GWAS helps to identify QTL and candidate genes of specific traits. Buffalo breeding mainly focused on milk production but its negative correlation with reproduction traits resulted in unfavourable decline in reproductive performance of buffalo. A genome wide scan was performed on a total of 120 Murrah buffaloes genotyped by ddRAD sequencing for 13 traits related to female fertility, production and growth. Identified 25 significant SNPs (P < 1x106) associated with Age at first calving (AFC), Age at first service (AFS), period from calving to 1st AI, Service period (SP) and 6 month body weight (6M). 15 genetic variants overlapped with different QTL regions of reported studies. Among the associated loci, outstanding candidate genes for fertility include, AQP1, TRNAE-CUC, NRIP1, CPNE4 and VOPP1 have role in different fertility traits. AQP1 gene expressed on different stages of pregnancy and in ovulatory phase. TRNAE-CUC gene related with AFC and no. of calving after 4 yrs of age. CPNE4 is glycogen content associated gene regulate muscle glycogen and upregulated in early pregnancy. NRIP1 gene have regulation over corpus luteum at pregnancy and control over ovulation and in mammary gland development. Objective to identify potential genomic regions and genetic variants associated with fertility related traits, milk production and growth traits and select most significant SNP which have positive effect on all the traits.
Mastitis brings on economic losses, declined milk production, uplifted treatment costs and accelerated culling in buffaloes. Also, being multi-etiological in nature, control of mastitis is challenging in dairy animals. Hence, knowing the risk factors governing clinical mastitis incidence in buffalo might help in minimizing its occurrence. So, the present study was undertaken in 96 adult Murrah buffaloes to investigate the effect of parity, period of calving, season of calving and level of milk production on incidence of clinical mastitis using logistic regression in SAS v 9.3. The data of mastitis incidence was collected over a period of eighteen years (1997–2014) from Health record register of Livestock Research Centre of the institute. The incidence of mastitis was maximum in second parity (7.65%) followed by parity five and above (7.41%). Parity and period of calving had significant effects (p < 0.05) on mastitis incidence. The odds ratio for incidence of mastitis of animals in parity (5 and above) was highest (3.832), in comparison to first lactation. The animals calving during the period (2004–2007), exhibited maximum incidence of clinical mastitis (14.75%). Higher mastitis incidence in higher parity animals may be due to the compromised immune system and widened teat canal. Therefore, proper management of animals especially for advanced pregnant animals is recommended for reducing incidence of mastitis.
Deep learning has emerged as a powerful tool in genomics, utilizing neural networks to uncover complex patterns in large datasets. This review explores the application of deep learning in genomics, focusing on supervised and unsupervised learning tasks.The process involves training models with appropriate evaluation metrics and curated datasets to optimize performance. Balancing training data and model flexibility is crucial to avoid underfitting or overfitting. Deep learning models, with their high capacity and flexibility, outperform traditional techniques like logistic regression and support vector machines in genomics. Various applications of deep learning in genomics are includes predicting protein sequence specificity, determining cis-regulatory elements, analyzing splicing regulation and gene expression, and predicting genomic variants. Deep learning proves particularly effective in studying functional genomics and regulatory elements, leveraging techniques from computer vision and natural language processing. Overall, deep learning shows promise in advancing genomics research and understanding complex biological processes.
GWAS helps to identify QTL and candidate genes of speci c traits. Buffalo breeding mainly focused on milk production but its negative correlation with reproduction traits resulted in unfavourable decline in reproductive performance of buffalo. A genome wide scan was performed on a total of 120 Murrah buffaloes genotyped by ddRAD sequencing for 13 traits related to female fertility, production and growth.Identi ed 25 signi cant SNPs (P < 1x10 6 ) associated with Age at rst calving (AFC), Age at rst service (AFS), period from calving to 1st AI, Service period (SP) and 6 month body weight (6M). 15 genetic variants overlapped with different QTL regions of reported studies. Among the associated loci, outstanding candidate genes for fertility include, AQP1, TRNAE-CUC, NRIP1, CPNE4 and VOPP1 have role in different fertility traits. AQP1 gene expressed on different stages of pregnancy and in ovulatory phase. TRNAE-CUC gene related with AFC and no. of calving after 4 yrs of age. CPNE4 is glycogen content associated gene regulate muscle glycogen and upregulated in early pregnancy. NRIP1 gene have regulation over corpus luteum at pregnancy and control over ovulation and in mammary gland development. Objective to identify potential genomic regions and genetic variants associated with fertility related traits, milk production and growth traits and select most signi cant SNP which have positive effect on all the traits.
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