From QTL to QTN identification in livestock--winning by points rather than knock-out: a review

Functional impact of splicing variants in the elaboration of complex traits in cattle

GWAS conducted directly on imputed whole genome sequence have led to the identification of numerous genetic variants associated with agronomic traits in cattle. However, such variants are often simply markers in linkage disequilibrium with the actual causal variants, which is a limiting factor for the development of accurate genomic predictions. It is possible to identify causal variants by integrating information on how variants impact gene expression into GWAS output. RNA splicing plays a major role in regulating gene expression. Thus, assessing the effect of variants on RNA splicing may explain their function. Here, we use a high-throughput strategy to functionally analyse putative splice-disrupting variants in the bovine genome. Using GWAS, massively parallel reporter assay and deep learning algorithms designed to predict splice-disrupting variants, we identify 38 splice-disrupting variants associated with complex traits in cattle, three of which could be classified as causal. Our results indicate that splice-disrupting variants are widely found in the quantitative trait loci related to these phenotypes. Using our combined approach, we also assess the validity of splicing predictors originally developed to analyse human variants in the context of the bovine genome.

Sequence-based GWAS in 180,000 German Holstein cattle reveals new candidate variants for milk production traits

BACKGROUND

Milk production traits are complex and influenced by many genetic and environmental factors. Although extensive research has been performed for these traits, with many associations unveiled thus far, due to their crucial economic importance, complex genetic architecture, and the fact that causal variants in cattle are still scarce, there is a need for a better understanding of their genetic background. In this study, we aimed to identify new candidate loci associated with milk production traits in German Holstein cattle, the most important dairy breed in Germany and worldwide. For that purpose, 180,217 cattle were imputed to the sequence level and large-scale genome-wide association study (GWAS) followed by fine-mapping and evolutionary and functional annotation were carried out to identify and prioritize new association signals.

RESULTS

Using the imputed sequence data of a large cattle dataset, we identified 50,876 significant variants, confirming many known and identifying previously unreported candidate variants for milk (MY), fat (FY), and protein yield (PY). Genome-wide significant signals were fine-mapped with the Bayesian approach that determines the credible variant sets and generates the probability of causality for each signal. The variants with the highest probabilities of being causal were further classified using external information about the function and evolution, making the prioritization for subsequent validation experiments easier. The top potential causal variants determined with fine-mapping explained a large percentage of genetic variance compared to random ones; 178 variants explained 11.5%, 104 explained 7.7%, and 68 variants explained 3.9% of the variance for MY, FY, and PY, respectively, demonstrating the potential for causality.

CONCLUSIONS

Our findings proved the power of large samples and sequence-based GWAS in detecting new association signals. In order to fully exploit the power of GWAS, one should aim at very large samples combined with whole-genome sequence data. These can also come with both computational and time burdens, as presented in our study. Although milk production traits in cattle are comprehensively investigated, the genetic background of these traits is still not fully understood, with the potential for many new associations to be revealed, as shown. With constantly growing sample sizes, we expect more insights into the genetic architecture of milk production traits in the future.

Unraveling the genetic basis of methane emission in dairy cattle: a comprehensive exploration and breeding approach to lower methane emissions

Ruminant animals, such as dairy cattle, produce CH4, which contributes to global warming emissions and reduces dietary energy for the cows. While the carbon foot print of milk production varies based on production systems, milk yield and farm management practices, enteric fermentation, and manure management are major contributors togreenhouse gas emissions from dairy cattle. Recent emerging evidence has revealed the existence of genetic variation for CH4 emission traits among dairy cattle, suggests their potential inclusion in breeding goals and genetic selection programs. Advancements in high-throughput sequencing technologies and analytical techniques have enabled the identification of potential metabolic biomarkers, candidate genes, and SNPs linked to methane emissions. Indeed, this review critically examines our current understanding of carbon foot print in milk production, major emission sources, rumen microbial community and enteric fermentation, and the genetic architecture of methane emission traits in dairy cattle. It also emphasizes important implications for breeding strategies aimed at halting methane emissions through selective breeding, microbiome driven breeding, breeding for feed efficiency, and breeding by gene editing.

Association of variants and expression levels of MYOD1 gene with carcass and muscle characteristic traits in domestic pigeons

This study was to investigate the correlations of myogenic differentiation 1 (MYOD1) gene polymorphisms with carcass traits and its expression with breast muscle development in pigeons. Four SNPs were found in the pigeon MYOD1 gene. Correlation analysis showed that individuals with AA genotype at both SNPs g.2967A > G (p < .01) and g.3044G > A (p < .05) have significantly higher live weight (LW), carcass weight (CW), semi-eviscerated weight (SEW), eviscerated weight (EW) and breast muscle weight (BMW). Moreover, the two SNPs also had the same significant effects on MYOD1 mRNA expression levels in breast muscle of pigeons, ie, the AA genotype showed higher MYOD1 mRNA expression levels. The diameter and cross-section area of muscle fibers continuously increased from 0w to 4w (p < .05), accompanied with the increasing expression of MYOD1 gene, while the density decreased (p < .05) dramatically from 0w to 1w and continuously fell over in the next few weeks (p > .05). What's more, the expression level of MYOD1 gene was positively correlated with a diameter (r = 0.937, p < .05) and cross-sectional area (r = 0.956, p < .01) of myofiber, and negatively correlated with density (r = -0.769, p < .01). The results showed that individuals with AA genotype at both SNPs g.2967A > G and g.3044G > A have showed higher carcass traits (LW, CW, SEW, EW, and BMW) and higher MYOD1 mRNA expression level in breast muscle than AB and BB genotypes. Moreover, the expression level of MYOD1 gene was closely correlated with muscle characteristic traits, indicating variants of MYOD1 gene was closely related to muscle development and could be a potential candidate gene in marker-assisted selection of pigeons.

Master-Key Regulators of Sex Determination in Fish and Other Vertebrates-A Review

In vertebrates, mainly single genes with an allele ratio of 1:1 trigger sex-determination (SD), leading to initial equal sex-ratios. Such genes are designated master-key regulators (MKRs) and are frequently associated with DNA structural variations, such as copy-number variation and null-alleles. Most MKR knowledge comes from fish, especially cichlids, which serve as a genetic model for SD. We list 14 MKRs, of which dmrt1 has been identified in taxonomically distant species such as birds and fish. The identification of MKRs with known involvement in SD, such as amh and fshr, indicates that a common network drives SD. We illustrate a network that affects estrogen/androgen equilibrium, suggesting that structural variation may exert over-expression of the gene and thus form an MKR. However, the reason why certain factors constitute MKRs, whereas others do not is unclear. The limited number of conserved MKRs suggests that their heterologous sequences could be used as targets in future searches for MKRs of additional species. Sex-specific mortality, sex reversal, the role of temperature in SD, and multigenic SD are examined, claiming that these phenomena are often consequences of artificial hybridization. We discuss the essentiality of taxonomic authentication of species to validate purebred origin before MKR searches.

