Animal QTLdb: an improved database tool for livestock

Animal QTLdb: an improved database tool for livestock
Published online 24 November 2012
Nucleic Acids Research, 2013, Vol. 41, Database issue D871–D879
Animal QTLdb: an improved database tool for
livestock animal QTL/association data dissemination
in the post-genome era
Zhi-Liang Hu1,*, Carissa A. Park1, Xiao-Lin Wu2 and James M. Reecy1,*
Department of Animal Science and Center for Integrated Animal Genomics, Iowa State University, 2255 Kildee
Hall, Ames, IA 50011 and 2Department of Meat and Animal Science, College of Agriculture and Life Sciences,
University of Wisconsin-Madison, Madison, WI 53706, USA
Received June 30, 2012; Revised October 24, 2012; Accepted October 25, 2012
The Animal QTL database (QTLdb; http://www. is designed to house all
publicly available QTL and single-nucleotide polymorphism/gene association data on livestock
animal species. An earlier version was published in
the Nucleic Acids Research Database issue in 2007.
Since then, we have continued our efforts to develop
new and improved database tools to allow more
data types, parameters and functions. Our efforts
have transformed the Animal QTLdb into a tool
that actively serves the research community as a
quality data repository and more importantly, a
provider of easily accessible tools and functions to
disseminate QTL and gene association information.
The QTLdb has been heavily used by the livestock
genomics community since its first public release in
2004. To date, there are 5920 cattle, 3442 chicken,
7451 pigs, 753 sheep and 88 rainbow trout data
points in the database, and at least 290 publications
that cite use of the database. The rapid advancement in genomic studies of cattle, chicken, pigs,
sheep and other livestock animals has presented
us with challenges, as well as opportunities for the
QTLdb to meet the evolving needs of the research
community. Here, we report our progress over the
recent years and highlight new functions and
services available to the general public.
Previously (1–3), we have reported on the success of Animal
QTLdb, which was developed to house publicly available
quantitative trait loci (QTL) data for cattle, chicken and
pigs, to provide tools for aligning various genome features
to QTL and to enable comparison of QTL results within
species and across experiments. As of 2007, tools had been
developed to allow map alignments of QTL against various
genome features, such as bacterial artificial chromosome
(BAC) end sequences, single-nucleotide polymorphisms
(SNPs), Affymetrix or oligo array elements and the
human genome via radiation hybrid (RH) map anchor
markers. In conjunction with Animal QTLdb, comparisons
of QTL across species have been made possible by virtual
comparative map (VCmap), a tool co-developed by Iowa
State University, Medical College of Wisconsin and
University of Iowa (
VCmap). These efforts have successfully improved the
public’s ability to retrieve and analyze QTL data.
Significant progress has been made over the past few
years. First, the database has been expanded to include
two more species, sheep and rainbow trout (http://www., to serve a larger
research community and to aid comparative mapping
efforts. Meanwhile, new QTL data have been actively
curated into the database. Since 2007, the number of QTL
in the database has increased by 5.5-fold, reaching 17 566
QTL (5920 cattle, 3442 chicken, 7451 pigs, 753 sheep and 88
rainbow trout). Second, the popularity of the Animal
QTLdb has been evidenced not only by daily web access
records but also by the number of publications that cite
use of the database—this number has reached 290 by the
summer of 2012 (search for ‘’ at Third, the Journal of Animal
Science has listed the Animal QTLdb as one of the databases in which to deposit new QTL data to meet their publication requirements (http://www.journalofanimalscience.
org/site/misc/JAS-InstructionsToAuthors.pdf). As a result,
an increasing number of volunteer curators have chosen to
enter their own data. Fourth, the Animal Trait Ontology
(ATO) has been further developed into the Vertebrate Trait
(VT) Ontology and livestock Product Trait (PT) Ontology,
with relevant terms submitted for the Clinical Measurement
*To whom correspondence should be addressed. Tel: +1 901 759 0643; Fax: +1 901 759 0643; Email: [email protected]
Correspondence may also be addressed to James M. Reecy. Tel: +1 515 294 9269; Fax: +1 515 294 2401; Email: [email protected]
ß The Author(s) 2012. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which
permits non-commercial reuse, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact
[email protected]
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Ontology (CMO) (4). The ability to annotate QTL data to
these more precisely defined traits and measurement
ontology terms allows for improved accuracy of trait
In the meantime, the landscape of animal genomics
research has been dramatically changed, with the
genome sequences for cattle, chicken, horse, pigs and
sheep becoming available over only 5–6 years of time.
