Application of genomics tools to animal breeding

Advancing Dairy and Beef Genetics Through Genomic Technologies

The US beef and dairy industries have made remarkable advances in sustainability and productivity through technological advancements, including selective breeding. Yet, challenges persist due to the complex nature of quantitative traits. While the beef industry has progressed in adopting genomic technologies, the availability of phenotypic data remains an obstacle. To meet the need for sustainable production systems, novel traits are being targeted for selection. Additionally, emerging approaches such as genome editing and high-throughput phenotyping hold promise for further genetic progress. Future research should address the challenges of translating functional genomic findings into practical applications, while simultaneously harnessing analytical methods.

Pheno- and genotyping in vitro dauer juvenile recovery in the nematode Heterorhabditis bacteriophora

The entomopathogenic nematode (EPN) Heterorhabditis bacteriophora is an effective biological-control agent of insect pests. The dauer juveniles (DJs) seek for, infect insects, and release cells of the carried symbiotic bacterium of the genus Photorhabdus. Inside the host, the DJs perceive signals from the insect's haemolymph that trigger the exit from the arrested stage and the further development to mature adults. This developmental step is called DJ recovery. In commercial production, a high and synchronous DJ recovery determines the success of liquid-culture mass production. To enhance the understanding about genetic components regulating DJ recovery, more than 160 mutant- and 25 wild type inbred lines (WT ILs) were characterized for DJ recovery induced by cell-free bacterial supernatant. The mutant lines exhibited a broader DJ recovery range than WT ILs (4.6-67.2% vs 1.6-35.7%). A subset of mutant lines presented high variability of virulence against mealworm (Tenebrio molitor) (from 22 to 78% mortality) and mean time survival under oxidative stress (70 mM H2O2; from 10 to 151 h). Genotyping by sequencing of 96 mutant lines resulted in more than 150 single nucleotide polymorphisms (SNPs), of which four results are strongly associated with the DJ recovery trait. The present results are the basis for future approaches in improving DJ recovery by breeding under in vitro liquid-culture mass production in H. bacteriophora. This generated platform of EMS-mutants is as well a versatile tool for the investigation of many further traits of interest in EPNs. KEYPOINTS: • Exposure to bacterial supernatants of Photorhabdus laumondii induces the recovery of Heterorhabditis bacteriophora dauer juveniles (DJs). Both, the bacteria and the nematode partner, influence this response. However, the complete identity of its regulators is not known. • We dissected the genetic component of DJ recovery regulation in H. bacteriophora nematodes by generating a large array of EMS mutant lines and characterizing their recovery pheno- and genotypes. • We determined sets of mutants with contrasting DJ recovery and genotyped a subset of the EMS-mutant lines via genotyping by sequencing (GBS) and identified SNPs with significant correlation to the recovery trait.

Development of TaqMan PCR assay for genotyping SNP rs211250281 of the bovine agpat6 gene

Milk fat percentage is an important production trait of dairy cattle and is one of the goals of breeding programs. Over 95% of the milk fat accounts for triacylglycerols. AGPAT6 (1-acylglycerol-3-phosphate O-acyltransferase 6) catalyzes an intermediary step of triglyceride synthesis in the mammary cells. Genome-wide association studies identified SNP rs211250281 (g27: 36520069 G/T) in the agpat6 gene associated with milk fat content and fat-to-protein ratio in dairy cattle. The article presents data on the development of TaqMan PCR assay for genotyping SNP rs211250281 of the bovine agpat6 gene. In this method, a primer pair, initiating amplification of 75-bp fragments of the agpat6 gene, and two allele-specific TaqMan probes are used. Identification of the G and T alleles is based on a comparison of the final fluorescence intensity of FAM and VIC dyes, respectively. The developed TaqMan PCR assay was validated by Sanger sequencing method. The results of both methods fully coincided, that confirmed high accuracy of the developed TaqMan PCR assay. This reliable, simple, rapid, and high-throughput method could be a suitable tool for studying the distribution of the SNP rs211250281 among different cattle breeds and its association with milk fat content.

Genome-wide association study for growth traits in Blanco Orejinegro and Romosinuano cattle

Growth traits are economically important characteristics for the genetic improvement of local cattle breeds. Genome-wide association studies (GWAS) provide valuable information to enhance the understanding on the genetics of complex traits. The aim of this study was to perform a GWAS to identify genomic regions and genes associated to birth weight, weaning weight adjusted for 240 days, 16 months, and 24 months weight in Romosinuano (ROMO) and Blanco Orejinegro (BON) cattle. A single-step genomic-BLUP was implemented using 596 BON and 569 ROMO individuals that were genotyped with an Illumina BovineSNP50 BeadChip. There were 25 regions of interest identified on different chromosomes, with few of them simultaneously associated with two or more growth traits and some were common to both breeds. The gene mapping allowed to find 173 annotations on these regions, from which 49 represent potential candidate genes with known growth-related functions in cattle and other species. Among the regions that were associated with several growth traits, that at 24 - 27 MB of BTA14, has important candidate genes such as LYPLA1, XKR4, TMEM68 and PLAG1. Another region of interest at 0.40-0.77 Mb of BTA23 was identified in both breeds, containing KHDRBS2 as a potential candidate gene influencing body weight. Future studies targeting these regions could provide more knowledge to uncover the genetic architecture underlying growth traits in BON and ROMO cattle. The genomic regions and genes identified in this study could be used to improve the prediction of genetic merit for growth traits in these creole cattle breeds.

Development of nutrigenomic based precision management model for Hanwoo steers

Focusing high marble deposition, Hanwoo feedlot system uses high-energy diet over the prolonged fattening period. However, due to the individual genetic variation, around 40% of them are graded into inferior quality grades (QG), despite they utilized the same resources. Therefore, focusing on development of a nutrigenomic based precision management model, this study was to evaluated the response to the divergent selection on genetic merit for marbling score (MS), under different dietary total digestible nutrient (TDN) levels. Total of 111 calves were genotyped and initially grouped according to estimated breeding value (high and low) for marbling score (MS-EBV). Subsequently, managed under two levels of feed TDN%, over the calf period, early, middle, and final fattening periods following 2 × 2 factorial arrangement. Carcasses were evaluated for MS, Back fat thickness (BFT) and Korean beef quality grading standard. As the direct response to the selection was significant, the results confirmed the importance of initial genetic grouping of Hanwoo steers for MS-EBV. However, dietary TDN level did not show an effect (p > 0.05) on the MS. Furthermore, no genetic-by-nutrition interaction for MS (p > 0.05) was also observed. The present results showed no correlation response on BFT (p > 0.05), which indicates that the selection based on MS-EBV can be used to enhance the MS without undesirable effect on BFT. Ultimate turnover of the Hanwoo feedlot operation is primarily determined by the QGs. The present model shows that the initial grouping for MS-EBV increased the proportion of carcasses graded for higher QGs (QG1⁺⁺ and QG1⁺) by approximately 20%. Moreover, there appear to be a potential to increase the proportion of QG 1⁺⁺ animals among the high-genetic group by further increasing the dietary energy content. Overall, this precision management strategy suggests the importance of adopting an MS based initial genetic grouping system for Hanwoo steers with a subsequent divergent management based on dietary energy level.

