Open Journal of Genetics, 2012, 2, 136-154 OJGen Published Online September 2012 (
Analysis of candidat e g enes of QTL and chromosomal
regions for essential hypertension in the rat model
Lishi Wang1,2, Jiaqian Zhu1,3, Yue Huang1, Qing Xiong4, Cong-Yi Wang5, Arnold Postlethwaite6,
Yongjun Wang7, Weikuan Gu1*
1Departments of Orthopaedic Surgery-Campbell Clinic and Pathology, University of Tennessee Health Science Center (UTHSC),
Memphis, USA
2Department of Basic Medicine, Inner Mongolia Medical University, Huhhot City, China
3Rust College, Holly Spring, USA
4Department of Biostatistics and Bioinformatics, Duke University, Durham, USA
5Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta, USA
6Department of Medicine, University of Tennessee Health Science Center (UTHSC), Memphis, USA
7Department of Neurology, Beijing Tiantan Hospital, Capital University of Medical Sciences, Beijing, China
Email:,,,,,,, *
Received 20 May 2012; revised 25 June 2012; accepted 10 July 2012
This is an in silico analysis of quantitative trait loci
(QTLs), genes, polymorphisms, and chromosomal
regions regulating hypertension in the rat genome.
Utilizing PGmapper, a program that matches phenol-
types to genes, we identified 266 essential hyperten-
sion-associated genes (HyperA), and 83 of these genes
contain known hypertension-associated polymorphi-
sms (HyperAP). The majority of HyperAP have been
reported in recent years. Surprisingly, only a few of
these HyperAP genes have been investigated for their
candidacy as the QTL for hypertension. The fre-
quency of candidate genes within peak regions of the
QTL is higher than the rest of the QTL region. We
also found that QTL located in both gene-rich regions
and gene-rich chromosomes contained the most can-
didate genes. However, the number of candidate
genes within a peak region is not associated with the
number of total genes in a QTL region. This data
could not only facilitate a more rapid and compre-
hensive identification for the causal genes underlying
hypertension in rats, but also provides new insights
into genomic structure in regulation of hypertension.
Keywords: Chromosome; Hypertension; QTL; Gene;
Polymorphism; Rat
Blood pressure and hypertension in humans are quantita-
tive traits controlled by many genes [1-3]. Because of the
difficulties in studying genetic variations relating to hy-
pertension in humans, researchers chose the rat as an
ideal model. In recently review articles, progress for the
genetic dissection of essential hypertension in humans
using a rat model have been summarized. Cowley [5]
pointed out that the next daunting task is gene identifica-
tion and validation.
Currently, more than 300 Quantitative trait loci (QTLs)
for blood pressure and hypertension are reported in the
rat genome (see the Rat Genome Database web site at:, and these QTLs are found in every
rat chromosome [6]. More than 200 candidate genes
relating to hypertension have been identified in rats and
some of them, such as Kcnj1 and Drd2, are positioned
within a QTL [7,8]. Most of these identified genes are
relatable to human chromosomes. However, further ana-
lysis on the relationship between these candidate genes
and their likelihood for being genes involved in the
QTLs for hypertension will enhance the identification of
the real causal genes responsible for QTLs.
In addition to just simply further analysis, more ques-
tions need to be answered in the investigation for the
candidate genes responsible for the hypertension QTLs.
There are three main questions that need answering: Do
these candidate genes include all the genes affecting
blood pressure and hypertension? How many candidate
genes are within the hypertension QTLs and how close
are these genes to peak markers? What is a candidate
gene’s relative potential candidacy for being a gene truly
responsible for the QTL? The third question is the most
important of the three and needs a definitive answer.
Currently, due to the combination of rapidly developed
genome sequence information and online literature, it is
*Corresponding author.
L. S. Wang et al. / Open Journal of Genetics 2 (2012) 136-154 137
now possible to pinpoint accurately genes within a QTL
and evaluate them much more efficiently.
2.1. Software
In this investigation, we used our recently developed
software named PGMapper
( to
systematically examine the candidate genes for the hy-
pertension QTLs across the whole rat genome [9].
PGMapper identified all the possible candidate genes for
the hypertension QTL by combining the mapping infor-
mation from Ensemble database, updated literature in-
formation from PubMed, and the Online Medelian In-
heritance in Man (OMIM) database.
2.2. Collection of QTL Loci
We first selected blood pressure as a trait to pick up all
of the possible hypertension QTLs from the Rat Genome
Database (RGD, We then selected
all the QTL that have a logarithm of odds (LOD) score >
2.8. This selection criterion was based on accepted link-
age criteria. A LOD score of >4.3 was considered sig-
nificant while a LOD score between 2.8 and 4.3 was
considered suggestive for linkage [10,11]. If two QTL
were overlapped and connected, we analyzed them inde-
pendently. However, if one QTL is located within an-
other QTL, we just used the QTL with the larger genome
2.3. Examination of Candidate Genes
We used flanking markers to search candidate genes for
QTLs that are fine-mapped and well-defined. If a flank-
ing marker has no sequence location on Ensemble data-
base, we used either nearby markers or markers at the
peak region of the QTL (according to the curve of the
LOD score). If a marker in the peak region of the QTL
was used, we searched candidate genes using genomic
regions of 20,000,000 base pairs (bp) on each side of the
peak marker.
After we searched for candidate genes using PGMap-
per, visual examination of these selected candidate genes
was conducted to determine the potential of each indi-
vidual gene involved. First, the genes full names were
checked to ensure their abbreviations. Next, at least one
abstract per gene from the PGMapper’s report detailing
each selected gene was read to determine the candidacy
of that gene. In the majority of cases, more than one ab-
stract was read to confirm the importance of that gene.
In order to discern whether any of these candidate
genes had been investigated for their candidacy in the
hypertension QTL, we conducted a separate literature
search on PubMed using the key words “QTL + hyper-
tension + ‘gene name’.” We then read the associated
literature from this search to determine any connection
between our preliminary candidate genes from PGMap-
per and essential hypertension. A gene is considered an
essential hypertension-associated gene if it is associated
with essential hypertension in at least one of the follow-
ing studies: 1) functional studies (i.e., knockouts, trans-
genics, mutagenesis, RNA interference, etc.); 2) associa-
tion studies; 3) clinical studies.
In case any genes associated to essential hypertension
by association studies resulted in an inconclusive result,
we excluded these genes from our candidate gene list.
Candidate genes were examined whether they have
been studied in the human population previously and
their polymorphisms have been linked with hypertension.
The key words used in our search were “hypertension
and polymorphism”. Thus, only when both hypertension
and polymorphism appear in the same abstracts with a
gene name will PGMapper collect it into our list of genes
needing further review.
2.4. Analysis of Gene Chromosomal Position
and Their Candidacy
We also searched for candidate genes within 1.5 cM re-
gions from the peak marker in a QTL by PGMapper.
These results indicated the importance of each selected
candidate genes in the QTL.