Identification of candidate genes responsible for innate fear behavior in the chicken

Identifying the genes responsible for quantitative traits remains a major challenge. We previously found a major QTL on chromosome 4 affecting several innate fear behavioral traits obtained by an open-field test in an F2 population between White Leghorn and Nagoya breeds of chickens (Gallus gallus). Here, an integrated approach of transcriptome, haplotype frequency, and association analyses was used to identify candidate genes for the QTL in phenotypically extreme individuals selected from the same segregating F2 population as that used in the initial QTL analysis. QTL mapping for the first principal component, which summarizes the variances of all affected behavioral traits in the F2 population, revealed the behavioral QTL located at 14-35 Mb on chromosome 4 with 333 genes. After RNA-seq analysis using two pooled RNAs from extreme F2 individuals, real-time qPCR analysis in the two parental breeds and their F1 individuals greatly reduced the number of candidate genes in the QTL interval from 333 to 16 genes. Haplotype frequency analysis in the two extreme F2 groups further reduced the number of candidate genes from 16 to 11. After comparing gene expression in the two extreme groups, a conditional correlation analysis of diplotypes between gene expression and phenotype of extreme individuals revealed that NPY5R and LOC101749214 genes were strong candidate genes for innate fear behavior. This study illustrates how the integrated approach can identify candidate genes more rapidly than fine mapping of the initial QTL interval and provides new information for studying the genetic basis of innate fear behavior in chickens.

Genomic Prediction of Complex Traits in Animal Breeding with Long Breeding History, the Dairy Cattle Case

In accordance with the infinitesimal model for quantitative traits, a very large number of genes affect nearly all economic traits. In only two cases has the causative polymorphism been determined for genes affecting economic traits in dairy cattle. Most current methods for genomic evaluation are based on the "two-step" method. Genetic evaluations are computed by the individual animal model, and functions of the evaluations of progeny-tested sires are the dependent variable for estimation of marker effects. With the adoption of genomic evaluation in 2008, annual rates of genetic gain in the US increased from ∼50-100% for yield traits and from threefold to fourfold for lowly heritable traits, including female fertility, herd-life and somatic cell concentration. Gradual elimination of the progeny test scheme has led to a reduction in the number of sires with daughter records and less genetic ties between years. As genotyping costs decrease, the number of cows genotyped will continue to increase, and these records will become the basic data used to compute genomic evaluations, most likely via application of "single-step" methodologies. Less emphasis in selection goals will be placed on milk production traits, and more on health, reproduction, and efficiency traits and "environmentally friendly" production. Genetic variance for economic traits is maintained by increase in frequency of rare alleles, new mutations, and changes in selection goals and management.

Accelerated discovery of functional genomic variation in pigs

The genotype-phenotype link is a major research topic in the life sciences but remains highly complex to disentangle. Part of the complexity arises from the number of genes contributing to the observed phenotype. Despite the vast increase of molecular data, pinpointing the causal variant underlying a phenotype of interest is still challenging. In this study, we present an approach to map causal variation and molecular pathways underlying important phenotypes in pigs. We prioritize variation by utilizing and integrating predicted variant impact scores (pCADD), functional genomic information, and associated phenotypes in other mammalian species. We demonstrate the efficacy of our approach by reporting known and novel causal variants, of which many affect non-coding sequences. Our approach allows the disentangling of the biology behind important phenotypes by accelerating the discovery of novel causal variants and molecular mechanisms affecting important phenotypes in pigs. This information on molecular mechanisms could be applicable in other mammalian species, including humans.

Comparative transcriptome profiling of a resistant vs susceptible bread wheat (Triticum aestivum L.) cultivar in response to water deficit and cold stress

Bread wheat (Triticum aestivum L.) is one of the most important agricultural plants wearing abiotic stresses, such as water deficit and cold, that cause its productivity reduction. Since resistance to abiotic factors is a multigenic trait, therefore modern genome-wide approaches can help to involve various genetic material in breeding. One technique is full transcriptome analysis that reveals groups of stress response genes serving marker-assisted selection markers. Comparing transcriptome profiles of the same genetic material under several stresses is essential and makes the whole picture. Here, we addressed this by studying the transcriptomic response to water deficit and cold stress for two evolutionarily distant bread wheat varieties: stress-resistant cv. Saratovskaya 29 (S29) and stress-sensitive cv. Yanetzkis Probat (YP). For the first time, transcriptomes for these cultivars grown under abiotic stress conditions were obtained using Illumina based MACE technology. We identified groups of genes involved in response to cold and water deficiency stresses, including responses to each stress factor and both factors simultaneously that may be candidates for resistance genes. We discovered a core group of genes that have a similar pattern of stress-induced expression changes. The particular expression pattern was revealed not only for the studied varieties but also for the published transcriptomic data on cv. Jing 411 and cv. Fielder. Comparative transcriptome profiling of cv. S29 and cv. YP in response to water deficit and cold stress confirmed the hypothesis that stress-induced expression change is unequal within a homeologous gene group. As a rule, at least one changed significantly while the others had a relatively lower expression. Also, we found several SNPs distributed throughout the genomes of cv. S29 and cv. YP and distinguished the studied varieties from each other and the reference cv. Chinese Spring. Our results provide new data for genomics-assisted breeding of stress-tolerant wheat cultivars.

GALLO: An R package for genomic annotation and integration of multiple data sources in livestock for positional candidate loci

BACKGROUND

The development of high-throughput sequencing and genotyping methodologies has enabled the identification of thousands of genomic regions associated with several complex traits. The integration of multiple sources of biological information is a crucial step required to better understand patterns regulating the development of these traits.

FINDINGS

Genomic Annotation in Livestock for positional candidate LOci (GALLO) is an R package developed for the accurate annotation of genes and quantitative trait loci (QTLs) located in regions identified in common genomic analyses performed in livestock, such as genome-wide association studies and transcriptomics using RNA sequencing. Moreover, GALLO allows the graphical visualization of gene and QTL annotation results, data comparison among different grouping factors (e.g., methods, breeds, tissues, statistical models, studies), and QTL enrichment in different livestock species such as cattle, pigs, sheep, and chickens.

CONCLUSIONS

Consequently, GALLO is a useful package for annotation, identification of hidden patterns across datasets, and data mining previously reported associations, as well as the efficient examination of the genetic architecture of complex traits in livestock.

Identification of QTNs and Their Candidate Genes for 100-Seed Weight in Soybean (Glycine max L.) Using Multi-Locus Genome-Wide Association Studies

100-seed weight (100-SW) in soybeans is a yield component trait and controlled by multiple genes with different effects, but limited information is available for its quantitative trait nucleotides (QTNs) and candidate genes. To better understand the genetic architecture underlying the trait and improve the precision of marker-assisted selection, a total of 43,834 single nucleotide polymorphisms (SNPs) in 250 soybean accessions were used to identify significant QTNs for 100-SW in four environments and their BLUP values using six multi-locus and one single-locus genome-wide association study methods. As a result, a total of 218 significant QTNs were detected using multi-locus methods, whereas eight QTNs were identified by a single-locus method. Among 43 QTNs or QTN clusters identified repeatedly across various environments and/or approaches, all of them exhibited significant trait differences between their corresponding alleles, 33 were found in the genomic region of previously reported QTLs, 10 were identified as new QTNs, and three (qHSW-4-1, qcHSW-7-3, and qcHSW-10-4) were detected in all the four environments. The number of seed weight (SW) increasing alleles for each accession ranged from 8 (18.6%) to 36 (83.72%), and three accessions (Yixingwuhuangdou, Nannong 95C-5, and Yafanzaodou) had more than 35 SW increasing alleles. Among 36 homologous seed-weight genes in Arabidopsis underlying the above 43 stable QTNs, more importantly, Glyma05g34120, GmCRY1, and GmCPK11 had known seed-size/weight-related genes in soybean, and Glyma07g07850, Glyma10g03440, and Glyma10g36070 were candidate genes identified in this study. These results provide useful information for genetic foundation, marker-assisted selection, genomic prediction, and functional genomics of 100-SW.

