GWAS of Reproductive Traits in Large White Pigs on Chip and Imputed Whole-Genome Sequencing Data

Identification of Heilongjiang crossbred beef cattle pedigrees and reveals functional genes related to economic traits based on whole-genome SNP data

Introduction: To enhance the beef cattle industry, Heilongjiang Province has developed a new Crossbred beef cattle variety through crossbreeding with exotic commercial breeds. This new variety exhibits relatively excellent meat quality, and efficient reproductive performance, catering to market demands. Method: This study employed whole genome resequencing technology to analyze the genetic pedigree and diversity of 19 Heilongjiang Crossbred beef cattle, alongside 59 published genomes from East Asian, Eurasian, and European taurine cattle as controls. In addition, genes related to production traits were also searched by identifying Runs of Homozygosity (ROH) islands and important fragments from ancestors. Results: A total of 14,427,729 biallelic SNPs were discovered, with the majority located in intergenic and intron regions and a small percentage in exon regions, impacting protein function. Population genetic analyses including Principal Component Analysis (PCA), Neighbor-Joining (NJ) tree, and ADMIXTURE identified Angus, Holstein, and Mishima as the main ancestors of Crossbred beef cattle. In genetic diversity analysis, nucleotide diversity, linkage disequilibrium, and inbreeding coefficient analysis reveal that the genetic diversity of Crossbred beef cattle is at a moderate level, and a higher inbreeding coefficient indicates the need for careful breeding management. In addition, some genes related to economic traits are identified through the identification of Runs of Homozygosity (ROH) islands and important fragments from ancestors. Conclusion: This comprehensive genomic characterization supports the targeted improvement of economically important traits in Crossbred beef cattle, facilitating advanced breeding strategies.

Imputation strategies for low-coverage whole-genome sequencing data and their effects on genomic prediction and genome-wide association studies in pigs

The uncertainty resulting from missing genotypes in low-coverage whole-genome sequencing (LCWGS) data complicates genotype imputation. The aim of this study is to find out an optimal strategy for accurately imputing LCWGS data and assess its effectiveness for genomic prediction (GP) and genome-wide association study (GWAS) on economically important traits of Large White pigs. The LCWGS data of 1 423 Large White pigs were imputed using three different strategies: (1) using the high-coverage whole-genome sequencing (HCWGS) of 30 key progenitors as the reference panel (Ref_LG); (2) mixing HCWGS of key progenitors with LCWGS (Mix_HLG) and (3) self-imputation in LCWGS (Within_LG). Additionally, to compare the imputation effects of LCWGS, we also imputed SNP chip data of 1 423 Large White pigs to the whole-genome sequencing level using the reference panel consisting of key progenitors (Ref_SNP). To evaluate effects of the imputed sequencing data, we compared the accuracies of GP and statistical power of GWAS for four reproductive traits based on the chip data, sequencing data imputed from chip data and LCWGS data using an optimal strategy. The average imputation accuracies of the Within_LG, Ref_LG and Mix_HLG were 0.9893, 0.9899 and 0.9875, respectively, which were higher than that of the Ref_SNP (0.8522). Using the imputed sequencing data from LCWGS with the Ref_LG imputation strategy, the accuracies of GP for four traits improved by approximately 0.31-1.04% compared to the chip data, and by 0.7-1.05% compared to the imputed sequencing data from chip data. Furthermore, by using the sequence data imputed from LCWGS with the Ref_LG, 18 candidate genes were identified to be associated with the four reproductive traits of interest in Large White pigs: total number of piglets born - EPC2, MBD5, ORC4 and ACVR2A; number of piglets born healthy - IKBKE; total litter weight of piglets born alive - HSPA13 and CPA1; gestation length - GTF2H5, ITGAV, NFE2L2, CALCRL, ITGA4, STAT1, HOXD10, MSTN, COL5A2 and STAT4. With the exception of EPC2, ORC4, ACVR2A and MSTN, others represent novel candidates. Our findings can provide a reference for the application of LCWGS data in livestock and poultry.