Total genomic bps within a QTL region were deter-
mined by subtracting the nucleotide base position of the
right flanking marker from the position of left flanking
marker. Bps per gene of a QTL region were calculated
by dividing the total bps of a QTL region by the total
genes according to data from Ensembl (bps per gene =
total bps/total genes). Bps per candidate gene of a QTL
region were calculated by dividing the total bps of a QTL
region by the number of candidate genes determined
from our aforementioned methods (bps per candidate
gene = total bps/total candidate genes).
Gene-rich and gene-poor regions on chromosomes
were determined by comparison of the bps per gene in a
QTL region or chromosome. P values were determined
using student’s t test. R values of correlation analysis
were obtained with Excel.
3.1. QTL and Their Candidate Genes
The 62 hypertension QTLs cover 2,015,062,129 bp ge-
nomic sequences, which is roughly 73% of the total rat
genome. Every autosomal and the X chromosome, except
chromosomes 19 and Y (Table 1), contain at least one
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L. S. Wang et al. / Open Journal of Genetics 2 (2012) 136-154
Copyright © 2012 SciRes.
Table 1. QTL and candidate genes identified from rat model.
Chromosomes QTLs Total genes Length (bp) Hypertension
candidate genesEssential hypertension
associated genes
within 1.5 cM
X 2 454 48,888,222 5 4 0 1
1 8 3419 247,322,280 75 63 22 1
2 5 1852 256,217,383 44 12 4 1
3 4 1706 178,038,027 25 16 2 3
4 4 1006 129,644,537 22 16 3 3
5 3 1562 151,887,861 20 17 7 0
6 2 1087 121,776,584 18 14 8 0
7 4 1037 109,297,243 22 16 2 2
8 3 1111 12,479,341 15 13 1 0
9 2 568 71,306,988 13 10 2 1
10 3 1502 89,111,128 26 22 6 0
11 2 569 71,856,931 5 3 1 0
12 2 714 72,770,456 12 11 3 1
13 4 745 87,298,215 15 15 10 0
14 2 459 56,035,440 9 7 5 1
15 3 192 54,324,114 1 0 0 1
16 1 400 41,833,193 6 6 1 2
17 3 757 87,545,639 6 6 1 1
18 3 657 81,554,492 10 10 3 1
20 2 469 45,874,055 6 5 2 2
Total 62 20,266 2,015,062,129 355 266 83 21
hypertension QTL. The genomic size of these QTL
ranges from 5,873,857 bps to 167,769,667 bps. Within
the 2,015,062,129 bp genomic sequences, a total of
20,266 genes have been located. The number of genes
within a QTL region ranges from 79 to 1398. The aver-
age gene density throughout the whole rat genome (ex-
cluding ChrY) is about one gene per 99,918 bp. Within
the total 2,015,062,129 bp genomic sequences represent-
ing the hypertension QTL, there are roughly one gene per
99,430 bp, which is similar to the average gene per bp
ratio found in the rat genome. From the total 20,266
genes related to hypertension, 266 were selected as can-
didate genes for essential hypertension (Table 1). Basi-
cally, about one candidate gene was chosen per 76 se-
lected genes. These 266 candidate genes are located
throughout all autosomal and X chromosomes excluding
chromosome 15 (which contains no obvious recognizable
candidate genes), chromosome 19, and chromosome Y.
The number of candidate genes within a QTL region
can be as low as zero to as high as 32. The number of
QTL on each individual chromosome varies from one to
five. There is a positive correlationship between the
number of QTL on a chromosome to the number of can-
didate genes on a chromosome (R = 0.787).
3.2. Essential Hypertension Associated (HyperA)
Candidate Genes and Essential
Hypertension Associated Gene
Polymorphisms (HyperAP)
While the majority of candidate genes have been reported
or listed in rat genome databases, we identified many new
candidate genes during our search. Particularly, we have
identified 83 polymorphic genes with publicized linkage
to human hypertension (HyperAP). While HyperA genes
are found on every chromosome containing one or more
QTL regions, HyperAP genes are found only on auto-
somal chromosomes containing QTL regions.
In general, the number of HyperA genes is positively
correlated to the number of HyperAP genes (Figure 1).
Chromosome 1 has the largest number of HyperA genes
and, consequentially, the largest number of HyperAP
genes too. For instance, Chromosome X has a small
number of HyperA genes and, therefore, does not have
any HyperAP genes.
Surprisingly, in spite of demonstrated linkage between
the polymorphic HyperAP genes and hypertension, the
candidacy for the majority of these genes for hyperten-
sion QTL has not been investigated. Examples of the few
L. S. Wang et al. / Open Journal of Genetics 2 (2012) 136-154 139
HyperAP genes investigated, with positive or negative
results, are monocyte chemotactic protein-1 (CCL2),
nitric oxide synthase 2A (NOS2), and natriuretic peptide
precursor B (NPPB). A study identifying candidate genes
for the QTL influencing blood pressure within Chr10
reported that the expression levels of CCL2 mRNA
showed no difference between the kidneys of Dahl
salt-sensitive (DS) and Lewis (LEW) rats fed a normal
diet. However, CCL2 mRNA expression levels in DS
were 10-fold higher than those in LEW with a high-salt
diet [12]. Yet another study reported that NOS2 is not
supported as a candidate for the QTL capable of causing
a blood pressure difference between the S and MNS rats
[13]. Nevertheless, it was suggested that the nitric oxide
system appears to be secondarily involved in the regula-
tion of blood pressure in the S rat, as evidenced by
physiological data. In a study using a congenic approach
with a rat model, the relationship between natriuretic
peptide precursor A (NPPA) and NPPB genes and hy-
pertension was examined, but an association between the
NPPB gene and blood pressure was not found [14].
3.3. Candidate Genes for the QTL on Gene- Rich
and/or Gene-Poor Chromosomes
We found that gene density for QTL on different chro-
mosomes varies significantly. QTLs on two chromo-
somes are located in gene-rich regions. These two chro-
mosomes are chromosome 1 (one gene per 72,337 bp)
and chromosome 10 (one gene per 59,328 bp). However,
QTLs on chromosome 15 reside in extremely gene-poor
regions (one gene per 282,938 bp). The other QTLs in
gene-poor regions are on chromosome 11 (one gene per
30,000 bp) (Figure 2). 1
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, X
Ch romosome
Number of Gene s
HyperA HyperAP
Figure 1. Number of essential hypertension associated genes (HyperA) and genes of
polymorphic associated to essential hypertension in human population (HyperAP) in
QTL regions on chromosomes.
Chr1 Chr10 Chr11 Chr15
Bps per Candidate
Figure 2. Number of candidate genes in QTL of gene rich chromosomes and
gene poor chromosomes.
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L. S. Wang et al. / Open Journal of Genetics 2 (2012) 136-154
3.3.1. Chromosome 1
There are 6 QTLs that overlap and cover a large propor-
tion of this chromosome. These QTL are Bp95, Bp96,
Bp173, Bp196, Bp 255, and Bp259 (Supplementary
Table 1). These QTL extend from 90,448,902 to
247,322,280 with a length of 156,873,379 bps. All of
these QTL are located in gene-dense regions. Gene den-
sities in four of these loci are relatively high.
