Whole-genome association analyses for lifetime reproductive traits in the pig

Structural variant landscapes reveal convergent signatures of evolution in sheep and goats

BACKGROUND

Sheep and goats have undergone domestication and improvement to produce similar phenotypes, which have been greatly impacted by structural variants (SVs). Here, we report a high-quality chromosome-level reference genome of Asiatic mouflon, and implement a comprehensive analysis of SVs in 897 genomes of worldwide wild and domestic populations of sheep and goats to reveal genetic signatures underlying convergent evolution.

RESULTS

We characterize the SV landscapes in terms of genetic diversity, chromosomal distribution and their links with genes, QTLs and transposable elements, and examine their impacts on regulatory elements. We identify several novel SVs and annotate corresponding genes (e.g., BMPR1B, BMPR2, RALYL, COL21A1, and LRP1B) associated with important production traits such as fertility, meat and milk production, and wool/hair fineness. We detect signatures of selection involving the parallel evolution of orthologous SV-associated genes during domestication, local environmental adaptation, and improvement. In particular, we find that fecundity traits experienced convergent selection targeting the gene BMPR1B, with the DEL00067921 deletion explaining ~10.4% of the phenotypic variation observed in goats.

CONCLUSIONS

Our results provide new insights into the convergent evolution of SVs and serve as a rich resource for the future improvement of sheep, goats, and related livestock.

Application of deep learning with bivariate models for genomic prediction of sow lifetime productivity-related traits

OBJECTIVE

Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP).

METHODS

Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip.

RESULTS

The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits.

CONCLUSION

This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP.

Application of Bayesian genomic prediction methods to genome-wide association analyses

Background

Bayesian genomic prediction methods were developed to simultaneously fit all genotyped markers to a set of available phenotypes for prediction of breeding values for quantitative traits, allowing for differences in the genetic architecture (distribution of marker effects) of traits. These methods also provide a flexible and reliable framework for genome-wide association (GWA) studies. The objective here was to review developments in Bayesian hierarchical and variable selection models for GWA analyses.

Results

By fitting all genotyped markers simultaneously, Bayesian GWA methods implicitly account for population structure and the multiple-testing problem of classical single-marker GWA. Implemented using Markov chain Monte Carlo methods, Bayesian GWA methods allow for control of error rates using probabilities obtained from posterior distributions. Power of GWA studies using Bayesian methods can be enhanced by using informative priors based on previous association studies, gene expression analyses, or functional annotation information. Applied to multiple traits, Bayesian GWA analyses can give insight into pleiotropic effects by multi-trait, structural equation, or graphical models. Bayesian methods can also be used to combine genomic, transcriptomic, proteomic, and other -omics data to infer causal genotype to phenotype relationships and to suggest external interventions that can improve performance.

Conclusions

Bayesian hierarchical and variable selection methods provide a unified and powerful framework for genomic prediction, GWA, integration of prior information, and integration of information from other -omics platforms to identify causal mutations for complex quantitative traits.

A major QTL at the LHCGR/FSHR locus for multiple birth in Holstein cattle

BACKGROUND

Twin and multiple births are rare in cattle and have a negative impact on the performance and health of cows and calves. Therefore, selection against multiple birth would be desirable in dairy cattle breeds such as Holstein. We applied different methods to decipher the genetic architecture of this trait using de-regressed breeding values for maternal multiple birth of ~ 2500 Holstein individuals to perform genome-wide association analyses using ~ 600 K imputed single nucleotide polymorphisms (SNPs).

RESULTS

In the population studied, we found no significant genetic trend over time of the estimated breeding values for multiple birth, which indicates that this trait has not been selected for in the past. In addition to several suggestive non-significant quantitative trait loci (QTL) on different chromosomes, we identified a major QTL on chromosome 11 for maternal multiple birth that explains ~ 16% of the total genetic variance. Using a haplotype-based approach, this QTL was fine-mapped to a 70-kb window on chromosome 11 between 31.00 and 31.07 Mb that harbors two functional candidate genes (LHCGR and FSHR). Analysis of whole-genome sequence data by linkage-disequilibrium estimation revealed a regulatory variant in the 5'-region of LHCGR as a possible candidate causal variant for the identified major QTL. Furthermore, the identified haplotype showed significant effects on stillbirth and days to first service.

CONCLUSIONS

QTL detection and subsequent identification of causal variants in livestock species remain challenging in spite of the availability of large-scale genotype and phenotype data. Here, we report for the first time a major QTL for multiple birth in Holstein cattle and provide evidence for a linked variant in the non-coding region of a functional candidate gene. This discovery, which is a first step towards the understanding of the genetic architecture of this polygenic trait, opens the path for future selection against this undesirable trait, and thus contributes to increased animal health and welfare.

The Effect of the Size of the Litter in Which the Sow Was Born on Her Lifetime Productivity

Simple Summary

Improving reproductive traits, including increased fertility and prolificacy, is important for efficiency behind production, but difficult to achieve due to the low heritability for those traits, their dependence on the environment, as well as maintenance and nutrition. It is possible to achieve good results in reproduction using various methods of improving the characteristics of pig reproduction, such as breeding work, crossbreeding, selection programmes, optimisation of the rearing environment, and maternal effects. The litter of sow origin is one of the features worth using in practice, as it can have a significant impact on improving the fertility, prolificacy, and reproductive longevity of sows and is therefore a factor analysed in the work presented.

Abstract

Improvement of lowly heritable traits is difficult, efforts must be made to take full advantage of the available information sources to improve them. The objective of the study was to determine the effect of the size of the litter in which the sow was born on her lifetime reproductive performance. Data on 22,683 litters were used to analyse the lifetime reproductive performance of 5623 Polish Large White sows. The sows from small litters (≤9) were on average the oldest at first farrowing, had the shortest herd life, the smallest number of litters, and the smallest sized litters (p ≤ 0.01). A positive relationship was established between the mean number of offspring born per litter and size of the litter in which the sow was born (p ≤ 0.01). For a sow to produce at least seven piglets per 100 days of reproduction, gilts from litters of at least 12 piglets should be selected for breeding.

Identification of Genomic Regions Associated with Concentrations of Milk Fat, Protein, Urea and Efficiency of Crude Protein Utilization in Grazing Dairy Cows

The objective of this study was to identify genomic regions associated with milk fat percentage (FP), crude protein percentage (CPP), urea concentration (MU) and efficiency of crude protein utilization (ECPU: ratio between crude protein yield in milk and dietary crude protein intake) using grazing, mixed-breed, dairy cows in New Zealand. Phenotypes from 634 Holstein Friesian, Jersey or crossbred cows were obtained from two herds at Massey University. A subset of 490 of these cows was genotyped using Bovine Illumina 50K SNP-chips. Two genome-wise association approaches were used, a single-locus model fitted to data from 490 cows and a single-step Bayes C model fitted to data from all 634 cows. The single-locus analysis was performed with the Efficient Mixed-Model Association eXpedited model as implemented in the SVS package. Single nucleotide polymorphisms (SNPs) with genome-wide association p-values ≤ 1.11 × 10⁻⁶ were considered as putative quantitative trait loci (QTL). The Bayes C analysis was performed with the JWAS package and 1-Mb genomic windows containing SNPs that explained > 0.37% of the genetic variance were considered as putative QTL. Candidate genes within 100 kb from the identified SNPs in single-locus GWAS or the 1-Mb windows were identified using gene ontology, as implemented in the Ensembl Genome Browser. The genes detected in association with FP (MGST1, DGAT1, CEBPD, SLC52A2, GPAT4, and ACOX3) and CPP (DGAT1, CSN1S1, GOSR2, HERC6, and IGF1R) were identified as candidates. Gene ontology revealed six novel candidate genes (GMDS, E2F7, SIAH1, SLC24A4, LGMN, and ASS1) significantly associated with MU whose functions were in protein catabolism, urea cycle, ion transportation and N excretion. One novel candidate gene was identified in association with ECPU (MAP3K1) that is involved in post-transcriptional modification of proteins. The findings should be validated using a larger population of New Zealand grazing dairy cows.

