Kang-Seok Seo

Reproductive performance is a critical aspect of livestock production and sustainability. The number of stillbirths (NS) and mummified (NM) piglets significantly affect reproductive efficiency in swine production. Identifying and understanding the genetic architecture and candidate genes influencing these reproductive loss traits are essential for genetic inheritance studies and selective breeding programs in the swine breeding industry. To elucidate the genetic architecture of these traits, we performed a genome-wide association study (GWAS) using porcine 70 K BeadChip arrays from 1,162 Duroc, 1,783 Landrace, and 2,366 Yorkshire pigs. Single-parity breed-specific genome-wide association (GWA) analyses revealed that SIDT2 on Chr9 was associated with NM in Duroc pigs, whereas GTF2H3, PPP1R9A, and LMTK2 on Chr14, 9, and 3, respectively, were associated with both traits in Landrace pigs. Several genes have been linked to NS and NM in Yorkshire pigs, including GRID1 (chr14), DLGAP2 (chr15), ZZEF1 (chr12), RNF17 (chr11), SEL1L2 (chr17), ASCC3 (chr1), KCND2 (chr18), AP5B1(chr2), KAT5 (chr2), and SPATS2 (chr5). Across the breeds, five genes (DLGAP2, RNF17, SEL1L2, AP5B1, and KAT5) were exclusively associated with NS, whereas the other five (SIDT2, NDUFAF5, AKAP12, RCC1L, and BIRC6) were specific to NM. In addition, eight genes (GTF2H3, PPP1R9A, LMTK2, GRID1, ZZEF1, ASCC3, KCND2, and SPATS2) were associated with both traits, suggesting shared molecular pathways involved in piglet mortality. This study showed genetic factors affecting reproductive performance loss in three commercial Korean pig breeds through first-parity GWA analysis and provided crucial genetic markers that breeders may use in marker-assisted breeding programs aimed at improving reproductive efficiency.

Background: Reproductive performance is critical in the pig industry, and improved sow performance could lead to increased economic benefits. GWAS and ROH analyses based on SNP array data were conducted to identify the breed-specific genetic architecture underlying the variation in NBA and TNB. Methods: A total of 7488 breeding pigs with phenotypic data from 1586 Duroc, 2256 Landrace, and 3646 Yorkshire breeds, along with 76,756 SNP markers from Korean grand-grand-parent (GGP) breeding farms, were used. Results: In the Duroc breeds, SNPs on SSC 9 and 17 were found to be associated with the SIDT2 and TGM2 genes, respectively. In the Landrace breed, PPP1R9A, LMTK2, and GTF2H3 on SSCs 9, 3, and 14, respectively, were associated with both TNB and NBA. With the Yorkshire breed genome, GRID1, DLGAP2, ZZEF1, PARG, RNF17, and NDUFAF5 in SSCs 14, 15, 12, 14, 11, and 17, respectively, were associated with NBA and TNB traits. These genes have distinct functions, ranging from synaptic transmission and cytoskeletal organization to DNA repair and cellular energy production. In the Duroc breed, six genes identified in the ROH islands were associated with various biological pathways, molecular functions, and cellular components. NT5DC1 was associated with metaphyseal chondrodysplasia, CRTAC1 with ion binding, CFAP43 with spermatogenic failure, CASC3 with intracellular mRNA localization, ERC2 with cellular component organization, and FOCAD with Focadhesin. In the Landrace and Yorkshire breeds, PDE6D was associated with GTPase inhibitor activity. Conclusions: Through GWAS and ROH analyses, we identified breed-specific SNP markers associated with NBA and TNB in three breed genotypes, providing insights for improving reproductive performance efficiency and contributing to future breeding strategies.

The purpose of this study was to estimate the homozygosity distribution and compute genomic and conventional inbreeding coefficients in three genetically diverse pig breed populations. The genomic and pedigree data of Duroc (1586), Landrace (2256), and Yorkshire (3646) were analyzed. We estimated and compared various genomic and pedigree inbreeding coefficients using different models and approaches. A total of 709,384 ROH segments in Duroc, 816,898 in Landrace, and 1,401,781 in Yorkshire, with average lengths of 53.59 Mb, 56.21 Mb, and 53.46 Mb, respectively, were identified. Relatively, the Yorkshire breed had the shortest ROH segments, whereas the Landrace breed had the longest mean ROH segments. Sus scrofa chromosome 1 (SSC1) had the highest chromosomal coverage by ROH across all breeds. Across breeds, an absolute correlation (1.0) was seen between F_ROH total and F_ROH1–2Mb, showing that short ROH were the primary contributors to overall F_ROH values. The overall association between genomic and conventional inbreeding was weak, with values ranging from 0.058 to 0.140. In contrast, total genomic inbreeding (F_ROH) and ROH classes showed a strong association, ranging from 0.663 to 1.00, across the genotypes. The results of genomic and conventional inbreeding estimates improve our understanding of the genetic diversity among genotypes.