This presents a great challenge, as well as a huge opportunity, for QTL analysis. As all QTL data published in the
past were linkage map-based, transfer of their linkage map
locations to genome coordinates is needed in order for
them to be useful in the genomics context. As gene sets
have become available for microarray expression analysis,
and high-density (HD) SNP arrays have been generated
for whole-genome association (WGA) studies, QTL
analysis is no longer the only way to link between
genomes and traits. It has been our vision for the
Animal QTLdb that it serves as a bridge between genotypes (genes) and phenotypes (traits) (3), which will inevitably necessitate inclusion of SNP/genome-wide
association study (GWAS) data. Under this concept, we
must bring related experimental results together for examination through a process called meta-analysis. Therefore,
continued improvements to the Animal QTLdb are extremely important, as huge amounts of similar data
continue to accumulate rapidly.
In this article, we report our recent progress in
redeveloping the Animal QTLdb to meet these challenges.
Data, data curation and data transformation
The QTLdb accepts either curated public data from journal
papers or private laboratory reports subject to publication.
More than 50 parameters/data types are subject to collection to describe a QTL, as reported earlier (3). We have
recently added a number of new data types to enhance
our ability to be more inclusive in QTL/association data
collection. These new types include ‘association’ data for
candidate gene or single marker associations; ‘eQTL’ from
microarray-based QTL scan analysis; ‘test scale’ to differentiate genome-wise, chromosome-wise, comparison-wise
and experiment-wise QTL/association reports; ‘test
model’ to indicate epistatic or maternally or paternally imprinted QTL; new test statistics such as Bayes value and
likelihood ratio, etc. We have also added animal breed information for future breed-associated QTL analysis. The
backbone maps to record QTL are from USDA-Meat
Animal Research Center (MARC; for pigs and cattle),
Wageningen University (for chicken), University of
Melbourne (for sheep) and the National Center for Cool
and Cold Water Aquaculture (NCCCWA; for rainbow
trout). Reported QTL genome locations were obtained by
interpolating their linkage map positions via anchor
QTL are mapping features recorded as linkage distances. In order for GBrowse to display QTL and for
users to easily port QTL data for customized analysis,
we established a process to convert the QTL linkage
map locations (centimorgan, cM) to the corresponding
physical locations (megabase pair, Mbp). The data conversion is a mathematical process built in a Perl script,
whereby interpolation or extrapolation is performed
with reference to the nearest common anchoring marker
locations on both maps.
The QTLdb has a three-tiered data curation structure so
that curators, editors and database administrators can
work together and share responsibilities in a workflow
to ensure data quality and smooth process control. In
the past few years, a set of new data debugging tools,
process control mechanisms and functions for the ease
of use of the tools have been developed in response to
lessons learned during data curation and debugging.
Platform and software
The QTLdb is built on a RedHat Linux platform with
MySQL (version 14.12) as the backend relational
database and Apache (2.2.13) as the web server. Perl
(5.8.8) was used to program the web interface for
user-controlled data presentations and interactive
curator tools for data entry. Some lightweight PHP
hypertext preprocessor and Javascript codes were also
used to develop web functions where needed. An
embedded R script was developed for QTL meta-plots.
Since 2007, we have made 14 database releases with both
new data and new functions, at a recent pace of three
releases per year. The number of publications curated
into the QTLdb has been steadily increasing at a rate of
30% per year on average (Supplementary Figure S1),
which indicates the importance of the research to the community. As the QTLdb is being increasingly used (http://, various user requests
continue to be received by our Helpdesk, which compels
us to further improve the QTLdb in order to better serve
the needs of the research community.
New data types and parameters
GWAS and eQTL data
Genome-wide association study (also known as
whole-genome association study, WGAS) and expression
QTL (eQTL) are newly emerged methods (relative to traditional QTL) to analyze the associations of abundant
genetic variants (typically SNPs) with traits of interest
(Table 1). Like QTL, GWAS adds value to our understanding of genome–trait relationships. GWAS data are
genome map based, whereas QTL data are linkage map
based. We have set up genome maps using the most
updated version of the genome build available for each
species. When a new build is available, we update both
the genome maps within the QTLdb and the genome
version information page linked from each genome
name on the web site. The genome maps are aligned
with their respective linkage maps in order to display
both types of data in parallel. Two methods were used
to align the maps: (i) linearly scale out both linkage and
genome maps with the same length base, such that their
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Table 1. New data types introduced to the Animal QTLdb since our
last NAR publication in 2007
Analysis types
Map types
Test models
Statistical parameters
QTL descriptors
Significance levels
Test scale
QTL term mapping
Reference information
Linkage map (cM)
Genome map (bp)a
Paternally imprinteda
Maternally imprinteda
LOD score
LS mean
Bayes valuea
Likelihood ratioa
Trait name
Flank Marker A2
Flank Marker A1
Peak Mark
Flank Marker B1
Flank Marker B2
Vertebrate Trait Ontologya
Product Trait Ontologya
Clinical Measurement Ontologya
Author emailsa
(Other reference parameters omitted)
Rainbow trouta
New data types. Note that only necessary parameters are listed to save
map locations are visually comparable and (ii) use anchor
markers between them to transfer map information.
