94 research outputs found
British sheep breeds as a part of world sheep gene pool landscape: looking into genomic applications
Sheep farming has been an important sector of the UKβs economy and rural life for many centuries. It is the favored source of wool, meat and milk products. In the era of exponential progress in genomic technologies, we can now address the questions of what is special about UK sheep breed genotypes and how they differ genetically form one another and from other countries. We can reflect how their natural history has been determined at the level of their genetic code and what traces have been left in their genomes because of selection for phenotypic traits. These include adaptability to certain environmental conditions and management, as well as resistance to disease. Application of these advancements in genetics and genomics to study sheep breeds of British domestic selection has begun and will continue in order to facilitate conservation solutions and production improvement
Genomic studies in domestic goats (Capra hircus L.): current advances and prospects (review] ΠΠ΅Π½ΠΎΠΌΠ½ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π΄ΠΎΠΌΠ°ΡΠ½ΠΈΡ ΠΊΠΎΠ· (Capra hircus L.): ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ΅ ΡΠΎΡΡΠΎΡΠ½ΠΈΠ΅ ΠΈ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Ρ (ΠΎΠ±Π·ΠΎΡ)
The domestic goat (Capra hircus) is a versatile small ruminant species spread on all continents, whose genomic features are becoming the subject of study by research teams from all over the world (A.M.A.M. Zonaed Siddiki et al., 2020; M.I. Selionova et al., 2021). The goal of this review is to elucidate the results of recent genomic studies on domestic goats using DNA chips and whole genome sequencing (WGS) analysis, and to compile a list of WGS-identified candidate genes associated with economically significant and adaption traits. This review summarizes and analyzes the results of WGS studies from 2020 to 2024. A list of candidate genes identified using WGS and associated with economically important and adaptive traits in goats is presented. An analysis of the methodological and bioinformatic approaches used to study WGS of domestic goats is executed. Using DNA chips, genetic relationships between different goat breeds and populations were established (T.E. Deniskova et al., 2021; V. Mukhina et al., 2022; A. Manunza et al., 2023), their genetic diversity was assessed (B.A. Vlaic et al., 2024; G. Chessari et al., 2024), and introgression from wild species of the genus Capra was studied (H. Asadollahpour Nanaei et al., 2023; N. Pogorevc et al., 2024). The decline in the WGS costs (B. Gu et al., 2022) has boosted an increase in the number of WGSs generated in goats (S. Belay et al., 2024). Genes under convergent selection pressure in sheep and goats have been identified, including DGKB, FAM155A, GRM5 (J. Yang et al., 2024) and CHST11 (L. Tao et al., 2021). An increase in the copy number of the GBP1 gene has been shown to be associated with immune resistance and prolificacy (R.Q. Zhang et al., 2019; R. Di Gerlando et al., 2020; M. Arslan, 2023). A large group of genes has been identified that affect milk productivity β ANPEP (J. Ni et al., 2024), ERBB4 (Z. Liu et al., 2024), NCAM2 (Z. Amiri Ghanatsaman et al., 2023) and GLYCAM1 (J. Xiong et al., 2023; H.B. Gebreselase et al., 2024), carcass quality β ACOX1, PGM1 (Z.X. An et al., 2024), ZNF385B and MYOT (H.B. Gebreselase et al., 2024), growth β HMGA2 and GJA3 (C. Li et al., 2024), live weight β STIM1 and ADM (R. Saif et al., 2021), and wool performance β CCNA2 (Y. Rong et al., 2024) and FGF5 (Q. Zhao et al., 2024). The TSHR and STC1 genes associated with domestication were discovered in Swiss breeds (H. Signer-Hasler et al., 2022). Genes involved in the formation of protective responses of the body to diseases and unfavorable climatic factors have been identified, including PIGR, TNFAIP2 (Q. Chen et al., 2021, 2022), KHDRBS2 (X. Sun et al., 2022), PPP2R3C (R. HuangFu et al., 2024), GNG2 (Z.X. An et al., 2024), HOXC12 and MAPK8IP2 (O. Sheriff et al., 2024). Genome-wide association studies (GWAS) based on WGS identified candidate genes associated with body size, including FNTB, CHURC1 (R. Yang et al., 2024), PSTPIP2 and SIPA1L (B. Gu et al., 2022), and milk production (H. Wu et al., 2023). To date, candidate genes have been identified on 21 of the 29 autosomes, with the largest number on CHI5 (9 genes), CHI18 (8 genes), CHI1, CHI3, CHI57 and CHI23 (7 genes on each chromosome). Thus, the compiled list of target candidate genes may be used in marker-assisted selection programs.
