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

Dominance is common in mammals and is associated with trans-acting gene expression and alternative splicing

BACKGROUND

Dominance and other non-additive genetic effects arise from the interaction between alleles, and historically these phenomena play a major role in quantitative genetics. However, most genome-wide association studies (GWAS) assume alleles act additively.

RESULTS

We systematically investigate both dominance-here representing any non-additive within-locus interaction-and additivity across 574 physiological and gene expression traits in three mammalian stocks: F2 intercross pigs, rat heterogeneous stock, and mice heterogeneous stock. Dominance accounts for about one quarter of heritable variance across all physiological traits in all species. Hematological and immunological traits exhibit the highest dominance variance, possibly reflecting balancing selection in response to pathogens. Although most quantitative trait loci (QTLs) are detectable as additive QTLs, we identify 154, 64, and 62 novel dominance QTLs in pigs, rats, and mice respectively that are undetectable as additive QTLs. Similarly, even though most cis-acting expression QTLs are additive, gene expression exhibits a large fraction of dominance variance, and trans-acting eQTLs are enriched for dominance. Genes causal for dominance physiological QTLs are less likely to be physically linked to their QTLs but instead act via trans-acting dominance eQTLs. In addition, thousands of eQTLs are associated with alternatively spliced isoforms with complex additive and dominant architectures in heterogeneous stock rats, suggesting a possible mechanism for dominance.

CONCLUSIONS

Although heritability is predominantly additive, many mammalian genetic effects are dominant and likely arise through distinct mechanisms. It is therefore advantageous to consider both additive and dominance effects in GWAS to improve power and uncover causality.

A composite strategy of genome-wide association study and copy number variation analysis for carcass traits in a Duroc pig population

BACKGROUND

Carcass traits are important in pig breeding programs for improving pork production. Understanding the genetic variants underlies complex phenotypes can help explain trait variation in pigs. In this study, we integrated a weighted single-step genome-wide association study (wssGWAS) and copy number variation (CNV) analyses to map genetic variations and genes associated with loin muscle area (LMA), loin muscle depth (LMD) and lean meat percentage (LMP) in Duroc pigs.

RESULTS

Firstly, we performed a genome-wide analysis for CNV detection using GeneSeek Porcine SNP50 Bead chip data of 3770 pigs. A total of 11,100 CNVs were detected, which were aggregated by overlapping 695 CNV regions (CNVRs). Next, we investigated CNVs of pigs from the same population by whole-genome resequencing. A genome-wide analysis of 21 pigs revealed 23,856 CNVRs that were further divided into three categories (851 gain, 22,279 loss, and 726 mixed), which covered 190.8 Mb (~ 8.42%) of the pig autosomal genome. Further, the identified CNVRs were used to determine an overall validation rate of 68.5% for the CNV detection accuracy of chip data. CNVR association analyses identified one CNVR associated with LMA, one with LMD and eight with LMP after applying stringent Bonferroni correction. The wssGWAS identified eight, six and five regions explaining more than 1% of the additive genetic variance for LMA, LMD and LMP, respectively. The CNVR analyses and wssGWAS identified five common regions, of which three regions were associated with LMA and two with LMP. Four genes (DOK7, ARAP1, ELMO2 and SLC13A3) were highlighted as promising candidates according to their function.

CONCLUSIONS

We determined an overall validation rate for the CNV detection accuracy of low-density chip data and constructed a genomic CNV map for Duroc pigs using resequencing, thereby proving a value genetic variation resource for pig genome research. Furthermore, our study utilized a composite genetic strategy for complex traits in pigs, which will contribute to the study for elucidating the genetic architecture that may be influenced and regulated by multiple forms of variations.

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.

Genome-wide association analyses identify known and novel loci for teat number in Duroc pigs using single-locus and multi-locus models

BACKGROUND

More teats are necessary for sows to nurse larger litters to provide immunity and nutrient for piglets prior to weaning. Previous studies have reported the strong effect of an insertion mutation in the Vertebrae Development Associated (VRTN) gene on Sus scrofa chromosome 7 (SSC7) that increased the number of thoracic vertebrae and teat number in pigs. We used genome-wide association studies (GWAS) to map genetic markers and genes associated with teat number in two Duroc pig populations with different genetic backgrounds. A single marker method and several multi-locus methods were utilized. A meta-analysis that combined the effects and P-values of 34,681 single nucleotide polymorphisms (SNPs) that were common in the results of single marker GWAS of American and Canadian Duroc pigs was conducted. We also performed association tests between the VRTN insertion and teat number in the same populations.

