A Combined LC-MS and Immunoassay Approach to Characterize Preservative-Induced Destabilization of Human Papillomavirus Virus-like Particles Adsorbed to an Aluminum-Salt Adjuvant.

During the multi-dose formulation development of recombinant vaccine candidates, protein antigens can be destabilized by antimicrobial preservatives (APs). The degradation mechanisms are often poorly understood since available analytical tools are limited due to low protein concentrations and the presence of adjuvants. In this work, we evaluate different analytical approaches to monitor the structural integrity of HPV16 VLPs adsorbed to Alhydrogel™ (AH) in the presence and absence of APs (i.e., destabilizing m-cresol, MC, or non-destabilizing chlorobutanol, CB) under accelerated conditions (pH 7.4, 50 °C). First, in vitro potency losses displayed only modest correlations with the results from two commonly used methods of protein analysis (SDS-PAGE, DSC). Next, results from two alternative analytical approaches provided a better understanding of physicochemical events occurring under these same conditions: (1) competitive ELISA immunoassays with a panel of mAbs against conformational and linear epitopes on HPV16 VLPs and (2) LC-MS peptide mapping to evaluate the accessibility/redox state of the 12 cysteine residues within each L1 protein comprising the HPV16 VLP (i.e., with 360 L1 proteins per VLP, there are 4320 Cys residues per VLP). These methods expand the limited analytical toolset currently available to characterize AH-adsorbed antigens and provide additional insights into the molecular mechanism(s) of AP-induced destabilization of vaccine antigens.

Effects of aluminum-salt, CpG and emulsion adjuvants on the stability and immunogenicity of a virus-like particle displaying the SARS-CoV-2 receptor-binding domain (RBD).

Second-generation COVID-19 vaccines with improved immunogenicity (e.g., breadth, duration) and availability (e.g., lower costs, refrigerator stable) are needed to enhance global coverage. In this work, we formulated a clinical-stage SARS-CoV-2 receptor-binding domain (RBD) virus-like particle (VLP) vaccine candidate (IVX-411) with widely available adjuvants. Specifically, we assessed the in vitro storage stability and in vivo mouse immunogenicity of IVX-411 formulated with aluminum-salt adjuvants (Alhydrogel™, AH and Adjuphos™, AP), without or with the TLR-9 agonist CpG-1018™ (CpG), and compared these profiles to IVX-411 adjuvanted with an oil-in-water nano-emulsion (AddaVax™, AV). Although IVX-411 bound both AH and AP, lower binding strength of antigen to AP was observed by Langmuir binding isotherms. Interestingly, AH- and AP-adsorbed IVX-411 had similar storage stability profiles as measured by antigen-binding assays (competitive ELISAs), but the latter displayed higher pseudovirus neutralizing titers (pNT) in mice, at levels comparable to titers elicited by AV-adjuvanted IVX-411. CpG addition to alum (AP or AH) resulted in a marginal trend of improved pNTs in stressed samples only, yet did not impact the storage stability profiles of IVX-411. In contrast, previous work with AH-formulations of a monomeric RBD antigen showed greatly improved immunogenicity and decreased stability upon CpG addition to alum. At elevated temperatures (25, 37°C), IVX-411 formulated with AH or AP displayed decreased in vitro stability compared to AV-formulated IVX-411and this rank-ordering correlated with in vivo performance (mouse pNT values). This case study highlights the importance of characterizing antigen-adjuvant interactions to develop low cost, aluminum-salt adjuvanted recombinant subunit vaccine candidates.

Correlating physicochemical and biological properties to define critical quality attributes of a rAAV vaccine candidate.