ΠΠΎΡΠΊΠΈΠ½Π° Π.Π., ΠΠ΅Π½ΠΈΡΠΊΠΎΠ²Π° Π’.Π., Π ΠΎΠΌΠ°Π½ΠΎΠ² Π.Π., ΠΠΈΠ½ΠΎΠ²ΡΠ΅Π²Π° Π.Π.
ΠΠΎΠΌΠ°ΡΠ½ΡΡ ΠΊΠΎΠ·Π° (Capra hircus L.) β ΡΡΠΎ ΡΠ½ΠΈΠ²Π΅ΡΡΠ°Π»ΡΠ½ΡΠΉ Π²ΠΈΠ΄ ΠΌΠ΅Π»ΠΊΠΎΠ³ΠΎ ΡΠΎΠ³Π°ΡΠΎΠ³ΠΎ ΡΠΊΠΎΡΠ°, ΡΠ°Π·Π²ΠΎΠ΄ΠΈΠΌΡΠΉ Π½Π° Π²ΡΠ΅Ρ
ΠΊΠΎΠ½ΡΠΈΠ½Π΅Π½ΡΠ°Ρ
, Π³Π΅Π½ΠΎΠΌΠ½ΡΠ΅ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΠΊΠΎΡΠΎΡΠΎΠ³ΠΎ ΡΡΠ°Π½ΠΎΠ²ΡΡΡΡ ΠΏΡΠ΅Π΄ΠΌΠ΅ΡΠΎΠΌ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π΄Π»Ρ Π½Π°ΡΡΠ½ΡΡ
ΠΊΠΎΠ»Π»Π΅ΠΊΡΠΈΠ²ΠΎΠ² Π²ΠΎ Π²ΡΠ΅ΠΌ ΠΌΠΈΡΠ΅ (A.M.A.M. Zonaed Siddiki Ρ ΡΠΎΠ°Π²Ρ., 2020; Π.Π. Π‘Π΅Π»ΠΈΠΎΠ½ΠΎΠ²Π° Ρ ΡΠΎΠ°Π²Ρ., 2021). Π¦Π΅Π»Ρ ΠΎΠ±Π·ΠΎΡΠ° β ΠΎΡΡΠ°Π·ΠΈΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ Π½Π΅Π΄Π°Π²Π½ΠΈΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ Π³Π΅Π½ΠΎΠΌΠΎΠ² Π΄ΠΎΠΌΠ°ΡΠ½ΠΈΡ
ΠΊΠΎΠ· Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΠΠ-ΡΠΈΠΏΠΎΠ² ΠΈ Π°Π½Π°Π»ΠΈΠ·Π° ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠ΅ΠΉ ΠΏΠΎΠ»Π½ΡΡ
Π³Π΅Π½ΠΎΠΌΠΎΠ² (WGS) ΠΈ ΡΠΎΡΡΠ°Π²ΠΈΡΡ ΡΠΏΠΈΡΠΎΠΊ Π³Π΅Π½ΠΎΠ²-ΠΊΠ°Π½Π΄ΠΈΠ΄Π°ΡΠΎΠ², Π²ΡΡΠ²Π»Π΅Π½Π½ΡΡ
Ρ ΠΏΠΎΠΌΠΎΡΡΡ WGS Π°Π½Π°Π»ΠΈΠ·Π°, ΠΊΠΎΡΠΎΡΡΠ΅ Π°ΡΡΠΎΡΠΈΠΈΡΠΎΠ²Π°Π½Ρ Ρ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈ Π·Π½Π°ΡΠΈΠΌΡΠΌΠΈ ΠΈ Π°Π΄Π°ΠΏΡΠΈΠ²Π½ΡΠΌΠΈ ΠΏΡΠΈΠ·Π½Π°ΠΊΠ°ΠΌΠΈ Ρ Π΄ΠΎΠΌΠ°ΡΠ½ΠΈΡ
ΠΊΠΎΠ·. Π Π½Π°ΡΡΠΎΡΡΠ΅ΠΌ ΠΎΠ±Π·ΠΎΡΠ΅ ΠΎΠ±ΠΎΠ±ΡΠ΅Π½Ρ ΠΈ ΠΏΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ WGS Ρ 2020 ΠΏΠΎ 2024 Π³ΠΎΠ΄. ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ ΡΠΏΠΈΡΠΎΠΊ Π³Π΅Π½ΠΎΠ²-ΠΊΠ°Π½Π΄ΠΈΠ΄Π°ΡΠΎΠ², ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ WGS ΠΈ Π°ΡΡΠΎΡΠΈΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
Ρ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈ Π·Π½Π°ΡΠΈΠΌΡΠΌΠΈ ΠΈ Π°Π΄Π°ΠΏΡΠΈΠ²Π½ΡΠΌΠΈ ΠΏΡΠΈΠ·Π½Π°ΠΊΠ°ΠΌΠΈ Ρ Π΄ΠΎΠΌΠ°ΡΠ½ΠΈΡ
