Transcript Genome-Wide Association Studies (GWAS)

Genome-Wide Association
Studies (GWAS)
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Association Studies of Genetic
Factors
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1st generation
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Very small studies (<100 cases)
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Usually not epidemiologic study design; 1-2 SNPs
2nd generation
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Small studies (100-500 cases)
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More epi focus; a few SNPs
3rd generation
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Large molecular epi studies (>500 cases)
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Proper epi design; pathways
4th generation
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Consortium-based pooled analyses (>2000 cases)
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GxE analyses
5th generation
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Post-GWS studies
Boffeta, 2007
International Lung Cancer Consortium (ILCCO)
Wichmann
McLaughlin
Schwarts
Wild
Boffetta
Harris
Goodman
Risch
Kiyohara
Brennan
Benhamou
Wiencke
Christiani
Zhang
Stucker
Yang
Tajima
Landi
Berwick
Hong
Vineis
Lan
Chen
Lazarus
Spitz
Thun
Le Marchand
3 cohort studies
17 population based case-control studies
13 hospital based case-control studies
2 studies with mixed controls
1 cross-sectional study
Issues in genetic association studies
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Many genes
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Many SNPs
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~25,000 genes, many can be candidates
~12,000,000 SNPs, ability to predict functional SNPs is limited
Methods to select SNPs:
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Only functional SNPs in a candidate gene
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Systematic screen of SNPs in a candidate gene
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Systematic screen of SNPs in an entire pathway
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Genomewide screen
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Systematic screen for all coding changes
Introduction
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A genome-wide association study is an approach that
involves rapidly scanning markers across the
complete sets of DNA, or genomes, of many people
to find genetic variations associated with a particular
disease.
Once new genetic associations are identified,
researchers can use the information to develop better
strategies to detect, treat and prevent the disease.
Such studies are particularly useful in finding genetic
variations that contribute to common, complex
diseases, such as asthma, cancer, diabetes, heart
disease and mental illnesses.
http://www.genome.gov/20019523
Definition of GWAS
A genome-wide association study is
defined as any study of genetic
variation across the entire human
genome that is designed to identify
genetic associations with observable
traits (such as blood pressure or
weight), or the presence or absence of
a disease (such as cancer) or condition.
Potential of GWAS
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Whole genome information, when combined with
epidemiological, clinical and other phenotype data,
offers the potential for increased understanding of
basic biological processes affecting human health,
improvement in the prediction of disease and patient
care, and ultimately the realization of the promise of
personalized medicine.
In addition, rapid advances in understanding the
patterns of human genetic variation and maturing
high-throughput, cost-effective methods for
genotyping are providing powerful research tools for
identifying genetic variants that contribute to health
and disease.
Potential of GWAS
Selection of SNPs
(Genome-wide association studies)
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Molecular
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Analytical
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Highest requirements: Data management, automation
Advantages
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Higher requirements: Affymetrix and Illumina
No biological assumptions and can identify novel
genes/pathways
Excellent chance to identify risk alleles
Utility in individual risk assessment
Disadvantages
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High costs
Concern of multiple tests
SNP Selection
Affymetrix® Genome-Wide
Human SNP Array
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The new Affymetrix® Genome-Wide Human
SNP Array 6.0 features 1.8 million genetic
markers, including more than 906,600 single
nucleotide polymorphisms (SNPs) and more
than 946,000 probes for the detection of copy
number variation. The SNP Array 6.0
represents more genetic variation on a single
array than any other product, providing
maximum panel power and the highest
physical coverage of the genome.
The need for GWA
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Current understanding of disease etiology is limited
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Current understanding of functional variants is limited
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Xu JF, 2007
Therefore, the focusing on nonsynonymous changes is not sufficient
Results from linkage studies are often inconsistent and broad
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Therefore, candidate genes or pathways are insufficient
Therefore, the utility of identified linkage regions is limited
GWA studies offer an effective and objective approach
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Better chance to identify disease associated variants
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Improve understanding of disease etiology
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Improve ability to test gene-gene interaction and predict disease risk
GWA is promising
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Many diseases and traits are influenced by genetic factors
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Over 12 millions SNPs are known in the genome
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i.e., it is affordable to genotype a large number of SNPs in the genome
Large numbers of cases and controls are available
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i.e., some SNPs will be directly or indirectly associated with causal variants
The cost of SNP Genotyping is reduced
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i.e., they are caused by sequence variants in the genome
i.e., there is statistical power to detect variants with modest effect
When the above conditions are met…
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…associated SNPs will have different frequencies between cases
GWA is challenging
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Many diseases and traits are influenced by genetic factors
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But probably due to multiple modest risk variants
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They confer a stronger risk when they interact
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True associated SNPs are not necessary highly significant
Too many SNPs are evaluated
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Single studies tend to be underpowered
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Xu, 2007
False positives due to multiple tests
False negatives
Considerable heterogeneity among studies
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Phenotypic and genetic heterogeneity
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False positives due to population stratification
Genome coverage
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Two major platforms for GWA
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Illumina: HumanHap300, HumanHap550, and HumanHap1M
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Affymetrix: GeneChip 100K, 500K, 1M, and 2.3M
Genome-wide coverage
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The percentage of known SNPs in the genome that are in LD with
the genotyped SNPs
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Calculated based on HapMap (Haplotype map)
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Xu, 2007
http://hapmap.ncbi.nlm.nih.gov/downloads/nature02168.pdf
Calculated based on ENCODE
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Encyclopedia of DNA Elements
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identify all functional elements in the human genome.