The first QTL, Bp95, is in the proximal region of this
chromosome, and is located in the interval from 0 bp to
22,869,052 bp. This region contains 248 genes and four
candidate genes (Table 1). Among them, transforming
growth factor beta 1 (TGF-beta 1) is a fibrogenic cyto-
kine implicated in hypertension in African-Americans.
Suthanthiran et al. [15] demonstrated that African-
Americans with end-stage renal disease (ESRD) have
higher circulating levels of TGF-beta 1 protein compared
to Caucasians with ESRD. They have also found that
hyperexpression of TGF-beta 1 is more frequent in Afri-
can-Americans with hypertension than in Caucasians.
Accordingly, they proposed that TGF-beta 1 hyperex-
pression may be an important mediator of hypertension
and hypertensive nephrosclerosis. More recently, and
importantly, polymorphisms in TGF-beta 1 have been
associated with cardiovascular and renal damage. This
data strongly suggests that TGF-beta 1 is the causal gene
for this QTL. The other noteworthy gene in this QTL is
osteoglycin (OGN). In a quantitative trait transcript
(QTT) analysis study of the cardiac transcriptome in the
rat, Petretto et al. [16] showed that OGN is a key regula-
tor of left ventricular mass (LVM) in rats, mice and hu-
mans, and suggested that OGN modifies the hypertrophic
response to extrinsic factors such as hypertension and
aortic stenosis.
QTL Bp96 overlaps and is connected to Bp95. Bp96
covers a region from 22,842,962 to 102,532,417 bps and
contains more than 1000 genes. Twenty genes were re-
garded as candidate genes. Among these 20 genes,
polymorphisms in at least five of them have been linked
to hypertension in human populations. TGF-beta 1 is also
among these candidates. The other important gene in this
group is Apolipoprotein E (APOE) [17]. Associations
between polymorphisms of APOE and hypertension have
been extensively studied in several countries and differ-
ent populations. However, the results are sometimes
controversial and vary from population to population.
Kcnj11 is another gene shown to be associated with hy-
pertension in humans [18]. Specifically, there is signifi-
cant evidence linking polymorphisms of Kcnj11 to blood
pressure variations in Korean [18] and Japanese [19]
populations. In addition, SNPs in the renal glomeru-
lus-specific cell adhesion receptor (Nphs1) have known
associations with hypertension in the Japanese popula-
tion [20]. Glucocorticoid-regulated kinase (SGK1) also
has a demonstrated relationship with hypertension in
populations specifically those in Germany [21] and Swi-
tzerland [22]. The Organic Cation Transporter 2, OCT2
(SLC22A2), implicated in both renal dopamine handling
as well as the inactivation of circulating catecholamines,
is also thought to be involved in blood pressure regula-
tion [23].
Two QTL, Bp173 and Bp196, largely overlap each
other and occupy the genome regions of 115,946,375 bp
to 189,900,838 bp and 119,780,561 bp to 204,280,279 bp,
respectively. From the more than 1200 known genes
within these two QTL, a total of 33 candidate genes were
found (Supplementary Table 1). Ten of these 33 genes
have been linked to hypertension in humans by polymer-
phic analysis. Those genes are the following: 1) Neuro-
trophic tyrosine kinase, receptor, type 3 (Ntrk3), a gene
studied in the Japanese population [20]; 2) Prolylcar-
boxypeptidase (Prcp) D allele, which is coupled to
chronic hypertension and associated with a significant
increased risk of preeclampsia in both African-American
and non-African-American women [24]; 3) Sodium chan-
nel gamma subunit gene (SCNN1G), multiple poly-
morphisms in human epithelial SCNN1G are associated
with essential hypertension in several populations [25-27];
4) Calpain-5 (Capn5), Capn5 variants are associated with
diastolic blood pressure and cholesterol levels in the
Spanish population [28]; 5) Solute Carrier family 9 mem-
ber 2 (SLC9A2), Uromodulin (UMOD), and Elastin
(ELN), five polymorphisms in these 3 genes were associ-
ated with hypertension status [29]; 6) Uncoupling protein
2 (Ucp2), a common polymorphism of this gene is asso-
ciated with hypertension in the Japanese population
[30,31]; 7) Inositol polyphosphate phosphatase-like 1
(INPPL1 and SHIP2), variants of these genes affect es-
sential hypertension [32]; 8) Purinergic receptor p2y, g
protein-coupled, 2 (P2ry2), P2ry2 is among 61 non-syn-
onymous polymorphisms of 41 hypertension candidate
genes with blood pressure variation in the Japanese popu-
lation [19]; 9) SCNN1B, this gene has shown a mod-
est-sized but highly significant effect on common genetic
variation in SCNN1B on plasma potassium [33]. Interac-
tions between the rs889299 SNP and functional SNPs in
other genes influencing aldosterone-responsive distal
tubular electrolyte transport may be important in the eti-
ology of essential hypertension; 10) Spontaneously hy-
pertensive rat-clone A-hypertension-associated gene (SAH),
SAH has been extensively investigated and its gene vari-
ants are associated with obesity-related hypertension in
Caucasians [34,35].
Among these 33 candidates, only two genes have been
studied for their role as the QTL for hypertension. ApoE
was investigated by Yuan et al. [36], and Okuda et al. [37]
found that the expression of SAH has been increased in
genetic hypertensive rats via microarray analysis.
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L. S. Wang et al. / Open Journal of Genetics 2 (2012) 136-154 141
The next pair of QTL are Bp255 and Bp259. These
two QTL overlap in the region from 195,927,908 bp to
247,322,280 bp. Within these two QTL, 20 candidate
genes were identified. However, polymorphisms of only
two of these genes (CPT1A and RGS20/GNA14) have
been linked to hypertension. Carnitine palmitoyltrans-
ferase 1A (CPT1A) is among four genes that have their
polymorphisms associated with left ventricular hyper-
trophy (LVH) in essential hypertension [38]. In “The
Millennium Genome Project for Hypertension”, Kohara
et al. [39] found the genes RGS20 and GNA14. Domi-
nant models for these minor alleles had significant asso-
ciation with hypertension in the Japanese population.
3.3.2. Chromosome 10
There are 3 overlapping QTLs (Bp57, Bp168, and Bp186)
that cover a large proportion of this chromosome (Sup-
plementary Table 1). All of these QTL are located in
gene-dense regions.
Bp57 covers the genome region from 21,607,720 to
84,443,858 bps. The genome region contains a total of
1026 genes and has an average of one gene per 61,243
bps. A total of 11 candidate genes were identified from
this QTL, which is basically one candidate gene per 93
selected genes. From these 11 candidate genes, the five
genes that were of special interest were NOS2, CCL2,
12(s)-HETE and ALOX12, and CIAS1.
Studying the ratio of circulating nitric oxide to endo-
thelin-1 in patients with both systemic sclerosis (SSc)
and pulmonary arterial hypertension (PAH), Kawaguchi
et al. linked polymorphisms of the nitric oxide synthase
2 (NOS2) gene to susceptibility of both PAH and SSc in
the Japanese population [40].
