Identification of a quantitative trait locus (QTL) related to a chronic respiratory disease such as Mycoplasmal pneumonia of swine (MPS) and immune-related traits is important for the genetic improvement of disease resistance in pigs. The objective of this study was to detect a novel QTL for a total of 22 production, respiratory disease, and immune-related traits in Landrace pigs. A total of 874 Landrace purebred pigs, which were selected based on MPS resistance, were genotyped using the Illumina PorcineSNP60 BeadChip. We performed single nucleotide polymorphism (SNP)-based and haplotype-based genome-wide association studies (GWAS) to detect a novel QTL and to evaluate the possibility of a pleiotropic QTL for these traits. SNP-based GWAS detected a total of six significant regions in backfat thickness, ratio of granular leucocytes to lymphatic cells, plasma concentration of cortisol at different ages, and complement alternative pathway activity in serum. The significant region detected by haplotype-based GWAS was overlapped across the region detected by SNP-based GWAS. Most of these detected QTL regions were novel regions with some candidate genes located in them. With regard to a pleiotropic QTL among traits, only three of these detected QTL regions overlapped among traits, and many detected regions independently affected the traits.

Background Pedigree-based inbreeding coefficients have been generally included in statistical models for genetic evaluation of Japanese Black cattle. The use of genomic data is expected to provide precise assessment of inbreeding level and depression. Recently, many measures have been used for genome-based inbreeding coefficients; however, with no consensus on which is the most appropriate. Therefore, we compared the pedigree- ($${F}_{PED}$$ F PED ) and multiple genome-based inbreeding coefficients, which were calculated from the genomic relationship matrix with observed allele frequencies ($${F}_{GRM}$$ F GRM ), correlation between uniting gametes ($${F}_{UNI}$$ F UNI ), the observed vs expected number of homozygous genotypes ($${F}_{HOM}$$ F HOM ), runs of homozygosity (ROH) segments ($${F}_{ROH}$$ F ROH ) and heterozygosity by descent segments ($${F}_{HBD}$$ F HBD ). We quantified inbreeding depression from estimating regression coefficients of inbreeding coefficients on three reproductive traits: age at first calving (AFC), calving difficulty (CD) and gestation length (GL) in Japanese Black cattle. Results The highest correlations with $${F}_{PED}$$ F PED were for $${F}_{ROH}$$ F ROH (0.86) and $${F}_{HBD}$$ F HBD (0.85) whereas $${F}_{GRM}$$ F GRM and $${F}_{UNI}$$ F UNI provided weak correlations with $${F}_{PED}$$ F PED , with range 0.33–0.55. Except for $${F}_{GRM}$$ F GRM and $${F}_{UNI}$$ F UNI , there were strong correlations among genome-based inbreeding coefficients ($$\ge$$ ≥ 0.94). The estimates of regression coefficients of inbreeding depression for $${F}_{PED}$$ F PED was 2.1 for AFC, 0.63 for CD and -1.21 for GL, respectively, but $${F}_{PED}$$ F PED had no significant effects on all traits. Genome-based inbreeding coefficients provided larger effects on all reproductive traits than $${F}_{PED}$$ F PED . In particular, for CD, all estimated regression coefficients for genome-based inbreeding coefficients were significant, and for GL, that for $${F}_{UNI}$$ F UNI had a significant.. Although there were no significant effects when using overall genome-level inbreeding coefficients for AFC and GL, $${F}_{ROH}$$ F ROH provided significant effects at chromosomal level in four chromosomes for AFC, three chromosomes for CD, and two chromosomes for GL. In addition, similar results were obtained for $${F}_{HBD}$$ F HBD . Conclusions Genome-based inbreeding coefficients can capture more phenotypic variation than $${F}_{PED}$$ F PED . In particular, $${F}_{ROH}$$ F ROH and $${F}_{HBD}$$ F HBD can be considered good estimators for quantifying inbreeding level and identifying inbreeding depression at the chromosome level. These findings might improve the quantification of inbreeding and breeding programs using genome-based inbreeding coefficients.