Recombinant adeno-associated viruses (rAAVs) are a preferred vector system in clinical gene transfer. A fundamental challenge to formulate and deliver rAAVs as stable and efficacious vaccines is to elucidate interrelationships between the vector's physicochemical properties and biological potency. To this end, we evaluated an rAAV-based coronavirus disease 2019 (COVID-19) vaccine candidate that encodes the Spike antigen (AC3) and is produced by a commercially viable process. First, state-of-the-art analytical techniques were employed to determine key structural attributes of AC3, including primary and higher-order structures, particle size, empty/full capsid ratios, aggregates, and multi-step thermal degradation pathway analysis. Next, several quantitative potency measures for AC3 were implemented, and data were correlated with the physicochemical analyses on thermally stressed and control samples. Results demonstrate links between decreasing AC3 physical stability profiles, in vitro transduction efficiency in a cell-based assay, and, importantly, in vivo immunogenicity in a mouse model. These findings are discussed in the general context of future development of rAAV-based vaccine candidates as well as specifically for the rAAV vaccine application under study.

Formulation development and comparability studies with an aluminum-salt adjuvanted SARS-CoV-2 spike ferritin nanoparticle vaccine antigen produced from two different cell lines.

The development of safe and effective second-generation COVID-19 vaccines to improve affordability and storage stability requirements remains a high priority to expand global coverage. In this report, we describe formulation development and comparability studies with a self-assembled SARS-CoV-2 spike ferritin nanoparticle vaccine antigen (called DCFHP), when produced in two different cell lines and formulated with an aluminum-salt adjuvant (Alhydrogel, AH). Varying levels of phosphate buffer altered the extent and strength of antigen-adjuvant interactions, and these formulations were evaluated for their (1) in vivo performance in mice and (2) in vitro stability profiles. Unadjuvanted DCFHP produced minimal immune responses while AH-adjuvanted formulations elicited greatly enhanced pseudovirus neutralization titers independent of ∼100%, ∼40% or ∼10% of the DCFHP antigen adsorbed to AH. These formulations differed, however, in their in vitro stability properties as determined by biophysical studies and a competitive ELISA for measuring ACE2 receptor binding of AH-bound antigen. Interestingly, after one month of 4°C storage, small increases in antigenicity with concomitant decreases in the ability to desorb the antigen from the AH were observed. Finally, we performed a comparability assessment of DCFHP antigen produced in Expi293 and CHO cells, which displayed expected differences in their N-linked oligosaccharide profiles. Despite consisting of different DCFHP glycoforms, these two preparations were highly similar in their key quality attributes including molecular size, structural integrity, conformational stability, binding to ACE2 receptor and mouse immunogenicity profiles. Taken together, these studies support future preclinical and clinical development of an AH-adjuvanted DCFHP vaccine candidate produced in CHO cells.

Using pre-selected variants from large-scale whole-genome sequence data for single-step genomic predictions in pigs.

BACKGROUND: Whole-genome sequence (WGS) data harbor causative variants that may not be present in standard single nucleotide polymorphism (SNP) chip data. The objective of this study was to investigate the impact of using preselected variants from WGS for single-step genomic predictions in maternal and terminal pig lines with up to 1.8k sequenced and 104k sequence imputed animals per line. METHODS: Two maternal and four terminal lines were investigated for eight and seven traits, respectively. The number of sequenced animals ranged from 1365 to 1491 for the maternal lines and 381 to 1865 for the terminal lines. Imputation to sequence occurred within each line for 66k to 76k animals for the maternal lines and 29k to 104k animals for the terminal lines. Two preselected SNP sets were generated based on a genome-wide association study (GWAS). Top40k included the SNPs with the lowest p-value in each of the 40k genomic windows, and ChipPlusSign included significant variants integrated into the porcine SNP chip used for routine genotyping. We compared the performance of single-step genomic predictions between using preselected SNP sets assuming equal or different variances and the standard porcine SNP chip. RESULTS: In the maternal lines, ChipPlusSign and Top40k showed an average increase in accuracy of 0.6 and 4.9%, respectively, compared to the regular porcine SNP chip. The greatest increase was obtained with Top40k, particularly for fertility traits, for which the initial accuracy based on the standard SNP chip was low. However, in the terminal lines, Top40k resulted in an average loss of accuracy of 1%. ChipPlusSign provided a positive, although small, gain in accuracy (0.9%). Assigning different variances for the SNPs slightly improved accuracies when using variances obtained from BayesR. However, increases were inconsistent across the lines and traits. CONCLUSIONS: The benefit of using sequence data depends on the line, the size of the genotyped population, and how the WGS variants are preselected. When WGS data are available on hundreds of thousands of animals, using sequence data presents an advantage but this remains limited in pigs.

