Beyond kinship
Pedigrees capture a wealth of information beyond individual
relationships produced purely by genomic data (Clutton-Brock & Sheldon,
2010). The behavioral and ecological observations required to provide
inter-individual relationships results in a rich ancillary dataset that
cannot be captured by genomic data alone. For example, pedigrees can
discern different relationships with identical relatedness coefficients
(e.g., half-siblings compared to grandparent-grandoffspring, R =
0.25), which can have different social and ecological consequences.
Pedigrees also carry rich demographic data that may be inaccessible from
molecular data, including sex for species without genetic sex
determination (Janzen & Paukstis, 1991), cohort, number of offspring,
age, and survival. Phenotypic data collected alongside ancestry is often
extensive, including morphometrics (e.g., weight, size, body condition),
cause of death, behaviour, and signs of inbreeding depression (e.g.,
disease susceptibility, infertility). On its own, the metadata captured
alongside pedigrees can be used to forecast best management practices
for small populations through population viability analysis (i.e., PVA;
Lacy & Pollak, 2021) and provides a critical resource for understanding
demography and fitness (e.g., variance in reproductive success). For
example, a recent study harnessed pedigree data from 15 species
(> 30K individuals) to show how generations in captivity
impact survival (Farquharson, Hogg, & Grueber, 2021). Another study
assessing breeding in 39 pedigreed populations of 21 wild animal species
(> 35K females) concluded that many species were able to
buffer annual fluctuations in optimal breeding date through phenotypic
plasticity (de Villemereuil et al., 2020). Meta-analyses on this scale
would be impossible to ascertain using genomic data alone, given these
studies rely on life history data carried in pedigrees. Further,
metadata captured in pedigrees can also be integrated with genomic
approaches, for example quantitative trait locus mapping (Pelgas,
Bousquet, Meirmans, Ritland, & Isabel, 2011), genome-wide association
studies (GWAS; Morris et al., 2013), assessing adaptive potential (de
Villemereuil et al., 2019a), genomic selection (GS) studies, and GxE
studies to test genotypes for association with environmental variation
(Crossa et al., 2017; see below).