An User Guide to the QTLdb

A comprehensive database tool set for curated QTL/association data comparison, dynamic linking to comparative structural genome information in order for positional gene mining, genetic architecture analysis, meta-analysis, and more

  1. What are QTL/GWAS?
  2. What is QTLdb? Are there any publications about it?
  3. What are the main differences between QTL and association mapping?
  4. What animal species are currently included in the QTLdb?
  5. What are "flanking markers" and what do they represent?
  6. What is Trait Ontology and how is it used in QTLdb?
  7. How are public QTL data curated into the QTLdb?
  8. Can I enter my QTL data into the QTLdb?
  9. I have a large amount of QTL/association data to submit to the QTLdb. Is there an easy way to "upload" them?
  10. What questions does the QTLdb attempt to address?
  11. What functionality does QTLdb offer?
  12. How to access the information in the QTLdb?
  13. What structural genomics information are aligned in the QTLdb and how to access them?
  14. Which trait(s) are found having more QTL?
  15. There are so many QTLs on a chromosome. Is there an easy way for me to narrow down the scope to the traits I am more interested? e.g. Can I view my selected sub-sets of traits?
  16. Are data within the QTLdb static?
  17. Are QTL_ID within the QTLdb stable?
  18. I wish to find a cytogenetic band region of interest for QTL, how can I do that?
  19. I wish to "zoom in" to examine a local region of my interested region, can I do it? how?
  20. Some chromosomes have awful lot of QTL that the chromosome view becomes a very wide picture (extends way out of computer screen to the right), making it hard to compare some alignments. Is there any way I can see a more manageable view?
  21. There are recently available genome sequences for cattle, chicken, and pigs. Has Animal QTLdb been made accessible to them?
  22. For genomic mining of a QTL region, we often need to align a QTL against its genome for underlining genes. Is it possible in the QTLdb?
  23. How accurate is the QTL location alignment to, say, transcript locations on the most recent genome assembly?
  24. Can I download the raw data from the QTLdb?
  25. I have downloaded some QTL data, and found many identical QTL (chromosome locations, traits, even publications. Is it possible that they are redundant curations?
  26. Are there any tools available that can help me to do some meta-analysis?
  27. What is an Animal QTLdb Release? What is involved in a release?
  28. What are Animal QTLdb data alliances?
  29. I have my data analysis on an older version of a genome assembly, is it possible to transfer QTL coordinates on the current assembly to the older assembly? How?
  30. Why sometimes I find QTL coordinates are not the same on your GBrowse / JBrowse and your web web site?
  31. Is there a better way to search QTLdb by multiple criteria for a literature without involving complicated multiple web form fields?
  32. I wonder if I can download the QTL/association data based on the trait classes? For example, health traits?
  33. Is there any license term on the QTLdb? (e.g. Open Data license or waiver)
  34. What are gene-centric, trait-centric, and SNP-centric QTL/association information?
  35. How can I find all QTL/associations reported by a publication?
  36. How can I quickly locate a QTL/association with a QTL_ID without going through the troubles looking it up in each of the 8 species in QTLdb?
  37. Terminologies

  1. What are QTL/GWAS?


    This graph is modified from the
    Rat GDB with kind permission

    Quantitative Trait Loci (QTL) are hypotheses that specific chromosomal regions contain genes that make a significant contribution to the expression of a complex trait. Hypothesized QTL are supported with, or generally identified by, analyzing or comparing the linkage (degree of co-variation) of polymorphic molecular markers and variation of phenotypic trait measurements. The methods to localize a QTL includes whole genome scan of linkage between genetic markers and phenotypes with specific family structure designed for such analysis, and association analysis of markers (e.g. SNPs) with certain traits (e.g. genome wide association analysis, GWAS). Therefore as a matter of fact they are genomic mappings of traits. On this database site, they are interchangeably called "QTL", "QTL/(SNP)associations".

    The ultimate goal of complex trait dissection is to identify the actual genes involved in the trait and to understand the cellular roles and functions of these genes. Thus the purpose of the Animal QTLdb is to provide resources and tools for QTL regions of data mining, to facilitate the identification of such genes.

    The accuracy and precision of locating QTL depends, in part, on the density of the linkage map created. The higher the density of the map, the more precise the location of the putative QTL. When QTL can be mapped to a relatively small chromosomal region or regions other methods, such as positional cloning, can be used effectively to isolate specific genes. Unfortunately, the denser the map, the more likely that false positive QTL will be detected with linkage map based QTL methods. More precise mapping of traits is possible with newly available genome sequences and GWAS.

    Most, but not all, complex traits are affected by more than one locus. QTL often interact in complex ways and their expression can also be influenced by non-genetic factors. Because QTL are hypotheses, they are subject to reinterpretation and revision. Because the location of QTL are provisional their nomenclature is likely to be fluid and temporary. (revised from Carol J. Bult)

  2. What is QTLdb? Are there any publications about it?

    QTLdb is an abbreviated name for "QTL Database", which contains published QTL/association data curated into structured tables managed in a relational database environment, MySQL. The user and manager interface to the database takes advantage of world-wide web (WWW) and is programmed with perl/CGI.