A genome-wide association study for loin depth and muscle pH in pigs from intensely selected purebred lines.

BACKGROUND: Genome-wide association studies (GWAS) aim at identifying genomic regions involved in phenotype expression, but identifying causative variants is difficult. Pig Combined Annotation Dependent Depletion (pCADD) scores provide a measure of the predicted consequences of genetic variants. Incorporating pCADD into the GWAS pipeline may help their identification. Our objective was to identify genomic regions associated with loin depth and muscle pH, and identify regions of interest for fine-mapping and further experimental work. Genotypes for ~ 40,000 single nucleotide morphisms (SNPs) were used to perform GWAS for these two traits, using de-regressed breeding values (dEBV) for 329,964 pigs from four commercial lines. Imputed sequence data was used to identify SNPs in strong ([Formula: see text] 0.80) linkage disequilibrium with lead GWAS SNPs with the highest pCADD scores. RESULTS: Fifteen distinct regions were associated with loin depth and one with loin pH at genome-wide significance. Regions on chromosomes 1, 2, 5, 7, and 16, explained between 0.06 and 3.55% of the additive genetic variance and were strongly associated with loin depth. Only a small part of the additive genetic variance in muscle pH was attributed to SNPs. The results of our pCADD analysis suggests that high-scoring pCADD variants are enriched for missense mutations. Two close but distinct regions on SSC1 were associated with loin depth, and pCADD identified the previously identified missense variant within the MC4R gene for one of the lines. For loin pH, pCADD identified a synonymous variant in the RNF25 gene (SSC15) as the most likely candidate for the muscle pH association. The missense mutation in the PRKAG3 gene known to affect glycogen content was not prioritised by pCADD for loin pH. CONCLUSIONS: For loin depth, we identified several strong candidate regions for further statistical fine-mapping that are supported in the literature, and two novel regions. For loin muscle pH, we identified one previously identified associated region. We found mixed evidence for the utility of pCADD as an extension of heuristic fine-mapping. The next step is to perform more sophisticated fine-mapping and expression quantitative trait loci (eQTL) analysis, and then interrogate candidate variants in vitro by perturbation-CRISPR assays.

Phenomic and genomic prediction of yield on multiple locations in winter wheat.

Genomic selection has recently become an established part of breeding strategies in cereals. However, a limitation of linear genomic prediction models for complex traits such as yield is that these are unable to accommodate Genotype by Environment effects, which are commonly observed over trials on multiple locations. In this study, we investigated how this environmental variation can be captured by the collection of a large number of phenomic markers using high-throughput field phenotyping and whether it can increase GS prediction accuracy. For this purpose, 44 winter wheat (Triticum aestivum L.) elite populations, comprising 2,994 lines, were grown on two sites over 2 years, to approximate the size of trials in a practical breeding programme. At various growth stages, remote sensing data from multi- and hyperspectral cameras, as well as traditional ground-based visual crop assessment scores, were collected with approximately 100 different data variables collected per plot. The predictive power for grain yield was tested for the various data types, with or without genome-wide marker data sets. Models using phenomic traits alone had a greater predictive value (R2 = 0.39-0.47) than genomic data (approximately R2 = 0.1). The average improvement in predictive power by combining trait and marker data was 6%-12% over the best phenomic-only model, and performed best when data from one full location was used to predict the yield on an entire second location. The results suggest that genetic gain in breeding programmes can be increased by utilisation of large numbers of phenotypic variables using remote sensing in field trials, although at what stage of the breeding cycle phenomic selection could be most profitably applied remains to be answered.

Multi-line ssGBLUP evaluation using preselected markers from whole-genome sequence data in pigs.

