(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) MAS5

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 cases

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 tissue

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 platform

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.

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 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 .CHP file: The expression data were generated using MAS5. 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.

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.

Citation

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.

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.

Notes

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.