Unveiling the Genetic Mechanism of Meat Color in Pigs through GWAS, Multi-Tissue, and Single-Cell Transcriptome Signatures Exploration.

Meat color traits directly influence consumer acceptability and purchasing decisions. Nevertheless, there is a paucity of comprehensive investigation into the genetic mechanisms underlying meat color traits in pigs. Utilizing genome-wide association studies (GWAS) on five meat color traits and the detection of selection signatures in pig breeds exhibiting distinct meat color characteristics, we identified a promising candidate SNP, 6_69103754, exhibiting varying allele frequencies among pigs with different meat color characteristics. This SNP has the potential to affect the redness and chroma index values of pork. Moreover, transcriptome-wide association studies (TWAS) analysis revealed the expression of candidate genes associated with meat color traits in specific tissues. Notably, the largest number of candidate genes were observed from transcripts derived from adipose, liver, lung, spleen tissues, and macrophage cell type, indicating their crucial role in meat color development. Several shared genes associated with redness, yellowness, and chroma indices traits were identified, including RINL in adipose tissue, ENSSSCG00000034844 and ITIH1 in liver tissue, TPX2 and MFAP2 in lung tissue, and ZBTB17, FAM131C, KIFC3, NTPCR, and ENGSSSCG00000045605 in spleen tissue. Furthermore, single-cell enrichment analysis revealed a significant association between the immune system and meat color. This finding underscores the significance of the immune system associated with meat color. Overall, our study provides a comprehensive analysis of the genetic mechanisms underlying meat color traits, offering valuable insights for future breeding efforts aimed at improving meat quality.

Exploring the Interplay between the Hologenome and Complex Traits in Bovine and Porcine Animals Using Genome-Wide Association Analysis.

Genome-wide association studies (GWAS) significantly enhance our ability to identify trait-associated genomic variants by considering the host genome. Moreover, the hologenome refers to the host organism's collective genetic material and its associated microbiome. In this study, we utilized the hologenome framework, called Hologenome-wide association studies (HWAS), to dissect the architecture of complex traits, including milk yield, methane emissions, rumen physiology in cattle, and gut microbial composition in pigs. We employed four statistical models: (1) GWAS, (2) Microbial GWAS (M-GWAS), (3) HWAS-CG (hologenome interaction estimated using COvariance between Random Effects Genome-based restricted maximum likelihood (CORE-GREML)), and (4) HWAS-H (hologenome interaction estimated using the Hadamard product method). We applied Bonferroni correction to interpret the significant associations in the complex traits. The GWAS and M-GWAS detected one and sixteen significant SNPs for milk yield traits, respectively, whereas the HWAS-CG and HWAS-H each identified eight SNPs. Moreover, HWAS-CG revealed four, and the remaining models identified three SNPs each for methane emissions traits. The GWAS and HWAS-CG detected one and three SNPs for rumen physiology traits, respectively. For the pigs' gut microbial composition traits, the GWAS, M-GWAS, HWAS-CG, and HWAS-H identified 14, 16, 13, and 12 SNPs, respectively. We further explored these associations through SNP annotation and by analyzing biological processes and functional pathways. Additionally, we integrated our GWA results with expression quantitative trait locus (eQTL) data using transcriptome-wide association studies (TWAS) and summary-based Mendelian randomization (SMR) methods for a more comprehensive understanding of SNP-trait associations. Our study revealed hologenomic variability in agriculturally important traits, enhancing our understanding of host-microbiome interactions.

PyAGH: a python package to fast construct kinship matrices based on different levels of omic data.

