digrees provide a cost-effective approach for sampling IBD across more
individuals over time.
Methods that identify the regions of the genome that contribute to trait
heritability, broadly termed ‘gene mapping’, often require or are
enhanced by pedigree information. Linkage mapping is one important
method of gene mapping, which leverages genetic markers, phenotypic
data, and recombination across a multi-generational pedigree to
understand the general location of genes controlling traits (Slate et
al., 2009; Laird & Lange, 2011). Tracking dense panels of genome-wide
markers over generations also forms the basis of genetic linkage maps,
which characterise the recombination landscape and show the position and
architecture of genes throughout the genome. For example, a linkage map
was created for collared flycatcher (Ficedula albicollis ) using
deep pedigree data and thousands of genome-wide SNPs, which has provided
an understanding of flycatcher genomic architecture in comparison to
other species (Kawakami et al., 2014). Beyond providing an understanding
of genome evolution, these maps provide useful context to how
populations are expected to respond to selection pressures (Stapley,
Feulner, Johnston, Santure, & Smadja, 2017). For example, mapping
resources and pedigrees developed in California condor are being used to
understand the genomic basis of chondrodystrophy, a lethal form of
dwarfism in this critically endangered species (Ralls, Ballou, Rideout,
& Frankham, 2000; Romanov et al., 2009). Besides traits that are
controlled by single genes of large effect, linkage maps and pedigree
data can be utilised for quantitative trait locus linkage mapping, or
QTL mapping, which enables the detection of many genomic loci that
contribute to continuous trait differences (Slate, 2005). For example,
QTL mapping identified candidate adaptive loci contributing to bud
phenology in white spruce (Picea glauca ; Pelgas et al., 2011) and
phenotypic differences between marine and freshwater nine-spined
stickleback (Pungitius pungitius ; Yang, Guo, Shikano, Liu, &
Merilä, 2016). Similarly, pedigree information can be incorporated into
GWAS, which leverages dense markers, putatively unrelated individuals,
and phenotypic information to understand the genomic basis of traits.
Studies have shown that GWAS that incorporate pedigree data are better
able to avoid type-I error and add greater precision to GWAS analyses,
especially in data sets with low marker density (Chen et al., 2013; Zhou
et al., 2017). Given that genetically-depauperate and/or species with
large genomes may be hampered by low marker density, we anticipate that
pedigrees will be an important tool that can complement genome-wide
association analyses.