    The active QTLdb development is a project carried out at the Iowa State University (see this note for an overview of its development history). The progress on the QTLdb development has been presented at the 13th North American Colloquium on Animal Cytogenetics & Gene Mapping (2003), Midwestern ADSA/ASAS Annual Meeting (2005), annual Plant and Animal Genome (PAG) conferences in 2005, 2006, 2007, 2008, 2009, 2011, 2012, 2015, 2016, 2018, and International Society for Animal Genetics (ISAG) in 2008. A number of papers by Hu et al. published on Mammalian Genome (2005, 2007), Nucleic Acids Research (2007, 2013, 2016, 2019) and full papers published on the World Congress of Genetics Applied to Livestock Production (WCGALP, 2010, 2018) representing the milestones in the course of the QTLdb development. The QTLdb has been listed by the NAR Database Collections, DataCite, LabWorm, FAIRsharing, OMICtools, AgroPortal (France), DatabaseCommons, National Ag Libriary (AgData Commons) and others.

  3. What are the main differences between QTL and association mapping?

    The main differences between QTL and association mapping are: (1) the methods. They are both to establish links between certain trait observations and genome locations, however, the former involves hypothetical points along a chromosome that form an array of neighboring points providing support for localization of a link under certain statistical thresholds, and these "neighboring points" ("clusters" if you wish to call) are found in inheritable patterns in a family (pedigreed analysis). (2) the level of resolution. QTL analyses can resolve the locations of a link in terms of chromosomal segments with sizes in terms of centimorgan (cM) (which translate into several million base pairs).

  4. What animal species are currently included in the QTLdb?

    The Animal QTLdb is designed to house QTL results from multiple livestock species. The database was originally developed to house pig QTL (2005). Subsequently, QTL data from cattle and chicken were added (2006). From 2007 to 2010, Jill Maddox's group started to curate sheep QTL into a copy of the Animal QTLdb installed at The University of Melbourne, Australia (It was migrated back to Iowa State University in 2010). Rainbow trout was added in 2011, horse was added in 2013, Catfish was added in early 2016, and Goat was added in late 2020, to the multiple species family served by the Animal QTLdb.

    QTL from other animal and aquaculture species will be added in the near future when possible. This is to serve our long term goal of assisting comparative QTL studies.

  5. What are "flanking markers" and what do they represent?

    There are different ways to determine a detected QTL is significant enough to be "real". Permutation test is one of those popular ones used by many people. According to Lander and Kruglyak (1995), a suggestive linkage is expected to occur one time at random in a genome scan and has an estimated minimum LOD score of 2.0; A significant linkage is expected to occur 0.05 times at random in a genome scan and has an estimated minimum LOD score of 3.4 (in real life the "cut-off" LOD scores may vary depend on actual permutation tests). Therefore, in an ideal situation, a QTL may be peaked by one marker and flanked by 2 pairs of markers (see Figure).

    In the QTLdb we try to use flanking markers A1, A2, B1, B2 when they are available.

  6. What is Trait Ontology and how is it used in QTLdb?

    Livestock production traits are sets of animal phenotypes described for their nature, quality, quantity and biological stage. Due to differences in methods of detection or measurement, scope of description and/or customs, a trait may be described in several different ways. In order to compare QTL discovered by different labs with different methods, we have to make a "standard" way of trait description in order to correctly compare them. To solve this problem, we introduced "Trait Ontology" to classify and organize the traits for management with database.

    Ontology is a classification methodology that defines a common vocabulary in a structured way for useful information sharing. Animal production traits may be classified in many different ways based on their functions, features, property, etc. One most useful construct of the trait ontology is that the animal traits may be classified by how they are measured as commercial products. In the QTLdb, we use three levels of controlled vocabulary to describe each production trait: Trait Class, Trait Type and Trait itself. For their definitions, see FAQ #129 "Terminology" below.

    The classification of traits helps to share common understanding of information structure among people or software agents.

  7. How are public QTL data curated into the QTLdb?

    Following are extracted from each publication: Experimental design, Population structure and design, Testing Model and Methods, Trait names on which significant QTL are detected, Trait Description and Measurements; QTL location (Chromosome, Position, 95% CI on the Location), Flanking markers (A1, A2, B1, B2 and the Peak; see Figure for FAQ #5), Test Statistics (LOD_score, LS_means, P_values, F_values, Variance), QTL effects (Dominance effect, Additive effect), Candidate genes, etc., when available. Publication title, authors, journal and abstracts are also included.

    Take pig data as an example, the QTLdb uses the USDA-MARC pig linkage map (MARC-Map) as a map reference to show relative locations of each QTL, as the MARC map is the single largest pig map to date, and its markers are used by most QTL studies for genome / chromosome scan. When a non-MARC-Map marker is used to describe a QTL, the actual marker location in the experimental map is interpolated to the MARC map and the interpolated map locations are stored in the QTLdb.

