Vol 13, Issue 1, 118-121, January 2003
METHODS
Multiple Cross and Inbred Strain Haplotype Mapping of Complex-Trait Candidate Genes
Yeong-Gwon Park,
Robert Clifford,
Kenneth H. Buetow and
Kent W. Hunter1
Laboratory of Population Genetics, Center for Cancer Research,
National Cancer Institute, National Institutes of Health,
Bethesda, Maryland 20892, USA
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ABSTRACT
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Identifying complex-trait candidate genes after initial
low-resolution mapping has proven to be a difficult and labor-intensive
undertaking, usually requiring years to develop and analyze congenic
strains. As a result, to date, few complex-trait genes have been
discovered. Recently it was suggested that SNP haplotype analysis in
inbred strains might be useful for mapping of complex traits. In this
study, we have combined medium-resolution haplotype mapping with
multiple experimental cross-mapping experiments to reduce the number of
potential candidate genes in a complex-trait candidate interval.
Coincident mapping of a modifier gene in multiple experimental crosses
using different inbred strains is consistent with the common
inheritance of a modifier allele. A haplotype map was developed in four
inbred strains of mice used in our complex-trait mapping crosses across
the proximal 10 cM of proximal Chromosome 19 to identify haplotype
blocks that segregate appropriately. Only  23 out of >400 genes met
this criteria. This strategy coupled with tissue and expression arrays,
as well as our recently described common pathway analysis to reduce the
number of high-priority candidates, may provide a rapid, efficient
method to identify and prioritize complex-trait candidate genes without
requiring construction of congenic mouse strains.
Identification of the genetic basis for complex
traits or phenotypic modification has been an extremely arduous task to
date. Although >1000 modifier loci have been mapped in the mouse
genome, only a handful have been linked to specific gene polymorphisms
(Korstanje and Paigen 2002 ). This is owing primarily to the laborious
and expensive process of making interval-specific congenic mice to
isolate candidate regions in a fixed genetic background, followed by
generation of subcongenic animals for high-resolution mapping.
As a result, investigators are increasingly attempting to develop novel
methods for high-resolution mapping of modifier genes that circumvent
this process. These strategies include mapping in outbred (Nagase et
al. 2001) or HS strains of mice (Mott and Flint 2002). Other
investigators have suggested making F1 crosses
between the members of recombinant inbred (RI) strains, which allows
repeated interrogation of a fixed genotype to reduce nongenetic
variance while increasing the mapping resolution of the panel (Williams
et al. 2001). Performing multiple mapping experiments using different
inbred strain partners, followed by microsatellite-based haplotype
mapping of the progenitor strains, has also been suggested as a method
of refining the initial modifier gene mapping (Hitzemann et al. 2000).
Recently it has been suggested that in silico SNP haplotype analysis
might be a useful strategy for mapping complex traits (Grupe et al.
2001). Although a controversial idea (Chesler et al. 2001; Darvasi
2001), it is possible that combining the developing mouse inbred strain
SNP databases (Lindblad-Toh et al. 2000; Grupe et al. 2001) with
multiple experimental crosses may provide an important tool for
investigators to narrow the large list of potential candidate genes to
a manageable, prioritized list for further analysis. For example,
coincident mapping of modifier loci to the same chromosomal region for
a particular trait using different inbred strain partners is consistent
with inheritance of a common allele. Examination of the haplotype
structure across the candidate region might reveal regions that
segregate appropriately with the phenotypic modification and therefore
may harbor the causative polymorphism. If the conserved haplotype
blocks are sufficiently small, this method has the potential of
significantly reducing the high-priority candidate genes in the
10–20-cM regions initially defined in preliminary complex-trait
analysis. The high-priority candidate list can then be further reduced
by tissue or microarray experiments to identify genes expressed in
tissues of interest or in appropriate biological pathways, or in
pathways shared among independent QTL candidate peaks affecting the
same phenotypic trait (Cozma et al. 2002). This "systems biology"
approach may have the capacity to significantly accelerate modifier
gene discovery, at least in some cases, by circumventing the need for
generation of congenic and subcongenic animals.
