Combining multi-OMICs information to identify key-regulator genes for pleiotropic effect on fertility and production traits in beef cattle

Fonseca, Pablo Augusto de Souza; Id‐Lahoucine, Samir; Reverter, Antônio; Medrano, Juan F.; Fortes, M. R. S.; Casellas, J.; Miglior, F.; Brito, Luiz F.; Carvalho, Maria Raquel Santos; Schenkel, F.S.; Nguyen, Loan; Porto-Neto, Laércio R.; Thomas, M. G.; Cánovas, Ángela

doi:10.1371/journal.pone.0205295

Cited by 34 publications

(25 citation statements)

References 92 publications

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“…After comparing the loci identified in the current study with the 22 previous GWAA, 46 genomic regions were validated (Table 5). These loci were identified both within the Holstein breed [19,33,35,[37][38][39] and among other breeds such as crossbreed Angus [34], Brahman [40], Brangus [40], Jersey [19] and a tropical composite breed [40]. Identifying genomic regions in independent populations and across breeds suggests that these regions are near the causal mutation and have a common function related to fertility in cattle.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, loci associated with HCR1 and TBRD were compared to 22 previous studies investigating both dairy and beef cattle fertility. These studies investigated a variety of fertility traits, including: days to first service, non-return rate and calving interval [19,33], pregnancy success at day 28 [34] or day 42 [35], calving to first service [36], conception rates in heifers and in cows [37][38][39], and postpartum anestrous interval [40]. Loci locations were provided based on UMD 3.1 (ftp://ftp.cbcb.umd.edu/pub/data/Bos_taurus/) and ARS-UCD 1.2 (https://www.animalgenome.org/repository/cattle/ UMC_bovine_coordinates/).…”

Section: Genome-wide Association Analysismentioning

confidence: 99%

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Validation of 46 loci associated with female fertility traits in cattle

et al. 2019

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Background: Subfertility is one challenge facing the dairy industry as the average Holstein heifer conception rate (HCR), the proportion of heifers that conceive and maintain a pregnancy per breeding, is estimated at 55-60%. Of the loci associated with HCR, few have been validated in an independent cattle population, limiting their usefulness for selection or furthering our understanding of the mechanisms involved in successful pregnancy. Therefore, the objectives here were to identify loci associated with HCR: 1) to the first artificial insemination (AI) service (HCR1), 2) to repeated AI services required for a heifer to conceive (TBRD) and 3) to validate loci previously associated with fertility. Breeding and health records from 3359 Holstein heifers were obtained after heifers were bred by AI at observed estrus, with pregnancy determined at day 35 via palpation. Heifer DNA was genotyped using the Illumina BovineHD BeadChip, and genome-wide association analyses (GWAA) were performed with additive, dominant and recessive models using the Efficient Mixed Model Association eXpedited (EMMAX) method with a relationship matrix for two phenotypes. The HCR1 GWAA compared heifers that were pregnant after the first AI service (n = 497) to heifers that were open following the first AI service (n = 405), which included those that never conceived. The TBRD GWAA compared only those heifers which did conceive, across variable numbers of AI service (n = 712). Comparison of loci previously associated with fertility, HCR1 or TBRD were considered the same locus for validation when in linkage disequilibrium (D' > 0.7). Results: The HCR1 GWAA identified 116, 187 and 28 loci associated (P < 5 × 10 − 8) in additive, dominant and recessive models, respectively. The TBRD GWAA identified 235, 362, and 69 QTL associated (P < 5 × 10 − 8) with additive, dominant and recessive models, respectively. Loci previously associated with fertility were in linkage disequilibrium with 22 loci shared with HCR1 and TBRD, 5 HCR1 and 19 TBRD loci. Conclusions: Loci associated with HCR1 and TBRD that have been identified and validated can be used to improve HCR through genomic selection, and to better understand possible mechanisms associated with subfertility.

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Section: Resultsmentioning

confidence: 99%

Section: Genome-wide Association Analysismentioning

confidence: 99%

Validation of 46 loci associated with female fertility traits in cattle

et al. 2019

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“…The production of cattle therefore involves GH, TSH, IGF-1, and insulin because they act together on individual animals [7]. Many genes that participate in the process have been identified, such as MYC, PPARG, GSK3B, TG, STAT5b, and IYD [8,9]. However, the growth and development of biological organisms is very complicated, and there are still many genes involved that need to be identified [10,11].…”

Section: Introductionmentioning

confidence: 99%

Comparative Transcriptomic Analysis of the Pituitary Gland between Cattle Breeds Differing in Growth: Yunling Cattle and Leiqiong Cattle

