Enrichment in Classical QTLs
The Chr 1 interval, from 172–178 Mb, harbors 32 relatively precisely mapped QTLs for classical traits such as alcohol dependency, escape latency, and emotionality (Mouse Genome Informatics at www.informatics.jax.org
, ). To compare the enrichment of QTLs in Qrr1
with that in other regions, we counted classical QTLs in 100 non-overlapping intervals covering almost the entire autosomal genome (table S1
). These intervals were selected to contain the same number of genes as Qrr1
. Numbers of QTLs ranged from 0 to 23, and averaged at 9.16±5.37 (SD). Compared to these regions, Qrr1
had the highest QTL number, over 4 SD above the mean, and over three times higher than average.
Enrichment in Expression QTLs in Neural Tissues
In this section, we summarize the number of expression phenotypes that map to Qrr1
in different tissues and mouse crosses. The results are based on the analysis of 18 array datasets that provide estimates of global mRNA abundance in neural and non-neural tissues from six different crosses. These crosses are—(i) BXD RI and advanced intercross RI strains derived from B6 and D2, (ii) CXB RI strains derived from B6y×BALB, (iii) LXS RI strains derived from ILS and ISS, (iv) B6×C3H F2 intercrosses, and (v & vi) two separate B6×D2 F2 intercrosses. These datasets were generated by collaborative efforts over the last few years 
and some were generated more recently (e.g., the Illumina datasets for BXD striatum and LXS hippocampus, and BXD Hippocampus UMUTAffy Exon Array dataset). All datasets can be accessed from GeneNetwork (www.genenetwork.org
We mapped loci that modulate transcript levels and selected only those transcripts that have peak QTLs in Qrr1 with a minimum LOD score of 3. This corresponds to a generally lenient threshold with genome-wide p-value of 0.1 to 0.05, but corresponds to a highly significant pointwise p-value. Because we are mainly interested in testing a short segment on Chr 1, a pointwise (region-wise) threshold is more appropriate to select those transcripts that are likely to be modulated by Qrr1. Qrr1 covers approximately 0.2% of the genome and extends from Fcgr3 (more precisely, SNP rs8242852 at 172.887364 Mb using Mouse Genome Assembly NCBI m36, UCSC Genome Browser mm8) through to Rgs7 (SNP rs4136041 at 177.273526 Mb). We defined this region on the basis of the large number of transcripts that have maximal LOD scores associated with markers between these SNPs.
Hundreds of transcripts map to Qrr1 with LOD scores ≥3 in neural tissue datasets of BXD RI strains, B6D2F2 intercrosses, and B6C3HF2 intercrosses (). The QTL counts in Qrr1 are far higher than the average of 15 to 35 expression QTLs in a typical 6 Mb interval. The fraction of QTLs in Qrr1 is as high as 14% of all trans-QTLs, and 5% of all cis-QTLs in the whole genome (). The enrichment in trans-QTLs in Qrr1 is even more pronounced when the QTL selection stringency is increased to a LOD threshold of 4 (genome-wide p-value of approximately 0.01). For example, 27% of all highly significant trans-QTLs in the BXD cerebellum dataset are in Qrr1 (). The BXD hippocampus dataset that was assayed on the Affymetrix Exon ST array is an exception—there are over a million probe sets in this array, and the percent enrichment of QTLs in Qrr1 appears to be relatively low. Nevertheless, about 1000 transcripts map to Qrr1 in this exon dataset.
Expression QTLs in Qrr1 in different crosses and tissues.
In contrast to the CNS datasets, relatively few transcripts map to Qrr1 in non-neural tissues of the BXD strains and B6C3HF2 intercrosses. While the number of cis-QTLs is still relatively high (1–3%), Qrr1 has limited or no trans-effect in these datasets ().