Genomic evaluations in pigs could benefit from using multi-line data along with whole-genome sequencing (WGS) if the data are large enough to represent the variability across populations. The objective of this study was to investigate strategies to combine large-scale data from different terminal pig lines in a multi-line genomic evaluation (MLE) through single-step GBLUP (ssGBLUP) models while including variants preselected from whole-genome sequence (WGS) data. We investigated single-line and multi-line evaluations for five traits recorded in three terminal lines. The number of sequenced animals in each line ranged from 731 to 1,865, with 60k to 104k imputed to WGS. Unknown parent groups (UPG) and metafounders (MF) were explored to account for genetic differences among the lines and improve the compatibility between pedigree and genomic relationships in the MLE. Sequence variants were preselected based on multi-line genome-wide association studies (GWAS) or linkage disequilibrium (LD) pruning. These preselected variant sets were used for ssGBLUP predictions without and with weights from BayesR, and the performances were compared to that of a commercial porcine single-nucleotide polymorphisms (SNP) chip. Using UPG and MF in MLE showed small to no gain in prediction accuracy (up to 0.02), depending on the lines and traits, compared to the single-line genomic evaluation (SLE). Likewise, adding selected variants from the GWAS to the commercial SNP chip resulted in a maximum increase of 0.02 in the prediction accuracy, only for average daily feed intake in the most numerous lines. In addition, no benefits were observed when using preselected sequence variants in multi-line genomic predictions. Weights from BayesR did not help improve the performance of ssGBLUP. This study revealed limited benefits of using preselected whole-genome sequence variants for multi-line genomic predictions, even when tens of thousands of animals had imputed sequence data. Correctly accounting for line differences with UPG or MF in MLE is essential to obtain predictions similar to SLE; however, the only observed benefit of an MLE is to have comparable predictions across lines. Further investigation into the amount of data and novel methods to preselect whole-genome causative variants in combined populations would be of significant interest.

Genomic selection strategies for clonally propagated crops.

KEY MESSAGE: For genomic selection in clonally propagated crops with diploid (-like) meiotic behavior to be effective, crossing parents should be selected based on genomic predicted cross-performance unless dominance is negligible. For genomic selection (GS) in clonal breeding programs to be effective, parents should be selected based on genomic predicted cross-performance unless dominance is negligible. Genomic prediction of cross-performance enables efficient exploitation of the additive and dominance value simultaneously. Here, we compared different GS strategies for clonally propagated crops with diploid (-like) meiotic behavior, using strawberry as an example. We used stochastic simulation to evaluate six combinations of three breeding programs and two parent selection methods. The three breeding programs included (1) a breeding program that introduced GS in the first clonal stage, and (2) two variations of a two-part breeding program with one and three crossing cycles per year, respectively. The two parent selection methods were (1) parent selection based on genomic estimated breeding values (GEBVs) and (2) parent selection based on genomic predicted cross-performance (GPCP). Selection of parents based on GPCP produced faster genetic gain than selection of parents based on GEBVs because it reduced inbreeding when the dominance degree increased. The two-part breeding programs with one and three crossing cycles per year using GPCP always produced the most genetic gain unless dominance was negligible. We conclude that (1) in clonal breeding programs with GS, parents should be selected based on GPCP, and (2) a two-part breeding program with parent selection based on GPCP to rapidly drive population improvement has great potential to improve breeding clonally propagated crops.

Measurement of Adenovirus-Based Vector Heterogeneity.

Adenovirus vectors have become an important class of vaccines with the recent approval of Ebola and COVID-19 products. In-process quality attribute data collected during Adenovirus vector manufacturing has focused on particle concentration and infectivity ratios (based on viral genome: cell-based infectivity), and data suggest only a fraction of viral particles present in the final vaccine product are efficacious. To better understand this product heterogeneity, lab-scale preparations of two Adenovirus viral vectors, (Chimpanzee adenovirus (ChAdOx1) and Human adenovirus Type 5 (Ad5), were studied using transmission electron microscopy (TEM). Different adenovirus morphologies were characterized, and the proportion of empty and full viral particles were quantified. These proportions showed a qualitative correlation with the sample's infectivity values. Liquid chromatography-mass spectrometry (LC-MS) peptide mapping was used to identify key adenovirus proteins involved in viral maturation. Using peptide abundance analysis, a ∼5-fold change in L1 52/55k abundance was observed between low-(empty) and high-density (full) fractions taken from CsCl ultracentrifugation preparations of ChAdOx1 virus. The L1 52/55k viral protein is associated with DNA packaging and is cleaved during viral maturation, so it may be a marker for infective particles. TEM and LC-MS peptide mapping are promising higher-resolution analytical characterization tools to help differentiate between relative proportions of empty, non-infectious, and infectious viral particles as part of Adenovirus vector in-process monitoring, and these results are an encouraging initial step to better differentiate between the different product-related impurities.

