(B6 x BTBR)F2-ob/ob Liver mRNA M430 RMA Database (Aug 2005 Freeze) modify this page

    Summary:

This August 2005 data freeze provides estimates of mRNA expression in adult liver from a selected set of 60 F2 animals generated by crossing strain C57BL/6J-ob/+ with BTBR and then intercrossing the F1-ob/+ progeny. The F2 progeny included, in a total of 350 progeny, 110 ob/ob progeny homozygous for the obese (ob) allele of leptin (Lep) on Chr 6. Sixty of the ob/ob progeny were selected for expression assays. This selection means that the data set is not useful for defining QTLs on Chr 6. Array data were generated at the University of Wisconsin by Alan Attie and colleagues. This data release accompanies the paper of Lan and colleagues (in submission, 2005). A set of 24 complementary phenotypes such as body weight, blood chemistry, and rtPCR results, are also available for these animals and an additional set of 50 F2s (see Phenotypes database. Samples were hybridized to 60 pairs of Affymetrix M430A and B arrays. This particular data set was processed using the RMA normalization method. To simplify comparison among transforms, RMA values of each array were adjusted to an average of 8 units and a standard deviation of two units.

    About the cases used to generate this set of data:

The F2-ob/ob mice were chosen from a mapping panel that we created to map diabetes related physiological phenotypes (Stoehr et al. 2000). About 110 of these F2-ob/ob mice were also used to map mRNA abundance traits derived by quantitative real-time RT-PCR (Lan et al. 2003). The sixty F2-ob/ob mice that were used to generate microarray-derived mRNA abundance traits were selected from the 110 mice based on a selective phenotyping algorithm (Jin et al. 2004). The F2-ob/ob mice were housed at weaning at the University of Wisconsin-Madison animal care facility on a 12-h light/dark cycle. Mice were provided Purina Formulab Chow 5008 (6.5% fat) and acidified water ad libitum. Mice were killed at 14 weeks of age by CO2 asphyxiation after a 4-hour fast. The livers, along with other tissues, were immediately foil wrapped and frozen in liquid nitrogen, and subsequently transferred to -80 °C freezers for storage.

    About the tissue used to generate this set of data:

Liver samples were taken from 29 male and 31 females. Total RNA was isolated with RNAzol Reagent (Tel-Test, Inc.) using a modification of the single-step acid guanidinium isothiocyanate phenol-chloroform extraction method according to the manufacturer's protocol. The extracted RNA was purified using RNeasy (Qiagen, Inc.). RNA samples were evaluated by UV spectroscopy for concentration. RNA quality was monitored by visualization on an ethidium bromide-stained denaturing formaldehyde agarose gel. RNA samples were converted to cDNA, and then biotin-labeled cRNA according to Affymetrix Expression Analysis Technical Manual. The labeled samples were hybridized to the M430A, and subsequently the M430B array. The hybridization, washing and scanning steps were carried out by Hong Lan using the Affymetrix core facility at the Gene Expression Center of University of Wisconsin-Madison.

    About the array

Liver samples were assayed individually using 60 M430A and B Affymetrix oligonucleotide microarray pairs. Each array ID is denoted by a 10-letter code: the first three letters represent the F2-ob/ob mouse ID number, the fourth letter (either A or B) denotes M430A or M430B arrays, and the last six letters represent the date the array was scanned (MMDDYY).
All 120 M430A and B arrays used in this project were purchased at one time and had the same Affymetrix lot number. The table below lists the arrays by Animal ID, sex, and ArrayID.
Animal ID
sex
MOE430A ArrayID
MOE430B ArrayID
2
M
002A100203
002B100503
12
M
012A100203
012B100503
22
M
022A100203
022B100503
44
M
044A100203
044B100503
46
M
046A100203
046B100503
61
M
061A100203
061B100503
100
M
100A100303
100B100503
105
F
105A100303
105B100503
111
F
111A100303
111B100503
123
M
123A100303
123B100503
156
F
156A100303
156B100503
165
M
165A100303
165B100503
167
M
167A100303
167B100503
173
M
173A100303
173B100503
186
F
186A100203
186B100503
190
F
190A100303
190B100503
194
M
194A100303
194B100503
200
F
200A100303
200B100503
207
F
207A100303
207B100503
209
F
209A100203
209B100503
212
F
212A100303
212B100503
223
M
223A100303
223B100503
224
M
224A100303
224B100503
253
F
253A100303
253B100503
254
F
254A100603
254B100703
260
F
260A100603
260B100703
264
F
264A100603
264B100703
310
F
310A100603
310B100703
317
M
317A100603
317B100703
318
F
318A100603
318B100703
324
F
324A100603
324B100703
327
F
327A100603
327B100703
343
M
343A100603
343B100703
416
M
416A100603
416B100703
419
F
419A100603
419B100703
438
M
438A100603
438B100703
440
M
440A100603
440B100803
455
M
455A100603
455B100803
458
F
458A100603
458B100803
472
M
472A100603
472B100803
474
F
474A100603
474B100803
479
F
479A100603
479B100803
484
F
484A100603
484B100803
486
F
486A100603
486B100803
489
F
489A100603
489B100803
493
F
493A100603
493B100803
499
M
499A100603
499B100803
513
M
513A100603
513B100803
517
M
517A100703
517B100803
523
M
523A100703
523B100803
549
M
549A100703
549B100803
553
F
553A100703
553B100803
554
F
554A100703
554B100803
559
F
559A100703
559B100803
560
F
560A100703
560B100803
566
M
566A100703
566B100803
608
F
608A100703
608B100803
615
F
615A100703
615B100803
617
M
617A100703
617B100803
620
M
620A100703
620B100803

    About Data Access:

The F2 data set used in the manuscript is available at GEO under the accession number "GSE3330".