Qrr1 does not have a strong trans-effect in the LXS and CXB hippocampus datasets (). This indicates that the sequence variants underlying the trans-QTLs do not segregate to nearly the same extent in the LXS and CXB RI panels as they do in B6×D2 and B6×C3H crosses. This contrast among crosses can be exploited to parse Qrr1 into sub-regions and identify stronger candidate genes.
Replication of trans-QTLs in Multiple Datasets
The trans-QTLs in Qrr1 are highly replicable. A large fraction of the transcripts, in some cases represented by multiple probes or probe sets, map to Qrr1 in multiple CNS datasets. For example, there are 747 unique trans-QTLs with LOD scores greater than 4 (genome-wide p-value≤0.01) in the BXD hippocampus dataset (assayed on Affymetrix M430v2 arrays). Out of these highly significant trans-QTLs, 155 are in Qrr1 and the remaining 592 are distributed across the rest of the genome (). We compared the trans-QTLs in the hippocampus dataset with a similar collection of trans-QTLs (LOD≥4) in the cerebellum dataset (assayed on Affymetrix M430 arrays). Only 101 trans-QTLs in the hippocampus are replicated in the cerebellum (for trans-QTLs that were declared as common, the average distance between peak QTL markers in the two datasets is 1.6 Mb). But it is remarkable that of the subset of common trans-QTLs, 64 are in Qrr1 (). The replication rate of trans-QTLs in Qrr1 is therefore about 6-fold higher relative to the rest of the genome. When we compared the BXD hippocampus dataset with the B6C3HF2 brain dataset (assayed on Agilent arrays), we found 54 trans-QTLs common to both datasets (for the common trans-QTLs, the average distance between peak markers in the two datasets is 2.7 Mb). Strikingly, out of the 54 trans-QTLs common to both crosses, 52 are in Qrr1 ().
Highly replicable trans-QTLs in Qrr1.
Among the transcripts with the most consistent trans
-QTLs are glycyl-tRNA synthetase (Gars
), cysteinyl-tRNA synthetase (Cars
), asparaginyl-tRNA synthetase (Nars
), isoleucyl tRNA synthetase (Iars
), asparagine synthetase (Asns
), and activating transcription factor 4 (Atf4
). These transcripts map to Qrr1
in almost all datasets in which the strong trans
-effect is detected. Gars
, and Nars
are aminoacyl-tRNA synthetases (ARS) that charge tRNAs with amino acids during translation. Asns
are also involved in amino acid metabolism—Asns
is required for asparagine synthesis and is under the regulation of Atf4
, which in turn is sensitive to cellular amino acid levels 
. Other transcripts that consistently map as trans
-QTLs to Qrr1
include brain expressed X-linked 2 (Bex2
), splicing factor Sfrs3
, ribonucleoproteins Snrpc
, ring finger protein 6 (Rnf6
), and RAS oncogene family member Rab2
Candidates in Qrr1
Qrr1 contains 164 known genes. The proximal part of Qrr1 is gene-rich and has several genes with high expression in the CNS (e.g. Pea15, Kcnj9, Kcnj10, Atp1a2). The middle to distal part of Qrr1 is relatively gene sparse and consists mostly of clusters of olfactory receptors and members of the interferon activated Ifi200 gene family. Though comparatively gene sparse, the middle to distal part of Qrr1 contains a small number of genes that have high expression in the CNS—Igsf4b, Dfy, Fmn2, and Rgs7.
A subset of 35 genes were initially selected as high priority candidates based on the number of known and inferred sequence differences between the B6 allele (B
) and D2 allele (D
) and based on expression levels in multiple CNS datasets (). Eleven of these candidates contain missense SNPs segregating in B6×D2 crosses. We also scanned Qrr1
for variation in copy number 
. Graubert et al. 