Genomic prediction with whole-genome sequence data in intensely selected pig lines.

BACKGROUND: Early simulations indicated that whole-genome sequence data (WGS) could improve the accuracy of genomic predictions within and across breeds. However, empirical results have been ambiguous so far. Large datasets that capture most of the genomic diversity in a population must be assembled so that allele substitution effects are estimated with high accuracy. The objectives of this study were to use a large pig dataset from seven intensely selected lines to assess the benefits of using WGS for genomic prediction compared to using commercial marker arrays and to identify scenarios in which WGS provides the largest advantage. METHODS: We sequenced 6931 individuals from seven commercial pig lines with different numerical sizes. Genotypes of 32.8 million variants were imputed for 396,100 individuals (17,224 to 104,661 per line). We used BayesR to perform genomic prediction for eight complex traits. Genomic predictions were performed using either data from a standard marker array or variants preselected from WGS based on association tests. RESULTS: The accuracies of genomic predictions based on preselected WGS variants were not robust across traits and lines and the improvements in prediction accuracy that we achieved so far with WGS compared to standard marker arrays were generally small. The most favourable results for WGS were obtained when the largest training sets were available and standard marker arrays were augmented with preselected variants with statistically significant associations to the trait. With this method and training sets of around 80k individuals, the accuracy of within-line genomic predictions was on average improved by 0.025. With multi-line training sets, improvements of 0.04 compared to marker arrays could be expected. CONCLUSIONS: Our results showed that WGS has limited potential to improve the accuracy of genomic predictions compared to marker arrays in intensely selected pig lines. Thus, although we expect that larger improvements in accuracy from the use of WGS are possible with a combination of larger training sets and optimised pipelines for generating and analysing such datasets, the use of WGS in the current implementations of genomic prediction should be carefully evaluated against the cost of large-scale WGS data on a case-by-case basis.

Genomic selection using random regressions on known and latent environmental covariates.

KEY MESSAGE: The integration of known and latent environmental covariates within a single-stage genomic selection approach provides breeders with an informative and practical framework to utilise genotype by environment interaction for prediction into current and future environments. This paper develops a single-stage genomic selection approach which integrates known and latent environmental covariates within a special factor analytic framework. The factor analytic linear mixed model of Smith et al. (2001) is an effective method for analysing multi-environment trial (MET) datasets, but has limited practicality since the underlying factors are latent so the modelled genotype by environment interaction (GEI) is observable, rather than predictable. The advantage of using random regressions on known environmental covariates, such as soil moisture and daily temperature, is that the modelled GEI becomes predictable. The integrated factor analytic linear mixed model (IFA-LMM) developed in this paper includes a model for predictable and observable GEI in terms of a joint set of known and latent environmental covariates. The IFA-LMM is demonstrated on a late-stage cotton breeding MET dataset from Bayer CropScience. The results show that the known covariates predominately capture crossover GEI and explain 34.4% of the overall genetic variance. The most notable covariates are maximum downward solar radiation (10.1%), average cloud cover (4.5%) and maximum temperature (4.0%). The latent covariates predominately capture non-crossover GEI and explain 40.5% of the overall genetic variance. The results also show that the average prediction accuracy of the IFA-LMM is [Formula: see text] higher than conventional random regression models for current environments and [Formula: see text] higher for future environments. The IFA-LMM is therefore an effective method for analysing MET datasets which also utilises crossover and non-crossover GEI for genomic prediction into current and future environments. This is becoming increasingly important with the emergence of rapidly changing environments and climate change.