Аpplication of massive parallel reporter analysis in biotechnology and medicine

The development and functioning of an organism relies on tissue-specific gene programs. Genome regulatory elements play a key role in the regulation of such programs, and disruptions in their function can lead to the development of various pathologies, including cancers, malformations and autoimmune diseases. The emergence of high-throughput genomic studies has led to massively parallel reporter analysis (MPRA) methods, which allow the functional verification and identification of regulatory elements on a genome-wide scale. Initially MPRA was used as a tool to investigate fundamental aspects of epigenetics, but the approach also has great potential for clinical and practical biotechnology. Currently, MPRA is used for validation of clinically significant mutations, identification of tissue-specific regulatory elements, search for the most promising loci for transgene integration, and is an indispensable tool for creating highly efficient expression systems, the range of application of which extends from approaches for protein development and design of next-generation therapeutic antibody superproducers to gene therapy. In this review, the main principles and areas of practical application of high-throughput reporter assays will be discussed.

An Appropriate Genetic Approach for Improving Reproductive Traits in Crossbred Thai-Holstein Cattle under Heat Stress Conditions

Thailand is a tropical country affected by global climate change and has high temperatures and humidity that cause heat stress in livestock. A temperature−humidity index (THI) is required to assess and evaluate heat stress levels in livestock. One of the livestock types in Thailand experiencing heat stress due to extreme climate change is crossbred dairy cattle. Genetic evaluations of heat tolerance in dairy cattle have been carried out for reproductive traits. Heritability values for reproductive traits are generally low (<0.10) because environmental factors heavily influence them. Consequently, genetic improvement for these traits would be slow compared to production traits. Positive and negative genetic correlations were found between reproductive traits and reproductive traits and yield traits. Several selection methods for reproductive traits have been introduced, i.e., the traditional method, marker-assisted selection (MAS), and genomic selection (GS). GS is the most promising technique and provides accurate results with a high genetic gain. Single-step genomic BLUP (ssGBLUP) has higher accuracy than the multi-step equivalent for fertility traits or low-heritability traits.

Analysis of Autozygosity Using Whole-Genome Sequence Data of Full-Sib Families in Pikeperch (Sander lucioperca)

Pikeperch (Sander lucioperca) has emerged as a high value species to the aquaculture industry. However, its farming techniques are at an early stage and its production is often performed without a selective breeding program, potentially leading to high levels of inbreeding. In this study, we identified and characterized autozygosity based on genome-wide runs of homozygosity (ROH) on a sample of parental and offspring individuals, determined effective population size (N_e), and assessed relatedness among parental individuals. A mean of 2,235 ± 526 and 1,841 ± 363 ROH segments per individual, resulting in a mean inbreeding coefficient of 0.33 ± 0.06 and 0.25 ± 0.06 were estimated for the progeny and parents, respectively. N_e was about 12 until four generations ago and at most 106 for 63 generations in the past, with varying genetic relatedness amongst the parents. This study shows the importance of genomic information when family relationships are unknown and the need of selective breeding programs for reproductive management decisions in the aquaculture industry.

SAP30 Gene Is a Probable Regulator of Muscle Hypertrophy in Chickens

Animals with muscle hypertrophy phenotype are targeted by the broiler industry to increase the meat production and the quality of the final product. Studies characterizing the molecular machinery involved with these processes, such as quantitative trait loci studies, have been carried out identifying several candidate genes related to this trait; however, validation studies of these candidate genes in cell culture is scarce. The aim of this study was to evaluate SAP30 as a candidate gene for muscle development and to validate its function in cell culture in vitro. The SAP30 gene was downregulated in C2C12 muscle cell culture using siRNA technology to evaluate its impact on morphometric traits and gene expression by RNA-seq analysis. Modulation of SAP30 expression increased C2C12 myotube area, indicating a role in muscle hypertrophy. RNA-seq analysis identified several upregulated genes annotated in muscle development in treated cells (SAP30-knockdown), corroborating the role of SAP30 gene in muscle development regulation. Here, we provide experimental evidence of the involvement of SAP30 gene as a regulator of muscle cell hypertrophy.

Impact of genotypic errors with equal and unequal family contribution on accuracy of genomic prediction in aquaculture using simulation

Genotypic errors, conflict between recorded genotype and the true genotype, can lead to false or biased population genetic parameters. Here, the effect of genotypic errors on accuracy of genomic predictions and genomic relationship matrix are investigated using a simulation study based on population and genomic structure comparable to black tiger prawn, Penaeus monodon. Fifty full-sib families across five generations with phenotypic and genotypic information on 53 K SNPs were simulated. Ten replicates of different scenarios with three heritability estimates, equal and unequal family contributions were generated. Within each scenario, four SNP densities and three genotypic error rates in each SNP density were implemented. Results showed that family contribution did not have a substantial impact on accuracy of predictions across different datasets. In the absence of genotypic errors, 3 K SNP density was found to be efficient in estimating the accuracy, whilst increasing the SNP density from 3 to 20 K resulted in a marginal increase in accuracy of genomic predictions using the current population and genomic parameters. In addition, results showed that the presence of even 10% errors in a 10 and 20 K SNP panel might not have a severe impact on accuracy of predictions. However, below 10 K marker density, even a 5% error can result in lower accuracy of predictions.

Evaluation of Genomic Prediction for Fusarium Head Blight Resistance with a Multi-Parental Population

Simple Summary

Genomic selection is a promising approach to select superior wheat lines with better resistance to Fusarium head blight. The accuracy of genomic selection is determined by many factors. In this study, we found a training population with large size, genomic selection models incorporating biological information, and multi-environment modelling led to considerably better predictabilities. A training population designed by the coefficient of determination (CDmean) could increase accuracy of prediction. Relatedness between training population (TP) and testing population is the key for accuracies of genomic selection across populations.

Abstract

Fusarium head blight (FHB) resistance is quantitatively inherited, controlled by multiple minor effect genes, and highly affected by the interaction of genotype and environment. This makes genomic selection (GS) that uses genome-wide molecular marker data to predict the genetic breeding value as a promising approach to select superior lines with better resistance. However, various factors can affect accuracies of GS and better understanding how these factors affect GS accuracies could ensure the success of applying GS to improve FHB resistance in wheat. In this study, we performed a comprehensive evaluation of factors that affect GS accuracies with a multi-parental population designed for FHB resistance. We found larger sample sizes could get better accuracies. Training population designed by CDmean based optimization algorithms significantly increased accuracies than random sampling approach, while mean of predictor error variance (PEVmean) had the poorest performance. Different genomic selection models performed similarly for accuracies. Including prior known large effect quantitative trait loci (QTL) as fixed effect into the GS model considerably improved the predictability. Multi-traits models had almost no effects, while the multi-environment model outperformed the single environment model for prediction across different environments. By comparing within and across family prediction, better accuracies were obtained with the training population more closely related to the testing population. However, achieving good accuracies for GS prediction across populations is still a challenging issue for GS application.

Prediction of complex phenotypes using the Drosophila melanogaster metabolome

Understanding the genotype-phenotype map and how variation at different levels of biological organization is associated are central topics in modern biology. Fast developments in sequencing technologies and other molecular omic tools enable researchers to obtain detailed information on variation at DNA level and on intermediate endophenotypes, such as RNA, proteins and metabolites. This can facilitate our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila melanogaster Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We performed NMR metabolomics on four replicate pools of male flies from each of 170 different isogenic lines. Our results show that metabolite profiles are variable among the investigated lines and that this variation is highly heritable. Second, we identify genes associated with metabolome variation. Third, using the metabolome gave better prediction accuracies than genomic information for four of five quantitative traits analyzed. Our comprehensive characterization of population-scale diversity of metabolomes and its genetic basis illustrates that metabolites have large potential as predictors of organismal phenotypes. This finding is of great importance, e.g., in human medicine, evolutionary biology and animal and plant breeding.

