Animal Quantitative Trait Loci Research Articles

Determining the Relationships Between Genotype and Phenotype Using Molecular Genetic Tools in Chickens

Context: Chicken meat is one of the rich sources of protein and is considered one of the most commonly consumed meats in the world. Enhancing poultry production and its products is achievable through chicken breeding. Effective breeding projects for future genetic improvement require an understanding of the genetic potential of a given population. Evidence Acquisition: Articles were selected based on novel molecular markers related to economic traits of chicken in Animal quantitative trait loci (QTL) database, Ensemble genome browser, CorrDB, ChickVD, Galbase, and Google Scholar. These databases contain QTLs, markers, and reference genomes of chicken. Galbase provides research communities with updated possibilities to conduct thorough functional genomics research on chickens. ChickVD manages high-quality sequence variation data, variation analysis in relation to chicken genes, cDNAs, genetic markers, and QTLs. CorrDB compiles all available chicken genetic and phenotypic trait correlation data to aid in the analysis of genetic networks and facilitate systems biology research. Animal Quantitative Trait Loci Database (Animal QTLdb) gathers and compiles all existing data on trait mapping, including QTL (phenotype/expression, eQTL), candidate gene, association data (GWAS), and copy number variations (CNV) for livestock animal genomes. This comprehensive database facilitates the easier identification and comparison of discoveries within and across species. Ensemble genome browser is a powerful tool for analyzing vertebrate genomes, offering valuable insights into comparative genomics, evolutionary processes, sequence variations, and transcriptional regulation. Results: Valuating the relatedness of the studied broilers and layers is important for maintaining and improving the genetic reserves of the same population. Candidate genes (CGs) are used to determine genes related to economic traits in chickens. Molecular genetic techniques such as whole-genome re-sequencing (WGRS) can identify single nucleotide polymorphisms (SNPs), insertion/deletion polymorphisms (indels), structural variations (SV), CNV, and simple sequence repeats (SSRs) and their chromosomal regions in terms of QTL. Recently, the pan-genome (PG) has become one of the most important tools to study genome variation and changes in important traits. The use of molecular markers and CGs can aid in evaluating the genetic relationships between phenotype and genotype of broilers and layers. Conclusions: This review introduces molecular genetic methods and new approaches to identify novel genes related to important biological pathways and significant production and reproductive traits.

Transcriptome analysis of the Bactrian camel (Camelus bactrianus) reveals candidate genes affecting milk production traits

BackgroundMilk production traits are complex traits with vital economic importance in the camel industry. However, the genetic mechanisms regulating milk production traits in camels remain poorly understood. Therefore, we aimed to identify candidate genes and metabolic pathways that affect milk production traits in Bactrian camels.MethodsWe classified camels (fourth parity) as low- or high-yield, examined pregnant camels using B-mode ultrasonography, observed the microscopic changes in the mammary gland using hematoxylin and eosin (HE) staining, and used RNA sequencing to identify differentially expressed genes (DEGs) and pathways.ResultsThe average standard milk yield over the 300 days during parity was recorded as 470.18 ± 9.75 and 978.34 ± 3.80 kg in low- and high-performance camels, respectively. Nine female Junggar Bactrian camels were subjected to transcriptome sequencing, and 609 and 393 DEGs were identified in the low-yield vs. high-yield (WDL vs. WGH) and pregnancy versus colostrum period (RSQ vs. CRQ) comparison groups, respectively. The DEGs were compared with genes associated with milk production traits in the Animal Quantitative Trait Loci database and in Alashan Bactrian camels, and 65 and 46 overlapping candidate genes were obtained, respectively. Functional enrichment and protein–protein interaction network analyses of the DEGs and candidate genes were conducted. After comparing our results with those of other livestock studies, we identified 16 signaling pathways and 27 core candidate genes associated with maternal parturition, estrogen regulation, initiation of lactation, and milk production traits. The pathways suggest that emerged milk production involves the regulation of multiple complex metabolic and cellular developmental processes in camels. Finally, the RNA sequencing results were validated using quantitative real-time PCR; the 15 selected genes exhibited consistent expression changes.ConclusionsThis study identified DEGs and metabolic pathways affecting maternal parturition and milk production traits. The results provides a theoretical foundation for further research on the molecular mechanism of genes related to milk production traits in camels. Furthermore, these findings will help improve breeding strategies to achieve the desired milk yield in camels.Graphical

