This ppt is related to the new era of association mapping,GWAP which is an emerging bud in mapping field.

1. Seminar on
By:Varsha Gaitonde
ID:PALB2235
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2.  Introduction
 Terminologies
 Comparison of AM v/s BM
GWAS Introduction
 Methodology
 Challenges –Conducting GWAS
Case studies
Advantages
Disadvantages
Future of GWAS
Revision
Conclusion
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3. • False negative: the declaration of an outcome as statistically
non-significant, when the effect is actually genuine.
• False positive: the declaration of an outcome as statistically
significant, when there is no true effect.
• Linkage: refers to coinheritance of different loci within a genetic
distance on the chromosome.
• Linkage equilibrium: LE is a random association of alleles at
different loci and equals the product of allele frequencies within
haplotypes.
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4. • Linkage disequilibrium: LD is a non-random association of alleles
at different loci, describing the condition with non-equal
frequency of haplotypes in a population.
• Minor allele Frequency(MAF):The frequency of the less common
alleles of a polymorphic locus. Its value lies between 0 to 0.5,and
can be vary between populations.
• Odd ratio: Measurement of association that is commonly used in
case control studies. Defined as odd of exposure to the
susceptible genetic variant in case compared with that in
controls. If OR significantly greater than 1,then the genetic
variant is associated with a disease.
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5. • It is the representation of information using special relationships
• Mapping methods
Linkage mapping: measures recombination between markers
and the unknown gene (linkage)
• Association mapping: measure correlation between marker
alleles and allele in a population (linkage disequilibrium)
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6. Bi-parental mapping
Association mapping
• No cross required, works
with existing germplasm.
• Phenotypic data can be
Phenotypes to be collected.
already available.
Limited mapping resolution. • High resolution.
Essentially 2 alleles are tested • More than 2 alleles are
tested.
Constraints to segregating loci • Many loci for a single trait
are concurrently analyzed.
between parental lines.
• Comparatively low.
High detection power
• Experimental cross required.
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8. • Aim to identify which regions (or SNPs) in the genome are
associated with disease or certain phenotype.
• Design:
– Identify population structure
– Select case subjects (those with disease)
– Select control subjects (healthy)
– Genotype a million SNPs for each subject
– Determine which SNP is associated.
• Encoded data
• Ranking SNPs
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9. Linkage vs Association
Linkage
Association
• Successful study published in 2005,with investigating patients
age related molecular degeneration.
1.
Family-based
Matching/ethnicity generally
• 2.Prior to GWAS in 2000
unimportant
Inheritance studies of
3. Few markers for genome
coverage (300-400
linkage families. STRs)
4.
Can be weak design
• 5.HapMap2003 detection; poor
Good for initial
6.
for fine-mapping
Powerful for rare variants
1.
Families or unrelateds
2.
Matching/ethnicity crucial
3.
Many markers req for genome
coverage (105 – 106 SNPs)
Powerful design
4.
5.
6.
Poor for initial detection; good
for fine-mapping
Powerful for common variants;
rare variants generally
impossible
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10. 1. Human Genome Project
 Good for consensus,
not good for individual
differences
Sept 01
Feb 02
April 04
Oct 04
2. Identify genetic variants
 Anonymous with respect
to traits.
April 1999 – Dec 01
3. Assay genetic variants
 Verify polymorphisms,
catalogue correlations
amongst sites
 Anonymous with respect to
traits
Oct 2002 - present
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11. • Multi-country effort to identify, catalog common human
genetic variants.
• Developed to better understand and catalogue LD patterns
across the genome in several populations.
• Genotyped ~4 million SNPs on samples of African, east Asian,
European ancestry.
• All genotype data in a publicly available data base.
• Can download the genotype data
– Able to examine LD patterns across genome
– Can estimate approximate coverage of a given SNP chip
• Can represent 80-90% of common SNPs with
~300,000 tag SNPs for European or Asian samples
~500,000 tag SNPs for African samples
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12. Linkage vs Association
Linkage
Association
1.
