Background: Over several decades, a wide range of natural and artificial selection events in response to subtropical environments, intensive pasture and intensive feedlot systems have greatly changed the customary behaviour, appearance, and important economic traits of Shanghai Holstein cattle. In particular, the longevity of the Shanghai Holstein cattle population is generally short, approximately the 2nd to 3rd lactation. In this study, two complementary approaches, integrated haplotype score (iHS) and runs of homozygosity (ROH), were applied for the detection of selection signatures within the genome using genotyping by genome-reduced sequence data from 1092 cows. Results: In total, 101 significant iHS genomic regions containing selection signatures encompassing a total of 256 candidate genes were detected. There were 27 significant |iHS| genomic regions with a mean |iHS| score > 2. The average number of ROH per individual was 42.15 ± 25.47, with an average size of 2.95 Mb. The length of 78 % of the detected ROH was within the range of 1–2 MB and 2–4 MB, and 99 % were shorter than 8 Mb. A total of 168 genes were detected in 18 ROH islands (top 1 %) across 16 autosomes, in which each SNP showed a percentage of occurrence > 30 %. There were 160 and 167 genes associated with the 52 candidate regions within health-related QTL intervals and 59 candidate regions within reproduction-related QTL intervals, respectively. Annotation of the regions harbouring clustered |iHS| signals and candidate regions for ROH revealed a panel of interesting candidate genes associated with adaptation and economic traits, such as IL22RA1, CALHM3, ITGA9, NDUFB3, RGS3, SOD2, SNRPA1, ST3GAL4, ALAD, EXOSC10, and MASP2. In a further step, a total of 1472 SNPs in 256 genes were matched with 352 cis-eQTLs in 21 tissues and 27 trans-eQTLs in 6 tissues. For SNPs located in candidate regions for ROH, a total of 108 cis-eQTLs in 13 tissues and 4 trans-eQTLs were found for 1092 SNPs. Eighty-one eGenes were significantly expressed in at least one tissue relevant to a trait (P value < 0.05) and matched the 256 genes detected by iHS. For the 168 significant genes detected by ROH, 47 gene-tissue pairs were significantly associated with at least one of the 37 traits. Conclusions: We provide a comprehensive overview of selection signatures in Shanghai Holstein cattle genomes by combining iHS and ROH. Our study provides a list of genes associated with immunity, reproduction and adaptation. For functional annotation, the cGTEx resource was used to interpret SNP-trait associations. The results may facilitate the identification of genes relevant to important economic traits and can help us better understand the biological processes and mechanisms affected by strong ongoing natural or artificial selection in livestock populations.

Jinhua pig, a well-known Chinese indigenous breed, has evolved as a pig breed with excellent meat quality, greater disease resistance, and higher prolificacy. The reduction in the number of Jinhua pigs over the past years has raised concerns about inbreeding. Runs of homozygosity (ROH) along the genome have been applied to quantify individual autozygosity to improve the understanding of inbreeding depression and identify genes associated with traits of interest. Here, we investigated the occurrence and distribution of ROH using next-generation sequencing data to characterize autozygosity in 202 Jinhua pigs, as well as to identify the genomic regions with high ROH frequencies within individuals. The average inbreeding coefficient, based on ROH longer than 1 Mb, was 0.168 ± 0.052. In total, 18,690 ROH were identified in all individuals, among which shorter segments (1–5 Mb) predominated. Individual ROH autosome coverage ranged from 5.32 to 29.14% in the Jinhua population. On average, approximately 16.8% of the whole genome was covered by ROH segments, with the lowest coverage on SSC11 and the highest coverage on SSC17. A total of 824 SNPs (about 0.5%) and 11 ROH island regions were identified (occurring in over 45% of the samples). Genes associated with reproduction (HOXA3, HOXA7, HOXA10, and HOXA11), meat quality (MYOD1, LPIN3, and CTNNBL1), appetite (NUCB2) and disease resistance traits (MUC4, MUC13, MUC20, LMLN, ITGB5, HEG1, SLC12A8, and MYLK) were identified in ROH islands. Moreover, several quantitative trait loci for ham weight and ham fat thickness were detected. Genes in ROH islands suggested, at least partially, a selection for economic traits and environmental adaptation, and should be subject of future investigation. These findings contribute to the understanding of the effects of environmental and artificial selection in shaping the distribution of functional variants in the pig genome.

Genomic prediction (GP) has revolutionized animal and plant breeding. However, better statistical models that can improve the accuracy of GP are required. For this reason, in this study, we explored the genomic-based prediction performance of a popular machine learning method, the Support Vector Machine (SVM) model. We selected the most suitable kernel function and hyperparameters for the SVM model in eight published genomic data sets on pigs and maize. Next, we compared the SVM model with RBF and the linear kernel functions to the two most commonly used genome-enabled prediction models (GBLUP and BayesR) in terms of prediction accuracy, time, and the memory used. The results showed that the SVM model had the best prediction performance in two of the eight data sets, but in general, the predictions of both models were similar. In terms of time, the SVM model was better than BayesR but worse than GBLUP. In terms of memory, the SVM model was better than GBLUP and worse than BayesR in pig data but the same with BayesR in maize data. According to the results, SVM is a competitive method in animal and plant breeding, and there is no universal prediction model.