BACKGROUND: Construction of kinship matrices among individuals is an important step for both association studies and prediction studies based on different levels of omic data. Methods for constructing kinship matrices are becoming diverse and different methods have their specific appropriate scenes. However, software that can comprehensively calculate kinship matrices for a variety of scenarios is still in an urgent demand. RESULTS: In this study, we developed an efficient and user-friendly python module, PyAGH, that can accomplish (1) conventional additive kinship matrces construction based on pedigree, genotypes, abundance data from transcriptome or microbiome; (2) genomic kinship matrices construction in combined population; (3) dominant and epistatic effects kinship matrices construction; (4) pedigree selection, tracing, detection and visualization; (5) visualization of cluster, heatmap and PCA analysis based on kinship matrices. The output from PyAGH can be easily integrated in other mainstream software based on users' purposes. Compared with other softwares, PyAGH integrates multiple methods for calculating the kinship matrix and has advantages in terms of speed and data size compared to other software. PyAGH is developed in python and C + + and can be easily installed by pip tool. Installation instructions and a manual document can be freely available from https://github.com/zhaow-01/PyAGH . CONCLUSION: PyAGH is a fast and user-friendly Python package for calculating kinship matrices using pedigree, genotype, microbiome and transcriptome data as well as processing, analyzing and visualizing data and results. This package makes it easier to perform predictions and association studies processes based on different levels of omic data.

Holo-omics analysis reveals the influence of gut microbiota on obesity indicators in Jinhua pigs.

BACKGROUND: The mechanisms behind obesity are complex and multi-faceted, involving the interplay of both host genomics and gut microbiome. In recent years, research has largely focused on these factors separately, but rarely from the viewpoint of holo-omics, which considers the host and microbiome as an integrated entity. To address this gap in knowledge, the present study aimed to investigate the holo-omics basis of obesity in Jinhua pigs, a Chinese indigenous breed known for its high degree of fat deposition and superior meat quality. METHODS: Six pigs with extreme obesity phenotype were selected from a larger cohort of eighteen Jinhua pigs, and the contents of the jejunum, cecum, and colon regions were collected after slaughter at 240 days of age. The data obtained was processed, denoised, and annotated using QIIME2, with expression differences being analyzed using edgeR software. RESULTS: The results showed significant differences in jejunal microbial diversity and composition between the two groups, with gut transcriptomics also indicating that differentially expressed genes in the jejunum were enriched in lipid metabolism pathways. These findings provide further evidence of the influence of the gut microbiome and host gene expression on fat deposition in Jinhua pigs. CONCLUSIONS: This study provides valuable insights into the mechanisms of fat deposition in Jinhua pigs from the viewpoint of holo-omics. The integration of host transcriptomics and microbiome data helps shed light on the complex interactions between the host and gut microbiome, and highlights the importance of considering both factors in our understanding of obesity.

A web tool for the global identification of pig breeds.

BACKGROUND: Natural and artificial selection for more than 9000 years have led to a variety of domestic pig breeds. Accurate identification of pig breeds is important for breed conservation, sustainable breeding, pork traceability, and local resource registration. RESULTS: We evaluated the performance of four selectors and six classifiers for breed identification using a wide range of pig breeds (N = 91). The internal cross-validation and external independent testing showed that partial least squares regression (PLSR) was the most effective selector and partial least squares-discriminant analysis (PLS-DA) was the most powerful classifier for breed identification among many breeds. Five-fold cross-validation indicated that using PLSR as the selector and PLS-DA as the classifier to discriminate 91 pig breeds yielded 98.4% accuracy with only 3K single nucleotide polymorphisms (SNPs). We also constructed a reference dataset with 124 pig breeds and used it to develop the web tool iDIGs ( http://alphaindex.zju.edu.cn/iDIGs_en/ ) as a comprehensive application for global pig breed identification. iDIGs allows users to (1) identify pig breeds without a reference population and (2) design small panels to discriminate several specific pig breeds. CONCLUSIONS: In this study, we proved that breed identification among a wide range of pig breeds is feasible and we developed a web tool for such pig breed identification.

The effect of high-density genotypic data and different methods on joint genomic prediction: A case study in large white pigs.

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.

The construction of a haplotype reference panel using extremely low coverage whole genome sequences and its application in genome-wide association studies and genomic prediction in Duroc pigs.

