A comparison of methods for whole-genome QTL mapping using dense markers in four livestock species

Identification and Functional Prediction of Circular RNAs Related to Growth Traits and Skeletal Muscle Development in Duroc pigs

Porcine skeletal muscle is a highly heterogeneous tissue type, and the Longissimus Dorsi muscle (LDM), as the most economical and physiologically metabolized skeletal muscle in pigs, has always been the focus of research and improvement in pig molecular breeding. Circular RNA, as an important new member of regulatory non-coding RNA after microRNA, has become a frontier hot spot in life science research. This study aims to explore candidate circRNAs related to growth, meat quality, and skeletal muscle development among Duroc pigs with different average daily gain (ADG). Eight pigs were selected and divided into two groups: H group (high-ADG) and L group (low-ADG), followed by RNA-Seq analysis to identify circRNAs. The results showed that backfat at 6-7 rib (BF) and Intramuscular fat (IMF) content in the H group was significantly lower than L group, but ribeye area (REA) in the H group was higher than in the L group. In RNA-seq, 296 Differentially expressed (DE) circRNAs (157 upregulated and 139 downregulated) were identified and exons flanking long introns are easier to circularize to produce circRNAs. Most of the DE circRNAs were enriched in Quantitative trait locus (QTL) regions related to meat quality and growth traits. In addition, a gene can produce one or more circRNA transcripts. It was also found that the source genes of DE circRNAs were enriched in MAPK, FoXO, mTOR, PI3K-Akt, and Wnt signaling pathways. The results showed that different ADG, carcass, and meat quality traits among half-sibling Duroc pigs with the same diet may be due to the DE circRNAs related to skeletal muscle growth and development.

Genetic × environment variation in sheep lines bred for divergent resistance to strongyle infection

Drug-resistant parasites threaten livestock production. Breeding more resistant hosts could be a sustainable control strategy. Environmental variation linked to animal management practices or to parasite species turnover across farms may however alter the expression of genetic potential. We created sheep lines with high or low resistance to Haemonchus contortus and achieved significant divergence on both phenotypic and genetic scales. We exposed both lines to chronic stress or to the infection by another parasite Trichostrongylus colubriformis, to test for genotype-by-environment and genotype-by-parasite species interactions respectively. Between-line divergence remained significant following chronic stress exposure although between-family variation was found. Significant genotype-by-parasite interaction was found although H. contortus-resistant lambs remained more resistant against T. colubriformis. Growth curves were not altered by the selection process although resistant lambs were lighter after the second round of divergence, before any infection took place. Breeding for resistance is a sustainable strategy but allowance needs to be made for environmental perturbations and worm species.

Genome-wide association studies reveal novel loci associated with carcass and body measures in beef cattle

Genomic selection has an essential role in the livestock economy by increasing selection productivity. Genomics provides a mechanism to increase the rate of genetic gain using marker-assisted selection. Various quantitative trait loci (QTL) associated with body, carcass and meat quality traits in beef cattle have been found. It is widely accepted that QTL traits in livestock species are regulated by several genes and factors from the environment. Genome-wide association studies (GWAS) are a powerful approach in identifying QTL and to establish genomic regions harboring the genes and polymorphisms associated with specific characteristics in beef cattle. Due to their impact on economic returns, growth, carcass and meat quality traits of cattle are frequently used as essential criteria in selection in breeding programs., GWAS has been used in beef cattle breeding and genetic program and some progress has been made. Furthermore, numerous genes and markers related to productivity traits in beef cattle have been found. This review summarizes the advances in the use of GWAS in beef cattle production and outlines the associations with growth, carcass, and meat quality.

Efficient Estimation of Marker Effects in Plant Breeding

The evaluation of prediction machines is an important step for a successful implementation of genomic-enabled selection in plant breeding. Computation time and predictive ability constitute key metrics to determine the methodology utilized for the consolidation of genomic prediction pipeline. This study introduces two methods designed to couple high prediction accuracy with efficient computational performance: 1) a non-MCMC method to estimate marker effects with a Laplace prior; and 2) an iterative framework that allows solving whole-genome regression within mixed models with replicated observations in a single-stage. The investigation provides insights on predictive ability and marker effect estimates. Various genomic prediction techniques are compared based on cross-validation, assessing predictions across and within family. Properties of quantitative trait loci detection and single-stage method were evaluated on simulated plot-level data from unbalanced data structures. Estimation of marker effects by the new model is compared to a genome-wide association analysis and whole-genome regression methods. The single-stage approach is compared to a GBLUP fitted via restricted maximum likelihood, and a two-stages approaches where genetic values fit a whole-genome regression. The proposed framework provided high computational efficiency, robust prediction across datasets, and accurate estimation of marker effects.