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https://www.ncbi.nlm.nih.gov/pubmed/21037257?dopt=Abstract
Strategies for pre-association
analysis
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Quality control
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Filter SNPs by genotype call rates
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Filter SNPs by minor allele frequencies
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Filter SNPs by testing for Hardy-Weinberg
Equilibrium (p + q)2 = p2 + 2pq + q2 = 1
Data Analysis
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Single SNP analysis using prespecified genetic models
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2 x 3 table (2-df)
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Additive model (1-df), and test for additivity
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All possible genetic models (recessive,
dominant)
Data Analysis
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Haplotype analysis
Gene-gene and gene-environment
interactions
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Interaction with main effect
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Logistic regression
Interaction without main effect: data mining
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Classification and recursive tree (CART)
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Multifactor Dimensionality Reduction (MDR)
Sample size needs as a function of
genotype prevalence and OR for
main effects
Boffeta, 2007
False Positives
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False positives: too many dependent tests
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Adjust for number of tests
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Bonferroni correction
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Nominal significance level = study-wide significance / number of tests
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Nominal significance level = 0.05/500,000 = 10-7
Effective number of tests
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Take LD into account
Permutation procedure
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Permute case-control status
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Mimic the actual analyses
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Obtain empirical distribution of maximum test statistic under null hypothesis
False Positives
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False discovery rate (FDR)
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Expected proportion of false discoveries among
all discoveries
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Offers more power than Bonferroni
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Holds under weak dependence of the tests
False Positives
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Bayesian approach
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Taking a priori into account, False-Positive
Report Probability (FPRP)
Confirmation in independent
study populations
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The approach may limit the number of false
positives
Confirmation is needed to dissect true from false
positives
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Replication, examine the results from the 2nd stage only
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Joint analysis, combining data from 1st stage with 2nd stage
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Multiple stages
Issues of GWAS
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Population stratification
Multiple Testing: False Positives
Gene-Environmental Interaction
High Costs
Kingsmore, 2008
Kingsmore, 2008
Hypothesis
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The overall hypothesis is that multiple
sequence variants in the genome are
associated with the risk of lung cancer
among non-smokers. Specifically, we
hypothesize that a number of common
nonsmoking lung cancer risk-modifying
SNPs are in strong LD with the SNPs
arrayed on the 500K GeneChip®.
Theoretical model of gene-gene/environmental interaction pathway for lung cancer
Tobacco consumption
Occupational
Exposures
Environmental Carcinogens /
Procarcinogens Exposures
Ile105Val 
Ala114Val
Environmental Exposure
Null 
GSTP1
GSTM1
CYP1A1
MspI
Ile462Val 
Tyr113His
His139Arg
PAHs,
Xenobiotics,
Arene,
Alkine, etc
Detoxified
carcinogens
Active carcinogens
Pro187Ser
mEH
mEH
NQO1
DNA damage
repaired
DNA Damage
Tyr113His
His139Arg
Normal cell
Defected DNA
repair gene
If DNA damage not
repaired
XRCC1
Arg194Trp,
Arg399Gln,
Arg280His
M
G1
G2
P53
P16
S
G0
G870A
Arg72Pro
Ala146Thr
Cyclin D1
If loose cell cycle
control
Carcinogenesis
Programmed cell
death
Figure 1. The effects of SNPs on the Risk of Lung
Cancer among Smokers and Non-smokers
8
OR
7
6
5
Smokers
Non-Smokers
ETS Exp
Non ETS Exp
4
3
2
1
0
BRCA1 CHEK1 XRCC3 INFG
IL-10 ALDH2
Flow cytometry analysis
Facsalibur sorting
Fortessa cytometer
Excitation Optics
The excitation optics consist of multiple fixed wavelength lasers,
beam shaping optics, and individual pinholes which result in spatially
separated beam spots.
A final lens focuses the laser light into the gel-coupled cuvette flow
cell. Since the optical pathway and the sample core stream are fixed,
alignment is constant from day to day and from experiment to
experiment.
Collection Optics
Emitted light from the gel-coupled cuvette is delivered by fiber optics
to the detector arrays. The collection optics are set up in patented
octagon- and trigon-shaped optical pathways that maximize signal
detection resulting from each laser illuminated beam spot. Bandpass
filters in front of each PMT allow spectral selection of the collected
wavelengths. Importantly, this arrangement allows filter and mirror
changes within the optical array to be made easily and requires no
additional alignment for maximum signal strength.
The analyzer can be configured
with up to 5 lasers to detect up
to 20 parameters simultaneously
to support ever increasing
demands in multicolor flow
cytometry. A wide range of up to
34 laser choices is available as
excitation sources, including
blue, red, violet, yellow-green,
and UV
FACSAria
Three lasers provide excitation at 407, 488, and 633 nm for analysis of up to 10 fluorescence channels plus forward and side scatter
Digital electronics
Sort up to four populations simultaneously
Spectral overlap
http://bitesizebio.com/13696/introduction-to-spectral-overlap-and-compensation-flow-cytometry-protocol/
Compensation is the process of correcting the
spillover from our primary signal in each
secondary channel it is measured in.
Figure 2: Fluorescein emission profile with two filters overlaid. The standard filter for fluorescein is a 530/30 filter. This filter allows
light between 515-545 nm to pass through the filter. The second filter, 585/42, is a common filter for the fluorescent molecule
phycoerythrin (PE) and allows light between 564-606 nm to pass. The overlap of the fluorescein molecule into the PE detector
indicates that approximately 12% of the fluorescein molecule is being measured in the PE detector. Figure generated using the
Invitrogen spectral viewer.
https://www.thermofisher.com/us/en/home/life-science/cell-analysis/labeling-chemistry/fluorescence-spectraviewer.html