RESULTS

A total of 97 SNPs were found to be associated with teat number. Among these, six, eight and seven SNPs were consistently detected with two, three and four multi-locus methods, respectively. Seven SNPs were concordantly identified between single marker and multi-locus methods. Moreover, 26 SNPs were newly found by multi-locus methods to be associated with teat number. Notably, we detected one consistent quantitative trait locus (QTL) on SSC7 for teat number using single-locus and meta-analysis of GWAS and the top SNP (rs692640845) explained 8.68% phenotypic variance of teat number in the Canadian Duroc pigs. The associations between the VRTN insertion and teat number in two Duroc pig populations were substantially weaker. Further analysis revealed that the effect of VRTN on teat number may be mediated by its LD with the true causal mutation.

CONCLUSIONS

Our study suggested that VRTN insertion may not be a strong or the only candidate causal mutation for the QTL on SSC7 for teat number in the analyzed Duroc pig populations. The combination of single-locus and multi-locus GWAS detected additional SNPs that were absent using only one model. The identified SNPs will be useful for the genetic improvement of teat number in pigs by assigning higher weights to associated SNPs in genomic selection.

GWAS for Meat and Carcass Traits Using Imputed Sequence Level Genotypes in Pooled F2-Designs in Pigs

In order to gain insight into the genetic architecture of economically important traits in pigs and to derive suitable genetic markers to improve these traits in breeding programs, many studies have been conducted to map quantitative trait loci. Shortcomings of these studies were low mapping resolution, large confidence intervals for quantitative trait loci-positions and large linkage disequilibrium blocks. Here, we overcome these shortcomings by pooling four large F2 designs to produce smaller linkage disequilibrium blocks and by resequencing the founder generation at high coverage and the F1 generation at low coverage for subsequent imputation of the F2 generation to whole genome sequencing marker density. This lead to the discovery of more than 32 million variants, 8 million of which have not been previously reported. The pooling of the four F2 designs enabled us to perform a joint genome-wide association study, which lead to the identification of numerous significantly associated variant clusters on chromosomes 1, 2, 4, 7, 17 and 18 for the growth and carcass traits average daily gain, back fat thickness, meat fat ratio, and carcass length. We could not only confirm previously reported, but also discovered new quantitative trait loci. As a result, several new candidate genes are discussed, among them BMP2 (bone morphogenetic protein 2), which we recently discovered in a related study. Variant effect prediction revealed that 15 high impact variants for the traits back fat thickness, meat fat ratio and carcass length were among the statistically significantly associated variants.

Genome-wide association studies and meta-analysis uncovers new candidate genes for growth and carcass traits in pigs

Genome-wide association studies (GWAS) have been widely used in the genetic dissection of complex traits. As more genomic data is being generated within different commercial or resource pig populations, the challenge which arises is how to collectively investigate the data with the purpose to increase sample size and implicitly the statistical power. This study performs an individual population GWAS, a joint population GWAS and a meta-analysis in three pig F2 populations. D1 is derived from European type breeds (Piétrain, Large White and Landrace), D2 is obtained from an Asian breed (Meishan) and Piétrain, and D3 stems from a European Wild Boar and Piétrain, which is the common founder breed. The traits investigated are average daily gain, backfat thickness, meat to fat ratio and carcass length. The joint and the meta-analysis did not identify additional genomic clusters besides the ones discovered via the individual population GWAS. However, the benefit was an increased mapping resolution which pinpointed to narrower clusters harboring causative variants. The joint analysis identified a higher number of clusters as compared to the meta-analysis; nevertheless, the significance levels and the number of significant variants in the meta-analysis were generally higher. Both types of analysis had similar outputs suggesting that the two strategies can complement each other and that the meta-analysis approach can be a valuable tool whenever access to raw datasets is limited. Overall, a total of 20 genomic clusters that predominantly overlapped at various extents, were identified on chromosomes 2, 7 and 17, many confirming previously identified quantitative trait loci. Several new candidate genes are being proposed and, among them, a strong candidate gene to be taken into account for subsequent analysis is BMP2 (bone morphogenetic protein 2).