    The flanking or underlining markers on the QTL map are linked to the NCBI Gene DB.

  8. Can I enter my QTL data into the QTLdb?

    Yes. The Animal QTLdb is open to public for data entry and update. One must apply to be a curator in order to do so. Being a curator, you will be able to

    • keep your data private
    • update your data any time
    • view your data either aligned to its chromosome or along with other public data
    • release your data to public access once you decide so
    • withdraw your data for any problem found
    • all released public data will be populated to NCBI database automatically
    By submitting your data to the QTLdb, your data set will join the other QTL data set published in the past 10+ years, and subject to within and cross species comparisons. See paper by Hu et al. ("Animal QTLdb: Beyond a Repository - A Public Platform for QTL Comparisons and Integration with Diverse Types of Structural Genomic Information. Mammalian Genome, Volume 18, 1-4 (2007) for more details).

  9. I have a large amount of QTL/association data to submit to the QTLdb. Is there an easy way to "upload" them?

    Yes, batch submission of your data is possible for our curators to process them into the database. There are only a few steps for doing so. Simply prepare/format your data according to the the minimum required information guidelines, then use our data sharing platform to upload.

    Please be aware that the uploaded data are subject to inspection and batch processing. It can be very efficient ONLY IF your data meet the requirements with little manual curation. It often requires follow-up communications for our curator to get all needed information before an data entry can be made final.

  10. What questions does the QTLdb attempt to address?

    The following questions were the initially projected to address:

    • What is the chromosomal location for a QTL? Can multiple QTL be viewed in a "synthetic" manner?
    • Is it possible that QTL from different studies be easily compared for their locations?
    • Can all markers underlining a QTL be shown and marker information easily retrievable?
    • What are the significance values for each QTL, with what method for detection?
    • Have any other phenotypic traits been mapped to the chromosome segment that my QTL appears to fall into or is part of it?
    • What percentage of phenotypic variation is associated with each QTL? Is the effect dominant or additive?
    • How is the possibility that markers flanking a QTL may be matched to sequences in a public database for data mining or annotation?

    As we build up the QTLdb, we find that the utility of the QTLdb can extend beyond what we originally anticipated. Efforts are continually made to add more functionality to the utility of the QTLdb.

  11. What functionality does QTLdb offer?

    The animal QTLdb offer a number of functions for user to easily retrieve, compare and synthesize QTL information.

    By searching or browsing the QTLdb, one can

    1. Find all QTL on one chromosome
    2. Find all chromosomes that bear QTL for the same trait
    3. List all QTL from a particular publication
    4. Find all markers underlining a QTL
    5. Find DNA sequences associated with certain markers
    6. Use GeneDB to further search for candidate genes by comparative maps
    7. Find experiment details in brief for a given publication
    8. Find all parameters describing a QTL, as well as test statistics
    9. Find all associated genome features (SNP, BAC, Genes, Transcripts, etc. - available since 2006).
    10. Meta-analysis of QTL for a single trait on a chromosome (available since 2010).
    11. ... more to come as we are continuously developing the QTLdb tools.

    The Figure on the right shows an example of multiple QTL identified by different studies map to pig chromosome 3. With further details, a user can synthesize a picture of his own on the most promising chromosomal region where best candidate gene for a trait may reside.

  12. How to access the information in the QTLdb?

    The QTLdb web interface is designed to be easily accessed by search and browse. Each searched or browsed resulting information is again dynamically linked for further search or browse. In this way users can quickly find information from the QTLdb with multi-directional information traverse. The following paths seem daunting to read but each is at only a couple mouse-clicks away:

    • Draw Single Trait QTL on Multiple Chromosomes
      Go to Search page, input a keyword Click "GO" Click on your trait Click on "Find all QTLs"
    • Draw Multiple QTL on Single Chromosome
      Go to the Browse page Click on your chromosome
    • List all QTL from a particular publication
      Go to Search page, input a keyword Click "GO" Click on "List QTLs"
    • Find a pig QTL in NCBI Gene Database
      Search or browse to a QTL map Click on a QTL symbol Clink on "GeneDB"
    • Find DNA sequences associated with certain markers
      Search or browse to a QTL map Click on a marker name Click on "UniSTS" link above the marker name Click on "GenBank Accession" link
    • Find experiment/ publication details that produced a QTL
      Search or browse to a QTL map Click on a QTL symbol Experiment detail in brief is in upper right box Publication detail is in the lower right box
    • Find all locations that QTL for a trait may have been mapped to
      Search or browse to a QTL map Click on a QTL symbol Click on "Trait Name" Click on "Find all QTL on this trait"
    • Find related traits from a known QTL trait
      Search or browse to a QTL map Click on a QTL symbol Click on a "Trait Name", or "Trait Type", or "Trait Class" Choose from returned trait ontology list other traits to search further
  13. What structural genomics information are aligned in the QTLdb and how to access them?