Extremely low coverage whole genome sequencing (lcWGS) is an economical technique to obtain high-density single nucleotide polymorphisms (SNPs). Here, we explored the feasibility of constructing a haplotype reference panel (lcHRP) using lcWGS and evaluated the effects of lcHRP through a genome-wide association study (GWAS) and genomic prediction in pigs. A total of 297 and 974 Duroc pigs were genotyped using lcWGS and a 50 K SNP array, respectively. We obtained 19,306,498 SNPs using lcWGS with an accuracy of 0.984. With the help of lcHRP, the accuracy of imputation from the SNP array to lcWGS was 0.922. Compared to the SNP array findings, those from the imputation-based GWAS identified more signals across four traits. With the integration of the top 1% imputation-based GWAS findings as genomic features, the accuracies of genomic prediction was improved by 6.0% to 13.2%. This study showed the great potential of lcWGS in pigs' molecular breeding.

The Role of SOCS3 in Regulating Meat Quality in Jinhua Pigs.

Meat quality is an important economic trait that influences the development of the pig industry. Skeletal muscle development and glycolytic potential (GP) are two crucial aspects that significantly impact meat quality. It has been reported that abnormal skeletal muscle development and high glycogen content results in low meat quality. However, the genetic mechanisms underlying these factors are still unclear. Compared with intensive pig breeds, Chinese indigenous pig breeds, such as the Jinhua pig, express superior meat quality characteristics. The differences in the meat quality traits between Jinhua and intensive pig breeds make them suitable for uncovering the genetic mechanisms that regulate meat quality traits. In this study, the Jinhua pig breed and five intensive pig breeds, including Duroc, Landrace, Yorkshire, Berkshire, and Pietrain pig breeds, were selected as experimental materials. First, the FST and XP-EHH methods were used to screen the selective signatures on the genome in the Jinhua population. Then, combined with RNA-Seq data, the study further confirmed that SOCS3 could be a key candidate gene that influences meat quality by mediating myoblast proliferation and glycometabolism because of the down-regulated expression of SOCS3 in Jinhua pigs compared with Landrace pigs. Finally, through SOCS3 knockout (KO) and overexpression (OE) experiments in mouse C2C12 cells, the results showed that SOCS3 regulated the cell proliferation of myoblasts. Moreover, SOCS3 is involved in regulating glucose uptake by the IRS1/PI3K/AKT signaling pathway. Overall, these findings provide a basis for the genetic improvement of meat quality traits in the pig industry.

[Chromosomal microarray analysis of 17 patients with unbalanced reciprocal translocations].

OBJECTIVE: To retrospectively analyze the results of chromosomal microarray analysis (CMA) and parental origins of unbalanced translocations among 17 patients, so as to provide reference for their genetic counseling. METHODS: The results of CMA for 7 001 samples tested in Chengdu Women and Children's Central Hospital from January 2019 to January 2022 were retrospectively reviewed. Unbalanced reciprocal translocation was defined as two non-homologous chromosomes with lost and gained segments respectively or both with gained segments, and their parental origins were identified by parental chromosomal karyotyping and/or fluorescence in situ hybridization (FISH). RESULTS: In total 17 unbalanced translocations were identified. In three cases, two non-homologous chromosomes both had gained segments, which constituted a derivative chromosome, with the total number of chromosomes being 47. In the remaining 14 cases, there was a terminal deletion on one chromosome and a terminal duplication on the other, 10 of which were confirmed by karyotyping, with the total number of chromosomes being 46. In the derivative chromosome, the lost segment was replaced by a gained segment from another chromosome. Among 15 cases undergoing parental origin analysis, 12 had paternal or maternal chromosomal abnormalities, including 11 balanced translocations and 1 unbalanced translocation. The unbalanced gametes therefore may form through meiosis. In 3 cases, the parental chromosomes were normal, indicating a de novo origin. CONCLUSION: Discovery of terminal duplication and deletion or gained segments on two non-homologous chromosomes by CMA is suggestive of parental balanced translocation, which can facilitate genetic counseling and assessment the recurrence risk for subsequent pregnancies.

