Forebrain weight was defined to include all brain rostral of the metencephalon, excluding olfactory bulbs. The forebrain was dissected free of the olfactory bulbs by cutting across the ventral midline at the waist of the olfactory peduncle behind the ventral-caudal end of the glomerular surface of the bulb, and was dissected free of the hindbrain by cutting at the junction of midbrain and pons. The brain was rolled quickly in tissue paper and immediately weighed to the nearest 0.1 mg. The forebrain dissection thus includes most of the forebrain and midbrain, bilaterally, but excludes the olfactory bulbs, retinas, and the posterior pituitary (all formally part of forebrain).
The gene expression data set used for these analyses was selected from a larger set that has been previously described . There is a link to extensive metadata describing the samples and sample processing on GeneNetwork http://www.genenetwork.org/dbdoc/BR_U_1203_M.html. Briefly, tissues were dissected from BXD animals (both sexes, aged 8, 20, or 52 weeks) in 32 of the same strains (but different animals) as for the forebrain weight analysis, but using unfixed tissue. Total RNA was extracted and labeled according to Affymetrix protocols and hybridized with Affymetrix U74Av2 microarrays http://www.affymetrix.com/index.affx. A total of 2–4 littermates were dissected and equal amounts of tissue were combined (pooled) for hybridization to each array. A total of 100 arrays were used. Array data were normalized using the MAS5 algorithm from Affymetrix. For a subset of confirmation analyses, we accessed a newer Affymetrix M430 microarray dataset (described at http://www.genenetwork.org/dbdoc/IBR_M_0106_R.html).
Modeling forebrain weight
The number of BXD mice used to collect forebrain weights was 386, with an average of 11 mice measured per strain (minimum, 25%, median, 75%, maximum: 3, 7, 10, 15, 21). Because our forebrain weight measurements were not taken from a population balanced for important covariates, we used multiple regression to fit effects of age, body weight, sex, and non-forebrain brain weight (weight of total brain after the weight of the forebrain was substracted). Residual forebrain weights are available on http://www.genenetwork.org (Trait 10701, standardized to the mean forebrain weight by addition of the average forebrain weight by strain) as are simple raw trait averages by strain (Trait 10699).
Genotyping and QTL mapping
QTL and eQTL mapping was performed using GeneNetwork http://www.genenetwork.org and a standardized set of 3795 genotyped markers (mapping algorithm and genotypes described at http://www.genenetwork.org/dbdoc/BXDGeno.html; genotypes downloadable as a text file from http://www.genenetwork.org/genotypes/BXD.geno). Residuals from the model described above (Trait 10701) were simple interval mapped using a modified Haley-Knott algorithm [36, 37], weighted by the within strain variances. Genome-wide significance was calculated by comparing the best likelihood ratio statistic of the original data set with the distribution of highest LRS computed for 10,000 permutations.
eQTL mapping is QTL mapping of gene transcript abundance, generally measured by microarray. eQTLs can be classified as either cis-eQTLs, that map to the same location of gene encoding the transcript being mapped, or trans-eQTLs, that map to locations other than the gene encoding the mapped transcript. Cis-eQTLs are suggestive of a polymorphism in the gene promoter.
Five filters for candidate gene discovery
Selection criteria for candidate genes included five filters. The first filter required a candidate gene to be located near to the mapped forebrain QTL, within 10 million bases from the genetic marker with the highest LRS resulting from simple interval mapping. Physical locations of genes in the BXD are known, because the genomes of the parental inbred strains C57BL/6J and DBA/2J have been sequenced. Physical positions from the mm6 assembly of the mouse genome http://genome.ucsc.edu/cgi-bin/hgGateway were used with Genenetwork to generate lists of genes residing in or near to the QTLs. The second filter required a significant genetic correlation between forebrain microarray gene expression and forebrain weight among BXD strains. GeneNetwork can be used to rapidly estimate genetic correlation between BXD phenotypes [9, 10]. We used GeneNetwork to correlate forebrain weight and gene expression from a dataset of 100 microarrays on 32 BXD RI lines (Spearman rho, alpha = 0.05). The third filter required a significant difference in forebrain microarray gene expression between the BXD parental inbred strains C57BL/6J and DBA/2J. Unpaired, equal variances t-tests were used to compare 3 and 3 Affymetrix U74Av2 microarrays (alpha = 0.05). Because gene microarray technology platforms change, we also verified candidate gene differences between C57BL/6J and DBA/2J using a newer microarray data set, based on the Affymetrix M430 A and B chips http://www.genenetwork.org/dbdoc/IBR_M_0106_R.html. The fourth filter required verification of gene expression differences by reverse transcriptase PCR (RT-PCR) in C57BL/6J and DBA/2J, and in a two BXD RI lines that had low and high transcript abundance by microarray. The fifth filter required protein differences by Western blot on genes verified by RT-PCR. Together, these five filters strongly nominate genes for classical trait QTLs that act by differences in gene expression.
