Accurately establishing the relationships among individuals lays the foundation for genetic analyses such as genome-wide association studies and identification of selection signatures. Of particular interest to the poultry industry are estimates of genetic merit based on molecular data. These estimates can be commercially exploited in marker-assisted breeding programs to accelerate genetic improvement. Here, we test the utility of a new method we have recently developed to estimate animal relatedness and applied it to genetic parameter estimation in commercial broilers. Our approach is based on the concept of data compression from information theory. Using the real-world compressor gzip to estimate normalized compression distance (NCD) we have built compression-based relationship matrices (CRM) for 988 chickens from 4 commercial broiler lines—2 male and 2 female lines. For all pairs of individuals, we found a strong negative relationship between the commonly used genomic relationship matrix (GRM) and NCD. This reflects the fact that “similarity” is the inverse of “distance.” The CRM explained more genetic variation than the corresponding GRM in 2 of 3 phenotypes, with corresponding improvements in accuracy of genomic-enabled predictions of breeding value. A sliding-window version of the analysis highlighted haplotype regions of the genome apparently under selection in a line-specific manner. In the male lines, we retrieved high population-specific scores for IGF-1 and a cognate receptor, INSR. For the female lines, we detected an extreme score for a region containing a reproductive hormone receptor (GNRHR). We conclude that our compression-based method is a valid approach to established relationships and identify regions under selective pressure in commercial lines of broiler chickens.