Genome-wide association study identifying genetic variants associated with carcass backfat thickness, lean percentage and fat percentage in a four-way crossbred pig population using SLAF-seq technology.

BACKGROUND: Carcass backfat thickness (BFT), carcass lean percentage (CLP) and carcass fat percentage (CFP) are important to the commercial pig industry. Nevertheless, the genetic architecture of BFT, CLP and CFP is still elusive. Here, we performed a genome-wide association study (GWAS) based on specific-locus amplified fragment sequencing (SLAF-seq) to analyze seven fatness-related traits, including five BFTs, CLP, and CFP on 223 four-way crossbred pigs. RESULTS: A total of 227, 921 highly consistent single nucleotide polymorphisms (SNPs) evenly distributed throughout the genome were used to perform GWAS. Using the mixed linear model (MLM), a total of 20 SNP loci significantly related to these traits were identified on ten Sus scrofa chromosomes (SSC), of which 10 SNPs were located in previously reported quantitative trait loci (QTL) regions. On SSC7, two SNPs (SSC7:29,503,670 and rs1112937671) for average backfat thickness (ABFT) exceeded 1% and 10% Bonferroni genome-wide significance levels, respectively. These two SNP loci were located within an intron region of the COL21A1 gene, which was a protein-coding gene that played an important role in the porcine backfat deposition by affecting extracellular matrix (ECM) remodeling. In addition, based on the other three significant SNPs on SSC7, five candidate genes, ZNF184, ZNF391, HMGA1, GRM4 and NUDT3 were proposed to influence BFT. On SSC9, two SNPs for backfat thickness at 6-7 ribs (67RBFT) and one SNP for CLP were in the same locus region (19 kb interval). These three SNPs were located in the PGM2L1 gene, which encoded a protein that played an indispensable role in glycogen metabolism, glycolysis and gluconeogenesis as a key enzyme. Finally, one significant SNP on SSC14 for CLP was located within the PLBD2 gene, which participated in the lipid catabolic process. CONCLUSIONS: A total of two regions on SSC7 and SSC9 and eight potential candidate genes were found for fatness-related traits in pigs. The results of this GWAS based on SLAF-seq will greatly advance our understanding of the genetic architecture of BFT, CLP, and CFP traits. These identified SNP loci and candidate genes might serve as a biological basis for improving the important fatness-related traits of pigs.

Genetic diversity and selection signatures of four indigenous pig breeds from eastern China.

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.

Identifying the unique characteristics of the Chinese indigenous pig breeds in the Yangtze River Delta region for precise conservation.

BACKGROUND: China is the country with the most abundant swine genetic resources in the world. Through thousands of years of domestication and natural selection, most of pigs in China have developed unique genetic characteristics. Finding the unique genetic characteristics and modules of each breed is an essential part of their precise conservation. RESULTS: In this study, we used the partial least squares method to identify the significant specific SNPs of 19 local Chinese pig breeds and 5 Western pig breeds. A total of 37,514 significant specific SNPs (p < 0.01) were obtained from these breeds, and the Chinese local pig breed with the most significant SNPs was Hongdenglong (HD), followed by Jiaxing black (JX), Huaibei (HB), Bihu (BH), small Meishan (SMS), Shengxian Hua (SH), Jiangquhai (JQ), Mi (MI), Chunan (CA), Chalu (CL), Jinhualiangtouwu (JHL), Fengjing (FJ), middle Meishan (MMS), Shanzhu (SZ), Pudong white (PD), Dongchuan (DC), Erhualian (EH), Shawutou (SW) and Lanxi Hua (LX) pig. Furthermore, we identified the breeds with the most significant genes, GO terms, pathways, and networks using KOBAS and IPA and then ranked them separately. The results showed that the breeds with the highest number of interaction networks were Hongdenglong (12) and Huaibei (12) pigs. In contrast, the breeds with the lowest interaction networks were Shawutou (4) and Lanxi Hua pigs (3), indicating that Hongdenglong and Huaibei pigs might have the most significant genetic modules in their genome, whereas Shawutou and Lanxi Hua pigs may have the least unique characteristics. To some degree, the identified specific pathways and networks are related to the number of genes and SNPs linked to the specific breeds, but they do not appear to be the same. Most importantly, more significant modules were found to be related to the development and function of the digestive system, regulation of diseases, and metabolism of amino acids in the local Chinese pig breeds, whereas more significant modules were found to be related to the growth rate in the Western pig breeds. CONCLUSION: Our results show that each breed has some relatively unique structural modules and functional characteristics. These modules allow us to better understand the genetic differences among local Chinese and Western pig breeds and therefore implement precise conservation methods. This study could provide a basis for formulating more effective strategies for managing and protecting these genetic resources in the future.