Figure 1b shows how genome and linkage maps are laid
out, and how the GWAS and QTL are plotted to facilitate
user analysis.
Animal breeds
This information is important for analysis of the source of
a QTL, although not all publications include such information. The introduction of breed information was
carried out in late 2007 aided by a breed ontology
database set up with initial data from the Breeds of
Livestock Project at Oklahoma State University (http:// The display of animal
breed information on QTL detail queries was implemented in 2010 (Figure 3b), and the ability to search for
QTL by animal breeds was implemented in 2012.
Genetic test models
The original Pig QTLdb was developed only to accommodate classical, simple QTL association data. With the
development of QTL tests under various genetic models,
the following options were added for data curation: ‘paternally imprinted’, ‘maternally imprinted’, ‘sex-specific’,
‘Mendelian’, ‘epistatic’ and ‘associated QTL’ information.
The epistasis types include ‘antagonistic’, ‘synergistic’,
‘genetic suppression’, ‘genetic enhancement’, ‘intragenic
complementation’, ‘allelic complementation’, ‘interallelic
complementation’, etc. Collection of these data is useful
to facilitate future genetic network analysis and metaanalysis.
New test statistics
In addition to logarithm of the odds (LOD) score, least
squares (LS) mean, P-value, F-statistic and variance, we
have added options for Bayes value and likelihood ratio.
This allows us to be as inclusive as possible for all QTL/
association reports.
QTL alignments to genome maps and cytogenetic
G-band maps
Genome maps and cytogenetic G-band maps
The alignment of QTL/association data is made both
within the QTLdb using our own graphic tools, and by
using GBrowse in a separate setup. The GBrowse setup is
to accommodate QTL alignments in newly available
genome assemblies for cattle (Bos taurus), chicken
(Gallus gallus) and pig (Sus scrofa). In order to accommodate both linkage maps and genome maps to integratively
display QTL data, a number of improvements had to be
made. First, the back-end relational database was
restructured to store and integrate genome maps parallel
to linkage maps. Second, the QTL graph tool was rebuilt
so that both genome coordinates (Mbp) and linkage map
locations (cM) can be comparatively displayed (Figure 1;
also see above).
Due to the large sizes of genome data (e.g. millions of
rows of high-density SNP data and increasing), efforts
have been made to optimize queries and snap views to
improve the MySQL query efficiency and minimize any
noticeable transit delays for users.
Conversion of coordinates
In order to transfer QTL from linkage maps to genome
maps, anchor marker-based coordinate interpolations are
used. Briefly, the genome Mbp coordinates corresponding
to a QTL on the linkage map are converted from their
linkage map locations using the closest anchor marker
locations between the linkage and the genome maps as a
reference. The error sizes of interpolated Mbp locations
vary depending on the anchor markers available and their
distances from the target QTL location. The map distance
is estimated using a Mbp–cM factor calculated based on
the actual linkage and genome map lengths of that particular genome or chromosome. As such, the interpolation
is only an estimate. Although the estimates are ‘gross’, the
error sizes are not significant relative to the size and test
errors of most QTL.
The converted QTL genome locations are ported to
GBrowse for display in alignment with NCBI (5) and/or
Ensembl (6) annotated genes, as well as with the locations
of array elements and HD SNPs. The data are also
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Figure 1. A screenshot of an Animal QTLdb chromosomal map view with new elements or functions in boxes or in circles: (a) options to view
different data types, allowing ‘QTL’, ‘eQTL’ and ‘association’ data to be displayed in various combinations; options to view ‘linkage map’, ‘genome
map’ or both; and options to view different genome map builds if applicable. (b) Cytogenetic bands and genome coordinates aligned side-by-side with
the linkage map. Note that the genome chromosome bar can be clicked to go to viewing QTL in GBrowse. (c) New shapes/colors for different data
types. (d) All QTL/traits on a chromosome can be easily retrieved to view by selecting from a pull-down menu or by their first initials. (e) A flexible
data download tool that allows users to download data in the current view or by chromsome. (f) A link to trigger Metaplot function (Figure 5 for
available for free download, with warning messages reminding users to verify data before any critical use of
the converted coordinates.