Identifying selection signatures for immune response and resilience to Aleutian disease in mink using genotype data

Aleutian disease (AD) brings tremendous financial losses to the mink industry. Selecting AD-resilient mink has been conducted to control AD. Such selections could have altered the patterns of genetic variation responding to selection pressures. This study aimed to identify selection signatures for immune response (IRE) and resilience to AD. A total of 1,411 mink from an AD-positive facility were used. For IRE, 264 animals were categorized according to the combined results of enzyme-linked immunosorbent assay (ELISA) and counterimmunoelectrophoresis (CIEP). For resilience, two grouping methods were used: 1) general resilience performance (GRP, n = 30) was evaluated based on the feed conversion ratio, Kleiber ratio, and pelt quality; and 2) female reproductive performance (FRP, n = 36) was measured based on the number of kits alive 24 h after birth. Detection methods were the pairwise fixation index, nucleotide diversity, and cross-population extended haplotype homozygosity. A total of 619, 569, and 526 SNPs were identified as candidates for IRE, GRP, and FRP, respectively. The annotated genes were involved in immune system process, growth, reproduction, and pigmentation. Two olfactory-related Gene Ontology (GO) terms were significant (q < 0.05) for all traits, suggesting the impact of AD on the sense of smell of infected mink. Differences in detected genes and GO terms among different color types for IRE indicated variations in immune response to AD among color types. The mitogen-activated protein kinase (MAPK) signaling pathway was significant (q < 0.05) for FRP, suggesting that AD may disrupt MAPK signaling and affect FRP. The findings of this research contribute to our knowledge of the genomic architecture and biological mechanisms underlying AD resilience in mink.

Effect of SNPs on Litter Size in Swine

Although sows do not directly enter the market, they play an important role in piglet breeding on farms. They consume large amounts of feed, resulting in a significant environmental burden. Pig farms can increase their income and reduce environmental pollution by increasing the litter size (LS) of swine. PCR-RFLP/SSCP and GWAS are common methods to evaluate single-nucleotide polymorphisms (SNPs) in candidate genes. We conducted a systematic meta-analysis of the effect of SNPs on pig LS. We collected and analysed data published over the past 30 years using traditional and network meta-analyses. Trial sequential analysis (TSA) was used to analyse population data. Gene set enrichment analysis and protein-protein interaction network analysis were used to analyse the GWAS dataset. The results showed that the candidate genes were positively correlated with LS, and defects in PCR-RFLP/SSCP affected the reliability of candidate gene results. However, the genotypes with high and low LSs did not have a significant advantage. Current breeding and management practices for sows should consider increasing the LS while reducing lactation length and minimizing the sows' non-pregnancy period as much as possible.

Identification of population-informative markers from high-density genotyping data through combined feature selection and machine learning algorithms: Application to European autochthonous and cosmopolitan pig breeds

Large genotyping datasets, obtained from high-density single nucleotide polymorphism (SNP) arrays, developed for different livestock species, can be used to describe and differentiate breeds or populations. To identify the most discriminating genetic markers among thousands of genotyped SNPs, a few statistical approaches have been proposed. In this study, we applied the Boruta algorithm, a wrapper of the machine learning random forest algorithm, on a database of 23 European pig breeds (20 autochthonous and three cosmopolitan breeds) genotyped with a 70k SNP chip, to pre-select informative SNPs. To identify different sets of SNPs, these pre-selected markers were then ranked with random forest based on their mean decrease accuracy and mean decrease gene indexes. We evaluated the efficiency of these subsets for breed classification and the usefulness of this approach to detect candidate genes affecting breed-specific phenotypes and relevant production traits that might differ among breeds. The lowest overall classification error (2.3%) was reached with a subpanel including only 398 SNPs (ranked based on their mean decrease accuracy), with no classification error in seven breeds using up to 49 SNPs. Several SNPs of these selected subpanels were in genomic regions in which previous studies had identified signatures of selection or genes associated with morphological or production traits that distinguish the analysed breeds. Therefore, even if these approaches have not been originally designed to identify signatures of selection, the obtained results showed that they could potentially be useful for this purpose.

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.