reported segmental duplication in Qrr1
with a copy number gain in D2 compared to B6 near the intelectin 1 (Itlna
) gene at 173.352 Mb. We failed to detect any expression signatures of a copy number variation around Itlna
in any of the GeneNetwork datasets. However, we did identify an apparent 150 kb deletion across the Ifi200
gene cluster (175.584–175.733 Mb). Affymetrix probe sets 1426906_at, 1452231_x_at, and 1452349_x_at detect Ifi204
transcripts in B6 but not in D2. The expression difference is robust enough to generate cis
-QTLs with very high LOD scores (>40). This gene cluster has low expression in the CNS (Affymetrix declares this probe sets to be “not present”), but high expression in tissues such as hematopoietic stem cells and kidney, in which the trans
-effect of Qrr1
is not detected. The Ifi200
gene cluster was therefore excluded as a high priority candidate.
cis-QTLs in Qrr1
Transcripts of 26 of the 35 selected candidate genes map as cis-QTLs (LOD≥3) in the BXD CNS datasets (). These putatively cis-regulated genes are among the strongest candidates in the QTL interval. The D allele in Qrr1 has the positive effect on the expression of Sdhc, Ndufs2, Adamts4, Dedd, Pfdn2, Ltap, Pea15, Atp1a2, Kcnj9, Kcnj10, Igsf4b, and Grem2. Increase in expression caused by the D allele ranges from about 10% for Adamts4 to over 2-fold for Atp1a2. In contrast, the B allele has the positive effect on the expression of Pcp4l1, Fcer1g, B4galt3, Ppox, Ufc1, Nit1, Usf1, Copa, Pex19, Wdr42a, Igsf8, Dfy, Fmn2, and Rgs7. Increase in expression caused by the B allele ranges from about 7% for Usf1 to 40% for Pex19.
Individual probes were screened to assess if the strong cis
-effects are due to hybridization artifacts caused by SNPs in probe targets. Thirteen candidate genes with cis
-QTLs were then selected for further analysis and validation of cis
-regulation by measuring allele specific expression (ASE) difference 
. This method exploits transcribed SNPs, and uses single base extension to assess expression difference in F1 hybrids. By means of ASE, we validated the cis
-regulation of 10 candidate genes—Ndufs2
, and Fmn2
failed to show significant allelic expression difference. In the case of Ufc1
, the polarity of the allele effect failed to agree with the ASE result (D
positive at p
Validation of cis-QTLs by measuring allele specific expression difference.
High-Resolution cis-QTL Mapping
The BXD CNS datasets were generated from a combined panel of conventional RI strains and advanced RI strains that were derived by inbreeding advanced intercross progeny. The advanced RIs have approximately twice as many recombinations compared to standard RIs and the merged panel offers over a 3-fold increase in mapping resolution 
. This expanded RI set combined with the relatively high intrinsic recombination rate within Qrr1 
provides comparatively high mapping resolution. Mapping precision can be empirically determined by analyzing cis
-QTLs in multiple large datasets, particularly the BXD Hippocampus Consortium, UMUTAffy Hippocampus, and Hamilton Eye datasets. These three datasets were selected because they have expression measurements from six BXD strains with recombinations in Qrr1
. These strains—BXD8, BXD29, BXD62, BXD64, BXD68, and BXD84—collectively provide six sets of informative markers and divide Qrr1
into six non-recombinant segments, labeled as segments 1–6 (haplotype structures shown in ).
Haplotype maps of Qrr1 recombinant BXD strains.