Potential functional variants in AHR signaling pathways are associated with age at puberty in swine

Puberty in female pigs is defined as age at first estrus and gilts that have an earlier age at puberty are more likely to have greater lifetime productivity. Because age at puberty is predictive for sow longevity and lifetime productivity, but not routinely measured in commercial herds, it would be beneficial to use genomic or marker-assisted selection to improve these traits. A GWAS at the US Meat Animal Research Center (USMARC) identified several loci associated with age at puberty in pigs. Candidate genes in these regions were scanned for potential functional variants using sequence information from the USMARC swine population founder animals and public databases. In total, 135 variants (SNP and insertion/deletions) in 39 genes were genotyped in 1284 phenotyped animals from a validation population sired by Landrace and Yorkshire industry semen using the Agena MassArray system. Twelve variants in eight genes were associated with age at puberty (P < 0.005) with estimated additive SNP effects ranging from 1.6 to 5.3 days. Nine of these variants were non-synonymous coding changes in AHR, CYP1A2, OR2M4, SDCCAG8, TBC1D1 and ZNF608, two variants were deletions of one and four codons in aryl hydrocarbon receptor, AHR, and the most significant SNP was near an acceptor splice site in the acetyl-CoA carboxylase alpha, ACACA. Several of the loci identified have a physiological and a genetic role in sexual maturation in humans and other animals and are involved in AHR-mediated pathways. Further functional validation of these variants could identify causative mutations that influence age at puberty in gilts and possibly sow lifetime productivity.

Porcine LIF gene polymorphisms and their association with litter size traits in four pig breeds

Leukemia inhibitory factor (LIF) is an important productivity-related gene in pigs. We found two polymorphisms — g.6646C>T and g.6988C>T — in exon 3 of LIF in pigs by using DNA sequencing and polymerase chain reaction-restriction fragment length polymorphism. Three genotypes were obtained and associated with litter size traits in Anqing Six-end-white (AQ), Wei (W), Wannan Black (WNB), and Large White (LW) pigs. At locus g.6646C>T, the g.6646C allele frequency variation was 0.6869 (AQ), 0.7473 (W), 1 (WNB), and 0.6852 (LW). In AQ pigs, sows with the TT genotype had higher total number of piglets born (TNB) and number of piglets born alive (NBA) in the first parity and multiparities (P < 0.01). In W and LW pigs, sows with the CC genotype had higher TNB and NBA in multiparities (P < 0.01). At locus g.6988C>T, the g.6988C allele frequency variation was 1 (AQ), 0.6154 (W), 1 (WNB), and 0.6667 (LW). The CC genotype significantly differed from CT or TT genotypes (P < 0.01) for TNB and NBA in W and LW pigs. Thus, LIF was shown to have a significant influence on litter size. Therefore, g.6646C>T and g.6988C>T loci of LIF could be potential marker-assisted selection tools for improving litter size in pig production.

GWAS on Imputed Whole-Genome Resequencing From Genotyping-by-Sequencing Data for Farrowing Interval of Different Parities in Pigs

The whole-genome sequencing (WGS) data can potentially discover all genetic variants. Studies have shown the power of WGS for genome-wide association study (GWAS) lies in the ability to identify quantitative trait loci and nucleotides (QTNs). However, the resequencing of thousands of target individuals is expensive. Genotype imputation is a powerful approach for WGS and to identify causal mutations. This study aimed to evaluate the imputation accuracy from genotyping-by-sequencing (GBS) to WGS in two pig breeds using a resequencing reference population and to detect single-nucleotide polymorphisms (SNPs) and candidate genes for farrowing interval (FI) of different parities using the data before and after imputation for GWAS. Six hundred target pigs, 300 Landrace and 300 Large White pigs, were genotyped by GBS, and 60 reference pigs, 20 Landrace and 40 Large White pigs, were sequenced by whole-genome resequencing. Imputation for pigs was conducted using Beagle software. The average imputation accuracy (allelic R ²) from GBS to WGS was 0.42 for Landrace pigs and 0.45 for Large White pigs. For Landrace pigs (Large White pigs), 4,514,934 (5,533,290) SNPs had an accuracy >0.3, resulting an average accuracy of 0.73 (0.72), and 2,093,778 (2,468,645) SNPs had an accuracy >0.8, resulting an average accuracy of 0.94 (0.93). Association studies with data before and after imputation were performed for FI of different parities in two populations. Before imputation, 18 and 128 significant SNPs were detected for FI in Landrace and Large White pigs, respectively. After imputation, 125 and 27 significant SNPs were identified for dataset with an accuracy >0.3 and 0.8 in Large White pigs, and 113 and 18 SNPs were found among imputed sequence variants. Among these significant SNPs, six top SNPs were detected in both GBS data and imputed WGS data, namely, SSC2: 136127645, SSC5: 103426443, SSC6: 27811226, SSC10: 3609429, SSC14: 15199253, and SSC15: 150297519. Overall, many candidate genes could be involved in FI of different parities in pigs. Although imputation from GBS to WGS data resulted in a low imputation accuracy, association analyses with imputed WGS data were optimized to detect QTNs for complex trait. The obtained results provide new insight into genotype imputation, genetic architecture, and candidate genes for FI of different parities in Landrace and Large White pigs.

Epigenome-wide skeletal muscle DNA methylation profiles at the background of distinct metabolic types and ryanodine receptor variation in pigs

BACKGROUND

Epigenetic variation may result from selection for complex traits related to metabolic processes or appear in the course of adaptation to mediate responses to exogenous stressors. Moreover epigenetic marks, in particular the DNA methylation state, of specific loci are driven by genetic variation. In this sense, polymorphism with major gene effects on metabolic and cell signaling processes, like the variation of the ryanodine receptors in skeletal muscle, may affect DNA methylation.

METHODS

DNA-Methylation profiles were generated applying Reduced Representation Bisulfite Sequencing (RRBS) on 17 Musculus longissimus dorsi samples. We examined DNA methylation in skeletal muscle of pig breeds differing in metabolic type, Duroc and Pietrain. We also included F2 crosses of these breeds to get a first clue to DNA methylation sites that may contribute to breed differences. Moreover, we compared DNA methylation in muscle tissue of Pietrain pigs differing in genotypes at the gene encoding the Ca2+ release channel (RYR1) that largely affects muscle physiology.

RESULTS

More than 2000 differently methylated sites were found between breeds including changes in methylation profiles of METRNL, IDH3B, COMMD6, and SLC22A18, genes involved in lipid metabolism. Depending on RYR1 genotype there were 1060 differently methylated sites including some functionally related genes, such as CABP2 and EHD, which play a role in buffering free cytosolic Ca²⁺ or interact with the Na⁺/Ca²⁺ exchanger.

CONCLUSIONS

The change in the level of methylation between the breeds is probably the result of the long-term selection process for quantitative traits involving an infinite number of genes, or it may be the result of a major gene mutation that plays an important role in muscle metabolism and triggers extensive compensatory processes.

