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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 - PowerPoint PPT Presentation
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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
Association Mapping
ancestral chromosomes
*TG
recombination throughevolutionary history
present-daychromosomesin natural population
*TG
*TA
CG
CA*TG
CA
Slide courtesy of Dave Neale
HEIG
HT
GENOTYPECCTCTT
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 GenotypesPotential 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
Typically accomplished with software such as IMPUTE, PLINK, MACH, BEAGLE, and fastPHASE
From Isik and Wetten 2011 Workshop on Genomic Selection
Detecting Associations: Single SNP Tests
Balding 2006
Armitage TestContingency tests
Chi-square
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
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
Balding 2006
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
Corrections for Multiple Testing
Bonferoni:
Where N is number of tests
Very conservative
Alternative: False Discovery Rate or Benjamani-Hochberg test
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 case-control diseases: “Missing heritability”
Manolio et al. 2009. Nature 461: 747–753.
15
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
16
Possible Causes of Missing Heritability
Manolio et al. 2009. Nature 461: 747–753.
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
Genomic Selection
ancestral chromosomes
recombination throughevolutionary history
present-daychromosomesin natural population
*G
*A*
HEIG
HT
Multilocus GENOTYPE
Blanket entire genome with markers and use these to predict genotypes
20
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
21
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
22
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