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BMC Genomics

, 18:425

Non-human and non-rodent vertebrate genomics


BackgroundAlthough genome-wide association and genomic selection studies have primarily focused on additive effects, dominance and imprinting effects play an important role in mammalian biology and development. The degree to which these non-additive genetic effects contribute to phenotypic variation and whether QTL acting in a non-additive manner can be detected in genetic association studies remain controversial.

ResultsTo empirically answer these questions, we analyzed a large cattle dataset that consisted of 42,701 genotyped Holstein cows with genotyped parents and phenotypic records for eight production and reproduction traits. SNP genotypes were phased in pedigree to determine the parent-of-origin of alleles, and a three-component GREML was applied to obtain variance decomposition for additive, dominance, and imprinting effects. The results showed a significant non-zero contribution from dominance to production traits but not to reproduction traits. Imprinting effects significantly contributed to both production and reproduction traits. Interestingly, imprinting effects contributed more to reproduction traits than to production traits. Using GWAS and imputation-based fine-mapping analyses, we identified and validated a dominance association signal with milk yield near RUNX2, a candidate gene that has been associated with milk production in mice. When adding non-additive effects into the prediction models, however, we observed little or no increase in prediction accuracy for the eight traits analyzed.

ConclusionsCollectively, our results suggested that non-additive effects contributed a non-negligible amount more for reproduction traits to the total genetic variance of complex traits in cattle, and detection of QTLs with non-additive effect is possible in GWAS using a large dataset.

KeywordsVariance decomposition Additive Dominance Imprinting Cattle Dairy traits Qtl AbbreviationsCCRCow conception rate


DPRDaughter pregnancy rate

FYFat yield

GEBVGenomic estimated breeding value

GRMGenomic relationship matrix

GWASGenome-wide association study

HCRHeifer conception rate

KbKilo base pairs = 1000 base pairs

LDLinkage disequilibrium

MbMega bases pairs = 1000 kb = 1 million base pairs

MYMilk yield

PYProtein yield

QTLQuantitative trait locus

SCSSomatic cell score

SNPSingle nucleotide polymorphism

STPLStandardized productive life

YDYield deviatoin

Electronic supplementary materialThe online version of this article doi:10.1186-s12864-017-3821-4 contains supplementary material, which is available to authorized users.

Autor: Jicai Jiang - Botong Shen - Jeffrey R. O’Connell - Paul M. VanRaden - John B. Cole - Li Ma

Fuente: https://link.springer.com/

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