Effect of single-nucleotide polymorphisms on specific reproduction parameters in Hungarian Large White sows

The aim of this study was to reveal the effect of single-nucleotide polymorphisms (SNPs) on the total number of piglets born (TNB), the litter weight born alive (LWA), the number of piglets born dead (NBD), the average litter weight on the 21st day (M21D) and the interval between litters (IBL). Genotypes were determined on a high-density Illumina Porcine SNP 60K BeadChip. Data screening and data identification were performed by a multi-locus mixed-model. Statistical analyses were carried out to find associations between individual genotypes of 290 Hungarian Large White sows and the investigated reproduction parameters. According to the analysis outcome, three SNPs were identified to be associated with TNB. These loci are located on chromosomes 1, 6 and 13 (-log₁₀P = 6.0, 7.86 and 6.22, the frequencies of their minor alleles, MAF, were 0.298, 0.299 and 0.364, respectively). Two loci showed considerable association (-log₁₀P = 10.35 and 10.46) with LWA on chromosomes 5 and X, the MAF were 0.425 and 0.446, respectively. Seven loci were found to be associated with NBD. These loci are located on chromosomes 5, 6, 13, 14, 15, 16 and 18 (-log₁₀P = 10.95, 5.43, 8.29, 6.72, 6.81, 5.90, and 5.15, respectively). One locus showed association (-log₁₀P = 5.62) with M21D on chromosome 1 (the MAF was 0.461). Another locus was found to be associated with IBL on chromosome 8 (-log₁₀P = 7.56; the MAF was 0.438). The above-mentioned loci provide a straightforward possibility to assist selection by molecular tools and, consequently, to improve the competitiveness of the Hungarian Large White (HLW) breed.

Ways to improve the gene pool of pigs of the Russian Federation

An analysis of the system of breeding work in the pig industry of our country has been carried out. The scientifc and organizational factors that determine the improvement of breed and productive qualities of pigs have been analyzed. On the basis of many years of experimental data, selection practices obtained in the process of creating new breeding achievements, and using the scientifc results of the world science on genetics and animal breeding, proposals have been developed for a new system for assessing and improving the genetic potential of animal productivity based on modern achievements in genetics. In particular, a critical analysis was performed on the existing instructions for boning pigs, linear breeding, which does not meet the criteria of reality in the systematization of biological objects. The positive effect of breeding pigs in a closed mode in the form of a “line-population”, using such genetic markers as erythrocyte antigens, erythrocyte enzymes, lipoproteins, allowing intensifcation of the selection process, has been experimentally proved. When hogging pigs and developing breeding plans with a herd, it was proposed to use such selection and genetic parameters as heritability factors, phenotypic and genetic correlations, selective differential, selective effect, etc. for assessment of animals and prognostication of productivity. A system of continuous scoring of each selectable feature was developed, in contrast to the interval to the classes used in our country at the present time. A model of the selection index, taking into account the selection and economic signifcance of each of its components, has been proposed. Theoretical paths have been shown towards and experimental proof given to a relatively rapid transformation of breeds of animals bred in Russia to world-class productivity, as opposed to the constant import of breeding animals.

SLA-11 mutations are associated with litter size traits in Large White and Chinese DIV pigs

Litter size is an important economic traits in pigs. SLA-11 gene is a member of SLA (swine leukocyte antigen) complex. In our previous study, the SLA-11 gene was differentially expressed in PMSG-hCG stimulated preovulatory ovarian follicles of Chinese Taihu and Large White sows. Here, we identified two mutations (c.754-132 T > C and c.1421 + 38 T > C) in SLA-11 gene and analyzed the associations of two SNPs with litter size traits in Large White (n = 263) and DIV (n = 117) sows. The results showed that in Large White pigs, SLA-11 c.754-132 CC sows produced 0.74 and 0.87 more pigs per litter for TNB and NBA of all parities than did TT sows (p < .05); In DIV pigs, SLA-11 c.754-132 CC sows produced 1.17 more pigs per litter for TNB of all parities than did TC sows (p < .05). In Large White pigs, SLA-11 c.1421 + 38 CC sows produced 0.9 more pigs per litter for TNB of all parities than did TT sows (p < .05), while in DIV pigs SLA-11 c.1421 + 38 CC sows produced 0.84 and 0.7 less pigs per litter for TNB and NBA of all parities than did TT sows (p < .05). Our research indicated that SLA-11 mutations were potential molecular markers for improving the litter size traits in pigs.

Genome-wide association study for reproductive traits in a Large White pig population

Using the PorcineSNP80 BeadChip, we performed a genome‐wide association study for seven reproductive traits, including total number born, number born alive, litter birth weight, average birth weight, gestation length, age at first service and age at first farrowing, in a population of 1207 Large White pigs. In total, we detected 12 genome‐wide significant and 41 suggestive significant SNPs associated with six reproductive traits. The proportion of phenotypic variance explained by all significant SNPs for each trait ranged from 4.46% (number born alive) to 11.49% (gestation length). Among them, 29 significant SNPs were located within known QTL regions for swine reproductive traits, such as corpus luteum number, stillborn number and litter size, of which one QTL region associated with litter size contained the ALGA0098819 SNP for total number born. Subsequently, we found that 376 functional genes contained or were near these significant SNPs. Of these, 14 genes—BHLHA15, OCM2, IL1B2, GCK, SMAD2, HABP2, PAQR5, GRB10, PRELID2, DMKN, GPI, GPIHBP1, ADCY2 and ACVR2B—were considered important candidates for swine reproductive traits based on their critical roles in embryonic development, energy metabolism and growth development. Our findings contribute to the understanding of the genetic mechanisms for reproductive traits and could have a positive effect on pig breeding programs.

Similar Genetic Architecture with Shared and Unique Quantitative Trait Loci for Bacterial Cold Water Disease Resistance in Two Rainbow Trout Breeding Populations

Bacterial cold water disease (BCWD) causes significant mortality and economic losses in salmonid aquaculture. In previous studies, we identified moderate-large effect quantitative trait loci (QTL) for BCWD resistance in rainbow trout (Oncorhynchus mykiss). However, the recent availability of a 57 K SNP array and a reference genome assembly have enabled us to conduct genome-wide association studies (GWAS) that overcome several experimental limitations from our previous work. In the current study, we conducted GWAS for BCWD resistance in two rainbow trout breeding populations using two genotyping platforms, the 57 K Affymetrix SNP array and restriction-associated DNA (RAD) sequencing. Overall, we identified 14 moderate-large effect QTL that explained up to 60.8% of the genetic variance in one of the two populations and 27.7% in the other. Four of these QTL were found in both populations explaining a substantial proportion of the variance, although major differences were also detected between the two populations. Our results confirm that BCWD resistance is controlled by the oligogenic inheritance of few moderate-large effect loci and a large-unknown number of loci each having a small effect on BCWD resistance. We detected differences in QTL number and genome location between two GWAS models (weighted single-step GBLUP and Bayes B), which highlights the utility of using different models to uncover QTL. The RAD-SNPs detected a greater number of QTL than the 57 K SNP array in one population, suggesting that the RAD-SNPs may uncover polymorphisms that are more unique and informative for the specific population in which they were discovered.

Single step genome-wide association studies based on genotyping by sequence data reveals novel loci for the litter traits of domestic pigs

In this study, data genotyping by sequence (GBS) was used to perform single step GWAS (ssGWAS) to identify SNPs associated with the litter traits in domestic pigs and search for candidate genes in the region of significant SNPs. After quality control, 167,355 high-quality SNPs from 532 pigs were obtained. Phenotypic traits on 2112 gilt litters from 532 pigs were recorded including total number born (TNB), number born alive (NBA), and litter weight born alive (LWB). A single-step genomic BLUP approach (ssGBLUP) was used to implement the genome-wide association analysis at a 5% genome-wide significance level. A total of 8, 23 and 20 significant SNPs were associated with TNB, NBA, and LWB, respectively, and these significant SNPs accounted for 62.78%, 79.75%, and 58.79% of genetic variance. Furthermore, 1 (SSC14: 16314857), 4 (SSC1: 81986236, SSC1: 66599775, SSC1: 161999013, and SSC1: 267883107), and 5 (SSC9: 29030061, SSC2: 32368561, SSC5: 110375350, SSC13: 45619882 and SSC13: 45647829) significant SNPs for TNB, NBA, and LWB were inferred to be novel loci. At SSC1, the AIM1 and FOXO3 genes were found to be associated with NBA; these genes increase ovarian reproductive capacity and follicle number and decrease gonadotropin levels. The genes SLC36A4 and INTU are involved in cell growth, cytogenesis and development were found to be associated with LWB. These significant SNPs can be used as an indication for regions in the Sus scrofa genome for variability in litter traits, but further studies are expected to confirm causative mutations.

