Abstract
Without appropriate relationships present in a given population, identifying dominance effects in the expression of desirable traits is challenging. Including non-additive effects is desirable to increase accuracy of breeding values. There is no current user-friendly tool package to investigate genetic relatedness in large pedigrees. The objective was to develop and implement efficient algorithms in R to calculate and visualize measures of relatedness (e.g., sibling and family structure, numerator relationship matrices) for large pedigrees. Comparisons to current R packages (Table 1) are also made. Functions to assign animals to families, summary of sibling counts, calculation of numerator relationship matrix (NRM), and NRM summary by groups were created, providing a comprehensive toolkit (Sibs package) not found in other packages. Pedigrees of various sizes (n = 20, 4,035, 120,000 and 132,833) were used to test functionality and compare to current packages. All runs were conducted on a Windows-based computer with an 8 GB RAM, 2.5 GHz Intel Core i7 processor. Other packages had no significant difference in runtime when constructing the NRM for small pedigrees (n = 20) compared to Sibs (0 to 0.05 s difference). However, packages such as ggroups, AGHmatrix, and pedigree were 10 to 15 min slower than Sibs for a 4,035-individual pedigree. Packages nadiv and pedigreemm competed with Sibs (0.30 to 60 s slower than Sibs), but no package besides Sibs was able to complete the 132,833-individual pedigree due to memory allocation issues in R. The nadiv package was closest with a pedigree of 120,000 individuals, but took 37 min to complete (13 min slower than Sibs). This package also provides easier input of pedigrees and is more encompassing of such relatedness measures than other packages (Table 1). Furthermore, it can provide an option to utilize other packages such as GCA for connectedness calculations when using large pedigrees.
Review of the Durability of Polymer Electrolyte Membrane Fuel Cell in Long-Term Operation: Main Influencing Parameters and Testing Protocols