A multi-breed reference panel and additional rare variants maximize imputation accuracy in cattle

Background

During the last decade, the use of common-variant array-based single nucleotide polymorphism (SNP) genotyping in the beef and dairy industries has produced an astounding amount of medium-to-low density genomic data. Although low-density assays work well in the context of genomic prediction, they are less useful for detecting and mapping causal variants and the effects of rare variants are not captured. The objective of this project was to maximize the accuracies of genotype imputation from medium- and low-density assays to the marker set obtained by combining two high-density research assays (~ 850,000 SNPs), the Illumina BovineHD and the GGP-F250 assays, which contains a large proportion of rare and potentially functional variants and for which the assay design is described here. This 850 K SNP set is useful for both imputation to sequence-level genotypes and direct downstream analysis.

Results

We found that a large multi-breed composite imputation reference panel that includes 36,131 samples with either BovineHD and/or GGP-F250 genotypes significantly increased imputation accuracy compared with a within-breed reference panel, particularly at variants with low minor allele frequencies. Individual animal imputation accuracies were maximized when more genetically similar animals were represented in the composite reference panel, particularly with complete 850 K genotypes. The addition of rare variants from the GGP-F250 assay to our composite reference panel significantly increased the imputation accuracy of rare variants that are exclusively present on the BovineHD assay. In addition, we show that an assay marker density of 50 K SNPs balances cost and accuracy for imputation to 850 K.

Conclusions

Using high-density genotypes on all available individuals in a multi-breed reference panel maximized imputation accuracy for tested cattle populations. Admixed animals or those from breeds with a limited representation in the composite reference panel were still imputed at high accuracy, which is expected to further increase as the reference panel expands. We anticipate that the addition of rare variants from the GGP-F250 assay will increase the accuracy of imputation to sequence level.

RNAseq analysis reveals drought-responsive molecular pathways with candidate genes and putative molecular markers in root tissue of wheat

Drought is one of the major impediments in wheat productivity. Traditional breeding and marker assisted QTL introgression had limited success. Available wheat genomic and RNA-seq data can decipher novel drought tolerance mechanisms with putative candidate gene and marker discovery. Drought is first sensed by root tissue but limited information is available about how roots respond to drought stress. In this view, two contrasting genotypes, namely, NI5439 41 (drought tolerant) and WL711 (drought susceptible) were used to generate ~78.2 GB data for the responses of wheat roots to drought. A total of 45139 DEGs, 13820 TF, 288 miRNAs, 640 pathways and 435829 putative markers were obtained. Study reveals use of such data in QTL to QTN refinement by analysis on two model drought-responsive QTLs on chromosome 3B in wheat roots possessing 18 differentially regulated genes with 190 sequence variants (173 SNPs and 17 InDels). Gene regulatory networks showed 69 hub-genes integrating ABA dependent and independent pathways controlling sensing of drought, root growth, uptake regulation, purine metabolism, thiamine metabolism and antibiotics pathways, stomatal closure and senescence. Eleven SSR markers were validated in a panel of 18 diverse wheat varieties. For effective future use of findings, web genomic resources were developed. We report RNA-Seq approach on wheat roots describing the drought response mechanisms under field drought conditions along with genomic resources, warranted in endeavour of wheat productivity.

Differences in gene expression profiles for subcutaneous adipose, liver, and skeletal muscle tissues between Meishan and Landrace pigs with different backfat thicknesses

Backfat thickness is one of the most important traits of commercially raised pigs. Meishan pigs are renowned for having thicker backfat than Landrace pigs. To examine the genetic factors responsible for the differences, we first produced female crossbred pig lines by mating Landrace (L) × Large White (W) × Duroc (D) females (LWD) with Landrace (L) or Meishan (M) boars (i.e., LWD × L = LWDL for Landrace offspring and LWD × M = LWDM for the Meishan offspring). We confirmed that LWDM pigs indeed had a thicker backfat than LWDL pigs. Next, we performed gene expression microarray analysis in both genetic lines to examine differentially expressed genes (DEGs) in energy metabolism-related tissues, subcutaneous adipose (fat), liver, and longissimus dorsi muscle tissues. We analyzed the annotation of DEGs (2-fold cutoff) to functionally categorize them by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. The number of DEGs in muscle tissues of both lines was much less than that in fat and liver tissues, indicating that DEGs in muscle tissues may not contribute much to differences in backfat thickness. In contrast, several genes related to muscle (in fat tissue) and lipid metabolism (in liver tissue) were more upregulated in LWDM pigs than LWDL pigs, indicating that those DEGs might be responsible for differences in backfat thickness. The different genome-wide gene expression profiles in the fat, liver, and muscle tissues between genetic lines can provide useful information for pig breeders.

Determination of quantitative trait nucleotides by concordance analysis between quantitative trait loci and marker genotypes of US Holsteins

Experimental designs that exploit family information can provide substantial predictive power in quantitative trait nucleotide discovery projects. Concordance between quantitative trait locus genotype as determined by the a posteriori granddaughter design and marker genotype was determined for 30 trait-by-chromosomal segment effects segregating in the US Holstein population with probabilities of <10^-20 to accept the null hypotheses of no segregating gene affecting the trait within the chromosomal segment. Genotypes for 83 grandsires and 17,217 sons were determined by either complete sequence or imputation for 3,148,506 polymorphisms across the entire genome; 471 Holstein bulls had a complete genome sequence, including 64 of the grandsires. Complete concordance was obtained only for stature on chromosome 14 and daughter pregnancy rate on chromosome 18. For each quantitative trait locus, effects of the 30 polymorphisms with highest concordance scores for the analyzed trait were computed by stepwise regression for predicted transmitting abilities of 26,750 bulls with progeny test and imputed genotypes. Effects for stature on chromosome 11, daughter pregnancy rate on chromosome 18, and protein percentage on chromosome 20 met 3 criteria: complete or almost complete concordance, nominal significance of the polymorphism effect after correction for all other polymorphisms, and marker coefficient of determination >40% of total multiple-regression coefficient of determination for the 30 polymorphisms with highest concordance. An intronic variant marker on chromosome 5 at 93,945,738 bp explained 7% of variance for fat percentage and 74% of total multiple-marker regression variance but was concordant for only 24 of 30 families. The missense polymorphism Phe279Tyr in GHR at 31,909,478 bp on chromosome 20 was confirmed as the causative mutation for fat and protein concentration. For effect on fat percentage on chromosome 14, 12 additional missense polymorphisms were found that had almost complete concordance with the suggested causative polymorphism (missense mutation Ala232Glu in DGAT1). The only polymorphism found likely to improve predictive power for genomic evaluation of dairy cattle was on chromosome 15; that polymorphism had a frequency of 0.45 for the allele with economically positive effects on all production traits.

