Strategies for gene identification in complex traits

1. Strategies for gene identification in complex traits --- Association studies ---

2. Phenotypic variation • - Presence/Absence of a disease- Levels of a disease-related trait • Genomic Variation • at one or more sites What is an association study? Objective:Is there a statistical relation? Principle:Compares 2 groups that are expected to differ in their prevalence of disease-susceptibility alleles

3. Analytical Issues in Genetic Association Studies • Sampling Design • Markers (typed; Map density) • Unit of Analysis • Statistical testing

4. Linkage disequilibrium between 2 tightly linked loci Marker 2 Allelic association  f(i,j)  f(i) x f(j) • Haplotype frequency product of allele frequencies LD decays with time/generations and genetic distance (recombination) Marker 1

5. D’ =1; r2 <1 D’ =1; r2 =1 Measures of allelic association D’ (Lewinson’s); r2 (correlation) 0 r2D’  1 D’~ recombinational events in the genomic regionr2 ~ The 2 SNPs carry same information D’ can be high but not r2

6. TDT Genotype 3 Subjects/Family Phenotype 1 Subject/Family Increased power with multiple affected sibs Generally, Immune to population stratification Family structure provides some error-checking and haplotype information Full trios may not be available Case-Control Genotype 2 Subjects to equal one trio Phenotype 2 Subjects to equal one trio Increased power with 3:1 controls:cases Susceptible to population stratification Power in Population-based vs. Family-basedAnalysis

7. Most common forms of markers • Repeated sequences of 2,3 or 4 nucleotide (Microsatellites) • reasonably frequent in genome • highly polymorphic/informative  useful in linkage analysis • few disease susceptibility gene variants are likely STRs • Single Nucleotide Polymorphisms (SNPs) “one” letter of the code is altered • very frequent in genome (1/500 to 1/1000 base pairs) • Exonic SNPs may or may not cause an amino acid change • many disease susceptibility gene variants are likely SNPs

8. Unit of Analysis in Genetic Association Studies • Allele vs. Genotype • Dominance can be considered in genotype analysis • Extra degree of freedom in genotype analysis • Not clear which is optimal • Single SNP vs. Haplotype • Haplotypes capture evolutionary history • Need for haplotype imputation • Single SNP optimal if functional SNP is included

9. OR The typed variant A second variant Marker= Causal VariantDirect Association Markerin LD with Causal variantIndirect Association What are we hoping from a genetic association study? Situation of Interest: Trait variation is influenced by

10. Likelihood of detecting a true association? • Genetic effects of the causal allele on trait susceptibility/variation --Relative Risk & allele frequency • LD between the marker and the causal variant (Marker map & LD patterns in the genomic region of the causal variant)

11. Detectable Genetic effects (1) Power under different Nominal P-values N=2,000 (1,000 cases + 1,000 controls)

12. Detectable Genetic effects (2) Power under different Nominal P-values N=2,000 (1,000 cases + 1,000 controls)

13. Detectable Genetic effects?  Association is powerful to detect causal variants that are - Common (>10%) with relatively modest effects (RR) - Less common (~5%) but with substantial effects (RR>2)

14. r2=0 r2=1 Direct 0< r2<1 • For a given N, PowerMaxnul Likelihood of detecting a true association? • For a given Power, required N with 1/r2 • r2= 1 0.8 0.5 0.20 0N= 1,000 1,250 2,000 5,000

15. Hot spots and Haplotype blocks • LD is variable : Recombination does not occur with equal probability at all points in the genome ---- there are « hot » and « cold » spots • Recently, it has been suggested that the genome falls into « blocks », with little haplotype diversity within blocks: Mean block size seems to be about ~14kb in Caucasians, and ~8 kb in Africans (but very variable; there are blocks up to 200kb in size)

16. Detectable Causal Variants? • Causal polymorphism is known and typed (direct association) or • There are markers that are highly correlated to the causal variant: - The causal locus lies in a « cold » spot (« LD blocks »)- The « best » map density to be used will depend on the LD patterns of the region  implications on statistical significance (multi-test correction)

17. Human Genome • The human genome consists of about 3x109base pairs (3-6 x106 SNPs) and contains about 25,000 genes • Much of the DNA is either in introns or in intergenic regions • Trait variation: A few hundred of (functional) variants may make a meaningful contribution to variation in any single phenotype  Prior probability that a variant selected at random will influence a given trait is very low

18. Genetic variants to be typed? --- Choices have to be made --- Two complementary approaches: • Functional: incorporates assessments of the likely functional effect of variation within a gene or region of interest. • Tagging: exploits presence of LD in many parts of the genome.