-acting regulatory elements are usually located within a few kilobases of a gene's coding sequence 
, we used the cis
-QTLs as an internal metric of mapping precision by measuring the offset distance between a cis
-QTL (position of peak QTL marker) and the parent gene (). For cis
-QTLs with LOD scores between 3–4 (genome-wide p
-value of 0.1–0.01) the mean gene-to-QTL peak distance is 900 kb. The offset decreases to a mean of 640 kb for cis
-QTLs with LOD scores greater than 4 (p
-value<0.001). Very strong cis
-QTLs with LOD scores greater than 11 (p
) have a mean gene-to-QTL peak distance of only 450 kb. In all, 60% of cis
-QTLs we examined have peak linkage on markers located precisely in the same non-recombinant segment as the parent gene, and 30% have peak linkage on markers in a segment adjacent to the parent gene (dataset S1
). These cis
-QTLs provide an empirical metric of mapping precision within Qrr1
Parsing trans-QTLs by High-Resolution Mapping and Gene Functions
Mapping precision of cis-QTLs is comparatively higher in the BXD hippocampus dataset (average offset of only 410 kb), and we used this set to examine the trans-QTLs (LOD≥3) at higher resolution. The trans-QTLs in Qrr1 were parsed into subgroups based on the location of peak LOD score markers (). This method of resolving trans-QTLs effectively grouped subsets of transcripts into functionally related cohorts. For instance, all the QTLs for the aminoacyl-tRNA synthetases (ARS) have peak LOD scores only within the distal three segments of Qrr1 (). This consistency in QTL peaks for transcripts of the same gene family is itself a good indicator of mapping precision. In addition to the ARS, numerous other genes involved in amino acid metabolism and translation map to the distal part of Qrr1 (e.g., Atf4, Asns, Eif4g2, and Pum2).
Segregation of trans-QTLs in Qrr1.
QTL for aminoacyl-tRNA synthetases in distal Qrr1.
We divided the trans-QTLs into two broad subgroups—those with peak QTLs on markers in the proximal part of Qrr1 (Qrr1p; 172–174.5 Mb or segments 1, 2, 3 in ), and those with peak QTLs on markers in the distal part of Qrr1 (Qrr1d; 174.5–177.5 Mb or segments 4, 5, and 6 in ). While Qrr1p is relatively gene-rich, only 35% of the trans-QTLs (129 out of 365 probe sets) have peak LOD scores in this region. The majority of trans-QTLs—about 65% (236 out of 365 probe sets)—have peak QTLs in the relatively gene-sparse Qrr1d.
The two subsets of transcripts—those with trans
-QTLs in Qrr1p
and those with trans
-QTLs in Qrr1d
—were analyzed for overrepresented gene functions using the DAVID functional annotation tool (http://david.abcc.ncifcrf.gov/
). This revealed distinct gene ontology (GO) categories enriched in the two subsets (dataset S2
). Enriched GOs among the transcripts modulated by Qrr1p
include GTPase-mediate signal transduction (modified Fisher's exact test p
0.001), and structural constituents of ribosomes (p
0.003). Transcripts modulated by Qrr1d
are highly enriched in genes involved in RNA metabolism (p
), tRNA aminoacylation (p
) and translation (p
), RNA transport (p
0.003), cell cycle (p
0.004), and ubiquitin mediated protein catabolism (p
0.006). Other GO categories show enrichment in both Qrr1p
. For example, genes involved in RNA metabolism and ubiquitin-mediated protein catabolism are also overrepresented among the transcripts modulated by Qrr1p
0.002 for RNA metabolism and p
0.005 for ubiquitin-protein ligases). This may either be due to limitations in QTL resolution, or due to multiple loci in Qrr1p
controlling these subsets of transcripts.
An Aminoacyl-tRNA Synthetase trans-QTL in Distal Qrr1
A remarkable number of transcripts of the ARS gene family map to Qrr1. A total of 16 ARS transcripts have trans-QTLs at a minimum LOD score of 3 in one or multiple BXD, B6D2F2, and B6C3H CNS datasets (). In almost all cases, QTLs peak on markers on the distal part of Qrr1. Except for Hars, the B allele in Qrr1 consistently increases expression by 10% to 30%. In the case of Hars, the D allele has the positive additive effect and increases expression by about 10%.
Transcripts of aminoacyl tRNA synthetases that have trans-QTLs in Qrr1 (LOD≥3) in one or multiple CNS datasets.