Toward Genome-Based Selection in Asian Seabass: What Can We Learn From Other Food Fishes and Farm Animals?

Due to the steadily increasing need for seafood and the plateauing output of fisheries, more fish need to be produced by aquaculture production. In parallel with the improvement of farming methods, elite food fish lines with superior traits for production must be generated by selection programs that utilize cutting-edge tools of genomics. The purpose of this review is to provide a historical overview and status report of a selection program performed on a catadromous predator, the Asian seabass (Lates calcarifer, Bloch 1790) that can change its sex during its lifetime. We describe the practices of wet lab, farm and lab in detail by focusing onto the foundations and achievements of the program. In addition to the approaches used for selection, our review also provides an inventory of genetic/genomic platforms and technologies developed to (i) provide current and future support for the selection process; and (ii) improve our understanding of the biology of the species. Approaches used for the improvement of terrestrial farm animals are used as examples and references, as those processes are far ahead of the ones used in aquaculture and thus they might help those working on fish to select the best possible options and avoid potential pitfalls.

Opportunities and Challenges for Improving the Productivity of Swamp Buffaloes in Southeastern Asia

The swamp buffalo is a domesticated animal commonly found in Southeast Asia. It is a highly valued agricultural animal for smallholders, but the production of this species has unfortunately declined in recent decades due to rising farm mechanization. While swamp buffalo still plays a role in farmland cultivation, this species' purposes has shifted from draft power to meat, milk, and hide production. The current status of swamp buffaloes in Southeast Asia is still understudied compared to its counterparts such as the riverine buffaloes and cattle. This review discusses the background of swamp buffalo, with an emphasis on recent work on this species in Southeast Asia, and associated genetics and genomics work such as cytogenetic studies, phylogeny, domestication and migration, genetic sequences and resources. Recent challenges to realize the potential of this species in the agriculture industry are also discussed. Limited genetic resource for swamp buffalo has called for more genomics work to be done on this species including decoding its genome. As the economy progresses and farm mechanization increases, research and development for swamp buffaloes are focused on enhancing its productivity through understanding the genetics of agriculturally important traits. The use of genomic markers is a powerful tool to efficiently utilize the potential of this animal for food security and animal conservation. Understanding its genetics and retaining and maximizing its adaptability to harsher environments are a strategic move for food security in poorer nations in Southeast Asia in the face of climate change.

A SNP of the hemocyanin gene (LvHc) is a marker for high growth and ammonia-tolerance in Pacific white shrimp Litopenaeus vannamei

Expression levels of hemocyanin (LvHc), activating transcription factor 4 (LvAtf4), glutathione S-transferase (LvGst), caspase 2 (LvCasp2) and anti-lipopolysaccharide factor (LvAlf) were examined in the hepatopancreas of Pacific white shrimp Litopenaeus vannamei juveniles exposed to a lethal concentration of ammonia-N (32.15 mg/l). The expression levels of all transcripts except LvAlf were significantly greater (P < 0.05) in tolerant shrimp (Lv-AT; N = 30) that survived up to 72 h post treatment (hpt) than in susceptible shrimp (Lv-AS24 and Lv-AS72; N = 45 and 15), that died within 24 h or between 24 and 72 hpt, respectively. Subsequently, effects of non-lethal concentrations of ammonia-N (control, 10 and 20 mg/l) on the expression of LvHc in juvenile shrimp were examined. Compared to the control, expression levels of LvHc transcripts in hemocytes and the hepatopancreas of tested shrimp changed after exposure to ammonia-N. One SNP (C > T₅₄₅) was found in the LvHc₃₂₂ gene segment. Real-time PCR amplification of specific alleles (real-time PASA) was developed for detection of C > T₅₄₅ genotypes. Juveniles in the lethal exposure test that carried a C/T₅₄₅ genotype showed a greater average body weight and total length (8.46 ± 0.36 g and 10.05 ± 0.16 cm) than those with a C/C₅₄₅ genotype (7.48 ± 0.31 g and 9.60 ± 0.13 cm) (P < 0.05). Similar results were found in the second generation (G2) of a growth-improved stock (3 and 4 families of BIOTEC-G2-L1 and BIOTEC-G2-L2) and in commercially farmed shrimp (2 groups). Accordingly, expression levels and SNP of LvHc can serve as markers for selection high growth performance in ammonia-tolerant L. vannamei.

Transcriptome Analysis and Expression of Selected Cationic Amino Acid Transporters in the Liver of Broiler Chicken Fed Diets with Varying Concentrations of Lysine

Amino acids are known to play a key role in gene expression regulation. Amino acid signaling is mediated via two pathways: the mammalian target of rapamycin complex 1 (mTORC1) and the amino acid responsive (AAR) pathways. Cationic amino acid transporters (CATs) are crucial in these pathways due to their sensing, signaling and transport functions. The availability of certain amino acids plays a key role in the intake of other amino acids, hence affecting growth in young birds. However, the specific mechanism for regulating lysine transport for growth is not clear. In this study, we analyze the transcriptome profiles and mRNA expression of selected cationic amino acid transporters in the livers of broilers fed low and high lysine diets. Birds consumed high-lysine (1.42% lysine) or low-lysine (0.85% lysine) diets while the control group consumed 1.14% lysine diet. These concentrations of lysine represent 125% (high lysine), 75% (low lysine) and 100% (control), respectively, of the National Research Council's (NRC) recommendation for broiler chickens. After comparing the two groups, 210 differentially expressed genes (DEGs) were identified (fold change >1 and false discovery rate (FDR) <0.05). When comparing the high lysine and the low lysine treatments, there were 67 upregulated genes and 143 downregulated genes among these DEGs. Analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) and the Gene Ontology (GO) enrichment analysis show that cellular growth, lipid metabolism and lysine metabolism pathways were among the significantly enriched pathways. This study contributes to a better understanding of the potential molecular mechanisms underlying the correlation between lysine intake, body weight gain (BWG) and feed intake (FI) in broiler chickens. Moreover, the DEGs obtained in this study may be used as potential candidate genes for further investigation of broiler growth customized responses to individualized nutrients such as amino acids.

Variation in autosomal and sex-linked genetic effects for growth traits in Markhoz goat using multivariate animal models

The present study was conducted to estimate autosomal and sex-linked genetic parameters by restricted maximum likelihood method using four different multivariate models for growth traits in Markhoz goat. Data were collected over a period of 23 years (1992-2014) from the breeding station of Markhoz goat. The studied growth traits were birth weight (BW), weaning weight (WW), 6-month weight (6WT), 9-month weight (9WT), and 12-month weight (12WT). The best model was determined by Akaike's information criterion. Based on the best model, direct autosomal heritabilities of 0.18, 0.10, 0.12, 0.22, and 0.24 were obtained for BW, WW, 6WT, 9WT, and 12WT, respectively. On the other hand, estimates of direct sex-linked heritability for BW, WW, 6WT, 9WT, and 12WT were 0.02, 0.01, 0.002, 0.03, and 0.04, respectively. The maternal permanent environmental component was remarkable for growth traits. Direct autosomal additive genetic correlations among the traits were positive and varied from 0.11 between BW and 9WT to 0.95 between BW and WW. The positive correlations were also estimated for direct sex-linked additive genetic effect except between BW-6WT (- 0.07) and WW-6WT (-0.19). The result of this study indicated that sex-linked chromosome effect had more considerable influence at post-weaning traits.

