1 / 14

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

Stratification Lon Cardon University of Oxford F:lon2001stratificationstratification.ppt. Population Stratification. Consider trait distribution only. Mean differences in population substrata. These differences alone do not influence genetic association under H o.

udell
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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 Allele ‘1’ Allele ‘2’ f(‘2’) >f(‘1’)

  4. Single sample, unequal marker allele frequencies Allele ‘1’ Allele ‘2’ 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

More Related