Genomic analysis of claw lesions in Holstein cows: Opportunities for genomic selection, quantitative trait locus detection, and gene identification

Claw lesions are the third most important health issue in dairy cattle, after mastitis and reproductive disorders, and genomic selection is a key component for long-term improvement of claw health. The objectives of this study were to assess the feasibility of a genomic evaluation for claw health in French Holstein cows, explore possibilities to increase evaluation accuracy, and gain a better understanding of the genetic determinism of claw health traits. The data set consisted of 48,685 trimmed Holstein cows, including 9,646 that were genotyped; 478 genotyped sires were also used. Seven claw lesion traits were evaluated using BLUP, genomic BLUP, BayesC, and single-step genomic BLUP, and the accuracies obtained using these approaches were measured through a validation study. The BayesC approach was used to detect quantitative trait locus (QTL) regions associated with the 7 individual traits (digital dermatitis, heel horn erosion, interdigital hyperplasia, sole hemorrhage circumscribed, sole hemorrhage diffused, sole ulcer, and white line fissure) based on their Bayes factor. Annotated genes on these regions were reported. Genomic evaluation approaches generally did not allow for greater accuracies than BLUP, except for single-step genomic BLUP. Accuracies were moderate, but best and worst validation animals were correctly discriminated and showed significant differences in lesion frequencies. A total of 192 QTL regions were identified, including 13 with major evidence or involved for 2 of the traits. A high number of genes were present on these regions, and several had functions associated with the immune system. In particular, the EPYC gene is located close to a major evidence QTL for resistance to digital dermatitis that is also a QTL for interdigital hyperplasia (on chromosome 5, around 20.9 MB) and has been associated with Ehlers-Danlos syndrome in cattle. Genomic selection can be used to improve resistance to individual claw lesions, and several possibilities exist to improve accuracies of genomic evaluations.

Mapping of QTL for chicken body weight, carcass composition, and meat quality traits in a slow-growing line

Slow-growing chicken lines are valuable genetic resources for the development of well-perceived alternative free-range production. While there is no constraint on increasing growth rate, breeding programs have to evolve in order to include new traits improving the positioning of such lines in the growing market for parts and processed products. In this study, we used dense genotyping to fine map QTL for chicken growth, body composition, and meat quality traits in view of developing new tools for selection of a slow-growing line. The dataset included a total of 836 birds (10 sires, 87 dams, 739 descendants) and 40,203 SNP. QTL for the 15 traits analyzed were detected by 3 different methods, i.e., linkage and linkage disequilibrium haplotype-based analysis (LDLA), family-based single marker association (FASTA), and Bayesian multi-marker regression (Bayes Cπ). After filtering for QTL redundancy, we found 16, 16, and 9 QTL when using the FASTA, LDLA, and Bayes Cπ methods, respectively, with a threshold of 2.49 × 10-5 for FASTA and LDLA, and a Bayes factor of 150 for the Bayes Cπ analysis. They comprised 17 QTL for body weight, 9 QTL for body composition, and 15 QTL for breast meat quality or behavior at slaughter. The 3 methods agreed in the detection of highly significant QTL such as that detected on GGA24 for body weight at 3, 6, and 9 wk, and the 2 QTL detected on GGA17 and GGA18 for breast meat yield. Several significant QTL were also detected for the different components of breast meat quality. This study provided new locations for investigation in order to improve our understanding of the genetic architecture of growth, carcass composition, and meat quality in the chicken and to develop molecular tools for the selection of these traits in a slow-growing line.