ΠΊΠΎΠ·. ΠΡΠΎΠ²Π΅Π΄Π΅Π½ Π°Π½Π°Π»ΠΈΠ· ΠΏΡΠΈΠΌΠ΅Π½ΡΠ΅ΠΌΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ Π±ΠΈΠΎΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ² Π΄Π»Ρ ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ WGS Π΄ΠΎΠΌΠ°ΡΠ½ΠΈΡ
ΠΊΠΎΠ·. Π‘ ΠΏΠΎΠΌΠΎΡΡΡ ΠΠΠΡΠΈΠΏΠΎΠ² ΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½Ρ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ Π²Π·Π°ΠΈΠΌΠΎΡΠ²ΡΠ·ΠΈ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΠΏΠΎΡΠΎΠ΄ ΠΈ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΉ ΠΊΠΎΠ· (T.E. Deniskova Ρ ΡΠΎΠ°Π²Ρ., 2021; V. Mukhina Ρ ΡΠΎΠ°Π²Ρ., 2022; A. Manunza Ρ ΡΠΎΠ°Π²Ρ., 2023), ΠΎΡΠ΅Π½Π΅Π½ΠΎ ΠΈΡ
Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΡΠ°Π·Π½ΠΎΠΎΠ±ΡΠ°Π·ΠΈΠ΅ (B.A. Vlaic Ρ ΡΠΎΠ°Π²Ρ., 2024; G. Chessari Ρ ΡΠΎΠ°Π²Ρ., 2024), ΠΈΠ·ΡΡΠ΅Π½Π° ΠΈΠ½ΡΡΠΎΠ³ΡΠ΅ΡΡΠΈΡ Ρ Π΄ΠΈΠΊΠΈΠΌΠΈ Π²ΠΈΠ΄Π°ΠΌΠΈ ΡΠΎΠ΄Π° Capra (H. Asadollahpour Nanaei Ρ ΡΠΎΠ°Π²Ρ., 2023; N. Pogorevc Ρ ΡΠΎΠ°Π²Ρ., 2024). Π‘Π½ΠΈΠΆΠ΅Π½ΠΈΠ΅ ΡΡΠΎΠΈΠΌΠΎΡΡΠΈ WGS (B. Gu Ρ ΡΠΎΠ°Π²Ρ., 2022) ΡΡΠΈΠΌΡΠ»ΠΈΡΠΎΠ²Π°Π»ΠΎ ΡΠΎΡΡ ΡΠΈΡΠ»Π° Π³Π΅Π½Π΅ΡΠΈΡΡΠ΅ΠΌΡΡ
WGS ΠΊΠΎΠ· (S. Belay Ρ ΡΠΎΠ°Π²Ρ., 2024). ΠΡΡΠ²Π»Π΅Π½Ρ Π³Π΅Π½Ρ, Π½Π°Ρ
ΠΎΠ΄ΡΡΠΈΠ΅ΡΡ ΠΏΠΎΠ΄ Π΄Π°Π²Π»Π΅Π½ΠΈΠ΅ΠΌ ΠΊΠΎΠ½Π²Π΅ΡΠ³Π΅Π½ΡΠ½ΠΎΠ³ΠΎ ΠΎΡΠ±ΠΎΡΠ° Ρ ΠΎΠ²Π΅Ρ ΠΈ ΠΊΠΎΠ·, Π²ΠΊΠ»ΡΡΠ°Ρ DGKB, FAM155A, GRM5 (J. Yang Ρ ΡΠΎΠ°Π²Ρ., 2024) ΠΈ CHST11 (L. Tao Ρ ΡΠΎΠ°Π²Ρ., 2021). ΠΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΡΠΎ ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΠ΅ ΡΠΈΡΠ»Π° ΠΊΠΎΠΏΠΈΠΉ Π³Π΅Π½Π° GBP1 ΡΠ²ΡΠ·Π°Π½ΠΎ Ρ ΠΈΠΌΠΌΡΠ½ΠΎΡΠ΅Π·ΠΈΡΡΠ΅Π½ΡΠ½ΠΎΡΡΡΡ ΠΈ ΠΌΠ½ΠΎΠ³ΠΎΠΏΠ»ΠΎΠ΄ΠΈΠ΅ΠΌ (R.Q. Zhang Ρ ΡΠΎΠ°Π²Ρ., 2019; R. Di Gerlando Ρ ΡΠΎΠ°Π²Ρ., 2020; M. Arslan, 2023). ΠΠ΄Π΅Π½ΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π° Π±ΠΎΠ»ΡΡΠ°Ρ Π³ΡΡΠΏΠΏΠ° Π³Π΅Π½ΠΎΠ², Π²Π»ΠΈΡΡΡΠΈΡ