Identification of genetic variation in equine collagenous lectins using targeted resequencing

Collagenous lectins are a family of soluble pattern recognition receptors that play an important role in innate immune resistance to infectious disease. Through recognition of carbohydrate motifs on the surface of pathogens, some collagenous lectins can activate the lectin pathway of complement, providing an effective means of host defense. Genetic polymorphisms in collagenous lectins have been shown in several species to predispose animals to a variety of infectious diseases. Infectious diseases are an important cause of morbidity in horses, however little is known regarding the role of equine collagenous lectins. Using a high-throughput, targeted re-sequencing approach, the relationship between genetic variation in equine collagenous lectin genes and susceptibility to disease was investigated. DNA was isolated from tissues obtained from horses submitted for post-mortem examination. Animals were divided into two populations, those with infectious or autoinflammatory diseases (n = 37) and those without (n = 52), and then subdivided by dominant pathological process for a total of 21 pools, each containing 4-5 horses. DNA was extracted from each horse and pooled in equimolar amounts, and the exons, introns, upstream (approximately 50 kb) and downstream (approximately 3 kb) regulatory regions for the 11 equine collagenous lectin genes and related MASP genes were targeted for re-sequencing. A custom target capture kit was used to prepare a sequencing library, which was sequenced on an Illumina MiSeq. After implementing quality control filters, 4559 variants were identified. Of these, 92 were present in the coding regions (43 missense, 1 nonsense, and 48 synonymous), 1414 in introns, 3029 in the upstream region, and 240 in the downstream region. In silico analysis of the missense short nucleotide variants identified 12 mutations with potential to disrupt collagenous lectin protein structure or function, 280 mutations located within predicted transcription factor binding sites, and 95 mutations located within predicted microRNA binding elements. Analysis of allelic association identified 113 mutations that segregated between the infectious/autoinflammatory and non-infectious populations. The variants discovered in this experiment represent potential genetic contributors to disease susceptibility of horses, and will serve as candidates for further population-level genotyping. This study contributes to the growing body of evidence that pooled, high-throughput sequencing is a viable strategy for cost-effective variant discovery.

Identification of polymorphisms in the bovine collagenous lectins and their association with infectious diseases in cattle

Infectious diseases are a significant issue in animal production systems, including both the dairy and beef cattle industries. Understanding and defining the genetics of infectious disease susceptibility in cattle is an important step in the mitigation of their impact. Collagenous lectins are soluble pattern recognition receptors that form an important part of the innate immune system, which serves as the first line of host defense against pathogens. Polymorphisms in the collagenous lectin genes have been shown in previous studies to contribute to infectious disease susceptibility, and in cattle, mutations in two collagenous lectin genes (MBL1 and MBL2) are associated with mastitis. To further characterize the contribution of variation in the bovine collagenous lectins to infectious disease susceptibility, we used a pooled NGS approach to identify short nucleotide variants (SNVs) in the collagenous lectins (and regulatory DNA) of cattle with (n = 80) and without (n = 40) infectious disease. Allele frequency analysis identified 74 variants that were significantly (p < 5 × 10⁻⁶) associated with infectious disease, the majority of which were clustered in a 29-kb segment upstream of the collectin locus on chromosome 28. In silico analysis of the functional effects of all the variants predicted 11 SNVs with a deleterious effect on protein structure and/or function, 148 SNVs that occurred within potential transcription factor binding sites, and 31 SNVs occurring within potential miRNA binding elements. This study provides a detailed look at the genetic variation of the bovine collagenous lectins and identifies potential genetic markers for infectious disease susceptibility.

Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs

Abstract. Quantitative or complex traits are controlled by many genes and environmental factors. Most traits in livestock breeding are quantitative traits. Mapping genes and causative mutations generating the genetic variance of these traits is still a very active area of research in livestock genetics. Since genome-wide and dense SNP panels are available for most livestock species, genome-wide association studies (GWASs) have become the method of choice in mapping experiments. Different statistical models are used for GWASs. We will review the frequently used single-marker models and additionally describe Bayesian multi-marker models. The importance of nonadditive genetic and genotype-by-environment effects along with GWAS methods to detect them will be briefly discussed. Different mapping populations are used and will also be reviewed. Whenever possible, our own real-data examples are included to illustrate the reviewed methods and designs. Future research directions including post-GWAS strategies are outlined.

Fine-mapping of BTA17 using imputed sequences for associations with de novo synthesized fatty acids in bovine milk

The aim of this study was to fine-map a genomic region associated with milk fatty acids (FA) on Bos taurus autosome (BTA) 17. This genomic region has been discovered with 50,000 (50k) single nucleotide polymorphisms (SNP) imputed to 777,000 (777k) SNP. In this study, high-density genotypes were imputed to whole-genome sequences level to identify candidate gene(s) associated with milk FA composition on BTA17. Phenotypes and genotypes were available for 1,640 cows sampled in winter, and for 1,581 cows sampled in summer. Phenotypes consisted of gas chromatography measurements in winter and in summer milk samples of 6 individual FA and the indicator of de novo synthesis, C6:0-C14:0. Genotypes consisted of imputed 777k SNP, and 89 sequenced ancestors of the population of genotyped cows. In addition, 450 whole-genome sequences from the 1,000 Bull Genome Consortium were available. Using 495 Holstein-Friesian sequences as a reference population, the 777k SNP genotypes of the cows were imputed to sequence level. We then applied single-variant analyses with an animal model, and identified thousands of significant associations with C6:0, C8:0, C10:0, C12:0, C14:0, and C6:0-C14:0. For C8:0 in summer milk samples, the genomic region located between 29 and 34 Mbp on BTA17 revealed a total of 646 significant associations. The most significant associations [-log₁₀(P-value) = 7.82] were 8 SNP in perfect linkage disequilibrium. After fitting one of these 8 SNP as a fixed effect in the model, and re-running the single-variant analyses, no further significant associations were found for any of the 6 FA or C6:0-C14:0. These findings suggest that one polymorphism underlying this QTL on BTA17 influences multiple de novo synthesized milk FA. Thirteen genes in the QTL region were identified and analyzed carefully. Six out of the 8 SNP that showed the strongest associations were located in the La ribonucleoprotein domain family, member 1B (LARP1B) gene, and we suggest LARP1B as a primary candidate gene. Another gene of interest for this QTL region might be PKL4. None of these suggested candidate genes have previously been associated with milk fat synthesis or milk FA composition.

Invited review: A perspective on the future of genomic selection in dairy cattle

Genomic evaluation has been successfully implemented in the United States, Canada, Great Britain, Ireland, New Zealand, Australia, France, the Netherlands, Germany, and the Scandinavian countries. Adoption of this technology in the major dairy producing countries has led to significant changes in the worldwide dairy industry. Gradual elimination of the progeny test system has led to a reduction in the number of sires with daughter records and fewer genetic ties between years. As genotyping costs decrease, the number of cows genotyped will continue to increase, and these records will become the basic data used to compute genomic evaluations, most likely via application of "single-step" methodologies. Although genomic selection has been successful in increasing rates of genetic gain, we still know very little about the genetic architecture of quantitative variation. Apparently, a very large number of genes affect nearly all economic traits, in accordance with the infinitesimal model for quantitative traits. Less emphasis in selection goals will be placed on milk production traits, and more on health, reproduction, and efficiency traits and on environmentally friendly production with reduced waste and gas emission. Genetic variance for economic traits is maintained by the increase in frequency of rare alleles, new mutations, and changes in selection goals and management. Thus, it is unlikely that a selection plateau will be reached in the near future.