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RESULTS
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To assess this possibility, we have constructed a medium-resolution
haplotype map across the proximal 8 Mb of mouse Chromosome 19 in four
strains of mice. These four strains, DBA/2J, FVB/NJ, AKR/J, and
NZB/B1NJ, were used to map a metastatic efficiency modifier locus to
the proximal 10 cM of the chromosome (Hunter et al. 2001). Two separate
mapping experiments were used; an intercross between the AKXD
recombinant inbred panel and FVB/N-TgN(MMTV-PyVT)634Mul; and
an [FVB/NJ x (NZB/B1NJ x FVB/N-TgN(MMTV-PyVT)634Mul)]
backcross. Analysis of the crosses revealed that the DBA/2J
and NZB/B1NJ alleles suppressed metastatic efficiency.
Candidate haplotype intervals therefore would include those regions
where DBA/2J and NZB/B1NJ shared a common haplotype and differed
from both AKR/J and FVB/NJ.
High-resolution haplotype analysis of human Chromosome 21 indicated
that human haplotypes varied between 5 and 100 kb in length (Patil
et al. 2001). It was anticipated, owing to the derivation of most
laboratory inbred strains from relatively few progenitors, that the
mouse haplotype blocks would probably be larger than humans. Therefore,
500-bp amplicons were designed approximately every 50 kb to try to
identify a large fraction of the potential haplotype blocks. An
800-kb region 5 Mb distal (4653–5690 kb) of the centromere
containing only putative repetitive elements was excluded from the SNP
discovery. A second gene-poor region (6000–6678 kb) containing only 1
predicted gene was also excluded from analysis. In addition, the most
telomeric 1.5 Mb of the candidate region consisting of a large number
of olfactory receptors, which were considered as unlikely metastasis
efficiency modifier genes, was also excluded.
Initially, 17 microsatellite markers and 84 PCR amplicons were assayed
for polymorphisms in the four inbred strains. Three microsatellites
(18%) and 31 of the amplicons (37%) were nonpolymorphic in the
four inbred strains assayed. The average spacing of the polymorphisms
in the assayed regions was 115 kb. Potential haplotype blocks were
defined as sets of polymorphisms that were present in two of the inbred
strains.
To assess the approximate size of the haplotype blocks, selected
regions were subjected to further analysis. Additional amplicons were
generated and sequenced in the Ntlk–FosL1 region to
determine the length of the haplotype block. Amplicons consisted of
inter- and intragenic noncoding regions as well as exons and their
flanking intronic regions in the Rela and HtaTip
loci. Analysis of polymorphisms in the four strains demonstrated the
presence of at least one haplotype that extends 150 kb, and possibly
as many as three haplotype blocks if the HtaTip NZB/B1NJ
polymorphism is a common polymorphism.
A significant fraction of the polymorphisms observed were present in
only one of the four strains, indicating that they may be rare
polymorphisms unique to that strain. This possibility was tested by
screening additional strains of mice for the presence of the SNPs.
Using the CIDR database (http://www.cidr.jhmi.edu/mouse/index.html), a
phylogenic tree was constructed to identify strains that clustered near
FVB/NJ and NZB/B1NJ, and therefore might be likely to share haplotype
structures. Subsets of SNPs were selected to specifically test whether
a subset of the unique SNPs observed in the original four strains was
present in the phylogenically related strains. As can be observed in
Figure 1, all of the 8 SNPs originally
identified in NZB/B1NJ were replicated, indicating that these are
common SNPs representing true haplotype blocks. In addition, additional
haplotype blocks were identified in NZW/LacJ and BUB/BnJ, indicating
that there were at least three common founder haplotypes for this
region of Chromosome 19 in the progenitors of the common laboratory
inbred strains.