Π½Π° ΠΌΠΎΠ»ΠΎΡΠ½ΡΡ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΠ²Π½ΠΎΡΡΡ, β ANPEP (J. Ni Ρ ΡΠΎΠ°Π²Ρ., 2024), ERBB4 (Z. Liu Ρ ΡΠΎΠ°Π²Ρ., 2024), NCAM2 (Z. Amiri Ghanatsaman Ρ ΡΠΎΠ°Π²Ρ., 2023), GLYCAM1 (J. Xiong Ρ ΡΠΎΠ°Π²Ρ., 2023; H.B. Gebreselase Ρ ΡΠΎΠ°Π²Ρ., 2024), Π½Π° ΠΊΠ°ΡΠ΅ΡΡΠ²ΠΎ ΡΡΡ β ACOX1, PGM1 (Z.X. An Ρ ΡΠΎΠ°Π²Ρ., 2024), ZNF385B ΠΈ MYOT (H.B. Gebreselase Ρ ΡΠΎΠ°Π²Ρ., 2024), Π½Π° ΡΠΎΡΡ β HMGA2 ΠΈ GJA3 (C. Li Ρ ΡΠΎΠ°Π²Ρ., 2024), ΠΆΠΈΠ²ΡΡ ΠΌΠ°ΡΡΡ β STIM1 ΠΈ ADM (R. Saif Ρ ΡΠΎΠ°Π²Ρ., 2021), Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠ΅ΡΡΡΠ½ΡΡ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΠ²Π½ΠΎΡΡΡ β CCNA2 (Y. Rong Ρ ΡΠΎΠ°Π²Ρ., 2024) ΠΈ FGF5 (Q. Zhao Ρ ΡΠΎΠ°Π²Ρ., 2024). ΠΠ±Π½Π°ΡΡΠΆΠ΅Π½Ρ Π³Π΅Π½Ρ TSHR ΠΈ STC1, ΡΠ²ΡΠ·Π°Π½Π½ΡΠ΅ Ρ ΠΎΠ΄ΠΎΠΌΠ°ΡΠ½ΠΈΠ²Π°Π½ΠΈΠ΅ΠΌ Ρ ΡΠ²Π΅ΠΉΡΠ°ΡΡΠΊΠΈΡ
ΠΏΠΎΡΠΎΠ΄ (H. Signer-Hasler Ρ ΡΠΎΠ°Π²Ρ., 2022). ΠΡΡΠ²Π»Π΅Π½Ρ Π³Π΅Π½Ρ, Π²ΠΎΠ²Π»Π΅ΡΠ΅Π½Π½ΡΠ΅ Π² ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ Π·Π°ΡΠΈΡΠ½ΡΡ
ΡΠ΅Π°ΠΊΡΠΈΠΉ ΠΏΡΠΈ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡΡ
ΠΈ Π΄Π΅ΠΉΡΡΠ²ΠΈΠΈ Π½Π΅Π±Π»Π°Π³ΠΎΠΏΡΠΈΡΡΠ½ΡΡ
ΠΊΠ»ΠΈΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ²: PIGR, TNFAIP2 (Q. Chen Ρ ΡΠΎΠ°Π²Ρ., 2021, 2022), KHDRBS2 (X. Sun Ρ ΡΠΎΠ°Π²Ρ., 2022), PPP2R3C (R. HuangFu Ρ ΡΠΎΠ°Π²Ρ., 2024), GNG2 (Z.X. An Ρ ΡΠΎΠ°Π²Ρ., 2024), HOXC12 ΠΈ MAPK8IP2 (O. Sheriff Ρ ΡΠΎΠ°Π²Ρ., 2024). ΠΡΠΈ ΠΏΠΎΠ»Π½ΠΎΠ³Π΅Π½ΠΎΠΌΠ½ΠΎΠΌ ΠΏΠΎΠΈΡΠΊΠ΅ Π°ΡΡΠΎΡΠΈΠ°ΡΠΈΠΉ (GWAS) Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ WGS ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Ρ Π³Π΅Π½Ρ-ΠΊΠ°Π½Π΄ΠΈΠ΄Π°ΡΡ, Π°ΡΡΠΎΡΠΈΠΈΡΠΎΠ²Π°Π½Π½ΡΠ΅ Ρ ΡΠ°Π·ΠΌΠ΅ΡΠ°ΠΌΠΈ ΡΡΠ»ΠΎΠ²ΠΈΡΠ°, Π²ΠΊΠ»ΡΡΠ°Ρ Π³Π΅Π½Ρ FNTB, CHURC1 (R. Yang Ρ ΡΠΎΠ°Π²Ρ., 2024), PSTPIP2 ΠΈ SIPA1L (B. Gu Ρ ΡΠΎΠ°Π²Ρ., 2022), ΠΈ Ρ ΠΌΠΎΠ»ΠΎΡΠ½ΠΎΠΉ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΠ²Π½ΠΎΡΡΡΡ (H. Wu Ρ ΡΠΎΠ°Π²Ρ., 2023). ΠΠ΅Π½Ρ-ΠΊΠ°Π½Π΄ΠΈΠ΄Π°ΡΡ Π²ΡΡΠ²Π»Π΅Π½Ρ Π½Π° 21 ΠΈΠ· 29 Π°ΡΡΠΎΡΠΎΠΌ, ΠΏΡΠΈ ΡΡΠΎΠΌ Π½Π°ΠΈΠ±ΠΎΠ»ΡΡΠ΅Π΅ ΠΈΡ