Variants in the 3' untranslated region of the ovine acetyl-coenzyme A acyltransferase 2 gene are associated with dairy traits and exhibit differential allelic expression

The acetyl-CoA acyltransferase 2 (ACAA2) gene encodes an enzyme of the thiolase family that is involved in mitochondrial fatty acid elongation and degradation by catalyzing the last step of the respective β-oxidation pathway. The increased energy needs for gluconeogenesis and triglyceride synthesis during lactation are met primarily by increased fatty acid oxidation. Therefore, the ACAA2 enzyme plays an important role in the supply of energy and carbon substrates for lactation and may thus affect milk production traits. This study investigated the association of the ACAA2 gene with important sheep traits and the putative functional involvement of this gene in dairy traits. A single nucleotide substitution, a T to C transition located in the 3' untranslated region of the ACAA2 gene, was used in mixed model association analysis with milk yield, milk protein yield and percentage, milk fat yield and percentage, and litter size at birth. The single nucleotide polymorphism was significantly associated with total lactation production and milk protein percentage, with respective additive effects of 6.81 ± 2.95 kg and -0.05 ± 0.02%. Additionally, a significant dominance effect of 0.46 ± 0.21 kg was detected for milk fat yield. Homozygous TT and heterozygous CT animals exhibited higher milk yield compared with homozygous CC animals, whereas the latter exhibited increased milk protein percentage. Expression analysis from age-, lactation-, and parity-matched female sheep showed that mRNA expression of the ACAA2 gene from TT animals was 2.8- and 11.8-fold higher in liver and mammary gland, respectively. In addition, by developing an allelic expression imbalance assay, it was estimated that the T allele was expressed at an average of 18% more compared with the C allele in the udder of randomly selected ewes. We demonstrated for the first time that the variants in the 3' untranslated region of the ovine ACAA2 gene are differentially expressed in homozygous ewes of each allele and exhibit allelic expression imbalance within heterozygotes in a tissue-specific manner, supporting the existence of cis-regulatory DNA variation in the ovine ACAA2 gene. This is the first study reporting differential allelic imbalance expression of a candidate gene associated with milk production traits in dairy sheep.

The Importance of Endophenotypes to Evaluate the Relationship between Genotype and External Phenotype

With the exception of a few Mendelian traits, almost all phenotypes (traits) in livestock science are quantitative or complex traits regulated by the expression of many genes. For most of the complex traits, differential expression of genes, rather than genomic variation in the gene coding sequences, is associated with the genotype of a trait. The expression profiles of the animal's transcriptome, proteome and metabolome represent endophenotypes that influence/regulate the externally-observed phenotype. These expression profiles are generated by interactions between the animal's genome and its environment that range from the cellular, up to the husbandry environment. Thus, understanding complex traits requires knowledge about not only genomic variation, but also environmental effects that affect genome expression. Gene products act together in physiological pathways and interaction networks (of pathways). Due to the lack of annotation of the functional genome and ontologies of genes, our knowledge about the various biological systems that contribute to the development of external phenotypes is sparse. Furthermore, interaction with the animals' microbiome, especially in the gut, greatly influences the external phenotype. We conclude that a detailed understanding of complex traits requires not only understanding of variation in the genome, but also its expression at all functional levels.

Cavefish and the basis for eye loss

Animals have colonized the entire world from rather moderate to the harshest environments, some of these so extreme that only few animals are able to survive. Cave environments present such a challenge and obligate cave animals have adapted to perpetual darkness by evolving a multitude of traits. The most common and most studied cave characteristics are the regression of eyes and the overall reduction in pigmentation. Studying these traits can provide important insights into how evolutionary forces drive convergent and regressive adaptation. The blind Mexican cavefish (Astyanax mexicanus) has emerged as a useful model to study cave evolution owing to the availability of genetic and genomic resources, and the amenability of embryonic development as the different populations remain fertile with each other. In this review, we give an overview of our current knowledge underlying the process of regressive and convergent evolution using eye degeneration in cavefish as an example.This article is part of the themed issue 'Evo-devo in the genomics era, and the origins of morphological diversity'.

Association of the Single Nucleotide Polymorphisms in RUNX1, DYRK1A, and KCNJ15 with Blood Related Traits in Pigs

The aim of this study was to detect positional candidate genes located within the support interval (SI) regions based on the results of red blood cell, mean corpuscular volume (MCV), and mean corpuscular hemoglobin quantitative trait locus (QTL) in Sus scrofa chromosome 13, and to verify the correlation between specific single-nucleotide polymorphisms (SNPs) located in the exonic region of the positional candidate gene and the three genetic traits. The flanking markers of the three QTL SI regions are SW38 and S0215. Within the QTL SI regions, 44 genes were located, and runt-related transcription factor 1, dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 1A (DYRK1A), and potassium inwardly-rectifying channel, subfamily J, member 15 KCNJ15-which are reported to be related to the hematological traits and clinical features of Down syndrome-were selected as positional candidate genes. The ten SNPs located in the exonic region of the three genes were detected by next generation sequencing. A total of 1,232 pigs of an F₂ resource population between Landrace and Korean native pigs were genotyped. To investigate the effects of the three genes on each genotype, a mixed-effect model which is the considering family structure model was used to evaluate the associations between the SNPs and three genetic traits in the F₂ intercross population. Among them, the MCV level was highly significant (nominal p = 9.8×10^-9) in association with the DYRK1A-SNP1 (c.2989 G<A), and explained 4.9% of the phenotypic variance. However, since linkage disequilibrium is quite extensive in an F₂ intercross, our approach has limited power to distinguish one particular positional candidate gene from a QTL region.

Detection of selection signatures in Piemontese and Marchigiana cattle, two breeds with similar production aptitudes but different selection histories

BACKGROUND

Domestication and selection are processes that alter the pattern of within- and between-population genetic variability. They can be investigated at the genomic level by tracing the so-called selection signatures. Recently, sequence polymorphisms at the genome-wide level have been investigated in a wide range of animals. A common approach to detect selection signatures is to compare breeds that have been selected for different breeding goals (i.e. dairy and beef cattle). However, genetic variations in different breeds with similar production aptitudes and similar phenotypes can be related to differences in their selection history.

METHODS

In this study, we investigated selection signatures between two Italian beef cattle breeds, Piemontese and Marchigiana, using genotyping data that was obtained with the Illumina BovineSNP50 BeadChip. The comparison was based on the fixation index (Fst), combined with a locally weighted scatterplot smoothing (LOWESS) regression and a control chart approach. In addition, analyses of Fst were carried out to confirm candidate genes. In particular, data were processed using the varLD method, which compares the regional variation of linkage disequilibrium between populations.

