Some Fertility Traits and Growing Characteristics of Kivircik Sheep Breed in the Extensive Farm Conditions

Author(s):  
Tuğçe Necla SELVİ ◽  
Hakan ÜSTÜNER
Keyword(s):  
2017 ◽  
Vol 52 (6) ◽  
pp. 1157-1165
Author(s):  
E.A. Gladyr ◽  
◽  
T.E. Deniskova ◽  
V.A. Bagirov ◽  
O.V. Kostyunina ◽  
...  

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rui Shi ◽  
Luiz Fernando Brito ◽  
Aoxing Liu ◽  
Hanpeng Luo ◽  
Ziwei Chen ◽  
...  

Abstract Background The effect of heat stress on livestock production is a worldwide issue. Animal performance is influenced by exposure to harsh environmental conditions potentially causing genotype-by-environment interactions (G × E), especially in highproducing animals. In this context, the main objectives of this study were to (1) detect the time periods in which heifer fertility traits are more sensitive to the exposure to high environmental temperature and/or humidity, (2) investigate G × E due to heat stress in heifer fertility traits, and, (3) identify genomic regions associated with heifer fertility and heat tolerance in Holstein cattle. Results Phenotypic records for three heifer fertility traits (i.e., age at first calving, interval from first to last service, and conception rate at the first service) were collected, from 2005 to 2018, for 56,998 Holstein heifers raised in 15 herds in the Beijing area (China). By integrating environmental data, including hourly air temperature and relative humidity, the critical periods in which the heifers are more sensitive to heat stress were located in more than 30 days before the first service for age at first calving and interval from first to last service, or 10 days before and less than 60 days after the first service for conception rate. Using reaction norm models, significant G × E was detected for all three traits regarding both environmental gradients, proportion of days exceeding heat threshold, and minimum temperature-humidity index. Through single-step genome-wide association studies, PLAG1, AMHR2, SP1, KRT8, KRT18, MLH1, and EOMES were suggested as candidate genes for heifer fertility. The genes HCRTR1, AGRP, PC, and GUCY1B1 are strong candidates for association with heat tolerance. Conclusions The critical periods in which the reproductive performance of heifers is more sensitive to heat stress are trait-dependent. Thus, detailed analysis should be conducted to determine this particular period for other fertility traits. The considerable magnitude of G × E and sire re-ranking indicates the necessity to consider G × E in dairy cattle breeding schemes. This will enable selection of more heat-tolerant animals with high reproductive efficiency under harsh climatic conditions. Lastly, the candidate genes identified to be linked with response to heat stress provide a better understanding of the underlying biological mechanisms of heat tolerance in dairy cattle.


2021 ◽  
Vol 12 (1) ◽  
pp. 113
Author(s):  
Edward H. Cabezas-Garcia ◽  
Mauricio Civiero ◽  
Alan Gordon ◽  
Conrad P. Ferris

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Nicole Pretini ◽  
Leonardo S. Vanzetti ◽  
Ignacio I. Terrile ◽  
Guillermo Donaire ◽  
Fernanda G. González

Abstract Background In breeding programs, the selection of cultivars with the highest yield potential consisted in the selection of the yield per se, which resulted in cultivars with higher grains per spike (GN) and occasionally increased grain weight (GW) (main numerical components of the yield). In this study, quantitative trait loci (QTL) for GW, GN and spike fertility traits related to GN determination were mapped using two doubled haploid (DH) populations (Baguette Premium 11 × BioINTA 2002 and Baguette 19 × BioINTA 2002). Results In total 305 QTL were identified for 14 traits, out of which 12 QTL were identified in more than three environments and explained more than 10% of the phenotypic variation in at least one environment. Eight hotspot regions were detected on chromosomes 1A, 2B, 3A, 5A, 5B, 7A and 7B in which at least two major and stable QTL sheared confidence intervals. QTL on two of these regions (R5A.1 and R5A.2) have previously been described, but the other six regions are novel. Conclusions Based on the pleiotropic analysis within a robust physiological model we conclude that two hotspot genomic regions (R5A.1 and R5A.2) together with the QGW.perg-6B are of high relevance to be used in marker assisted selection in order to improve the spike yield potential. All the QTL identified for the spike related traits are the first step to search for their candidate genes, which will allow their better manipulation in the future.