We examined all probes or probe sets that target ARS and ARS-like genes in the B6×D2 CNS datasets. The Affymetrix platform measures the expression of 34 ARS and ARS-like genes; 24 of these map to Qrr1 at LOD scores ranging from a low of 2 to a high of 12. Even in the case of the suggestive trans-QTLs (i.e., LOD values between 2 and 3), the B allele in Qrr1 has the positive effect on expression. The ARS family is also highly represented among trans-QTLs in the B6C3HF2 brain dataset. Thirty-seven probes in this dataset target the tRNA synthetases, eleven of these have trans-QTLs in Qrr1d (LOD scores ranging from 2 to 20), and almost all have a B positive additive effect (exceptions are Hars and Qars). The co-localization of trans-QTLs to Qrr1d, the general consensus in parental allele effect, and their common biological function indicate that there is a single QTL in the distal part of Qrr1 modulating the expression of the ARS. It is crucial to note that this genetic modulation is only detected in CNS tissues.
In the LXS hippocampus dataset, Qrr1 has only a limited trans-effect on gene expression. Despite the weak effect, expression of Dars2 (probe ID ILM580427) maps to the distal part of Qrr1 at a LOD of 3. Although this is only a weak detection of the ARS QTL in the LXS dataset, it nonetheless demonstrates the strong regulatory effect of Qrr1 on the expression of this gene family. In the case of the CXB hippocampus dataset, not a single trans-QTL for the ARS is detected in Qrr1.
trans-QTLs for Transcripts Localized in Neuronal Processes
In addition to the high overrepresentation of transcripts involved in translation and RNA metabolism, several transcripts known to be transported to neuronal processes or involved in RNA transport also map to Qrr1d
, including Camk2a
, and Pum2 
. An interesting example is provided by the brain derived neurotrophic factor (Bdnf
). Two alternative forms of Bdnf
mRNA are known—one isoform has a long 3′ UTR and is specifically transported into the dendrites; the other isoform has a short 3′ UTR and remains primarily in the somatic cytosol 
. The Affymetrix M430 arrays contain two different probe sets that target these Bdnf
isoforms. Probe set 1422169_a_at targets the distal 3′ UTR and is essentially specific for the dendritic isoform, and probe set 1422168_a_at targets a coding sequence common to both isoforms. Although both probe sets detect high expression signal in the hippocampus, only the dendritic isoform maps as a trans
-QTL to Qrr1d
. This enrichment in transcripts that are transported to neuronal processes raises the possibility that this CNS specific trans
-effect may be related to local protein synthesis.
tRNAs in Qrr1
Prompted by the many ARS transcripts that consistently map to Qrr1d
, we searched the genomic tRNA database 
for tRNAs in this region. Interestingly, distal Chr 1 is one of many tRNA hotspots in the mouse genome and several predicted tRNAs are clustered in the non-coding regions of Qrr1
(). The majority of these tRNA sequences are in the proximal end of Qrr1
, over 2 Mb away from Qrr1d
. We scanned the intergenic non-coding regions in Qrr1d
for tRNAs using the tRNAscan-SE software 
and uncovered tRNAs for arginine and serine, and three pseudo-tRNA sequences between genes Igsf4b
(175.204–175.257 Mb) in Qrr1d
). Transfer RNAs are involved in regulating transcription of the ARS in response to cellular amino acid levels 
and are functionally highly relevant candidates in Qrr1d
. Polymorphism in the tRNA clusters (e.g., possible copy number variants, differences in tRNA species) may have significant impact on the expression of the ARS.