Genome-wide selection signatures detection in Shanghai Holstein cattle population identified genes related to adaption, health and reproduction traits.

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.

Long-term impact of conventional and optimal contribution conservation methods on genetic diversity and genetic gain in local pig breeds.

There are rich and vast genetic resources of indigenous pig breeds in the world. Currently, great attention is paid to either crossbreeding or conservation of these indigenous pig breeds, and insufficient attention is paid to the combination of conservation and breeding along with their long-term effects on genetic diversity. Therefore, the objective of this study is to compare the long-term effects of using conventional conservation and optimal contribution selection methods on genetic diversity and genetic gain. A total of 11 different methods including conventional conservation and optimal contribution selection methods were investigated using stochastic simulations. The long-term effects of using these methods were evaluated in terms of genetic diversity metrices such as expected heterozygosity (He) and the rate of genetic gain. The results indicated that the rates of true inbreeding in these conventional conservation methods were maintained at around 0.01. The optimal contribution selection methods based either on the pedigree (POCS) or genome (GOCS) information showed more genetic gain than conventional methods, and POCS achieved the largest genetic gain. Furthermore, the effect of using GOCS methods on most of the genetic diversity metrics was slightly better than the conventional conservation methods when the rate of true inbreeding was the same, but this also required more sires used in OCS methods. According to the rate of true inbreeding, there was no significant difference among these conventional methods. In conclusion, there is no significant difference in different ways of selecting sows on inbreeding when we use different conventional conservation methods. Compared with conventional methods, POCS method could achieve the most genetic gain. However, GOCS methods can not only achieve higher genetic gain, but also maintain a relatively high level of genetic diversity. Therefore, GOCS is a better choice if we want to combine conservation and breeding in actual production in the conservation farms.

Estimation of genetic parameters for reproductive traits in connectedness groups of Duroc, Landrace and Yorkshire pigs in China.

The objective of this study was to estimate group- and breed-specific genetic parameters for reproductive traits in Chinese Duroc, Landrace, and Yorkshire populations. Records for reproductive traits between April 1998 and December 2017 from 92 nucleus pig breeding farms, which were involved in the China Swine Genetic Improvement Program, were analysed. Due to weak genetic connectedness across all farms, connectedness groups consisting of related farms were used. Three, two and four connectedness groups for Duroc, Landrace and Yorkshire were firstly established according to the genetic connectedness rating among farms. For each connectedness group a five-trait animal model was implemented, and via restricted maximum likelihood procedure the genetic parameters were estimated for five reproductive traits i.e., total number born (TNB), number born alive (NBA), litter weight at farrowing (LWF), farrowing interval (FI) and age at first farrowing (AFF). The average of heritabilities among connectedness groups ranged from .01 (for FI in Yorkshire) to .30 (for AFF in Duroc). Estimates of repeatability for litter traits ranged from .14 to .20 and were consistent for each breed, and for FI, the estimates varied from .01 to .11 across breeds and groups. The estimated genetic correlations among litter traits (i.e., TNB, NBA and LWF) were all significantly high (>.56) and similar across breeds. Averaged genetic correlations over three breeds were -.25, -.27, -.18, -.04, -.10, -.02, and .28 for FI-TNB, FI-NBA, FI-LWF, AFF-TNB, AFF-NBA, AFF-LWF and FI-AFF, respectively. The standard errors of the estimates were all very low (<0.01) in most situations. Results from this study suggest that selection based on TNB which is currently used in dam line selection index can improve NBA and LWF simultaneously. However, care should be taken on FI and AFF as they are both greatly influenced by non-genetic factors such as management and measurement.