Family-based
1.
Families or unrelateds
2.
Matching/ethnicity generally
unimportant
Few markers for genome
coverage (300-400 STRs)
Can be weak design
2.
Matching/ethnicity crucial
3.
Many markers req for genome
coverage (105 – 106 SNPs)
Powerful design
Good for initial detection; poor
for fine-mapping
Powerful for rare variants
5.
3.
4.
5.
6.
4.
6.
Poor for initial detection; good
for fine-mapping
Powerful for common variants;
rare variants generally
impossible
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13. Linkage vs Association
Linkage
Association
1.
Family-based
1.
Families or unrelateds
2.
Matching/ethnicity generally
unimportant
Few markers for genome
coverage (300-400 STRs)
Can be weak design
2.
Matching/ethnicity crucial
3.
Many markers req for genome
coverage (105 – 106 SNPs)
Powerful design
Good for initial detection; poor
for fine-mapping
Powerful for rare variants
5.
3.
4.
5.
6.
4.
6.
Poor for initial detection; good
for fine-mapping
Powerful for common variants;
rare variants generally
impossible
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15. Samples
SNPs
Joint analysis
Replication-based analysis
SNPs
SNPs
Stage 2
Samples
Stage 1
Stage 2
Samples
Stage 1
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16. • Joint analysis has more power than replication.
• p-value in Stage 1 must be liberal.
• CaTs power calculator.
• Here signals from an initial, First-stage GWAare used to
define a subset of SNPsthat are retyped in additional
second stage samples.
• Lower cost—do not gain power.
http://www.sph.umich.edu/csg/abecasis/CaTS/inde
x.html
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17. • Most common approach: look at each SNP one-at-a-time.
• Possibly add in multi-marker information.
• Further investigate / report top SNPs only.
Or backwards replication…Most commonly trend test.
• Log additive model, logistic regression.
• Adjust for potential population stratification.
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18. Calculate the odd ratio
• If 2 events are considered
• odds of A and B is
OR = Odds(A)/Odds(B) = (A)/(~A) / (B)/(~B)
• Symmetry in odds ratio
OR = Odds(D|G=1)/Odds(D|G=0) =
= Odds(G|D=1)/Odds(G|D=0).
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19. • Significance?
Chi- square test.
Rank SNP by P-value. (Statistical test of association )
• Search for SNPs that deviate from the independence
assumption.
• Rank SNPs by p-values
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(Abdurakhmonov and Abdukarimov, 2008)

23. • “Graphical overview of linkage disequilibrium” (GOLD) to
depict the structure and pattern of LD.
• “Trait Analysis by aSSociation, Evolution and Linkage” (TASSEL)
and PowerMarker
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25. • Maize- (Zea mays spp mays )Studies conducted to investigate
LD over a wide range of population and marker type.
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26. The factors, which lead to an increase in LD, include
•
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Inbreeding,
Small population size,
Genetic isolation between lineages,
Population subdivision,
Low recombination rate,
Population admixture,
Natural and artificial selection,
Balancing selection, etc.
The factors, which lead to a decrease/disruption in LD,
include
• Outcrossing,
• High recombination rate,
• High mutation rate, etc.
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27. Basic biology
• Understand the makeup of molecular pathways.
• Dissect the genetic component of molecular variation.
• Genotype environmental interaction.
Breeding
• Mining of markers causal for phenotype.
• to assist in breeding decisions.
• Maximization of yield, pathogen resistance
etc.
Brown and Brown 2008
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28. Genotype
• Imputing of missing value.
• Hidden Mankov models and related approaches.
Beagle,IMPUTE
• In GWAS based on full sequencing data some alleles
may be rare or even private.
Phenotype
• Most parametric models are based on Gaussian assumptions.
• Phenotypic residues are often non Gaussian.
• Phenotypic transformation on suitable scale.
• Use of prior knowledge.
•
eg.Growth rate , generation doubling time etc.