Ascertaining the genetic background of the Celtic-Iberian pig strain: A signatures of selection approach

Celtic-Iberian pig breeds were majority in Spain and Portugal until the first half of the 20th century. In the 1990s, they were nearly extinct as a result of the introduction of foreign improved pig breeds. Despite its historical importance, the genetic background of the Celtic-Iberian pig strain is poorly documented. In this study, we have identified genomic regions that might contain signatures of selection peculiar of the Celtic-Iberian genetic lineage. A total of 153 DNA samples of Celtic-Iberian pigs (Spanish Gochu Asturcelta and Portuguese Bísara breeds), Iberian pigs (Spanish Iberian and Portuguese Alentejano breeds), Cinta Senese pig, Korean local pig and Cosmopolitan pig (Hampshire, Landrace and Large White individuals) were analysed. A pairwise-comparison approach was applied: the Gochu Asturcelta and the Bísara samples as test populations and the five other pig populations as reference populations. Three different statistics (XP-EHH, FST and ΔDAF) were computed on each comparison. Strict criteria were used to identify selection sweeps in order to reduce the noise brought on by the Gochu Asturcelta and Bísara breeds' severe population bottlenecks. Within test population, SNPs used to construct potential candidate genomic areas under selection were only considered if they were identified in four of ten two-by-two pairwise comparisons and in at least two of three statistics. Genomic regions under selection constructed within test population were subsequently overlapped to construct candidate regions under selection putatively unique to the Celtic-Iberian pig strain. These genomic regions were finally used for enrichment analyses. A total of 39 candidate regions, mainly located on SSC5 and SSC9 and covering 3130.5 kb, were identified and could be considered representative of the ancient genomic background of the Celtic-Iberian strain. Enrichment analysis allowed to identify a total of seven candidate genes (NOL12, LGALS1, PDXP, SH3BP1, GGA1, WIF1, and LYPD6). Other studies reported that the WIF1 gene is associated with ear size, one of the characteristic traits of the Celtic-Iberian pig strain. The function of the other candidate genes could be related to reproduction, adaptation and immunity traits, indirectly fitting with the rusticity of a non-improved pig strain traditionally exploited under semi-extensive conditions.

Genome-Wide Association Study for Somatic Skeletal Traits in Duroc × (Landrace × Yorkshire) Pigs

The pig bone weight trait holds significant economic importance in southern China. To expedite the selection of the pig bone weight trait in pig breeding, we conducted molecular genetic research on these specific traits. These traits encompass the bone weight of the scapula (SW), front leg bone weight (including humerus and ulna) (FLBW), hind leg bone weight (including femur and tibia) (HLBW), and spine bone weight (SBW). Up until now, the genetic structure related to these traits has not been thoroughly explored, primarily due to challenges associated with obtaining the phenotype data. In this study, we utilized genome-wide association studies (GWAS) to discern single nucleotide polymorphisms (SNPs) and genes associated with four bone weight traits within a population comprising 571 Duroc × (Landrace × Yorkshire) hybrid pigs (DLY). In the analyses, we employed a mixed linear model, and for the correction of multiple tests, both the false discovery rate and Bonferroni methods were utilized. Following functional annotation, candidate genes were identified based on their proximity to the candidate sites and their association with the bone weight traits. This study represents the inaugural application of GWAS for the identification of SNPs associated with individual bone weight in DLY pigs. Our analysis unveiled 26 SNPs and identified 12 promising candidate genes (OPRM1, SLC44A5, WASHC4, NOPCHAP1, RHOT1, GLP1R, TGFB3, PLCB1, TLR4, KCNJ2, ABCA6, and ABCA9) associated with the four bone weight traits. Furthermore, our findings on the genetic mechanisms influencing pig bone weight offer valuable insights as a reference for the genetic enhancement of pig bone traits.

Multi-trait genomic prediction in pigs using single and multistep methods based on the absorption of ungenotyped animals