19. Significance of association withAD, for SNPs immediatelysurrounding APOE (<100 kb)[Martin et al., AJHG, 2000]

20. Selection of variants: Functional approach Target polymorphisms which are themselves putative causal variants. Critical issues: • Identification of candidate polymorphisms • Beyond mutations altering aminoacid sequence (nSNPs), little is known on the potential effect of non-coding sequence on gene regulation & expression? • MAF of functional variants is skewed (MAF<5%)Power to detect uncommon variants with modest effects?  Potential to be the most powerful (Direct association) design, but may be limited to the discovery of some of the genetic causes of disease-related traits.

21. Selection of variants: Indirect Association The polymorphism is a surrogate for the causal variant But, necessary to type several surrounding markers to have a high chance of picking up the indirect association Questions: Do we need to type all markers in the region? Can we reduce genotyping costs & multi-test burden without decreasing « too much » the power?

22. Tagging approaches Type a subset of variants that captures a high amount of the information in common regional haplotypes Various strategies ---SNP & haplotype tagging --- but still debate as to the best methods [Johnson et al. Nat Genet, 200]

23. r2=0.8 r2=1 r2=0.3 random Power as a function of average spacing of tags[De Bakker, Nat Genet, Nov 2005]  A marker map density of ~1 tagSNP/5kb (r2>0.8) captures >80% of common variation kb Tags picked at r2 = 1,0.8, 0.5 and 0.3

24. Tagging approach: Limits • Less powerful than direct studies, • There cannot be a definite negative result, since we cannot exclude the possibility that a causal variant exists but is not picked up by the markers chosen, • Intrinsic biological merit of tagSNPs as markers for complex trait susceptibility variants?  « Common disease, common variant » hypothesis Supported by the few variants consistently shown to be associated to common diseases: -- APOE & Alzheimer --- Macular degeneration & Complement Factor H

25. Inpractical terms, an observedstatistical association will be due to … • Direct association: The allele itself is functional and directly affects the expression of the phenotype • Indirect association: The allele is in linkage disequilibrium with an allele at another locus that directly affects the expression of the phenotype • The finding could be due to chance or artifact, e.g., confounding or selection bias  Study design aims to maximize detection of “true” findings while controlling (minimizing) rate of “false” findings

26. “False” Association findings • Chance: measured by the nominal P value of the test, i.e., prior probability that a typed marker is found associated when HO (no association) is true.  Multi-test problem: The rate of “false” findings of a given experiment increases with the number of markers tested. • Solutions • Simulation: Empirical p-values • Replication and/or use Multi-Phases design

27. Multi-phase designs Are efficient to reduce the multi-test problem For example: 1. 2,000 cases + 2,000 controls with 500,000 SNP chip 2. Further 2,000 + 2,000 for best 100,000 SNPs • Further 4,000 + 4,000 for best 10,000 SNPs • Computation of the characteristics of such designs requires Monte Carlo integration --optimization is computationally intensive

28. “False” Association findings • Artifact (confounding, selection bias, pop stratification, genotyping): affects the Prior probability of a “chance” finding  The significance of a finding is no longer controlled by the nominal P-value. • Solutions - Careful matching of cases & controls- use homogenous populations- use family-based controls- use genomic control or other similar methods- use QC methods for scoring genotyping errors (Clayton et al., Nat Genet, 2005)

29. Prospects for whole-genome screens: Estimated numbers of «common» SNPs (MAF>5%) • Direct studies of nsSNPs: ~30,000 - 50,000 SNPs • Indirect studies of genes: ~300,000 -500,000 SNPs • «Nearly» whole genome: 500,000 - 1,000,000 • Whole genome: ~ 2,000,000 – 4,000, 000 Choice of markers • Optimal choice of markers requires detailed mapping of LD, e.g. based on HapMap data • Truly optimal solutions are computationally intensive. Current chip designers are using single marker r2 cluster-based algorithms

30. Choices of markers have to be made • The strategy used to define the subsets of variants to be typed has a substantial effect on the power & the quality of the study. • Greater understanding of genomic variation has allowed more logical choices. Nonetheless, variant selection is always a pragmatic compromise.

31. Research key questions • Are common human diseases due to common variants or multiple rare variants? • Will rare or common SNPs be better candidates for a particular disease? • Can large differences between populations in the frequency of an allele be merely dueto chance?