genotype

ID: GENO_0000536

A specification of the genetic state of an organism, whether complete (defined over the whole genome) or incomplete (defined over a subset of the genome). Genotypes typically describe this genetic state as a diff between some variant component and a canonical reference.

1. Scope of 'Genetic State': 'Genetic state' is considered quite broadly in GENO to describe two general kinds of 'states'. First, is traditional notion of 'allelic state' - defined as the complement of alleles present at a particular location or locations in a genome (i.e. across all homologous chromosomes containing this location). Here, a genotype can describe allelic state at a specific locus in a genome (an 'allelic genotype'), or describe the allelic state across the entire genome ('genomic genotype'). Second, this concept can also describe states of genomic features 'extrinsic' to their intrinsic sequence, such as the expression status of a gene as a result of being specifically targeted by experimental interventions such as RNAi, morpholinos, or CRISPRs. 2. Genotype Subtypes: In GENO, we use the term 'intrinsic' for genotypes describing variation in genomic sequence, and 'extrinsic' for genotypes describing variation in gene expression (e.g. resulting from the targeted experimental knock-down or over-expression of endogenous genes). We use the term 'effective genotype' to describe the total intrinsic and extrinsic variation in a cell or organism at the time a phenotypic assessment is performed. Two more precise conccepts are subsumed by the notion of an 'intrinsic genotype': (1) 'allelic genotypes', which specify allelic state at a single genomic location; and (2) 'genomic genotypes', which specify allelic state across an entire genome. In both cases, allelic state is typically specified in terms of a differential between a reference and a set of 1 or more known variant features. 3. The Genotype Partonomy: 'Genomic genotypes' describing sequence variation across an entire genome are 'decomposed' in GENO into a partonomy of more granular levels of variation. These levels are defined to be meaningful to biologists in their attempts to relate genetic variation to phenotypic features. They include 'genomic variation complement' (GVC), 'variant single locus complement' (VSLC), 'allele', 'haplotype', 'sequence alteration', and 'genomic background' classes. For example, the components of the zebrafish genotype "fgf8a; fgf3[AB]", described at https://zfin.org/ZDB-FISH-150901-9362, include the following elements: - GVC: fgf8a; fgf3 (total intrinsic variation in the genome) - Genomic Background: AB (the reference against which the GVC is variant) - VSLC1: fgf8a (homozygous complement of gene alleles at one known variant locus) - VSLC2: fgf3 (heterozygous complement of gene alleles at another known variant locus) - Allele 1: fgf8a (variant version of the fgf8a gene, present in two copies) - Allele 2: fgf3 (variant version of the fgf3 gene, present in one copy) - Allele 3: fgf3<+> (wild-type version of the fgf3 gene, present in one copy) - Sequence Alteration1: (the specific mutation within the fgf8a gene that makes it variant) - Sequence Alteration2: (the specific mutation within the fgf3 gene that makes it variant) A graphical representation of this decomposition that maps each element to a visual depiction of the portion of a genome it denotes can be found here: https://github.com/monarch-initiative/GENO-ontology/blob/develop/README.md One reason that explicit representation of these levels is important is because it is at these levels that phenotypic features are annotated to genetic variations in different clinical and model organism databases For example, ZFIN typically annotates phenotypes to effective genotypes, MGI to intrinsic genotypes, Wormbase to variant alleles, and ClinVar to haplotypes and sequence alterations. The ability to decompose a genotype into representations at these levels allows us to "propagate phenotypes" up or down the partonomy (e.g. infer associations of phenotypes annotated to a genotype to its more granular levels of variation and the gene(s) affected). This helps to supporting integrated analysis of G2P data.