Dissection of a QTL Hotspot on Mouse Distal
Chromosome 1 that Modulates Neurobehavioral
Phenotypes and Gene Expression
Khyobeni Mozhui, Daniel C. Ciobanu, Thomas Schikorski, Xusheng Wang, Lu Lu, Robert W. Williams*
Department of Anatomy and Neurobiology, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
A remarkably diverse set of traits maps to a region on mouse distal chromosome 1 (Chr 1) that corresponds to human Chr
1q21–q23. This region is highly enriched in quantitative trait loci (QTLs) that control neural and behavioral phenotypes,
including motor behavior, escape latency, emotionality, seizure susceptibility (Szs1), and responses to ethanol, caffeine,
pentobarbital, and haloperidol. This region also controls the expression of a remarkably large number of genes, including
genes that are associated with some of the classical traits that map to distal Chr 1 (e.g., seizure susceptibility). Here, we ask
whether this QTL-rich region on Chr 1 (Qrr1) consists of a single master locus or a mixture of linked, but functionally unrelated,
QTLs. To answer this question and to evaluate candidate genes, we generated and analyzed several gene expression,
haplotype, and sequence datasets. We exploited six complementary mouse crosses, and combed through 18 expression
datasets to determine class membership of genes modulated by Qrr1. Qrr1 can be broadly divided into a proximal part (Qrr1p)
and a distal part (Qrr1d), each associated with the expression of distinct subsets of genes. Qrr1d controls RNA metabolism and
protein synthesis, including the expression of ,20 aminoacyl-tRNA synthetases. Qrr1d contains a tRNA cluster, and this is a
functionally pertinent candidate for the tRNA synthetases. Rgs7 and Fmn2 are other strong candidates in Qrr1d. FMN2 protein
has pronounced expression in neurons, including in the dendrites, and deletion of Fmn2 had a strong effect on the expression
of few genes modulated by Qrr1d. Our analysis revealed a highly complex gene expression regulatory interval in Qrr1,
composed of multiple loci modulating the expression of functionally cognate sets of genes.
Citation: Mozhui K, Ciobanu DC, Schikorski T, Wang X, Lu L, et al. (2008) Dissection of a QTL Hotspot on Mouse Distal Chromosome 1 that Modulates
Neurobehavioral Phenotypes and Gene Expression. PLoS Genet 4(11): e1000260. doi:10.1371/journal.pgen.1000260
Editor: Jonathan Flint, The Wellcome Trust Centre for Human Genetics, University of Oxford, United Kingdom
Received May 27, 2008; Accepted October 14, 2008; Published November 14, 2008
Copyright: ? 2008 Mozhui et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was supported by NIAAA INIA (grants U01AA13499, U24AA13513, and U01AA014425). GeneNetwork is supported by NIDA, NIMH and NIAAA
(grant P20-DA 21131), the NCRR BIRN (U01NR 105417), and the NCI MMHCC (U01CA105417).
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com
The distal part of mouse Chr 1 harbors a large number of QTLs
that generate differences in behavior. Open field activity , fear
conditioning , rearing behavior , and several other measures
of emotionality [4,5] have been repeatedly mapped to distal Chr 1.
This region is also notable because it appears to influence responses
to a wide range of drugs including ethanol , caffeine ,
pentobarbital , and haloperidol . In addition to the behavioral
traits, a number of metabolic, physiological and immunological
phenotypes have been mapped to this region (table 1) [10–36]. This
QTL rich region on mouse distal Chr 1 exhibits reasonably
compelling functional and genetic concordance with the ortholo-
gous region on human Chr 1q21–q23. Prime examples of genes in
human are Rgs2 (anxiety in both species), Apoa2 (atherosclerosis),
and Kcnj10 (seizure susceptibility) [37–42].
Studies of gene expression in the central nervous system (CNS)
of mice have revealed major strain differences in the expression
level of numerous genes located on distal Chr 1, e.g., Copa, Atp1a2,
and Kcnj9 [26,43–45]. These differentially expressed genes are
strong candidates for the behavioral and neuropharmacological
traits that map to this region. We have recently shown that
sequence variants near each of these candidate genes are often
responsible for the prominent differences in expression [26,46,47].
In other words, sequence differences near genes such as Kcnj9
cause expression to differ, and variation in transcript level maps
back to the location of the source gene itself. Transcripts of this
type are associated with cis-QTLs.
These expression genetic studies have also uncovered another
unusual characteristic of mouse distal Chr 1. In addition to the
extensive cis-effects, a large number of transcripts of genes located
on other chromosomes map into this same short interval on distal
Chr 1 [47,48]. These types of QTLs are often referred to as trans-
QTLs. The clustering of trans-QTLs to distal Chr 1 has been
replicated in multiple crosses and CNS microarray datasets .
We refer to this region of Chr 1, extending from Fcgr3 (172.5 Mb)
to Rgs7 (177.5 Mb) as the QTL-rich region on Chr 1, or Qrr1. It is
possible that these modulatory effects on expression are the first
steps in a cascade of events that are ultimately responsible for
many of the prominent differences in behavior and neurophar-
macology. For example, Qrr1 modulates the expression of several
genes that have been implicated in seizure (e.g., Scn1b, Pnpo,
Cacna1g), and this may be a basis for the strong influence Qrr1 has
on seizure susceptibility .
In this study, we exploited 18 diverse array datasets derived
from different mouse crosses to systematically dissect the
expression QTLs in Qrr1. The strong trans effects are consistently
PLoS Genetics | www.plosgenetics.org1November 2008 | Volume 4 | Issue 11 | e1000260
detected in CNS tissues of C57BL/6J (B6)6DBA/2J (D2) and
B66C3H/HeJ (C3H) crosses, but are largely absent in ILS/Ibg
(ILS)6ISS/Ibg (ISS) and C57BL/6By (B6y)6BALB/cBy (BALB),
and in all non-neural tissues we have examined. We applied high-
resolution mapping and haplotype analysis of Qrr1 using a large
panel of BXD recombinant inbred (RI) strains that included highly
recombinant advanced intercross RI lines. Our analyses revealed
multiple distinct loci in Qrr1 that regulate gene expression
specifically in the CNS. The distal part of Qrr1 (Qrr1d) has a
strong effect on the expression of numerous genes involved in
RNA metabolism and protein synthesis, including more than half
of all aminoacyl-tRNA synthetases. Fmn2 and Rgs7, and a cluster
of tRNAs are the strongest candidates in Qrr1d.