The use of multiple imputation for the accurate measurements of individual feed intake by electronic feeders

Obtaining accurate individual feed intake records is the key first step in achieving genetic progress toward more efficient nutrient utilization in pigs. Feed intake records collected by electronic feeding systems contain errors (erroneous and abnormal values exceeding certain cutoff criteria), which are due to feeder malfunction or animal-feeder interaction. In this study, we examined the use of a novel data-editing strategy involving multiple imputation to minimize the impact of errors and missing values on the quality of feed intake data collected by an electronic feeding system. Accuracy of feed intake data adjustment obtained from the conventional linear mixed model (LMM) approach was compared with 2 alternative implementations of multiple imputation by chained equation, denoted as MI (multiple imputation) and MICE (multiple imputation by chained equation). The 3 methods were compared under 3 scenarios, where 5, 10, and 20% feed intake error rates were simulated. Each of the scenarios was replicated 5 times. Accuracy of the alternative error adjustment was measured as the correlation between the true daily feed intake (DFI; daily feed intake in the testing period) or true ADFI (the mean DFI across testing period) and the adjusted DFI or adjusted ADFI. In the editing process, error cutoff criteria are used to define if a feed intake visit contains errors. To investigate the possibility that the error cutoff criteria may affect any of the 3 methods, the simulation was repeated with 2 alternative error cutoff values. Multiple imputation methods outperformed the LMM approach in all scenarios with mean accuracies of 96.7, 93.5, and 90.2% obtained with MI and 96.8, 94.4, and 90.1% obtained with MICE compared with 91.0, 82.6, and 68.7% using LMM for DFI. Similar results were obtained for ADFI. Furthermore, multiple imputation methods consistently performed better than LMM regardless of the cutoff criteria applied to define errors. In conclusion, multiple imputation is proposed as a more accurate and flexible method for error adjustments in feed intake data collected by electronic feeders.

Identification of growth trait related genes in a Yorkshire purebred pig population by genome-wide association studies

Objective

The aim of this study is to identify genomic regions or genes controlling growth traits in pigs.

Methods

Using a panel of 54,148 single nucleotide polymorphisms (SNPs), we performed a genome-wide Association (GWA) study in 562 pure Yorshire pigs with four growth traits: average daily gain from 30 kg to 100 kg or 115 kg, and days to 100 kg or 115 kg. Fixed and random model Circulating Probability Unification method was used to identify the associations between 54,148 SNPs and these four traits. SNP annotations were performed through the Sus scrofa data set from Ensembl. Bioinformatics analysis, including gene ontology analysis, pathway analysis and network analysis, was used to identify the candidate genes.

Results

We detected 6 significant and 12 suggestive SNPs, and identified 9 candidate genes in close proximity to them (suppressor of glucose by autophagy [SOGA1], R-Spondin 2 [RSPO2], mitogen activated protein kinase kinase 6 [MAP2K6], phospholipase C beta 1 [PLCB1], rho GTPASE activating protein 24 [ARHGAP24], cytoplasmic polyadenylation element binding protein 4 [CPEB4], GLI family zinc finger 2 [GLI2], neuronal tyrosine-phosphorylated phosphoinositide-3-kinase adaptor 2 [NYAP2], and zinc finger protein multitype 2 [ZFPM2]). Gene ontology analysis and literature mining indicated that the candidate genes are involved in bone, muscle, fat, and lung development. Pathway analysis revealed that PLCB1 and MAP2K6 participate in the gonadotropin signaling pathway and suggests that these two genes contribute to growth at the onset of puberty.

Conclusion

Our results provide new clues for understanding the genetic mechanisms underlying growth traits, and may help improve these traits in future breeding programs.

Genetic correlations between first parity and accumulated second to last parity reproduction traits as selection aids to improve sow lifetime productivity

Objective

The objective of this research was to estimate genetic correlations between number of piglets born alive in the first parity (NBA1), litter birth weight in the first parity (LTBW1), number of piglets weaned in the first parity (NPW1), litter weaning weight in the first parity (LTWW1), number of piglets born alive from second to last parity (NBA2+), litter birth weight from second to last parity (LTBW2+), number of piglets weaned from second to last parity (NPW2+) and litter weaning weight from second to last parity (LTWW2+), and to identify the percentages of animals (the top 10%, 25%, and 50%) for first parity and sums of second and later parity traits.

Methods

The 9,830 records consisted of 2,124 Landrace (L), 724 Yorkshire (Y), 2,650 LY, and 4,332 YL that had their first farrowing between July 1989 and December 2013. The 8-trait animal model included the fixed effects of first farrowing year-season, additive genetic group, heterosis of the sow and the litter, age at first farrowing, and days to weaning (NPW1, LTWW1, NPW2+, and LTWW2+). Random effects were animal and residual.

Results

Heritability estimates ranged from 0.08±0.02 (NBA1 and NPW1) to 0.29±0.02 (NPW2+). Genetic correlations between reproduction traits in the first parity and from second to last parity ranged from 0.17±0.08 (LTBW1 and LTBW2+) to 0.67±0.06 (LTWW1 and LTWW2+). Phenotypic correlations between reproduction traits in the first parity and from second to last parity were close to zero. Rank correlations between LTWW1 and LTWW2+ estimated breeding value tended to be higher than for other pairs of traits across all replacement percentages.

Conclusion

These rank correlations indicated that selecting boars and sows using genetic predictions for first parity reproduction traits would help improve reproduction traits in the second and later parities as well as lifetime productivity in this swine population.

Evidence that oxidative stress is higher in replacement gilts than in multiparous sows

The recent success obtained in term of increasing the litter size of sows has not correlated with a reduction of replacement rate. There is thus an increased economic demand for gilts with optimal reproductive potential and longevity. Unfortunately, replacement gilts are known to be more susceptible to diseases and less productive than multiparous sows. Interestingly, reproductive performance, resistance to diseases and longevity could all be largely affected by oxidative stress. To investigate whether oxidative stress conditions could account for the poor longevity of gilts, three distinct groups of conventional Yorkshire × Landrace sows were formed based on their similar age and parity (gilts, second parity sows as well as fourth to fifth parity sows). All animals were slaughtered during the post-ovulatory period, and blood as well as tissue samples were collected. Biomarkers of oxidative damage to proteins (carbonyls) and DNA (8-OHdG) were analysed in samples. Specific mRNA expression of major antioxidants such as glutathione peroxidases 1, 3 and 4 (GPx1, GPx3, GPx4) as well as superoxide dismutases 1 and 2 (Sod1, Sod2) were monitored in liver and kidney samples by quantitative RT-PCR. Specific enzymatic activities of both GPx and SOD were measured by spectrophotometric assays. The plasma concentration of protein carbonyls was significantly different between the three groups with the highest concentration being observed in gilts (p ≤ 0.001). The mRNA expression levels of GPx1 and GPx4 were also significantly increased in the liver of gilts when compared to multiparous sows (p ≤ 0.05). SOD2 enzymatic activity was found to be higher in the liver of gilts than multiparous sows (p ≤ 0.05). Taken together, these results indicate that replacement gilts sustain significantly higher oxidative conditions than multiparous sows. Current findings may contribute to the design of nutritional regimens that will increase the productivity of gilts by counteracting oxidative stress.