RESULTS

Genome scans confirmed the presence of selective sweeps in the genomic regions that harbour candidate genes that are known to affect productive traits in cattle such as DGAT1, ABCG2, CAPN3, MSTN and FTO. In addition, several new putative candidate genes (for example ALAS1, ABCB8, ACADS and SOD1) were detected.

CONCLUSIONS

This study provided evidence on the different selection histories of two cattle breeds and the usefulness of genomic scans to detect selective sweeps even in cattle breeds that are bred for similar production aptitudes.

Genomic imprinting effects on complex traits in domesticated animal species

Monoallelically expressed genes that exert their phenotypic effect in a parent-of-origin specific manner are considered to be subject to genomic imprinting, the most well understood form of epigenetic regulation of gene expression in mammals. The observed differences in allele specific gene expression for imprinted genes are not attributable to differences in DNA sequence information, but to specific chemical modifications of DNA and chromatin proteins. Since the discovery of genomic imprinting some three decades ago, over 100 imprinted mammalian genes have been identified and considerable advances have been made in uncovering the molecular mechanisms regulating imprinted gene expression. While most genomic imprinting studies have focused on mouse models and human biomedical disorders, recent work has highlighted the contributions of imprinted genes to complex trait variation in domestic livestock species. Consequently, greater understanding of genomic imprinting and its effect on agriculturally important traits is predicted to have major implications for the future of animal breeding and husbandry. In this review, we discuss genomic imprinting in mammals with particular emphasis on domestic livestock species and consider how this information can be used in animal breeding research and genetic improvement programs.

Genetic and genomic analyses for economically important traits and their applications in molecular breeding of cultured fish

The traits of cultured fish must continually be genetically improved to supply high-quality animal protein for human consumption. Economically important fish traits are controlled by multiple gene quantitative trait loci (QTL), most of which have minor effects, but a few genes may have major effects useful for molecular breeding. In this review, we chose relevant studies on some of the most intensively cultured fish and concisely summarize progress on identifying and verifying QTLs for such traits as growth, disease and stress resistance and sex in recent decades. The potential applications of these major-effect genes and their associated markers in marker-assisted selection and molecular breeding, as well as future research directions are also discussed. These genetic and genomic analyses will be valuable for elucidating the mechanisms modulating economically important traits and to establish more effective molecular breeding techniques in fish.

Two novel polymorphisms of bovine SIRT2 gene are associated with higher body weight in Nanyang cattle

Identification of polymorphisms associated with economic traits is important for successful marker-assisted selection in cattle breeding. The family of mammalian sirtuin regulates many biological functions, such as life span extension and energy metabolism. SIRT2, a most abundant sirtuin in adipocytes, acts as a crucial regulator of adipogenic differentiation and plays a key role in controlling adipose tissue function and mass. Here we investigated single nucleotide polymorphisms (SNPs) of bovine SIRT2 in 1226 cattle from five breeds and further evaluated the effects of identified SNPs on economically important traits of Nanyang cattle. Our results revealed four novel SNPs in bovine SIRT2, one was located in intronic region and the other three were synonymous mutations. Linkage disequilibrium and haplotype analyses based on the identified SNPs showed obvious difference between crossbred breed and the other four beef breeds. Association analyses demonstrated that SNPs g.17333C > T and g.17578A > G have a significantly effect on 18-months-old body weight of Nanyang population. Animals with combined genotype TTGG at the above two loci exhibited especially higher body weight. Our data for the first time demonstrated that polymorphisms in bovine SIRT2 are associated with economic traits of Nanyang cattle, which will be helpful for future cattle selection practices.

Genome-wide delineation of natural variation for pod shatter resistance in Brassica napus

Resistance to pod shattering (shatter resistance) is a target trait for global rapeseed (canola, Brassica napus L.), improvement programs to minimise grain loss in the mature standing crop, and during windrowing and mechanical harvest. We describe the genetic basis of natural variation for shatter resistance in B. napus and show that several quantitative trait loci (QTL) control this trait. To identify loci underlying shatter resistance, we used a novel genotyping-by-sequencing approach DArT-Seq. QTL analysis detected a total of 12 significant QTL on chromosomes A03, A07, A09, C03, C04, C06, and C08; which jointly account for approximately 57% of the genotypic variation in shatter resistance. Through Genome-Wide Association Studies, we show that a large number of loci, including those that are involved in shattering in Arabidopsis, account for variation in shatter resistance in diverse B. napus germplasm. Our results indicate that genetic diversity for shatter resistance genes in B. napus is limited; many of the genes that might control this trait were not included during the natural creation of this species, or were not retained during the domestication and selection process. We speculate that valuable diversity for this trait was lost during the natural creation of B. napus. To improve shatter resistance, breeders will need to target the introduction of useful alleles especially from genotypes of other related species of Brassica, such as those that we have identified.

Concordance analysis for QTL detection in dairy cattle: a case study of leg morphology

BACKGROUND

The present availability of sequence data gives new opportunities to narrow down from QTL (quantitative trait locus) regions to causative mutations. Our objective was to decrease the number of candidate causative mutations in a QTL region. For this, a concordance analysis was applied for a leg conformation trait in dairy cattle. Several QTL were detected for which the QTL status (homozygous or heterozygous for the QTL) was inferred for each individual. Subsequently, the inferred QTL status was used in a concordance analysis to reduce the number of candidate mutations.

METHODS

Twenty QTL for rear leg set side view were mapped using Bayes C. Marker effects estimated during QTL mapping were used to infer the QTL status for each individual. Subsequently, polymorphisms present in the QTL regions were extracted from the whole-genome sequences of 71 Holstein bulls. Only polymorphisms for which the status was concordant with the QTL status were kept as candidate causative mutations.

RESULTS

QTL status could be inferred for 15 of the 20 QTL. The number of concordant polymorphisms differed between QTL and depended on the number of QTL statuses that could be inferred and the linkage disequilibrium in the QTL region. For some QTL, the concordance analysis was efficient and narrowed down to a limited number of candidate mutations located in one or two genes, while for other QTL a large number of genes contained concordant polymorphisms.

CONCLUSIONS

For regions for which the concordance analysis could be performed, we were able to reduce the number of candidate mutations. For part of the QTL, the concordant analyses narrowed QTL regions down to a limited number of genes, of which some are known for their role in limb or skeletal development in humans and mice. Mutations in these genes are good candidates for QTN (quantitative trait nucleotides) influencing rear leg set side view.

Deciphering the genetic blueprint behind Holstein milk proteins and production

Holstein is known to provide higher milk yields than most other cattle breeds, and the dominant position of Holstein today is the result of various selection pressures. Holstein cattle have undergone intensive selection for milk production in recent decades, which has left genome-wide footprints of domestication. To further characterize the bovine genome, we performed whole-genome resequencing analysis of 10 Holstein and 11 Hanwoo cattle to identify regions containing genes as outliers in Holstein, including CSN1S1, CSN2, CSN3, and KIT whose products are likely involved in the yield and proteins of milk and their distinctive black-and-white markings. In addition, genes indicative of positive selection were associated with cardiovascular disease, which is related to simultaneous propagation of genetic defects, also known as inbreeding depression in Holstein.