Genome-wide haplotype-based association analysis of key traits of plant lodging and architecture of maize identifies major determinants for leaf angle: hapLA4

Traits related to plant lodging and architecture are important determinants of plant productivity in intensive maize cultivation systems. Motivated by the identification of genomic associations with the leaf angle, plant height (PH), ear height (EH) and the EH/PH ratio, we characterized approximately 7,800 haplotypes from a set of high-quality single nucleotide polymorphisms (SNPs), in an association panel consisting of tropical maize inbred lines. The proportion of the phenotypic variations explained by the individual SNPs varied between 7%, for the SNP S1_285330124 (located on chromosome 9 and associated with the EH/PH ratio), and 22%, for the SNP S1_317085830 (located on chromosome 6 and associated with the leaf angle). A total of 40 haplotype blocks were significantly associated with the traits of interest, explaining up to 29% of the phenotypic variation for the leaf angle, corresponding to the haplotype hapLA4.04, which was stable over two growing seasons. Overall, the associations for PH, EH and the EH/PH ratio were environment-specific, which was confirmed by performing a model comparison analysis using the information criteria of Akaike and Schwarz. In addition, five stable haplotypes (83%) and 15 SNPs (75%) were identified for the leaf angle. Finally, approximately 62% of the associated haplotypes (25/40) did not contain SNPs detected in the association study using individual SNP markers. This result confirms the advantage of haplotype-based genome-wide association studies for examining genomic regions that control the determining traits for architecture and lodging in maize plants.

Five genomic regions have a major impact on fat composition in Iberian pigs

The adipogenic nature of the Iberian pig defines many quality attributes of its fresh meat and dry-cured products. The distinct varieties of Iberian pig exhibit great variability in the genetic parameters for fat deposition and composition in muscle. The aim of this work is to identify common and distinct genomic regions related to fatty acid composition in Retinto, Torbiscal, and Entrepelado Iberian varieties and their reciprocal crosses through a diallelic experiment. In this study, we performed GWAS using a high density SNP array on 382 pigs with the multimarker regression Bayes B method implemented in GenSel. A number of genomic regions showed strong associations with the percentage of saturated and unsaturated fatty acid in intramuscular fat. In particular, five regions with Bayes Factor >100 (SSC2 and SSC7) or >50 (SSC2 and SSC12) explained an important fraction of the genetic variance for miristic, palmitoleic, monounsaturated (>14%), oleic (>10%) and polyunsaturated (>5%) fatty acids. Six genes (RXRB, PSMB8, CHGA, ACACA, PLIN4, PLIN5) located in these regions have been investigated in relation to intramuscular composition variability in Iberian pigs, with two SNPs at the RXRB gene giving the most consistent results on oleic and monounsaturated fatty acid content.

GWAS by GBLUP: Single and Multimarker EMMAX and Bayes Factors, with an Example in Detection of a Major Gene for Horse Gait

Bayesian models for genomic prediction and association mapping are being increasingly used in genetics analysis of quantitative traits. Given a point estimate of variance components, the popular methods SNP-BLUP and GBLUP result in joint estimates of the effect of all markers on the analyzed trait; single and multiple marker frequentist tests (EMMAX) can be constructed from these estimates. Indeed, BLUP methods can be seen simultaneously as Bayesian or frequentist methods. So far there is no formal method to produce Bayesian statistics from GBLUP. Here we show that the Bayes Factor, a commonly admitted statistical procedure, can be computed as the ratio of two normal densities: the first, of the estimate of the marker effect over its posterior standard deviation; the second of the null hypothesis (a value of 0 over the prior standard deviation). We extend the BF to pool evidence from several markers and of several traits. A real data set that we analyze, with ours and existing methods, analyzes 630 horses genotyped for 41711 polymorphic SNPs for the trait “outcome of the qualification test” (which addresses gait, or ambling, of horses) for which a known major gene exists. In the horse data, single marker EMMAX shows a significant effect at the right place at Bonferroni level. The BF points to the same location although with low numerical values. The strength of evidence combining information from several consecutive markers increases using the BF and decreases using EMMAX, which comes from a fundamental difference in the Bayesian and frequentist schools of hypothesis testing. We conclude that our BF method complements frequentist EMMAX analyses because it provides a better pooling of evidence across markers, although its use for primary detection is unclear due to the lack of defined rejection thresholds.

