Genetic architecture of quantitative traits in beef cattle revealed by genome wide association studies of imputed whole genome sequence variants: II: carcass merit traits

Wang, Yining; Zhang, Feng; Mukiibi, Robert; Chen, Liuhong; Vinsky, Michael; Plastow, Graham; Basarab, J. A.; Stothard, Paul; Li, Changxi

doi:10.1186/s12864-019-6273-1

Cited by 41 publications

(47 citation statements)

References 77 publications

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“…Lu et al 11 (2020) reported SLCO2A1, LY6K, RALYL, AADACL3, C17H4orf45, BICC1, SHROOM2 as candidate genes for birth weight in Chinese Fine-Wool Sheep 11 . The candidate genes for Dry Matter Intake, Average Daily Gain, and Metabolic Body Weight based on the imputed 7.8 M WGS in chattels were reported: SNORA70, B3GALT1, DDR2, GPR37, SYT1, LYZL1, RGS2, F13A1, SNORA31, LCORL, DPH6, PARD3, MOS, CRB1, CUL1, CCND2, ARRDC3, PLAG1, STC2, CARD11, TMEM72, SCGB1A1, ERICH6, ARRDC3, GALNT14, PLAG1, ERGIC1, AP3S2, A1CF 40 . An, B. et al (2020) 41 identified the candidate genes for growth traits in Simmental beef cattle, including SOX2, SNRPD1, RASGEF1B, EFNA5, PTBP1, SNX9, SV2C, PKDCC, SYNDIG1, AKR1E2, PRIM2, SLC37A1, LAP3, PCDH7, MANEA, LHCGR, P2RY1, MPZL1, LINGO2, CMIP, and WSCD1 .…”

Section: Discussionmentioning

confidence: 99%

Genomic prediction for growth using a low-density SNP panel in dromedary camels

Sani

Harofte

Banabazi

et al. 2021

Sci Rep

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For thousands of years, camels have produced meat, milk, and fiber in harsh desert conditions. For a sustainable development to provide protein resources from desert areas, it is necessary to pay attention to genetic improvement in camel breeding. By using genotyping-by-sequencing (GBS) method we produced over 14,500 genome wide markers to conduct a genome- wide association study (GWAS) for investigating the birth weight, daily gain, and body weight of 96 dromedaries in the Iranian central desert. A total of 99 SNPs were associated with birth weight, daily gain, and body weight (p-value < 0.002). Genomic breeding values (GEBVs) were estimated with the BGLR package using (i) all 14,522 SNPs and (ii) the 99 SNPs by GWAS. Twenty-eight SNPs were associated with birth weight, daily gain, and body weight (p-value < 0.001). Annotation of the genomic region (s) within ± 100 kb of the associated SNPs facilitated prediction of 36 candidate genes. The accuracy of GEBVs was more than 0.65 based on all 14,522 SNPs, but the regression coefficients for birth weight, daily gain, and body weight were 0.39, 0.20, and 0.23, respectively. Because of low sample size, the GEBVs were predicted using the associated SNPs from GWAS. The accuracy of GEBVs based on the 99 associated SNPs was 0.62, 0.82, and 0.57 for birth weight, daily gain, and body weight. This report is the first GWAS using GBS on dromedary camels and identifies markers associated with growth traits that could help to plan breeding program to genetic improvement. Further researches using larger sample size and collaboration of the camel farmers and more profound understanding will permit verification of the associated SNPs identified in this project. The preliminary results of study show that genomic selection could be the appropriate way to genetic improvement of body weight in dromedary camels, which is challenging due to a long generation interval, seasonal reproduction, and lack of records and pedigrees.

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Section: Discussionmentioning

confidence: 99%

Genomic prediction for growth using a low-density SNP panel in dromedary camels

Sani

Harofte

Banabazi

et al. 2021

Sci Rep

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“…In carp ( Cyprinus carpio ), the same gene was reported to increase the efficiency of abdominal fat deposition (Zheng et al., 2016). KCNJ1 (BTA29) belongs to the potassium channel family and was reported to be associated with BFT in Angus and Charolais cattle (Wang et al., 2020).…”

Section: Discussionmentioning

confidence: 99%

Genome‐wide association study and pathway analysis for fat deposition traits in nellore cattle raised in pasture–based systems