Sequence Analysis of Crosses
-regulation of large number of transcripts by Qrr1
is a strong feature of crosses between B6 and D2—both the BXD RI set and B6D2F2 intercrosses—and in the B6 and C3H intercrosses. The feature is much weaker in the large LXS RI set and in the small CXB panel. The effect specificity demonstrates that a major source of the Qrr1
signal is generated by variations between B
, and B
and C3H alleles (H
) but not by variations between the ILS and ISS alleles (L
, respectively), and B
and BALB alleles (C
). This contrast can be exploited to identify sub-regions that underlie the trans
SNPs were counted for all four pairs of parental haplotypes—B vs D, B vs H, B vs C, and L vs S—and SNP profiles for the four crosses were compared (). Qrr1 is a highly polymorphic interval in the B6×D2 crosses. The flanking regions, however, have few SNPs (170–172.25 Mb proximally, and 177.5–179.5 Mb distally) and are almost identical-by-descent between B6 and D2. The B6×BALB crosses, despite being negative for the trans-effect, have moderate to high SNP counts in Qrr1 and share a SNP profile somewhat similar to B6×D2 crosses. The B6×C3H crosses also have moderate to high SNP counts in Qrr1, with a relatively higher SNP count in Qrr1d compared to Qrr1p. In contrast, in the LXS, Qrr1p is more SNP-rich than Qrr1d. Most notably, the segments that harbor the tRNAs and candidates Fmn2, Grem2, and Rgs7 are almost identical by descent between ILS and ISS. This SNP comparison indicates that the strongest trans-effect is from Qrr1d. A possible reason why the trans-effect is not detected in the CXB RI strains, despite being SNP rich in Qrr1, is that the crucial SNPs underlying the trans-QTLs may not be segregating in this cross or that undetected copy number variants make important contributions to the Qrr1 effects. A final explanation may be that the small CXB dataset (13 strains) is simply underpowered.
High-Ranking Candidates Based on Cross Specificity of cis-QTLs
We used the specificity of cis-QTLs in the multiple crosses to identify higher priority candidates in Qrr1. The assumption is that candidate genes whose transcripts have cis-QTLs (LOD score above 3) in the B6×D2 and B6×C3H crosses but not in the LXS and CXB RI strains are stronger candidates for trans-QTLs that are detected in the former two crosses but not in the latter two crosses. In contrast, cis-QTLs with the inverse cross specificity are less likely to underlie these trans-QTLs. Based on this criterion, there are four high-ranking candidates in Qrr1p—Purkinje cell protein 4-like 1 (Pcp4l1), prefoldin (Pfdn2), WD repeat domain 42 a (Wdr42a), and Kcnj10 (). There are only two high-ranking candidates in Qrr1d—formin 2 (Fmn2), an actin binding protein involved in cytoskeletal organization, and regulator of G-protein signaling 7 (Rgs7) ().
Both Fmn2 and Rgs7 are almost exclusively expressed in the CNS and are high priority candidates for the CNS specific trans-QTLs. A point of distinction between the two candidates is that while expression of Rgs7 maps as a cis-QTL only in the B6×D2 and B6×C3H crosses, expression of Fmn2 maps as a cis-QTL in B6×D2 and B6×C3H crosses, and in the CXB RI strains in which the trans-effect is not detected (). Based on the pattern of specificity of cis-QTLs in multiple crosses, Rgs7 is a more appealing candidate. However, Fmn2 has known missense SNPs that segregate in the B6×D2 (Glu610Asp, Pro1077Leu, Asp1431Glu) and B6×C3H crosses (Val372Ala). There are no known missense mutations in Fmn2 in the CXB and LXS RI strains, and no known missense mutation in Rgs7 in any of the four crosses.