Combined GWAS and LDLA approaches to improve genome-wide quantitative trait loci detection affecting carcass and meat quality traits in pig

Many QTL affecting meat quality and carcass traits have been reported. However, in most of the cases these QTL have been detected in non-commercial populations. Therefore, a family structured population of 457 F2 pigs issued from an inter-cross between 2 commercial sire lines was used to detect QTL affecting meat quality and carcass traits. All animals were genotyped using the Illumina PorcineSNP60 BeadChip platform. Genome-wide association studies were used in combination with linkage disequilibrium-linkage analysis to identify QTL. A total of 32 QTL were detected. Nine of these QTL exceeded the genome-wide 5% significance threshold. We detected 18 QTL affecting carcass composition traits and 16 QTL affecting meat quality traits. Using post-QTL bioinformatics analysis we highlighted 26 functional candidate genes related to fatness, muscle development, meat color and meat pH. Finally, our results shed light on the advantage of using different QTL detection methodologies to get a global overview of the QTL present in the studied population.

Helitrons and Retrotransposons Are Co-localized in Bos taurus Genomes

Background:

DNA transposons helitrons are mobile genetic elements responsible for major movements of the genetic material within and across different genomes. This ability makes helitrons suitable candidate elements for the development of new approaches of multilocus genotyping of live-stock animals, along with the well-known microsatellite loci.

Objective:

We aimed to estimate the informativeness of helitron and microsatellite markers in assessing the consolidation and the “gene pool” standards of two commercial dairy cattle breeds (Ayrshire breed and holsteinized Black-and-White cattle) and one local breed of Kalmyk cattle, and to reveal any inter-breed difference in the organization of genomic regions flanked by helitrons in the studied cattle breeds.

Method:

We used the combination of two highly-polymorphic genomic elements – helitrons and trinu-cleotide microsatellites (AGC)6G and (GAG)6C, respectively – for genome scanning of the sampled groups of cattle. Also, we pyrosequenced the genomic regions flanked by the inverted repeats of 3'-end of Heligloria family of helitron fragments.

Results:

Generally, the both combinations of markers generated polymorphic spectra, based on which certain interbreed differentiation could be observed. The analysis of the identified interspersed repeats suggests that in factory and local cattle the genomic regions flanked by helitron fragments are shaped differently and contain different superfamilies of transposable elements, especially retrotransposons.

Conclusion:

Despite the well-known fact of retrotransposon-dependent microsatellite expansion, our data suggest that, in the cattle genome, the DNA transposons and microsatellites can also be found in close neighbourhood, and that helitrons and retrotransposons may form domains of increased variability – targets for factors of artificial selection.

Whole genome structural analysis of Caribbean hair sheep reveals quantitative link to West African ancestry

Hair sheep of Caribbean origin have become an important part of the U.S. sheep industry. Their lack of wool eliminates a number of health concerns and drastically reduces the cost of production. More importantly, Caribbean hair sheep demonstrate robust production performance even in the presence of drug-resistant gastrointestinal nematodes, a rising concern to the industry. Despite the growing importance of hair sheep in the Americas their genetic origins have remained speculative. Prior to this report no genetic studies were able to identify a unique geographical origin of hair sheep in the New World. Our study clarifies the African and European ancestry of Caribbean hair sheep. Whole-genome structural analysis was conducted on four established breeds of hair sheep from the Caribbean region. Using breeds representing Africa and Europe we establish an objective measure indicating Caribbean hair sheep are derived from Iberian and West African origins. Caribbean hair sheep result from West African introgression into established ecotypes of Iberian descent. Genotypes from 47,750 autosomal single nucleotide polymorphism markers scored in 290 animals were used to characterize the population structure of the St. Croix, Barbados Blackbelly, Morada Nova, and Santa Ines. Principal components, admixture, and phylogenetic analyses results correlate with historical patterns of colonization and trade. These patterns support co-migration of these sheep with humans.

Detection of QTL for traits related to adaptation to sub-optimal climatic conditions in chickens

BACKGROUND

Growth traits can be used as indicators of adaptation to sub-optimal conditions. The current study aimed at identifying quantitative trait loci (QTL) that control performance under variable temperature conditions in chickens.

METHODS

An F2 population was produced by crossing the Taiwan Country chicken L2 line (selected for body weight, comb area, and egg production) with an experimental line of Rhode Island Red layer R- (selected for low residual feed consumption). A total of 844 animals were genotyped with the 60 K Illumina single nucleotide polymorphism (SNP) chip. Whole-genome interval linkage mapping and a genome-wide association study (GWAS) were performed for body weight at 0, 4, 8, 12, and 16 weeks of age, shank length at 8 weeks of age, size of comb area at 16 weeks of age, and antibody response to sheep red blood cells at 11 weeks of age (7 and 14 days after primary immunization). Relevant genes were identified based on functional annotation of candidate genes and potentially relevant SNPs were detected by comparing whole-genome sequences of several birds between the parental lines.