Copy number variations and genome-wide associations reveal putative genes and metabolic pathways involved with the feed conversion ratio in beef cattle

The use of genome-wide association results combined with other genomic approaches may uncover genes and metabolic pathways related to complex traits. In this study, the phenotypic and genotypic data of 1475 Nellore (Bos indicus) cattle and 941,033 single nucleotide polymorphisms (SNPs) were used for genome-wide association study (GWAS) and copy number variations (CNVs) analysis in order to identify candidate genes and putative pathways involved with the feed conversion ratio (FCR). The GWAS was based on the Bayes B approach analyzing genomic windows with multiple regression models to estimate the proportion of genetic variance explained by each window. The CNVs were detected with PennCNV software using the log R ratio and B allele frequency data. CNV regions (CNVRs) were identified with CNVRuler and a linear regression was used to associate CNVRs and the FCR. Functional annotation of associated genomic regions was performed with the Database for Annotation, Visualization and Integrated Discovery (DAVID) and the metabolic pathways were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG). We showed five genomic windows distributed over chromosomes 4, 6, 7, 8, and 24 that explain 12 % of the total genetic variance for FCR, and detected 12 CNVRs (chromosomes 1, 5, 7, 10, and 12) significantly associated [false discovery rate (FDR) < 0.05] with the FCR. Significant genomic regions (GWAS and CNV) harbor candidate genes involved in pathways related to energetic, lipid, and protein metabolism. The metabolic pathways found in this study are related to processes directly connected to feed efficiency in beef cattle. It was observed that, even though different genomic regions and genes were found between the two approaches (GWAS and CNV), the metabolic processes covered were related to each other. Therefore, a combination of the approaches complement each other and lead to a better understanding of the FCR.

Effects of Gold Nanorods on Imprinted Genes Expression in TM-4 Sertoli Cells

Gold nanorods (GNRs) are among the most commonly used nanomaterials. However, thus far, little is known about their harmful effects on male reproduction. Studies from our laboratory have demonstrated that GNRs could decrease glycine synthesis, membrane permeability, mitochondrial membrane potential and disrupt blood-testis barrier factors in TM-4 Sertoli cells. Imprinted genes play important roles in male reproduction and have been identified as susceptible loci to environmental insults by chemicals because they are functionally haploid. In this original study, we investigated the extent to which imprinted genes become deregulated in TM-4 Sertoli cells when treated with low dose of GNRs. The expression levels of 44 imprinted genes were analyzed by quantitative real-time PCR in TM-4 Sertoli cells after a low dose of (10 nM) GNRs treatment for 24 h. We found significantly diminished expression of Kcnq1, Ntm, Peg10, Slc22a2, Pwcr1, Gtl2, Nap1l5, Peg3 and Slc22a2, while Plagl1 was significantly overexpressed. Additionally, four (Kcnq1, Slc22a18, Pwcr1 and Peg3) of 10 abnormally expressed imprinted genes were found to be located on chromosome 7. However, no significant difference of imprinted miRNA genes was observed between the GNRs treated group and controls. Our study suggested that aberrant expression of imprinted genes might be an underlying mechanism for the GNRs-induced reproductive toxicity in TM-4 Sertoli cells.

Genome-wide association and identification of candidate genes for age at puberty in swine

Background

Reproductive efficiency has a great impact on the economic success of pork production. Gilts comprise a significant portion of breeding females and gilts that reach puberty earlier tend to stay in the herd longer and be more productive. About 10 to 30 % of gilts never farrow a litter and the most common reasons for removal are anestrus and failure to conceive. Puberty in pigs is usually defined as the female’s first estrus in the presence of boar stimulation. Genetic markers associated with age at puberty will allow for selection on age at puberty and traits correlated with sow lifetime productivity.

Results

Gilts (n = 759) with estrus detection measurements ranging from 140–240 days were genotyped using the Illumina PorcineSNP60 BeadChip and SNP were tested for significant effects with a Bayesian approach using GenSel software. Of the available 8111 five-marker windows, 27 were found to be statistically significant with a comparison-wise error of P < 0.01. Ten QTL were highly significant at P < 0.005 level. Two QTL, one on SSC12 at 15 Mb and the other on SSC7 at 75 Mb, explained 16.87 % of the total genetic variance. The most compelling candidate genes in these two regions included the growth hormone gene (GH1) on SSC12 and PRKD1 on SSC7. Several loci confirmed associations previously identified for age at puberty in the pig and loci for age at menarche in humans.

Conclusions

Several of the loci identified in this study have a physiological role for the onset of puberty and a genetic basis for sexual maturation in humans. Understanding the genes involved in regulation of the onset of puberty would allow for the improvement of reproductive efficiency in swine. Because age at puberty is a predictive factor for sow longevity and lifetime productivity, but not routinely measured or selected for in commercial herds, it would be beneficial to be able to use genomic or marker-assisted selection to improve these traits.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-016-0352-y) contains supplementary material, which is available to authorized users.

Genomewide association analysis for average birth interval and stillbirth in swine

Reproductive efficiency has a great impact on the economic success of pork production. Stillborn pigs and average birth interval contribute to the number of pigs born alive in a litter. To better understand the underlying genetics of these traits, a genomewide association study was undertaken. Samples of DNA were collected and tested using the Illumina Porcine SNP60 BeadChip from 798 females farrowing over a 4-yr period (all first parity). Birth intervals and piglet birth status (stillborn or alive) were determined by videotaping each farrowing event. A total of 41,148 SNP were tested using the Bayes C option of GenSel (version 4.61) and 1-Mb windows. These 1-Mb windows explained proportions of 0.017, 0.002, 0.032, 0.029, and 0.030 of the total variation, respectively, for litter average birth interval after deletion of the last piglet born, last birth interval in the litter, number of stillborn piglets ignoring the last piglet born, number of stillborns in the last birth position, and percent stillborn ignoring the last piglet. Significant 1-Mb nonoverlapping SNP windows were identified by using a conservative approach requiring 1-Mb windows to have a genetic variance ≥1.0% of genomic variance and these were considered to be QTL. Quantitative trait loci were located for number of stillborn piglets ignoring the last piglet born (1 QTL), number of stillborns in the last birth position (1 QTL), and percent stillborn ignoring the last piglet (3 QTL). In addition, 2, 13, 3, and 6 suggestive 1-Mb nonoverlapping SNP windows were identified for litter average birth interval after deletion of the last piglet born, number of stillborn piglets ignoring the last piglet born, number of stillborns in the last birth position, and percent stillborn ignoring the last piglet, respectively. Possible candidate genes affecting both birth interval and stillbirth included () and (). Possible genes affecting only birth interval included (), and (), and those affecting only stillbirth included (), LOC100518697 (a nostrin-like gene), and (). The QTL and the suggestive 1-Mb nonoverlapping SNP windows may lead to genetic markers for marker assisted selection, marker assisted management, or genomic selection applications in commercial pig populations.

Genome-wide association study reveals novel loci for litter size and its variability in a Large White pig population

Background

In many traits, not only individual trait levels are under genetic control, but also the variation around that level. In other words, genotypes do not only differ in mean, but also in (residual) variation around the genotypic mean. New statistical methods facilitate gaining knowledge on the genetic architecture of complex traits such as phenotypic variability. Here we study litter size (total number born) and its variation in a Large White pig population using a Double Hierarchical Generalized Linear model, and perform a genome-wide association study using a Bayesian method.

Results

In total, 10 significant single nucleotide polymorphisms (SNPs) were detected for total number born (TNB) and 9 SNPs for variability of TNB (varTNB). Those SNPs explained 0.83 % of genetic variance in TNB and 1.44 % in varTNB. The most significant SNP for TNB was detected on Sus scrofa chromosome (SSC) 11. A possible candidate gene for TNB is ENOX1, which is involved in cell growth and survival. On SSC7, two possible candidate genes for varTNB are located. The first gene is coding a swine heat shock protein 90 (HSPCB = Hsp90), which is a well-studied gene stabilizing morphological traits in Drosophila and Arabidopsis. The second gene is VEGFA, which is activated in angiogenesis and vasculogenesis in the fetus. Furthermore, the genetic correlation between additive genetic effects on TNB and on its variation was 0.49. This indicates that the current selection to increase TNB will also increase the varTNB.