ΡΠΈΡΠ»ΠΎ ΠΊ Π½Π°ΡΡΠΎΡΡΠ΅ΠΌΡ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΎ Π½Π° CHI5 (9 Π³Π΅Π½ΠΎΠ²), CHI18 (8 Π³Π΅Π½ΠΎΠ²), CHI1, CHI3, CHI57 ΠΈ CHI23 (ΠΏΠΎ 7 Π³Π΅Π½ΠΎΠ² Π½Π° ΠΊΠ°ΠΆΠ΄ΠΎΠΉ Ρ
ΡΠΎΠΌΠΎΡΠΎΠΌΠ΅). Π’Π°ΠΊΠΈΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ, ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ ΡΠΏΠΈΡΠΎΠΊ ΡΠ΅Π»Π΅Π²ΡΡ
Π³Π΅Π½ΠΎΠ²-ΠΊΠ°Π½Π΄ΠΈΠ΄Π°ΡΠΎΠ², ΠΊΠΎΡΠΎΡΡΠ΅ ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Ρ Π² ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ°Ρ
ΠΌΠ°ΡΠΊΠ΅Ρ-ΠΎΡΠΈΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΠ΅Π»Π΅ΠΊΡΠΈΠΈ
Editorial: Traditional and up-to-date genomic insights into domestic animal diversity
Domesticated animals play a significant role in local, national, and international agricultural output as well as in daily human life and culture. Additionally, they make up a sizeable portion of the biodiversity of the planet, which is essential for producing food and other animal products for human consumption. The present Frontiers in Genetics Research Topic (Figure 1) is devoted to various issues pertinent diversity of farm animals. The latter is at serious risk today, which could result in a reduction in the resources available to produce breed-specific food products and other necessities of everyday living. Importantly, genetic diversity is necessary for future animal breeding to be flexible enough to adapt livestock populations to changing customer demands and climatic conditions. Continued efforts are required to protect biodiversity, stop the loss of animal breeds, and maintain genetic diversity and develop strategies to use resource population in regional (niche) production systems