Many quantitative traits measured in breeding programs are genetically correlated. The genetic correlations between the traits indicate that the measurement of one trait carries information on others. To benefit from this information, multi-trait genomic prediction (MTGP) is preferable to use. However, MTGP is more difficult to implement compared to single-trait genomic prediction (STGP), and even more challenging for the goal to exploit not only the information on other traits but also the information on ungenotyped animals. This could be accomplished using both single and multistep methods. The single-step method was achieved by implementing a single-step genomic best linear unbiased prediction (ssGBLUP) approach using a multi-trait model. Here, we examined a multistep analysis based on an approach called "Absorption" to achieve this goal. The Absorption approach absorbed all available information including the phenotypic information on ungenotyped animals as well as the information on other traits if applicable, into mixed model equations of genotyped animals. The multistep analysis included (1) to apply the Absorption approach that exploits all available information and (2) to implement genomic BLUP (GBLUP) prediction on the absorbed dataset. In this study, the ssGBLUP and multistep analysis were applied to 5 traits in Duroc pigs, which were slaughter percentage, feed consumption from 40 to 120 kg (FC40_120), days of growth from 40 to 120 kg (D40_120), age at 40 kg (A40) and lean meat percentage. The results showed that MTGP yielded higher accuracy than STGP, which on average was 0.057 higher for the multistep method and 0.045 higher for ssGBLUP. The multistep method achieved similar prediction accuracy as ssGBLUP. However, the prediction bias of the multistep method was in general lower than that of ssGBLUP.

Genomic Association Analysis of Growth and Backfat Traits in Large White Pigs

The pig industry is significantly influenced by complex traits such as growth rate and fat deposition, which have substantial implications for economic returns. Over the years, remarkable genetic advancements have been achieved through intense artificial selection to enhance these traits in pigs. In this study, we aimed to investigate the genetic factors that contribute to growth efficiency and lean meat percentages in Large White pigs. Specifically, we focused on analyzing two key traits: age at 100 kg live weight (AGE100) and backfat thickness at 100 kg (BF100), in three distinct Large White pig populations-500 Canadian, 295 Danish, and 1500 American Large White pigs. By employing population genomic techniques, we observed significant population stratification among these pig populations. Utilizing imputed whole-genome sequencing data, we conducted single population genome-wide association studies (GWAS) as well as a combined meta-analysis across the three populations to identify genetic markers associated with the aforementioned traits. Our analyses highlighted several candidate genes, such as CNTN1-which has been linked to weight loss in mice and is potentially influential for AGE100-and MC4R, which is associated with obesity and appetite and may impact both traits. Additionally, we identified other genes-namely, PDZRN4, LIPM, and ANKRD22-which play a partial role in fat growth. Our findings provide valuable insights into the genetic basis of these important traits in Large White pigs, which may inform breeding strategies for improved production efficiency and meat quality.

Genetic gain and inbreeding from simulation of different genomic mating schemes for pig improvement

BACKGROUND

Genomic selection involves choosing as parents those elite individuals with the higher genomic estimated breeding values (GEBV) to accelerate the speed of genetic improvement in domestic animals. But after multi-generation selection, the rate of inbreeding and the occurrence of homozygous harmful alleles might increase, which would reduce performance and genetic diversity. To mitigate the above problems, we can utilize genomic mating (GM) based upon optimal mate allocation to construct the best genotypic combinations in the next generation. In this study, we used stochastic simulation to investigate the impact of various factors on the efficiencies of GM to optimize pairing combinations after genomic selection of candidates in a pig population. These factors included: the algorithm used to derive inbreeding coefficients; the trait heritability (0.1, 0.3 or 0.5); the kind of GM scheme (focused average GEBV or inbreeding); the approach for computing the genomic relationship matrix (by SNP or runs of homozygosity (ROH)). The outcomes were compared to three traditional mating schemes (random, positive assortative or negative assortative matings). In addition, the performance of the GM approach was tested on real datasets obtained from a Large White pig breeding population.

RESULTS

Genomic mating outperforms other approaches in limiting the inbreeding accumulation for the same expected genetic gain. The use of ROH-based genealogical relatedness in GM achieved faster genetic gains than using relatedness based on individual SNPs. The G_ROH-based GM schemes with the maximum genetic gain resulted in 0.9%-2.6% higher rates of genetic gain ΔG, and 13%-83.3% lower ΔF than positive assortative mating regardless of heritability. The rates of inbreeding were always the fastest with positive assortative mating. Results from a purebred Large White pig population, confirmed that GM with ROH-based GRM was more efficient than traditional mating schemes.

CONCLUSION

Compared with traditional mating schemes, genomic mating can not only achieve sustainable genetic progress but also effectively control the rates of inbreeding accumulation in the population. Our findings demonstrated that breeders should consider using genomic mating for genetic improvement of pigs.