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

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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

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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