Identification of Carcass and Meat Quality QTL in an F(2) Duroc × Pietrain Pig Resource Population Using Different Least-Squares Analysis Models

MOBILE pipeline enables identification of context-specific networks and regulatory mechanisms

Robust identification of context-specific network features that control cellular phenotypes remains a challenge. We here introduce MOBILE (Multi-Omics Binary Integration via Lasso Ensembles) to nominate molecular features associated with cellular phenotypes and pathways. First, we use MOBILE to nominate mechanisms of interferon-γ (IFNγ) regulated PD-L1 expression. Our analyses suggest that IFNγ-controlled PD-L1 expression involves BST2, CLIC2, FAM83D, ACSL5, and HIST2H2AA3 genes, which were supported by prior literature. We also compare networks activated by related family members transforming growth factor-beta 1 (TGFβ1) and bone morphogenetic protein 2 (BMP2) and find that differences in ligand-induced changes in cell size and clustering properties are related to differences in laminin/collagen pathway activity. Finally, we demonstrate the broad applicability and adaptability of MOBILE by analyzing publicly available molecular datasets to investigate breast cancer subtype specific networks. Given the ever-growing availability of multi-omics datasets, we envision that MOBILE will be broadly useful for identification of context-specific molecular features and pathways.

Integration of multi-omics data reveals cis-regulatory variants that are associated with phenotypic differentiation of eastern from western pigs

Background

The genetic mechanisms that underlie phenotypic differentiation in breeding animals have important implications in evolutionary biology and agriculture. However, the contribution of cis-regulatory variants to pig phenotypes is poorly understood. Therefore, our aim was to elucidate the molecular mechanisms by which non-coding variants cause phenotypic differences in pigs by combining evolutionary biology analyses and functional genomics.

Results

We obtained a high-resolution phased chromosome-scale reference genome with a contig N50 of 18.03 Mb for the Luchuan pig breed (a representative eastern breed) and profiled potential selective sweeps in eastern and western pigs by resequencing the genomes of 234 pigs. Multi-tissue transcriptome and chromatin accessibility analyses of these regions suggest that tissue-specific selection pressure is mediated by promoters and distal cis-regulatory elements. Promoter variants that are associated with increased expression of the lysozyme (LYZ) gene in the small intestine might enhance the immunity of the gastrointestinal tract and roughage tolerance in pigs. In skeletal muscle, an enhancer-modulating single-nucleotide polymorphism that is associated with up-regulation of the expression of the troponin C1, slow skeletal and cardiac type (TNNC1) gene might increase the proportion of slow muscle fibers and affect meat quality.

Conclusions

Our work sheds light on the molecular mechanisms by which non-coding variants shape phenotypic differences in pigs and provides valuable resources and novel perspectives to dissect the role of gene regulatory evolution in animal domestication and breeding.

Identification and validation of a regulatory mutation upstream of the BMP2 gene associated with carcass length in pigs

Background

Carcass length is very important for body size and meat production for swine, thus understanding the genetic mechanisms that underly this trait is of great significance in genetic improvement programs for pigs. Although many quantitative trait loci (QTL) have been detected in pigs, very few have been fine-mapped to the level of the causal mutations. The aim of this study was to identify potential causal single nucleotide polymorphisms (SNPs) for carcass length by integrating a genome-wide association study (GWAS) and functional assays.

Results

Here, we present a GWAS in a commercial Duroc × (Landrace × Yorkshire) (DLY) population that reveals a prominent association signal (P = 4.49E−07) on pig chromosome 17 for carcass length, which was further validated in two other DLY populations. Within the detected 1 Mb region, the BMP2 gene stood out as the most likely causal candidate because of its functions in bone growth and development. Whole-genome gene expression studies showed that the BMP2 gene was differentially expressed in the cartilage tissues of pigs with extreme carcass length. Then, we genotyped an additional 267 SNPs in 500 selected DLY pigs, followed by further whole-genome SNP imputation, combined with deep genome resequencing data on multiple pig breeds. Reassociation analyses using genotyped and imputed SNP data revealed that the rs320706814 SNP, located approximately 123 kb upstream of the BMP2 gene, was the strongest candidate causal mutation, with a large association with carcass length, with a ~ 4.2 cm difference in length across all three DLY populations (N = 1501; P = 3.66E−29). This SNP segregated in all parental lines of the DLY (Duroc, Large White and Landrace) and was also associated with a significant effect on body length in 299 pure Yorkshire pigs (P = 9.2E−4), which indicates that it has a major value for commercial breeding. Functional assays showed that this SNP is likely located within an enhancer and may affect the binding affinity of transcription factors, thereby regulating BMP2 gene expression.

Conclusions

Taken together, these results suggest that the rs320706814 SNP on pig chromosome 17 is a putative causal mutation for carcass length in the widely used DLY pigs and has great value in breeding for body size in pigs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-021-00689-0.

Comparative Analyses of Sperm DNA Methylomes Among Three Commercial Pig Breeds Reveal Vital Hypomethylated Regions Associated With Spermatogenesis and Embryonic Development

Identifying epigenetic changes is essential for an in-depth understanding of phenotypic diversity and pigs as the human medical model for anatomizing complex diseases. Abnormal sperm DNA methylation can lead to male infertility, fetal development failure, and affect the phenotypic traits of offspring. However, the whole genome epigenome map in pig sperm is lacking to date. In this study, we profiled methylation levels of cytosine in three commercial pig breeds, Landrace, Duroc, and Large White using whole-genome bisulfite sequencing (WGBS). The results showed that the correlation of methylation levels between Landrace and Large White pigs was higher. We found that 1,040-1,666 breed-specific hypomethylated regions (HMRs) were associated with embryonic developmental and economically complex traits for each breed. By integrating reduced representation bisulfite sequencing (RRBS) public data of pig testis, 1743 conservated HMRs between sperm and testis were defined, which may play a role in spermatogenesis. In addition, we found that the DNA methylation patterns of human and pig sperm showed high similarity by integrating public data from WGBS and chromatin immunoprecipitation sequencing (ChIP-seq) in other mammals, such as human and mouse. We identified 2,733 conserved HMRs between human and pig involved in organ development and brain-related traits, such as NLGN1 (neuroligin 1) containing a conserved-HMR between human and pig. Our results revealed the similarities and diversity of sperm methylation patterns among three commercial pig breeds and between human and pig. These findings are beneficial for elucidating the mechanism of male fertility, and the changes in commercial traits that undergo strong selection.