Selection signature reveals genes associated with susceptibility loci affecting respiratory disease due to pleiotropic and hitchhiking effect in Chinese indigenous pigs.

BACKGROUND: Porcine respiratory disease is one of the most important health problems which causes significant economic losses. OBJECTIVE: To understand the genetic basis for susceptibility to swine enzootic pneumonia (EP) in pigs, we detected 102,809 SNPs in a total of 249 individuals based on genome-wide sequencing data. METHODS: Genome comparison of three susceptibility to swine EP pig breeds (Jinhua, Erhualian and Meishan) with two western lines that are considered more resistant (Duroc and Landrace) using XP-EHH and FST statistical approaches identified 691 positively selected genes. Based on QTLs, GO terms and literature search, we selected 14 candidate genes that have convincible biological functions associated with swine EP or human asthma. RESULTS: Most of these genes were tested by several methods including transcription analysis and candidated genes association study. Among these genes: CYP1A1 and CTNNB1 are involved in fertility; TGFBR3 plays a role in meat quality traits; WNT2, CTNNB1 and TCF7 take part in adipogenesis and fat deposition simultaneously; PLAUR (completely linked to AXL, r2=1) plays an essential role in the successful ovulation of matured oocytes in pigs; CLPSL2 (strongly linked to SPDEF, r2=0.848) is involved in male fertility. CONCLUSION: These adverse genes susceptible to swine EP may be selected while selecting for economic traits (especially reproduction traits) due to pleiotropic and hitchhiking effect of linked genes. Our study provided a completely new point of view to understand the genetic basis for susceptibility or resistance to swine EP in pigs thereby, provide insight for designing sustainable breed selection programs. Finally, the candidate genes are crucial due to their potential roles in respiratory diseases in a large number of species, including human.

Genotype-by-environment interaction of fertility traits in Danish Holstein cattle using a single-step genomic reaction norm model.

Genotype-by-environment (G × E) interactions could play an important role in cattle populations, and it should be considered in breeding programmes to select the best sires for different environments. The objectives of this study were to study G × E interactions for female fertility traits in the Danish Holstein dairy cattle population using a reaction norm model (RNM), and to detect the particular genomic regions contributing to the performance of these traits and the G × E interactions. In total 4534 bulls were genotyped by an Illumina BovineSNP50 BeadChip. An RNM with a pedigree-based relationship matrix and a pedigree-genomic combined relationship matrix was used to explore the existence of G × E interactions. In the RNM, the environmental gradient (EG) was defined as herd effect. Further, the genomic regions affecting interval from calving to first insemination (ICF) and interval from first to last insemination (IFL) were detected using single-step genome-wide association study (ssGWAS). The genetic correlations between extreme EGs indicated that G × E interactions were sizable for ICF and IFL. The genomic RNM (pedigree-genomic combined relationship matrix) had higher prediction accuracy than the conventional RNM (pedigree-based relationship matrix). The top genomic regions affecting the slope of the reaction norm included immunity-related genes (IL17, IL17F and LIF), and growth-related genes (MC4R and LEP), while the top regions influencing the intercept of the reaction norm included fertility-related genes such as EREG, AREG and SMAD4. In conclusion, our findings validated the G × E interactions for fertility traits across different herds and were helpful in understanding the genetic background of G × E interactions for these traits.

Application of partial least squares in exploring the genome selection signatures between populations.