This study aimed to estimate carcass trait characteristics, proportions, and their correlation with the preslaughter body weight of indigenous chickens in Southeastern Ethiopia. Data from 42 healthy male chickens were collected and analyzed using SAS 2012 ver. 9.4, R software ver.4.3.1, and MetaboAnalyst 5.0. The results indicated significant fixed effect of districts on dressed carcass weight, drumsticks, thighs, wings, and gizzard between Goba and Agarfa chickens (p < 0.05). The fixed effect of age and the district–age interaction effect were not significant. Principal component analysis revealed that PC1, accounting for 96.8% of the total variation between Goba and Agarfa chickens, was contributed by preslaughter and dressed carcass weight. Among the carcass components, the key contributors to differentiation between the Goba and Agarfa indigenous chicken carcass yields were the drumstick, thigh, breast (with/without keel bone), and back with thoracic weight (VIP > 1); there was a higher yield in Goba chickens. Preslaughter weight exhibited stronger correlations with most other traits, while dressing percentages displayed a negative correlation with various carcass components and edible giblet yields across the study districts. This study provides useful insights into carcass component characteristics and yields of indigenous Ethiopian chickens, which can increase our understanding of carcass components and their relationships with other qualities for improvement and further studies on poultry production.

The biometric characterization of autochthonous Ethiopian chickens has not been fully investigated in the study area. In this study, we aimed to conduct biometric trait characterization and multivariate discriminant analysis of traditionally bred autochthonous chickens in Ethiopia and assess the wide range of phenotypic diversity within these populations. A multi-stage sampling procedure was used, and data on biometric traits and body weight were collected from adult chickens. Principal component and correlation analyses were performed to explore the discriminating factors and relationships among traits. All autochthonous chickens showed clear sexual dimorphism, with the Agarfa chickens having the highest biometric traits and body weight. Across the study area, the majority of biometric values showed variation between age group 1 (AG-1) and age group 3 (AG-3). All autochthonous chickens showed a strong correlation between wingspan and back length (p ≤ 0.001). Roosters and hens also showed a strong correlation between the keel and neck length (p ≤ 0.001). Principal component 1 (PC1) and principal component 2 (PC2) explained 56.44%, 55.09%, and 47.86% of the total variation in the original variables for all autochthonous chickens, roosters, and hens, respectively. Univariate and multivariate analyses revealed the existence of biometric trait and body weight variations among autochthonous chickens from different districts. Therefore, genetic profiling should be performed to better understand the genetic potential of autochthonous Ethiopian chickens.

Native chickens in Ethiopia are characterized in a fragmented manner for their performance characteristics and genotypes. This review aimed to explore the production and reproduction performance characteristics as well as the morphometric and morphological diversity of Ethiopian native chickens. The investigation was performed on four production performance characteristics, including average egg per clutch, average clutch/hen/year, average egg set/hen, and average egg/hen/year, as well as six reproductive performance characteristics, including age at first laying, age of male chickens at first bred, age at which female chickens are first bred, the reproductive life span of males and females, and fertility percentage in various parts of Ethiopia. Some economically practical morphometric characteristics of native chickens, such as shank length, chest circumference, comb length, body weight, body length, keel length, wattle length, neck length, back length, and morphological diversity, were also summarized. Regarding performance characteristics, there were some variations in eggs’ average production performance per clutch (13.56-15.4 eggs) and clutch/hen/year (3.0-4.29) in Ethiopia. The average reproduction performance characteristics of Ethiopian native chickens for age at first laying (6.90-7.13 months), age of male chickens at first bred (5.87-6.15 months), female at first bred (5.20-5.93 months), the reproductive life span of males (3.79 years) and hens (3.56 years), and chicks hatched from set eggs revealed variation across Ethiopia. In various locations of Ethiopia, the average trait values reported for Ethiopian native chickens under the farmer’s management differed in terms of morphometric and morphological features. The variation observed in performance characteristics, as well as morphometrics and morphological characteristics for Ethiopian native chicken ecotype population, can help the native breed classification, unique trait conservation, and breed improvement intervention programs.

MicroRNAs (miRNAs) play a vital role in improving meat quality by binding to messenger RNAs (mRNAs). We performed an integrated analysis of miRNA and mRNA expression profiling between bulls and steers based on the differences in meat quality traits. Fat and fatty acids are the major phenotypic indices of meat quality traits to estimate between-group variance. In the present study, 90 differentially expressed mRNAs (DEGs) and 18 differentially expressed miRNAs (DEMs) were identified. Eighty-three potential DEG targets and 18 DEMs were used to structure a negative interaction network, and 75 matching target genes were shown in this network. Twenty-six target genes were designated as intersection genes, screened from 18 DEMs, and overlapped with the DEGs. Seventeen of these genes enriched to 19 terms involved in lipid metabolism. Subsequently, 13 DEGs and nine DEMs were validated using quantitative real-time PCR, and seven critical genes were selected to explore the influence of fat and fatty acids through hub genes and predict functional association. A dual-luciferase reporter and Western blot assays confirmed a predicted miRNA target (bta-miR-409a and PLIN5). These findings provide substantial evidence for molecular genetic controls and interaction among genes in cattle.