A combinatorial approach implementing new database structures to facilitate practical data curation management of QTL, association, correlation and heritability data on trait variants

A precise description of traits is essential in genetics and genomics studies to facilitate comparative genetics and meta-analyses. It is an ongoing challenge in research and production environments to unambiguously and consistently compare traits of interest from data collected under various conditions. Despite previous efforts to standardize trait nomenclature, it remains a challenge to fully and accurately capture trait nomenclature granularity in a way that ensures long-term data sustainability in terms of the data curation processes, data management logistics and the ability to make meaningful comparisons across studies. In the Animal Quantitative Trait Loci Database and the Animal Trait Correlation Database, we have recently introduced a new method to extend livestock trait ontologies by using trait modifiers and qualifiers to define traits that differ slightly in how they are measured, examined or combined with other traits or factors. Here, we describe the implementation of a system in which the extended trait data, with modifiers, are managed at the experiment level as 'trait variants'. This has helped us to streamline the management and curation of such trait information in our database environment. Database URL https://www.animalgenome.org/PGNET/.

Proteomics Recapitulates Ovarian Proteins Relevant to Puberty and Fertility in Brahman Heifers (Bos indicus L.).

High fertility and early puberty in Bos indicus heifers are desirable and genetically correlated traits in beef production. The hypothalamus–pituitary–ovarian (HPO) axis synthesizes steroid hormones, which contribute to the shift from the pre-pubertal state into the post-pubertal state and influence subsequent fertility. Understanding variations in abundance of proteins that govern steroid synthesis and ovarian signaling pathways remains crucial to understanding puberty and fertility. We used whole ovaries of six pre-pubertal and six post-pubertal Brahman heifers to conduct differential abundance analyses of protein profiles between the two physiological states. Extracted proteins were digested into peptides followed by identification and quantification with massspectrometry (MS) by sequential window acquisition of all instances of theoretical fragment ion mass spectrometry (SWATH-MS). MS and statistical analysis identified 566 significantly differentially abundant (DA) proteins (adjusted p < 0.05), which were then analyzed for gene ontology and pathway enrichment. Our data indicated an up-regulation of steroidogenic proteins contributing to progesterone synthesis at luteal phase post-puberty. Proteins related to progesterone signaling, TGF-β, retinoic acid, extracellular matrix, cytoskeleton, and pleiotrophin signaling were DA in this study. The DA proteins probably relate to the formation and function of the corpus luteum, which is only present after ovulation, post-puberty. Some DA proteins might also be related to granulosa cells signaling, which regulates oocyte maturation or arrest in ovaries prior to ovulation. Ten DA proteins were coded by genes previously associated with reproductive traits according to the animal quantitative trait loci (QTL) database. In conclusion, the DA proteins and their pathways were related to ovarian activity in Bos indicus cattle. The genes that code for these proteins may explain some known QTLs and could be targeted in future genetic studies.