Single-Step Genome Wide Association Study Identifies QTL Signals for Untrimmed and Trimmed Thigh Weight in Italian Crossbred Pigs for Dry-Cured Ham Production

Simple Summary

Along with the traditional traits, swine breeding programs for Italian dry-cured ham production have recently aimed to include novel phenotypes. The identification of the genomic regions underlying such new traits helps to untangle their genetic architecture and may provide useful information to be integrated in genetic selection. With this aim, we estimated genetic parameters and conducted a single step genome wide association studies (GWAS) on untrimmed and trimmed thigh weight considering two pig crossbred lines approved for Italian Protected Designation of Origin ham production. Quantitative trait loci (QTLs) were characterized based on the variance of 10-SNP sliding windows genomic estimated breeding values. In particular, we identified interesting QTL signals on several chromosomes, notably on chromosome 4, 6, 7 and 15. A high heritability and genetic correlation were observed for the two traits under investigation and although independent studies including other pig populations are required to disentangle the possible effects of specific linkage disequilibrium in our population, our findings suggest that such QTL could be investigated in future pig breeding programs to improve the reliability of genomic estimated breeding values for the dry-cured ham production.

Abstract

Protected Designation of Origin (PDO) dry-cured ham is the most important product in the Italian pig breeding industry, mainly oriented to produce heavy pig carcasses to obtain hams of the right weight and maturity. Recently, along with the traditional traits swine breeding programs have aimed to include novel carcass traits. The identification at the genome level of quantitative trait loci (QTLs) affecting such new traits helps to reveal their genetic determinism and may provide information to be integrated in prediction models in order to improve prediction accuracy as well as to identify candidate genes underlying such traits. This study aimed to estimate genetic parameters and perform a single step genome wide association studies (ssGWAS) on novel carcass traits such as untrimmed (UTW) and trimmed thigh weight (TTW) in two pig crossbred lines approved for the ham production of the Italian PDO. With this purpose, phenotypes were collected from ~1800 animals and 240 pigs were genotyped with Illumina PorcineSNP60 Beadchip. The single-step genomic BLUP procedure was used for the heritability estimation and to implement the ssGWAS. QTL were characterized based on the variance of 10-SNP sliding window genomic estimated breeding values. Moderate heritabilities were detected and QTL signals were identified on chromosome 1, 4, 6, 7, 11 and 15 for both traits. As expected, the genetic correlation among the two traits was very high (~0.99). The QTL regions encompassed a total of 249 unique candidate genes, some of which were already reported in association with growth, carcass or ham weight traits in pigs. Although independent studies are required to further verify our findings and disentangle the possible effects of specific linkage disequilibrium in our population, our results support the potential use of such new QTL information in future breeding programs to improve the reliability of genomic prediction.

Weighted Single-Step GWAS Identified Candidate Genes Associated with Growth Traits in a Duroc Pig Population

Growth traits are important economic traits of pigs that are controlled by several major genes and multiple minor genes. To better understand the genetic architecture of growth traits, we performed a weighted single-step genome-wide association study (wssGWAS) to identify genomic regions and candidate genes that are associated with days to 100 kg (AGE), average daily gain (ADG), backfat thickness (BF) and lean meat percentage (LMP) in a Duroc pig population. In this study, 3945 individuals with phenotypic and genealogical information, of which 2084 pigs were genotyped with a 50 K single-nucleotide polymorphism (SNP) array, were used for association analyses. We found that the most significant regions explained 2.56–3.07% of genetic variance for four traits, and the detected significant regions (>1%) explained 17.07%, 18.59%, 23.87% and 21.94% for four traits. Finally, 21 genes that have been reported to be associated with metabolism, bone growth, and fat deposition were treated as candidate genes for growth traits in pigs. Moreover, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses implied that the identified genes took part in bone formation, the immune system, and digestion. In conclusion, such full use of phenotypic, genotypic, and genealogical information will accelerate the genetic improvement of growth traits in pigs.

Genome-Wide Association Analysis Identified BMPR1A as a Novel Candidate Gene Affecting the Number of Thoracic Vertebrae in a Large White × Minzhu Intercross Pig Population

Simple Summary

The number of thoracic vertebrae (NTV) and number of vertebrae (NV) varies among pig breeds with a high correlation of about 0.8. It is important to discover variants associated with the NTV by considering the effect of the NV in pig. The results suggest that regulation variants on SSC7 might play crucial roles in the NTV and the FOS on SSC7 should be further studied as a critical candidate gene. In addition, BMPR1A was identified as a novel candidate gene affecting the NTV in pigs.