Recombinant protein technology has emerged as an excellent option for vaccine development. However, prior to our study, the immune induction ability of recombinant Mycoplasma suis alpha-enolase (rMseno) in animals remained unclear. The purpose of this study was to develop a rMseno protein subunit vaccine and to determine its ability to elicit an immunological response. To accomplish this, we cloned the gene into pET-15b, expressed it in BL21 cells, and purified it. Following the establishment of immunity, the immunogenicity and potential for protection of rMseno were evaluated in mice and piglets. The results demonstrate that anti-M. suis serum recognized the pure rMseno protein in both mice and piglets as evidenced by high levels of specific anti-rMseno antibodies, significantly increased levels of IFN-γ and IL-4 cytokines, and significantly increased T lymphocyte proliferation index. Piglets also had significantly increased levels of specific IgG₁, IgG_2a, CD4⁺, and CD8⁺ cells. The rMseno findings demonstrated a robust immunological response in mice and piglets, affording partial clinical protective efficacy in piglets.

This study was conducted to estimate the genetic parameters of different feeding pattern traits, including average daily feed intake (ADFI), average occupation time per day (AOTD), average occupation time per visit (AOTV), average daily feeding rate (ADFR), average feeding rate per feeding visit (AFRV), average feed intake per feeding visit (AFIV), and average number of visits per day (ANVD), and their genetic relationship to production traits, such as on-test average daily gain (ADG), backfat thickness (BFT), loin muscle area (LMA), lean percentage (LP), and feed efficiency traits, such as feed conversion ratio (FCR) and five measures of residual feed intake (RFI1 to RFI5), in Duroc pigs (DD). The non-heritable common spatial pen effect was also estimated in all studied traits. The feeding pattern traits used in this study were derived from filtered feeding visits of 602 DD pigs. Using three animal models and the REML method, the genetic parameters revealed low to moderate heritability for ADFI (0.19 to 0.32) and AFIV (0.18 to 0.33), moderate heritability for ANVD (0.28 to 0.35) and AOTV (0.21 to 0.31), and high heritability for AOTD (0.73), ADFR (0.62 to 0.64), and AFRV (0.59 to 0.63). The addition of a common spatial pen effect in models 2 and 3 had a substantial impact, ranging from 8% to 23%, on the total variability of most feeding pattern traits, with the exception of AOTD, which only had a percentage variance of 0.30% due to the pen effect. The genetic and phenotypic correlation revealed that ADFI had consistent moderate to high genetic and phenotypic correlation with production and feed efficiency (FE) traits. However, selection against ADFI would negatively affect on-test ADG. Interestingly, the AOTD had no genetic correlation with ADG (0.04), low to moderate positive genetic correlation with FCR (0.27) and all RFI measures (0.24 to 0.33), and moderate negative correlation with LP (−0.39), indicating that selection for DD pigs with lower AOTD would not influence on-test ADG but may increase LP and improve feed efficiency by lowering FCR and all RFI measures. However, the corresponding phenotypic correlation of AOTD with production and feed efficiency traits was mostly weak, which can be attributed to the low residual or environmental correlation between these correlated traits. At the genetic level, the feeding pattern traits showed potential in improving feed efficiency and production traits. However, further studies are needed to evaluate their impact at phenotypic level.

Residual feed intake (RFI) gained attention as a potential alternative to the feed conversion ratio (FCR). Thus, this study aimed to estimate genetic parameters for different feed efficiency (FE) traits (FCR, RFI1 to RFI5) and their genetic correlation to on-test daily weight gain (ADG), backfat (BFT), loin muscle area (LMA), lean percentage (LP), and total feed intake (FI) for 603 Male Duroc (DD), 295 Landrace (LL), and 341 Yorkshire (YY). The common spatial pen effect was also estimated in these traits. Five RFI measures were estimated by regressing daily feed intake on initial testing age (ITA), initial testing weight (IBW), and ADG for RFI1; other models were the same as RFI1 except for additional BFT for RFI2; LMA for RFI3; BFT and LMA for RFI4; BFT, LMA, and average metabolic body weight (AMBW) instead of IBW for RFI5. Genetic parameters estimated using two animal models and the REML method showed moderate heritability for FCR in all breeds (0.22 and 0.28 for DD, 0.31 and 0.39 for LL, 0.17 and 0.22 for YY), low heritability for the majority of RFI measures in DD (0.15 to 0.23) and YY (0.14 to 0.20) and moderate heritability for all RFI measures in LL (0.31 to 0.34). Pen variance explained 7% to 22% for FE and 0% to 9% for production traits’ phenotypic variance. The genetic correlation revealed that selection against less complex RFI1 in DD and LL and RFI2 in YY would bring the most advantageous reduction to FI (0.71 for DD, 0.49 for LL, 0.43 YY) without affecting ADG in all breeds (0.06 for DD, −0.11 for LL, 0.05 for YY), decrease in BFT, and increase in LP in DD (0.51 in BFT, −0.77 in LP) and LL (0.45 in BFT, −0.83 in LP). Therefore, inclusion of these breed-specific RFI measures in the future selection criteria would help improve feed efficiency in the swine industry.