Chemokine, cc motif, ligand 2 (CCL2) has shown a
significant and independent association between the
-2518G/A polymorphism of the MCP-1 gene (presence
of G allele) and hypertension in the Tunisian population
The arachidonic acid-derived metabolite 12-(S) hy-
droxyeicosatetraenoic acid (12(S)-HETE), catalyzed by
12-lipoxygenase (12-LOX, ALOX12), exhibits a variety
of biological activities with implications in cardiovascular
disease. In a study of 200 patients with essential hyper-
tension (aged 56 1 years, mean s.e.m., including 97
males) and 166 matched controls (aged 54 1 years, 91
males), Quintana et al., [42] found that a non-synonymous
polymorphism in ALOX12 is associated with both essen-
tial hypertension and urinary levels of 12(S)-HETE.
An intronic variable number of tandem repeat poly-
morphisms in the cold-induced autoinflammatory syn-
drome 1 (CIAS1) gene modifies gene expression, and is
associated with essential hypertension in the Japanese
population [43]. Timasheva et al. [44] found that carriers
of the IL12B 1159 *A/*A genotype have a lower risk of
stroke during a study to reveal the association of inter-
leukin-6, interleukin-12, and interleukin-10 gene poly-
morphisms with essential hypertension, and its clinical
complications in a Tatar ethnic group from Bashkor-
tostan, Russia.
Bp168, overlaps Bp57 and covers a region from
27,184,742 bp to 102,587,587 bp and has a total of 1398
genes, which is roughly one gene per 53,936 bps. From
these genes, 20 were found to be candidate genes for hy-
pertension, and eight of these 20 genes, including NOS2,
CCL2, ALOX12, are also found in Bp57. On average,
every 3,770,142 bp within Bp168 has one candidate gene.
Also, from these 20 genes Angiotensin Converting En-
zyme (ACE) is a promising candidate gene for essential
hypertension (EH) as it plays a key role in blood pressure
regulation. ACE has been extensively studied in a variety
of human populations with both positive [45] and nega-
tive [46] results.
Another promising candidate from Bp168 is Solute
Carrier family 4, anion exchanger, member 1 (SLC4A1).
SLC4A1 is a polymorphism of one of our candidate genes
and has been associated with both blood pressure varia-
tion and hypertension [47].
Bp186 is another overlapped QTL on this chromosome.
It covers a region from 95,066,219 bp to 110,718,848 bp
and contains 263 genes, which is roughly one gene per
59,515 bps. A total of five candidate genes were selected
from these 263 genes, including ACE and Gh1, which are
both overlapped by genes in Bp168.
Both NOS2 and ACE have been disqualified as QTLs
in the Dahl salt-sensitive (DSS) and Lewis (LEW) rat
comparison [48]. However, we did not find any report on
the investigation of candidacy for either ALOX12,
CIAS1, or SLC4A1 as the QTL for hypertension.
3.3.3. Chromosome 11
Chromosome 11 includes two QTL, Bp187 and Bp-
QTLcluster 10. Bp187 is located in a 41,381,787 bp re-
gion. It contains as many as 296 genes with an average
of 139,803 bps per gene. However, it contains only two
candidate genes, which means there is only one candi-
date gene per 20,690,893 bps. No polymorphisms of
these two genes, adp-ribosylation factor-like 6 and (ARl6)
superoxide dismutase 1 (SOD1), have been linked to
human hypertension.
BpQTLcluster 10 covers a 45,000,000 bp region and
has a total of 406 genes, with one gene per every 110,837
bps. It contains four candidate genes. This means there is a
candidate gene from every 101 genes or every 11,250,000
bps has one candidate gene. Among these four genes, SNP
variations in calcium-sensing receptor (CASR) has been
recently linked to human hypertension [49,50]. However,
it has never been investigated as a candidate gene in this
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3.3.4. Chromosome 15
The three QTL found in Chromosome 15 are Bp191,
Bp190, and Bp126. Bp191 is located on the proximal
region covering a region of 14,323,976 bps. It contains
as many as 118 genes; however, none of them were se-
lected as candidate genes by our bioinformatic searching.
Since Bp190 is located within Bp126 they are considered
the same QTL. Bp126 is found within a region of
2,000,000 bps on each side of the peak marker, Ednrb,
and contains as many as 158 genes. However, similar to
Bp191, there is no obvious candidate gene for hyperten-
sion from these 158 genes.
3.4. Candidate Genes in Different QTL on the
Same Chromosome Are Correlated t o the
Density of Genes in Each QTL Region
In general, the number of candidate genes per QTL is
directly correlated to the gene density in of the QTL re-
gion. The phenomenon is not restricted to the chromo-
somes in this study but holds true throughout every ge-
nome. The correlation between the total number of genes
and the number of nucleotides in the QTL region is 0.817
(Figure 3(a)). The correlation between the total number
of genes and the number of candidate genes in the QTL
region is 0.827 (Figure 3(b)). QTL from several typical
chromosomes are described as below.
3.4.1. Chromosome 2
Chromosome 2 includes the five QTL Bp115, Bp205,
Bp14, Bp175, and Bp203. Bp115 is located on the
proximal region covering a large 33,349,984 bp region. It
contains as many as 218 genes, with an average of
152,981 bps per gene. However, since it contains only
five candidate genes there are an average of 669,966 bps
per candidate gene. Of these five candidate genes, CAST,
THBS4, and F2R are the most noteworthy.
Calpastatin (CAST) is among 16 genes that have
shown linkage to human hypertension in the Japanese
population. Wessel et al. [51] suggest that the A387P
variant of the THBS4 gene may be an important deter-
minant for the development of myocardial infarction at
any age. Gigante B et al. [52] reported that F2R genetic
020000000400000006000000080000000100000000 120000000140000000160000000 180000000
Bps of nucl eotides
number of ge ne s
05001000 1500
Gene number
C andidate gene number
Figure 3. (a) Correlationship between number of base pairs of genome and number of genes in QTL regions;
(b) Correlationship between number of genes and number of candidate genes for essential hypertension in
QTL regions.
L. S. Wang et al. / Open Journal of Genetics 2 (2012) 136-154 143
variants may influence the natural history of coronary
heart disease (CHD) in patients at high-risk for cardio-
vascular events.
Bp205 cover a 16,765,253 bp region and has on aver-
age one gene per 153,809 bps. However, it contains no
candidate gene.
Bp14 is located next to Bp205. It covers 167,769,667
bps and has a total of 1284 genes, with one gene per
130,661 bps. It contains 32 candidate genes or one can-
didate gene per 53 selected genes. On average, every
5,242,802 bps has one candidate gene. Several important
candidate genes include: NPR, GHR, CRH, GSTM, and
ARNT. Allelic variants of natriuretic peptide receptor
(NPR) genes are associated with a family history of hy-
pertension and cardiovascular phenotypes [53], including
Npr1 and Npr3. Another of the candidate genes, growth
hormone receptor (GHR), has been linked to hyperten-
sion in two independent studies [19,54]. In “The Québec
Family Study”, a study of genome-wide linkage analysis
for systolic and diastolic blood pressure, Rice et al. [55]
found an association between polymorphisms of cortico-
trophin releasing hormone gene (CRH) and hypertension.