2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 83-84
Author(s):  
Marina Fortes ◽  
Wei Liang Andre Tan ◽  
Laercio R Porto-Neto ◽  
Antonio Reverter ◽  
Gry B Boe-Hansen

Abstract Traits such as sperm morphology and motility are routine in veterinarian evaluations of bull fertility. However, they rarely are included in livestock breeding programs, which typically use only scrotal circumference (SC) and some female traits for fertility selection. We studied 25 male fertility traits measured in two research populations of bulls (1,099 Brahman, and 1,719 Tropical Composite) and one commercial population (2,490 Santa Gertrude bulls). Measurements included standard semen evaluation (e.g. sperm motility and morphology) and SC. In the research data, we also measured sperm DNA fragmentation and sperm protamine deficiency for about 50% of the bulls. Using a mixture of genomic and pedigree analyses, we estimated heritabilities and genetic correlations for all traits, in each population. Our analyses suggest that bull fertility traits have a heritable component, which makes selective breeding possible. The phenotype variation in sperm DNA fragmentation and sperm protamine deficiency traits also have a heritable component (h2 ~ 0.05–0.22). These first estimates for heritability of sperm chromatin phenotypes require further studies, with larger datasets, to corroborate present results. In all three populations, we observed genetic correlations across traits that were favorable, but not high. For example, the percentage of normal sperm (PNS) from the sperm morphology evaluation was positively correlated with SC. In the research data, sperm DNA fragmentation was negatively correlated with PNS (r2 ~ 0.23–0.33), meaning that bulls with a higher PNS had less DNA fragmentation, being therefore more fertile according to both indicators. Given the favorable and yet not high genetic correlations between traits, it is possible to envision that sperm chromatin phenotypes might form a panel, together with PNS and SC, for a comprehensive bull fertility index. Selection indices that include fertility traits are being implemented in the dairy industry and could be recommended for beef cattle, too. An index that benefits from the favorable genetic correlations between traits that describe different aspects of bull fertility is a sensible approach to selective breeding. The clinical use of complementary indicators for male fertility is largely accepted, when deciding on bull fitness for the mating season. We propose extending this rationale to create a multi-trait index that captures genetic merit for bull fertility. In addition, we performed genome-wide association analyses in the research data and identified eight QTLs in the X chromosome. Correlations and shared SNP associations support the hypothesis that these phenotypes have the same underlying cause: abnormal spermatogenesis. In conclusion, it is possible to improve bull fertility through selective breeding, by measuring complementary fertility traits. Genomic selection for bull fertility might be more accurate if the X chromosome mutations that underlie the discovered QTL are included in the analyses. Polymorphisms associated with fertility in the bull accumulate in the X chromosome, as they do in humans and mice, thus suggesting specialization of this chromosome.


2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Suphunnee Mookprom ◽  
Monchai Duangjinda ◽  
Somsook Puangdee ◽  
Wootichai Kenchaiwong ◽  
Wuttigrai Boonkum

2018 ◽  
Vol 2 (suppl_1) ◽  
pp. S175-S179
Author(s):  
Maggie K Reynolds ◽  
Gwinyai E Chibisa ◽  
Amin Ahmadzadeh ◽  
John B Hall

2019 ◽  
Vol 102 (12) ◽  
pp. 11207-11216 ◽  
Author(s):  
G.M. Tarekegn ◽  
P. Gullstrand ◽  
E. Strandberg ◽  
R. Båge ◽  
E. Rius-Vilarrasa ◽  
...  

PLoS ONE ◽  
2014 ◽  
Vol 9 (1) ◽  
pp. e87823 ◽  
Author(s):  
Dianna Bowles ◽  
Amanda Carson ◽  
Peter Isaac

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