Conclusions

To the best of our knowledge, this is the first study reporting SNPs associated with variation of a trait in pigs. Detected genomic regions associated with varTNB can be used in genomic selection to decrease varTNB, which is highly desirable to avoid very small or very large litters in pigs. However, the percentage of variance explained by those regions was small. The SNPs detected in this study can be used as indication for regions in the Sus scrofa genome involved in maintaining low variability of litter size, but further studies are needed to identify the causative loci.

Genome-wide association studies (GWAS) identify a QTL close to PRKAG3 affecting meat pH and colour in crossbred commercial pigs

Background

Improving meat quality is a high priority for the pork industry to satisfy consumers’ preferences. GWAS have become a state-of-the-art approach to genetically improve economically important traits. However, GWAS focused on pork quality are still relatively rare.

Results

Six genomic regions were shown to affect loin pH and Minolta colour a* and b* on both loin and ham through GWAS in 1943 crossbred commercial pigs. Five of them, located on Sus scrofa chromosome (SSC) 1, SSC5, SSC9, SSC16 and SSCX, were associated with meat colour. However, the most promising region was detected on SSC15 spanning 133–134 Mb which explained 3.51% - 17.06% of genetic variance for five measurements of pH and colour. Three SNPs (ASGA0070625, MARC0083357 and MARC0039273) in very strong LD were considered most likely to account for the effects in this region. ASGA0070625 is located in intron 2 of ZNF142, and the other two markers are close to PRKAG3, STK36, TTLL7 and CDK5R2. After fitting MARC0083357 (the closest SNP to PRKAG3) as a fixed factor, six SNPs still remained significant for at least one trait. Four of them are intragenic with ARPC2, TMBIM1, NRAMP1 and VIL1, while the remaining two are close to RUFY4 and CDK5R2. The gene network constructed demonstrated strong connections of these genes with two major hubs of PRKAG3 and UBC in the super-pathways of cell-to-cell signaling and interaction, cellular function and maintenance. All these pathways play important roles in maintaining the integral architecture and functionality of muscle cells facing the dramatic changes that occur after exsanguination, which is in agreement with the GWAS results found in this study.

Conclusions

There may be other markers and/or genes in this region besides PRKAG3 that have an important effect on pH and colour. The potential markers and their interactions with PRKAG3 require further investigation

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-015-0192-1) contains supplementary material, which is available to authorized users.

Feed intake, average daily gain, feed efficiency, and real-time ultrasound traits in Duroc pigs: II. Genomewide association

Efficient use of feed resources has become a clear challenge for the U.S. pork industry as feed costs continue to be the largest variable expense. The availability of the Illumina Porcine60K BeadChip has greatly facilitated whole-genome association studies to identify chromosomal regions harboring genes influencing those traits. The current study aimed at identifying genomic regions associated with variation in feed efficiency and several production traits in a Duroc terminal sire population, including ADFI, ADG, feed conversion ratio, residual feed intake (RFI), real-time ultrasound back fat thickness (BF), ultrasound muscle depth, intramuscular fat content (IMF), birth weight (BW at birth), and weaning weight (BW at weaning). Single trait association analyses were performed using Bayes B models with 35,140 SNP on 18 autosomes after quality control. Significance of nonoverlapping 1-Mb length windows (n = 2,380) were tested across 3 QTL inference methods: posterior distribution of windows variances from Monte Carlo Markov Chain, naive Bayes factor, and nonparametric bootstrapping. Genes within the informative QTL regions for the traits were annotated. A region ranging from166 to 140 Mb (4-Mb length) on SSC 1, approximately 8 Mb upstream of the MC4R gene, was significantly associated with ADFI, ADG, and BF, where SOCS6 and DOK6 are proposed as the most likely candidate genes. Another region affecting BW at weaning was identified on SSC 4 (84-85 Mb), harboring genes previously found to influence both human and cattle height: PLAG1, CHCHD7, RDHE2 (or SDR16C5), MOS, RPS20, LYN, and PENK. No QTL were identified for RFI, IMF, and BW at birth. In conclusion, we have identified several genomic regions associated with traits affecting nutrient utilization that could be considered for future genomic prediction to improve feed utilization.

Identification of genomic regions associated with feed efficiency in Nelore cattle

Background

Feed efficiency is jointly determined by productivity and feed requirements, both of which are economically relevant traits in beef cattle production systems. The objective of this study was to identify genes/QTLs associated with components of feed efficiency in Nelore cattle using Illumina BovineHD BeadChip (770 k SNP) genotypes from 593 Nelore steers. The traits analyzed included: average daily gain (ADG), dry matter intake (DMI), feed-conversion ratio (FCR), feed efficiency (FE), residual feed intake (RFI), maintenance efficiency (ME), efficiency of gain (EG), partial efficiency of growth (PEG) and relative growth rate (RGR). The Bayes B analysis was completed with Gensel software parameterized to fit fewer markers than animals. Genomic windows containing all the SNP loci in each 1 Mb that accounted for more than 1.0% of genetic variance were considered as QTL region. Candidate genes within windows that explained more than 1% of genetic variance were selected by putative function based on DAVID and Gene Ontology.

Results

Thirty-six QTL (1-Mb SNP window) were identified on chromosomes 1, 2, 3, 5, 6, 7, 8, 9, 10, 12, 14, 15, 16, 18, 19, 20, 21, 22, 24, 25 and 26 (UMD 3.1). The amount of genetic variance explained by individual QTL windows for feed efficiency traits ranged from 0.5% to 9.07%. Some of these QTL minimally overlapped with previously reported feed efficiency QTL for Bos taurus. The QTL regions described in this study harbor genes with biological functions related to metabolic processes, lipid and protein metabolism, generation of energy and growth. Among the positional candidate genes selected for feed efficiency are: HRH4, ALDH7A1, APOA2, LIN7C, CXADR, ADAM12 and MAP7.

Conclusions

Some genomic regions and some positional candidate genes reported in this study have not been previously reported for feed efficiency traits in Bos indicus. Comparison with published results indicates that different QTLs and genes may be involved in the control of feed efficiency traits in this Nelore cattle population, as compared to Bos taurus cattle.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-014-0100-0) contains supplementary material, which is available to authorized users.

Genomewide association and identification of candidate genes for ovulation rate in swine

Reproductive efficiency has a great impact on the economic success of pork production. Ovulation rate is an early component of reproduction efficiency and contributes to the number of pigs born in a litter. To better understand the underlying genetics of ovulation rate, a genomewide association study was undertaken. Samples of DNA were collected and tested using the Illumina Porcine SNP60 BeadChip from 1,180 females with ovulation measurements ranging from never farrowed to measurements taken after parity 2. A total of 41,848 SNP were tested using the Bayes C option of GenSel. After the Bayes C analysis, SNP were assigned to sliding windows of 5 consecutive SNP by chromosome-position order beginning with the first 5 SNP on SSC1 and ending with the last 5 SNP on SSCX. The 5-SNP windows were analyzed using the Predict option of GenSel. From the Predict analysis, putative QTL were selected having no overlap with other 5-SNP window groups, no overlap across chromosomes, and the highest genetic variation. These putative QTL were submitted to statistical testing using the bootstrap option of GenSel. Of the putative QTL tested, 80 were found to be statistically significant (P < 0.01). Ten QTL were found on SSC1, 12 on SSC2, 4 on SSC3, 8 on SSC4, 3 on SSC5, 3 on SSC6, 3 on SSC7, 4 on SSC8, 2 on SSC9, 4 on SSC10, 1 on SSC12, 4 on SSC13, 2 on SSC14, 4 on SSC15, 4 on SSC16, 6 on SSC17, 4 on SSC18, and 1 on SSCX. Sixteen QTL were found to be statistically significant at the P < 0.001 level. Six additional QTL were significant at the P = 0.001 level. These 22 QTL accounted for 71.10% of the total genetic variance. The most compelling candidate genes in these regions include Estrogen receptor 1, growth differentiation factor 9, and inhibin βA. These QTL, when combined with information on genes found in the same regions, should provide useful information that could be used for marker assisted selection, marker assisted management, or genomic selection applications in commercial pig populations.

