The rate and spectrum of germline mutations in chicken from a commercial pedigree line

Abstract Background Pedigree-based studies of germline mutations provide a direct understanding of the origin and consequences of genetic variation, and allow quantification of how mutations affect functional variation. Here, we use whole-genome sequencing data from 194 chicken ( Gallus gallus ) trios from a commercial pedigree line to estimate the germline mutation rate and spectrum, and to characterize patterns of mutational variation across the genome and genes, aiming to rank protein coding genes by their expected contributions to mutational variance ( V m ). Results We estimated a mutation rate of µ = 3.49 × 10 −9 mutations per nucleotide site per generation. A goodness-of-fit test (χ 2 13 = 10.97, P = 0.61) indicated that the number of new mutations per generation follows a Poisson distribution ( λ = 6.22). A read-based phasing approach revealed a strong sex bias, with males contributing approximately twice as many mutations as females. Microchromosomes showed a point estimate of mutation rate approximately 8% higher than macrochromosomes, likely owing to their higher GC content and a mutation spectrum dominated by C→T transitions. From this spectrum, we built a codon transition matrix and show that genes vary widely in their propensity to mutation based on codon composition, likely reflecting large differences in their contributions to V m . Specifically, we found that genes involved in immunity and development rank among those most prone to non-silent mutations. Conclusions Our results reveal predictable patterns of gene-level mutational variation and provide a framework for anticipating how new mutations generate functional variation. By bridging quantitative genetics and molecular biology, we provide a mechanistic basis for variation in V m across the genome, showing that heterogeneity in V m can arise from gene-specific features, particularly codon composition and mutational target size. These metrics offer a way to rank genes and prioritize genetic variants by their expected contributions to biological functions as a consequence of de novo mutation. This provides a new basis for informed management of genetic variation in breeding populations and for understanding the evolutionary dynamics of functionally important genes.