A review of traditional and machine learning methods applied to animal breeding

The current livestock management landscape is transitioning to a high-throughput digital era where large amounts of information captured by systems of electro-optical, acoustical, mechanical, and biosensors is stored and analyzed on a daily and hourly basis, and actionable decisions are made based on quantitative and qualitative analytic results. While traditional animal breeding prediction methods have been used with great success until recently, the deluge of information starts to create a computational and storage bottleneck that could lead to negative long-term impacts on herd management strategies if not handled properly. A plethora of machine learning approaches, successfully used in various industrial and scientific applications, made their way in the mainstream approaches for livestock breeding techniques, and current results show that such methods have the potential to match or surpass the traditional approaches, while most of the time they are more scalable from a computational and storage perspective. This article provides a succinct view on what traditional and novel prediction methods are currently used in the livestock breeding field, how successful they are, and how the future of the field looks in the new digital agriculture era.

Genomic Signatures of Selection Associated With Litter Size Trait in Jining Gray Goat

Litter size (LS), an important economic trait in livestock, is so complicate that involves many aspects of reproduction, the underlying mechanism of which particularly in goat has always been scanty. To uncover the genetic basis of LS, the genomic sequence of Jining Gray goat groups (one famous breed for high prolificacy in China) with LS 1, 2, and 3 for firstborn was analyzed, obtaining 563.67 Gb sequence data and a total of 31,864,651 high-quality single nucleotide polymorphisms loci were identified. Particularly, the increased heterozygosity in higher LS groups, and large continuous homozygous segments associated with lower LS group had been uncovered. Through an integrated analysis of three popular methods for detecting selective sweeps (Fst, nucleotide diversity, and Tajima’s D statistic), 111 selected regions and 42 genes associated with LS were scanned genome wide. The candidate genes with highest selective signatures included KIT, KCNH7, and KMT2E in LS2 and PAK1, PRKAA1, and SMAD9 in LS3 group, respectively. Meanwhile, functional terms of programmed cell death involved in cell development and regulation of insulin receptor signaling pathway were mostly enriched with 42 candidate genes, which also included reproduction related terms of steroid metabolic process and cellular response to hormone stimulus. In conclusion, our study identified novel candidate genes involving in regulation of LS in goat, which expand our understanding of genetic fundament of reproductive ability, and the novel insights regarding to LS would be potentially applied to improve reproductive performance.

Evaluation of SNP Genotyping in Alpacas Using the Bovine HD Genotyping Beadchip

Alpacas are one of four South American Camelid species living in the highlands of the Andes. Production of alpaca fiber contributes to the economy of the region and the livelihood of many rural families. Fiber quantity and quality are important and in need of a modern breeding program based on genomic selection to accelerate genetic gain. To achieve this is necessary to discover enough molecular markers, single nucleotide polymorphisms (SNPs) in particular, to provide genome coverage and facilitate genome wide association studies to fiber production characteristics. The aim of this study was to discover alpaca SNPs by genotyping forty alpaca DNA samples using the BovineHD Genotyping Beadchip. Data analysis was performed with GenomeStudio (Illumina) software. Because different filters and thresholds are reported in the literature we investigated the effects of no-call threshold (≥0.05, ≥0.15, and ≥0.25) and call frequency (≥0.9 and =1.0) in identifying positive SNPs. Average GC Scores, calculated as the average of the 10% and 50% GenCall scores for each SNP (≥0.70) and the GenTrain score ≥ 0.25 parameters were applied to all comparisons. SNPs with minor allele frequency (MAF) ≥ 0.05 or ≥ 0.01 were retained. Since detection of SNPs is based on the stable binding of oligonucleotide probes to the target DNA immediately adjacent to the variant nucleotide, all positive SNP flanking sequences showing perfect alignments between the bovine and alpaca genomes for the first 21 or 26 nucleotides flanking the variant nucleotide at either side were selected. Only SNPs localized in one scaffold were assumed unique. Unique SNPs identified in both reference genomes were kept and mapped on the Vicugna_pacos 2.0.2 genome. The effects of the no-call threshold ≥ 0.25, call frequency = 1 and average GC ≥ 0.7 were meaningful and identified 6756 SNPs of which 400 were unique and polymorphic (MAF ≥ 0.01). Assignment to alpaca chromosomes was possible for 292 SNPs. Likewise, 209 SNPs were localized in 202 alpaca gene loci and 29 of these share the same loci with the dromedary. Interestingly, 69 of 400 alpaca SNPs have 100% similarity with dromedary.

High-Density Linkage Map and QTLs for Growth in Snapper (Chrysophrys auratus)

Characterizing the genetic variation underlying phenotypic traits is a central objective in biological research. This research has been hampered in the past by the limited genomic resources available for most non-model species. However, recent advances in sequencing technologies and related genotyping methods are rapidly changing this. Here we report the use of genome-wide SNP data from the ecologically and commercially important marine fish species Chrysophrys auratus (snapper) to 1) construct the first linkage map for this species, 2) scan for growth QTL, and 3) search for putative candidate genes in the surrounding QTL regions. The newly constructed linkage map contained ∼11K SNP markers and is one of the densest maps to date in the fish family Sparidae. Comparisons with genome scaffolds of the recently assembled snapper genome indicated that marker placement was mostly consistent between the scaffolds and linkage map (R = 0.7), but that at fine scales (< 5 cM) some precision limitations occurred. Of the 24 linkage groups, which likely reflect the 24 chromosomes of this species, three were found to contain QTL with genome-wide significance for growth-related traits. A scan of 13 candidate growth genes located the growth hormone, myogenin, and parvalbumin genes within 5.3, 9.6, and 25.0 cM of these QTL, respectively. The linkage map and QTL found in this study will advance the investigation of genome structure and aquaculture breeding efforts in this and related species.

Enhancing the rate of genetic gain in public-sector plant breeding programs: lessons from the breeder's equation

The integration of new technologies into public plant breeding programs can make a powerful step change in agricultural productivity when aligned with principles of quantitative and Mendelian genetics. The breeder's equation is the foundational application of quantitative genetics to crop improvement. Guided by the variables that describe response to selection, emerging breeding technologies can make a powerful step change in the effectiveness of public breeding programs. The most promising innovations for increasing the rate of genetic gain without greatly increasing program size appear to be related to reducing breeding cycle time, which is likely to require the implementation of parent selection on non-inbred progeny, rapid generation advance, and genomic selection. These are complex processes and will require breeding organizations to adopt a culture of continuous optimization and improvement. To enable this, research managers will need to consider and proactively manage the, accountability, strategy, and resource allocations of breeding teams. This must be combined with thoughtful management of elite genetic variation and a clear separation between the parental selection process and product development and advancement process. With an abundance of new technologies available, breeding teams need to evaluate carefully the impact of any new technology on selection intensity, selection accuracy, and breeding cycle length relative to its cost of deployment. Finally breeding data management systems need to be well designed to support selection decisions and novel approaches to accelerate breeding cycles need to be routinely evaluated and deployed.

