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Lecture 26: Advanced
Association Genetics
December 3, 2012
Announcements
 Extra credit lab this Wednesday: up to
10 points
 Extra credit report due at final exam
 Review session on Friday, Dec. 7
 Final exam on Monday, Dec. 10 at 11
am in computer lab
NOT on Dec. 11 like syllabus and lecture
notes say!
Last Time
 Association genetics
 Effects of population structure
 Transmission Disequilibrium Tests
Today
 Limitations of association genetics
approaches
 Solutions:
Imputation of genotypes
Multiple testing corrections
Genomic selection
 The Case of the Missing Heritability
ancestral
chromosomes
G
T
HEIGHT
Association Mapping
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*
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TT
TC
GENOTYPE
CC
recombination
through
evolutionary
history
present-day
chromosomes
in natural
population
G
C
G
T
A
C
A
C
*
G
T
A
T
*
*
Slide courtesy of Dave Neale
Association Study Limitations
 Population structure: differences
between cases and controls
 Genetic heterogeneity underlying
trait
 Inadequate genome
coverage/Missing Genotypes
 Random error/false positives
 Multiple testing
Missing Genotypes
Potential source of bias in analysis
Some alleles under-represented
Problem if data gathered differently
in case and control populations
Missing genotypes degrade power of
analysis
More complex statistical models
required
Solution: Imputation
Imputing Missing Genotypes
From Isik and Wetten 2011 Workshop on Genomic Selection
Typically accomplished with software such as IMPUTE, PLINK, MACH,
BEAGLE, and fastPHASE
Detecting Associations: Single SNP Tests
Contingency tests
 Chi-square
Armitage Test
 Fisher’s Exact Test
Armitage test fits a line to
relationship between genotype
score (number of alleles) and
“genotypic risk”
Null hypothesis: slope=0
 Assumes additivity
Genomic control (GC): threshold
of significance set by
background SNPs: inflate critical
value by a constant
Balding 2006
Genome-Wide Association Studies and
Multiple Testing
 With Next-Gen sequencing,
true genome-wide association
studies are a reality
 Millions of tests of association
 How to set proper P-value
cutoff?
 With P=0.05, expect 50,000
type I errors per million tests
 Need protection from type I
error
Null
Multiple Testing: Quantile-Quantile (Q-Q) Plot
 Assess the effects
of multiple testing
 Expected value of
negative log of ith
smallest P value is
−log (i / (L + 1)),
where L is the
number of tests
(loci)
 Points above the
line are significant
beyond the null
expectation
Balding 2006
Corrections for Multiple Testing
 Bonferoni:
a'=
a
N
Where N is number of tests
 Very conservative
 Alternative: False Discovery Rate or BenjamaniHochberg test
ai =
a
N -i
Where i is the number of P-values that are less than or equal to the
current P. Test is performed with smallest P first, in sorted order
 P-values can also be set by permutation: randomize the
phenotype data across genotypes, generate a
distribution
Manhattan Plot
How Successful have GWAS Been?
Thousands of associations have been identified for
many different traits
Each locus explains a very small proportion of the
variation in complex traits (typically <1%)
Overall percentage of variation explained is
substantially less than trait heritability, even for casecontrol diseases: “Missing heritability”
Manolio et al. 2009. Nature 461: 747–753.
Possible Causes of Missing Heritability
 Much larger numbers of common variants of smaller
effect yet to be found
 Gene-environment interaction
 Trait heterogeneity
 Rare variants (possibly with larger effects)
 De novo mutations
 Structural variations such as copy number variants
 Gene–gene interactions, epistasis
 Beyond DNA sequence: epigenetic markers
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Possible Causes of Missing Heritability
Manolio et al. 2009. Nature 461: 747–753.
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Association Genetics of Human Height
2010 Nature Genetics 42: 565-571
 Human height has
heritability of 0.8
 Study of 4,259
individuals
 Nearly 500K SNP
markers
 A large fraction of
missing heritability
recaptured with
genome-wide marker
predictions
ancestral
chromosomes
HEIGHT
Genomic Selection
Multilocus GENOTYPE
recombination
through
evolutionary
history
present-day
chromosomes
in natural
population
*
G
A
*
*
Blanket entire genome with markers and
use these to predict genotypes
Trait Heterogeneity: Height
 Pygmy population has genome regions that show a high frequency of derived
alleles (Ancestry-Informative Markers) and high divergence from other
human populations (Locus-Specific Branch Length outliers)
 Genes in these regions show association with height
 Mechanisms are related to pituitary function: totally different than loci
controlling height in Eurasian populations
2012 Cell 150: 457-469
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De novo Mutations
 Mutations commonly occur in germ line and are passed down to
offspring
 Mutations increase with parental age
 Possible association with human conditions like cancer, autism and
schizophrenia
2012 Nature 288:471-475
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Rare Mutations
 Increasing accumulation of mutations in human populations
 Polymorphisms are much younger in European americans than in
African Americans
 Deleterious mutations are rapidly increasing: decline of human
fitness?
November 2012 Nature doi:10.1038/nature11690
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