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, Table 1). To compare the
enrichment of QTLs in Qrr1 with that in other regions, we counted
classical QTLs in100non-overlapping intervalscovering almost the
entire autosomal genome (table S1). These intervals were selected to
contain the same number of genes as Qrr1. Numbers of QTLs
ranged from0 to23,andaveraged at 9.1665.37 (SD).Comparedto
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 B6y6BALB, (iii) LXS RI
strains derived from ILS and ISS, (iv) B66C3H F2 intercrosses,
and (v & vi) two separate B66D2 F2 intercrosses. These datasets
were generated by collaborative efforts over the last few years
[46,47,49–52] 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
withgenome-wide p-valueof0.1to0.05,butcorrespondstoa highly
significant pointwise p-value. Because we are mainly interested in
testing a short segment on Chr1, 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 (table 2). 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 (table 2).
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%
ofallhighlysignificant trans-QTLsinthe BXD cerebellumdatasetare
in Qrr1 (table 2). 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.
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 (table 2).
Qrr1 does not have a strong trans-effect in the LXS and CXB
hippocampus datasets (table 2). 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 B66D2
and B66C3H crosses. This contrast among crosses can be
exploited to parse Qrr1 into sub-regions and identify stronger
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 (figure 1). 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 (figure 1).
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
A major goal of genetics is to understand how variation in
DNA sequence gives rise to differences among individuals
that influence traits such as disease risk. This is challenging.
Most traits are the result of a complex interplay of genetic
and environmental factors. One of the first steps in the
path from DNA to these complex traits is the production of
mRNA molecules. Understanding how sequence differenc-
es modulate expression of different RNAs is fundamental
to understanding the molecular origins of complex traits.
Here, we combine classic gene mapping methods with
microarray technology to characterize and quantify RNA
levels in different crosses of mice. We focused on a hotspot
on chromosome 1 that controls the expression of a large
number of different types of RNAs in the brain. This
hotspot also controls many disease traits, including anxiety
levels, and vulnerability to seizure in mice and humans. We
show that this hotspot is made up of several distinct
functional regions, one of which has an unusually strong
and selective effect on aminoacyl-tRNA synthetases and
other genes involved in protein translation.
QTL Hotspot on Mouse Distal Chromosome 1
PLoS Genetics | www.plosgenetics.org2November 2008 | Volume 4 | Issue 11 | e1000260
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
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, Cars, and Nars are
aminoacyl-tRNA synthetases (ARS) that charge tRNAs with
amino acids during translation. Asns and Atf4 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 and
Snrpd1, ring finger protein 6 (Rnf6), and RAS oncogene family
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 (table 3). Eleven of
these candidates contain missense SNPs segregating in B66D2
Table 1. Classical QTLs on Chr 1 from 172–178 Mb; listed by approximate position from proximal to distal end (adapted from
Mouse Genome Informatics).
MGI ID SymbolNameTypeCross Reference
2389129 Bmd5Bone mineral density 5 boneC3H/HeJ6C57BL/6J
1349434 Bmd1 Bone mineral density 1bone C57BL/6J6CAST/Ei 
3624655 Scgq1Spontaneous crescentic glomerulonephritis QTL 1 kidneyC57BL/6J6SCG/Kj 
2680094 Rrodp1Rotarod performance 1behavior129S6/SvEvTac6C57BL/6J 
1891474 Tir3cTrypansomiasis infection response 3c immuneA/JOlaHsd; BALB/cJOlaHsd; C57BL/6JOlaHsd 
2387316 Elnt Escape latencies during navigation taskbehaviorC57BL/6J6DBA/2J
1350920 Emo1Emotionality 1behavior BALB/cJ6C57BL/6J 
3050452 Alcdp1 Alcohol dependency 1behavior C57BL/6J6DBA/2J
1309452 Alcw1 Alcohol withdrawal 1behavior C57BL/6J6DBA/2J
2150827 Cafq1Caffeine metabolism QTL 1 metabolismC3H/HeJ6APN
1098770 Szs1Seizure susceptibility 1CNSC57BL/66DBA/2 
2661242 Cd8mts1CD8 T memory cell subset 1immune BALB/c6C3H6C57BL/66DBA/2 
3613641 Chlq1 Circulating hormone level QTL 1endocrineBALB/cJ6C3H/HeJ6C57BL/6J6DBA/2J 
1345638 Pbw1Pentobarbital withdrawal QTL 1 behaviorC57BL/6J6DBA/2J
2661145 Ssta2 Susceptibility to Salmonella typhimurium antigens 2 immuneHIII6LIII
3522039 Trglyd TriglyceridesmetabolismC57BL/6J6RR 
1346066 Gvhd1 Graft-versus-host disease 1Immune B10.D2-H2d6C57BL/10J 
2155287 Radpf2 Radiation pulmonary fibrosis 2Immune C3H/Kam6C57BL/6J
2151854 Pbwm Pentobarbital withdrawal modifier behaviorC57BL/6J6DBA/2J
1890350 Ath9Atherosclerosis 9 metabolismC57BL/6J6FVB/NCr
2682357 Bslm4 Basal locomotor activity 4 behaviorBALB/cJ6C57BL/6J; C57BL/6J6DBA/2J; C57BL/6J6LP/J 
1891174 Cbm1 Cerebellum weight 1CNSC57BL/6J6DBA/2J
2137602 Cq2 Cholesterol QTL 2metabolism C57BL/6J6KK-Ay 
2680927 Eila1Ethanol induced locomotor activitybehavior C3H/HeJ6C57BL/6J 
2660561 Fglu2Fasting glucose 2metabolismC57BL/6J6KK-Ay 
2137474 Hpic2 Haloperidol induced catalepsy 2behavior C57BL/6J6DBA/2J 
1890554 Melm2Melanoma modifier 2 tumorBALB/cJ6C57BL/6J
2684308 MnotchModifier of Notch 129X1/SvJ6C57BL/6J
2149094 Sle9Systematic lupus erythematosus susceptibility 9immuneBXSB/J6C57BL/10Ola
3579342 Sphsr1Spermatocyte heat stress resistance 1other C57BL/6CrSlc6MRL/MpJSlc
2148991 Yaa4Y-linked autoimmune accelerationimmune BXSB/J6C57BL/10Ola
3613551 Bglu3 Blood glucose level 3metabolismC3H/HeJ6C57BL/6J
QTL Hotspot on Mouse Distal Chromosome 1
PLoS Genetics | www.plosgenetics.org3 November 2008 | Volume 4 | Issue 11 | e1000260
crosses. We also scanned Qrr1 for variation in copy number
[54,55]. 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,
Table 2. Expression QTLs in Qrr1 in different crosses and tissues.