Discover and Track Obesity Relevant Research Resources Using dkNET (NIDDK Information Network)

The NIDDK Information Network (dknet.org) is a portal for basic and clinical investigators that makes it easier to discover, obtain, and reuse scientific research resources. Here we demonstrate how dkNET can connect researchers to resources for obesity research. A search for “obesity” returns 264,498 results (Table 1), including physical resources (i.e., antibodies, cell lines, model organisms), digital resources, data, models, funding, and knowledge bases. dkNET facilitates discovery not only within well known databases like GEO, Mouse Phenome Database, or model organism databases such as MMRRC, but also many unique but less well known databases, such as Animal Quantitative Trait Loci or Aging Gene/Interventions databases. dkNET provides some basic analytic tools, e.g., word clouds, category graphs, to aid exploration. dkNET provides in depth information on how resources are used through Research Resource Identifier (RRID), a unique ID allowing dkNET to aggregate and provide information, e.g., about known problems with antibodies or cell lines. To assist researchers in complying with NIH guidelines on rigor and transparency, dkNET provides a tool to suggest authentication plans for key research resources. dkNET also provides reports to track resource utilization. These new services aim to not only enhance transparency of research but also ensure that resource creators are appropriately credited. Disclosure K. Lin: None. A. Bandrowski: None. J. Go: None. I. Ozyurt: None. T. Gillespie: None. J.S. Grethe: None. M.E. Martone: None.

Identification of molecular pathways and candidate genes associated with cocks\u2019 comb size trait by genome-wide transcriptome analysis

The comb of the male is an important secondary sexual characteristic. Although quantitative trait loci (QTLs) related to comb size have been identified, molecular mechanisms underlying this trait remain mostly unknown. In this study, RNA sequencing (RNA-seq) was employed to compare whole transcriptomic differences between two groups of Partridge Shank chickens that are divergent in comb sizes. A total of 563 differentially expressed genes (DEGs) were identified, including 277 up-regulated and 286 down-regulated DEGs. According to the animal QTL database, eight DEGs including BMP2 and CHADL matching the reported QTLs were associated with the comb size. Functional annotation analysis revealed that DEGs were involved in cell communication and calcium signaling. Protein-protein interaction network analysis showed that STK32A, PIK3R1, EDN1, HSPA5, and HSPA8 have an impact on comb growth. Moreover, potential alternative splicing events and single nucleotide polymorphisms were also identified. Our data provide a source for identifying genes and pathways with functions critical to comb size and accelerate studies involving molecular mechanisms of this sexual ornament.

Identification of genes for controlling swine adipose deposition by integrating transcriptome, whole-genome resequencing, and quantitative trait loci data.

Backfat thickness is strongly associated with meat quality, fattening efficiency, reproductive performance, and immunity in pigs. Fat storage and fatty acid synthesis mainly occur in adipose tissue. Therefore, we used a high-throughput massively parallel sequencing approach to identify transcriptomes in adipose tissue, and whole-genome differences from three full-sibling pairs of pigs with opposite (high and low) backfat thickness phenotypes. We obtained an average of 38.69 million reads for six samples, 78.68% of which were annotated in the reference genome. Eighty-nine overlapping differentially expressed genes were identified among the three pair comparisons. Whole-genome resequencing also detected multiple genetic variations between the pools of DNA from the two groups. Compared with the animal quantitative trait loci (QTL) database, 20 differentially expressed genes were matched to the QTLs associated with fatness in pigs. Our technique of integrating transcriptome, whole-genome resequencing, and QTL database information provided a rich source of important differentially expressed genes and variations. Associate analysis between selected SNPs and backfat thickness revealed that two SNPs and one haplotype of ME1 significantly affected fat deposition in pigs. Moreover, genetic analysis confirmed that variations in the differentially expressed genes may affect fat deposition.

Increased prediction accuracy using a genomic feature model including prior information on quantitative trait locus regions in purebred Danish Duroc pigs.