Analytical and Preformulation Characterization Studies of Human Papillomavirus Virus-Like Particles to Enable Quadrivalent Multi-Dose Vaccine Formulation Development.

Introducing multi-dose formulations of Human Papillomavirus (HPV) vaccines will reduce costs and enable improved global vaccine coverage, especially in low- and middle-income countries. This work describes the development of key analytical methods later utilized for HPV vaccine multi-dose formulation development. First, down-selection of physicochemical methods suitable for multi-dose formulation development of four HPV (6, 11, 16, and 18) Virus-Like Particles (VLPs) adsorbed to an aluminum adjuvant (Alhydrogel®, AH) was performed. The four monovalent AH-adsorbed HPV VLPs were then characterized using these down-selected methods. Second, stability-indicating competitive ELISA assays were developed using HPV serotype-specific neutralizing mAbs, to monitor relative antibody binding profiles of the four AH-adsorbed VLPs during storage. Third, concentration-dependent preservative-induced destabilization of HPV16 VLPs was demonstrated by addition of eight preservatives found in parenterally administered pharmaceuticals and vaccines, as measured by ELISA, dynamic light scattering, and differential scanning calorimetry. Finally, preservative stability and effectiveness in the presence of vaccine components were evaluated using a combination of RP-UHPLC, a microbial growth inhibition assay, and a modified version of the European Pharmacopoeia assay (Ph. Eur. 5.1.3). Results are discussed in terms of analytical challenges encountered to identify and develop high-throughput methods that facilitate multi-dose formulation development of aluminum-adjuvanted protein-based vaccine candidates.

Concordance of in vitro and in vivo measures of non-replicating rotavirus vaccine potency.

Rotavirus infections remain a leading cause of morbidity and mortality among infants residing in low- and middle-income countries. To address the large need for protection from this vaccine-preventable disease we are developing a trivalent subunit rotavirus vaccine which is currently being evaluated in a multinational Phase 3 clinical trial for prevention of serious rotavirus gastroenteritis. Currently, there are no universally accepted in vivo or in vitro models that allow for correlation of field efficacy to an immune response against serious rotavirus gastroenteritis. As a new generation of non-replicating rotavirus vaccines are developed the lack of an established model for evaluating vaccine efficacy becomes a critical issue related to how vaccine potency and stability can be assessed. Our previous publication described the development of an in vitro ELISA to quantify individual vaccine antigens adsorbed to an aluminum hydroxide adjuvant to address the gap in vaccine potency methods for this non-replicating rotavirus vaccine candidate. In the present study, we report on concordance between ELISA readouts and in vivo immunogenicity in a guinea pig model as it relates to vaccine dosing levels and sensitivity to thermal stress. We found correlation between in vitro ELISA values and neutralizing antibody responses engendered after animal immunization. Furthermore, this in vitro assay could be used to demonstrate the effect of thermal stress on vaccine potency, and such results could be correlated with physicochemical analysis of the recombinant protein antigens. This work demonstrates the suitability of the in vitro ELISA to measure vaccine potency and the correlation of these measurements to an immunologic outcome.

Rare and population-specific functional variation across pig lines.