Dataset & Normalization TissueArray
% cisd% transc
B6D2F2 58 OHSU/VA (Sep05) PDNNStriatumAffymetrix M430v2 1975685 185
B6D2F256OHSU/VA mRNA (Aug05) PDNN Whole brain Affymetrix M43079301252
BXD 45SJUT (Mar05) PDNN CerebellumAffymetrix M4304394492 272
BXD 69 Hippocampus Consortium (Dec05) PDNN HippocampusAffymetrix M430v2 345 5471 221
BXD39INIA (Jan06) PDNN Forebrain Affymetrix M430279 3951131
BXD64 Hamilton Eye Institute (Sep06) RMAEyeAffymetrix M430v2156 432121
BXD54HQF (Nov 07) RankInv Striatum Illumina M6.1 97311121
BXD 29 HBP/Rosen(Apr05) PDNNStriatumAffymetrix M430v2 94 252161
BXD 63UMUTAffy RMA (Mar08) HippocampusAffymetrix Exon 1.0 ST 700 3020.41 0.51
BXD 40 UNC (Jan06) BothSexes LOWESSLiver Agilent G4121A9 200.31 0.71
BXD53 Kidney Consortium (Aug06) PDNN KidneyAffymetrix M430v28 33 0.2101
BXD30 GNF (Mar03) MAS5 Hematopoietic CellsAffymetrix U74Av2060303
LXS75 NIAAA INIA (May07) RankInv HippocampusIllumina M6.110 280.4111
B6C3F2 238UCLA BHHBF2 (2005) mlratioBrain Agilent516 51143232
B6C3F2306UCLA BHHBF2 (2005) mlratioMuscle Agilent1533 0.32 0.32
B6C3F2298UCLA BHHBF2 (2005) mlratio LiverAgilent 6346 0.730.63
B6C3F2 282UCLA BHHBF2 (2005) mlratio AdiposeAgilent56 34 0.53 0.43
CXB 13Hippocampus Consortium (Dec05) PDNN HippocampusAffymetrix M430v27 120.082 0.12
aNumber of RI strains or F2 mice.
bNumber of cis- and trans-QTLs in Qrr1 at minimum LOD of 3; complete list of these transcripts can be retrieved from www.genenetwork .org using search key ‘‘LRS=(15
500 Chr1 172 178)’’.
cPercent of trans-QTLs in Qrr1=[(number of trans-QTLs in Qrr1)/(total number of trans-QTLs in the whole genome)6100].
dPercent of cis-QTLs in Qrr1=[(number of cis-QTLs in Qrr1)/(total number of cis-QTLs in the whole genome)6100].
Figure 1. Highly replicable trans-QTLs in Qrr1. The charts illustrate the total number of trans-QTLs (LOD$4) in Qrr1 (shaded) and in other
regions of the genome (non-shaded) in three datasets—BXD cerebellum, BXD hippocampus, and B6C3H F2 brain. The smaller charts represent the
trans-QTLs in BXD hippocampus that are also detected in BXD cerebellum, and B6C3HF2 brain datasets. Out of the 101 trans-QTLs common to both
BXD hippocampus and cerebellum, 64 are in Qrr1 and the remaining 37 are located in other regions of the genome. The BXD hippocampus and
B6C3HF2 brain datasets have 54 common trans-QTLs, and almost all (52 out of 54) are in Qrr1.
QTL Hotspot on Mouse Distal Chromosome 1
PLoS Genetics | www.plosgenetics.org4November 2008 | Volume 4 | Issue 11 | e1000260
and 1452349_x_at detect Ifi204 and Mnda 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 (table 3). 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, Nit1,
Pfdn2, Usf1, Copa, Atp1a2, Kcnj9, Kcnj10, Dfy, and Fmn2 (table 4).
Adamts4 and Igsf4b 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-value=0.02).
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 recombina-
tions 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 figure 2).
As cis-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 (figure 3). 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-value,1026) 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
Parsing trans-QTLs by High-Resolution Mapping and
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
Table 3. Candidate genes in Qrr1.
Fcgr3 172.9812 8.2cis
Sdhc 173.0592 12.3 cis cis
Pcp4l1 173.1038.7 ciscis
Tomm40l173.148 9.67cis cis
Apoa2 173.1557.2cis ciscis
Fcer1g173.160 8.5cis cis
Ndufs2173.1652 13.6 cis
Adamts4 173.1811 8.1cis ciscis cis
Ppox 173.207 7.8cis cis cis
Ufc1173.219 10.8 ciscisciscis
Dedd 173.260 9.7cis
Nit1 173.2721 9.8ciscisciscis
Pfdn2173.276 12.8ciscis cis
Refbp2 173.4342 9.7ciscis
Vangl2 173.9357.6 ciscisciscis
Ncstn 173.9968.5 ciscis
Pex19174.0571 9.9 cisciscis
Wdr42a 174.07810.3cis cis
Pea15 174.12714.1 cis
Atp1a2 174.20215.4cis ciscis cis
Kcnj10 174.271111.2 cisciscis
Dfy175.262 10.3 cisciscis
Igsf4b 175.26410.6 cis
Fmn2 176.4193 10.4 cis ciscis
Grem2 176.7648.2 cis
aNumber of missense mutations between B and D alleles.
bMean expression signal of probe sets in BXD Hippocampus PDNN dataset;
below 7 is considered to be below background.
cCis-QTLs in BXD, B6C3HF2, CXB, and LXS crosses.
QTL Hotspot on Mouse Distal Chromosome 1
PLoS Genetics | www.plosgenetics.org5 November 2008 | Volume 4 | Issue 11 | e1000260
based on the location of peak LOD score markers (figure 4). 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 (figure 5). 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).
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 figure 2), 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 figure 2). 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 overrep-
resented 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
Table 4. Validation of cis-QTLs by measuring allele specific expression difference.
Gene ProbeSet IDSNP IDCis-LODAdd. effect (QTL)a
High allele (ASE) P-value
Ndufs21451096_at rs824521612 0.172D 2.461025
Adamts4 1455965_at rs3153783225
Nit1 1417468_atrs31552469 15
Pfdn2 1421950_atrs315499985 0.174D 4.161027
Atp1a21455136_at rs3157090249 1.186D 0.02
Kcnj91450712_at rs3156911819 0.511D 0.01
Kcnj101419601_atrs30789204 280.349D 0.003
Igsf4b 1418921_atrs316136267 0.171 0.3
Fmn21450063_at rs33800912 17
aAdditive effect is computed as [(mean expression in DD homozygote)2(mean expression in BB homozygote)]/2 on a log2scale. Positive value means D high expression,
and negative value means B high expression.
Figure 2. Haplotype maps of Qrr1 recombinant BXD strains. BXD8, BXD29, BXD62, BXD64, BXD68, and BXD84 have recombinations in Qrr1. B
haplotype is assigned blue (2), D haplotype is assigned pink (+), and recombination regions are shown in grey. The Qrr1 interval (in Mb scale) is
shown above and approximate positions of recombination are highlighted (red). The recombinant strains collectively divide Qrr1 into six segments
(labeled 1–6), and provide six sets of informative markers. Markers are shown below and approximate positions of candidate genes (yellow bars) and
tRNA clusters (orange triangles) are indicated.