• Variance stabiliztion.
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29. Multiple hypothesis testing
• In GWAS the number of statistical tests is commonly is on the
order of 10⁶.
• At significance level of 0.01we would expect 10,000 false
positive. Thus individual p-value <0.01are not significant
anymore.
• Correction of multiple hypothesis testing is critical.
Population structure
• Confounding structure leads to false positive.
Statistical power and resolution
• Small samples, large number of hypothesis.
• Increased power
• Testing compound hypothesis.
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30. • Compare the Quartiles of the empirical test statistic distribution
to
assume null distribution.
• Sort test statistic.
• Plot test statistic against (Y-axis) quartile of the theoretical null
distribution.
• If the plot is close to diagonal the distribution makeup.
• Deviation from the diagonal indicates inflation or deflation of
test statistics.
• Repair the plot with HW equilibrium.
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Gonçalo Abecasis

36. •
Why in Arabidopsis?
• Hermophodite.so large existence of spp.
• Behaves as naturally existing inbred population
• LD is more extensive.
• False positive rates strongly differed between the
traits
.
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37. • They considered 4 phenotypes for which the major loci were
known.
• Vernalization response locus-FRI.
• 3 pathogen resistant loci-Rpm1,Rps2,Rps5.
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44. • Biparental and QTL approaches are not scalable to
investigate genetic potential of 12000 accessions.
• GWAS simultaneously screened large number of genetic
accessions.
• Genotype once and sequence repeatedly.
• Took Global collection of 413 accessions of sativa.
• Collected from 82 countries and designed 440000
oligonucleotides.
Results
• Correlation analysis between different phenotypes.
• Correlation ranged from -0.41 to 0.9(seed width and length).
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46. Analysis of Naive and mixed model approaches
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49. • Smut (Sphacelotheca reiliana) study using Illumina maize SNP
50 array.
• 45868 SNPs in the panel of 144 inbred lines.
• Classified candidate genes as resistant genes, disease
response genes and other genes.
Outcome
• 50K SNP offers highest array.
• Chromosomal region and specific SNP that affect resistance
level in commercial build up population.
• Assessed the extent of LD in target population.
• Identified genes or QTLs that significantly affect smut
resistance.
• Characterized those genes based on known function and colocation.
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Barley
Lettuce
Tomato
Sorghum
Wheat
Foxtail millet
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53. • Biological pathway of the trait does not have to be known.
• Discovering novel candidate genes.
• Encourages collaborative consortia.
• Rules out specific genetic association.
• Provides more robust data.
• Identifies the mutations explaining few percent of phenotypic
variant.
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54. • Results need replication in independent samples in different
population.
• A large study of population is required, detects association not
causation.
• Identifies specific location not complete gene.
• Focus on common variants and many associated variants are
not causal.
• Detect any variant(>5%) in a population.
• Cost of each DNA sample and pooling them.
• Unavailability of funding agencies.
• Not predictive and explains less heritability.
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56. Candidate gene
• Hypothesis-driven
• Low-cost: small genotyping
requirements
• Multiple-testing less
important
– Possible many misses,
fewer false positives
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57. • The dropping genotyping costs. It involves whole genome.
• Resequencing of all the individuals in a population, large
structure variation such as copy number variation.
• Eg.Resequencing of Arabidopsis lyrata.
• In future this will help in RNA-seq data to include in e-QTL
mapping in GWAS studies.
• Population choice will no longer restricted to model organisms
will slowly become more focused on the spp which are more
relevant in answering biological questions.
• The accuracy depends on 1 time genotyping and repeated
phenotyping.
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58. •
All phenotype and genotype data should to be made public
and be deposited in public databases.
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As such file format and minimum information standards should
to be established.
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Priority to storage and dissemination of phenotypic and
genotypic data.
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59. Systems Biology
Moving beyond Genomics
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60. The more we find, the more we see,
the more we come to learn.
The more that we explore, the more we shall
return.”
Sir Tim Rice, Aida, 2000
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