RESULTS

Whole-genome QTL analysis revealed 47 QTL and 714 effects associated with 178 SNPs were identified by GWAS with 5% Bonferroni genome-wide significance. Little overlap was observed between the QTL and GWAS results, with only two chromosomal regions detected by both approaches, i.e. one on GGA24 (GGA for Gallus gallus chromosome) for BW04 and one on GGAZ for six growth-related traits. Based on whole-genome sequence, differences between the parental lines based on several birds were screened in the genome-wide QTL regions and in a region detected by both methods, resulting in the identification of 106 putative candidate genes with a total of 15,443 SNPs, of which 41 were missense and 1698 were not described in the dbSNP archive.

CONCLUSIONS

The QTL detected in this study for growth and morphological traits likely influence adaptation of chickens to sub-tropical climate. Using whole-genome sequence data, we identified candidate SNPs for further confirmation of QTL in the F2 design. A strong QTL effect found on GGAZ underlines the importance of sex-linked inheritance for growth traits in chickens.

Application of a Bayesian dominance model improves power in quantitative trait genome-wide association analysis

BACKGROUND

Multi-marker methods, which fit all markers simultaneously, were originally tailored for genomic selection purposes, but have proven to be useful also in association analyses, especially the so-called BayesC Bayesian methods. In a recent study, BayesD extended BayesC towards accounting for dominance effects and improved prediction accuracy and persistence in genomic selection. The current study investigated the power and precision of BayesC and BayesD in genome-wide association studies by means of stochastic simulations and applied these methods to a dairy cattle dataset.

METHODS

The simulation protocol was designed to mimic the genetic architecture of quantitative traits as realistically as possible. Special emphasis was put on the joint distribution of the additive and dominance effects of causative mutations. Additive marker effects were estimated by BayesC and additive and dominance effects by BayesD. The dependencies between additive and dominance effects were modelled in BayesD by choosing appropriate priors. A sliding-window approach was used. For each window, the R. Fernando window posterior probability of association was calculated and this was used for inference purpose. The power to map segregating causal effects and the mapping precision were assessed for various marker densities up to full sequence information and various window sizes.

RESULTS

Power to map a QTL increased with higher marker densities and larger window sizes. This held true for both methods. Method BayesD had improved power compared to BayesC. The increase in power was between -2 and 8% for causative genes that explained more than 2.5% of the genetic variance. In addition, inspection of the estimates of genomic window dominance variance allowed for inference about the magnitude of dominance at significant associations, which remains hidden in BayesC analysis. Mapping precision was not substantially improved by BayesD.

CONCLUSIONS

BayesD improved power, but precision only slightly. Application of BayesD needs large datasets with genotypes and own performance records as phenotypes. Given the current efforts to establish cow reference populations in dairy cattle genomic selection schemes, such datasets are expected to be soon available, which will enable the application of BayesD for association mapping and genomic prediction purposes.

Unravelling the genomic architecture of bull fertility in Holstein cattle

BACKGROUND

Fertility is considered an important economic trait in dairy cattle. Most studies have investigated cow fertility while bull fertility has received much less consideration. The main objective of this study was to perform a comprehensive genomic analysis in order to unravel the genomic architecture underlying sire fertility in Holstein dairy cattle. The analysis included the application of alternative genome-wide association mapping approaches and the subsequent use of diverse gene set enrichment tools.

RESULTS

The association analyses identified at least eight genomic regions strongly associated with bull fertility. Most of these regions harbor genes, such as KAT8, CKB, TDRD9 and IGF1R, with functions related to sperm biology, including sperm development, motility and sperm-egg interaction. Moreover, the gene set analyses revealed many significant functional terms, including fertilization, sperm motility, calcium channel regulation, and SNARE proteins. Most of these terms are directly implicated in sperm physiology and male fertility.

CONCLUSIONS

This study contributes to the identification of genetic variants and biological processes underlying sire fertility. These findings can provide opportunities for improving bull fertility via marker-assisted selection.