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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=461027), tRNA aminoacylation
(p=0.003), cell cycle (p=0.004), and ubiquitin mediated protein
catabolism (p=0.006). Other GO categories show enrichment in
both Qrr1p and Qrr1d. For example, genes involved in RNA
metabolism and ubiquitin-mediated protein catabolism are also
overrepresented among the transcripts modulated by Qrr1p
(p=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 and Qrr1d 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 (table 5). 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%.
We examined all probes or probe sets that target ARS and
ARS-like genes in the B66D2 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
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, Bdnf, Cdc42,
Eif4e, Eif4g2, Hnrpab, Ppp1cc, Pabpc1, Eif5, Kpnb1, Rhoip3, Stau2, and
Pum2 [60–63]. 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 39 UTR and is
specifically transported into the dendrites; the other isoform has a
short 39 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 39 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 (figure 2). 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 and Aim2 (175.204–
175.257 Mb) in Qrr1d (dataset S3). 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
Trans-regulation of large number of transcripts by Qrr1 is a
strong feature of crosses between B6 and D2—both the BXD RI
Figure 3. QTL mapping precision in Qrr1. Mapping precision was
empirically determined by measuring the distance between a cis-QTL
peak and location of parent gene. Cis-QTLs in BXD Hippocampus
Consortium, UMUTAffy Hippocampus, and Hamilton Eye datasets were
used for this purpose. Mean gene-to-QTL peak distance (y-axis) was
plotted as a function of LOD score (LOD score range on x-axis). Number
of probe sets in each LOD range is shown. Mapping precision increases
with increase in LOD score. The mean offset for cis-QTLs with LOD
scores 3–4 (genome-wide adjusted p-value of 0.1–0.01) is 900 kb, and
the offset decreases to 650 kb at 4–5 LOD scores (p-value of 0.01–
0.001). Cis-QTLs with LOD scores greater than 11 (p-value,1026) have
mean offset of only 450 kb.
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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 D, and B and C3H alleles (H) but not by variations
between the ILS and ISS alleles (L and S, respectively), and B and
BALB alleles (C). This contrast can be exploited to identify sub-
regions that underlie the trans-QTLs .
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 (figure 6). Qrr1 is a highly polymorphic
interval in the B66D2 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 B66BALB crosses, despite being negative for the trans-effect,
have moderate to high SNP counts in Qrr1 and share a SNP profile
somewhat similar to B66D2 crosses. The B66C3H 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
Figure 4. Segregation of trans-QTLs in Qrr1. Expression of Atp5j2, Cplx2, and Nars are modulated by trans-QTLs in Qrr1 (blue plot). D allele has
the positive additive effect (green plot; allele effect scale shown on the right) on the expression of Atp5j2 and Cplx2; peak LOD scores are on markers
near candidate genes Ndufs2 and Kcnj10. B allele has the positive additive effect (red plot) on the expression of Nars; peak LOD score is on markers
near candidate gene Fmn2. The horizontal lines indicate the genome-wide significant thresholds (p-value=0.05). Yellow seismograph tracks the SNP
density between B and D alleles. Affymetrix probe set ID for each transcript in the BXD hippocampus dataset is shown.
QTL Hotspot on Mouse Distal Chromosome 1
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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 contribu-
tions 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
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 B66D2 and B66C3H 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 (table 3). 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) (table 3).
Figure 5. QTL for aminoacyl-tRNA synthetases in distal Qrr1. Transcripts of Gars, Cars, Nars, Mars, and Yars map as trans-QTLs to Qrr1 at
LOD.4 (genome-wide p-value,0.01) in the BXD hippocampus dataset. The trans-QTLs have peak LOD precisely on markers in distal part of Qrr1,
,175–177.5 Mb (shaded regions). Yellow seismograph on Chr 1 (x-axis) tracks SNP density between B and D alleles. Affymetrix probe set ID for each
transcript is shown.
Table 5. Transcripts of aminoacyl tRNA synthetases that have trans-QTLs in Qrr1 (LOD$3) in one or multiple CNS datasets.
Nars asparaginyl-tRS1452866_at_AChr 18BXD cerebellum 12.0B
Gars glycyl-tRS1423784_atChr 6 BXD hippocampus10.6B
Rars arginyl-tRS 1416312_at_AChr 11 BXD forebrain8.9B
Carscysteinyl-tRS 10024406001 Chr 7B6C3HF2 brain 8.9B
Yarstyrosyl-tRS10024399842Chr 4 B6C3HF2 brain8.0B
Iars isoleucine-tRS 1426705_s_atChr 13BXD cerebellum 7.8B
Sarsseryl-tRS1426257_a_at Chr 3BXD cerebellum6.9B
Mars methionine-tRS1455951_atChr 10 BXD hippocampus6.5B
Harshistidyl-tRS1438510_a_at Chr 18 BXD hippocampus5.2D
Iars2 isoleucine-tRS 1426735_at Chr 1BXD hippocampus 4.3B
Tarsthreonyl-tRS10024395655Chr 15 B6C3HF2 brain4.0B
Aarsalanyl-tRS 1451083_s_atChr 8 BXD eye 3.9B
Lars leucyl-tRS 1448403_at_AChr 18 BXD cerebellum3.7B
Ears2 glutmyl-tRSILM5290446Chr 7 BXD ILM striatum3.7B
Aarsd1 alanyl-tRS domain 11424006_at Chr 11B6D2F2 brain 3.5B
Dars aspartyl-tRS1423800_at_A Chr 1BXD cerebellum 3.2B
aProbe/Probe set ID.
bPhysical location of gene; Iars2 is located on Chr 1 at 186.9 Mb, and Dars on Chr 1 at 130 Mb.
cDataset in which transcript has highest trans-QTL in Qrr1.
dHighest LOD score in Qrr1.
eAllele that increases expression.
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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 B66D2
and B66C3H crosses, expression of Fmn2 maps as a cis-QTL in
B66D2 and B66C3H crosses, and in the CXB RI strains in which
the trans-effect is not detected (table 3). 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 B66D2 (Glu610Asp, Pro1077Leu, Asp1431Glu)
and B66C3H 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
Figure 6. SNP comparison between crosses. SNPs in Qrr1 were counted for (A) C57BL/6J (B6)6DBA/2J (D2), (B) B66BALB/cBy (BALB), (C)
B66C3H/HeJ (C3H), and (D) ILS6ISS. The SNP distribution profiles were generated by plotting the number of SNPs in 250 kb bins. Vertical red lines
mark the approximate positions of recombination (corresponds to figure 2). Region covered by Qrr1p (horizontal line), candidate genes in Qrr1d
(yellow bars), and position of tRNA clusters (triangles) are shown above the graphs. The B66D2, B66BALB, and B66C3H crosses have moderate to
high SNP counts throughout Qrr1. In the ILSxISS cross, Qrr1p is relatively SNP-rich but Qrr1d is SNP-sparse.
