The widespread incorporation of genomic information in dairy genetic evaluations allows for the opportunity to develop and implement methods to manage populations at the genomic level. While for the most part genome-wide metrics are currently employed in managing livestock inbreeding, genomic information offers the ability to identity region-specific homozygosity causing inbreeding depression or with reduced levels of diversity. Here we emphasize the importance of dedicated simulation tools to better understand the impact of alternative management schemes on genetic diversity, inbreeding depression and the fitness of a population. Also, methods to understand inbreeding depression have so far primarily focused on the effect of a region being in a run of homozygosity (ROH), which doesn’t account for the region potentially containing multiple unique ROH genotypes with variable effect. We discuss alternative heuristic methods to identify haplotypes contained within a ROH that give rise to reduced performance. This class of algorithms comprises multiple steps that scan the genome for unfavorable haplotypes, which are then contrasted using a linear mixed model. The identified haplotypes are then used to generate a functional inbreeding load metric across individuals (IIL). We present results from simulated scenarios combining different levels of linkage disequilibrium (LD) and number of loci impacting a quantitative trait and show that with increased LD in the population, these algorithms identify a greater proportion of the true unfavorable ROH effects (mean for high LD scenario: 41% of highly unfavorable haplotypes). In real data the accuracy of predicting phenotypes based on IIL across milk yield traits in Jersey cattle is different from 0 (mean ± SD: 0.20 ± 0.02), suggesting the method is capable of capturing functional inbreeding. Results from previous analysis are then discussed in the context of characterizing the expected progeny genome based on the observed parental genotypes. The results illustrate how genomic information can be utilized to manage the genetic diversity and degree of inbreeding depression that exists within a population.