    About the marker set:

The 60 mice were each genotyped at 194 MIT microsatellite markers an average of approximately 10 cM (and always < 30 cM) apart across the entire genome (Y chromsome, excepted). The genotyping error-check routine implemented within R/qtl (Broman et al. 2003) showed no likely errors at p <0.01 probability.

    About the array platfrom :

Affymetrix Mouse Genome 430A and 430B array pairs: The 430A and B array pairs collectively consist of 992936 25-nucleotide probes that estimate the expression of approximately 39,000 transcripts (some are variant transcipts and many are duplicates). The array sequences were selected late in 2002 using Unigene Build 107. The arrays nominally contain the same probe sequence as the 430 2.0 series. However, roughy 75000 probes differ between those on A and B arrays and those on the 430 2.0.

    About the data processing:

Probe (cell) level data from the CEL file: These CEL values produced by GCOS are 75% quantiles from a set of 91 pixel values per cell.
  • Step 1: We added an offset of 1.0 to the CEL expression values for each cell to ensure that all values could be logged without generating negative values.
  • Step 2: We took the log base 2 of each cell.
  • Step 3: We computed the Z scores for each cell.
  • Step 4: We multiplied all Z scores by 2.
  • Step 5: We added 8 to the value of all Z scores. The consequence of this simple set of transformations is to produce a set of Z scores that have a mean of 8, a variance of 4, and a standard deviation of 2. The advantage of this modified Z score is that a two-fold difference in expression level corresponds approximately to a 1 unit difference.
  • Step 6a: The 430A and 430B GeneChips include a set of 100 shared probe sets (2200 probes) that have identical sequences. These probes and probe sets provide a way to calibrate expression of the two GeneChips to a common scale. The absolute mean expression on the 430B array is almost invariably lower than that on the 430A array. To bring the two arrays into alignment, we regressed Z scores of the common set of probes to obtain a linear regression corrections to rescale the 430B arrays to the 430A array. In our case this involved multiplying all 430B Z scores by the slope of the regression and adding or subtracting a very small offset. The result of this step is that the mean of the 430A GeneChip expression is fixed at a value of 8, whereas that of the 430B chip is typically 7. Thus average of A and B arrays is approximately 7.5.
  • Step 6b: We recenter the whole set of 430A and B transcripts to a mean of 8 and a standard deviation of 2. This involves reapplying Steps 3 through 5 above but now using the entire set of probes and probe sets from a merged 430A and B data set.
Probe set data from the TXT file: These TXT files were generated using the RMA (Robust Multiarray Average; (IRIZARRY et al. 2003)). RMA is implemented in the affy package (11/24/03 version) within Bioconductor. RMA functions provide options for background correction and normalization resulting in a single summary score of expression for every transcript in every condition. The same simple steps described above were also applied to these values. A 1-unit difference represents roughly a two-fold difference in expression level. Expression levels below 5 are usually close to background noise levels.

    Data source acknowledgment:

This project was supported in part by NIH/NIDDK 5803701, NIH/NIDDK 66369-01 and American Diabetes Association 7-03-IG-01 to Alan D. Attie, USDA CSREES grants to the University of Wisconsin-Madison to Brian S. Yandell, and HHMI grant A-53-1200-4 to Christina Kendziorski.
B6BTBRF2 Liver Database. All of the original (B6 x BTBR)F2-ob/ob liver mRNA M430AB array data were generated by Hong Lan and Alan Attie at The University of Wisconsin-Madison. For contact and citations and other information on these data sets, please review the INFO pages and contact Drs. Alan Attie, Christina Kendziorski, and Brian Yandell regarding use of this data set in publications or projects.

    References:

Lan H, Chen M, Byers JE, Yandell BS, Stapleton DS, Mata CM, Mui ET, Flowers MT, Schueler KL, Malnly KF, Williams RW, Kendziorski CM, Attie AD (2005) Combined expression trait correlations and expression quantitative trait locus mapping. Submitted, Aug. 2005.

Broman KW, Wu H, Sen S, Churchill GA (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics 19: 889-890.

Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP (2003) Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res 31: e15.

Jin C, Lan H, Attie AD, Churchill GA, Bulutuglo D, Yanell BS (2004) Selective phenotyping for increased efficiency in genetic mapping studies. Genetics 168:2285-2293.

Lan H, Stoehr JP, Nadler ST, Schueler KL, Yandel BS, Attie AD (2003) Dimension reduction for mapping mRNA abundance as quantitative traits. Genetics 164: 1607-1614.

Stoehr JP, Nadler ST, Schueler KL, Rabaglia ME, Yandell BS, Metz SA, Attie AD (2000) Genetic obesity unmasks nonlinear interactions between murine type 2 diabetes susceptibility loci. Diabetes 49: 1946-1954.

Zhang L, Miles MF, Aldape KD (2003) A model of molecular interactions on short oligonucleotide microarrays. Nat Biotechnol 21: 818-821.

    Information about this text file:

This text file originally generated by RWW and Alan Attie, July 2, 2004. Updated by RWW, Aug 20, 5, 2004; April 7, 2005; August 20, 2005.