An expression QTL of closely linked candidate genes affects pH of meat in chickens

Genetical genomics has been suggested as a powerful approach to study the genotype-phenotype gap. However, the relatively low power of these experiments (usually related to the high cost) has hindered fulfillment of its promise, especially for loci (QTL) of moderate effects.One strategy with which to overcome the issue is to use a targeted approach. It has two clear advantages: (i) it reduces the problem to a simple comparison between different genotypic groups at the QTL and (ii) it is a good starting point from which to investigate downstream effects of the QTL. In this study, from 698 F2 birds used for QTL mapping, gene expression profiles of 24 birds with divergent homozygous QTL genotypes were investigated. The targeted QTL was on chromosome 1 and affected initial pH of breast muscle. The biological mechanisms controlling this trait can be similar to those affecting malignant hyperthermia or muscle fatigue in humans. The gene expression study identified 10 strong local signals that were markedly more significant compared to any genes on the rest of the genome. The differentially expressed genes all mapped to a region <1 Mb, suggesting a remarkable reduction of the QTL interval. These results, combined with analysis of downstream effect of the QTL using gene network analysis, suggest that the QTL is controlling pH by governing oxidative stress. The results were reproducible with use of as few as four microarrays on pooled samples (with lower significance level). The results demonstrate that this cost-effective approach is promising for characterization of QTL.

Use of locally weighted scatterplot smoothing (LOWESS) regression to study selection signatures in Piedmontese and Italian Brown cattle breeds

Selection is the major force affecting local levels of genetic variation in species. The availability of dense marker maps offers new opportunities for a detailed understanding of genetic diversity distribution across the animal genome. Over the last 50 years, cattle breeds have been subjected to intense artificial selection. Consequently, regions controlling traits of economic importance are expected to exhibit selection signatures. The fixation index (Fst ) is an estimate of population differentiation, based on genetic polymorphism data, and it is calculated using the relationship between inbreeding and heterozygosity. In the present study, locally weighted scatterplot smoothing (LOWESS) regression and a control chart approach were used to investigate selection signatures in two cattle breeds with different production aptitudes (dairy and beef). Fst was calculated for 42 514 SNP marker loci distributed across the genome in 749 Italian Brown and 364 Piedmontese bulls. The statistical significance of Fst values was assessed using a control chart. The LOWESS technique was efficient in removing noise from the raw data and was able to highlight selection signatures in chromosomes known to harbour genes affecting dairy and beef traits. Examples include the peaks detected for BTA2 in the region where the myostatin gene is located and for BTA6 in the region harbouring the ABCG2 locus. Moreover, several loci not previously reported in cattle studies were detected.

A suite of new genes defining salinity stress tolerance in seedlings of contrasting rice genotypes

Salinity is one of the major constraints adversely influencing crop productivity. Saltol QTL is a major QTL associated with Na⁺-K⁺ ratio and seedling stage salinity tolerance in rice. With an aim to understand the contribution of individual genes localized within saltol towards salinity tolerance, we analysed the transcript abundance of a set of these genes in seedlings of contrasting genotypes of rice. We hypothesize that this approach may be helpful in identifying new 'candidate genes' for improving salinity tolerance in crops. For this purpose, seedlings of Oryza sativa cv. IR64 (sensitive) and the landrace Pokkali (tolerant) were subjected to short/long durations of salinity. qRT-PCR analysis clearly exhibited differential regulation of genes encoding signaling related protein (SRPs), where higher transcript abundance for most of them was observed in Pokkali than IR64 under non-stress conditions, thereby indicating towards well preparedness of the former to handle stress, in anticipation. Genes encoding proteins of unknown function (PUFs), though, constitute a considerable portion of plant genome, have so far been neglected in most studies. Time course analysis of these genes showed a continuous increase in their abundance in Pokkali, while in IR64, their abundance increased till 24 h followed by a clear decrease, thereby justifying their nomenclature as 'salinity induced factors' (SIFs). This is the first report showing possible involvement of SIFs localized within salinity related QTL towards salinity stress response. Based on the phenotypes of insertional mutants, it is proposed that these SIFs may have a putative function in vegetative growth (SIFVG), fertility (SIFF), viability (SIFV) or early flowering (SIFEF).

QTL fine mapping with Bayes C(π): a simulation study

BACKGROUND

Accurate QTL mapping is a prerequisite in the search for causative mutations. Bayesian genomic selection models that analyse many markers simultaneously should provide more accurate QTL detection results than single-marker models. Our objectives were to (a) evaluate by simulation the influence of heritability, number of QTL and number of records on the accuracy of QTL mapping with Bayes Cπ and Bayes C; (b) estimate the QTL status (homozygous vs. heterozygous) of the individuals analysed. This study focussed on the ten largest detected QTL, assuming they are candidates for further characterization.

METHODS

Our simulations were based on a true dairy cattle population genotyped for 38,277 phased markers. Some of these markers were considered biallelic QTL and used to generate corresponding phenotypes. Different numbers of records (4387 and 1500), heritability values (0.1, 0.4 and 0.7) and numbers of QTL (10, 100 and 1000) were studied. QTL detection was based on the posterior inclusion probability for individual markers, or on the sum of the posterior inclusion probabilities for consecutive markers, estimated using Bayes C or Bayes Cπ. The QTL status of the individuals was derived from the contrast between the sums of the SNP allelic effects of their chromosomal segments.

RESULTS

The proportion of markers with null effect (π) frequently did not reach convergence, leading to poor results for Bayes Cπ in QTL detection. Fixing π led to better results. Detection of the largest QTL was most accurate for medium to high heritability, for low to moderate numbers of QTL, and with a large number of records. The QTL status was accurately inferred when the distribution of the contrast between chromosomal segment effects was bimodal.

CONCLUSIONS

QTL detection is feasible with Bayes C. For QTL detection, it is recommended to use a large dataset and to focus on highly heritable traits and on the largest QTL. QTL statuses were inferred based on the distribution of the contrast between chromosomal segment effects.

Application of a posteriori granddaughter and modified granddaughter designs to determine Holstein haplotype effects

A posteriori and modified granddaughter designs were applied to determine haplotype effects for Holstein bulls and cows with BovineSNP50 [~50,000 single nucleotide polymorphisms (SNP); Illumina Inc., San Diego, CA] genotypes. The a posteriori granddaughter design was applied to 52 sire families, each with ≥100 genotyped sons with genetic evaluations based on progeny tests. For 33 traits (milk, fat, and protein yields; fat and protein percentages; somatic cell score; productive life; daughter pregnancy rate; heifer and cow conception rates; service-sire and daughter calving ease; service-sire and daughter stillbirth; 18 conformation traits; and net merit), the analysis was applied to the autosomal segment with the SNP with the greatest effect in the genomic evaluation of each trait. All traits except 2 had a within-family haplotype effect. The same design was applied with the genetic evaluations of sons corrected for SNP effects associated with chromosomes besides the one under analysis. The number of within-family contrasts was 166 without adjustment and 211 with adjustment. Of the 52 bulls analyzed, 36 had BovineHD (high density; Illumina Inc.) genotypes that were used to test for concordance between sire quantitative trait loci and SNP genotypes; complete concordance was not obtained for any effects. Of the 31 traits with effects from the a posteriori granddaughter design, 21 were analyzed with the modified granddaughter design. Only sires with a contrast for the a posteriori granddaughter design and ≥200 granddaughters with a record usable for genetic evaluation were included. Calving traits could not be analyzed because individual cow evaluations were not computed. Eight traits had within-family haplotype effects. With respect to milk and fat yields and fat percentage, the results on Bos taurus autosome (BTA) 14 corresponded to the hypothesis that a missense mutation in the diacylglycerol O-acyltransferase 1 (DGAT1) gene is the main causative mutation, although other polymorphisms in that gene also modify fat yield and percentage. The positive allele for protein concentration was less frequent, which indicated that selection on that locus could be effective. Although the results can be used to determine causative polymorphisms for most of the analyzed traits, complete DNA sequencing of most of the analyzed sires probably will be required.