Abstract

The number of vertebrae (NV), especially the number of thoracic vertebrae (NTV), varies among pig breeds. The NTV is controlled by vertebral segmentation and the number of somites during embryonic development. Although there is a high correlation between the NTV and NV, studies on a fixed NV have mainly considered the absolute numbers of thoracic vertebrae instead of vertebral segmentation. Therefore, this study aimed to discover variants associated with the NTV by considering the effect of the NV in pigs. The NTV and NV of 542 F2 individuals from a Large White × Minzhu pig crossbreed were recorded. All animals were genotyped for VRTN g.19034 A > C, LTBP2 c.4481A > C, and 37 missense or splice variants previously reported in a 951-kb interval on SSC7 and 147 single nucleotide polymorphisms (SNPs) on SSC14. To identify NTV-associated SNPs, we firstly performed a genome-wide association study (GWAS) using the Q + K (population structure + kinship matrix) model in TASSEL. With the NV as a covariate, the obtained data were used to identify the SNPs with the most significant genome-wide association with the NTV by performing a GWAS on a PorcineSNP60K Genotyping BeadChip. Finally, a conditional GWAS was performed by fixing this SNP. The GWAS showed that 31 SNPs on SSC7 have significant genome-wide associations with the NTV. No missense or splice variants were found to be associated with the NTV significantly. A linkage disequilibrium analysis suggested the existence of quantitative trait loci (QTL) in a 479-Kb region on SSC7, which contained a critical candidate gene FOS for the NTV in pigs. Subsequently, a conditional GWAS was performed by fixing M1GA0010658, the most significant of these SNPs. Two SNPs in BMPR1A were found to have significant genome-wide associations and a significant dominant effect. The leading SNP, S14_87859370, accounted for 3.86% of the phenotypic variance. Our study uncovered that regulation variants in FOS on SSC7 and in BMPR1A on SSC14 might play important roles in controlling the NTV, and thus these genetic factors may be harnessed for increasing the NTV in pigs.

Haplotype Block Analysis Reveals Candidate Genes and QTLs for Meat Quality and Disease Resistance in Chinese Jiangquhai Pig Breed

The Jiangquhai (JQ) pig breed is one of the most widely recognized pig populations in China due to its unique and dominant characteristics. In this study, we examined the extent of Linkage disequilibrium (LD) and haplotype block structure of the JQ pig breed, and scanned the blocks for possible genes underlying important QTLs that could either be responsible for some adaptive features in these pigs or might have undergone some selection pressure. We compared some of our results with other Chinese and Western pig breeds. The results show that the JQ breed had the highest total block length (349.73 Mb ≈ 15% of its genome), and the coverage rate of blocks in most of its chromosomes was larger than those of other breeds except for Sus scrofa chromosome 4 (SSC4), SSC6, SSC7, SSC8, SSC10, SSC12, SSC13, SSC14, SSC17, SSC18, and SSCX. Moreover, the JQ breed had more SNPs that were clustered into haplotype blocks than the other breeds examined in this study. Our shared and unique haplotype block analysis revealed that the Hongdenglong (HD) breed had the lowest percentage of shared haplotype blocks while the Shanzhu (SZ) breed had the highest. We found that the JQ breed had an average r² > 0.2 at SNPs distances 10–20 kb and concluded that about 120,000–240,000 SNPs would be needed for a successful GWAS in the breed. Finally, we detected a total of 88 genes harbored by selected haplotype blocks in the JQ breed, of which only 4 were significantly enriched (p-value ≤ 0.05). These genes were significantly enriched in 2 GO terms (p-value < 0.01), and 2 KEGG pathways (p-value < 0.02). Most of these enriched genes were related to health. Also, most of the overlapping QTLs detected in the haplotype blocks were related to meat and carcass quality, as well as health, with a few of them relating to reproduction and production. These results provide insights into the genetic architecture of some adaptive and meat quality traits observed in the JQ pig breed and also revealed the pattern of LD in the genome of the pig. Our result provides significant guidance for improving the statistical power of GWAS and optimizing the conservation strategy for this JQ pig breed.

Survey of SNPs Associated with Total Number Born and Total Number Born Alive in Pig

Reproductive productivity depend on a complex set of characteristics. The number of piglets at birth (Total number born, Litter size, TNB) and the number of alive piglets at birth (Total number born alive, NBA) are the main indicators of the reproductive productivity of sows in pig breeding. Great hopes are pinned on GWAS (Genome-Wide Association Studies) to solve the problems associated with studying the genetic architecture of reproductive traits of pigs. This paper provides an overview of international studies on SNP (Single nucleotide polymorphism) associated with TNB and NBA in pigs presented in PigQTLdb as "Genome map association". Currently on the base of Genome map association results 306 SNPs associated with TNB (218 SNPs) and NBA (88 SNPs) have been identified and presented in the Pig QTLdb database. The results are based on research of pigs such as Large White, Yorkshire, Landrace, Berkshire, Duroc and Erhualian. The presented review shows that most SNPs found in chromosome areas where candidate genes or QTLs (Quantitative trait locus) have been identified. Further research in the given direction will allow to obtain new data that will become an impulse for creating breakthrough breeding technologies and increase the production efficiency in pig farming.

The Genetics and Epigenetics of Sex Change in Fish

Fish show extraordinary sexual plasticity, changing sex naturally as part of their life cycle or reversing sex because of environmental stressors. This plasticity shows that sexual fate is not an irreversible process but the result of an ongoing tug-of-war for supremacy between male and female signaling networks. The behavioral, gonadal, and morphological changes involved in this process are well described, yet the molecular events that underpin those changes remain poorly understood. Epigenetic modifications emerge as a critical link between environmental stimuli, the onset of sex change, and subsequent maintenance of sexual phenotype. Here we synthesize current knowledge of sex change, focusing on the genetic and epigenetic processes that are likely involved in the initiation and regulation of sex change. We anticipate that better understanding of sex change in fish will shed new light on sex determination and development in vertebrates and on how environmental perturbations affect sexual fate.

