1. Association Analysis Spotted history
Many real and presumed false positives
Very difficult to know which results are ‘real’
Few replicable outcomes

3. human complex trait genetics?
Why so few successes in
human complex trait genetics?
Obvious explanations
Polygenic systems too complicated
GxE interaction
epistasis
too many genes genes of small effect
heterogeneity
Phenotypes poorly defined/unreliable low validity
Too few markers available
Sample sizes (effect sizes) too small
Multiple testing problem unresolved …

4. Genotyping Error
Genotyping accuracy one of most critical components of any
mapping study
Small amounts error cause real findings to be missed or lead to false claims of real effects
Once genotyping completed, several main ways to detect errors
1) Look at departures from Hardy-Weinberg Equilibrium (HWE)
2) Look for sample mixups, incorrect relationships
3) Identify Mendelian inconsistencies in families
(also can detect excess recombinants)
Note that (1) is at marker level (‘good’ SNP, ‘bad’ SNP), (2) is at sample level; while (3) is at level of individual genotype
None of these guaranteed to detect majority of errors
Best solution is to emphasise accuracy before analysis starts…

5. Genotyping Error Hardy-Weinberg Equilibrium
For a SNP with two alleles, A1 and A2, and frequencies p = f(A1) and q = f(A2)….
If there is no selection, excess mutation or nonrandom mating,
The genotype frequencies will be
Genotype A1A1: p2
Genotype A1A2, A2A1: 2pq
Genotype A2A2: q2
Genotyping error perturbs these ratios
- errors often have directional bias (e.g, under-represent heterozygotes)
- can have dramatic results
exaggerate false-positives (esp in homozygosity mapping)
lose statistical power (esp acute in complex traits)
The program ‘pedstats’ tests for HWE deviations

6. Are Pedigree Errors Still an Issue?
Excerpt from Am J Hum Genet, 2000

7. Pedigree Errors Type I error increases come from, e.g.:
MZ twins coded as full-sibs, who share 2 alleles IBD at all loci
Full-siblings coded as half-sibs (expect ¼ sharing, observe ½)
Any close relative coded as more distant
Power reduction comes from:
Half-siblings coded as full-sibs
Any distant relative coded as more related than they are
How many studies have unknowingly suffered (Type I or power loss) because of this?

8. How can this be fixed?
Different relative pairs are characterized by different patterns of allele sharing
half-sibs share more alleles on average (ibs) than full sibs
Parent-offspring pairs share the same number of alleles on average as sib pairs, but with less variability (they always share one allele)
Unrelated pairs share less than relatives

9. Identity by State AA x AA Aa x Aa aa x aa AA x Aa Aa x aa AA x aa
2 alleles shared ibs
1 allele shared ibs
0 alleles shared ibs
With genome scan of G markers, can easily compute mean and variance of genome-wide ibs sharing for any pair of individuals i,j (the individuals need not be in the same pedigree)

10. - data published in last 2 years -
Pedigree errors amongst close relatives are easy to detect in genome scans
- data published in last 2 years -
GRR (Abecasis et al, 2001), for other methods see McPeek & Sun (2000), Epstein et al. (2000)

11. Mendelian Inheritance Errors
Modest levels are likely
Up to 1% may be typical
Mendelian inheritance checks
Can detect up 30% of errors for SNPs
(Gordon, Heath, Ott, Hum Hered, 1998)
Large effect on power, accuracy
Linkage vs. Association
SNPs vs. Microsatellites
Pairwise LD
Haplotype estimation
(Abecasis et al, EJHG 2001; Akey et al., AJHG 2001, Kirk & Cardon, EJHG 2002)

12. Mendelian Error Detection11 12 22 12

13. Mendelian Error Detection11 12 22 ??
Nuclear families individually consistent with Mendelian inheritance

14. Mendelian Error Detection11 12 22 ??
Consistent only if missing
offspring has 22 genotype
inconsistent
Consistent only if missing
parent has 12 genotype
Error detection by direct observation can miss errors

15. Genotyping Error: Affected Sib Pair Sample
No error
0.5% error
1% error
2% error
5% error
ls = 1.5; Lods calculated using Kong & Cox (signed) procedure

16. Genotyping Error: Quantitative Trait Linkage Analysis
Dense SNP map (1 SNP/2cM)

17. Association Analysis Allele frequency differences 0% 10% 20% 30% 40%
50%
60%
70%
80%
90%
100% 2% 4% 6% 8%
Error rate
Average LOD retained (% of
maximum)
0.050
0.100
0.200
0.300
0.400
0.5
Allele frequency
differences

18. Genotype Error
Small error rates can have dramatic consequences
Effects depend on study design
ASPs lose power; DSPs inflate Type I; common allele association not great influence; rare allele worse
Crucial issue is detection
not essential that errors are resolved, just detected (LRC2003: this may turn out to be wrong!)
What levels can be tolerated in pharmacogenetics, pooling or large-scale association studies?
Detection without families hard problem
Is genotype error partly responsible for marginal linkage outcomes and/or unreplicable associations?

19. Genotyping Error: Effects on Haplotype Estimation
Estimating haplotypes important for LD, association studies
Several different methods available to estimate haplotypes
Families (segregation)
Molecular (haploid cell lines)
Unrelated individuals (if high LD)
What effect does genotyping error have on haplotype estimation?
Kirk & Cardon, Euro J Hum Genet 2002

20. Unequal allele frequencies
Low LD
Equifrequent alleles A)
Unequal allele frequencies D)
Unrelateds
Trios
4-sibs
Moderate LD B) E)
Haplotype
Block C) F)

21. Given methodological differences in haplotype accuracy, what is influence of error on each design?

22. Genotyping Error and Haplotype Estimation
At modest levels, genotyping error not great concern for family designs
Haplotype estimation in unrelateds is surprisingly robust when LD is high
But when LD low or many common alleles, serious consequences
Problem: Generally don’t know LD in advance so can’t predict outcome
Trios inefficient design
Perform slightly better than unrelateds, but too little power to detect many errors
With regard to error, trios least desirable approach
Conditional on baseline differences in haplotype estimation, individual haplotype estimation influenced about same in all designs
Genotyping error serious problem for linkage, association studies, but less so for estimation of haplotypes themselves

23. Simulation Study ‘Genome’ of 22 autosomes each of 100 cM (a lie)
10 markers/chromosome
5 equifrequent alleles/marker
252 unselected sib pairs
>= 1 QTL somewhere in the genome
background h2 moderate (30%)

24. How many QTLs? Where are they?

25. Simulation Study: Exercise
FILES: F:\lon\2003\scan?.ped, scan?.dat, scan.map
Run pedstats to view HWE tests
pedstats –p scan1.ped –d scan1.ped --ignore --hardy | more
2) Find the sample mixups using GRR. How many mixups are there? What family(ies) are involved?
Check for Mendelian errors using pedstats or merlin. Are there any? What would you do about this?
pedstats –p scan1.ped –d scan1.dat | more
merlin –p scan1.ped –d scan1.dat –m scan.map | more
What differences do you see between the programs? Can you predict the impact on the results?

26. Clean Data
Mixed-up Data

27. Clean Data
Genotype-error Data