GWA analysis for milk production traits in dairy sheep and genetic support for a QTN influencing milk protein percentage in the LALBA gene

In this study, we used the Illumina OvineSNP50 BeadChip to conduct a genome-wide association (GWA) analysis for milk production traits in dairy sheep by analyzing a commercial population of Spanish Churra sheep. The studied population consisted of a total of 1,681 Churra ewes belonging to 16 half-sib families with available records for milk yield (MY), milk protein and fat yields (PY and FY) and milk protein and fat contents (PP and FP). The most significant association identified reached experiment-wise significance for PP and FP and was located on chromosome 3 (OAR3). These results confirm the population-level segregation of a previously reported QTL affecting PP and suggest that this QTL has a significant pleiotropic effect on FP. Further associations were detected at the chromosome-wise significance level on 14 other chromosomal regions. The marker on OAR3 showing the highest significant association was located at the third intron of the alpha-lactalbumin (LALBA) gene, which is a functional and positional candidate underlying this association. Sequencing this gene in the 16 Churra rams of the studied resource population identified additional polymorphisms. One out of the 31 polymorphisms identified was located within the coding gene sequence (LALBA_g.242T>C) and was predicted to cause an amino acid change in the protein (Val27Ala). Different approaches, including GWA analysis, a combined linkage and linkage disequilibrium study and a concordance test with the QTL segregating status of the sires, were utilized to assess the role of this mutation as a putative QTN for the genetic effects detected on OAR3. Our results strongly support the polymorphism LALBA_g.242T>C as the most likely causal mutation of the studied OAR3 QTL affecting PP and FP, although we cannot rule out the possibility that this SNP is in perfect linkage disequilibrium with the true causal polymorphism.

A catalogue of validated single nucleotide polymorphisms in bovine orthologs of mammalian imprinted genes and associations with beef production traits

Genetic (or 'genomic') imprinting, a feature of approximately 100 mammalian genes, results in monoallelic expression from one of the two parentally inherited chromosomes. To date, most studies have been directed on imprinted genes in murine or human models; however, there is burgeoning interest in the effects of imprinted genes in domestic livestock species. In particular, attention has focused on imprinted genes that influence foetal growth and development and that are associated with several economically important production traits in cattle, sheep and pigs. We have re-sequenced regions in 20 candidate bovine imprinted genes in order to validate single nucleotide polymorphisms (SNPs) that may influence important production traits in cattle. Putative SNPs detected via re-sequencing were subsequently re-formatted for high-throughput SNP genotyping in 185 cattle samples comprising 138 performance-tested European Bos taurus (all Limousin bulls), 29 African B. taurus and 18 Indian B. indicus samples. Analysis of the resulting genotypic data identified 117 validated SNPs. Preliminary genotype-phenotype association analyses using 83 SNPs that were polymorphic in the Limousin samples with minor allele frequencies ⩾0.05 revealed significant associations between two candidate bovine imprinted genes and a range of important beef production traits: average daily gain, average feed intake, live weight, feed conversion ratio, residual feed intake and residual gain. These genes were the Ras protein-specific guanine nucleotide releasing factor gene (RASGRF1) and the zinc finger, imprinted 2 gene (ZIM2). Despite the relatively small sample size used in these analyses, the observed associations with production traits are supported by the purported biological function of the RASGRF1 and ZIM2 gene products. These results support the hypothesis that imprinted genes contribute significantly to important complex production traits in cattle. Furthermore, these SNPs may be usefully incorporated into future marker-assisted and genomic selection breeding schemes.

The integration of 'omic' disciplines and systems biology in cattle breeding

Enormous progress has been made in the selection of animals, including cattle, for specific traits using traditional quantitative genetics approaches. Nevertheless, considerable variation in phenotypes remains unexplained, and therefore represents potential additional gain for animal production. In addition, the paradigm shift in new disciplines now being applied to animal breeding represents a powerful opportunity to prise open the 'black box' underlying the response to selection and fully understand the genetic architecture controlling the traits of interest. A move away from traditional approaches of animal breeding toward systems approaches using integrative analysis of data from the 'omic' disciplines represents a multitude of exciting opportunities for animal breeding going forward as well as providing alternatives for overcoming some of the limitations of traditional approaches such as the expressed phenotype being an imperfect predictor of the individual's true genetic merit, or the phenotype being only expressed in one gender or late in the lifetime of an animal. This review aims to discuss these opportunities from the perspective of their potential application and contribution to cattle breeding. Harnessing the potential of this paradigm shift also poses some new challenges for animal scientists - and they will also be discussed.

Polymorphism of the FAM13A, ABCG2, OPN, LAP3, HCAP-G, PPARGC1A genes and somatic cell count of Jersey cows--preliminary study

The aim of this study was to investigate association between genotypes/combined genotypes of selected genes located on BTA6 (FAM13A, ABCG2, OPN, LAP3, HCAP-G, PPARGC1A) and somatic cell count (SCC) in milk. The study included 181 Jersey, all of which were genotyped. Allele frequencies were also determined. Genotypes were identified by the PCR-RFLP method and the results showed statistically significant (P≤0.05, P≤0.01, P≤0.001) differences between mean values of SCC in analysed cows with different genotypes of FAM13A1 G85A and combined genotypes OPN and FAM13A1. Data resulting from the present studies may be useful in further analysis in order to define the role of analysed genes (FAM13A, ABCG2, OPN, LAP3, HCAP-G, PPARGC1A) in relation to mastitis.

Genes contributing to genetic variation of muscling in sheep

Selective breeding programs aiming to increase the productivity and profitability of the sheep meat industry use elite, progeny tested sires. The broad genetic traits of primary interest in the progeny of these sires include skeletal muscle yield, fat content, eating quality, and reproductive efficiency. Natural mutations in sheep that enhance muscling have been identified, while a number of genome scans have identified and confirmed quantitative trait loci (QTL) for skeletal muscle traits. The detailed phenotypic characteristics of sheep carrying these mutations or QTL affecting skeletal muscle show a number of common biological themes, particularly changes in developmental growth trajectories, alterations of whole animal morphology, and a shift toward fast twitch glycolytic fibers. The genetic, developmental, and biochemical mechanisms underpinning the actions of some of these genetic variants are described. This review critically assesses this research area, identifies gaps in knowledge, and highlights mechanistic linkages between genetic polymorphisms and skeletal muscle phenotypic changes. This knowledge may aid the discovery of new causal genetic variants and in some cases lead to the development of biochemical and immunological strategies aimed at enhancing skeletal muscle.