High-resolution association mapping of number of teats in pigs reveals regions controlling vertebral development

BACKGROUND

Selection pressure on the number of teats has been applied to be able to provide enough teats for the increase in litter size in pigs. Although many QTL were reported, they cover large chromosomal regions and the functional mutations and their underlying biological mechanisms have not yet been identified. To gain a better insight in the genetic architecture of the trait number of teats, we performed a genome-wide association study by genotyping 936 Large White pigs using the Illumina PorcineSNP60 Beadchip. The analysis is based on deregressed breeding values to account for the dense family structure and a Bayesian approach for estimation of the SNP effects.

RESULTS

The genome-wide association study resulted in 212 significant SNPs. In total, 39 QTL regions were defined including 170 SNPs on 13 Sus scrofa chromosomes (SSC) of which 5 regions on SSC7, 9, 10, 12 and 14 were highly significant. All significantly associated regions together explain 9.5% of the genetic variance where a QTL on SSC7 explains the most genetic variance (2.5%). For the five highly significant QTL regions, a search for candidate genes was performed. The most convincing candidate genes were VRTN and Prox2 on SSC7, MPP7, ARMC4, and MKX on SSC10, and vertebrae δ-EF1 on SSC12. All three QTL contain candidate genes which are known to be associated with vertebral development. In the new QTL regions on SSC9 and SSC14, no obvious candidate genes were identified.

CONCLUSIONS

Five major QTL were found at high resolution on SSC7, 9, 10, 12, and 14 of which the QTL on SSC9 and SSC14 are the first ones to be reported on these chromosomes. The significant SNPs found in this study could be used in selection to increase number of teats in pigs, so that the increasing number of live-born piglets can be nursed by the sow. This study points to common genetic mechanisms regulating number of vertebrae and number of teats.

A genome-wide linkage analysis for reproductive traits in F2 Large White × Meishan cross gilts

Female reproductive performance traits in pigs have low heritabilities thus limiting improvement through traditional selective breeding programmes. However, there is substantial genetic variation found between pig breeds with the Chinese Meishan being one of the most prolific pig breeds known. In this study, three cohorts of Large White × Meishan F2 cross-bred pigs were analysed to identify quantitative trait loci (QTL) with effects on reproductive traits, including ovulation rate, teat number, litter size, total born alive and prenatal survival. A total of 307 individuals were genotyped for 174 genetic markers across the genome. The genome-wide analysis of the trait-recorded F2 gilts in their first parity/litter revealed one QTL for teat number significant at the genome level and a total of 12 QTL, which are significant at the chromosome-wide level, for: litter size (three QTL), total born alive (two QTL), ovulation rate (four QTL), prenatal survival (one QTL) and teat number (two QTL). Further support for eight of these QTL is provided by results from other studies. Four of these 12 QTL were mapped for the first time in this study: on SSC15 for ovulation rate and on SSC18 for teat number, ovulation rate and litter size.

Genome-wide association study for intramuscular fat deposition and composition in Nellore cattle

Background

Meat from Bos taurus and Bos indicus breeds are an important source of nutrients for humans and intramuscular fat (IMF) influences its flavor, nutritional value and impacts human health. Human consumption of fat that contains high levels of monounsaturated fatty acids (MUFA) can reduce the concentration of undesirable cholesterol (LDL) in circulating blood. Different feeding practices and genetic variation within and between breeds influences the amount of IMF and fatty acid (FA) composition in meat. However, it is difficult and costly to determine fatty acid composition, which has precluded beef cattle breeding programs from selecting for a healthier fatty acid profile. In this study, we employed a high-density single nucleotide polymorphism (SNP) chip to genotype 386 Nellore steers, a Bos indicus breed and, a Bayesian approach to identify genomic regions and putative candidate genes that could be involved with deposition and composition of IMF.

Results

Twenty-three genomic regions (1-Mb SNP windows) associated with IMF deposition and FA composition that each explain ≥ 1% of the genetic variance were identified on chromosomes 2, 3, 6, 7, 8, 9, 10, 11, 12, 17, 26 and 27. Many of these regions were not previously detected in other breeds. The genes present in these regions were identified and some can help explain the genetic basis of deposition and composition of fat in cattle.

Conclusions

The genomic regions and genes identified contribute to a better understanding of the genetic control of fatty acid deposition and can lead to DNA-based selection strategies to improve meat quality for human consumption.

A genome-wide association study of production traits in a commercial population of Large White pigs: evidence of haplotypes affecting meat quality

BACKGROUND

Numerous quantitative trait loci (QTL) have been detected in pigs over the past 20 years using microsatellite markers. However, due to the low density of these markers, the accuracy of QTL location has generally been poor. Since 2009, the dense genome coverage provided by the Illumina PorcineSNP60 BeadChip has made it possible to more accurately map QTL using genome-wide association studies (GWAS). Our objective was to perform high-density GWAS in order to identify genomic regions and corresponding haplotypes associated with production traits in a French Large White population of pigs.

METHODS

Animals (385 Large White pigs from 106 sires) were genotyped using the PorcineSNP60 BeadChip and evaluated for 19 traits related to feed intake, growth, carcass composition and meat quality. Of the 64,432 SNPs on the chip, 44,412 were used for GWAS with an animal mixed model that included a regression coefficient for the tested SNPs and a genomic kinship matrix. SNP haplotype effects in QTL regions were then tested for association with phenotypes following phase reconstruction based on the Sscrofa10.2 pig genome assembly.

RESULTS

Twenty-three QTL regions were identified on autosomes and their effects ranged from 0.25 to 0.75 phenotypic standard deviation units for feed intake and feed efficiency (four QTL), carcass (12 QTL) and meat quality traits (seven QTL). The 10 most significant QTL regions had effects on carcass (chromosomes 7, 10, 16, 17 and 18) and meat quality traits (two regions on chromosome 1 and one region on chromosomes 8, 9 and 13). Thirteen of the 23 QTL regions had not been previously described. A haplotype block of 183 kb on chromosome 1 (six SNPs) was identified and displayed three distinct haplotypes with significant (0.0001 < P < 0.03) associations with all evaluated meat quality traits.

CONCLUSIONS

GWAS analyses with the PorcineSNP60 BeadChip enabled the detection of 23 QTL regions that affect feed consumption, carcass and meat quality traits in a LW population, of which 13 were novel QTL. The proportionally larger number of QTL found for meat quality traits suggests a specific opportunity for improving these traits in the pig by genomic selection.

Identification of species-specific novel transcripts in pig reproductive tissues using RNA-seq

Although structural properties of the porcine reproductive system are shared by many placental mammals, some combination of these properties is unique to pigs. To explore whether genomic elements specific to pigs could potentially underlie this uniqueness, we made the first step to identify novel transcripts in two representative pig reproductive tissues by the technique of massively parallel sequencing. To automate the whole process, we built a computational pipeline, which can also be easily extended for similar studies in other species. In total, 5516 and 9061 novel transcripts were found, and 159 and 252 novel transcripts appear to be specific to pigs for the placenta and testis respectively. Furthermore, these novel transcripts were found to be enriched in quantitative trait loci (QTL) regions for reproduction traits in pigs. We validated eight of these novel transcripts by quantitative real-time PCR. With respect to their genomic organization and their functional relationship to reproduction, these transcripts need to be further validated and explored in various pig breeds to better comprehend the relevant aspects of pig physiology that contribute to reproductive performance.

Whole Genome Association Studies of Residual Feed Intake and Related Traits in the Pig

BACKGROUND

Residual feed intake (RFI), a measure of feed efficiency, is the difference between observed feed intake and the expected feed requirement predicted from growth and maintenance. Pigs with low RFI have reduced feed costs without compromising their growth. Identification of genes or genetic markers associated with RFI will be useful for marker-assisted selection at an early age of animals with improved feed efficiency.

