1. Stratification Lon Cardon University of Oxford
F:\lon\2001\stratification\stratification.ppt

2. Population Stratification
Consider trait distribution only
Mean differences in population substrata
These differences alone do not influence genetic association under Ho

3. Single sample, unequal marker allele frequencies
f(‘2’) > f(‘1’)

4. Single sample, unequal marker allele frequencies
f(‘1’) > f(‘2’)

5. Stratified sample, equal marker allele frequencies
f(‘1’) = f(‘2’)
More ‘1’ in high end
More ‘2’ in low end.
Association evidence

6. A Simple Model of Stratification
Consider:
Two subsamples of equal sample size, but opposite allele frequencies (e.g., sample 1, 52:48; sample 2, 48:52);
Within sample variance of the usual form of Va + Vq(w) + Ve, which is the same for both subsamples;
Mean effects arising from a true QTL and stratification: m= mq + ms
Then:
Total variance in the combined sample has additional ‘between-strata’ effects due to the QTL and stratification: Vq(b) and Vs, so Vq = Vq(b) + Vq(w) and the total variance is Va + Vq + Vs + Ve
Let Vq = Vs = 0.05 and p1, p2 vary from .1 to .9.

7. Stratification only

8. QTL effect only

9. QTL and stratification effects

10. Stratification Summary
Stratification not only yields increases in Type I error
Can also mask real effects
Could see ‘true’ case/control results but no TDT
Difficult area of research.

11. Stratification detection/correction
using the Genome
Idea:
Don’t necessarily need to use family-based controls to detect/control for stratification, can use other markers in ‘cases’
Pritchard & Rosenberg (1999). Am J Hum Genet
Procedure:
Interested in candidate marker, C1, genotype ~ 40 other anonymous (unlinked) markers, M1 .. M40.
Calculate association c2 for M1 .. M40. Test is on sum of c2. If find evidence in background, worry about stratification; else, do not.
Extensions:
Use same idea to gain estimate of background ‘inflation factor’ of test statistic. Use this factor to correct candidate gene test.
Pritchard et al. (2000) Am J Hum Genet
Devlin & Roeder (1999) Biometrics (‘Genomic Control’)
Bacanu, Devlin & Roeder (2000) Am J Hum Genet.

12. How bad is the stratification problem?

13. When is Stratification Tricky?

14. Stratification Detection Using the Genome
Promising idea to allow large studies of popln cohorts
Appears to detect large stratification differences easily
Small frequency differences much more difficult to detect. Can still obtain large (> 2-fold) increases in Type I error rate.
Unfortunately, these differences may be precisely what we seek in complex traits
Tough cases: many sub-strata, uninformative markers, effects of linked background markers. Watch this area: very active