Walking through the statistical black boxes of plant breeding

The main statistical procedures in plant breeding are based on Gaussian process and can be computed through mixed linear models. Intelligent decision making relies on our ability to extract useful information from data to help us achieve our goals more efficiently. Many plant breeders and geneticists perform statistical analyses without understanding the underlying assumptions of the methods or their strengths and pitfalls. In other words, they treat these statistical methods (software and programs) like black boxes. Black boxes represent complex pieces of machinery with contents that are not fully understood by the user. The user sees the inputs and outputs without knowing how the outputs are generated. By providing a general background on statistical methodologies, this review aims (1) to introduce basic concepts of machine learning and its applications to plant breeding; (2) to link classical selection theory to current statistical approaches; (3) to show how to solve mixed models and extend their application to pedigree-based and genomic-based prediction; and (4) to clarify how the algorithms of genome-wide association studies work, including their assumptions and limitations.

Insights into the genetic variation of maternal behavior and suckling performance of continental beef cows

BACKGROUND

In beef cattle, maternal care is critical for calf survival and growth. Our objective was to evaluate the major sources of additive genetic variation in maternal behavior and suckling performance in two genetically close beef breeds.

METHODS

Maternal performance was assessed based on maternal behavior (MB), milk yield (MY) and udder swelling score (US) of 1236 Blonde d'Aquitaine cows and 1048 Limousin cows. MB was scored just after calving to describe the intensity of the dam's protective behavior towards her calf. Most of the cows were genotyped using the low-density chip EuroG10K BeadChip, and imputed to the high-density 770K panel within breed. Genetic parameters for each trait were estimated for each breed under a multi-trait best linear unbiased prediction animal model. Genomic analysis was performed for each breed using the high-density genotypes and a Bayesian variable selection method.

RESULTS

Heritabilities were low for MB (0.11-0.13), intermediate for MY (0.33-0.45) and high for US (0.47-0.64). Genetic correlations between the traits ranged from 0.31 to 0.58 and 0.72 to 0.99 for the Blonde d'Aquitaine and Limousin breeds, respectively. Two quantitative trait loci (QTL) were detected for MB in Blonde d'Aquitaine with NPY1R and ADRA2A as candidate causative genes. Thirty to 56 QTL were detected for MY and US in both breeds and 12 candidate genes were identified as having a role in the genetic variation of suckling performance. Since very few pleiotropic QTL were detected, there was little biological explanation for the moderate (0.57) to very high (0.99) genetic correlations estimated between MY and US in the Blonde d'Aquitaine and Limousin cows, respectively. In Blonde d'Aquitaine, the correlation was largely due to the pleiotropic QTL detected in the region upstream of the CG gene, while in Limousin, this region was only identified for US, thus attesting the difference in genetic architecture between the breeds.

CONCLUSIONS

Our findings question the assumption that two populations that have close genetic links share many QTL. Nevertheless, we identified four candidate genes that may explain a substantial amount of the genetic variation in suckling performance of these two breeds.

Detection of quantitative trait loci for maternal traits using high-density genotypes of Blonde d'Aquitaine beef cattle

Background

The genetic determinism of the calving and suckling performance of beef cows is little known whereas these maternal traits are of major economic importance in beef cattle production systems. This paper aims to identify QTL regions and candidate genes that affect maternal performance traits in the Blonde d’Aquitaine breed. Three calving performance traits were studied: the maternal effect on calving score from field data, the calving score and pelvic opening recorded in station for primiparous cows. Three other traits related to suckling performance were also analysed: the maternal effect on weaning weight from field data, milk yield and the udder swelling score recorded in station for primiparous cows. A total of 2,505 animals were genotyped from various chip densities and imputed in high density chips for 706,791 SNP. The number of genotyped animals with phenotypes ranged from 1,151 to 2,284, depending on the trait considered.

Results

QTL detections were performed using a Bayes C approach. Evidence for a QTL was based on Bayes Factor values. Putative candidate genes were proposed for the QTL with major evidence for one of the six traits and for the QTL shared by at least two of the three traits underlying either calving or suckling performance. Nine candidate genes were proposed for calving performance among the nine highlighted QTL regions. The neuroregulin gene on chromosome 27 was notably identified as a very likely candidate gene for maternal calving performance. As for suckling abilities, seven candidate genes were identified among the 15 highlighted QTL. In particular, the Group-Specific Component gene on chromosome 6, which encodes vitamin D binding protein, is likely to have a major effect on maternal weaning weight in the Blonde d’Aquitaine breed. This gene had already been linked to milk production and clinical mastitis in dairy cattle.