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Qrr1d—Fmn2 and Rgs7—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
Fmn2, and 14 transcripts representative of the different GOs that
map to Qrr1d (dataset S4). The highest partial correlations are
between Fmn2 and Rnf6 (r=0.68, p-value,10213), Atf4 (r=0.6, p-
value,1029), Asns (r=0.55, p-value,1027), Ube2d3 (r=0.5, p-
value,1026), Hnrpk (r=0.5, p-value=1025), Rab2 (r=20.5, p-
value=1025), and Gars (r=0.5, p-value=1025). The strongest
correlate of Fmn2 is Rnf6, 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
Fmn2 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-null (Fmn22/2)
and coisogenic (Fmn2+/+) 129/SvEv controls. At a stringent
statistical threshold (Bonferroni corrected p,0.05), only eight genes
have significant expression differences between Fmn22/2and
Fmn2+/+genotypes (table 6). Five out of the eight genes, including
Pou6f1, Usp53, 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
[72,73]. 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 Fmn22/2line. 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
Sub-Cellular Localization of FMN2 Protein in
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 (figure 7A). 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.
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 . Wefound 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, Cacna1g, Pnpo, and Dapk1
effect on the expression of these seizure related transcripts, increasing
expression 5% to 20%. The two potassium channel genes, Kcnj9 and
Kcnj10, 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 approachto evaluate thesecandidates .
Kcnj9 (figure 8A) is expressed most heavily in neurons within the
dentate gyrus, whereas Kcnj10 (figure 8B) 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. Dapk1 and Cacna1g (figure 8C)
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 (figure 8D), Adora1, Pnpo, and Kcnma1 complement the
expression of Kcnj9 with comparatively strong expression in CA2 and
CA3, and weak expression in CA1 and dentate gyrus.
Table 6. Genes that have significant expression difference between Fmn2+/+and Fmn22/2.
Pou6f1 ILM62001681511.966.48 45361026
Zfp420ILM2570632 710.127.705 0.002
Txnl1 ILM28501481810.726.70160.002 3.0B6D2F2 striatum
Usp53ILM10319006837.17 9.324 0.009 3.3BXD Hippocampus
LOC331139ILM103170273414.45 10.59 15 0.01
Slc11a2ILM104050242 159.929.1720.02 3.9BXD Hippocampus
Pgbd5 ILM1039404358 13.4012.122 0.023.3BXD HBP Striatum
aIllumina probe ID.
bPhysical location of gene.
cAverage expression signal in Fmn2-null and wild-type lines.
dFold difference in expression between Fmn2-null and wild-type lines
eBonferroni adjusted p-values; corrected for 46,620 tests.
fHighest LOD in Qrr1 and dataset in which transcript has highest LOD in Qrr1.
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Qrr1 is a complex regulatory region that modulates expression of
many genes and classical phenotypes. By exploiting a variety of
microarray datasets and by applying a combination of high-
resolution mapping, sequence analysis, and multiple cross analysis,
we have dissected Qrr1 into segments that are primarily responsible
for variation in the expression of functionally coherent sets of
transcripts. The distal portion of Qrr1 (Qrr1d) has a strong trans-
effect on RNA metabolism, translation, tRNA aminoacylation,
and transcripts that are transported into neuronal dendrites. Fmn2,
Rgs7, and a cluster of tRNAs are strong candidates in Qrr1d. We
analyzed gene expression changes in the CNS of Fmn2-null mice
and detected a profound effect on the expression of a small
number of transcripts that map to Qrr1d, particularly on the
expression of the transcription factor Pou6f1. We have shown that
the FMN2 protein is highly expressed in the cell body and
processes of neurons, and is a high priority candidate in Qrr1d.
Kcnj9 vs. Kcnj10 and Seizure Susceptibility
The two inwardly rectifying potassium channel genes—Kcnj9
and Kcnj10—are strong candidates for the seizure susceptibility
QTL in Qrr1p that has been unambiguously narrowed to the short
interval from Atp1a2 to Kcnj10 . In BXD CNS datasets, Qrr1
also modulates the expression of a set of genes implicated in the
etiology of seizure and epilepsy, including Pnpo, Scn1b, Kcnma1,
Socs2, and Cacna1g. Polymorphisms in the Kcnj9/Kcnj10 interval
that influence expression of these genes are excellent candidates
for the Szs1 locus.
The in situ expression data in the ABA shows a striking spatial
correlation between expression of Kcnj9 and other seizure-related
transcripts that have trans-QTLs in Qrr1p. The complementary
expression of Kcnj9 and the seizure-related transcripts (figure 8)
make Kcnj9 a stronger candidate than Kcnj10. Kcnj9 has over a 2-
fold higher expression in D2 [our data, and cf. 26,86], a seizure
prone strain, compared to B6, a relatively seizure resistant strain,
suggesting that the proximal cause of Szs1 may be high expression
of this gene, perhaps due to the promoter polymorphism
discovered by Hitzemann and colleagues .
Multiple Loci in a Major QTL Interval
Fine mapping of complex traits have often yielded multiple
constituent loci within a QTL interval [87,88]. Our mapping
analyses of expression traits also show that multiple gene variants,
rather than one master regulatory gene, cause the aggregation of
expression QTLs in Qrr1. Subgroups of genes with tight
coexpression can be dissected from the dense cluster of QTLs.
Most notable is the strong trans-regulatory effect of Qrr1d on genes
involved in amino acid metabolism and translation, including a
host of ARS transcripts. However, there are limits to our ability to
dissect Qrr1, and genes associated with protein degradation and
RNA metabolism map throughout the region. In part this may be
due to inadequate mapping resolution, but it may also reflect
Figure 7. Expression of FMN2 protein in hippocampal neurons. (A) Neurons exhibited pronounced fine granular immunoreactivity for FMN2.
The cell body had the strongest signal. The fine granular staining extended into apical and distal dendrites (arrows). Thin axon-like processes were
also labeled (arrow head). (B) The fine granular staining is not detected in controls of sister cultures processed in parallel without the first antibody.
Figure 8. Expression patterns of seizure related genes with cis-
and trans-QTLs in Qrr1p. Candidate gene Kcnj9 (A) has heavy
expression in neurons. Kcnj9 shows a regionally restricted expression in
the hippocampus with intense labeling in dentate gyrus, strong
labeling in CA1, and relatively weak labeling in CA2 and CA3. Candidate
gene Kcnj10 (B) has a more diffused pattern and expressed primarily in
glial cells. There is almost no labeling for Kcnj10 in the hippocampus.
Transcripts of seizure-related genes, Cacna1g (C) and Socs2 (D), have
trans-QTLs in Qrr1p. Both genes show high expression in neurons.