Investigation of gene pool and genealogical links between sheep breeds of southern Russia by blood groups and DNA microsatellites
To study the gene pool and the establishment of genealogical relationships between breeds of sheep of different directions productivity bred in Russia, were used two classes of genetic markers - blood and DNA microsatellites. The included sample sheep are fine-wool Merino breeds: Grozny (GR), Caucasian (CA), Manychskij merino (MM), the Soviet Merino (SM), Stavropol (ST) and coarse wool breeds: Edilbaevskaya (ED), Karakul (CR) and Romanov (RO). For the study of erythrocyte, were selected antigens (blood group) in 1159 samples from 11 breeding farms. For microsatellite DNA study - 598 from 10 breeding farms. Microsatellite analysis revealed that the most polymorphic were Stavropol breed sheep that have identified an average of 18.27 alleles per locus were relatively conservative Romanov breed sheep - 9.7 alleles per locus. The minimum genetic distances established between Grozny and Soviet Merino - 0.0569 (for microsatellites) and 0.0741 (blood groups - later in the same sequence). The rocks of the Stavropol - Grozny were 0.0861 and 0, 0810. Whereas Stavropol and Soviet Merino 0.0861 and 0.1094. Also relatively close between Grozny - Edilbaevskoy, Grozny Karakul, Edilbaevskoy - Karakul: 0.1364 and 0.0851, respectively; 0.1620 and 0.1208; 0.1875 and 0.1192. The highest genetic distances were between Stavropol and Karakul 0.2664 and 0.1804, as well as between the Romanov and all studied species - 0.2491 ... 0.3211 and 0.1734 ... 0.2235
[Genome-wide association study of testes development indicators in roosters (Gallus gallus L.)] ΠΠΎΠ»Π½ΠΎΠ³Π΅Π½ΠΎΠΌΠ½ΡΠ΅ Π°ΡΡΠΎΡΠΈΠ°ΡΠΈΠ²Π½ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠ΅ΠΌΠ΅Π½Π½ΠΈΠΊΠΎΠ² Ρ ΠΏΠ΅ΡΡΡ ΠΎΠ² (Gallus gallus L.)