A more recent study suggests that knowledge of glu-
tathione s-transferase M1 (GSTM1) variant statuses is a
potentially useful method for predicting a possible hy-
pertensive status after 80 years of age in the Italian popu-
lation [56]. Aryl hydrocarbon receptor nuclear transloca-
tor-like (ARNT) is associated with susceptibility to hy-
pertension in a genetic association study designed to test
the relevance of these findings in 1304 individuals from
424 families [57]. Glutathione s-transferase M3 (GSTM3)
-63A/C polymorphisms were associated with essential
hypertension in Chinese Han populations. It is thought
that the C allele might be a risk factor for EH in the Chi-
nese Han ethnic group [58]. In a study by Delles et al.,
[59], it was found that a single nucleotide polymorphism
in the 3' region of GSTM5 (rs11807) was associated with
hypertension (P = 0.01), and with the T-allele being
over-transmitted to hypertensive offspring.
The one exception that gene density is not associated
with the number of candidate genes on chromosome 2 is
the Bp203 QTL, which is overlapped with Bp14, that
covers a 30,053,784 bp region. It contains as many as
267 genes, which is an average of 112,560 bps per gene.
It has nine candidate genes, which is one candidate gene
per 3,339,309 bps. An interesting fact is that out of these
nine candidate genes six of them are also found in Bp14.
Bp175 covers a 33,278,084 bp region. It contains as
many as 220 genes (151,264 bp per gene). It contains 4
candidate genes, which is one candidate gene per
8,319,521 bps. However, none of these four genes have
been linked to hypertension in humans.
3.4.2. Chromosome 4
Chromosome 4 includes the four QTL Bp179, Bp86,
Bp124, and Bp209, located proximal to distal, respect-
tively. Bp179 locates on the proximal region covers a
40,000,132 bp region. It contains as many as 239 genes,
with an average of 167,364 bps per gene. However, it
contains only 10 candidate genes, which is an average of
4,000,013 bps per candidate gene. Key candidate genes
from this QTL include HGF and LEP. Hepatocyte
growth factor (HGF) is a growth factor that contributes
to protection and/or repair of vascular endothelial cells.
Two studies showed that C/A polymorphisms in intron
13 of the HGF gene are associated with susceptibility to
essential hypertension in Japanese female subjects [60,
61]. Leptin (LEP) has been widely investigated for its
role in hypertension. A recent study indicates that com-
mon LEP polymorphisms are associated with blood
pressure in the Brazilian Tunisian population [62].
Bp86 has a little overlap with Bp179 and covers a
35,960,924 bp region. It contains total of 1284 genes,
with one gene per 92,207 bps. It has 11 candidate genes,
or one candidate gene per 35 selected genes. On average,
every 3,269,174 bp has at least one candidate gene. The
most probable candidate gene from Bp86 is Lep, which
is overlapped from Bp179, since none of other genes are
linked to human hypertension.
Bp124 is largely overlapped with Bp86 and covers a
40,000,190 bp region. It contains as many as 456 genes,
and has an average of 87,719 bps per gene. It has 10
candidate genes, which is one candidate gene per
4,000,019 bps. Eight of these 10 candidate genes are
overlapped with Bp86. The other two genes have no
known association with hypertension.
Bp209 covers a 40,000,115 bp region. It contains as
many as 251 genes, with an average of 159,363 bps per
gene. It has only three candidate genes, or one candidate
gene per 13,333,371 bps. Among those three genes, only
the CC genotype in oxidized LDL receptor gene (OLR-1)
is an independent risk factor for hypertension in the
Chinese population [63].
3.4.3. Chromosome 8
On chromosome 8 the three QTL, are Bp184, Bp262,
and Bp263. Bp262 is located in a 75,560,673 bp region
close to proximal end. It has one gene per 88,999 bps and
one candidate gene from every 85 genes. One average,
there is one candidate gene per 7,556,067 bps. Three of
the ten selected candidate genes showed association with
human hypertension. Recently, it was reported that five
polymorphisms in the KCNJ1 gene coding for the potas-
sium channel, ROMK, showed associations with mean
24-hour systolic or diastolic blood pressure [25,46]. An-
other candidate gene, Cytochrome P450, family 1, sub-
family A, polypeptide 1 (CYP1A1) has been studied in
several populations, and, particularly, the T6325C poly-
morphism is thought to modulate essential hyperten-
Copyright © 2012 SciRes. OPEN ACCESS
L. S. Wang et al. / Open Journal of Genetics 2 (2012) 136-154
sion-associated stroke risk [64].
Bp 184 is partially overlapped with Bp262 and has one
gene per 101,726 bps with one candidate gene per 80
genes. One average, there is one candidate gene per
8,188,965 bps.
Bp 263 has one gene per 82,646 bps. Six genes were
selected as candidate genes, with one candidate gene per
71 genes. One average, one candidate gene is found per
5,900,984 bps. Only one candidate gene is associated
with hypertension in human populations. In a study of
the 206 M polymorphic variant of the SLC26A6 gene
encoding a Cl(-)-oxalate transporter in patients with pri-
mary hyperparathyroidism, Corbetta et al. [65] found
that the SLC26A6 206M alleles were significantly re-
lated to the presence of hypertension.
Bp263 is essentially the same as Bp184 and SLC26A6
is also included as a candidate gene for this locus.
3.4.4. Chromosome 9
Chromosome 9 includes the two QTL Bp34 and Bp185.
Bp34 is located on a region covering 37,780,765 bps. It
contains as many as 311 genes, with an average of
121,482 bps per gene. However, there is an average of
5,397,252 bps per candidate gene. Of the candidate genes,
Cytotoxic T-lymphocyte-associated protein 4 (CTLA4)
is among the polymorphisms from 16 genes that are sig-
nificantly associated with blood pressure variations (29)
in the Japanese population.
Bp185 cover a 40,000,158 bp region and has a total of
343 genes, with 116,618 bps per gene. It also has one
candidate gene per 43 genes. On average, every
6,666,669 bps contains one candidate gene. Wen et al.
[66] reported evidence for the association between a
regulatory polymorphism in Secretogranin II (SCG2) and
hypertension in African-American subjects.
3.4.5. Chromosome 14
Chromosome 14 includes the two QTL Bp189 and Bp59.
Bp189 is located on a region covering 40,799,268 bps. It
contains as many as 245 genes, with an average of
166,527 bps per gene. However, it only contains three
candidate genes, which is an average of 13,599,756 bps
per candidate gene. Dries et al. [67] showed that the
Corin gene minor allele defined by 2 missense mutations
is common in African-Americans and is associated with
high blood pressure and hypertension. Their study was
also confirmed in later study comprising the Chinese
population [68]. Another of the candidate genes is ex-
tracellular superoxide dismutase gene (SOD3). Based on
the results from Naganuma et al. [69] who utilized a
haplotype-based case-control study, a T-A haplotype of
the SOD gene may be a genetic marker for essential hy-
Bp59 covers a 15,236,276 bp region and has a total of
214 genes, with 71,197 bps per gene. It has one candi-
date gene per 36 selected gene. On average, every
2,539,379 bps contains one candidate gene. Adducin
(ADD1) is a candidate gene for salt-sensitive hyperten-
sion. The association between ADD1 and essential hy-
pertension has been well documented [70,71]. G pro-
tein-coupled receptor kinases (GRKs) polymorphisms
that lead to aberrant action of GRKs cause a number of
conditions, which include hypertension and salt sensi-
tive- ity. Polymorphisms in one particular member of this
family, GRK4, have been shown to cause hyperphos-
phorylation, desensitization, and internalization [72].