Genome Association Study for Visual Scores in Nellore Cattle Measured at Weaning

BACKGROUND

Genome-wide association studies (GWAS) are utilized in cattle to identify regions or genetic variants associated with phenotypes of interest, and thus, to identify design strategies that allow for the increase of the frequency of favorable alleles. Visual scores are important traits of cattle production in Brazil because they are utilized as selection criteria, helping to choose more harmonious animals. Despite its importance, there are still no studies on the genome association for these traits. This study aimed to identify genome regions associated with the traits of conformation, precocity and muscling, based on a visual score measured at weaning.

RESULTS

Bayesian approaches with BayesC and Bayesian LASSO were utilized with 2873 phenotypes of Nellore cattle for a GWAS. The animals were genotyped with Illumina BovineHD BeadChip, and a total of 309,865 SNPs were utilized after quality control. In the analyses, phenotype and deregressed breeding values were utilized as dependent variables; a threshold model was utilized for the former and a linear model for the latter. The association criterion was the percentage of genetic variance explained by SNPs found in 1 Mb-long windows. The Bayesian approach BayesC was better adjusted to the data because it could explain a larger phenotypic variance for both dependent variables.

CONCLUSIONS

There were no large effects for the visual scores, indicating that they have a polygenic nature; however, regions in chromosomes 1, 3, 5, 7, 14, 15, 16, 19, 20 and 23 were identified and explained a large part of the genetic variance.

Bayesian analysis and prediction of hybrid performance

BACKGROUND

The selection of hybrids is an essential step in maize breeding. However, evaluating a large number of hybrids in field trials can be extremely costly. However, genomic models can be used to predict the expected performance of un-tested genotypes. Bayesian models offer a very flexible framework for hybrid prediction. The Bayesian methodology can be used with parametric and semi-parametric assumptions for additive and non-additive effects. Furthermore, samples from the posterior distribution of Bayesian models can be used to estimate the variance due to general and specific combining abilities even in cases where additive and non-additive effects are not mutually orthogonal. Also, the use of Bayesian models for analysis and prediction of hybrid performance has remained fairly limited.

RESULTS

We provided an overview of Bayesian parametric and semi-parametric genomic models for prediction of agronomic traits in maize hybrids and discussed how these models can be used to decompose the genotypic variance into components due to general and specific combining ability. We applied the methodology to data from 906 single cross tropical maize hybrids derived from a convergent population. Our results show that: (1) non-additive effects make a sizable contribution to the genetic variance of grain yield; however, the relative importance of non-additive effects was much smaller for ear and plant height; (2) genomic prediction can achieve relatively high accuracy in predicting phenotypes of un-tested hybrids and in pre-screening.

CONCLUSIONS

Genomic prediction can be a useful tool in pre-screening of hybrids and could contribute to the improvement of the efficiency and efficacy of maize hybrids breeding programs. The Bayesian framework offers a great deal of flexibility in modeling hybrid performance. The methodology can be used to estimate important genetic parameters and render predictions of the expected hybrid performance as well measures of uncertainty about such predictions.

Single nucleotide polymorphisms in candidate genes associated with milk yield in Argentinean Holstein and Holstein x Jersey cows

BACKGROUND

Research on loci influencing milk production traits of dairy cattle is one of the main topics of investigation in livestock. Many genomic regions and polymorphisms associated with dairy production have been reported worldwide. In this context, the purpose of this study was to identify candidate loci associated with milk yield in Argentinean dairy cattle. A database of candidate genes and single nucleotide polymorphisms (SNPs) for milk production and composition was developed. Thirty-nine SNPs belonging to 22 candidate genes were genotyped on 1643 animals (Holstein and Holstein x Jersey). The genotypes obtained were subjected to association studies considering the whole population and discriminating the population by Holstein breed percentage. Phenotypic data consisted of milk production values recorded during the first lactation of 1156 Holstein and 462 Holstein x Jersey cows from 18 dairy farms located in the central dairy area of Argentina. From these records, 305-day cumulative milk production values were predicted.

RESULTS

Eight SNPs (rs43375517, rs29004488, rs132812135, rs137651874, rs109191047, rs135164815, rs43706485, and rs41255693), located on six Bos taurus autosomes (BTA4, BTA6, BTA19, BTA20, BTA22, and BTA26), showed suggestive associations with 305-day cumulative milk production (under Benjamini-Hochberg procedure with a false discovery rate of 0.1). Two of those SNPs (rs43375517 and rs135164815) were significantly associated with milk production (Bonferroni adjusted p-values < 0.05) when considering the Holstein population.

CONCLUSIONS

The results obtained are consistent with previously reported associations in other Holstein populations. Furthermore, the SNPs found to influence bovine milk production in this study may be used as possible candidate SNPs for marker-assisted selection programs in Argentinean dairy cattle.

Phenotyping Whole Forests Will Help to Track Genetic Performance

Phenotyping is the accurate and precise physical description of organisms. Accurate and quantitative phenotyping underpins the delivery of benefits from genetic improvement programs in agriculture. In forest trees, phenotyping at an equivalent precision has been impossible because trees and forests are large, long-lived, and highly variable. These facts have restricted the delivery of genetic gains in forestry compared to other agricultural sectors. We describe a landscape-scale phenotyping platform that integrates remote sensing, spatial information systems, and genomics to facilitate the delivery of greater gains enabling forestry to catch up with other sectors. Combining remote sensing at a range of spatial and temporal scales with genomics will ultimately impact on tree breeding globally.

The State of "Omics" Research for Farmed Penaeids: Advances in Research and Impediments to Industry Utilization

Elucidating the underlying genetic drivers of production traits in agricultural and aquaculture species is critical to efforts to maximize farming efficiency. "Omics" based methods (i.e., transcriptomics, genomics, proteomics, and metabolomics) are increasingly being applied to gain unprecedented insight into the biology of many aquaculture species. While the culture of penaeid shrimp has increased markedly, the industry continues to be impeded in many regards by disease, reproductive dysfunction, and a poor understanding of production traits. Extensive effort has been, and continues to be, applied to develop critical genomic resources for many commercially important penaeids. However, the industry application of these genomic resources, and the translation of the knowledge derived from "omics" studies has not yet been completely realized. Integration between the multiple "omics" resources now available (i.e., genome assemblies, transcriptomes, linkage maps, optical maps, and proteomes) will prove critical to unlocking the full utility of these otherwise independently developed and isolated resources. Furthermore, emerging "omics" based techniques are now available to address longstanding issues with completing keystone genome assemblies (e.g., through long-read sequencing), and can provide cost-effective industrial scale genotyping tools (e.g., through low density SNP chips and genotype-by-sequencing) to undertake advanced selective breeding programs (i.e., genomic selection) and powerful genome-wide association studies. In particular, this review highlights the status, utility and suggested path forward for continued development, and improved use of "omics" resources in penaeid aquaculture.

Functional Validation of Candidate Genes Detected by Genomic Feature Models

Understanding the genetic underpinnings of complex traits requires knowledge of the genetic variants that contribute to phenotypic variability. Reliable statistical approaches are needed to obtain such knowledge. In genome-wide association studies, variants are tested for association with trait variability to pinpoint loci that contribute to the quantitative trait. Because stringent genome-wide significance thresholds are applied to control the false positive rate, many true causal variants can remain undetected. To ameliorate this problem, many alternative approaches have been developed, such as genomic feature models (GFM). The GFM approach tests for association of set of genomic markers, and predicts genomic values from genomic data utilizing prior biological knowledge. We investigated to what degree the findings from GFM have biological relevance. We used the Drosophila Genetic Reference Panel to investigate locomotor activity, and applied genomic feature prediction models to identify gene ontology (GO) categories predictive of this phenotype. Next, we applied the covariance association test to partition the genomic variance of the predictive GO terms to the genes within these terms. We then functionally assessed whether the identified candidate genes affected locomotor activity by reducing gene expression using RNA interference. In five of the seven candidate genes tested, reduced gene expression altered the phenotype. The ranking of genes within the predictive GO term was highly correlated with the magnitude of the phenotypic consequence of gene knockdown. This study provides evidence for five new candidate genes for locomotor activity, and provides support for the reliability of the GFM approach.

