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Unveiling Comparative Genomic Trajectories of Selection and Key Candidate Genes in Egg-Type Russian White and Meat-Type White Cornish Chickens

Abstract

Comparison of genomic footprints in chicken breeds with different selection history is a powerful tool in elucidating genomic regions that have been targeted by recent and more ancient selection. In the present work, we aimed at examining and comparing the trajectories of artificial selection in the genomes of the native egg-type Russian White (RW) and meat-type White Cornish (WC) breeds. Combining three different statistics (top 0.1% SNP by value at pairwise breed comparison, hapFLK analysis, and identification of ROH island shared by more than 50% of individuals), we detected 45 genomic regions under putative selection including 11 selective sweep regions, which were detected by at least two different methods. Four of such regions were breed-specific for each of RW breed (on GGA1, GGA5, GGA8, and GGA9) and WC breed (on GGA1, GGA5, GGA8, and GGA28), while three remaining regions on GGA2 (two sweeps) and GGA3 were common for both breeds. Most of identified genomic regions overlapped with known QTLs and/or candidate genes including those for body temperatures, egg productivity, and feed intake in RW chickens and those for growth, meat and carcass traits, and feed efficiency in WC chickens. These findings were concordant with the breed origin and history of their artificial selection. We determined a set of 188 prioritized candidate genes retrieved from the 11 overlapped regions of putative selection and reviewed their functions relative to phenotypic traits of interest in the two breeds. One of the RW-specific sweep regions harbored the known domestication gene, . Gene ontology and functional annotation analysis provided additional insight into a functional coherence of genes in the sweep regions. We also showed a greater candidate gene richness on microchromosomes relative to macrochromosomes in these genomic areas. Our results on the selection history of RW and WC chickens and their key candidate genes under selection serve as a profound information for further conservation of their genomic diversity and efficient breeding.

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References
1.
de Freitas Dionizio A, de Souza Khatlab A, Alcalde C, Gasparino E, Feihrmann A . Supplementation with free methionine or methionine dipeptide improves meat quality in broilers exposed to heat stress. J Food Sci Technol. 2021; 58(1):205-215. PMC: 7813893. DOI: 10.1007/s13197-020-04530-2. View

2.
Ferencakovic M, Solkner J, Curik I . Estimating autozygosity from high-throughput information: effects of SNP density and genotyping errors. Genet Sel Evol. 2013; 45:42. PMC: 4176748. DOI: 10.1186/1297-9686-45-42. View

3.
Deniskova T, Dotsev A, Selionova M, Kunz E, Medugorac I, Reyer H . Population structure and genetic diversity of 25 Russian sheep breeds based on whole-genome genotyping. Genet Sel Evol. 2018; 50(1):29. PMC: 5968526. DOI: 10.1186/s12711-018-0399-5. View

4.
Tarsani E, Kranis A, Maniatis G, Avendano S, Hager-Theodorides A, Kominakis A . Deciphering the mode of action and position of genetic variants impacting on egg number in broiler breeders. BMC Genomics. 2020; 21(1):512. PMC: 7379350. DOI: 10.1186/s12864-020-06915-1. View

5.
Shah T, Patel N, Patel A, Upadhyay M, Mohapatra A, Singh K . A genome-wide approach to screen for genetic variants in broilers (Gallus gallus) with divergent feed conversion ratio. Mol Genet Genomics. 2016; 291(4):1715-25. DOI: 10.1007/s00438-016-1213-0. View