Yamada et al. suggested that the genotypes for ITGA2,
GCK, and PTGIS may prove reliable for the assessment
of the genetic component of hypertension. There claim
was based on their study of a population comprised of
4853 unrelated Japanese individuals, including 2818
subjects with hypertension [73].
3.4.6. Chromosome 18
Chromosome 18 includes the three QTL Bp2, Bp233,
and Bp48.
Bp2 is located on the proximal region of the chromo-
some and consists of 33,239,845 bps. It contains as many
as 282 genes, and has on average 117,871 bps per gene.
However, there is an average of 16,619,922 bps per can-
didate gene. There are only two candidate genes in this
QTL, and only one (ROCK) with any linkage to EH in
the human population. A Rho kinase (ROCK) polymer-
phism influences blood pressure and systemic vascular
resistance in human twins [74].
Bp233 covers a 40,000,179 bp region and has a total
of 319 genes, with 125,392 bps per gene. It has one can-
didate gene per 40 selected genes. On average, every
5,000,022 bps contains one candidate gene.
Bp48 covers a 19,324,774 bp region and has a total of
128 genes, with 150,974 bps per gene. There is one can-
didate gene per 43 selected genes. On average, every
6,441,591 bps contains one candidate gene.
3.5. Candidate Genes in the Peak Region of the
QTL Compared to Candidate Genes in the
QTL Region
As indicated in our method, we selected QTL with LOD
scores that are 2.8. However, QTL usually produce a
curved or bell-shaped region. Thus, the LOD score at the
top of the bell, the peak point, has the highest value.
Theoretically, the higher a LOD score is the higher the
probability there is a QTL. We then ask the question, if
the LOD score in the peak region is high is the probabil-
ity of a QTL gene in or near the peak point also high?
Therefore, based on our assumptions we examined the
number of candidate genes within 1.5 mbps on each side
Copyright © 2012 SciRes. OPEN ACCESS
L. S. Wang et al. / Open Journal of Genetics 2 (2012) 136-154 145
of the peak region.
As expected, there was a much high probability of
candidate genes in those 1.5 mbp regions. In the whole
rat genome, we obtained one candidate gene from every
12,594,138 bps. Of all the QTL, we have information on
the peak positions of 45 QTL. From these 45 peak re-
gions, we obtained 21 candidate genes (Table 1). On
average, there are one candidate gene per 7.105 mbps in
the peak region of a QTL. However, these 21 candidate
genes are from only 13 QTL peak regions. The peak re-
gions for the rest of 26 QTL do not contain obvious can-
didate genes (Supplementary Table 1).
Among these 13 peak regions, one region has three
candidate genes while in each of other four regions there
are 2 candidate genes (Table 1). There is no correlation
between the number of candidate genes in a peak region
and the number of total genes in a QTL region (R =
–0.084). However, there is a weak negative correlation
between the bps per candidate gene and the number of
candidate genes in a peak region (R = –0.325). Overall,
the number of candidate genes in a peak region appears
independent from other factors in this study.
Among all the QTL we examined, only Bp124 on
chromosome 4 contained three candidate genes in its
peak region. This QTL is located on a gene rich region
with a LOD score of 3. It also contains a total of 456
genes with an average of 87,719 bps per gene. The three
candidate genes from this QTL are from a pool of 10
candidates. The three candidates are Phosphodiesterase
1c (PDE1c), Aquaporin 1 (AQP1) and Corticotropin-
Releasing Hormone Receptor 2 (CRHR2). Recent re-
ports indicate that members in the calcium/calmodulin-
dependent phosphodiesterase 1 (PDE1) family may play
a major role in vascular smooth muscle cell proliferation.
Particularly, PDE1c has been known to contribute to
decreased cAMP production and increased proliferation
of pulmonary areterial smooth muscle cells (PASMC) in
patients with pulmonary hypertension (PHT) [75]. AQP1
is abundant in renal proximal tubular epithelium, the thin
descending limb of the loop of Henle, and the descend-
ing vasa recta of the kidney. In 2006, Lee et al. [76] re-
ported that the expression of AQP1-3 channels is in-
creased in the kidney in association with enhanced activ-
ity of the AVP/cAMP pathway in spontaneously hyper-
tensive rats.
CRHR2 is known for its important role in coordinating
the endocrine, autonomic, and behavioral responses to
stress and immune challenges [77]. In a study of an
Antalarmin blockade of corticotropin releasing hormone-
induced hypertension in rats, Briscoe and colleagues [78]
reported that the hypertension produced by central CRH
administration is mediated through central CRHR1 re-
ceptors, whereas, the hypertension produced by par-
enteral CRH administration is mediated through periph-
eral CRHR2 receptors. It would be very interesting to
know whether all, or any of these three genes, are the
causal genes for hypertension’s QTL.
Our investigation raises an important issue in the selec-
tion of candidate genes for specific QTLs, the considera-
tion of the position of a gene in the QTL region. Our data
suggests that a gene in the peak region of a QTL is of
more relative importance than a gene further from the
peak position. Theoretically, this agrees with the defi-
nition of the LOD score. From experience, many QTL
genes have been identified from positions that are close
to the peak region of the QTL.
The other issue our investigation discovered is whether
candidate genes can be easily identified from a QTL lo-
cated in either gene-rich or gene-poor regions. In our
study, we found that QTL located in both gene-rich re-
gions and gene-rich chromosomes contained the most
candidate genes. However, the number of candidate genes
within a peak region is not associated with the number of
total genes in a QTL region. Also, more candidate genes
does not necessary indicate that the real, causal gene is
among these genes. Similarly, less candidate genes in the
QTL of gene-poor regions or gene-poor chromosomes
does not necessary mean that the analysis is easy due to
the smaller number of candidate genes.
The fact from our study is that there are an extremely
large number of genome regions and genes covered by
the hypertension QTLs. Even though our search for can-
didate genes was limited to just essential hypertension,
we still obtained 266 candidate genes. Considering the
large number of QTL and genetic and environmental
factors, identification of the QTL for hypertension with-
out defined environmental conditions and a unified ge-
nomic background is extremely difficult.
Support for this work is from Center of Genomics and Bioinformatics
(WG) and Center in Connective Tissue Research (WG), at University
of Tennessee Health Science Center; Veterans Administration (WG);
National Institute on Alcohol Abuse and Alcoholism (NIAAA), Na-
tional Institutes of Health (1R01AA016342 to WG) and the Center of
Integrating Genomics and Bioinformatics for International Studying of
Strokes, the University of Tennessee Health Science Center, and the
Veterans Administration Medical Center, Memphis, TN. Funding for
YJW is from the “Major Project of Chinese National Programs for
Fundamental Research and Development (No.2009CB521905).”