Data display
Display of QTL and association data in the QTLdb native
chromosomal view and in GBrowse are illustrated in
Figures 1 and 2. Improvements have been made to the
QTL data organization by trait hierarchies/trait-type
groups to aid data dissemination.
Development of ATO and mapping to QTL traits
In an effort to standardize trait nomenclature across
species, the ATO has undergone active development into
VT, PT and CMO (Table 2) as part of a collaboration
between Animal QTLdb, Rat Genome Database (http://, Mouse Genome Informatics (http://www., the French National Institute for
Agricultural Research (INRA;, SABRE Research UK (
and EADGENE ( To facilitate
a smooth transfer of QTL information to the new trait
terms, we have developed an interactive ‘ATO to VT/
PT/CMO mapping tool’ as part of the QTLdb curator
tool set, for curators to assign mapping relationships.
We have also undertaken a database restructuring for
dynamic yet consistent trait hierarchy management to
allow for easier future development. Figure 3a shows an
example of the trait mapping results. As a by-product, a
new trait hierarchy tree structure display and navigation
tool using combined java scripts and W3 cascading style
sheets has been created. Figure 4 shows a simple example,
including new features built into the tool.
QTL meta-analysis
Probably the biggest advantage provided by the QTLdb is
its utility for metadata analysis. Our preliminary work on
making QTL meta-plots was reported earlier (7).
Subsequent minor improvements have been made for
better browser compatibility, among other issues.
Currently available tools include a ‘pile plot’ (histogram)
and a ‘kernel density plot’. The pile plot is based on the
actual counts (y-value) of the reported QTLs, plotted at
1-cM (x-value) bins along the target chromosome. The
kernel density estimation is a non-parametric approach
to estimate the probability density function of
location-wise QTL incidence treated as a random
variable. We have also recently modified the rules so
that a group of selected traits, rather than the previous
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Figure 2. A snapshot of GBrowse display of QTL and association data on part of bovine chromosome 3 in alignment with annotated genes and
transcripts on UMD3.1 genome build of Bos taurus.
With the increased complexity and diversity of data types
within the QTLdb, the post-curation data quality control
(QC) and tasks required to run them through a release
pipeline have introduced new challenges. We have
improved the tools and methods for how this is handled,
using redeveloped curator/editor tools and improved QC
inconvenience for curators and editors. We have unified
the curator accounts so that one only needs to login once,
then has options to go into any of the available species’
‘realms’ for his or her work. The most important improvement made to the curator/editor tools is the implementation of a number of QC rule sets and codes to alert the
curator when a data integrity problem is identified. The
problematic data are not accepted until the problem is
fixed. At the database level, the problematic data are
recorded rather than denied acceptance, but only flagged
for their interim status. As such, multiple curators/editors
may be able to have access to problematic data (as long as
access rights are properly granted) in order to work
together to solve the problem.
Curator/editor ‘realms’
Originally, a curator was given an account to curate data
within a species. Additional accounts had to be created for
each species for which a curator needed to have access.
The various accounts were unwieldy, creating
Automated PUBMED search and data pre-load
To reduce the curator’s workload and keep track of
incoming and curated data, an automated procedure was
implemented in Perl script to perform periodic searches of
PubMed via NCBI eUtil portal (http://www.ncbi.nlm.nih.
limit of one trait, can be subjected to meta-plot analysis.
For example, the trait for a meta-plot can be either a trait
analyzed across multiple experiments or a group of similar
traits abstracted to describe a scenario (Figure 5).
Improved curator and editor tools
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Figure 3. A snapshot of an Animal QTLdb data details page, showing new parameters and features added to the database. The new parameters
subject to collection into the database include data analysis types (c), test models (c) and animal breeds (b). The ATO is now linked to VT, PT and
CMO (a).
Table 2. Definitions of VT, PT and CMO—ontology realms within which the livestock ATO is undergoing development for unified trait term
standards (8)
. The VT is a controlled vocabulary for the description of traits (measurable or observable characteristics) pertaining to the morphology,
physiology or development of vertebrate organisms.