Cacna1g matches the expression of Kcnj9 with strong labeling in
dentate gyrus and CA1, and weak labeling in CA2 and CA3. Socs2
complements the expression of Kcnj9 and Cacna1g with intense
labeling in CA2 and CA3. In Situ expression data are from the Allen
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clusters of functionally related loci and genes . At this stage we
are also unable to discern whether there is a single or multiple
QTLs within Qrr1d. While it is likely that a single QTL modulates
the expression of the ARS, there may be additional gene variants
in Qrr1d that modulate other transcripts involved in translation
and RNA metabolism. With increased resolving power it may be
possible to further subdivide transcripts that map to Qrr1p and
Qrr1d into smaller functional modules.
There may be multiple loci in Qrr1 that modulate different
stages of protein metabolism in the CNS. Maintenance of cellular
protein homeostasis requires finely tuned cross talk between
transcription and RNA processing, the translation machinery, and
protein degradation [90–92], gene functions highly overrepresent-
ed among the transcripts that map to Qrr1. While these are generic
cellular processes, there are unique demands on protein
metabolism in the nervous system. Neurons are highly polarized
cells and specialized mechanisms are in place to manage local
protein synthesis and degradation in dendrites and axons .
The nervous system is also particularly sensitive to imbalances in
protein homeostasis [94,95], a possible reason why the trans-effects
of Qrr1 are detected only in neural tissues.
Candidates in Qrr1d and Possible Links with Local Protein
Transfer RNAs are direct biological partners of the ARS, and
the cluster of tRNAs in the highly polymorphic intergenic region
of Qrr1d (figure 6) is an enticing candidate. In addition to their role
in shuttling amino acids, tRNAs also act as sensors of cellular
amino acid levels and regulate transcription of genes involved in
amino acid metabolism and the ARS . There is tissue
specificity in the expression of different tRNA isoforms , and
we speculate that the tRNA cluster in Qrr1d is specifically
functional in neural tissues.
Rgs7, a member of the RGS (regulator of G-protein signaling)
family, is another high-ranking candidate in Qrr1d. RGS proteins
are important regulators of G-protein mediated signal transduction.
Rgs7 is predominantly expressed in the brain and has been
implicated in regulation of neuronal excitability and synaptic
transmission [97,98]. Although RGS proteins are usually localized
in the plasma membrane, RGS7 has been found to shuttle between
themembraneandthe nucleus.Thisimpliesa role forRGS7in
gene expression regulation in response to external stimuli.
Our final high-ranking candidate in Qrr1d is Fmn2. It codes for
an actin binding protein exclusively expressed in the CNS and
oocytes, and is involved in the establishment of cell polarity
[70,71]. In Drosophila, the formin homolog, cappuccino, has a role
in RNA transport and in localizing the staufen protein to oocyte
poles [100–102]. It is possible that FMN2 has parallel functions in
mammalian neurons. Interestingly, Staufen 2 (Stau2), a gene
involved in RNA transport to dendrites , maps to Qrr1d in
BXD CNS datasets. Furthermore, deletion of formin homologs in
yeast results in inhibition of protein translation , compelling
evidence for an interaction between the protein translation system
and formins. Evidence for a role for Fmn2 in dendrites also comes
from our immunocytochemical analysis that clearly demonstrates
the expression of FMN2 protein in dendrites. Taken together,
Fmn2 is a functionally relevant candidate gene in Qrr1d and may be
related to RNA transport and protein synthesis in the CNS.
The microarray datasets used in this study (table 2) were
generated by collaborative efforts [46,47,49–52]. All datasets can
be accessed from www.genenetwork.org. They provide estimates
of global mRNA abundance in neural and non-neural tissues in
the BXD, LXS, and CXB RI strains, B6D2F2 intercrosses, and
B6C3HF2 intercrosses. Detailed description of each set, tissue
acquisition, RNA extraction and array hybridization methods, and
data processing and normalization methods are provided in the
‘‘Info’’ page linked to each dataset. In brief, the datasets are:
1)BXD CNS transcriptomes: The BXD CNS datasets
measure gene expression in the forebrain and midbrain
(INIA Forebrain), striatum (HBP/Rosen Striatum and HQF
Striatum), hippocampus (Hippocampus Consortium and
UMUTAffy Hippocampus), cerebellum (SJUT Cerebellum
mRNA), and eye (Hamilton Eye) of BXD RI strains (table 2).
The INIA Brain and HBP/Rosen Striatum datasets have
been described in Peirce et al. . The Hippocampus
Consortium dataset measures gene expression in the adult
hippocampus of 69 BXD RI strains, the parental B6 and D2
strains, and F1 hybrids. The SJUT Cerebellum dataset
measures gene expression in the adult cerebellum of 45
BXD RI strains, parental strains, and F1 hybrids. The Eye
dataset measures gene expression in the eyes of 64 BXD RI
strains, parental strains, and F1 hybrids. The HQF BXD
Striatum is one of the newest datasets and was generated on
Illumina Sentrix Mouse–6.1 arrays. It is similar to the HBP/
Rosen Striatum and measures gene expression in the
striatum of 54 BXD RI strains, parental strains, and F1
2)BXD non-neural transcriptomes: The non-neural BXD
array sets measure gene expression in the liver (UNC Liver)
of 40 BXD strains, kidney (Kidney Consortium) of 53 BXD
strains, and hematopoietic stem cells (GNF Hematopoietic
Cells) of 30 BXD strains [49,50].
3) LXS hippocampus transcriptome: The LXS Hippocampus
dataset measures gene expression in the adult hippocampus
of 75 LXS RI strains and the parental ILS and ISS strains.
4) B6D2F2 CNS transcriptomes: The B6D2F2 datasets
measure gene expression in the whole brain (OHSU/VA
Brain), and striatum (OHSU/VA Striatum) of B66D2 F2
intercrosses [47,52]. The whole brain dataset comprises of
samples from 56 F2 animals, and the striatum dataset
comprises of samples from 58 F2 animals.
5) B6C3HF2 transcriptomes: These datasets were generated
from large numbers of B66C3H F2 intercross progeny and
assayed using Agilent arrays . These datasets have been
described in Yang et al .
Mouse Strains and Genotype Data
The conventional BXD RI strains were derived from the B6
and D2 inbred mice [104,105]. The newer sets of advanced RI
strains were derived by inbreeding intercrosses of the RI strains
. The parental B6 and D2 strains differ significantly in
sequence and have approximately 2 million informative SNP. A
subset of 14,000 SNPs and microsatellite markers have been used
to genotype the BXD strains [106,107]. We used 3,795
informative markers for QTL mapping. Thirty such informative
markers are in Qrr1 and we queried these markers to identify
strains with recombinations in Qrr1; genes with strong cis-QTLs
(Sdhc, Atp1a2, Dfy, and Fmn2) were used as additional markers.
Smaller sub-sets of markers were used to genotype the two F2
panels (total of 306 markers for the whole brain, and 75 markers
for the striatum F2 datasets).