Meta-analysis of genome-wide association studies for loin muscle area and loin muscle depth in two Duroc pig populations

Loin muscle area (LMA) and loin muscle depth (LMD) are important traits influencing the production performance of breeding pigs. However, the genetic architecture of these two traits is still poorly understood. To discern the genetic architecture of LMA and LMD, a material consisting of 6043 Duroc pigs belonging to two populations with different genetic backgrounds was collected and applied in genome-wide association studies (GWAS) with a genome-wide distributed panel of 50K single nucleotide polymorphisms (SNPs). To improve the power of detection for common SNPs, we conducted a meta-analysis in these two pig populations and uncovered additional significant SNPs. As a result, we identified 75 significant SNPs for LMA and LMD on SSC6, 7, 12, 16, and 18. Among them, 25 common SNPs were associated with LMA and LMD. One pleiotropic quantitative trait locus (QTL), which was located on SSC7 with a 283 kb interval, was identified to affect LMA and LMD. Marker ALGA0040260 is a key SNP for this QTL, explained 1.77% and 2.48% of the phenotypic variance for LMA and LMD, respectively. Another genetic region on SSC16 (709 kb) was detected and displayed prominent association with LMA and the peak SNP, WU_10.2_16_35829257, contributed 1.83% of the phenotypic variance for LMA. Further bioinformatics analysis determined eight promising candidate genes (GCLC, GPX8, DAXX, FGF21, TAF11, SPDEF, NUDT3, and PACSIN1) with functions in glutathione metabolism, adipose and muscle tissues development and lipid metabolism. This study provides the first GWAS for the LMA and LMD of Duroc breed to analyze the underlying genetic variants through a large sample size. The findings further advance our understanding and help elucidate the genetic architecture of LMA, LMD and growth-related traits in pigs.

Assessment of Autozygosity Derived From Runs of Homozygosity in Jinhua Pigs Disclosed by Sequencing Data

Jinhua pig, a well-known Chinese indigenous breed, has evolved as a pig breed with excellent meat quality, greater disease resistance, and higher prolificacy. The reduction in the number of Jinhua pigs over the past years has raised concerns about inbreeding. Runs of homozygosity (ROH) along the genome have been applied to quantify individual autozygosity to improve the understanding of inbreeding depression and identify genes associated with traits of interest. Here, we investigated the occurrence and distribution of ROH using next-generation sequencing data to characterize autozygosity in 202 Jinhua pigs, as well as to identify the genomic regions with high ROH frequencies within individuals. The average inbreeding coefficient, based on ROH longer than 1 Mb, was 0.168 ± 0.052. In total, 18,690 ROH were identified in all individuals, among which shorter segments (1-5 Mb) predominated. Individual ROH autosome coverage ranged from 5.32 to 29.14% in the Jinhua population. On average, approximately 16.8% of the whole genome was covered by ROH segments, with the lowest coverage on SSC11 and the highest coverage on SSC17. A total of 824 SNPs (about 0.5%) and 11 ROH island regions were identified (occurring in over 45% of the samples). Genes associated with reproduction (HOXA3, HOXA7, HOXA10, and HOXA11), meat quality (MYOD1, LPIN3, and CTNNBL1), appetite (NUCB2) and disease resistance traits (MUC4, MUC13, MUC20, LMLN, ITGB5, HEG1, SLC12A8, and MYLK) were identified in ROH islands. Moreover, several quantitative trait loci for ham weight and ham fat thickness were detected. Genes in ROH islands suggested, at least partially, a selection for economic traits and environmental adaptation, and should be subject of future investigation. These findings contribute to the understanding of the effects of environmental and artificial selection in shaping the distribution of functional variants in the pig genome.

A whole-genome sequence based association study on pork eating quality traits and cooking loss in a specially designed heterogeneous F6 pig population

To determine the genetic basis of pork eating quality traits and cooking loss, we herein performed a genome-wide association study (GWAS) for tenderness, juiciness, oiliness, umami, overall liking and cooking loss by using whole genome sequences of heterogeneous stock F6 pigs which were generated by crossing 4 typical western pig breeds (Duroc, Landrace, Large White and Pietrain) and 4 typical Asian pig breeds (Erhualian, Laiwu, Bamaxiang and Tibetan). We identified 50 associated loci (QTLs) and most of them are novel. Seven loci also showed pleiotropic associations with different traits. In addition, we identified multiple promising candidate genes for these traits, including PAK1 and AQP11 for cooking loss, EP300 for tenderness, SDK1 for juiciness, FITM2 and 5-linked MYH genes for oiliness, and TNNI2 and TNNT3 for overall liking. Our results provide not only a better understanding of the genetic basis for meat quality, but also a potential application in future breeding for these complex traits.

Linkage disequilibrium pattern and genome-wide association mapping for meat traits in multiple porcine F2 crosses

In the present study, data from four F2 crosses were analysed and used to study the linkage disequilibrium (LD) structure within and across the crosses. Genome-wide association analyses (GWASes) for conductivity and dressing out meat traits were conducted using single-marker and Bayesian multi-marker models using the pooled data from all F2 crosses. Porcine F2 crosses generated from the distantly related founder breeds Wild Boar, Piétrain and Meishan, as well as from a porcine F2 cross from the closely related founder breed Piétrain and an F1 Large White × Landrace cross were pooled. A total of 2572 F2 animals were genotyped using a 62K SNP chip. The positions of the SNPs were based on genome assembly Sscrofa11.1. After post-alignment and genotype filtering, approximately 50K SNPs were usable for LD studies and GWASes. The main findings of the present study are that the breakdown of LD was faster in crosses from closely related founder breeds compared to crosses from distantly related founders. The fastest breakdown of LD was observed by pooling the data. Based on the single-marker results and LD structure, clusters and windows were built for 1-Mb intervals. For conductivity and dressing out, 183 and 191 nominal significant associations respectively and six and five clusters respectively were found. Dominance was important for conductivity, and considering dominance in GWASes improved the mapping signals. Most clear signals were found for conductivity on SSC6, 8 and 15 and for dressing out on SSC2 and 7. Considering dominance might contribute to the accuracy of genomic selection and serve as a guide for choosing mating pairs with good combining abilities. However, further research is needed to investigate if dominance is also important in crossbreed pig breeding schemes.