Genetic characterization of Chinese indigenous pig breeds is essential to promote scientific conservation and sustainable development of pigs. Here, we systematically surveyed the genomes of 75 unrelated Diannan small-ear (DSE) pigs from three diverse regions (Yingjiang County, Jinping County, and Sipsongpanna in Yunnan Province) to describe their population structures, genetic diversity, inbreeding coefficients, and selection signatures. First, these individuals were sequenced and genotyped using the genome reducing and sequencing (GGRS) protocol. A total of 438,038 autosomal single-nucleotide polymorphisms (SNPs) were obtained and used for subsequent statistical analysis. The results showed that these DSE pigs were clearly differentiated into three separate clades revealed by the population structure and principal component analysis, which is consistent with their geographical origins. Diannan small-ear pigs owned lower genetic diversity when compared with some other pig breeds, which demonstrated the need to strengthen the conservation strategies for DSE pigs. In addition, the inbreeding coefficients based on runs of homozygosity (ROH) length (F_ROH) were calculated in each ROH length categories, respectively. And the results indicated that the ancient (up to 50 generations ago) inbreeding had greater impacts than recent (within the last five generations) inbreeding within DSE pigs. Some candidate selection signatures within the DSE pig population were detected through the ROH islands and integrated haplotype homozygosity score (iHS) methods. And genes associated with meat quality (COL15A1, RPL3L, and SLC9A3R2), body size (PALM2-AKAP2, NANS, TRAF7, and PACSIN1), adaptability (CLDN9 and E4F1), and appetite (GRM4) were identified. These findings can help to understand the genetic characteristics and provide insights into the molecular background of special phenotypes of DSE pigs to promote conservation and sustainability of the breed.

Pigs not only function as a major meat source worldwide but also are commonly used as an animal model for studying human complex traits. A large haplotype reference panel has been used to facilitate efficient phasing and imputation of relatively sparse genome-wide microarray chips and low-coverage sequencing data. Using the imputed genotypes in the downstream analysis, such as GWASs, TWASs, eQTL mapping and genomic prediction (GS), is beneficial for obtaining novel findings. However, currently, there is still a lack of publicly available and high-quality pig reference panels with large sample sizes and high diversity, which greatly limits the application of genotype imputation in pigs. In response, we built the pig Haplotype Reference Panel (PHARP) database. PHARP provides a reference panel of 2012 pig haplotypes at 34 million SNPs constructed using whole-genome sequence data from more than 49 studies of 71 pig breeds. It also provides Web-based analytical tools that allow researchers to carry out phasing and imputation consistently and efficiently. PHARP is freely accessible at http://alphaindex.zju.edu.cn/PHARP/index.php. We demonstrate its applicability for pig commercial 50 K SNP arrays, by accurately imputing 2.6 billion genotypes at a concordance rate value of 0.971 in 81 Large White pigs (~ 17 × sequencing coverage). We also applied our reference panel to impute the low-density SNP chip into the high-density data for three GWASs and found novel significantly associated SNPs that might be casual variants.

Hu sheep, a famous breed in the Taihu Basin, has the advantages of non-seasonal estrus, multiple fetuses, coarse feeding tolerance, and suitability for house feeding. Runs of homozygosity (ROHs) were found to be an effective tool to detect the animal population structure and economic traits. The detection of ROHs is beneficial for reducing the incidence of inbreeding as well as identifying harmful variants in the genome. However, there is a lack of systemic reports on ruminants in previous studies of ROHs. Here, we sequenced 108 Hu sheep, detected ROHs in Hu sheep to calculate their inbreeding coefficient, and selected genes of Hu sheep breeds within the ROH islands which are relevant to agricultural economic characteristics. Then, we compared the characteristics of the occurrences of SNPs between Hu sheep and other sheep breeds, and also investigated the distribution of the frequencies of SNPs within specific gene regions of Hu sheep breeds to select their breed-specific genes. Furthermore, we performed a comparative genome and transcriptome analysis in human and sheep breeds to identify important reproduction-related genes. In this way, we found some significant SNPs, and mapped these with a set of interesting candidate genes which are related to the productive value of livestock (FGF9, BMPR1B, EFNB3, MICU2, GFRA3), healthy characteristics (LGSN, EPHA5, ALOX15B), and breed specificity (FGF9, SAP18, MICU2). These results in our study describe various production traits of Hu sheep from a genetic perspective, and provide insights into the genetic management and complementary understanding of Hu sheep.