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Chinese indigenous pig breeds have been undergoing selection for thousands of years, and have become invaluable genetic sources over the world. To investigate the population structure and genetic diversity of Jinhua (JH), Longyou Black (LYW), Shengxian Spotted (SXH), and Lanxi Spotted (LXH) breeds, a total of 200 pigs belonging to 10 diverse population were genotyped using SNP chips. The results showed that LYW pigs exhibited higher level of heterozygosity than the other indigenous pigs. In addition, gene introgression from intensively reared commercial pig breeds to LYW pigs was detected. Moreover, selection signature analysis revealed the possibility of differences between Chinese indigenous and intensively reared commercial pig breeds were mainly present for meat and carcass traits. Furthermore, we found that ANXA13, DISP1, and SRSF6 were the nearest genes located around the common selection signatures detected between each indigenous pig breed and Chinese wild boars. Our findings provide new insights into the selection signatures of Chinese indigenous pigs, and may contribute to future pig breeding. [ABSTRACT FROM AUTHOR]

In livestock, genome‐wide association studies (GWAS) are usually conducted in a single population (single‐GWAS) with limited sample size and detection power. To enhance the detection power of GWAS, meta‐analysis of GWAS (meta‐GWAS) and mega‐analysis of GWAS (mega‐GWAS) have been proposed to integrate data from multiple populations at the level of summary statistics or individual data, respectively. However, there is a lack of comparison for these different strategies, which makes it difficult to guide the best practice of GWAS integrating data from multiple study populations. To maximize the comparison of different association analysis strategies across multiple populations, we conducted single‐GWAS, meta‐GWAS, and mega‐GWAS for the backfat thickness of 100 kg (BFT_100) and days to 100 kg (DAYS_100) within each of the three commercial pig breeds (Duroc, Yorkshire, and Landrace). Based on controlling the genome inflation factor to one, we calculated corrected p‐values (pC). In Yorkshire, with the largest sample size, mega‐GWAS, meta‐GWAS and single‐GWAS detected 149, 38 and 20 significant SNPs (pC < 1E‐5) associated with BFT_100, as well as 26, four, and one QTL, respectively. Among them, pC of SNPs from mega‐GWAS was the lowest, followed by meta‐GWAS and single‐GWAS. The correlation of pC among the three GWAS strategies ranged from 0.60 to 0.75 and the correlation of SNP effect values between meta‐GWAS and mega‐GWAS was 0.74, all showing good agreement. Collectively, even though there are differences in the integration of individual data or summary statistics, integrating data from multiple populations is an effective means of genetic argument for complex traits, especially mega‐GWAS versus single‐GWAS. [ABSTRACT FROM AUTHOR]

Breed identification utilizing multiple information sources and methods is widely applicated in the field of animal genetics and breeding. Simultaneously, with the development of artificial intelligence, the integration of high‐throughput genomic data and machine learning techniques is increasingly used for breed identification. In this context, we used 654 individuals from 15 pig breeds, evaluating the performance of machine learning and stacking ensemble learning classifiers, as well as the function of feature selection and anomaly detection in different scenarios. Our results showed that, when using a training set of 16 individuals per breed and 32 features (SNPs), the accuracy of breed identification with feature selection (eXtreme Gradient Boosting, XGBoost) could exceed 95.00% (nine breeds), and was improved by 7.04% over the results with random selection. For stacking ensemble learning, feature selection methods (including random selection method) were used before different base learners. When these base learners' training set had 16 individuals per breed and 32 features, the accuracy of stacking ensemble learning improved by 9.24% over the best base learner (nine breeds), but did not significantly increase the advantage over the models with XGBoost feature selection. When using a training set of 16 individuals and 512 features per breed, breed identification with anomaly detection (local outlier factor, LOF) and random selection could achieve an accuracy of 89.06% (15 breeds). These results show that machine learning could be an effective tool for breed identification and this study will also provide useful information for the application of machine learning in animal genetics and breeding. [ABSTRACT FROM AUTHOR]

Joint genomic prediction (GP) is an attractive method to improve the accuracy of GP by combining information from multiple populations. However, many factors can negatively influence the accuracy of joint GP, such as differences in linkage disequilibrium phasing between single nucleotide polymorphisms (SNPs) and causal variants, minor allele frequencies and causal variants' effect sizes across different populations. The objective of this study was to investigate whether the imputed high‐density genotype data can improve the accuracy of joint GP using genomic best linear unbiased prediction (GBLUP), single‐step GBLUP (ssGBLUP), multi‐trait GBLUP (MT‐GBLUP) and GBLUP based on genomic relationship matrix considering heterogenous minor allele frequencies across different populations (wGBLUP). Three traits, including days taken to reach slaughter weight, backfat thickness and loin muscle area, were measured on 67 276 Large White pigs from two different populations, for which 3334 were genotyped by SNP array. The results showed that a combined population could substantially improve the accuracy of GP compared with a single‐population GP, especially for the population with a smaller size. The imputed SNP data had no effect for single population GP but helped to yield higher accuracy than the medium‐density array data for joint GP. Of the four methods, ssGLBUP performed the best, but the advantage of ssGBLUP decreased as more individuals were genotyped. In some cases, MT‐GBLUP and wGBLUP performed better than GBLUP. In conclusion, our results confirmed that joint GP could be beneficial from imputed high‐density genotype data, and the wGBLUP and MT‐GBLUP methods are promising for joint GP in pig breeding. [ABSTRACT FROM AUTHOR]