Conclusion

In the near future, these QTL findings and the preliminary proposals of candidate genes which act on the maternal performance of beef cows should help to identify putative causal mutations based on sequence data from different cattle breeds.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-016-0397-y) contains supplementary material, which is available to authorized users.

A comparison of methods to estimate genomic relationships using pedigree and markers in livestock populations

Accurate prediction of breeding values depends on capturing the variability in genome sharing of relatives with the same pedigree relationship. Here, we compare two approaches to set up genomic relationship matrices for precision of genomic relationships (GR) and accuracy of estimated breeding values (GEBV). Real and simulated data (pigs, 60k SNP) were analysed, and GR were estimated using two approaches: (i) identity by state, corrected with either the observed (G_VR-O ) or the base population (G_VR-B ) allele frequencies and (ii) identity by descent using linkage analysis (G_IBD-L ). Estimators were evaluated for precision and empirical bias with respect to true pedigree IBD GR. All three estimators had very low bias. G_IBD-L displayed the lowest sampling error and the highest correlation with true genome-shared values. G_VR-B approximated G_IBD-L 's correlation and had lower error than G_VR-O . Accuracy of GEBV for selection candidates was significantly higher when G_IBD-L was used and identical between G_VR-O and G_VR-B . In real data, G_IBD-L 's sampling standard deviation was the closest to the theoretical value for each pedigree relationship. Use of pedigree to calculate GR improved the precision of estimates and the accuracy of GEBV.

SNP- and haplotype-based genome-wide association studies for growth, carcass, and meat quality traits in a Duroc multigenerational population

Background

The aim of the present study was to compare the power of single nucleotide polymorphism (SNP)-based genome-wide association study (GWAS) and haplotype-based GWAS for quantitative trait loci (QTL) detection, and to detect novel candidate genes affecting economically important traits in a purebred Duroc population comprising seven-generation pedigree. First, we performed a simulation analysis using real genotype data of this population to compare the power (based on the null hypothesis) of the two methods. We then performed GWAS using both methods and real phenotype data comprising 52 traits, which included growth, carcass, and meat quality traits.

Results

In total, 836 animals were genotyped using the Illumina PorcineSNP60 BeadChip and 14 customized SNPs from regions of known candidate genes related to the traits of interest. The power of SNP-based GWAS was greater than that of haplotype-based GWAS in a simulation analysis. In real data analysis, a larger number of significant regions was obtained by SNP-based GWAS than by haplotype-based GWAS. For SNP-based GWAS, 23 genome-wide significant SNP regions were detected for 17 traits, and 120 genome-wide suggestive SNP regions were detected for 27 traits. For haplotype-based GWAS, 6 genome-wide significant SNP regions were detected for four traits, and 11 genome-wide suggestive SNP regions were detected for eight traits. All genome-wide significant SNP regions detected by haplotype-based GWAS were located in regions also detected by SNP-based GWAS. Four regions detected by SNP-based GWAS were significantly associated with multiple traits: on Sus scrofa chromosome (SSC) 1 at 304 Mb; and on SSC7 at 35–39 Mb, 41–42 Mb, and 103 Mb. The vertnin gene (VRTN) in particular, was located on SSC7 at 103 Mb and was significantly associated with vertebrae number and carcass lengths. Mapped QTL regions contain some candidate genes involved in skeletal formation (FUBP3; far upstream element binding protein 3) and fat deposition (METTL3; methyltransferase like 3).

Conclusion

Our results show that a multigenerational pig population is useful for detecting QTL, which are typically segregated in a purebred population. In addition, a novel significant region could be detected by SNP-based GWAS as opposed to haplotype-based GWAS.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-016-0368-3) contains supplementary material, which is available to authorized users.