Genome-wide association study in an F2 Duroc x Pietrain resource population for economically important meat quality and carcass traits

Meat quality is essential for consumer acceptance, it ultimately impacts pork production profitability and it is subject to genetic control. The objective of this study was to map genomic regions associated with economically important meat quality and carcass traits. We performed a genome-wide association (GWA) analysis to map regions associated with 38 meat quality and carcass traits recorded for 948 F2 pigs from the Michigan State University Duroc × Pietrain resource population. The F0, F1, and 336 F2 pigs were genotyped with the Illumina Porcine SNP60 BeadChip, while the remaining F2 pigs were genotyped with the GeneSeek Genomic Profiler for Porcine Low Desnisty (LD) chip, and imputed with high accuracy ( = 0.97). Altogether the genomic dataset comprised 1,019 animals and 44,911 SNP. A Gaussian linear mixed model was fitted to estimate the breeding values and the variance components. A linear transformation was performed to estimate the marker effects and variances. Type I error rate was controlled at a False Discovery Rate of 5%. Seven putative QTL found in this study were previously reported in other studies. Two novel QTL associated with tenderness (TEN) were located on SSC3 [135.6:137.5Mb; False Discovery rate (FDR) < 0.03] and SSC5 (67.3:69.1Mb; FDR < 0.02). The QTL region identified on SSC15 includes Protein Kinase AMP-activated ɣ 3-subunit gene (), which has been associated with 24-h pH (pH24), drip loss (DL) and cook yield (CY). Also, novel candidate genes were identified for TEN in the region on SSC5 [A Kinase (PRKA) Anchor Protein 3 (], and for tenth rib backfat thickness (BF10) [Carnitine O-Acetyltransferase ()] on SSC1. The association of gene polymorphisms with pork quality traits has been reported for several pig populations. However, there are no SNP for this gene on the chip used, thus we genotyped the animals for 2 non-synonymous variants ( and ). We then performed a GWA conditioning on the genotype of both SNP and was associated with pH24, DL, protein content (PRO) and CY ( < 0.004) and T30N with Juiciness, TEN, shear force, pH24, PRO, and CY < 0.04). Finally, we performed a GWA conditioning on the genotype of the SNP peak detected in this study, and T30N remained associated only with PRO ( < 0.02). Therefore, in this study we identified 2 novel QTL regions, suggest 2 novel candidate genes, and conclude that other SNP in PRKAG3 or nearby gene(s) explain the observed associations on SSC15 in this population.

Genomewide association of piglet responses to infection with one of two porcine reproductive and respiratory syndrome virus isolates

Porcine reproductive and respiratory syndrome (PRRS) is a devastating disease in the swine industry. Identification of host genetic factors that enable selection for improved performance during PRRS virus (PRRSV) infection would reduce the impact of this disease on animal welfare and production efficiency. We conducted genomewide association study (GWAS) analyses of data from 13 trials of approximately 200 commercial crossbred nursery-age piglets that were experimentally infected with 1 of 2 type 2 isolates of PRRSV (NVSL 97-7985 [NVSL] and KS2006-72109 [KS06]). Phenotypes analyzed were viral load (VL) in blood during the first 21 d after infection (dpi) and weight gain (WG) from 0 to 42 dpi. We accounted for the previously identified QTL in the region on SSC4 in our models to increase power to identify additional regions. Many regions identified by single-SNP analyses were not identified using Bayes-B, but both analyses identified the same regions on SSC3 and SSC5 to be associated with VL in the KS06 trials and on SSC6 in the NVSL trials ( < 5 × 10); for WG, regions on SSC5 and SSC17 were associated in the NVSL trials ( < 3 × 10). No regions were identified with either method for WG in the KS06 trials. Except for the region on SSC4, which was associated with VL for both isolates (but only with WG for NVSL), identified regions did not overlap between the 2 PRRSV isolate data sets, despite high estimates of the genetic correlation between isolates for traits based on these data. We also identified genomic regions whose associations with VL or WG interacted with either PRRSV isolate or with genotype at the SSC4 QTL. Gene ontology (GO) annotation terms for genes located near moderately associated SNP ( < 0.003) were enriched for multiple immunologically (VL) and metabolism- (WG) related GO terms. The biological relevance of these regions suggests that, although it may increase the number of false positives, the use of single-SNP analyses and a relaxed threshold also increased the identification of true positives. In conclusion, although only the SSC4 QTL was associated with response to both PRRSV isolates, genes near associated SNP were enriched for the same GO terms across PRRSV isolates, suggesting that host responses to these 2 isolates are affected by the actions of many genes that function together in similar biological processes.

Characterization and management of long runs of homozygosity in parental nucleus lines and their associated crossbred progeny

BACKGROUND

In nucleus populations, regions of the genome that have a high frequency of runs of homozygosity (ROH) occur and are associated with a reduction in genetic diversity, as well as adverse effects on fitness. It is currently unclear whether, and to what extent, ROH stretches persist in the crossbred genome and how genomic management in the nucleus population might impact low diversity regions and its implications on the crossbred genome.

METHODS

We calculated a ROH statistic based on lengths of 5 (ROH5) or 10 (ROH10) Mb across the genome for genotyped Landrace (LA), Large White (LW) and Duroc (DU) dams. We simulated crossbred dam (LA × LW) and market [DU × (LA × LW)] animal genotypes based on observed parental genotypes and the ROH frequency was tabulated. We conducted a simulation using observed genotypes to determine the impact of minimizing parental relationships on multiple diversity metrics within nucleus herds, i.e. pedigree-(A), SNP-by-SNP relationship matrix or ROH relationship matrix. Genome-wide metrics included, pedigree inbreeding, heterozygosity and proportion of the genome in ROH of at least 5 Mb. Lastly, the genome was split into bins of increasing ROH5 frequency and, within each bin, heterozygosity, ROH5 and length (Mb) of ROH were evaluated.

RESULTS

We detected regions showing high frequencies of either ROH5 and/or ROH10 across both LW and LA on SSC1, SSC4, and SSC14, and across all breeds on SSC9. Long haplotypes were shared across parental breeds and thus, regions of ROH persisted in crossbred animals. Averaged across replicates and breeds, progeny had higher levels of heterozygosity (0.0056 ± 0.002%) and lower proportion of the genome in a ROH of at least 5 Mb (-0.015 ± 0.003%) than their parental genomes when genomic relationships were constrained, while pedigree relationships resulted in negligible differences at the genomic level. Across all breeds, only genomic data was able to target low diversity regions.

