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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]

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]

The low heritability of reproduction traits such as total number born (TNB), number born alive (NBA) and adjusted litter weight until 21 days at weaning (ALW) poses a challenge for genetic improvement. In this study, we aimed to identify genetic variants that influence these traits and evaluate the accuracy of genomic selection (GS) using these variants as genomic features. We performed single‐step genome‐wide association studies (ssGWAS) on 17 823 Large White (LW) pigs, of which 2770 were genotyped by 50K single nucleotide polymorphism (SNP) chips. Additionally, we analyzed runs of homozygosity (ROH) in the population and tested their effects on the traits. The genomic feature best linear unbiased prediction (GFBLUP) was then carried out in an independent population of 350 LW pigs using identified trait‐related SNP subsets as genomic features. As a result, our findings identified five, one and four SNP windows that explaining more than 1% of genetic variance for ALW, TNB, and NBA, respectively and discovered 358 hotspots and nine ROH islands. The ROH SSC1:21814570–27186456 and SSC11:7220366–14276394 were found to be significantly associated with ALW and NBA, respectively. We assessed the genomic estimated breeding value accuracy through 20 replicates of five‐fold cross‐validation. Our findings demonstrate that GFBLUP, incorporating SNPs located in effective ROH (p‐value < 0.05) as genomic features, might enhance GS accuracy for ALW compared with GBLUP. Additionally, using SNPs explaining more than 0.1% of the genetic variance in ssGWAS for NBA as genomic features might improve the GS accuracy, too. However, it is important to note that the incorporation of inappropriate genomic features can significantly reduce GS accuracy. In conclusion, our findings provide valuable insights into the genetic mechanisms of reproductive traits in pigs and suggest that the ssGWAS and ROH have the potential to enhance the accuracy of GS for reproductive traits in LW pigs. [ABSTRACT FROM AUTHOR]