    Thanks to many collaborators who provided a number of useful structural genomics information for aligning to the QTL maps (Acknowledgement are on each respective web pages). These data include radiation hybrid (RH) maps, BAC clone finger printed contig (FPC) maps, SNP maps, consensus linkage maps, genome maps, etc. For example, 6,500+ cattle SNPs and 1,300+ pig SNPs were aligned to respective QTL maps via RH to human comparative maps. 4,528 new porcine microsatellites from the Sino-Danish Pig Genome Sequencing Consortium were aligned to the pig QTL maps (see following table for a summary).

    Table 1. Data alignment status summary
    SpeciesGenome mapRH mapBAC FPCSNPsMicroarray ElementsHuman map
    AffyOligo
    Pigs
    Cattle
    Chicken----
    Horse-----
    Sheep----
    Rainbow trout-------

    (With reference to paper by Hu et al., "Animal QTLdb: a livestock QTL database tool set for positional QTL information mining and beyond. Nucleic Acids Research, 2007, 35 [Database issue]: D604-D609.). Note: With added function of GBrowse that we implemented in October 2008, QTL alignments to more genome feature are available. See FAQ #19 for more details.

    All aligned data can be accessed via either pop-up links or web forms on the "chromosome view" page of the QTLdb (see Figure below). Users can bring a QTL region (or interested map locations) across the aligned maps to serve the purpose of data mining, by using either the QTL bars or the web forms where map locations in cM must be provided.

    Please be aware that more data types are continually being added, and data updates are actively going on. Don't be surprised if you see things new.

  14. Which trait(s) are found having more QTLs?

    Backfat, Loin-eye area and Meat Color-L are the top three pig traits having highest number of QTL reported. Body weight in chicken has a dominant number of more QTL than other traits. Fat yield, milk yield and twinning are the three top cattle traits that QTL are measured for. For more, see respective species QTL database "summary" for details.

  15. There are so many QTLs on a chromosome. Is there an easy way for me to narrow down the scope to the traits I am more interested? e.g. Can I view my selected sub-sets of traits?

    Yes. There are two added features on the chromosome view of the QTLdb for you to easily view your interested traits: (1) all traits found on a chromosome are listed in a new pull-down menu below the chromosome graph, from which you can click to select the trait you wish to see; (2) all trait name first initials are also listed at the same spot, with which you can simply clisk the initials to see all traits started with that initial.

    On the top of the chromosome view screen, there is a "Search for QTL" search box. Within this search box, you can put in multiple trait symbols, delimited by a space, to get them all at once to a chromosome view. Note that wild card (*) are allowed for partial non-specific matches.

  16. Are data within QTLdb static?

    No. The Animal QTLdb as an online database tool being kept up-to-date with most current data curated from various resources in public domain. Since its first release in June 2004, release have been made as of . There are more than just new data additions in each release. For details, see #27 "What's involved in a release", and Animal QTLdb Releases History.

    If you see any new data that has not been included in the QTLdb, please drop us a note with the source of the publication - we will curate it into the database as soon as we can get around. Or better yet, you can register to become a QTL data curator for the QTLdb yourself. In this way you can enter your data, update your data, and also use the curator tools as a research platform (see below).

  17. Are QTL_ID within the QTLdb stable?

    Yes. A QTL_ID is assigned as a unique identifier when a QTL is entered into the database. Once the QTL is entered, the ID becomes permanent. i.e. Even if the QTL is deleted, modified, or becomes obsolete, the ID cannot be re-used.

    When the QTL data is synchronized to NCBI GeneDB, NCBI assigns each QTL a Gene ID, which is also permanent. The QTL_ID record goes along with the Gene ID, and displayed as: Primary source - AnimalQTLdb:#### (QTL_ID). For deleted QTL data, NCBI site will show "DISCONTINUED: This record was withdrawn by the AnimalQTLdb".

  18. I wish to find a cytogenetic band region of interest for QTL, how can I do that?

    This has been deprectaed: The QTLdb is based on linkage maps. Before we may add the cyto-genetic band alignments to the linkage maps within the QTLdb, users have to make that alignment with other tools, such as the Arkdb (http://www.thearkdb.org/anubis), i.e. translate your cytogenetic band locations into linkage map locations, and then come to use the QTLdb to look for QTLs. In the Arkdb, you wish to build a within-species comparative map between the "Cytogenetic" map and the "USDA-MARC_v.2" map on the same chromosome. (see updates below)

    We have added cyto-genetic G-band chromosome drawings to the QTL maps for cattle, chicken, pigs and sheep as of November 16, 2010. The cyto-genetic chromosome maps and linkage maps are aligned side by side, so that you can visually find cytogenetic band locations where your interested QTL is likely to land. Please note that the alignment is scaled to fit the chromosome length, therefore the band to cM/bp locations are only approximate. Use your best visual judgment plus more evidences if there are any, to assist you when it comes to fine localizations.