BACKGROUND: It is expected that functional, mainly missense and loss-of-function (LOF), and regulatory variants are responsible for most phenotypic differences between breeds and genetic lines of livestock species that have undergone diverse selection histories. However, there is still limited knowledge about the existing missense and LOF variation in commercial livestock populations, in particular regarding population-specific variation and how it can affect applications such as across-breed genomic prediction. METHODS: We re-sequenced the whole genome of 7848 individuals from nine commercial pig lines (average sequencing coverage: 4.1×) and imputed whole-genome genotypes for 440,610 pedigree-related individuals. The called variants were categorized according to predicted functional annotation (from LOF to intergenic) and prevalence level (number of lines in which the variant segregated; from private to widespread). Variants in each category were examined in terms of their distribution along the genome, alternative allele frequency, per-site Wright's fixation index (FST), individual load, and association to production traits. RESULTS: Of the 46 million called variants, 28% were private (called in only one line) and 21% were widespread (called in all nine lines). Genomic regions with a low recombination rate were enriched with private variants. Low-prevalence variants (called in one or a few lines only) were enriched for lower allele frequencies, lower FST, and putatively functional and regulatory roles (including LOF and deleterious missense variants). On average, individuals carried fewer private deleterious missense alleles than expected compared to alleles with other predicted consequences. Only a small subset of the low-prevalence variants had intermediate allele frequencies and explained small fractions of phenotypic variance (up to 3.2%) of production traits. The significant low-prevalence variants had higher per-site FST than the non-significant ones. These associated low-prevalence variants were tagged by other more widespread variants in high linkage disequilibrium, including intergenic variants. CONCLUSIONS: Most low-prevalence variants have low minor allele frequencies and only a small subset of low-prevalence variants contributed detectable fractions of phenotypic variance of production traits. Accounting for low-prevalence variants is therefore unlikely to noticeably benefit across-breed analyses, such as the prediction of genomic breeding values in a population using reference populations of a different genetic background.

A genome-wide association analysis for body weight at 35 days measured on 137,343 broiler chickens.

BACKGROUND: Body weight (BW) is an economically important trait in the broiler (meat-type chickens) industry. Under the assumption of polygenicity, a "large" number of genes with "small" effects is expected to control BW. To detect such effects, a large sample size is required in genome-wide association studies (GWAS). Our objective was to conduct a GWAS for BW measured at 35 days of age with a large sample size. METHODS: The GWAS included 137,343 broilers spanning 15 pedigree generations and 392,295 imputed single nucleotide polymorphisms (SNPs). A false discovery rate of 1% was adopted to account for multiple testing when declaring significant SNPs. A Bayesian ridge regression model was implemented, using AlphaBayes, to estimate the contribution to the total genetic variance of each region harbouring significant SNPs (1 Mb up/downstream) and the combined regions harbouring non-significant SNPs. RESULTS: GWAS revealed 25 genomic regions harbouring 96 significant SNPs on 13 Gallus gallus autosomes (GGA1 to 4, 8, 10 to 15, 19 and 27), with the strongest associations on GGA4 at 65.67-66.31 Mb (Galgal4 assembly). The association of these regions points to several strong candidate genes including: (i) growth factors (GGA1, 4, 8, 13 and 14); (ii) leptin receptor overlapping transcript (LEPROT)/leptin receptor (LEPR) locus (GGA8), and the STAT3/STAT5B locus (GGA27), in connection with the JAK/STAT signalling pathway; (iii) T-box gene (TBX3/TBX5) on GGA15 and CHST11 (GGA1), which are both related to heart/skeleton development); and (iv) PLAG1 (GGA2). Combined together, these 25 genomic regions explained ~ 30% of the total genetic variance. The region harbouring significant SNPs that explained the largest portion of the total genetic variance (4.37%) was on GGA4 (~ 65.67-66.31 Mb). CONCLUSIONS: To the best of our knowledge, this is the largest GWAS that has been conducted for BW in chicken to date. In spite of the identified regions, which showed a strong association with BW, the high proportion of genetic variance attributed to regions harbouring non-significant SNPs supports the hypothesis that the genetic architecture of BW35 is polygenic and complex. Our results also suggest that a large sample size will be required for future GWAS of BW35.

Genetic architecture and major genes for backfat thickness in pig lines of diverse genetic backgrounds.