Mapping of genome-wide copy number variations in the Iranian indigenous cattle using a dense SNP data set

The objective of this study was to present the first map of the copy number variations (CNVs) in Iranian indigenous cattle based on a high-density single nucleotide polymorphism (SNP) dataset. A total of 90 individuals were genotyped using the Illumina BovineHD BeadChip containing 777 962 SNPs. The QuantiSNP algorithm was used to perform a genome-wide CNV detection across autosomal genome. After merging the overlapping CNV, a total of 221 CNV regions were identified encompassing 36.4 Mb or 1.44% of the bovine autosomal genome. The length of the CNV regions ranged from 3.5 to 2252.8 Kb with an average of 163.8 Kb. These regions included 147 loss (66.52%) and 74 gain (33.48%) events containing a total of 637 annotated Ensembl genes. Gene ontology analysis revealed that most of genes in the CNV regions were involved in environmental responses, disease susceptibility and immune system functions. Furthermore, 543 of these genes corresponded to the human orthologous genes, which involved in a wide range of biological functions. Altogether, 73% of the 221 CNV regions overlapped either completely or partially with those previously reported in other cattle studies. Moreover, novel CNV regions involved several quantitative trait loci (QTL)-related to adaptative traits of Iranian indigenous cattle. These results provided a basis to conduct future studies on association between CNV regions and phenotypic variations in the Iranian indigenous cattle.

O-GlcNAcylation mediates the control of cytosolic phosphoenolpyruvate carboxykinase activity via Pgc1α

PGC1α is a coactivator of many transcription factors and cytosolic phosphoenolpyruvate carboxykinase (PCK1) is a key enzyme for gluconeogenesis. PGC1α interacts with the transcription factor PPARγ to stimulate PCK1 expression and thus de novo glucose synthesis. These proteins are not only important for central energy metabolism but also for supplying intermediates for other metabolic pathways, including lipidogenesis and protein synthesis and might therefore be important factors in the ethiopathogenesis of metabolic disorders like diabetes but also in other pathologies like cancer. Since polymorphisms in these proteins have been related to some phenotypic traits in animals like pigs and PGC1α G482S polymorphism increases fat deposition in humans, we have investigated the molecular basis of such effects focusing on a commonly studied polymorphism in pig Pgc1α, which changes a cysteine at position 430 (WT) of the protein to a serine (C430S). Biochemical analyses show that Pgc1α WT stimulates higher expression of human PCK1 in HEK293T and HepG2 cells. Paradoxically, Pgc1α WT is less stable than Pgc1α p.C430S in HEK293T cells. However, the study of different post-translational modifications shows a higher O-GlcNAcylation level of Pgc1α p.C430S. This higher O-GlcNAcylation level significantly decreases the interaction between Pgc1α and PPARγ demonstrating the importance of post-translational glycosylation of PGC1α in the regulation of PCK1 activity. This, furthermore, could explain at least in part the observed epistatic effects between PGC1α and PCK1 in pigs.

The Valdostana goat: a genome-wide investigation of the distinctiveness of its selective sweep regions

The Valdostana goat is an alpine breed, raised only in the northern Italian region of the Aosta Valley. This breed's main purpose is to produce milk and meat, but is peculiar for its involvement in the "Batailles de Chèvres," a recent tradition of non-cruel fight tournaments. At both the genetic and genomic levels, only a very limited number of studies have been performed with this breed and there are no studies about the genomic signatures left by selection. In this work, 24 unrelated Valdostana animals were screened for runs of homozygosity to identify highly homozygous regions. Then, six different approaches (ROH comparison, Fst single SNPs and windows based, Bayesian, Rsb, and XP-EHH) were applied comparing the Valdostana dataset with 14 other Italian goat breeds to confirm regions that were different among the comparisons. A total of three regions of selection that were also unique among the Valdostana were identified and located on chromosomes 1, 7, and 12 and contained 144 genes. Enrichment analyses detected genes such as cytokines and lymphocyte/leukocyte proliferation genes involved in the regulation of the immune system. A genetic link between an aggressive challenge, cytokines, and immunity has been hypothesized in many studies both in humans and in other species. Possible hypotheses associated with the signals of selection detected could be therefore related to immune-related factors as well as with the peculiar battle competition, or other breed-specific traits, and provided insights for further investigation of these unique regions, for the understanding and safeguard of the Valdostana breed.

Fifteen years of quantitative trait loci studies in fish: challenges and future directions

Understanding the genetic basis of phenotypic variation is a major challenge in biology. Here, we systematically evaluate 146 quantitative trait loci (QTL) studies on teleost fish over the last 15 years to investigate (i) temporal trends and (ii) factors affecting QTL detection and fine-mapping. The number of fish QTL studies per year increased over the review period and identified a cumulative number of 3632 putative QTLs. Most studies used linkage-based mapping approaches and were conducted on nonmodel species with limited genomic resources. A gradual and moderate increase in the size of the mapping population and a sharp increase in marker density from 2011 onwards were observed; however, the number of QTLs and variance explained by QTLs changed only minimally over the review period. Based on these findings, we discuss the causative factors and outline how larger sample sizes, phenomics, comparative genomics, epigenetics and software development could improve both the quantity and quality of QTLs in future genotype-phenotype studies. Given that the technical limitations on DNA sequencing have mostly been overcome in recent years, a renewed focus on these and other study design factors will likely lead to significant improvements in QTL studies in the future.

Genomic predictions can accelerate selection for resistance against Piscirickettsia salmonis in Atlantic salmon (Salmo salar)

BACKGROUND

Salmon Rickettsial Syndrome (SRS) caused by Piscirickettsia salmonis is a major disease affecting the Chilean salmon industry. Genomic selection (GS) is a method wherein genome-wide markers and phenotype information of full-sibs are used to predict genomic EBV (GEBV) of selection candidates and is expected to have increased accuracy and response to selection over traditional pedigree based Best Linear Unbiased Prediction (PBLUP). Widely used GS methods such as genomic BLUP (GBLUP), SNPBLUP, Bayes C and Bayesian Lasso may perform differently with respect to accuracy of GEBV prediction. Our aim was to compare the accuracy, in terms of reliability of genome-enabled prediction, from different GS methods with PBLUP for resistance to SRS in an Atlantic salmon breeding program. Number of days to death (DAYS), binary survival status (STATUS) phenotypes, and 50 K SNP array genotypes were obtained from 2601 smolts challenged with P. salmonis. The reliability of different GS methods at different SNP densities with and without pedigree were compared to PBLUP using a five-fold cross validation scheme.