    Chromosomal banding graphs for goat are introduced in 2020.

  19. I wish to "zoom in" to examine a local region of my interested region, can I do that? how?

    The QTL map does not have an intuitive "zooming" feature but offer users to choose the size of the map to view. The size options are listed by a pull-down menu selection on the top tool bar. On a "very large" scale, users can see all details of the map at highest marker densities.

    Also, combining the use of "Marker density" pull-down menu, you can see more markers in a larger picture, to achieve the "zooming" effects.

  20. Some chromosomes have awful lot of QTL that the chromosome view becomes a very wide picture (extends way out of computer screen to the right), making it hard to compare some alignments. Is there any way I can see a more manageable view?

    On the "chromosome view" of the QTL, there is a "Display QTL" search box in the top tool menu. If you type the QTL abbreviations of your interests and click on "Go", the database will return you a new chromosome view with the QTL of your choice only, making it more effective for you to make comparisons.

  21. There are recently available genome sequences for cattle, chicken, and pigs. Has Animal QTLdb been made accessible to them? Which genome build version are used?

    Yes. We have built in a function to convert (see FAQ #23) all QTL locations (cM) on the linkage maps to genome maps (Mbp), thus made it possible to align all QTL on respective genomes on their most recent genome builds.

  22. For genomic mining of a QTL region, we often need to align a QTL against its genome for underlining genes. Is it possible in the QTLdb?

    Yes it is possible.

    Previously, we aligned some genomics features such as SNPs, microarray elements, microsatellites and RH map markers against QTL in terms of their genomics locations, within the QTLdb. (Reference to FAQ #13)

    In October 2008, we have implemented GBrowse for QTL alignments against multiple genomic features. Now we are able to align the QTL locations against all genome features stored in Genbank, such as locations of transcripts, mRNA, CDS, Annotated Repeats, etc. We also custom add more elements for alignment. The most recent addition to the alignments is the 60K SNP chip elements for cattle and pig.

    The link to Gbrowse view can be found in the Animal QTLdb main pages for respective QTLdb species, and at the GBrowse directory page: https://www.animalgenome.org/gbrowse/.

    If you have your own set of genes and wish to see what QTL/association is/are aligned them, as long as you have their genome coordinates of your gene, you can do so:

    (1) Create a GFF file with your own data, in a tab delimited 9-column plain text file, as in:

    Chr.4  Source  FeatureName  106510877  125474284   .   .   .   my_ID=49;Name="FUS1"
    Chr.4  Source  FeatureName  107478402  107478893   .   .   .   my_ID=51;Name="FUS2"
    .....  ......  ...........  .........  .........   .   .   .   ....................
    (2) Click Add your own tracks towards the bottom of your GBrowse page to upload the GFF file you just made, to the GBrowse window where you may already browse to a chromosome or a genome region of interests. In case the window is not refreshed, click Update Image to refresh.

  23. How accurate is the QTL location alignment to, say, transcript locations on the most recent genome assembly?

    The alignment of genome locations of QTL against that of transcripts or genes is accomplished by converting the linkage map QTL locations (cM) to its genome locations (bp) on the most recent assembly build (the updated genome build version used in the QTLdb can be found here). This is accomplished with references to available anchoring markers mapped on both (linkage and genome) maps.

    Often, a QTL boundary may not located exactly by an anchor marker. In such cases, the relative genome location of the QTL is estimated with an algorithm taking into account of the distance between the marker and the QTL boundary, the chromosome lengths of the linkage and genome maps, relative cM versis bp unit ratio for that particular chromosome, to calculate the offset of the QTL location relative to that of the anchor marker (interpolate). As such, the "bp" location of a QTL from its "cM" location is only a rough approximation. In addition, the sizes of QTL is often on the scale of centiMorgans ("cM"), which translate into genome maps in terms of a few hundred kelo- or mega-base pairs. Therefore large error sizes may be expected on such interpolation. On the other hand, the error ranges of original QTL reports are already on scale of "cM", we consider the current "bp" conversion pretty close to their "real" locations, and provide useful land marks for structural genome mining.

    While we caution users about the accuracy of exact "bp" locations found on the GBrowse or the downloaded data, we encourage users to re-estimate the QTL "bp" map locations, when possible, with their own methods/data. Nonetheless, it would be relatively safe to consider it a range instead of exact "bp" locations.

  24. Can I download the raw data from the QTLdb?

    Yes. Functions have been implemented so that there are multiple ways for you to download data from the QTLdb. (1) Links for downloading QTL coordinates (in cM or in bp) within a species can be found on respective species main pages of the QTLdb; (2) QTL coordinates and related data within a chromosome can be downloaded from the chromosomal view of a species; (3) Subsets of QTL data on a chromosome can be downloaded when term searches are applied to limited the view of QTL to that of your interests.