CONCLUSIONS

We show that long stretches of ROH present in the parents persist in crossbred animals. Furthermore, compared to using pedigree relationships, using genomic information to constrain parental relationships resulted in maintaining more genetic diversity and more effectively targeted low diversity regions.

Taking Bioinformatics to Systems Medicine

Systems medicine promotes a range of approaches and strategies to study human health and disease at a systems level with the aim of improving the overall well-being of (healthy) individuals, and preventing, diagnosing, or curing disease. In this chapter we discuss how bioinformatics critically contributes to systems medicine. First, we explain the role of bioinformatics in the management and analysis of data. In particular we show the importance of publicly available biological and clinical repositories to support systems medicine studies. Second, we discuss how the integration and analysis of multiple types of omics data through integrative bioinformatics may facilitate the determination of more predictive and robust disease signatures, lead to a better understanding of (patho)physiological molecular mechanisms, and facilitate personalized medicine. Third, we focus on network analysis and discuss how gene networks can be constructed from omics data and how these networks can be decomposed into smaller modules. We discuss how the resulting modules can be used to generate experimentally testable hypotheses, provide insight into disease mechanisms, and lead to predictive models. Throughout, we provide several examples demonstrating how bioinformatics contributes to systems medicine and discuss future challenges in bioinformatics that need to be addressed to enable the advancement of systems medicine.

Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli

A significant obstacle in training predictive cell models is the lack of integrated data sources. We develop semi-supervised normalization pipelines and perform experimental characterization (growth, transcriptional, proteome) to create Ecomics, a consistent, quality-controlled multi-omics compendium for Escherichia coli with cohesive meta-data information. We then use this resource to train a multi-scale model that integrates four omics layers to predict genome-wide concentrations and growth dynamics. The genetic and environmental ontology reconstructed from the omics data is substantially different and complementary to the genetic and chemical ontologies. The integration of different layers confers an incremental increase in the prediction performance, as does the information about the known gene regulatory and protein-protein interactions. The predictive performance of the model ranges from 0.54 to 0.87 for the various omics layers, which far exceeds various baselines. This work provides an integrative framework of omics-driven predictive modelling that is broadly applicable to guide biological discovery.

Multi-omics data integration is a great challenge. Here, the authors compile a database of E. coli proteomics, transcriptomics, metabolomics and fluxomics data to train models of recurrent neural network and constrained regression, enabling prediction of bacterial responses to perturbations.

Network analysis and juvenile idiopathic arthritis (JIA): a new horizon for the understanding of disease pathogenesis and therapeutic target identification

Juvenile idiopathic arthritis (JIA) is a clinically diverse and genetically complex autoimmune disease. Currently, there is very limited understanding of the potential underlying mechanisms that result in the range of phenotypes which constitute JIA.The elucidation of the functional relevance of genetic associations with phenotypic traits is a fundamental problem that hampers the translation of genetic observations to plausible medical interventions. Genome wide association studies, and subsequent fine-mapping studies in JIA patients, have identified many genetic variants associated with disease. Such approaches rely on 'tag' single nucleotide polymorphisms (SNPs). The associated SNPs are rarely functional variants, so the extrapolation of genetic association data to the identification of biologically meaningful findings can be a protracted undertaking. Integrative genomics aims to bridge the gap between genotype and phenotype.Systems biology, principally through network analysis, is emerging as a valuable way to identify biological pathways of relevance to complex genetic diseases. This review aims to highlight recent findings in systems biology related to JIA in an attempt to assist in the understanding of JIA pathogenesis and therapeutic target identification.

A study of vertebra number in pigs confirms the association of vertnin and reveals additional QTL

Background

Formation of the vertebral column is a critical developmental stage in mammals. The strict control of this process has resulted in little variation in number of vertebrae across mammalian species and no variation within most mammalian species. The pig is quite unique as considerable variation exists in number of thoracic vertebrae as well as number of lumbar vertebrae. At least two genes have been identified that affect number of vertebrae in pigs yet considerable genetic variation still exists. Therefore, a genome-wide association (GWA) analysis was conducted to identify additional genomic regions that affect this trait.

Results

A total of 1883 animals were phenotyped for the number of ribs and thoracolumbar vertebrae as well as successfully genotyped with the Illumina Porcine SNP60 BeadChip. After data editing, 41,148 SNP markers were included in the GWA analysis. These animals were also phenotyped for kyphosis. Fifty-three 1 Mb windows each explained at least 1.0 % of the genomic variation for vertebrae counts while 16 regions were significant for kyphosis. Vertnin genotype significantly affected vertebral counts as well. The region with the largest effect for number of lumbar vertebrae and thoracolumbar vertebrae were located over the Hox B gene cluster and the largest association for thoracic vertebrae number was over the Hox A gene cluster. Genetic markers in significant regions accounted for approximately 50 % of the genomic variation. Less genomic variation for kyphosis was described by QTL regions and no region was associated with kyphosis and vertebra counts.

Conclusions

The importance of the Hox gene families in vertebral development was highlighted as significant associations were detected over the A, B and C families. Further evaluation of these regions and characterization of variants within these genes will expand our knowledge on vertebral development using natural genetic variants segregating in commercial swine.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-015-0286-9) contains supplementary material, which is available to authorized users.

Exploring causal networks underlying fat deposition and muscularity in pigs through the integration of phenotypic, genotypic and transcriptomic data

BACKGROUND

Joint modeling and analysis of phenotypic, genotypic and transcriptomic data have the potential to uncover the genetic control of gene activity and phenotypic variation, as well as shed light on the manner and extent of connectedness among these variables. Current studies mainly report associations, i.e. undirected connections among variables without causal interpretation. Knowledge regarding causal relationships among genes and phenotypes can be used to predict the behavior of complex systems, as well as to optimize management practices and selection strategies. Here, we performed a multistep procedure for inferring causal networks underlying carcass fat deposition and muscularity in pigs using multi-omics data obtained from an F2 Duroc x Pietrain resource pig population.