RESULTS

Heritability estimated from GS methods was significantly higher than PBLUP. Pearson's correlation between predicted GEBV from PBLUP and GS models ranged from 0.79 to 0.91 and 0.79-0.95 for DAYS and STATUS, respectively. The relative increase in reliability from different GS methods for DAYS and STATUS with 50 K SNP ranged from 8 to 25% and 27-30%, respectively. All GS methods outperformed PBLUP at all marker densities. DAYS and STATUS showed superior reliability over PBLUP even at the lowest marker density of 3 K and 500 SNP, respectively. 20 K SNP showed close to maximal reliability for both traits with little improvement using higher densities.

CONCLUSIONS

These results indicate that genomic predictions can accelerate genetic progress for SRS resistance in Atlantic salmon and implementation of this approach will contribute to the control of SRS in Chile. We recommend GBLUP for routine GS evaluation because this method is computationally faster and the results are very similar with other GS methods. The use of lower density SNP or the combination of low density SNP and an imputation strategy may help to reduce genotyping costs without compromising gain in reliability.

Impact of young ewe fertility rate on risk and genetic gain in sheep-breeding programs using genomic selection

Genomic selection could be useful in sheep-breeding programs, especially if rams and ewes are first mated at an earlier age than is the current industry practice. However, young-ewe (1 year old) fertility rates are known to be lower and more variable than those of mature ewes. The aim of the present study was to evaluate how young-ewe fertility rate affects risk and expected genetic gain in Australian sheep-breeding programs that use genomic information and select ewes and rams at different ages. The study used stochastic simulation to model different flock age structures and young-ewe fertility levels with and without genomic information for Merino and maternal sheep-breeding programs. The results from 10 years of selection were used to compare breeding programs on the basis of the mean and variation in genetic gain. Ram and ewe age, availability of genomic information on males and young-ewe fertility level all significantly (P &lt; 0.05) affected expected genetic gain. Higher young-ewe fertility rates significantly increased expected genetic gain. Low fertility rate of young ewes (10%) resulted in net genetic gain similar to not selecting ewes until they were 19 months old and did not increase breeding-program risk, as the likelihood of genetic gain being lower than the range of possible solutions from a breeding program with late selection of both sexes was zero. Genomic information was of significantly (P &lt; 0.05) more value for 1-year-old rams than for 2-year-old rams. Unless genomic information was available, early mating of rams offered no greater gain in Merino breeding programs and increased breeding-program risk. It is concluded that genomic information decreases the risk associated with selecting replacements at 7 months of age. Genetic progress is unlikely to be adversely affected if fertility levels above 10% can be achieved. Whether the joining of young ewes is a viable management decision for a breeder will depend on the fertility level that can be achieved in their young ewes and on other costs associated with the early mating of ewes.

Genome-Wide Association Study for Identifying Loci that Affect Fillet Yield, Carcass, and Body Weight Traits in Rainbow Trout (Oncorhynchus mykiss)

Fillet yield (FY, %) is an economically-important trait in rainbow trout aquaculture that affects production efficiency. Despite that, FY has received little attention in breeding programs because it is difficult to measure on a large number of fish and cannot be directly measured on breeding candidates. The recent development of a high-density SNP array for rainbow trout has provided the needed tool for studying the underlying genetic architecture of this trait. A genome-wide association study (GWAS) was conducted for FY, body weight at 10 (BW10) and 13 (BW13) months post-hatching, head-off carcass weight (CAR), and fillet weight (FW) in a pedigreed rainbow trout population selectively bred for improved growth performance. The GWAS analysis was performed using the weighted single-step GBLUP method (wssGWAS). Phenotypic records of 1447 fish (1.5 kg at harvest) from 299 full-sib families in three successive generations, of which 875 fish from 196 full-sib families were genotyped, were used in the GWAS analysis. A total of 38,107 polymorphic SNPs were analyzed in a univariate model with hatch year and harvest group as fixed effects, harvest weight as a continuous covariate, and animal and common environment as random effects. A new linkage map was developed to create windows of 20 adjacent SNPs for use in the GWAS. The two windows with largest effect for FY and FW were located on chromosome Omy9 and explained only 1.0-1.5% of genetic variance, thus suggesting a polygenic architecture affected by multiple loci with small effects in this population. One window on Omy5 explained 1.4 and 1.0% of the genetic variance for BW10 and BW13, respectively. Three windows located on Omy27, Omy17, and Omy9 (same window detected for FY) explained 1.7, 1.7, and 1.0%, respectively, of genetic variance for CAR. Among the detected 100 SNPs, 55% were located directly in genes (intron and exons). Nucleotide sequences of intragenic SNPs were blasted to the Mus musculus genome to create a putative gene network. The network suggests that differences in the ability to maintain a proliferative and renewable population of myogenic precursor cells may affect variation in growth and fillet yield in rainbow trout.

Assessing Predictive Properties of Genome-Wide Selection in Soybeans

Many economically important traits in plant breeding have low heritability or are difficult to measure. For these traits, genomic selection has attractive features and may boost genetic gains. Our goal was to evaluate alternative scenarios to implement genomic selection for yield components in soybean (Glycine max L. merr). We used a nested association panel with cross validation to evaluate the impacts of training population size, genotyping density, and prediction model on the accuracy of genomic prediction. Our results indicate that training population size was the factor most relevant to improvement in genome-wide prediction, with greatest improvement observed in training sets up to 2000 individuals. We discuss assumptions that influence the choice of the prediction model. Although alternative models had minor impacts on prediction accuracy, the most robust prediction model was the combination of reproducing kernel Hilbert space regression and BayesB. Higher genotyping density marginally improved accuracy. Our study finds that breeding programs seeking efficient genomic selection in soybeans would best allocate resources by investing in a representative training set.

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.

Linkage disequilibrium, persistence of phase and effective population size estimates in Hereford and Braford cattle

Background

The existence of moderate to high levels of linkage disequilibrium (LD) between genetic markers and quantitative trait loci (QTL) affecting traits of interest is fundamental for the success of genome-wide association (GWAS) and genomic selection (GS) studies. Knowledge about the extent and the pattern of LD in livestock populations is essential to determine the density of single nucleotide polymorphisms (SNP) required for accurate GWAS and GS. Moreover, observed LD is related to historical effective population sizes (N_e), and can provide insights into the genetic diversity history of populations. Estimates of the consistency of linkage phase across breeds (R_H,B) can be used to determine if there is sufficient relationship to use pooled reference populations in multi-breed GS programs. The objective of this study was to estimate LD levels, persistence of phase and effective population size in Hereford and Braford cattle populations sampled in Brazil.

Results

Mean LD estimates, measured using the squared correlation of alleles at two loci (r²), obtained between adjacent SNP across all chromosomes were 0.21 ± 0.27 for Herefords (391 samples with 41,241 SNP) and 0.16 ± 0.22 for Brafords (2044 samples and 41,207 SNP). Estimated r² was > 0.2 and 0.3, respectively, for 34 and 25 % of adjacent markers in Herefords, and 26 and 17 % in Brafords. Estimated N_e for Brafords and Herefords at the current generation was 220 and 153 individuals, respectively. The two breeds demonstrated moderate to strong persistence of phase at all distances (R_H,B = 0.53 to 0.97). The largest phase correlations were found in the 0 to 50 Kb bins (R_H,B = 0.92 to 0.97). Estimated LD decreased rapidly with increasing distance between SNP, however, useful linkage for GWAS and GS (r² > 0.2) was found spanning to ~50 Kb.

Conclusions

Panels containing about 50,000 and 150,000 SNP markers are necessary to detect minimal levels of LD between adjacent markers that would be useful for GWAS and GS studies to Hereford and Braford breeds, respectively. Markers are expected to be linked to the same QTL alleles in distances < 50 Kb in both populations due to observed high persistence of phase levels.

