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Advances in Multiple Testing: Affy and Illumina Chips, Correction Methods, and QQ Plotting

This overview discusses recent technological advancements in genetic testing, focusing on the Affymetrix (Affy) 500K and Illumina chips. It delves into multiple testing approaches, including the Bonferroni correction and false discovery methodologies, highlighting key works by Benjamin Neale and others. The necessity of estimating independent tests and the thresholds for genome-wide significance, exemplified by studies like Risch and Merikangas (1996) and the HapMap project, are explained. Furthermore, the importance of QQ plotting in assessing test statistics' inflation and deflation is examined in detail, along with practical tips for utilizing R.

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Advances in Multiple Testing: Affy and Illumina Chips, Correction Methods, and QQ Plotting

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Presentation Transcript

  1. Multiple testing etc. Benjamin Neale Leuven 2008

  2. Technology advances – Affy 500K

  3. How the Affy Chip works

  4. Illumina’s Chip

  5. Multiple testing approaches • Bonferroni Correction • Conservative • Probability of observing at least 1 hit at that level • False Discovery methodology • Capitalizes on a distribution of positive results • See work by Benjamin, Hochberg, Storey • Bayes Factor • Like a P-value, but for Bayesians • Marginal likelihood of the two sets of parameters • Correlation approaches 1 under the alternative

  6. Genome-wide significance • Multiple testing theory requires an estimate of the number of ‘independent tests’ • Risch and Merikangas 1996 estimated a threshold of 10-6 = (0.05/(5*10000)) • HapMap 2005 estimate 10-8 based on encode deep sequencing in ENCODE regions • Dudbridge and Gusnato and Pe’er et al. 2008 Genetic Epidemiology estimate based on ‘infinite density’ like Lander and Kruglyak 1995 generates 5x10-8

  7. Guessing game • I’ll buy a beer for the closest guess • We’re each going to simulate a single chi square • There are about 25 groups

  8. Distribution of chi squares 1 df chi square expectation Distribution of maximum 1 df chi square from 25 trials

  9. QQ plotting

  10. QQ plotting Expectation

  11. QQ plotting Observation

  12. QQ plotting Observation Expectation

  13. QQ plotting Above the line indicates inflation of the test statistic Observation Expectation

  14. QQ plotting Observation Below the line indicates deflation of the test statistic Expectation

  15. Plotting the results in R CHANGE DIR… This is the menu item you must change to change where the PLINK results file is found Note you must have the R console highlighted

  16. Picture of the dialog box Either type the path name or browse to where you saved plink.cmh

  17. Screenshot of source code selection This is the file qqplot.R for the source code

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