QTL Hotspot on Mouse Distal Chromosome 1
PLoS Genetics | www.plosgenetics.org13 November 2008 | Volume 4 | Issue 11 | e1000260
The LXS RI strains were derived from the ILS and ISS inbred
strains. They have been genotyped using 13,377 SNPs, and some
microsatellite markers . 2,659 informative SNPs and
microsatellite markers were used for QTL mapping.
The CXB panel consists of 13 RI strains derived from C57BL/
6By and BALB/cBy inbred strains. A total of 1384 informative
markers were used for QTL mapping.
The B66C3H/HeJ F2 intercrosses have been genotyped using
13,377 SNPs and microsatellite markers, and 8,311 informative
markers were used for QTL mapping.
Animals and Tissue Acquisition
Majority of the BXD and LXS tissues (cerebellum, eye,
forebrain, hippocampus, kidney, liver, and striatum for the HQF
Illumina dataset) were dissected at the University of Tennessee
Health Science Center (UTHSC). Mice were housed at the
UTHSC in pathogen-free colonies, at an average of three mice per
cage. All animal procedures were approved by the Animal Care
and Use Committee. Mice were killed by cervical dislocation, and
tissues were rapidly dissected and placed in RNAlater (Ambion,
www.ambion.com) and kept overnight at 4u C, and subsequently
stored at 280 degree C. Tissue were then processed at UTHSC or
shipped to other locations for processing.
RNA Isolation and Sample Preparation
For the tissues that were processed at UTHSC (all BXD and
LXS CNS tissues except HBP Affymetrix striatum), RNA was
isolated using RNA STAT-60 (Tel-Test Inc., www.tel-test.com) as
per manufacturer’s instructions. Samples were then purified using
standard sodium acetate methods prior to microarray hybridiza-
tion. The eye samples required additional purification steps to
remove eye pigment; this was done using the RNeasy MinElute
Cleanup Kit (Qiagen, www.qiagen.com). RNA purity and
concentration was evaluated with a spectrophotometer using
260/280 nm absorbance ratio, and RNA quality was checked
using Agilent Bioanalyzer 2100 prior to hybridization. Array
hybridizations were then done according to standard protocols.
Microarray Probe Set Annotation
We have re-annotated a majority of Affymetrix probe sets to
ensure more accurate description of probe targets. Each probe set
represents a concatenations of eleven 25-mer probes, and these
have been aligned to the NCBI built 36 version of the mouse
genome (mm8 in UCSC Genome Browser) by BLAT analysis. We
have also re-annotated the Illumina probes and incorporated these
annotations into GeneNetwork. Each probe in the Illumina
Mouse–6 and Mouse–6.1 arrays is 50 nucleotides in length, and
these have been aligned to NCBI built 36.
We used the strain average expression signal detected by a
probe or probe set. QTL mapping was done for all transcripts
using QTL Reaper . The mapping algorithm combines simple
regression mapping, linear interpolation, and standard Haley-
Knott interval mapping . QTL Reaper performs up to a
million permutations of an expression trait to calculate the
genome-wide empirical p-value and the LOD score associated
with a marker. We selected only those transcripts that have highest
LOD scores, i.e., genome-wide adjusted best p-values, on markers
located on Chr 1 from 172 to 178 Mb. This selected transcripts
that are primarily modulated by Qrr1 but excluded transcripts that
have QTLs in Qrr1 but have higher LOD scores on markers
located on other chromosomal regions. Cis- and trans-QTLs were
distinguished based on criteria described by Peirce et al. . To
identify trans-QTLs common to multiple datasets, we selected
probes/probe sets that target the same genes and have peak LOD
scores within 10 Mb in the different datasets.
Screening Local QTLs
We screened all Affymetrix probe sets with cis-QTLs in Qrr1 for
SNPs in target sequences. This step was taken to identity false cis-
QTLs caused by differences in hybridization. As probe design is
based on the B6 sequence, such spurious cis-QTLs show high
expression for the B allele, and low expression for the D allele. Our
screening identified only two probe sets in which SNPs result in
spurious local QTLs—1429382_at (Tomm40l), and 1452308_a_at
(Atp1a2). The majority of cis-QTLs in Qrr1 are likely to be due to
actual differences in mRNA abundance. We did not detect a bias
in favor of the B allele on cis-regulated expression and the ratio of
transcripts with B- and D- positive additive effects is close to 1:1.
Analysis of Allele-Specific Expression Difference
To measure expression differencebetween theB and D alleles, we
exploited transcribed SNPs to capture allelic expression difference
in F1 hybrids  using a combination of RT-PCR and a single
base extension technology (SNaPshot, Applied Biosystems, www.
appliedbiosystems.com). For each transcript we analyzed, Primer 3
 was used to design a pair of PCR primers that target
sequences on the same exon and flanking an informative SNP.
We prepared four pools of RNA from the hippocampus, and
four pools of genomic DNA from the spleen of F1 hybrids (male
and female B66D2 and D26B6 F1 hybrids). To avoid
contamination by genomic DNA, the four RNA pools were
treated with Turbo DNase (Ambion, www.ambion.com), and then
first strand cDNA was synthesized (GE Healthcare, www.
gehealthcare.com). The genomic DNA samples were used as
controls, and both cDNA and genomic DNA samples were tested
concurrently using the same assay to compare expression levels of
B and D transcripts.
We amplified the cDNA and genomic DNA samples using
GoTaq Flexi DNA polymerase (Promega Corporation, www.
promega.com). PCR products were purified using ExoSap-IT
(USB Corporation, www.usbweb.com) followed by SNaPshot to
extend primer by a single fluorescently labeled ddNTPs.
Fluorescently labeled products were purified using calf intestinal
phosphatase (CIP, New England BioLabs, www.neb.com) and
separated by capillary electrophoresis on ABI3130 (Applied
Biosystems). Quantification was done using GeneMapper v4.0
software (Applied Biosystems), and transcript abundance was
measured by peak intensities associated with each allele. Ratio of B
and D allele in both cDNA and gDNA pools was computed, and t-
test (one tail, unequal variance) was done to validate expression
difference and polarity of parental alleles.
SNP Analysis in Multiple Crosses
GeneNetwork has compiled SNP data from different sources—
mouse.perlegen.com/mouse/download.html), BROAD institute
(http://www.broad.mit.edu/snp/mouse), Wellcome–CTC ,
dbSNP, and Mouse Phenome Database (http://www.jax.org/
phenome/SNP). SNP counts were done on the GeneNetwork SNP
Partial Correlation Analysis
A partial correlation is the correlation between X and Y
conditioned on one or more control variables. In this study, first
QTL Hotspot on Mouse Distal Chromosome 1
PLoS Genetics | www.plosgenetics.org 14 November 2008 | Volume 4 | Issue 11 | e1000260
order partial correlation was used to detect the interaction
between trans-regulated transcripts and cis-regulated candidate
genes conditioned on the genotype (marker rs8242481 at
175.058 Mb). If x, y and z are trans-regulated transcripts, cis-
regulated transcript, and genotype in the QTL, respectively, then
the first order partial correlation coefficient is calculated as—
where rxycan be either Pearson correlation or Spearman’s rank
correlation between x and y. We employed the Spearman’s rank
correlation because the expression levels of many transcripts do
not follow a normal distribution.