Arbab

Zhang

et al. 2020

Animals

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The hypothalamic–pituitary–thyroid (HPT) axis hormones regulate the growth and development of ruminants, and the pituitary gland plays a decisive role in this process. In order to identify pivotal genes in the pituitary gland that could affect the growth of cattle by regulating the secretion of hormones, we detected the content of six HPT hormones related to growth in the plasma of two cattle breeds (Yunling and Leiqiong cattle, both also known as the zebu cattle) with great differences in growth and compared the transcriptome data of their pituitary glands. Our study found that the contents of GH, IGF, TSH, thyroxine, triiodothyronine, and insulin were significantly different between the two breeds, which was the main cause of the difference in growth; 175 genes were identified as differentially expressed genes (DEGs). Functional association analyses revealed that DEGs were mainly involved in the process of transcription and signal transduction. Combining the enrichment analysis and protein interaction analysis, eight DEGs were predicted to control the growth of cattle by affecting the expression of growth-related hormones in the pituitary gland. In summary, our results suggested that SLC38A1, SLC38A3, DGKH, GNB4, GNAQ, ESR1, NPY, and GAL are candidates in the pituitary gland for regulating the growth of Yunling and Leiqiong cattle by regulating the secretion of growth-related hormones. This study may help researchers further understand the growth mechanisms and improve the artificial selection of zebu cattle.

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“…RNA-Seq experiments in livestock studies have identified significant SNPs in candidate genes associated with metabolic pathways that may play a role in the regulation of production traits [4,[8][9][10][11][12]. This has resulted in an improved understanding of the genetic architecture and a reduction in genome complexity of important traits such as feed efficiency, health, fertility, and meat quality traits in beef cattle [4,8,[13][14][15]. More specifically, the study of genetic variants that may serve as markers to select for feed efficiency or residual feed intake (RFI) may help lead to the genetic improvement of feed efficiency and result in economic and environmental benefits for beef production, as feed costs represent approximately 70% of livestock production expenses [16].…”

Section: Introductionmentioning

confidence: 99%

Development and comparison of RNA-sequencing pipelines for more accurate SNP identification: practical example of functional SNP detection associated with feed efficiency in Nellore beef cattle

et al. 2020

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Background Optimization of an RNA-Sequencing (RNA-Seq) pipeline is critical to maximize power and accuracy to identify genetic variants, including SNPs, which may serve as genetic markers to select for feed efficiency, leading to economic benefits for beef production. This study used RNA-Seq data (GEO Accession ID: PRJEB7696 and PRJEB15314) from muscle and liver tissue, respectively, from 12 Nellore beef steers selected from 585 steers with residual feed intake measures (RFI; n = 6 low-RFI, n = 6 high-RFI). Three RNA-Seq pipelines were compared including multi-sample calling from i) non-merged samples; ii) merged samples by RFI group, iii) merged samples by RFI and tissue group. The RNA-Seq reads were aligned against the UMD3.1 bovine reference genome (release 94) assembly using STAR aligner. Variants were called using BCFtools and variant effect prediction (VeP) and functional annotation (ToppGene) analyses were performed. Results On average, total reads detected for Approach i) non-merged samples for liver and muscle, were 18,362,086.3 and 35,645,898.7, respectively. For Approach ii), merging samples by RFI group, total reads detected for each merged group was 162,030,705, and for Approach iii), merging samples by RFI group and tissues, was 324,061,410, revealing the highest read depth for Approach iii). Additionally, Approach iii) merging samples by RFI group and tissues, revealed the highest read depth per variant coverage (572.59 ± 3993.11) and encompassed the majority of localized positional genes detected by each approach. This suggests Approach iii) had optimized detection power, read depth, and accuracy of SNP calling, therefore increasing confidence of variant detection and reducing false positive detection. Approach iii) was then used to detect unique SNPs fixed within low- (12,145) and high-RFI (14,663) groups. Functional annotation of SNPs revealed positional candidate genes, for each RFI group (2886 for low-RFI, 3075 for high-RFI), which were significantly (P < 0.05) associated with immune and metabolic pathways. Conclusion The most optimized RNA-Seq pipeline allowed for more accurate identification of SNPs, associated positional candidate genes, and significantly associated metabolic pathways in muscle and liver tissues, providing insight on the underlying genetic architecture of feed efficiency in beef cattle.

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Combining multi-OMICs information to identify key-regulator genes for pleiotropic effect on fertility and production traits in beef cattle

Cited by 34 publications

References 92 publications

Validation of 46 loci associated with female fertility traits in cattle

Validation of 46 loci associated with female fertility traits in cattle

Comparative Transcriptomic Analysis of the Pituitary Gland between Cattle Breeds Differing in Growth: Yunling Cattle and Leiqiong Cattle

Development and comparison of RNA-sequencing pipelines for more accurate SNP identification: practical example of functional SNP detection associated with feed efficiency in Nellore beef cattle

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