# Data structures * Species, e.g. 'Mouse', are split into groups, such as 'BXD bone studies' * A group can contain multiple families (see rat below) divided into subgroups * A trait, e.g. 'body weight' is a vector of data points the belongs to a study * A genotype vector can be a trait * A trait is always a member of group * A trait is part of a study/sample described in metadata * Theoretically traits can belong to multiple groups * An attribute can be a trait * An attribute can be a cofactor (also a vector) * An attribute is like a trait, but not used in computations, other than as a cofactor * Attributes are editable by group owners * We can have shared vocabulary for traits and attributes But * A trait is shown with attributes as cofactors * A cofactor can be a trait * A cofactor can be an attribute * A cofactor is not stored in the database - it is an optional vector (cofactors and attributes and traits overlap) ## Groups In GN datasets are organised in groups. On the main menu you can see BXD datasets are grouped into BXD aged hippocampus or BXD bone studies reflecting higher level interests. Groups are formed around a strain (here BXD) and are linked to experiments, or sample lists. A group, family, cohort, population is almost always a set of N cases or individuals or isogenic animals treated as "individuals". The BXD family of strains is a good and complex example. We can treat the 100+ BXD strains as if they were 100 "genetic" individuals and collapse traits for 10 animals each into one value with an error term. Or we can treat all 100 x 10 animals as actual individuals. Even though we use the same animals in both cases, they are treated in GN as two separate GROUPS. From a computational perspective a GROUP is a set that can be used to compute correlations among traits. Coming back to the two BXD groups (N = 100 strain means; or N = 1000 individuals), we can only compute correlations within either mean data or individual data. Groups are maintained in the inaptly named 'InbredSet' table. E.g. ``` MariaDB [db_webqtl]> select * from InbredSet limit 3; +----+-------------+-------------------+--------+-----------+-------------------+--------+-----------------+-------------+--------------------------------------------------+-------------+-------------+---------------+ | Id | InbredSetId | InbredSetName | Name | SpeciesId | FullName | public | MappingMethodId | GeneticType | Family | FamilyOrder | MenuOrderId | InbredSetCode | +----+-------------+-------------------+--------+-----------+-------------------+--------+-----------------+-------------+--------------------------------------------------+-------------+-------------+---------------+ | 1 | 1 | BXD | BXD | 1 | BXD Family | 2 | 1 | riset | Reference Populations (replicate average, SE, N) | 1 | 0 | BXD | | 2 | 2 | B6D2F2 OHSU Brain | B6D2F2 | 1 | B6D2F2 OHSU Brain | 2 | 1 | intercross | Crosses, AIL, HS | 3 | 0 | NULL | | 4 | 4 | AXB/BXA | AXBXA | 1 | AXB/BXA Family | 2 | 1 | NULL | Reference Populations (replicate average, SE, N) | 1 | 0 | AXB | +----+-------------+-------------------+--------+-----------+-------------------+--------+-----------------+-------------+--------------------------------------------------+-------------+-------------+---------------+ 3 rows in set (0.000 sec) ``` ## What is a trait? A trait is a vector of floats or integers for a GROUP. Body weight is a simple example of a trait. Eye color, if coded numerically, is a trait. A trait will usually have metadata, but the trait data itself boils down to single vector of values for a specific group that can be used to compute univariate statistics, (means, variances, etc), correlations between traits within a group, maps of trait variance for that group, and higher order properties. A trait could be a vector of more complex numerical types than just scalars. But up to now all traits that we have mapped or studied in GN are just simple vectors of numbers. Traits can also be genotypes that are coded as integers (usually). Some genotypes are coded as floats if genotype probabilities. In GeneNetwork a single trait value (a scalar) always belongs to a genetically-defined unit/case/individual/clone/strain/F1 hybrid. A single trait vector (what I usually mean when I talk about a trait) always belongs to at least one GROUP. A trait vector can belong to multiple Groups if the groups overlap in membership. For example, the rat Hybrid Rat Diversity Population (HRDP) consists of the HXB family, the LEXF family, and a bunch of other inbred rat strains. HRDP traits can therefore be split into subgroups. This is a pain from a programming perspective, since a data matrix of TRAITS-by-GROUP may be a sparse matrix. And the GUI become more complex, since the user may want to slice and dice the GROUP in multiple ways, for example—just map the HXB family, just map the LEXF family, or map everything together. ## Case attributes Case attributes, such as body weight or gene expression, are "strain/sample metadata" at the group level. All traits within a group share the same sample list. The other way, case attribute are connected to samples within a group. An attribute can be any trait as defined above, or it can be a short alphanumeric code used primarily as a cofactor in analysis. Sex is a good example of an attribute that can be coded as an integer (0 or 1 or x=unknown) and used computationally as if it were any other trait, or it can be coded as M and F and use for display and as a cofactor. But some attributes are not even cofactors. For example, an Attribute column may define which strains or cases were used in Study X by Roy et al in 2021. In this situation, the GUI and the attribute are used to quickly sort or select or exclude particular cases. Attributes are a recent addition to GeneNetwork. The motivation was to provide the user with a display of the most important cofactors of a study. For example, in our large study of lifespan in the BXD mice, we wanted to provide "low level" data on each individual animal. In this situation, the sex, strain, diet, aar tag number, resource reference ID, the epoch of the BXD strain (when the BXD strain was made), and even the study in which cases were specifically used—all of those are considered attributes. The last three attribute columns that you see in the screenshot below (KM20, SR21, EW21) refer to three papers (e.g. SR21 = Suheeta Roy 2021) that have used subsets of these animals. None of these attributes are used directly in computations. They are used to sort and filter. But notice that one of these ATTRIBUTES is also the most important trait in this study—the Longevity column attribute is the same as the VALUE (Trait BDL_10001). This highlights the fact that a trait can become an attribute, but not all attributes can become traits. Who would compute a correlation against ear tag number? => https://genenetwork.org/show_trait?trait_id=10001&dataset=BXD-LongevityPublish Attributes generally belong to a GROUP, not to an individual TRAIT. But for display purposes, every trait will show a set of ATTRIBUTES. This is a source of potential confusion. Who can edit case attributes? Attributes should only be editable by the GROUP owner or perhaps by GeneNetwork curators. How do we make sure we can compare attributes between datasets if the naming is haphazard? Attributes are only a GROUP property (e.g. BXD Family, AKXD Family, GTEx). The way I think about them today, they cannot be used computationally across GROUPS. They can be used across traits within GROUPS. Can we have global case attributes? We could have shared vocabulary for attributes, but I do not know how a global case attribute would be used computationally. For example, sex, age, body weight, lab identifiers, date of analysis, will almost always be useful attributes (and also some of those are traits) no matter what the GROUP, provided the GROUP consists of true individuals. So a common vocabulary of ATTRIBUTES make great sense, but computationally ATTRIBUTES as I think about them today, belong just to a group (or overlapping set of groups). However, it would be cool to compare differences in gene expression in the liver of BXD mice, HXD rats, and GTEx humans as a function of sex and age.