Natural and artificial selection have led to substantial variation in the phenotypic traits of different populations. Therefore, there is a need to develop methods that are based on cross-population comparisons to discover loci related to specific traits. Here, we suggested a strategy to detect the genome selection signatures between populations based on the partial least squares (PLS) theory. Using the binary population indicator as the response variable in the PLS analysis, alleles under selection between populations were identified from the first PLS component. We explored the theory behind the PLS analysis to reveal its usefulness in detecting the loci under selection. Through the simulation study, the results showed that the PLS method had a better performance than the FST and EigenGWAS methods. In addition, by using the real data hapmap3, we found that rs11150606 in PRSS53 gene and rs1800414 in OCA2 gene were under selection between East Asian populations and three other populations, including African, American, and European populations. We concluded that this strategy was easily carried out and might supplement for the deficiency of the EigenGWAS method in some cases. To facilitate the application of this method, we developed an R script that is freely accessible at http://klab.sjtu.edu.cn/PLS/ .

Detection of genome-wide structural variations in the Shanghai Holstein cattle population using next-generation sequencing.

OBJECTIVE: The Shanghai Holstein cattle breed is susceptible to severe mastitis and other diseases due to the hot weather and long-term humidity in Shanghai, which is the main distribution centre for providing Holstein semen to various farms throughout China. Our objective was to determine the genetic mechanisms influencing economically important traits, especially diseases that have huge impact on the yield and quality of milk as well as reproduction. METHODS: In our study, we detected the structural variations of 1,092 Shanghai Holstein cows by using next-generation sequencing. We used the DELLY software to identify deletions and insertions, cn.MOPS to identify copy-number variants (CNVs). Furthermore, we annotated these structural variations using different bioinformatics tools, such as gene ontology, cattle quantitative trait locus (QTL) database and ingenuity pathway analysis (IPA). RESULTS: The average number of high-quality reads was 3,046,279. After filtering, a total of 16,831 deletions, 12,735 insertions and 490 CNVs were identified. The annotation results showed that these mapped genes were significantly enriched for specific biological functions, such as disease and reproduction. In addition, the enrichment results based on the cattle QTL database showed that the number of variants related to milk and reproduction was higher than the number of variants related to other traits. IPA core analysis found that the structural variations were related to reproduction, lipid metabolism, and inflammation. According to the functional analysis, structural variations were important factors affecting the variation of different traits in Shanghai Holstein cattle. Our results provide meaningful information about structural variations, which may be useful in future assessments of the associations between variations and important phenotypes in Shanghai Holstein cattle. CONCLUSION: Structural variations identified in this study were extremely different from those of previous studies. Many structural variations were found to be associated with mastitis and reproductive system diseases; these results are in accordance with the characteristics of the environment that Shanghai Holstein cattle experience.

Distribution of runs of homozygosity in Chinese and Western pig breeds evaluated by reduced-representation sequencing data.

Inbreeding, which has several causes including genetic drift, population bottlenecks, mating of close relatives and selection, can leave tracts of runs of homozygosity (ROH) along genomes. Recently, decreasing performance on reproductive traits, which might have resulted from inbreeding, has been observed in Chinese pigs. In this study, 830 individuals from Western and Chinese pig breeds were genotyped using the reduced-representation sequencing method. After imputation and quality control, 60 850 high-confidence SNPs were retained for ROH detection. A simulation was performed to explore the reliability of ROH detection with imputed data. Different ROH-related variables were compared between imputed and non-missing genotypes used in ROH detection. Furthermore, ROH islands were evaluated and annotated to find genes influenced by inbreeding in these pigs. The simulation results showed that imputed data with 0.7 as the average missing genotype rate and three heterozygotes allowed in a sliding window have comparable ROH detected compared with data with no missing genotypes. Compared with Western pig breeds, Chinese pigs had more autosomes covered by ROH longer than 5 Mb, indicating higher inbreeding in Chinese pigs in recent times. Genes related to reproduction, immunity, meat quality and adaptability in Chinese pigs and several genes related to growth speed and immunity in Western pigs were observed in short ROH islands. The reproduction-related gene PRM1 was found to be located in the most frequent long ROH island in Chinese pigs, which might explain the decreasing fertility in Chinese pig breeds.