. The CMO is designed to be used to standardize morphological and physiological measurement records generated from clinical and model
organism research and health programs.
. The PT is a controlled vocabulary for the description of traits (measurable or observable characteristics) pertaining to products produced by
or obtained from the body of an agricultural animal or bird maintained for use and profit.
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gov/books/NBK25501/). We built a local search and track
database, with which we keep a record of which papers
have been curated, which have high priority in the queue,
which are being reviewed or are on hold, and which are
not applicable. This effectively facilitates collaboration by
multiple curators. Recently, we have established a
‘curators’ mailing listserv’ to share experiences and
lessons, to inform curators of the curation queue status,
Database release procedures and tools
We have implemented a nine-step data release procedure,
which includes a number of post-curation data fixes [such
as flanking marker/location (cM) validation, curation
evidence updates, pre-release integrity check-ups, etc.]
and administrative actions. The latter includes rolling
new data into public portals, exporting data for external
collaborative database synchronization, such as to NCBI
Reuters Digital Resources (
new/) and generation of download files in each of the
Figure 4. A new hierarchy display tool showing a partial cattle trait
tree, along with main features embedded in the display.
four supported formats (raw, GFF, SAM and BED), generation of new database statistics and updating the web
portal, rebuilding of GBrowse database tracks for each
species and finally, retrieval of updates from NCBI on
newly assigned Gene Database unique identifiers for
dynamic inter-database links. Supplementary Figure S2
shows the conceptual work/data flow pipeline implemented in the QTLdb data curator tools, where the roles
of curators, editors and database administrators are
sketched along with the data flow. The same procedure
is used for problematic data debugging, rollback or data
obsoletion (not shown).
With each release, new functions are introduced,
although their actual implementations are made seamlessly over time. That is, whenever a new function is developed, tested and passed, it is quietly rolled into public
portals. Quality assurance is fulfilled by routine maintenance and problem fixes. New functions are normally
announced collectively along with data releases.
Miscellaneous improvements
Many ‘small’ improvements and bug fixes were made over
the past 5 years. These include, but are not limited to: (i)
improved literature search tools (structured search for
QTL publications that helps users to better target what
they look for); (ii) a pull-down menu to list unique traits
on a chromosome view (with QTL counts for each trait
and an option to display selection); (iii) improved tool bar
on chromosome view (to allow display choices of QTL/
eQTL/association data or linkage/genome maps); (iv)
improved QTL search on chromosome view (to allow
the display of combinations of QTL per user’s choice);
(v) intelligent alert of search results if not found within a
species but similar results exist in another species (QTL
ID, traits, etc.); (vi) genome-wide view of QTL by trait
types; (vii) improvement of QTL map image quality (facilitates use for publications); (viii) customized data
download of user’s searched/browsed dataset (at chromosomal level) and (ix) data downloads, exports and sharing
(in the formats of GFF, BED and SAM, as well as
tab-delimited plain text files for each individual chromosome and for the whole genome of a species). Data are
also exported into specific formats designed to work with
Figure 5. An example of QTL meta-plots, showing a pile plot and a kernel density (KD) plot, for average daily gain (ADG) QTL on pig
chromosome 4. The meta-plot peak is supported by the number of reported QTL that overlap. Note this function is active only when there are
more than three QTL in a chromosome view (Figure 1).
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the NCBI Gene Database and Thomson Reuters Digital
Resources, respectively.
A QTL is a map feature that describes a location in a
genome where genes underlying quantitative traits of
interest may reside. Therefore, the development of
QTLdb and incorporated tools and utilities is all
map-centric. Numerous improvements to the QTLdb
have been made over the past 5 years. Although the
most notable changes include the addition of sheep and
rainbow trout data, as well as a number of new parameters
and features seen on the web portal, the most significant
‘upgrade’ has been the inclusion of GWAS data. As such,
the QTLdb is in fact an animal QTL/association database.