RESULTS

We initially explored marginal associations between genotypes and phenotypic and expression traits through whole-genome scans, and then, in genomic regions with multiple significant hits, we assessed gene-phenotype network reconstruction using causal structural learning algorithms. One genomic region on SSC6 showed significant associations with three relevant phenotypes, off-midline10th-rib backfat thickness, loin muscle weight, and average intramuscular fat percentage, and also with the expression of seven genes, including ZNF24, SSX2IP, and AKR7A2. The inferred network indicated that the genotype affects the three phenotypes mainly through the expression of several genes. Among the phenotypes, fat deposition traits negatively affected loin muscle weight.

CONCLUSIONS

Our findings shed light on the antagonist relationship between carcass fat deposition and lean meat content in pigs. In addition, the procedure described in this study has the potential to unravel gene-phenotype networks underlying complex phenotypes.

Copy number variation-based genome wide association study reveals additional variants contributing to meat quality in Swine

Pork quality is important both to the meat processing industry and consumers' purchasing attitude. Copy number variation (CNV) is a burgeoning kind of variants that may influence meat quality. In this study, a genome-wide association study (GWAS) was performed between CNVs and meat quality traits in swine. After false discovery rate (FDR) correction, a total of 8 CNVs on 6 chromosomes were identified to be significantly associated with at least one meat quality trait. All of the 8 CNVs were verified by next generation sequencing and six of them were verified by qPCR. Only the haplotype block containing CNV12 is adjacent to significant SNPs associated with meat quality, suggesting the effects of those CNVs were not likely captured by tag SNPs. The DNA dosage and EST expression of CNV12, which overlap with an obesity related gene Netrin-1 (Ntn1), were consistent with Ntn1 RNA expression, suggesting the CNV12 might be involved in the expression regulation of Ntn1 and finally influence meat quality. We concluded that CNVs may contribute to the genetic variations of meat quality beyond SNPs, and several candidate CNVs were worth further exploration.

A hidden Markov approach for ascertaining cSNP genotypes from RNA sequence data in the presence of allelic imbalance by exploiting linkage disequilibrium

BACKGROUND

Allelic specific expression (ASE) increases our understanding of the genetic control of gene expression and its links to phenotypic variation. ASE testing is implemented through binomial or beta-binomial tests of sequence read counts of alternative alleles at a cSNP of interest in heterozygous individuals. This requires prior ascertainment of the cSNP genotypes for all individuals. To meet the needs, we propose hidden Markov methods to call SNPs from next generation RNA sequence data when ASE possibly exists.

RESULTS

We propose two hidden Markov models (HMMs), HMM-ASE and HMM-NASE that consider or do not consider ASE, respectively, in order to improve genotyping accuracy. Both HMMs have the advantages of calling the genotypes of several SNPs simultaneously and allow mapping error which, respectively, utilize the dependence among SNPs and correct the bias due to mapping error. In addition, HMM-ASE exploits ASE information to further improve genotype accuracy when the ASE is likely to be present. Simulation results indicate that the HMMs proposed demonstrate a very good prediction accuracy in terms of controlling both the false discovery rate (FDR) and the false negative rate (FNR). When ASE is present, the HMM-ASE had a lower FNR than HMM-NASE, while both can control the false discovery rate (FDR) at a similar level. By exploiting linkage disequilibrium (LD), a real data application demonstrate that the proposed methods have better sensitivity and similar FDR in calling heterozygous SNPs than the VarScan method. Sensitivity and FDR are similar to that of the BCFtools and Beagle methods. The resulting genotypes show good properties for the estimation of the genetic parameters and ASE ratios.

CONCLUSIONS

We introduce HMMs, which are able to exploit LD and account for the ASE and mapping errors, to simultaneously call SNPs from the next generation RNA sequence data. The method introduced can reliably call for cSNP genotypes even in the presence of ASE and under low sequencing coverage. As a byproduct, the proposed method is able to provide predictions of ASE ratios for the heterozygous genotypes, which can then be used for ASE testing.

A genome-wide association study reveals dominance effects on number of teats in pigs

Dominance has been suggested as one of the genetic mechanisms explaining heterosis. However, using traditional quantitative genetic methods it is difficult to obtain accurate estimates of dominance effects. With the availability of dense SNP (Single Nucleotide Polymorphism) panels, we now have new opportunities for the detection and use of dominance at individual loci. Thus, the aim of this study was to detect additive and dominance effects on number of teats (NT), specifically to investigate the importance of dominance in a Landrace-based population of pigs. In total, 1,550 animals, genotyped for 32,911 SNPs, were used in single SNP analysis. SNPs with a significant genetic effect were tested for their mode of gene action being additive, dominant or a combination. In total, 21 SNPs were associated with NT, located in three regions with additive (SSC6, 7 and 12) and one region with dominant effects (SSC4). Estimates of additive effects ranged from 0.24 to 0.29 teats. The dominance effect of the QTL located on SSC4 was negative (−0.26 teats). The additive variance of the four QTLs together explained 7.37% of the total phenotypic variance. The dominance variance of the four QTLs together explained 1.82% of the total phenotypic variance, which corresponds to one-fourth of the variance explained by additive effects. The results suggest that dominance effects play a relevant role in the genetic architecture of NT. The QTL region on SSC7 contains the most promising candidate gene: VRTN. This gene has been suggested to be related to the number of vertebrae, a trait correlated with NT.

Detection of independent associations in a large epidemiologic dataset: a comparison of random forests, boosted regression trees, conventional and penalized logistic regression for identifying independent factors associated with H1N1pdm influenza infections

Background

Big data is steadily growing in epidemiology. We explored the performances of methods dedicated to big data analysis for detecting independent associations between exposures and a health outcome.

