaxLactose intoleranceEye colorBitter tasteAsparagus smell
Class GWAS (n=79)2. Allele p-values
rs4988235 rs7495174 rs713598 rs17822931 rs4481887
Earwax
Eyes
Asparagus
Bitter
Lactose
Class GWAS (n=79)2. Allele p-values
Class GWAS3. genotype counts
T is a null allele in ABC11T/T has dry wax. T/C and C/C have wet earwax usually.
Class GWAS3. genotype counts
rs17822931Allelic p value = 5 x 10-6
Genotype p value, T is dominant = 0.15Genotype p value, T is recessive = 3 x 10-9
Class GWASresults
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 = 1.82/0..167 = 10.9
Dry wax C/T = 4/24 = .167
Class GWASOdds Ratio, Likelihood Ratio, Relative Risk
P-value OR RR
Lactose Intolerance
rs4988235 7x10-6
Eye Color rs7495174 0.4
Asparagus rs4481887 .01
Bitter Taste rs713598 3x10-4
Earwax rs17822931 3x10-9 10.9
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 earwax14 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 is11 Dry/11 students = 1.0
Class GWASOdds Ratio, Relative Risk
P-value OR RR
Lactose Intolerance
rs4988235 7x10-6
Eye Color rs7495174 0.4
Asparagus rs4481887 .01
Bitter Taste rs713598 3x10-4
Earwax rs17822931 3x10-9 10.7 4.0
Class GWASOdds Ratio, Likelihood Ratio, Relative Risk
P-value OR RR
Lactose Intolerance
rs4988235 7x10-6 high high
Eye Color rs7495174 0.4 3.6 2.9
Asparagus rs4481887 .01 4.4 1.9
Bitter Taste rs713598 3x10-4 7.1 2.1
Earwax rs17822931 3x10-9 10.7 14.7
GWAS guides on genotation
http://www.stanford.edu/class/gene210/web/html/exercises.html
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
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.
Ear Wax
Rs17822931“the allele T arose in northeast Asia and thereafter spread through the world.”
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
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
SNPediaThe SNPedia website
http://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
SNPediaSummarize the traitSummarize 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 worldResolution 10,000PC1 and PC2
Then go to Ancestry, painting
Ancestry Analysispeople1 10,000
SNPs
1
1M
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
etc.
Uninformative traitsshirt colorPants color
favorite toothpastefavorite color
etc.
~SNPs informative for ancestry
~SNPs not informative for ancestry
PCA example
Skin ColorEye color
Lactose intolerantAsparagus
Ear WaxBitter taste
SexHeight
WeightPant sizeShirt size
Hair colorShirt Color
Favorite ColorEtc.
100
Skin colorEye color
Hair colorLactose intolerant
Ear WaxBitter taste
SexHeight
WeightPant sizeShirt size
AsparagusShirt Color
Favorite ColorEtc.
100
RACE
Bitter taste
SIZE
AsparagusShirt Color
Favorite ColorEtc.
100
PCA example
Skin colorEye color
Hair colorLactose intolerant
Ear WaxBitter taste
SexHeight
WeightPant sizeShirt size
AsparagusShirt Color
Favorite ColorEtc.
100
RACE
Bitter taste
SIZE
AsparagusShirt Color
Favorite ColorEtc.
100
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
Ancestry Analysis1 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
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
Ancestry Analysis1 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
1-6 7
Snp1 A T
Snp3 A T
Snp5 A G
Snp7 C A
Snp9 G T
Snp11 T C
1
Snp1 A
Snp3 A
Snp5 A
Snp7 C
Snp9 G
Snp11 T
7
Snp1 T
Snp3 T
Snp5 G
Snp7 A
Snp9 T
Snp11 C
=X =x
Ancestry Analysis1 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
M N
PC1 X x
Ancestry Analysis1 2 3 4 5 6 7
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
1-3
Snp4 C
Snp6 G
Snp8 T
4-7
Snp4 T
Snp6 A
Snp8 G
1-3 4-7
PC2 Y y
=Y =y
Ancestry Analysis1 2 3 4 5 6 7
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
Snp2
Snp10
Snp12
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
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
A B C
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