BACKGROUND: Backfat thickness is an important carcass composition trait for pork production and is commonly included in swine breeding programmes. In this paper, we report the results of a large genome-wide association study for backfat thickness using data from eight lines of diverse genetic backgrounds. METHODS: Data comprised 275,590 pigs from eight lines with diverse genetic backgrounds (breeds included Large White, Landrace, Pietrain, Hampshire, Duroc, and synthetic lines) genotyped and imputed for 71,324 single-nucleotide polymorphisms (SNPs). For each line, we estimated SNP associations using a univariate linear mixed model that accounted for genomic relationships. SNPs with significant associations were identified using a threshold of p < 10-6 and used to define genomic regions of interest. The proportion of genetic variance explained by a genomic region was estimated using a ridge regression model. RESULTS: We found significant associations with backfat thickness for 264 SNPs across 27 genomic regions. Six genomic regions were detected in three or more lines. The average estimate of the SNP-based heritability was 0.48, with estimates by line ranging from 0.30 to 0.58. The genomic regions jointly explained from 3.2 to 19.5% of the additive genetic variance of backfat thickness within a line. Individual genomic regions explained up to 8.0% of the additive genetic variance of backfat thickness within a line. Some of these 27 genomic regions also explained up to 1.6% of the additive genetic variance in lines for which the genomic region was not statistically significant. We identified 64 candidate genes with annotated functions that can be related to fat metabolism, including well-studied genes such as MC4R, IGF2, and LEPR, and more novel candidate genes such as DHCR7, FGF23, MEDAG, DGKI, and PTN. CONCLUSIONS: Our results confirm the polygenic architecture of backfat thickness and the role of genes involved in energy homeostasis, adipogenesis, fatty acid metabolism, and insulin signalling pathways for fat deposition in pigs. The results also suggest that several less well-understood metabolic pathways contribute to backfat development, such as those of phosphate, calcium, and vitamin D homeostasis.

Phasing and imputation of single nucleotide polymorphism data of missing parents of biparental plant populations.

This paper presents an extension to a heuristic method for phasing and imputation of genotypes of descendants in biparental populations so that it can phase and impute genotypes of parents that are ungenotyped or partially genotyped. The imputed genotypes of the parent are used to impute low-density (Ld) genotyped descendants to high density (Hd). The extension was implemented as part of the AlphaPlantImpute software and works in three steps. First, it identifies whether a parent has no or Ld genotypes and identifies its relatives that have Hd genotypes. Second, using the Hd genotypes of relatives, it determines whether the parent is homozygous or heterozygous for a given locus. Third, it phases heterozygous positions of the parent by matching haplotypes to its relatives. We measured the accuracy (correlation between true and imputed genotypes) of imputing parent genotypes in simulated biparental populations from different scenarios. We tested the imputation accuracy of the missing parent's descendants using the true genotype of the parent and compared this with using the imputed genotypes of the parent. Across all scenarios, the imputation accuracy of a parent was >0.98 and did not drop below ∼0.96. The imputation accuracy of a parent was always higher when it was inbred than outbred. Including ancestors of the parent at Hd, increasing the number of crosses and the number of Hd descendants increased the imputation accuracy. The high imputation accuracy achieved for the parent translated to little or no impact on the imputation accuracy of its descendants.

Genetic variation in recombination rate in the pig.

BACKGROUND: Meiotic recombination results in the exchange of genetic material between homologous chromosomes. Recombination rate varies between different parts of the genome, between individuals, and is influenced by genetics. In this paper, we assessed the genetic variation in recombination rate along the genome and between individuals in the pig using multilocus iterative peeling on 150,000 individuals across nine genotyped pedigrees. We used these data to estimate the heritability of recombination and perform a genome-wide association study of recombination in the pig. RESULTS: Our results confirmed known features of the recombination landscape of the pig genome, including differences in genetic length of chromosomes and marked sex differences. The recombination landscape was repeatable between lines, but at the same time, there were differences in average autosome-wide recombination rate between lines. The heritability of autosome-wide recombination rate was low but not zero (on average 0.07 for females and 0.05 for males). We found six genomic regions that are associated with recombination rate, among which five harbour known candidate genes involved in recombination: RNF212, SHOC1, SYCP2, MSH4 and HFM1. CONCLUSIONS: Our results on the variation in recombination rate in the pig genome agree with those reported for other vertebrates, with a low but nonzero heritability, and the identification of a major quantitative trait locus for recombination rate that is homologous to that detected in several other species. This work also highlights the utility of using large-scale livestock data to understand biological processes.