Genome-wide association study for conformation traits in three Danish pig breeds

The differences in artificial and natural selection have been some of the factors contributing to phenotypic diversity between Chinese and western pigs. Here, 830 individuals from western and Chinese pig breeds were genotyped using the reduced-representation genotyping method. First, we identified the selection signatures for different pig breeds. By comparing Chinese pigs and western pigs along the first principal component, the growth gene IGF1R; the immune genes IL1R1, IL1RL1, DUSP10, RAC3 and SWAP70; the meat quality-related gene SNORA50 and the olfactory gene OR1F1 were identified as candidate differentiated targets. Further, along a principal component separating Pudong White pigs from others, a potential causal gene for coat colour (EDNRB) was discovered. In addition, the divergent signatures evaluated by Fst within Chinese pig breeds found genes associated with the phenotypic features of coat colour, meat quality and feed efficiency among these indigenous pigs. Second, admixture and genomic introgression analysis were performed. Shan pigs have introgressed genes from Berkshire, Yorkshire and Hongdenglong pigs. The results of introgression mapping showed that this introgression conferred adaption to the local environment and coat colour of Chinese pigs and the superior productivity of western pigs.

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“… 16 TDRD9 Conformation traits Le, et al . 102 DPP10 Stillbirth Schneider, et al . 103 ENSSSCG00000016409 Growth Yang, et al .…”

Section: Resultsmentioning

confidence: 99%

Genomic analysis reveals genes affecting distinct phenotypes among different Chinese and western pig breeds

Zhang

Xiao

Zhang

et al. 2018

Sci Rep

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“…In this regard, the first step in diagnosing the disease is an exterior assessment of the legs and gait. Typically, pig legs are visually assessed by specially trained personnel using a point system (Le et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

PigLeg: prediction of swine phenotype using machine learning

Bakoev

Getmantseva

Kolosova

et al. 2020

PeerJ

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Industrial pig farming is associated with negative technological pressure on the bodies of pigs. Leg weakness and lameness are the sources of significant economic loss in raising pigs. Therefore, it is important to identify the predictors of limb condition. This work presents assessments of the state of limbs using indicators of growth and meat characteristics of pigs based on machine learning algorithms. We have evaluated and compared the accuracy of prediction for nine ML classification algorithms (Random Forest, K-Nearest Neighbors, Artificial Neural Networks, C50Tree, Support Vector Machines, Naive Bayes, Generalized Linear Models, Boost, and Linear Discriminant Analysis) and have identified the Random Forest and K-Nearest Neighbors as the best-performing algorithms for predicting pig leg weakness using a small set of simple measurements that can be taken at an early stage of animal development. Measurements of Muscle Thickness, Back Fat amount, and Average Daily Gain were found to be significant predictors of the conformation of pig limbs. Our work demonstrates the utility and relative ease of using machine learning algorithms to assess the state of limbs in pigs based on growth rate and meat characteristics.

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“…The MA is widely used in human genetics, where access to the original datasets is usually limited due to privacy protection policies. In the last years, the meta-analysis has been employed for pig association studies in an effort to maximize the use of available genomic information from commercial or experimental pig populations [ 8 – 10 ].…”

Section: Introductionmentioning

confidence: 99%

Genome-wide association studies and meta-analysis uncovers new candidate genes for growth and carcass traits in pigs

et al. 2018

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Genome-wide association studies (GWAS) have been widely used in the genetic dissection of complex traits. As more genomic data is being generated within different commercial or resource pig populations, the challenge which arises is how to collectively investigate the data with the purpose to increase sample size and implicitly the statistical power. This study performs an individual population GWAS, a joint population GWAS and a meta-analysis in three pig F2 populations. D1 is derived from European type breeds (Piétrain, Large White and Landrace), D2 is obtained from an Asian breed (Meishan) and Piétrain, and D3 stems from a European Wild Boar and Piétrain, which is the common founder breed. The traits investigated are average daily gain, backfat thickness, meat to fat ratio and carcass length. The joint and the meta-analysis did not identify additional genomic clusters besides the ones discovered via the individual population GWAS. However, the benefit was an increased mapping resolution which pinpointed to narrower clusters harboring causative variants. The joint analysis identified a higher number of clusters as compared to the meta-analysis; nevertheless, the significance levels and the number of significant variants in the meta-analysis were generally higher. Both types of analysis had similar outputs suggesting that the two strategies can complement each other and that the meta-analysis approach can be a valuable tool whenever access to raw datasets is limited. Overall, a total of 20 genomic clusters that predominantly overlapped at various extents, were identified on chromosomes 2, 7 and 17, many confirming previously identified quantitative trait loci. Several new candidate genes are being proposed and, among them, a strong candidate gene to be taken into account for subsequent analysis is BMP2 (bone morphogenetic protein 2).

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Genome-wide association study for conformation traits in three Danish pig breeds

Cited by 40 publications

References 62 publications

Genomic analysis reveals genes affecting distinct phenotypes among different Chinese and western pig breeds

Genomic analysis reveals genes affecting distinct phenotypes among different Chinese and western pig breeds

PigLeg: prediction of swine phenotype using machine learning

Genome-wide association studies and meta-analysis uncovers new candidate genes for growth and carcass traits in pigs

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