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Supplementary Ta ble 1. Candidate Genes of hypertension in QTL on chromosomes.
QTL Markers Search
region (bp) Total genes Candidate genes
(Essential and secondary)
Candidate genes
within 1.5 M/
polymorphic genes
Bp95 D1UIA8,
D1Rat18 0 - 22,869,052
248 total
194 known
38 novel
16 predicted
Ogn, Gprk6, Tgfb1, Lama2(2nd) No/2
Bp96 D1Mgh2,
22,842,962 -
1211 total
946 known
138 novel
Tgfb1, Abcc8, Apoe, Kcnj11, Abcc6(2nd),
Nphs1(2nd), Akt2(2nd), Ptgir(2nd), Vip, Sgk1, Trpm4,
Sod2, Tph1, Axl, Slc22a2, Zfp260, Myadm Tctex 1,
Boat1, Slc9a3(2nd), Sdha(2nd)
Bp173 D1Rat33
115,946,375 -
954 total
120 novel
62 predicted
29. Iqgap1(2nd), Prc1, Fah, Plin, Ntrk3, Nr2f2,
Prcp, Nox4, Scnn1g, Nmb Capn5, Anpep, Serpinh1,
Pak1, Igf1r. Umod, Ucp2, Ucp3, Pde3b(2nd,), spon1,
Inppl1, Phox2a, P2ry2, Scnn1b, Fgfr2, Sah, Xylt1,
Hbb, Acsm3
Bp196 D1Mgh10,
119,780,561 -
946 known,
128 novel,
172 predicted
Nox, Nmb, Capn5, Anpep, Serpinh1, Pak1, Fah,
Iqgap1(2nd), Prc1, Plin, Nr2f2, Igf1r
Scnn1b, Scnn1g, Fgfr2, Umod, Xylt1, Hbb, Prcp,
Dhcr7, Th, Acsm3, Adm ,
Bp255 D1Rat208,
195,927,908 -
915 total
749 known
66 novel
80 predicted
Dhcr7, Th, Cyp17a1, Bbs1(2nd,) Jak2, Gnaq,
Adrbk1, Ccnd1, Cyp2c23, Pax2(2nd), Cyp2e1,
Kcnq1, Cpt1a, Gldc(2nd)
Peak marker is NA.
Bp259 D1Rat71,
216,663,010 -
262 total
195 known
40 novel
27 predicted
Gldc(2nd), Pten, Rbp4(2nd), Mbl2, Kcnv2, Gna14,
Gnaq, Jak2
0.29 M./1
Bp115 D2Uia17
(peak) 0 - 33,349,984
218 total
138 known
49 novel
31 predicted
Cast, Arts1, Thbs4, F2r, Pik3r1(2nd), No./3
Bp205 D2Rat73,
26,101,089 -
109 total
80 known
19 novel
10 predicted
No. NA
Bp14 D2Mgh12,
42,866,342 -
1284 total
939 known
216 novel
129 predicted
Lmna, Fga, ATP1a, Npr1, Gucy1a3, Gucy1b3,
Fgf2, Bbs7, Npr3, Lifr(2nd), Ghr, Ca2, Cp, Crh,
Itga2, Gstm1, Arnt, Gstm2, Gstm3, Hsd3b1, Dear,
Bche, PLD, Gdnf, Gstm4, Gstm5,, Kcna2,
Gucy1a3, Kcnab1, Terc, Ccl28, Trpc3(2nd),
Peak marker is not
Bp203 D2Mit14,
197,256,224 -
267 Total
210 known
34 novel
23 predicted
Edg1, Kcna10, Vcam1, F3, Gstm1, Gstm2, Gstm3,
Gstm4, Gstm5,, Kcna2, Edg1 (1.22 M)/0 + 1
Bp175 222,939,299 -
220 total
155 known
35 novel
30 predicted
Cyr61, Edg7,
Mttp, Ddah1 No.
Bp15 D2Mgh12,
D2Mgh14 0 - 26,373,454
402 total
333 known
42 novel
27 predicted
Dbh, Pax8, Kynu, Ptgs1 or COX1, Vav2, Il1r(2nd), No/1
Bp264 D3Rat54,
10,267,753 -
1274 total
1039 known
146 novel
88 predicted
Ptgs1 or COX1, Kynu, Avp, Nphp1(2nd), Cat, Bbs5,
Pcna(2nd), Scn7a, Dpp4, Itgav Bbs5 (1.27 M)/2+1
Copyright © 2012 SciRes. OPEN ACCESS
L. S. Wang et al. / Open Journal of Genetics 2 (2012) 136-154
111,188,559 -
495 total
391 known
67 novel
37 predicted
Jag1(2nd), MKKS(2nd), Avp, Nphp1, Src, Il1b, Peak marker is not
138,037,867 -
510 total
404 known
71 novel
35 predicted
Src(2nd) Ptpn1, Hnf4a, Ada(2nd), Ahcy(2nd),
Hrh3(2nd), Mmp9(2nd), Myh7b_predicted(2nd) Ptpn1 ptgds (1.03M)
Bp179 D4Rat12,
6,178,308 -
239 total
159 known
61 novel
19 predicted
Nos3, Hgf, Cav(2nd), Pon-1,
Cftr(2nd), Pon-2, ABCB1,, Colna2, Sema3a,
Bp86 D4Mit2,
55,369,932 -
390 total
325 known
43 novel
22 predicted
10+1. Lep, Npy,
Pde1c(2nd), Trpv5, Aqp1, Crhr2, Hoxa9,
Hoxa5(2nd), Casp2, Mtpn, Tbxas1(2nd),
Peak marker is not
65,014,032 -
456 total
294 known
124 novel
38 predicted
Npy, Pde1c, Trpv5, Aqp1, Crhr2, Hoxa9, Hoxa5,
Casp2, Cntnap2, Fabp1,
Pde1c(.077 M),
Aqp1(.92 M)
crhr2(1.17 M)
163,954,705 -
203,954,820 251 total Lrp6, Pded3a(2nd), Olr1, No/1
Bp119 D5Mgh2
(peak) 0 - 37,517,235
214 total
192 known
40 novel
22 predicted
Slc26a7, No
Bp254 D5Rat9,
61,080,143 -
506 total
371 known
90 novel
Abca1, Tek(2nd), Cyp2j4 No/1
Bp147 D5Rat41(pe
ak marker)
135,450,641 -
744 total
606 known
89 novel
49 predicted
Ece1, Nppa, Tnfrsf1b, Cda(2nd), Slc9a1, Nppb,
Uts2, Alpl, Cyp4a1, Slc2a5, Edn2(2nd), Tnfrsf4,
Fabp3, Guca2b, Ptpru, Clcnkb
14,873,099 -
372 total
255 known
69 novel
48 predicted
9. Id2(2nd), Apob, Ahr, Rock2, Slc26a4, Lpin1,
Ucn(2nd), Hpcal1, Emilin1
Peak marker is not
Bp211 D6Mit3,
75,049,528 -
715 total
443 known
102 novel
170 predicted
Chga, Esr2, Yy1. Hif1a(2nd), Bdkrb1, Arg2, Dio2,
Serpina1(2nd), Bdk rb2,
Peak marker is not
Bp182 D7Rat27,
32,004,505 -
351 total
2501 known
63 novel
38 predicted