Reproductive ability is one of the main indicators of the male breeding value that depends primarily on the functional state of testes cells. Male fertility is defined by complex physiological processes affecting the formation of mature germ cells, i.e., spermatozoa in the process of spermatogenesis. The forming and accumulation of germ cells occur in the seminiferous tubules of the testes, therefore the gonad development can serve as an indicator characterizing spermatogenesis and the reproductive potential of males. A number of studies on farm animals, including poultry, have shown the genetic determinacy of this trait, with identification of respective single nucleotide polymorphisms (SNPs) and genes determining the male gonad growth and development. In the present investigation, a genome-wide association study (GWAS) of the testes development parameters in roosters (Gallus gallus L.) of the F2 resource population were conducted. For the first time, new significant SNPs and candidate genes (Ρ < 1.05Β½104) determining gonad growth and development in roosters were identified. The aim of the research was to seek SNPs and identify genes associated with testes growth parameters in roosters. The object of the study were F2 roosters from a model resource population (n = 115) that was obtained by interbreeding two breeds, Russian White and White Cornish. DNA was extracted from feather pulp using a commercial kit DNK Extran-2 (OOO NPF Sintol, Russia) in accordance with the manufacturerβs protocol. Genotyping was carried out using the medium-density Illumina Chicken 60K SNP iSelect BeadChip chip. At the age of 63 days, the experimental birds were slaughtered and the mass and morphometric indices of testes (length and thickness) were examined. Based on the obtained genotypic and phenotypic data, the GWAS analysis was performed in F2 resource population roosters using PLINK 1.9 software. The examined population was characterized by a high coefficient of variation in the measured indices, 96.1 % for the testes mass and 39.1 % for the linear measurements. The mass and linear measurements of the left testis were 5-14 % higher (Ρ 0.05) compared to the right testis. The GWAS analysis revealed 36 significant SNPs (Ρ < 1.05Β½104) associated with testes growth and development parameters in 63-day-old cockerels, in particular with the mass, length and thickness of the testes, 3, 26 and 7 SNPs, respectively. SNPs were localized on chromosomes GGA1, GGA3, GGA6, GGA7, GGA12, GGA15, and GGA18. A total of 156 genes were identified in the regions of the detected SNPs, including 16 genes that coincided with the positions of these SNPs. In particular, the latter were one gene (WNT7A) associated with the testis mass, 13 genes (LHFPL1, GALNT3, TMEM198, CACNA2D3, CCDC66, CACNA1D, DENND6A, CELSR3, WNT7A, IP6K2, ERC2, ABHD6, and DEPDC5) associated with the testis length, and three genes (ESR1, POLE, and RNFT2) associated with the testis thickness. These data can be used in genomic selection of roosters aimed at increasing their reproductive potential
Eggology and mathematics of a quail egg: an innovative non-destructive technology for evaluating egg parameters in Japanese quail
Quail eggs, the smallest ones among poultry species, require special methodological aspects for their non-destructive examination and quality analysis. Using eggs from a cross between the Japanese and Texas breeds, we devised a methodology for defining the main geometric parameters of quail eggs. Calculation formulae were proposed to estimate indirectly egg volume and surface area. Our findings on the weights of structural egg components enabled to obtain mathematical equations for computing the weights of shell, yolk and albumen, depending on the complex of measured parameters including the egg weight, its volume and surface area. When taken as a whole, the results of our study can be regarded as the most thorough methodological approach to date for the execution of comprehensive investigations of quail egg quality. They will be applicable and instrumental in areas of food research and emerging technologies, including the aspects of storage, packing, and processing of quail eggs
Shared Ancestry and Signatures of Recent Selection in Gotland Sheep
Gotland sheep, a breed native to Gotland, Sweden (an island in the Baltic Sea), split from the Gute sheep breed approximately 100 years ago, and since, has probably been crossed with other breeds. This breed has recently gained popularity, due to its pelt quality. This study estimates the shared ancestors and identifies recent selection signatures in Gotland sheep using 600 K single nucleotide polymorphism (SNP) genotype data. Admixture analysis shows that the Gotland sheep is a distinct breed, but also has shared ancestral genomic components with Gute (similar to 50%), Karakul (similar to 30%), Romanov (similar to 20%), and Fjallnas (similar to 10%) sheep breeds. Two complementary methods were applied to detect selection signatures: A Bayesian population differentiation F-ST and an integrated haplotype homozygosity score (iHS). Our results find that seven significant SNPs (q-value < 0.05) using the F-ST analysis and 55 significant SNPs (p-value < 0.0001) using the iHS analysis. Of the candidate genes that contain significant markers, or are in proximity to them, we identify several belongings to the keratin genes, RXFP2, ADCY1, ENOX1, USF2, COX7A1, ARHGAP28, CRYBB2, CAPNS1, FMO3, and GREB1. These genes are involved in wool quality, polled and horned phenotypes, fertility, twining rate, meat quality, and growth traits. In summary, our results provide shared founders of Gotland sheep and insight into genomic regions maintained under selection after the breed was formed. These results contribute to the detection of candidate genes and QTLs underlying economic traits in sheep
Metabolic rate and egg production in Japanese quails can be predicted by assessing growth parameters of laying hens
Simple Summary:
Quails are becoming increasingly popular for their meat and eggs, and thus, the productivity of laying hens, and how that can be predicted, is of growing interest to quail producers. Because of this, we wanted to find out whether we could predict the performance of laying hens (typically expressed as the number of eggs produced multiplied by the egg weightβthe so-called total egg mass) simply by looking at certain growth traits (i.e., body weight, surface area, and volume), as well as the metabolic rate among eight Japanese quail breeds. To succeed in this analysis, we developed a novel method for calculating the volume and surface area of a quail body. As a result, we derived a new mathematical formula called the metabolic index, which included the measurements of body weight, surface area, and volume. We discovered that the total egg mass in quails can be judged from these growth parameters, particularly when we examined the slope angles of the trend lines in the graphs pertaining to these parameters.
