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.