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Figure 1. Haplotype map of proximal Chromosome 19. The mouse strains screened are
indicated across the top of the figure. Primer pairs are listed in the
left-hand column. Haplotypes are indicated by colors. The polymorphisms
present in DBA/2J (yellow), AKR/J (green), FVB/NJ (purple), NZB/B1NJ
(light blue), BUB/BnJ and NZW/LacJ (pink) are indicated. The
polymorphisms observed are shown in each cell. (Del) Deletion, (ins)
insertion. When multiple polymorphisms were observed, commas separate
the polymorphisms. Microsatellite-based polymorphisms are shown as PCR
product size, in base pairs. For those microsatellites that were
significantly different in size, as determined by agarose gel
electrophoresis, the exact size was not determined, so no base pair
size was included in the figure. The physical position column indicates
the distance, in kilobases, from the centromere according to the Celera
database. The haplotype blocks that segregate appropriately according
to the coinheritance hypothesis are boxed.
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DISCUSSION
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Unlike the NZB/B1NJ haplotypes, only half of the FVB/NJ-identified
SNPs replicated in the expanded set of inbred mice. Several
possibilities might explain this. First, the SNPs might be unique to
FVB/NJ, having arisen after the foundation of this strain. Although we
cannot exclude this possibility with the current data, the
juxtaposition of the FVB/NJ SNPs indicates that this may represent a
real haplotype block. Second, although the mouse strains selected for
screening were chosen based on their phylogenic relationship, only two
of the strains were Swiss-derived, as is FVB/NJ. Screening additional
Swiss-derived mice might reveal that these SNPs are part of a conserved
haplotype block.
Interestingly, examination of the SNP map indicates that the rate of
mutation may not be identical across this region of the genome. The
majority of the amplicons contained a single SNP or a single small
indel. However, in a 300-kb interval of the chromosome (1827–2128
kb), all of the amplicons show multiple polymorphisms in the strains
tested. It is possible that this is simply caused by random chance and
the number of strains screened at these loci. The juxtaposition of
these loci and the lack of similar results at other loci screened in
the larger set of animals, however, imply that the increased
polymorphism rate may have a biological basis.
The analysis presented here is based on the hypothesis that the
colocalization of the metastasis modifier loci in the independent
crosses was due to the inheritance of alleles from common progenitors.
Although our genetic data are consistent with this hypothesis, there is
a major caveat. If, instead of a single locus or tightly linked group
of polymorphic loci that were inherited from a common progenitor, there
were two different linked QTLs in the modifying strains, one of which,
for example, is the basis of the metastasis suppression in DBA/2J and
the other in NZB/B1NJ, then this strategy would not be effective.
However, it might be useful to reduce the number of genes to be
considered as candidates. By comparing between two strains instead of
all four, for example, AKR/J and DBA/2J, haplotype blocks that are
shared between the strains in a QTL candidate region could be
eliminated from initial consideration. For this example it would reduce
the number of genes approximately twofold. The utility of this method,
however, will be directly determined by the mouse strains in question.
As shown in Figure 1, comparison of FVB/NJ and NZB/B1NJ would not
result in a significant reduction of potential candidate genes.
Examination of the haplotype blocks also indicates that there are five
regions that meet the criteria for candidate-gene consideration. These
regions encompass 25 genes, according to the Celera genome database.
This is a 16-fold reduction of the number of potential candidate genes
(>400) in the original candidate region, not including the >100
olfactory receptor genes excluded from consideration. Haplotype mapping
may, therefore, significantly reduce the number of genes in a QTL
candidate region for candidate-gene analysis, but significant numbers
of genes may still need to be examined. Previously Belknap et al.
(2001) suggested that combinations of techniques may be useful to
provide strong evidence that a particular gene may be the genetic basis
for a QTL effect. Similarly, different techniques and strategies may
serve as additional filters to reduce the number of high-priority
candidate genes. These techniques may include using microarray analysis
to identify genes in interesting haplotype blocks that are expressed in
tissues of interest or that are differentially expressed between inbred
strains of interest; pathway analysis, the identification of
biochemical pathways that have members associated with independent QTL
peaks affecting the same trait (Cozma et al. 2002); and surveys of
literature for genes lying in the haplotype blocks of interest that
were previously known to affect the trait of interest. Applying these
filters on top of the haplotype block mapping may reduce the number of
candidate genes to a handful that could subsequently be screened for
polymorphisms and subjected to in vitro and in vivo analysis for their
role in the phenotype of interest.
