1 / 26

Genetical Genomics in the Mouse

Genetical Genomics in the Mouse. Finding Genes with Microarray Expression Data. Genetical Genomics. Jansen, R.C. and J.P. Nap (2001). Genetical genomics: the added value from segregation. Trends Genet 17(7): 388-91. Mouse Genetical Genomics. BXD recombinant inbred lines

vianca
Download Presentation

Genetical Genomics in the Mouse

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. Genetical Genomics in the Mouse Finding Genes with Microarray Expression Data

  2. Genetical Genomics Jansen, R.C. and J.P. Nap (2001). Genetical genomics: the added value from segregation. Trends Genet 17(7): 388-91.

  3. Mouse Genetical Genomics • BXD recombinant inbred lines • 21 strains + parents and F1 • genotypes • 508 markers • traits • forebrain RNA assayed by Affymetrix U74Av2 • PM probe sequences • MM probe sequences • 1 to 4 microarrays per RI line (average 2.5)

  4. QTL mapping by regression • Trait vs genotype association • Genetically determined difference • in expressed RNA level • in hybridization of probe sequence • in competing hybridization • Measured by LRS (likelihood ratio statistic)

  5. BXD Marker Distribution

  6. Trait Data Preparation • 12,422 probesets (traits) • 16 PM & 16 MM probes (oligonucleotides) • average PM-MM difference • log2-transform average difference • normalize data of each microarray to common mean and standard deviation • average replicate microarrays • 400,000 PM & MM probes (cells) • log2-transform cell intensity • normalize and average replicate arrays

  7. Multiple testing problem • Two levels of multiple testing • Each trait or probe vs 508 loci • 12,422 traits or 400,000 probes • Strategy • Empirical p-value for multiple loci • measures significance of single best association • Benjamini-Hochberg procedure for multiple traits or probes • may declare many significant associations • assumes at least one significant association

  8. Empirical p-value • Measures genome-wide significance • converts multiple test into single test • significance of best association among all loci • Permutation test for distribution under null • up to 106 scans with permuted trait values • record largest LRS for each permutation • Find p-value of original regression from its rank in the null distribution

  9. Outliers • Examine permutation test distribution for bimodality • Compare 37th and 95th percentile values • Find outlier and assign next most extreme value • Redo permutation test and regression

  10. Benjamini-Hochberg test • Test of 100 uniformly distributed p-values (p-values from non-significant results) • P-values as blue dots • Significance threshold for FDR = 0.2 as red line

  11. Declare significant Benjamini-Hochberg test • Test of 10 low p-values (significant results) mixed with 90 p-values from non-significant results • P-values as blue dots • Significance threshold for FDR = 0.2 as red line • Eleven cases declared significant

  12. Empirical P-value Calculation 500x Permutation test Marker regression mapping ? p-value 5000x Perm Maximumgenome-wideLRS ? p-value 50000x Perm ? p-value 1000000x Perm p-value

  13. Trait-locus associations • Ranked P-values as blue dots (90 smallest from 12,422) • Significance threshold as red line • Cases below red line are significant for FDR = 0.2 • 75 significant trait-locus associations

  14. Probe-locus associations • Ranked P-values as blue dots (600 smallest from ~400,000) • Significance threshold as red line • Cases below red line are significant for FDR = 0.2 • 576 significant probe-locus associations

  15. QTLs from MM probes • 576 QTLs defined by single microarray probes • 454 (79%) by PM probes • 122 (21%) by MM probes • Proportion of PM probes QTLs declines as p-value increases A B C

  16. QTLs from cell-level mapping • 576 cell-marker associations (QTLs) • 339 traits (probesets) represented • most probesets represented by a single probe • rarely, two or more significant probes from same probeset • all probes from one probeset identify same locus • 79% of probes are PM

  17. QTLs from PM cells only • 454 PM cells defining QTLs • 288 traits (probesets) represented • 184 controlled by location on the same chr • 88 controlled by location on different chr • 16 unknown location for probeset • 147 locations (marker loci) with nearby QTLs, distributed on all chromosomes

  18. Probe-locus associations among traits • 339 traits (probesets) with probes identifying significant QTLs • 186 traits represented by a single probes • 2 traits represented by 10 probes

  19. QTL distribution among marker loci • 147 loci identified by at least one significant probes-locus association • multiple associations to one locus • multiple probes from one probeset • multiple QTL near locus

  20. Profiles of probe sensitivity Li, C & Wong, WH (2001) Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection. PNAS 98: 31-36

  21. Probe profiles (best) • LRS vs probe number • Probesets with highest significance in probeset-level mapping PMMM

  22. Probe profiles (worst) • LRS vs probe number • Probesets with lowest significant association in probeset-level mapping PMMM

  23. Distribution of controlled loci

  24. Distribution of controlling loci

  25. Chr 9 QTLs • Unusual number of chr 9 QTLs (22) controlling sequences on other chrs • Normalized frequency 3-fold greater than average chr • Many of these QTLs cluster near 2 loci on chr 9

  26. Robert W Williams Lu Lu S Shou Yanhua Qu Elissa Chesler John D Mountz Hui Chen Hsu David Threadgill Gene Hwang Dan Nettleton Jintao Wang Ram Varma Jianxin Wang Mark Brady Gene Sobel Acknowledgments U Tennessee, Memphis Gene Expression Core Bioinformatics U Alabama, Birmingham GOG U North Carolina Cornell U Iowa State U

More Related