Genome-Wide Association Study Singles Out SCD and LEPR as the Two Main Loci Influencing Intramuscular Fat Content and Fatty Acid Composition in Duroc Pigs

Intramuscular fat (IMF) content and fatty acid composition affect the organoleptic quality and nutritional value of pork. A genome-wide association study was performed on 138 Duroc pigs genotyped with a 60k SNP chip to detect biologically relevant genomic variants influencing fat content and composition. Despite the limited sample size, the genome-wide association study was powerful enough to detect the association between fatty acid composition and a known haplotypic variant in SCD (SSC14) and to reveal an association of IMF and fatty acid composition in the LEPR region (SSC6). The association of LEPR was later validated with an independent set of 853 pigs using a candidate quantitative trait nucleotide. The SCD gene is responsible for the biosynthesis of oleic acid (C18:1) from stearic acid. This locus affected the stearic to oleic desaturation index (C18:1/C18:0), C18:1, and saturated (SFA) and monounsaturated (MUFA) fatty acids content. These effects were consistently detected in gluteus medius, longissimus dorsi, and subcutaneous fat. The association of LEPR with fatty acid composition was detected only in muscle and was, at least in part, a consequence of its effect on IMF content, with increased IMF resulting in more SFA, less polyunsaturated fatty acids (PUFA), and greater SFA/PUFA ratio. Marker substitution effects estimated with a subset of 65 animals were used to predict the genomic estimated breeding values of 70 animals born 7 years later. Although predictions with the whole SNP chip information were in relatively high correlation with observed SFA, MUFA, and C18:1/C18:0 (0.48–0.60), IMF content and composition were in general better predicted by using only SNPs at the SCD and LEPR loci, in which case the correlation between predicted and observed values was in the range of 0.36 to 0.54 for all traits. Results indicate that markers in the SCD and LEPR genes can be useful to select for optimum fatty acid profiles of pork.

Fine mapping of QTL and genomic prediction using allele-specific expression SNPs demonstrates that the complex trait of genetic resistance to Marek's disease is predominantly determined by transcriptional regulation

Background

Marek’s disease (MD) is a lymphoproliferative disease of poultry induced by Marek’s disease virus (MDV), a highly oncogenic alphaherpesvirus. Identifying the underlying genes conferring MD genetic resistance is desired for more efficacious control measures including genomic selection, which requires accurately identified genetic markers throughout the chicken genome.

Methods

Hypothesizing that variants located in transcriptional regulatory regions are the main mechanism underlying this complex trait, a genome-wide association study was conducted by genotyping a ~1,000 bird MD resource population derived from experimental inbred layers with SNPs containing 1,824 previously identified allele-specific expression (ASE) SNPs in response to MDV infection as well as 3,097 random SNPs equally spaced throughout the chicken genome. Based on the calculated associations, genomic predictions were determined for 200 roosters and selected sires had their progeny tested for Marek’s disease incidence.

Results

Our analyses indicate that these ASE SNPs account for more than 83 % of the genetic variance and exhibit nearly all the highest associations. To validate these findings, 200 roosters had their genetic merit predicted from the ASE SNPs only, and the top 30 and bottom 30 ranked roosters were reciprocally mated to random hens. The resulting progeny showed that after only one generation of bidirectional selection, there was a 22 % difference in MD incidence and this approach gave a 125 % increase in accuracy compared to current pedigree-based estimates.

Conclusions

We conclude that variation in transcriptional regulation is the major driving cause for genetic resistance to MD, and ASE SNPs identify the underlying genes and are sufficiently linked to the causative polymorphisms that they can be used for accurate genomic prediction as well as help define the underlying molecular basis. Furthermore, this approach should be applicable to other complex traits.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2016-0) contains supplementary material, which is available to authorized users.

NAM: association studies in multiple populations

MOTIVATION

Mixed linear models provide important techniques for performing genome-wide association studies. However, current models have pitfalls associated with their strong assumptions. Here, we propose a new implementation designed to overcome some of these pitfalls using an empirical Bayes algorithm.

RESULTS

Here we introduce NAM, an R package that allows user to take into account prior information regarding population stratification to relax the linkage phase assumption of current methods. It allows markers to be treated as a random effect to increase the resolution, and uses a sliding-window strategy to increase power and avoid double fitting markers into the model.

AVAILABILITY AND IMPLEMENTATION

NAM is an R package available in the CRAN repository. It can be installed in R by typing install.packages ('NAM').

CONTACT

krainey@purdue.edu.

SUPPLEMENTARY INFORMATION

Supplementary date are available at Bioinformatics online.