Based on this combined strategy, we have applied these filters to our
haplotype data. Pathway analysis has been performed on the data
obtained from the initial metastasis efficiency mapping studies (Hunter
et al. 2001) using the publicly available gene lists. Three molecular
pathways were observed to have members present in each of the QTL
candidate regions. The Chromosome 19 candidate region contains 20
genes of known function that are members of these molecular pathways,
and thus might be considered candidates for further analysis. Combining
this information with expression array data (T. Qiu, G.V.R.
Chandramouli, N.W. Alkharouf, K.W. Hunter, and E.T. Liu, in
prep.), as well as literature searches to identify pathways
known to be associated with metastatic progression reduces the
candidate genes to two. Sequence analysis of these genes has revealed a
polymorphism in a conserved domain in one of the candidates that would
be likely to have functional consequences (data not shown). In vitro
and in vivo experiments are presently underway to evaluate this gene as
the Chromosome 19 metastasis efficiency modifier gene Mtes1.
In conclusion, these data indicate that using multiple crosses to find
shared putative modifier alleles, combined with haplotype maps, may be
a useful method for candidate modifier gene identification. The ability
to perform the multiple cross-haplotype analyses will clearly improve
as the SNP density across large numbers of inbred strains increases. At
present, because it is not clear what the exact size of a mouse
haplotype block is, the density of SNPs to achieve saturation is not
known. Our data are consistent with the existence of a large number of
haplotype blocks of >80–100 kb. However, it is not unlikely that we
may have missed some haplotype blocks, including some that fit the
criteria used to select candidate genes, either from insufficient
density of SNPs or by not screening putatively gene-poor regions.
Nevertheless, we believe that as the SNP maps are expanded across more
inbred strains, haplotype mapping in inbred strains from multiple cross
experiments may prove a valuable tool for rapidly identifying and
prioritizing candidate genes for more detailed analysis.
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METHODS
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The haplotype map was developed using a variety of resources. Based
on the Celera database (http://www.celera.com), the approximate
physical position and order of the MIT microsatellite markers relative
to the gene order in the first 10 Mb of the chromosome were identified.
Relative sizes of the microsatellite markers were determined by
analysis on either 4% agarose gels or 6% acrylamide gels. Putative
SNPs in a number of genes were identified using the CGAP GAI SNP
discovery tools (http://lpgws.nci.nih.gov:82/perl/snp2ref), primers
were designed, and potential SNPs were analyzed by direct sequencing.
Additional SNPs were developed by designing primers from the genome
sequence, followed by sequence analysis in the four strains of
interest. For sequencing, PCR products were purified with QIAGEN PCR
purification kits, and double-strand sequencing was performed with a
Perkin Elmer BigDye Dye Terminator sequence kit. Analysis was performed
on a Perkin Elmer 3100 Automated Fluorescent Sequencer. Sequences were
compiled and analyzed with the computer software packages PHRED and
PHRAP (Gordon et al. 1998) to identify SNPs.
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WEB SITE REFERENCES
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http://lpgws.nci.nih.gov:82/perl/snp2ref; CG AP GAI SNP
discovery tools.
http://www.celera.com; Celera database.
http://www.cidr.jhmi.edu/mouse/index.html; CIDR database.
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Acknowledgements
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These data were generated through the use of the Celera Discovery
System and Celera's associated databases.
The publication
costs of this article were defrayed in part by payment of page charges.
This article must therefore be hereby marked "advertisement" in
accordance with 18 USC section 1734 solely to indicate this fact.
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Footnotes
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1 Corresponding author. 
E-MAIL hunterk{at}mail.nih.gov; FAX (301) 435-8963.
Article and publication are at
http://www.genome.org/cgi/doi/10.1101/gr.786403.
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Received September 9, 2002;
accepted in revised format October 30, 2002.
13:118-121 © by 2003 Cold Spring Harbor Laboratory Press ISSN 1088-9051/03 $5.00
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ABSTRACT
RESULTS