METHODOLOGY/PRINCIPAL FINDINGS

Whole genome association studies (WGAS) for RFI, average daily feed intake (ADFI), average daily gain (ADG), back fat (BF) and loin muscle area (LMA) were performed on 1,400 pigs from the divergently selected ISU-RFI lines, using the Illumina PorcineSNP60 BeadChip. Various statistical methods were applied to find SNPs and genomic regions associated with the traits, including a Bayesian approach using GenSel software, and frequentist approaches such as allele frequency differences between lines, single SNP and haplotype analyses using PLINK software. Single SNP and haplotype analyses showed no significant associations (except for LMA) after genomic control and FDR. Bayesian analyses found at least 2 associations for each trait at a false positive probability of 0.5. At generation 8, the RFI selection lines mainly differed in allele frequencies for SNPs near (<0.05 Mb) genes that regulate insulin release and leptin functions. The Bayesian approach identified associations of genomic regions containing insulin release genes (e.g., GLP1R, CDKAL, SGMS1) with RFI and ADFI, of regions with energy homeostasis (e.g., MC4R, PGM1, GPR81) and muscle growth related genes (e.g., TGFB1) with ADG, and of fat metabolism genes (e.g., ACOXL, AEBP1) with BF. Specifically, a very highly significantly associated QTL for LMA on SSC7 with skeletal myogenesis genes (e.g., KLHL31) was identified for subsequent fine mapping.

CONCLUSIONS/SIGNIFICANCE

Important genomic regions associated with RFI related traits were identified for future validation studies prior to their incorporation in marker-assisted selection programs.

QTL fine mapping with Bayes C(π): a simulation study

BACKGROUND

Accurate QTL mapping is a prerequisite in the search for causative mutations. Bayesian genomic selection models that analyse many markers simultaneously should provide more accurate QTL detection results than single-marker models. Our objectives were to (a) evaluate by simulation the influence of heritability, number of QTL and number of records on the accuracy of QTL mapping with Bayes Cπ and Bayes C; (b) estimate the QTL status (homozygous vs. heterozygous) of the individuals analysed. This study focussed on the ten largest detected QTL, assuming they are candidates for further characterization.

METHODS

Our simulations were based on a true dairy cattle population genotyped for 38,277 phased markers. Some of these markers were considered biallelic QTL and used to generate corresponding phenotypes. Different numbers of records (4387 and 1500), heritability values (0.1, 0.4 and 0.7) and numbers of QTL (10, 100 and 1000) were studied. QTL detection was based on the posterior inclusion probability for individual markers, or on the sum of the posterior inclusion probabilities for consecutive markers, estimated using Bayes C or Bayes Cπ. The QTL status of the individuals was derived from the contrast between the sums of the SNP allelic effects of their chromosomal segments.

RESULTS

The proportion of markers with null effect (π) frequently did not reach convergence, leading to poor results for Bayes Cπ in QTL detection. Fixing π led to better results. Detection of the largest QTL was most accurate for medium to high heritability, for low to moderate numbers of QTL, and with a large number of records. The QTL status was accurately inferred when the distribution of the contrast between chromosomal segment effects was bimodal.

CONCLUSIONS

QTL detection is feasible with Bayes C. For QTL detection, it is recommended to use a large dataset and to focus on highly heritable traits and on the largest QTL. QTL statuses were inferred based on the distribution of the contrast between chromosomal segment effects.

Associations of TCF12, CTNNAL1 and WNT10B gene polymorphisms with litter size in pigs

In previous research, several WNT signaling pathway genes including transcription factor 12 (TCF12), catenin alpha-like protein 1 (CTNNAL1) and wingless-type MMTV integration site family, member 10B (WNT10B) were differentially expressed in PMSG-hCG stimulated preovulatory ovarian follicles of Large White and Chinese Taihu sows. In the present research, these three genes were selected as the candidate genes for litter size traits in pigs. Four mutations (TCF12 c.-201+65 G>A, TCF12 c.-200-300 G>A, CTNNAL1 c.1878 G>C and WNT10B c.*12 C>T) were detected in eleven pig populations, and results indicated CTNNAL1 c.1878 G and WNT10B c.*12 C were the major alleles in all tested pig populations, while TCF12 c.-201+65 A and TCF12 c.-200-300 A were the major alleles in several Chinese native pig breeds. Association analysis of four mutations with litter size in Large White and DIV pigs showed that both the signficant differences of total number born (TNB) and number born alive (NBA) among three genotypes and the significance of additive effects appeared at TCF12 c.-200-300 G>A and CTNNAL1 c.1878 G>C loci, suggesting these two mutations might be reliable markers for pig selection and breeding.

Heritability and Bayesian genome-wide association study of first service conception and pregnancy in Brangus heifers

Brangus [3/8 Brahman (Bos indicus) × 5/8 Angus (Bos taurus); n ≈ 800] heifers from 67 sires were used to estimate heritability and conduct a genome-wide association study (GWAS) for 2 binary fertility traits: first service conception (FSC) and heifer pregnancy (HPG). Genotypes were from 53,692 loci on the BovineSNP50 (Infinium Bead Chips, Illumina, San Diego, CA). Yearling heifers were estrous synchronized, bred by AI, and then exposed to natural service breeding. Reproductive ultrasound and DNA-based parentage testing were used to determine if the heifer conceived by AI or natural service, and code for FSC and HPG traits. Success rates for FSC and HPG were 53.3% and 78.0% ± 0.01%, and corresponding heritability estimates were 0.18 ± 0.07 and 0.10 ± 0.06, respectively. The models used in obtaining these heritability estimates and GWAS included fixed effects of year (i.e., 2005 to 2007), birth location, calving season, age of dam, and contemporary group. In GWAS, simultaneous associations of 1 Mb SNP windows with phenotype were undertaken with Bayes C analyses using GenSel software. The 1 Mb windows contained 21.3 ± 1.1 SNP. Analyses fitted a mixture model that treated SNP effects as random, with an assumed fraction pi = 0.9995 having no effect on phenotype. The windows that accounted for 1.0% of genetic variance were considered as QTL associated with FSC or HPG. Eighteen QTL existed on 15 chromosomes for the 2 traits. On average, each QTL accounted for 2.43% ± 0.2% of the genetic variance. Chromosome 8 harbored 2 QTL for FSC and 1 for HPG; however, these regions did not overlap. Chromosomes 3, 15, 16, 19, 24, 26, 27, 29, and X included QTL only for FSC, whereas chromosomes 2, 4, 10, 13, and 20 contained QTL only for HPG. The multitude of QTL detected for FSC and HPG in this GWAS involving Brangus heifers exemplifies the complex regulation of variation in heifer fertility traits of low heritability.

[Genome-wide association study of total number born and number born alive in pigs using both compressed mixed linear model and Bayes model]

GWAS (Genome-wide association study) strategy has been extensively used for identification of economical trait loci in livestock animals. Using Illumina's PorcineSNP60 BeadChip, a GWA study of 820 commercial pigs with reproductive traits recorded was performed. The PCA analysis showed that there was no significant population stratification. Two different statistical models Compressed Mixed Linear Model(GAPIT program package)and Bayes CPi (GenSel software) were used to implement GWAS on total number born and number born alive of the first and second parity. To compare the most significant 50 SNPs from each method, a total of 31 and 20 coincided SNPs for total number born in the first parity were identified, and there were 20 coincided SNPs for number born alive in the first parityinthe results of bothmethods. The most significant SNPs were also significant in the results of the other method. The most significantly associated regions for total number born in the first parity were located on SSC1, 2, 3, 7, 13, 16, and 18. The most significantly associated regions for number born alive in the first parity were locatedon SSC1, 3, 4, 13, and 16. There were 5 common regions significantly associated with bothtraits on SSC1, 3, 13, and 16. The most significantly associated regions forbothtotal number born and number born alive for the second parity were mainly located on six common regions on SSC7, 10, 12, 13, 14, and 16.