    Several file formats are available for the downloads: (1) Tab delimited plain text file containing QTL chromosomal locations in cM; (2) GFF files in which the QTL locations are in bp. The GFF download allows you to use the downloaded data file directly with other tools that take GFF file as input.

    The ways to download QTLdb data are updated on a data download page.

  25. I have downloaded some QTL data, and found many identical QTL (chromosome locations, traits, even publications. Is it possible that they are redundant curations?

    You may want to realize that the downloaded QTL data are only a snap shot of the data stored in the QTLdb. It depends on how you look at the data -- in some sense certain information may be "missing" if you scrutinize the data in a context that require extended information. For example, when you see two QTL that starts and ends on exact the same locations, your first impression might be that they are the same. It can be confusion in downloaded data sets since not all (comments and other loosely related) information are included, in a GFF download file. Verification of the data with the QTLdb online tools may be necessary. A useful way to verify if they are really "redundant" to compare them for all parameters, e.g. experiment methods, analysis methods, test statistics, flanking markers, etc.

    • Were they published in the same paper? -- Sometimes the same author(s) may analyze their data set with different methods, or re-analyzed their data with new data additions, and publish the results in a subsequent paper. It is likely the new results may resemble the previous ones.
    • Are their peak locations also the same? Are the peak/flanking markers also the same? (This would be an immediate indication that they are from different tests/analysis).
    • Are they for the same trait? Any trait variations on respective reports? -- Please note that there are many trait variations, e.g. ADG during gestation and ADG in feedlot; Fat content measured in loin and measured in ham; etc. In the QTLdb, "similar" QTL representations may all follow a "super trait" name for categorized comparisons (see FAQ #129 "Terminology" for more details).
    Cases exist where the same set of experiment data were analyzed with two different statistical methods, each was flagged with their significant p-values. In this case the information was only noted in "Comments" of a curation (found in QTL details).

    It is practically useful if you bring two suspicious QTL into respective QTL detail views for a side-by-side comparison. (Hint: formulate your URL to view details directly without having to go through browsing the web layers, i.e. put those two parts together:
    " https://www.animalgenome.org/cgi-bin/QTLdb/BT/qdetails?QTL_ID= " + " QTL_ID ", leaving no space, as in " https://www.animalgenome.org/cgi-bin/QTLdb/BT/qdetails?QTL_ID=4415 ".

    Feel free to contact us if you cannot resolve your confusions.

  26. Are there any tools available that can help me to do some meta-analysis?

    Yes, we have recently (October, 2010) developed a set of QTL meta-plot tools to help users performing simple meta-analysis on the fly. To use the tool, briefly, users will first need to select/browse to a species/chromosome of interest, then search for a trait or QTL to examine. When only the QTL for a single trait are displayed, a "Show MetaPlot" link appears (in the lower portion of a QTLdb chromosome view). When this link is clicked, the meta-plots will be displayed to the right of the QTL graphs on the QTLdb chromosome view. See this tutorial for more details.

  27. What is an Animal QTLdb Release? What is involved in a release?

    A "Release" of the Animal QTLdb is a process in which the new updates of the database on it's content (data), functions (database tools), and utilities (improved user accessibility to data and data analysis) are made available. Each release usually comes with a description of what's new on the release (see Releases History for examples). Upon each release, updated information are proliferated to the following sites in 5-10 days:

  28. What are Animal QTLdb data alliances?

    An Animal QTLdb Data Alliance is a genomics database partner with which we share data and make user experiences exploring these at each site seamlessly. Our current data alliances include: NCBI Entrez GeneDB, Ensembl, Reuters Data Center, and UCSC. Newly released QTL/association data are exported to them timely. Users can fully explore the power of their great tools for QTL and association data mining or exploration in the context of genome features.

    User contributed
  29. I have my data analysis on an older version of a genome assembly, is it possible to transfer QTL coordinates on the current assembly to the older assembly? How?

    Yes. While you may device your own ways to map genome coordinates of your interests from one genome assembly version to another, there are tools to help you to do so. These tools include but not limited to, UCSC liftOver, NCBI Remap, Ensembl API, CrossMap, among others. Sometimes, the flanking QTL genome coordinate pairs may not work out nicely with some tools. A trick used by Shangang Jia was to use only one side flanking location of a QTL to try out the LiftOver. You may like to repeat the same procedure in a separate run with flanking location of the other site, then assemble the results.

  30. Why sometimes I find QTL coordinates are not the same on your GBrowse / JBrowse and your web web site?

    We realize that while we commit our diligent works to keep the QTL data current and updated we should also keep users informaed of possible gaps from the steps how we update our data.

    The discrepancies you observe are most likely from our updates procedures in which the estimated genome coordinates are maintained (e.g. estimated bp from cM locations is subsequently corrected by actual SNP map coordinates when a SNP is present.)

    We do also realize there are time gaps between our data updates on the QTLdb, data download sites, GBrowse, and JBrowse sites, although we try to keep the gaps minimum as possible. A general advice is that the online Animal QTLdb web site reflects the most current data updates, i.e. data on other sites are propagated from here.