Partial Correlation Analysis
Linkage disequilibrium (LD) is a major confounding factor that limits fine-scale discrimination among physically linked candidates in a QTL. To further evaluate the two high-priority candidates in Qrr1d
—we implemented a partial correlation analysis 
in which the effect of genotype at Qrr1d
was controlled. For this analysis, we computed the partial correlation coefficient between cis
-regulated transcripts and each trans
-regulated transcript after regression against the Qrr1d
genotype. This partial correlation reveals residual variance that links cis
candidates with trans
targets, independent of genetic variance at Qrr1d
. We computed the partial correlation between Rgs7
, and 14 transcripts representative of the different GOs that map to Qrr1d
). The highest partial correlations are between Fmn2
), and Gars
). The strongest correlate of Fmn2
, a gene involved in regulating actin dynamics in axonal growth cones 
. Although not unequivocal, this analysis provides stronger support for Fmn2
than for Rgs7
Effect of Fmn2 Deletion on Gene Expression
is almost exclusively expressed in the nervous system 
and is a strong candidate for a trans
-effect specific to neural tissues. However, its precise function in the brain has not been established. Fmn2
-null mice do not have notable CNS abnormalities 
, but to evaluate a possible role of Fmn2
on expression of genes that map to Qrr1d
, we generated array data from brains of Fmn2
) and coisogenic (Fmn2+/+
) 129/SvEv controls. At a stringent statistical threshold (Bonferroni corrected p
<0.05), only eight genes have significant expression differences between Fmn2−/−
genotypes (). Five out of the eight genes, including Pou6f1
, and Slc11a
, have trans
-QTLs in Qrr1d
. Deletion of Fmn2
had the most drastic effect on the expression of the transcription factor gene Pou6f1
, a gene implicated in CNS development and regulation of brain-specific gene expression 
. Expression of Pou6f1
maps as a trans
-QTL (at LOD score of 3) to Qrr1d
in the hippocampus dataset, and its expression was down-regulated more than 44-fold in the Fmn2−/−
line. While the expression analysis of Fmn2
-null mice does not definitively link all the trans
-QTLs to Fmn2
, variation in this gene is likely to underlie some of the trans
-QTLs in Qrr1d
. The possible compensatory mechanism in the Fmn2
-null CNS, and the different genetic background of the mice (129/SvEv) are factors that may have contributed to the weak detection of trans
-effects in the knockout line.
Genes that have significant expression difference between Fmn2+/+ and Fmn2−/−.
Sub-Cellular Localization of FMN2 Protein in Hippocampal Neurons
We examined the intracellular distribution of FMN2 protein in neurons using immunocytochemical techniques. All hippocampal pyramidal neurons on a culture dish exhibited distinct and fine granular immunoreactivity for FMN2. The cell body itself had the strongest signal (). This fine punctate labeling extended into proximal dendrites and could be followed into distal dendrites. In some instances very thin processes, possibly the axons, were also labeled.
Expression of FMN2 protein in hippocampal neurons.
Linking Expression and Classical QTLs: Szs1
The strong trans
-effect that Qrr1
has on gene expression is a likely basis for the classical QTLs that map to this region. For example, the major seizure susceptibility QTL (Szs1
) has been precisely narrowed to Qrr1p 
. We found that 10 genes already known to be associated with seizure or epilepsy have trans
-QTLs with peak LOD scores near Szs1
and in Qrr1p
. These include Scn1b
, and Dapk1
. In every case, the D
allele has the positive additive effect on the expression of these seizure related transcripts, increasing expression 5% to 20%. The two potassium channel genes, Kcnj9
, are the primary candidates 
. Both are strongly cis
-regulated. The tight linkage between these genes (within 100 kb) limits further genetic dissection, but in situ
expression data from the Allen Brain Atlas (ABA, www.brain-map.org
) provides us with a powerful complementary approach to evaluate these candidates 
() is expressed most heavily in neurons within the dentate gyrus, whereas Kcnj10
() is expressed diffusely in glial cells in all parts of the CNS. The seizure-related transcripts with trans
-QTLs near Szs1
are most highly expressed in neurons, and all have comparatively high expression in the hippocampus. Furthermore, expression patterns of six of the seizure transcripts that map to Qrr1p
show spatial correlations with Kcnj9
() have expression pattern that match Kcnj9
with strong labeling in the dentate gyrus and CA1, and weaker labeling in CA2 and CA3. In contrast, Socs2
, and Kcnma1
complement the expression of Kcnj9
with comparatively strong expression in CA2 and CA3, and weak expression in CA1 and dentate gyrus.
Expression patterns of seizure related genes with cis- and trans-QTLs in Qrr1p.