Methods

We searched for associations between 303 covariates and influenza infection in 498 subjects (14% infected) sampled from a dedicated cohort. Independent associations were detected using two data mining methods, the Random Forests (RF) and the Boosted Regression Trees (BRT); the conventional logistic regression framework (Univariate Followed by Multivariate Logistic Regression - UFMLR) and the Least Absolute Shrinkage and Selection Operator (LASSO) with penalty in multivariate logistic regression to achieve a sparse selection of covariates. We developed permutations tests to assess the statistical significance of associations. We simulated 500 similar sized datasets to estimate the True (TPR) and False (FPR) Positive Rates associated with these methods.

Results

Between 3 and 24 covariates (1%-8%) were identified as associated with influenza infection depending on the method. The pre-seasonal haemagglutination inhibition antibody titer was the unique covariate selected with all methods while 266 (87%) covariates were not selected by any method. At 5% nominal significance level, the TPR were 85% with RF, 80% with BRT, 26% to 49% with UFMLR, 71% to 78% with LASSO. Conversely, the FPR were 4% with RF and BRT, 9% to 2% with UFMLR, and 9% to 4% with LASSO.

Conclusions

Data mining methods and LASSO should be considered as valuable methods to detect independent associations in large epidemiologic datasets.

Plant systems biology: insights, advances and challenges

Plants dwelling at the base of biological food chain are of fundamental significance in providing solutions to some of the most daunting ecological and environmental problems faced by our planet. The reductionist views of molecular biology provide only a partial understanding to the phenotypic knowledge of plants. Systems biology offers a comprehensive view of plant systems, by employing a holistic approach integrating the molecular data at various hierarchical levels. In this review, we discuss the basics of systems biology including the various 'omics' approaches and their integration, the modeling aspects and the tools needed for the plant systems research. A particular emphasis is given to the recent analytical advances, updated published examples of plant systems biology studies and the future trends.

Pediatric perspective on pharmacogenomics

The advances in high-throughput genomic technologies have improved the understanding of disease pathophysiology and have allowed a better characterization of drug response and toxicity based on individual genetic make up. Pharmacogenomics is being recognized as a valid approach used to identify patients who are more likely to respond to medication, or those in whom there is a high probability of developing severe adverse drug reactions. An increasing number of pharmacogenomic studies are being published, most include only adults. A few studies have shown the impact of pharmacogenomics in pediatrics, highlighting a key difference between children and adults, which is the contribution of developmental changes to therapeutic responses across different age groups. This review focuses on pharmacogenomic research in pediatrics, providing examples from common pediatric conditions and emphasizing their developmental context.

Insights into the pathophysiology of catch-up compared with non-catch-up growth in children born small for gestational age: an integrated analysis of metabolic and transcriptomic data

Small for gestational age (SGA) children exhibiting catch-up (CU) growth have a greater risk of cardiometabolic diseases in later life compared with non-catch-up (NCU) SGA children. The aim of this study was to establish differences in metabolism and gene expression profiles between CU and NCU at age 4-9 years. CU children (n=22) had greater height, weight and body mass index standard deviation scores along with insulin-like growth factor-I (IGF-I) and fasting glucose levels but lower adiponectin values than NCU children (n=11; all P<0.05). Metabolic profiling demonstrated a fourfold decrease of urine myo-inositol in CU compared with NCU (P<0.05). There were 1558 genes differentially expressed in peripheral blood mononuclear cells between the groups (P<0.05). Integrated analysis of data identified myo-inositol related to gene clusters associated with an increase in insulin, growth factor and IGF-I signalling in CU children (P<0.05). Metabolic and transcriptomic profiles in CU SGA children showed changes that may relate to cardiometabolic risk.

Pharmacogenomics of insulin-like growth factor-I generation during GH treatment in children with GH deficiency or Turner syndrome

Individual responses to growth hormone (GH) treatment are variable. Short-term generation of insulin-like growth factor-I (IGF-I) is recognized as a potential marker of sensitivity to GH treatment. This prospective, phase IV study used an integrated genomic analysis to identify markers associated with 1-month change in IGF-I (ΔIGF-I) following initiation of recombinant human (r-h)GH therapy in treatment-naïve children with GH deficiency (GHD) (n=166) or Turner syndrome (TS) (n=147). In both GHD and TS, polymorphisms in the cell-cycle regulator CDK4 were associated with 1-month ΔIGF-I (P<0.05). Baseline gene expression was also correlated with 1-month ΔIGF-I in both GHD and TS (r=0.3; P<0.01). In patients with low IGF-I responses, carriage of specific CDK4 alleles was associated with MAPK and glucocorticoid receptor signaling in GHD, and with p53 and Wnt signaling pathways in TS. Understanding the relationship between genomic markers and early changes in IGF-I may allow development of strategies to rapidly individualize r-hGH dose.

The role of longitudinal cohort studies in epigenetic epidemiology: challenges and opportunities

Longitudinal cohort studies are ideal for investigating how epigenetic patterns change over time and relate to changing exposure patterns and the development of disease. We highlight the challenges and opportunities in this approach.

Evaluation of QTL for carcass merit and meat quality traits in a US commercial Duroc population

Putative quantitative trait loci (QTL) regions on 5 chromosomes (SSC3, 6, 12, 15, and 18) were selected from our previous genome scans of a Duroc×Pietrain F(2) resource population for further evaluation in a US commercial Duroc population (n=331). A total of 81 gene-specific single nucleotide polymorphism (SNP) markers were genotyped and 33 markers were segregating. The MDH1 SNP on SSC3 was associated with 45-min and ultimate pH (pHu), and pH decline. PRKAG3 on SSC15 was associated with pHu. The HSPG2 SNP on SSC6 was associated with marbling score and days to 113kg. Markers for NUP88 and FKBP10 on SSC12 were associated with 45-min pH and L*, respectively. The SSC15 marker SF3B1 was associated with L* and LMA, and the SSC18 marker ARF5 was associated with pHu and color score. These results in a commercial Duroc population showed a general consistency with our previous genome scan.