  31. Is there a better way to search QTLdb by multiple criteria for a literature without involving complicated multiple web form fields?

    Yes it is possible. The "publication search" within the QTLdb take a formulated search string that includes several lines of information. For instance, in this example:

    title:Carcass; authors:Casas; year<2003

    Multiple criteria search string may be separated by semi-colon (";"), and each criteria is led with a key word separated from the search string with a colon (":"). Supported key words include 'author', 'title', 'journal', 'abstract', 'text', 'institute', 'affiliation' (actually only the first 4 letters should be sufficient). The only exception is "year", which can be in the form like: "year<1999", "year=1999", or "year>1999".

  32. I wonder if I can download the QTL/association data based on the trait classes? For example, health traits?

    Sure you can. Actually there are multiple ways to achieve that.

    (1) Try a trait search under “Search and Analysis” tools of a species; the search will bring up a trait-centric view of your search results. Fine tune with improved keyword combinations on the same results page to get what you expected to find, then scroll down to the bottom of the page for a “Download” button.

    (2) Download all data from a species (on a species page, upfront), choose gff format to download; bring the data to an Excel sheet to expand column 9 into multiple columns with semicolon “;” as the delimiter. On the resulting page you can sort all data by traits (or whatever) to get what you need.

  33. Is there any license term on the QTLdb? (e.g. Open Data license or waiver)

    The data is made freely available for anyone to use as they want. For example, NCBI, UCSC, Ensembl and Thompson Reuters ingest the database and make it available via their sites. We have left it up to the user as to what they want to do with the data.

    We generally ask folks to cite on the publications that we have on the site.

  34. What are gene-centric, trait-centric, and SNP-centric QTL/association information?

    The gene-centric, trait-centric, and SNP-centric information views are dynamic web pages showing QTL/association data gathered surrounding each of these entities.

    The gene-centric and trait-centric data views were introduced on Release 34 in 2017; and SNP-centric data view was introduced on Release 42 in 2020. All those data views are built in the data format moduals of QTLdb search tools.

  35. How can I find all QTL/associations reported by a publication?

    In 2017 we introduced a "permanent record locator" link upon the Release 33. The record locator link can bring up a web page with all curated QTL/association data from one spesific publication listed by traits, by genome locations, and by data types (e.g. eQTL, pleiotropics, epistasis, etc). The link is in the form of an URL: https://www.animalgenome.org/QTLdb/supp/?t=[Identifyer_string], where the identifyer string is assigned by the QTLdb and is stable. It can be retrived by the data owner or curator. The link can be used by the authors to refer to their published data, to provide proof of evidence of data entry at the Animal QTLdb to journal editors/manuscript reviewers, and to serve other purposes such as supplementary data pointer of a publication, or a link from their web sites.

  36. How can I quickly locate a QTL/association with a QTL_ID without going through the troubles looking it up in each of the 8 species in QTLdb?

    In 2020 we introduced dbxref links to each QTL/association record in the QTLdb on Release 43. The syntax for the specific URL link is in the form of https://www.animalgenome.org/QTLdb/q?id=[QTL_ID], where QTL_ID is a numeric QTL id; found in QTLdb data downloads or when browse the QTLdb web site. It's often used by web tools, API tools, or database dbxref references. Refer to DBxREF list for syntax definition details on "Animal QTLdb".

  37. Terminologies

    • Clinical Measurement Ontology (CMO) - is designed to be used to standardize morphological and physiological measurement records generated from clinical and model organism research and health programs.
    • eQTL: - "eQTL" is abbreviated from "expression QTL". It's a locus that explains a fraction of the genetic variance of a gene expression phenotype. eQTL is usually detected from association between genotype and RNA expression levels, which can be resolved to genes mapped to the genome, representing gene-eQTL. The use of selective transcriptional profiling using available information on individual quantitative traits can link an expression pattern to traits, resulting in trait-eQTL representations.

      Cis/Trans eQTL: eQTLs mapped to the approximate location of their gene-of-origin are referred to as local eQTL (cis eQTL). In contrast, those that map far from the location of their gene of origin (often on different chromosomes) are referred to as distant eQTL (trans eQTL). Usually the use of these terms are best reserved for instances when the regulatory mechanism (cis vs. trans) of the underlying sequence has been established (source: wikipedia.org). Related/corresponding terms include cis-acting/trans-acting, cis-SNP/trans-SNP, etc.