BackgroundIn animal breeding, genetic variance for complex traits is often estimated using linear mixed models that incorporate information from single nucleotide polymorphism (SNP) markers using a realized genomic relationship matrix. In such models, individual genetic markers are weighted equally and genomic variation is treated as a “black box.” This approach is useful for selecting animals with high genetic potential, but it does not generate or utilise knowledge of the biological mechanisms underlying trait variation. Here we propose a linear mixed-model approach that can evaluate the collective effects of sets of SNPs and thereby open the “black box.” The described genomic feature best linear unbiased prediction (GFBLUP) model has two components that are defined by genomic features.ResultsWe analysed data on average daily gain, feed efficiency, and lean meat percentage from 3,085 Duroc boars, along with genotypes from a 60 K SNP chip. In addition information on known quantitative trait loci (QTL) from the animal QTL database was integrated in the GFBLUP as a genomic feature. Our results showed that the most significant QTL categories were indeed biologically meaningful. Additionally, for high heritability traits, prediction accuracy was improved by the incorporation of biological knowledge in prediction models. A simulation study using the real genotypes and simulated phenotypes demonstrated challenges regarding detection of causal variants in low to medium heritability traits.ConclusionsThe GFBLUP model showed increased predictive ability when enough causal variants were included in the genomic feature to explain over 10 % of the genomic variance, and when dilution by non-causal markers was minimal. In the observed data set, predictive ability was increased by the inclusion of prior QTL information obtained outside the training data set, but only for the trait with highest heritability.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-015-0322-9) contains supplementary material, which is available to authorized users.

Identification of pleiotropic genes and gene sets underlying growth and immunity traits: a case study on Meishan pigs

Both growth and immune capacity are important traits in animal breeding. The animal quantitative trait loci (QTL) database is a valuable resource and can be used for interpreting the genetic mechanisms that underlie growth and immune traits. However, QTL intervals often involve too many candidate genes to find the true causal genes. Therefore, the aim of this study was to provide an effective annotation pipeline that can make full use of the information of Gene Ontology terms annotation, linkage gene blocks and pathways to further identify pleiotropic genes and gene sets in the overlapping intervals of growth-related and immunity-related QTLs. In total, 55 non-redundant QTL overlapping intervals were identified, 1893 growth-related genes and 713 immunity-related genes were further classified into overlapping intervals and 405 pleiotropic genes shared by the two gene sets were determined. In addition, 19 pleiotropic gene linkage blocks and 67 pathways related to immunity and growth traits were discovered. A total of 343 growth-related genes and 144 immunity-related genes involved in pleiotropic pathways were also identified, respectively. We also sequenced and genotyped 284 individuals from Chinese Meishan pigs and European pigs and mapped the single nucleotide polymorphisms (SNPs) to the pleiotropic genes and gene sets that we identified. A total of 971 high-confidence SNPs were mapped to the pleiotropic genes and gene sets that we identified, and among them 743 SNPs were statistically significant in allele frequency between Meishan and European pigs. This study explores the relationship between growth and immunity traits from the view of QTL overlapping intervals and can be generalized to explore the relationships between other traits.

ALDB: a domestic-animal long noncoding RNA database.

BackgroundLong noncoding RNAs (lncRNAs) have attracted significant attention in recent years due to their important roles in many biological processes. Domestic animals constitute a unique resource for understanding the genetic basis of phenotypic variation and are ideal models relevant to diverse areas of biomedical research. With improving sequencing technologies, numerous domestic-animal lncRNAs are now available. Thus, there is an immediate need for a database resource that can assist researchers to store, organize, analyze and visualize domestic-animal lncRNAs.ResultsThe domestic-animal lncRNA database, named ALDB, is the first comprehensive database with a focus on the domestic-animal lncRNAs. It currently archives 12,103 pig intergenic lncRNAs (lincRNAs), 8,923 chicken lincRNAs and 8,250 cow lincRNAs. In addition to the annotations of lincRNAs, it offers related data that is not available yet in existing lncRNA databases (lncRNAdb and NONCODE), such as genome-wide expression profiles and animal quantitative trait loci (QTLs) of domestic animals. Moreover, a collection of interfaces and applications, such as the Basic Local Alignment Search Tool (BLAST), the Generic Genome Browser (GBrowse) and flexible search functionalities, are available to help users effectively explore, analyze and download data related to domestic-animal lncRNAs.ConclusionsALDB enables the exploration and comparative analysis of lncRNAs in domestic animals. A user-friendly web interface, integrated information and tools make it valuable to researchers in their studies. ALDB is freely available from http://res.xaut.edu.cn/aldb/index.jsp.