The third filter, requiring a gene expression difference in the parental lines of the BXD, may be conservative, because an absence of a difference in the parental lines doesn't necessarily preclude heritability in the BXD. Shockley and Churchill  found more gene expression differences between A/J:C57BL/6J consomic lines than between the A/J and C57BL/6J parental inbred strains. One interpretation is that A/J and C57BL/6J carry compensating (epistatic) increaser and decreaser alleles that are segregated in the consomic lines. This has also been described in the BXD, when the RI lines have a range in phenotypic scores than is greater than ("transgresses") the difference between the C57BL/6J and DBA/2J parental inbred strains (e.g., ).
Microarrays have been shown capable of yielding quantitative estimates of RNA levels [40, 41]. However, it is generally accepted that differences benefit from verification with independent samples and methods. Also, in the present application of the short probe Affymetrix U74Av2 platform, it is possible that expression differences on the chip (but not in vivo) could arise from polymorphism between C57BL/6J and DBA/2J in Affymetrix probe sequences, because these were designed from C57BL/6J sequence information. BXD RI lines inheriting C57BL/6J alleles at such a location could in theory exhibit stronger hybridization than lines inheriting DBA/2J alleles. We used real-time PCR to verify expression differences, using duplicate samples for the parental strains C57BL/6J and DBA/2J, as well as duplicate samples for a high and low expressing BXD RIL, BXD40 and BXD25. Total RNA was isolated from whole brains using TRIzol reagent (Invitrogen, Carlsbad, CA). RT-PCR was performed on a SmartCycler (Cepheid, Sunnyvale, CA) using the AccessQuick RT-PCR system (Promega, Madison, WI), and SYBR green I (Molecular Probes, Eugene, Oregon) according to the manufacturer's instructions. The primers used to target mouse genes were (F is the forward primer, and R is the reverse): Tnni1, F: CAC CAG AGA GAT CAA GGA CC, R: TGT GCT TAG AGC CCA GTA GG; Asb3, F: TTT CAT CCA TCA GTT GCC AC, R: GCC TTG CTG GTT TCT CCA TC. Reverse transcription was performed at 48°C for 45 min and RT-PCR cycling parameters were as follows: denaturation at 95°C for 2 min followed by 35 cycles of amplification (94°C, 30 sec; 62°C, 30 sec). Product size was initially monitored by agarose gel electrophoresis and melting curves were analyzed to control for specificity of PCR reactions. The data on the target genes was normalized to the expression of the housekeeping gene β-actin and the relative units were calculated from a standard curve, plotting 3 different concentrations against the PCR cycle number at which the measured intensity reaches a fixed value (with a 10 fold increment equivalent to ~3.1 cycles).
Western blot verification of protein abundance difference in Asb3
BXD25 and BXD40 mouse brains were lysed directly in radioimmunoprecipitation (RIPA) buffer for analysis of whole cell lysates. 50 μg protein, calculated using a BCA (Bicinchoninic Acid) Protein Assay Kit (Pierce, Rockford, IL), were subjected to SDS-PAGE. Proteins were transferred to nitrocellulose membranes, immunoblotted with Asb3 specific antibodies (Santa Cruz Biotechnology, Inc., Santa Cruz, CA; antibody sc-19932) and visualized by enhanced chemiluminescence using the SuperSignal western blotting detection system (Pierce, Rockford, IL). The average intensity of bands was calculated using ImageJ http://rsbweb.nih.gov/ij/. Unfortunately, the only available antibody for Tnni1 protein (Santa Cruz Biotechnology, Inc.) could not be made to work successfully in our lab, and we therefore only report results for Asb3 protein.
Promoter sequence analysis
PCR fragments of Tnni1 were amplified from mice genomic DNA and subcloned into the pCR2.1 TA vector (Invitrogen). The sequences for primers P1 and P2 were: P1, 5' GAA TGG TAC CCC AGG TCG ACT TG 3' and P2, 5' AAG TCT GCT CTT CAC AGG TCA CA 3'. Sequencing was done by Macrogen (Rockville, MD).
The transcriptional start site (TSS) of Tnni1 was determined using the Database of Transcriptional Start Sites (DBTSS; http://dbtss.hgc.jp) . Potential transcription factor-binding sites (TFBSs) were then identified using the TRANSFAC database and P-Match software by screening the upstream region of the Tnni1 indel sequence. All sites were found by the P-Match using the default parameters .