    • DOI - Digital Object Identifier, a unique alphanumeric string assigned by a registration agency (the International DOI Foundation) to identify content locations on the Internet. This is useful in the QTLdb for direct links from curated QTL/association data to the original publications.
    • dbxref - The dbxref or db_xref is a database qualifier used in a method to serve as a stable vehicle for between-database links of records. It often takes the form of a unique identifier built into a stable URL without any space. This is often used by web tools, API tools, or database dbxref references.
    • Epistatic QTL - refers to to observations that multiple QTL/associations have interactions in terms of a combined effect on traits they influence.
    • Flanking markers - Genetic markers that boundary either sides of a hypothesized QTL. A flanking marker can also represent the level of statistical significance when the QTL is detected. ( see FAQ #5 above for more details )
    • LocusLink - It was a deprecated NCBI database that organizes information around genes to generate a central hub for accessing gene-specific information for multiple species. It provides a single query interface to curated sequence and descriptive information about genetic loci and presents information on official nomenclature, aliases, sequence accessions, phenotypes, EC numbers, MIM numbers, UniGene clusters, homology, map locations, and related web sites.

      The Animal QTLdb data were first started to be synchronized to the LocusLink in 2004. Following the LocusLink transition to "Gene"DB that started in 2005, the data synchronization was re-established in early 2009 to the NCBI GeneDB.

    • Livestock Product Trait Ontology (LPT) - 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.
    • Pleiotropic QTL - Refers to observations that a QTL/association locus has influnces on two or more seemingly unrelated phenotypic traits.
    • Quantitative Trait Loci - Genetic loci contributing to quantitative traits variations. ( see FAQ #1 above for more info.)
    • Statistics test parameters
      • False Discovery Rate (FDR) - is the number of false positives in all of the rejected hypotheses, i.e. the rate of type I errors in null hypothesis testing when conducting multiple comparisons.
      • Posterior Probability of Association (PPA) - can be can be thought of as the Bayesian analogue of a p-value obtained, interpreted directly as a probability, irrespective of power, sample size or how many other SNPs were tested. (Matthew Stephens* and David J. Balding, Bayesian statistical methods for genetic association studies. Nature Reviews - Genetics, 2009; 10:681).
      • Posterior Probability Inclusion (PPI) - In Bayesian. the proportion of samples in which at least one SNP from a given window was included in the model with a non-zero effect, is used for significance test in pleiotropy tests (Garrick DJ, Fernando RL, "Implementing a QTL detection study (GWAS) using genomic prediction methodology". Methods Mol Biol. 2013;1019:275-98). (This is also known as PIP.)
      • iHS - Signature of Selection test: Log ratio between two integrated haplotype homozygosity scores
      • CLR - Signature of Selection test: Multilocus composite likelihood ratio test.
      • XPCLR - Signature of Selection test: Cross-population composite Likelihood ratio test (multilocus allele frequency differentiation between two populations).
      • EHH - Signature of Selection test: Extended haplotype homozygosity
      • XPEHH - Signature of Selection test: Cross-population extended haplotype homozygosity
      • Fst - Fixation index (FST) is a measure of population differentiation due to genetic structure. It is frequently estimated from genetic polymorphism data, such as single-nucleotide polymorphisms (SNP) or microsatellites. Developed as a special case of Wright's F-statistics, it is one of the most commonly used statistics in population genetics.
    • Suggestive linkage - ( see FAQ #5 above )
    • Significant linkage - ( see FAQ #5 above )
    • Trait Ontology - ( see FAQ #6 above )
    • Trait Class - A trait hierarchy term used in the Animal QTLdb/CorrDB trait management system. It's usually a generic name to refer to a category of traits that are similar in terms of a concept (production traits), or a function (reproduction traits; disease resistance traits), or a process (growth traits), or traits under a concept (meat quality traits), etc.
    • Trait Name - A trait hierarchy term used in the Animal QTLdb/CorrDB trait management system. The names are usually defined to reflect the nature, the conceptural representartion, and the measurements (locations, time, methods, units, etc). e.g. 24hr post mortem pH. Each trait is distinguished by its characteristics, methods of measurement, and product merit.
    • Trait Type / SuperTrait - A trait hierarchy term used in the Animal QTLdb/CorrDB trait management system. It concepturally represents a collection of similar traits that share the same type of specified property or feature(s), general physical or chemical property of, or the processes that lead to, or types of measurements that result in, an observation, regardless possible naming variations by measuring times, locations or methods ("trait modifiers"). e.g. Meat Color.
    • Vertebrate Trait Ontology (VT) - is a controlled vocabulary for the description of traits (measurable or observable characteristics) pertaining to the morphology, physiology, or development of vertebrate organisms.

First draft: January 5, 2005
Version 2: August, 12, 2006
Version 3: January, 11, 2007
Version 4: May, 17, 2007
Version 5: January, 6, 2009
Version 6: March, 23, 2010
Version 7: December, 29, 2010
Version 8: December, 11, 2011
Version 9: November, 20, 2012
Version 10: September 2, 2013
Version 11: December 12, 2016
Version 12: September 18, 2017
Version 13: August 27, 2019
Version 14: December 12, 2020

Last update: March 12 2021 14:27:36.

By Zhiliang Hu
Associate Scientist
Dept of Animal Science
Iowa State University
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Contact: NAGRP Bioinformatics Team
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