Martins

Machado

Pinto

et al. 2020

J Animal Breeding Genetics

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Genome-wide association study (GWAS) is a powerful tool to identify candidate genes and genomic regions underlying key biological mechanisms associated with economically important traits. In this context, the aim of this study was to identify genomic regions and metabolic pathways associated with backfat thickness (BFT) and rump fat thickness (RFT) in Nellore cattle, raised in pasture-based systems. Ultrasound-based measurements of BFT and RFT (adjusted to 18 months of age) were collected in 11,750 animals, with 39,903 animals in the pedigree file.Additionally, 1,440 animals were genotyped using the GGP-indicus 35K SNP chip, containing 33,623 SNPs after the quality control. The single-step GWAS analyses were performed using the BLUPF90 family programs. Candidate genes were identified through the Ensembl database incorporated in the BioMart tool, while PANTHER and REVIGO were used to identify the key metabolic pathways and gene networks. A total of 18 genomic regions located on 10 different chromosomes and harbouring 23 candidate genes were identified for BFT. For RFT, 22 genomic regions were found on 14 chromosomes, with a total of 29 candidate genes identified. The results of the pathway analyses showed important genes for BFT, including TBL1XR1, AHCYL2, SLC4A7, AADAT, VPS53, IDH2 and ETS1, which are involved in lipid metabolism, synthesis of cellular amino acids, transport of solutes, transport between Golgi Complex membranes, cell differentiation and cellular development.The main genes identified for RFT were GSK3β, LRP1B, EXT1, GRB2, SORCS1 and SLMAP, which are involved in metabolic pathways such as glycogen synthesis, lipid transport and homeostasis, polysaccharide and carbohydrate metabolism. Polymorphisms located in these candidate genes can be incorporated in commercial genotyping platforms to improve the accuracy of imputation and genomic evaluations for carcass fatness. In addition to uncovering biological mechanisms associated with carcass quality, the key gene pathways identified can also be incorporated in biology-driven genomic prediction methods.

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“…Identification of positional candidate genes was conducted using the UCSC Genome Browser for the Ensembl annotation of the Sscrofa11.1 build of the swine genome ( https://genome.ucsc.edu ). One collective gene list was created for each trait by combing all candidate genes in associated genomic regions for IPA [ 94 , 95 ]. Human, mouse, and rat genes in the IPA knowledge base database were used as background for biological function analyses in diseases, molecular and cellular functions, and physiological system development and function categories.…”

Section: Methodsmentioning

confidence: 99%

“…A biological function was considered significantly enriched if the p-value for the overlap comparison test between the input list of candidate genes and the IPA database was less than 0.05 [94][95][96].…”

Section: Post-gwas Bioinformatics Analysesmentioning

confidence: 99%

Investigating the genetic architecture of disease resilience in pigs by genome-wide association studies of complete blood count traits collected from a natural disease challenge model

et al. 2021

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Background Genetic improvement for disease resilience is anticipated to be a practical method to improve efficiency and profitability of the pig industry, as resilient pigs maintain a relatively undepressed level of performance in the face of infection. However, multiple biological functions are known to be involved in disease resilience and this complexity means that the genetic architecture of disease resilience remains largely unknown. Here, we conducted genome-wide association studies (GWAS) of 465,910 autosomal SNPs for complete blood count (CBC) traits that are important in an animal’s disease response. The aim was to identify the genetic control of disease resilience. Results Univariate and multivariate single-step GWAS were performed on 15 CBC traits measured from the blood samples of 2743 crossbred (Landrace × Yorkshire) barrows drawn at 2-weeks before, and at 2 and 6-weeks after exposure to a polymicrobial infectious challenge. Overall, at a genome-wise false discovery rate of 0.05, five genomic regions located on Sus scrofa chromosome (SSC) 2, SSC4, SSC9, SSC10, and SSC12, were significantly associated with white blood cell traits in response to the polymicrobial challenge, and nine genomic regions on multiple chromosomes (SSC1, SSC4, SSC5, SSC6, SSC8, SSC9, SSC11, SSC12, SSC17) were significantly associated with red blood cell and platelet traits collected before and after exposure to the challenge. By functional enrichment analyses using Ingenuity Pathway Analysis (IPA) and literature review of previous CBC studies, candidate genes located nearby significant single-nucleotide polymorphisms were found to be involved in immune response, hematopoiesis, red blood cell morphology, and platelet aggregation. Conclusions This study helps to improve our understanding of the genetic basis of CBC traits collected before and after exposure to a polymicrobial infectious challenge and provides a step forward to improve disease resilience.

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Genetic architecture of quantitative traits in beef cattle revealed by genome wide association studies of imputed whole genome sequence variants: II: carcass merit traits

Cited by 41 publications

References 77 publications

Genomic prediction for growth using a low-density SNP panel in dromedary camels

Genomic prediction for growth using a low-density SNP panel in dromedary camels

Genome‐wide association study and pathway analysis for fat deposition traits in nellore cattle raised in pasture–based systems

Investigating the genetic architecture of disease resilience in pigs by genome-wide association studies of complete blood count traits collected from a natural disease challenge model

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