Genome-wide association analysis reveals loci associated with resistance against Piscirickettsia salmonis in two Atlantic salmon (Salmo salar L.) chromosomes

Background

Pisciricketssia salmonis is the causal agent of Salmon Rickettsial Syndrome (SRS), which affects salmon species and causes severe economic losses. Selective breeding for disease resistance represents one approach for controlling SRS in farmed Atlantic salmon. Knowledge concerning the architecture of the resistance trait is needed before deciding on the most appropriate approach to enhance artificial selection for P. salmonis resistance in Atlantic salmon. The purpose of the study was to dissect the genetic variation in the resistance to this pathogen in Atlantic salmon.

Methods

2,601 Atlantic salmon smolts were experimentally challenged against P. salmonis by means of intra-peritoneal injection. These smolts were the progeny of 40 sires and 118 dams from a Chilean breeding population. Mortalities were recorded daily and the experiment ended at day 40 post-inoculation. Fish were genotyped using a 50K Affymetrix® Axiom® myDesign^TM Single Nucleotide Polymorphism (SNP) Genotyping Array. A Genome Wide Association Analysis was performed on data from the challenged fish. Linear regression and logistic regression models were tested.

Results

Genome Wide Association Analysis indicated that resistance to P. salmonis is a moderately polygenic trait. There were five SNPs in chromosomes Ssa01 and Ssa17 significantly associated with the traits analysed. The proportion of the phenotypic variance explained by each marker is small, ranging from 0.007 to 0.045. Candidate genes including interleukin receptors and fucosyltransferase have been found to be physically linked with these genetic markers and may play an important role in the differential immune response against this pathogen.

Conclusions

Due to the small amount of variance explained by each significant marker we conclude that genetic resistance to this pathogen can be more efficiently improved with the implementation of genetic evaluations incorporating genotype information from a dense SNP array.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2038-7) contains supplementary material, which is available to authorized users.

Comparison of significant single nucleotide polymorphisms selections in GWAS for complex traits

The goal of this study was to compare significant SNP selection approaches in the context of complex traits based on SNP estimates obtained by models: a model fitting a single SNP (M1), a model fitting a single SNP and a random polygenic effect (M2), the nonparametric CAR score (M3), a SNP-BLUP model with random effects of all SNPs fitted simultaneously (M4). There were 46,267 SNPs tested in a population of 2601 Holstein Friesian bulls, four traits (milk and fat yields, somatic cell score, non-return rate for heifers) were considered. The numbers of SNPs selected as significant differed among models. M1 selected a very large number of SNPs, except for a NRH in which no SNPs were significant. M2 and M3 both selected similar and low number of SNPs for each trait. M4 selected more SNPs than M2 and M3. Considering linkage disequilibrium between SNPs, for MY M2 and M3 selected SNPs more highly correlated with each other than in the case of M4, while for FY M3 selection contained more correlated SNPs than M2 and M4. In conclusion, if the research interest is to identify SNPs not only with strong, but also with moderate effects on a complex trait a multiple-SNP model is recommended. Such models are capable of accounting for at least a part of linkage disequilibrium between SNPs through the design matrix of SNP effects. Functional annotation of SNPs significant in M4 reveals good correspondence between selected polymorphisms and functional information as well as with QTL mapping results.

Genome-Wide Association Study of Insect Bite Hypersensitivity in Swedish-Born Icelandic Horses

Insect bite hypersensitivity (IBH) is the most common allergic skin disease in horses and is caused by biting midges, mainly of the genus Culicoides. The disease predominantly comprises a type I hypersensitivity reaction, causing severe itching and discomfort that reduce the welfare and commercial value of the horse. It is a multifactorial disorder influenced by both genetic and environmental factors, with heritability ranging from 0.16 to 0.27 in various horse breeds. The worldwide prevalence in different horse breeds ranges from 3% to 60%; it is more than 50% in Icelandic horses exported to the European continent and approximately 8% in Swedish-born Icelandic horses. To minimize the influence of environmental effects, we analyzed Swedish-born Icelandic horses to identify genomic regions that regulate susceptibility to IBH. We performed a genome-wide association (GWA) study on 104 affected and 105 unaffected Icelandic horses genotyped using Illumina® EquineSNP50 Genotyping BeadChip. Quality control and population stratification analyses were performed with the GenABEL package in R (λ = 0.81). The association analysis was performed using the Bayesian variable selection method, Bayes C, implemented in GenSel software. The highest percentage of genetic variance was explained by the windows on X chromosomes (0.51% and 0.36% by 73 and 74 mb), 17 (0.34% by 77 mb), and 18 (0.34% by 26 mb). Overlapping regions with previous GWA studies were observed on chromosomes 7, 9, and 17. The windows identified in our study on chromosomes 7, 10, and 17 harbored immune system genes and are priorities for further investigation.

Identification of candidate genes and mutations in QTL regions for immune responses in chicken

There are two categories of immune responses – innate and adaptive immunity – both having polygenic backgrounds and a significant environmental component. In our study, adaptive immunity was represented by the specific antibody response toward keyhole limpet hemocyanin (KLH); innate immunity was represented by natural antibodies toward lipopolysaccharide (LPS) and lipoteichoic acid (LTA). Defining genetic bases of immune responses leads from defining quantitative trait loci (QTL) toward a single mutation responsible for variation in the phenotypic trait. The goal of the reported study was to define candidate genes and mutations for the immune traits of interest in chicken by performing an association study of SNPs located in candidate genes defined in QTL regions. Candidate genes and SNPs in QTL regions were selected in silico. SNP association was based on a custom SNP panel, GoldenGate genotyping assay (Illumina) and two statistical models: random mixed model and CAR score. The most significant SNP for immune response toward KLH was located in the JMJD6 gene located on GGA18. Four SNPs in candidate genes FOXJ1 (GGA18), EPHB1 (GGA9), PTGER4 (GGAZ) and PRKCB (GGA14) showed association with natural antibodies for LPS. A single SNP in ITGB4 (GGA18) was associated with natural antibodies for LTA. All associated SNPs mentioned above showed additive effects.

Meta - and combined - QTL analysis of different experiments on immune traits in chickens

Meta and/or combined QTL analysis from multiple studies can improve quantitative trait loci (QTL) position estimates compared to the individual experiments. Hereby we present results of a meta-analysis of QTL on chicken chromosome 9, 14 and 18 using data from three separate experiments and joint QTL analysis for chromosome 14 and 18. Meta QTL analysis uses information from multiple QTLs studies. Joint QTL analysis is based on combining raw data from different QTL experimental populations. QTLs under the study were related to specific antibody response to keyhole lymphet hemocyanin (KLH), and natural antibodies to environmental antigens, lipopolisaccharide (LPS) and lipoteichoic acid (LTA). Meta QTL analysis resulted in narrowing down the confidence interval for two QTLs on GGA14. The first one for natural antibodies against LTA and the second one for specific antibody response toward KLH. Also, a confidence interval of a QTL for natural antibodies against LPS located on GGA18 was narrowed down. Combined QTL analysis was successful for two QTLs: for specific antibody response toward KLH on GGA14, and for natural antibodies against LPS on GGA18. The greatest statistical power for QTL detection in joint analysis was achieved when raw data from segregating half–sib families from different populations under the study was used.