Lecture 26: Advanced Association Genetics

1. Lecture 26: Advanced Association Genetics December 3, 2012

2. 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!

3. Last Time • Association genetics • Effects of population structure • Transmission Disequilibrium Tests

4. Today • Limitations of association genetics approaches • Solutions: • Imputation of genotypes • Multiple testing corrections • Genomic selection • The Case of the Missing Heritability

5. * G T    HEIGHT       TT TC CC GENOTYPE * G T G C C A * T G A C Association Mapping ancestral chromosomes recombination through evolutionary history present-day chromosomes in natural population * T A Slide courtesy of Dave Neale

6. 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

7. 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

8. Imputing Missing Genotypes From Isik and Wetten 2011 Workshop on Genomic Selection Typically accomplished with software such as IMPUTE, PLINK, MACH, BEAGLE, and fastPHASE

9. Detecting Associations: Single SNP Tests • Contingency 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 Armitage Test Balding 2006

10. 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

11. 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

12. 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

13. Manhattan Plot

14. 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.

17. 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

18. HEIGHT Multilocus GENOTYPE * G * Genomic Selection ancestral chromosomes recombination through evolutionary history present-day chromosomes in natural population * A 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