The significance of a partial correlation with n data points was
assessed with a two-tailed t test on t~r
is the first order correlation coefficient, and k is the number of
variables on which we are conditioning.
Cultured hippocampal neurons from male B6 mice, prepared as
described in Schikorski et al.  and cultured for 23 days, were
fixed with 4% paraformaldehyde and 0.1% glutaraldehyde in
HEPES buffered saline (pH 7.2) for 15 min. Cell membranes were
permeabilized with 0.1% triton X-100 and unspecific binding sites
were quenched with 10% BSA for 20 min at room temperature
(RT). Neurons were incubated with a polyclonal anti-FMN2
antibody (Protein Tech Group, www.ptglab.com) diluted to
0.3 mg/ml at RT overnight. An anti-rabbit antibody raised in
donkey (1:500, Invitrogen; http://www.invitrogen.com) conjugat-
ed with the fluorescent dye Alexa488 was used for the detection of
the first antibody. All regions of interest were photographed with
identical illumination and camera settings to allow for a direct
comparison of the staining in labeled and control neurons.
Fmn22/2and Fmn2+/+Microarray Analysis
The Fmn22/2mice were generated using 129/SvEv (now strain
129S6/SvEvTac) derived TC-1 embryonic stem cells. Chimeric
mice were backcrossed to 129/SvEv . The Fmn2-null and
littermate controls are therefore coisogenic. To validate the
isogenicity of regions surrounding the targeted locus , we
genotyped the Fmn2+/+, Fmn2+/2, and Fmn22/2mice using ten
microsatellite markers located on, and flanking Fmn2 (markers
distributed from 172 Mb to 182 Mb). These markers are
D1Mit355, D1Mit150, D1Mit403, D1Mit315, and D1Mit426. With
the exception of a marker at Fmn2 (D1Mit150), all alleles in null,
heterozygote, and wildtype animals were identical.
RNA was isolated from whole brain samples of Fmn2+/+and
Fmn22/2mice, and assayed on Illumina Mouse-6 array slides (six
samples per slide). We compared five samples from Fmn22/2nulls,
and five samples from Fmn2+/+wildtype. Equal numbers of each
genotypes were placed on each slide to avoid batch confounds.
Microarray data were processed using both raw and rank invariant
protocols provided by Illumina as part of the BeadStation software
suite (www.illumina.com). We subsequently
expression values and stabilized the variance of each array. To
identify genes with significant expression difference between the
Fmn22/2and Fmn2+/+cases, we carried out two-tailed t-tests and
applied a Bonferroni correction for multiple testing, and selected
probes with a minimum adjusted p-value,0.05.
Classical QTLs counts are based on the April 2008 version of
Mouse Genome Informatics (MGI: www.informatics.jax.org)
. Search for tRNAs was done using tRNAscan-SE 1.21
(http://lowelab.ucsc.edu/tRNAscan-SE/) . GO analysis was
done using the analytical tool DAVID 2007 (http://david.abcc.
ncifcrf.gov/) . Overrepresented GO terms were identified
and statistical significance of enrichment was calculated using a
modified Fisher’s Exact Test or EASE score . We used the
Allen Brain Atlas to analyze expression pattern in the brain of
young C57BL/6J male mice (www.brain-map.org) [85,116].
Control for Non-Syntenic Association and Paralogous
In RI strains, non-syntenic associations can lead to LD between
distant loci [89,106]. In the BXDs, we detected such non-syntenic
associations between markers in Qrr1 and markers on distal Chr 2
and proximal Chr 15. As a result of these associations, some
transcripts that have strong cis- or trans-QTLs in Qrr1 tend to have
weak LOD peaks, usually below the suggestive threshold, on distal
Chr 2 and proximal Ch15. However, there is no bias for genes
located in these intervals in LD with Qrr1 to have trans-QTLs in
The Qrr1 segment has been reported to have paralogues on
mouse Chrs 1 (proximal region), 2, 3, 6, 7, 9, and 17 [117,118].
We examined if the trans-QTLs in Qrr1 are of genes located in
these paralogous regions. However, genes located in the
paralogous regions are not overrepresented among the trans-QTL.
other chromosomal intervals.
Found at: doi:10.1371/journal.pgen.1000260.s001 (0.23 MB
Number of classical QTLs in Qrr1 and in hundred
that have trans-QTLs in Qrr1p near the seizure susceptibility QTL.
Found at: doi:10.1371/journal.pgen.1000260.s002 (0.05 MB
Transcripts of genes associated with seizure or epilepsy
Found at: doi:10.1371/journal.pgen.1000260.s003 (0.13 MB
Precision of Cis-QTLs in Qrr1.
Qrr1p and Qrr1d in the BXD hippocampus dataset.
Found at: doi:10.1371/journal.pgen.1000260.s004 (0.03 MB
Gene ontology analysis of transcripts that map to
Found at: doi:10.1371/journal.pgen.1000260.s005 (0.09 MB
tRNAs in Qrr1d.
Found at: doi:10.1371/journal.pgen.1000260.s006 (0.04 MB
Partial correlation analysis.
We thank Drs. John K. Belknap and Robert Hitzemann for access to the
B6D2F2 datasets. We thank Drs. Gerd Kempermann and Rupert Overall
and members of the Hippocampus Consortium for access to their dataset,
and we thank Dr. Glenn Rosen for access to the BXD striatum datasets.
We thank Drs. Ivan Rusyn and David Threadgill for access to the BXD
UNC liver dataset, and we thank Drs. Gerald de Haan and Michael Cooke
for access to the BXD hematopoietic stem cells dataset. We thank Drs.
Erwin P. Bottinger and Kremena V. Star for access to the BXD kidney
QTL Hotspot on Mouse Distal Chromosome 1
PLoS Genetics | www.plosgenetics.org 15November 2008 | Volume 4 | Issue 11 | e1000260
dataset. We thank Drs. Aldons J. Lusis and Eric Schadt for access to the
B6C3HF2 datasets. We thank Drs. Markus Dettenhofer, Philip Leder and
their colleagues for providing brains from Fmn2+/+, Fmn2+/2, and Fmn22/2
mice. We thank Michael Hawrylycz and Ed Lein for their assistance with
analysis on the Allen Brain Atlas. Finally, we thank Feng Jiao and Shu Hua
Qi for technical assistance.
Conceived and designed the experiments: LL RWW. Performed the
experiments: KM DCC TS. Analyzed the data: KM DCC TS XW RWW.
Contributed reagents/materials/analysis tools: LL RWW. Wrote the
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