121 Copy number variation: From mapping to variance components in Holsteins

Abstract Copy number variations (CNVs) modify transcription levels mainly through changing genes and regulatory elements dosages. Hence, CNVs can contribute to the additive genetic variance of quantitative traits, which might not be fully captured by SNPs. Here, CNVs were mapped from 3,601 Holsteins and their contribution to complex traits was explored by i) quantitative trait loci (QTL) enrichment analysis, and ii) variance component analysis for metritis (1 = disease, 0 = no disease). The 4,113 CNVs derived from high-density (777k) genotypic information were compiled into 1,184 CNV regions (CNVRs) overlapping 7,832 QTLs reported in dairy cattle and available in the AnimalQTLdb. Fisher’s exact test was applied to perform the QTL enrichment analysis followed by False Discovery Rate (FDR) correction. Then, QTLs overlapping CNVRs were enriched for traits when the corrected P-value (PFDR) was lower than 0.05. For variance components estimation, genomic information from all CNVs loci was used to build the CNV-derived genomic relationship matrix (CNV_GRM). CNV loci showing double deletions, single deletions, and normal state in the population were coded as 0, 1, and 2, respectively, whereas CNV loci presenting normal state, single duplications, and double duplications were coded as 0, 1, and 2, respectively. Given that some CNVs presented both deletions and duplications at the same position in the population, these mixed-CNVs were treated as two distinct CNVs loci, one to represent deletions and the other to represent duplications. The variance components for metritis were estimated by fitting either only a SNP-derived GRM (SNP_GRM) or jointly SNP_GRM and CNV_GRM considering 3,272 cows in models that included the female category and farm-year-season as fixed effects. QTLs overlapped with CNVRs were enriched for milk (e.g., milk fat yield, milk yield, and milk protein percentage), reproduction (e.g., calving ease, non-return rate, and heat intensity), exterior (e.g., teat length, udder depth, and udder height), production (e.g., length of productive life, lifetime profit index, and net merit), and health traits (e.g., immune globulin G level, somatic cell, and bovine respiratory susceptibility). Regarding variance components, copy number variations were able to capture a fraction of the additive genetic variance not captured by SNPs alone, increasing the heritability estimate for metritis. In summary, our findings suggest that CNVs overlap QTLs associated with economically important traits more than only by chance and CNVs can capture additional additive genetic variance not fully captured by SNPs alone.