Ideally, a well-designed database should not only completely fit the scope of data that are subject to curation
(entry) and employ well-structured database management
but also allow for meta-analysis, where data can be easily
fit into an analysis grid. The addition of the meta-plot
tools is only a starting point, because there are
numerous research questions to address before more
tools may be implemented (8). Moreover, the nature of
QTL/GWAS/eQTL experiments presents some challenges
for glitch-free database development in terms of ideal fit of
data into database metrics. This is because the genetic
data structure has multiple dimensions, and each of
these dimensions may follow different standards for data
representation and recording. As genetics and genomics
rapidly evolve, database development also needs to keep
up with the challenges, not only for proper recording of
these data, but also for building platforms for the data to
be comparable, so that general conclusions may be drawn
through meta-analysis. We have made efforts to define the
minimum information necessary (9) to describe a QTL or
association for effective database management. This effort
has been geared at setting up standards for data submission, as well as toward the inclusion of related data that
are usually not publishable, but may be useful for
meta-analysis. Recently, Nature Genetics (10) has requested that authors, when reporting GWAS data, also
report the co-localization of trait-associated variants
identified by other methods. In particular, they ask
authors to publish or enter into databases the genotype
frequencies, association P-values, etc. for data that may or
may not reach genome-wide significance thresholds. This
reflects a consensus within the community that having
more information available will be useful for future
combined meta-analyses. We have enhanced our efforts
toward developing and implementing minimum
information for QTL and association studies (MIQAS)
through the continued improvement of the QTLdb.
Complete inclusion of all published data is crucial for
the QTLdb to avoid any possible bias in the representation of a true QTL. Since a comprehensive data update
requires extensive time and effort, we have endeavored to
open the QTLdb to the public for data entry, and welcome
users to volunteer their data to the database. Although it
takes time to catch up with the incorporation of newly
available data, it also takes time to roll out new features
and utilities for complex database development. This is
because developmental processes need to go through
trials, system integration, debugging, overall tests and
possibly rollback and application of new patches, etc.
Often, changes to an external database require us to redevelop certain functions (as when we went through
changes in coordination with the transition at NCBI
from Locus Link to Gene Database). One recent
example is our cooperation with NCBI to implement the
deletion of a QTL record when one becomes obsolete at
the QTLdb site (primary data source). It is thus important
to work closely with partner databases and check for any
functional failure and/or incompatibility as a matter of
For successful development of a comprehensive and
shared tool set for multiple curators and users like the
Animal QTLdb, details are extremely important. We
deem nothing so minor as to not be relevant to the advancement of science or users’ desire to easily find information. This applies to the development of curator tools,
user interfaces, web layout and cosmetics as well. In terms
of presentation of relevant information, we have attempted to make the user interface as brief, slick, interactive and to-the-point as possible. For example, there are
more than two dozen new features or functions that we
have implemented to improve the user experience. Some
may seem like only small or minor improvements, but
altogether they help to keep the QTLdb a user-friendly
and steadily useful tool.
Due to the complexity and size of genome data, as well
as the relatively fast update pace of new genome builds,
keeping track of the updates and versioning of genome
builds within the QTLdb has been a challenge, especially
considering that it also involves mapping of many
features. We envision that while QTL/eQTL/association
studies will continue to be map based, future development
of the database will be more sequence centered and gene
or genetic network analysis oriented. As such, future
QTLdb development will involve not only mapping features but also genetic factors contributing to our understanding of the connection between genes and traits.
The database contents and online tools are all freely available at The
Animal QTLdb welcomes users to directly deposit their
data by applying for a curator account (http://www. We also maintain a frequently asked questions (FAQ) page to serve as a user
guide to database functions (http://www.animalgenome.
Supplementary Data are available at NAR Online:
Supplementary Figures S1 and S2. Supplementary data
to this manuscript can be found at: http://www
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We thank Jill Maddox from the University of Melbourne,
Australia and Yniv Palti from the National Center for
Cool and Cold Water Aquaculture (NCCCWA), for initiation of the sheep and rainbow trout QTL data curation,
respectively. Deep appreciations are due to Andy Law
from Roslin Institute for kindly providing the cytogenetic
G-band measurements of cattle, chicken, pigs and sheep;
to Wonhee Jang and Donna Maglott from NCBI for their
efforts to streamline the QTL updates into the NCBI Gene
Database; to Daniel Auld from Thomson Reuters for
QTL data streamlining to their Digital Resources and to
James Koltes for his useful suggestions from a user’s perspective. User feedback, various requests, constructive
criticisms and suggestions received through the Helpdesk
or related collaborations over the past years have
been in valuable in our determination of the most useful
new developments and in improvement of QTLdb ease of
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The USDA NRSP-8 National Animal Genome Research
Program, Bioinformatics Coordination Project and partly
by the USDA-NRI [2007-04187]. Funding for open access
charge: USDA-NIFA Research Funds to the National
Research Service Program, NRSP-8, National Animal
Genome Research Program.
Conflict of interest statement. None declared.
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