Nts, Cdk4,(2nd), Admr (2nd), Lrp1, No/1
Bp181 D7Rat73,
57,326,197 -
338 total
240 known
56 known
42 predicted
Cdk4(2nd), Admr (2nd), Lrp1, Myc, Ndrg1. Tr hr,
Angpt1, Gpr(2nd), No/1+1
Bp214 D7Rat135
88,021,524 -
490 total
399 known
47 novel
44 predicted
cyt11 b1, cyt11 b2, Ppara,, Pdgfb(2nd)
Commd5, Mb, Adm2(2nd), Naga, Ptk2
Bp183 D7Rat13,
124,315,013 -
248 total
206 known
27 novel
15 predicted
Adm2(2nd), Acvrl1(2nd), Sp1, Vdr, Aqp2
(1.43 M),
(0.14 M)/0
Copyright © 2012 SciRes. OPEN ACCESS
L. S. Wang et al. / Open Journal of Genetics 2 (2012) 136-154 153
28,124,112 -
849 total
669 known
102 novel
78 predicted
Kcnj1, Hmbs(2nd), Atm, Cyp1a1, Cyp1a2, Drd2,
Gclc, Rab27a, Rdx, Htr1b,
Peak marker is not
Bp184 D8Rat114,
74,281,611 -
123,415,406 483 total Gclc, Rab27a, Trf, Slc26a6, Pthr1, Gnai2, No./1
NA or
or use
98,451,122 -
357 total Trf, Slc26a6, Pthr1, Gnai2, Cx3cr1(2nd) Peak marker is not
Bp34 D9Uia6,
39,390,033 -
311 total
243 known
43 novel
25 predicted
Bmpr2(2nd), Fn1, Cps1, Ctla4, Igfbp2, Casp8(2nd),
Hspd1, No./1
Bp185 D9Rat16,
70,696,863 -
343 total
277 known
32 novel
34 predicted
Fn1, Igfbp2, Htr2b(2nd), Mlk, Ramp1, Col4a4,
Scg2, Nppc
(0.29 M)/1
Bp57 D10Mit5,
21,607,720 -
1026 total
859 known
97 novel
70 predicted
Shbg, Nos2, CD68, Ccl2, Trpv2, Adra1b, Tbx2,
Alox12, Atp1b2, Cias1, Il12b, No./4
Bp168 D10Mit10,
27,184,742 -
1398 total
1182 known
122 novel
94 predicted
Wnk4, Ramp2(2nd), Xylt2(2nd), Nos2, Ace, CD68,
Gip, Ccl2, Trpv2, Adra1b, Apoh, Gh1, Crhr1,
Slc4a1, Tbx2, Alox12, Cias1, Il12b, Atp2a3, Ser
Peak marker is not
Bp186 *D10Rat17,
95,066,219 -
263 total
217 known
28 novel
18 predicted
Ace, Gh1, Tmp2(2nd), Sstr2(2nd), Uts2r2 Peak marker is not
Bp187 D11Rat15
9,053,659 -
296 total
214 known
51 novel
31 predicted
Sod1, No
35,910,590 -
406 total
310 known
66 novel
30 predicted
Arl6(2nd), Casr, Mylk,
Peak marker is not
Bp294 D12Mit6
(peak) 0 - 32,770,333
480 total
361 known
62 novel
57 predicted
Retn, Kl, Prkar1b, Eln, Pdgfa(2nd), Gna12, Rac1,
Insr, Slc7a1, Pdgfa(2nd)
(0.89 M)/2
Bp218 D12Mgh5
9,130,181 -
553 total
469 known
52 novel’
32 predicted
Pla2g1b, Eln, Gna12, Rac1, Aldh2, Nos1, No/1+1
Bp222 D13Mgh4 18,489,802 -
261 total
189 known
42 novel
30 predicted
Rgs2, Adipor1, Il10, Ren1, Adora1, No/2
Bp241 D13Arb5-
38,985,718 -
681 total
525 known
99 novel
57 predicted
Sele, Rgs5, Fmo3, Rgs2, Atp1b1, F5, Adipor1,
Atp1a2, Tnfsf4, F11r, Ptgs2, Adora1, Serpinc1.
Peak marker is not
available/ 7+1
Bp189 D14Rat10,
33,157,433 -
245 total
166 known
53 novel
Corin, Sod3, Med28, No./2
Copyright © 2012 SciRes. OPEN ACCESS
L. S. Wang et al. / Open Journal of Genetics 2 (2012) 136-154
Copyright © 2012 SciRes.
Bp59 D14Rat90,
73,956,597 -
214 total
173 known
19 novel
22 predicted
Drd5, Fgfr3(2nd), Add1, Grk4(Gprk2l, Igfbp3(2nd),
(0 M),/3
Bp191 D15Rat114,
D15Rat75 0 - 14,323,976
118 total
81 known
30 novel
7 predicted
No No.
9,376,539 -
34 total
19 known
10 novel
5 predicted
Ednrb(2nd) Peak marker is not
67,893,681 -
158 total
95 known
36 novel
27 predicted
Ednrb(2nd) Ednrb (0 M)
Bp190 D15Mgh9,
89,569,707 -
57 total
37 known
13 novel
7 predicted
Ednrb(2nd) No.
4,304,397 -
400 total
287 known
70 novel
43 predicted
Glud1, Lpl, Prkcd , Nisch, Mapk8, NPY1R
(0 M)
(1.04 M)/1
Bp247 D17Rat102
7,118,352 -
356 total
282 known
49 novel
25 predicted
Agtr1a, Drd1a, Edn1, Ogn, Prl Edn1
(1.19 M)/1
Bp242 D17Rat98
44,047,700 -
336 total
239 known
69 novel
28 predicted
Mtr, Prl No
Bp192 D17Rat16,
54,663,819 -
280 total
194 known
61 novel
25 predicted
Mtr No
Bp2 D18Mit7 0 - 33,239,845
282 total
228 known
23 novel
31 predicted
Nr3c1, Rock No/1
Bp233 D18Rat57 34,605,733 -
319 total
245 known
42 novel
32 predicted
Mex3c, Adrb2, Acaa2, Hsd17b4, Slc12a, Lox,
Pdgfrb Nr3c1, Lipg
(0.93 M)/2+1
63,595,606 -
128 total
89 known
21 novel
12 predicted
Mex3c, Acaa2,
Peak marker is not
Bp195 D20UW1,
3,660,639 -
179 total
162 known
12 novel
5 predicted
Tnf, Tap 1, Glp1r, Cdkn1a (2nd), Tnf (0.000362 M),
Tap1 (1.13 M)/1
Bp60 D20Rat40
13,791,487 -
290 total
211 known,
47 novel
32 predicted
Ros1, Adora2a(371) No/1
Bp65 DXRat4(pe
ak marker)
3,494,725 -
43,494,887 373 total Rhoa, Trpc5, Timp1 No
Bp56 DXMgh9,
79,626,109 -
88,514,169 79 total Ar(, Cybb(2nd) No/1