Abstract:
The aim of the current study was to assess the female metabolic rate and test the hypothesis that there is a relationship between the egg productivity of Japanese quails from eight breeds and their morphometric, or growth, parameters. Parameters measured were body weight (B), volume (V), and surface area (S), as well as the metabolism level expressed by the ratio S/V. The collected egg performance traits were as follows: the number of eggs produced (N), the average egg weight (W), and the total egg mass (M) (i.e., N multiplied by W). To measure the S and V values, a novel technique was developed that takes into account the similarity of the quailβs body to an ellipsoid. An analysis of the relationships between productivity indicators allowed us to introduce a new index called the metabolic index, BΒ·S/V, based on all three main growth parameters in quails. Using the values of this index, we were then able to judge indirectly the level of quailsβ egg productivity. We went on to assess the N, W, and M values, not only depending on the size of the birdβs growth parameters but also according to the degree of their changes during quail growth. These changes were expressed as the slope angles of trend lines describing the growth process data. This approach produced more accurate results for predicting the egg productivity in terms of W and M
Multivariate Analysis Identifies Eight Novel Loci Associated with Meat Productivity Traits in Sheep.
peer reviewedDespite their economic value, sheep remain relatively poorly studied animals in terms of the number of known loci and genes associated with commercially important traits. This gap in our knowledge can be filled in by performing new genome-wide association studies (GWAS) or by re-analyzing previously documented data using novel powerful statistical methods. This study is focused on the search for new loci associated with meat productivity and carcass traits in sheep. With a multivariate approach applied to publicly available GWAS results, we identified eight novel loci associated with the meat productivity and carcass traits in sheep. Using an in silico follow-up approach, we prioritized 13 genes in these loci. One of eight novel loci near the FAM3C and WNT16 genes has been replicated in an independent sample of Russian sheep populations (N = 108). The novel loci were added to our regularly updated database increasing the number of known loci to more than 140
Genome-wide association study reveals the genetic architecture of growth and meat production traits in a chicken F2 resource population
Background/Objectives: For genomic selection to enhance the efficiency of broiler production, finding SNPs and candidate genes that define the manifestation of main selected traits is essential. We conducted a genome-wide association study (GWAS) for growth and meat productivity traits of roosters from a chicken F2 resource population (n = 152). Methods: The population was obtained by crossing two breeds with contrasting phenotypes for performance indicators, i.e., Russian White (slow-growing) and Cornish White (fast-growing). The birds were genotyped using the Illumina Chicken 60K SNP iSelect BeadChip. After LD filtering of the data, 54,188 SNPs were employed for the GWAS analysis that allowed us to reveal significant specific associations for phenotypic traits of interest and economic importance. Results: At the threshold value of p < 9.2 Γ 10β7, 83 SNPs associated with body weight at the age of 28, 42, and 63 days were identified, as well as 171 SNPs associated with meat qualities (average daily gain, slaughter yield, and dressed carcass weight and its components). Moreover, 34 SNPs were associated with a group of three or more traits, including 15 SNPs significant for a group of growth traits and 5 SNPs for a group of meat productivity indicators. Relevant to these detected SNPs, nine prioritized candidate genes associated with the studied traits were revealed, including WNT2, DEPTOR, PPA2, UNC80, DDX51, PAPPA, SSC4D, PTPRU, and TLK2. Conclusions: The found SNPs and candidate genes can serve as genetic markers for growth and meat performance characteristics in chicken breeding in order to achieve genetic improvement in broiler production
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