Closed‐pig line breeding could change the genetic structure at a genome‐wide scale because of the selection in a pig breeding population. We investigated the changes in population structure among generations at a genome‐wide scale and the selected loci across the genome by comparing the observed and expected allele frequency changes in mycoplasma pneumonia of swine (MPS)‐selected pigs. Eight hundred and seventy‐four Landrace pigs, selected for MPS resistance without reducing average daily gain over five generations, had 37,299 single nucleotide polymorphisms (SNPs) and were used for genomic analyses. Regarding population structure, individuals in the first generation were the most widely distributed and then converged into a specific group, as they were selected over five generations. For allele frequency changes, 96 and 14 SNPs had higher allele frequency changes than the 99.9% and 99.99% thresholds of the expected changes, respectively. These SNPs were evenly spread across the genome, and a few of these selected regions overlapped with previously detected quantitative trait loci for MPS and immune‐related traits. Our results indicated that the considerable changes in allele frequency were identified in many regions across the genome by closed‐pig line breeding based on estimated breeding value.

Background: The genetic improvement of disease resistance in pig has been well-received. Identification of a quantitative trait locus (QTL) related to a chronic respiratory disease such as Mycoplasmal pneumonia of swine (MPS) and immune-related traits is important for understanding the genomic background of disease resistance and to apply marker-assisted selection. The objective of this study was to understand the influence of genomic factors on respiratory disease and immune-related traits in MPS-selected pigs.Results: A total of 874 Landrace purebred pigs, which were selected based on MPS resistance, were genotyped using the Illumina PorcineSNP60 BeadChip, and were then used for genomic analyses. First, we performed genome-wide association studies (GWAS) to detect a novel QTL for a total of 22 performance, respiratory disease, and immune-related traits using additive and nonadditive genetic effects. Second, we evaluated the changes in allele frequency due to selection for MPS resistance and compared the putative selected regions with the detected QTL. GWAS detected a total of 11 genome-wide significant single nucleotide polymorphisms (SNPs) with an additive effect in five traits and a total of three significant SNPs with a nonadditive effect in three traits. Most of these detected QTL regions were novel regions with some candidate genes located in them. With regard to a pleiotropic region among traits, only five of these detected QTL regions overlapped among traits. Changes in allele frequencies at the many putative selected regions were spread across the whole genome and overlapped with the detected QTL. Some of these selected regions were the ones that contained the detected QTL for MPS score and other traits.Conclusion: These results suggest that a closed-line breeding population is a useful target population to refine and confirm QTL regions by integrating the results of GWAS and allele frequency changes. The study provides new insights into the genomic factors that affect respiratory disease and immune-related traits in pigs.

We examined the prediction accuracies of genomic best linear unbiased prediction (GBLUP), various weighted GBLUP according to the degrees of marker effects (WGBLUP) and machine learning (ML) methods, and compared them with traditional BLUP for age at first calving (AFC), calving difficulty (CD), and gestation length in Japanese Black cattle. For WGBLUP, firstly, BayesC and FarmCPU were used to estimate marker effects. Then, we constructed three weighted genomic relationship matrices from information of estimated marker effects in the first step: absolute value of the estimated marker‐effect WGBLUP, estimated marker‐variance WGBLUP, and genomic‐feature WGBLUP. For ML, we applied Gaussian kernel, random forest, extreme gradient boost, and support vector regression. We collected a total of 2583 animals having both phenotypic records and genotypes with 30,105 markers and 16,406 pedigree records. For AFC, prediction accuracies of WGBLUP methods using FarmCPU exceeded BLUP by 25.7%–39.5%. For CD, estimated marker‐variance WGBLUP using BayesC achieved the highest prediction accuracy. Among ML methods, extreme gradient boost, support vector regression, and Gaussian kernel increased prediction accuracies by 28.4%, 19.0%, and 36.4% for AFC, CD, and gestation length compared with BLUP, respectively. Thus, prediction performance could be improved using suitable WGBLUP and ML methods according to target reproductive traits for the population used.