Genome wide association studies for body conformation traits in the Chinese Holstein cattle population

BackgroundGenome-wide association study (GWAS) is a powerful tool for revealing the genetic basis of quantitative traits. However, studies using GWAS for conformation traits of cattle is comparatively less. This study aims to use GWAS to find the candidates genes for body conformation traits.ResultsThe Illumina BovineSNP50 BeadChip was used to identify single nucleotide polymorphisms (SNPs) that are associated with body conformation traits. A least absolute shrinkage and selection operator (LASSO) was applied to detect multiple SNPs simultaneously for 29 body conformation traits with 1,314 Chinese Holstein cattle and 52,166 SNPs. Totally, 59 genome-wide significant SNPs associated with 26 conformation traits were detected by genome-wide association analysis; five SNPs were within previously reported QTL regions (Animal Quantitative Trait Loci (QTL) database) and 11 were very close to the reported SNPs. Twenty-two SNPs were located within annotated gene regions, while the remainder were 0.6–826 kb away from known genes. Some of the genes had clear biological functions related to conformation traits. By combining information about the previously reported QTL regions and the biological functions of the genes, we identified DARC, GAS1, MTPN, HTR2A, ZNF521, PDIA6, and TMEM130 as the most promising candidate genes for capacity and body depth, chest width, foot angle, angularity, rear leg side view, teat length, and animal size traits, respectively. We also found four SNPs that affected four pairs of traits, and the genetic correlation between each pair of traits ranged from 0.35 to 0.86, suggesting that these SNPs may have a pleiotropic effect on each pair of traits.ConclusionsA total of 59 significant SNPs associated with 26 conformation traits were identified in the Chinese Holstein population. Six promising candidate genes were suggested, and four SNPs showed genetic correlation for four pairs of traits.

The clinical measurement, measurement method and experimental condition ontologies: expansion, improvements and new applications.

BackgroundThe Clinical Measurement Ontology (CMO), Measurement Method Ontology (MMO), and Experimental Condition Ontology (XCO) were originally developed at the Rat Genome Database (RGD) to standardize quantitative rat phenotype data in order to integrate results from multiple studies into the PhenoMiner database and data mining tool. These ontologies provide the framework for presenting what was measured, how it was measured, and under what conditions it was measured.ResultsThere has been a continuing expansion of subdomains in each ontology with a parallel 2–3 fold increase in the total number of terms, substantially increasing the size and improving the scope of the ontologies. The proportion of terms with textual definitions has increased from ~60% to over 80% with greater synchronization of format and content throughout the three ontologies. Representation of definition source Uniform Resource Identifiers (URI) has been standardized, including the removal of all non-URI characters, and systematic versioning of all ontology files has been implemented. The continued expansion and success of these ontologies has facilitated the integration of more than 60,000 records into the RGD PhenoMiner database. In addition, new applications of these ontologies, such as annotation of Quantitative Trait Loci (QTL), have been added at the sites actively using them, including RGD and the Animal QTL Database.ConclusionsThe improvements to these three ontologies have been substantial, and development is ongoing. New terms and expansions to the ontologies continue to be added as a result of active curation efforts at RGD and the Animal QTL database. Use of these vocabularies to standardize data representation for quantitative phenotypes and quantitative trait loci across databases for multiple species has demonstrated their utility for integrating diverse data types from multiple sources. These ontologies are freely available for download and use from the NCBO BioPortal website at http://bioportal.bioontology.org/ontologies/1583 (CMO), http://bioportal.bioontology.org/ontologies/1584 (MMO), and http://bioportal.bioontology.org/ontologies/1585 (XCO), or from the RGD ftp site at ftp://rgd.mcw.edu/pub/ontology/.

Incorporation of Discrete Genotype Effects for Multiple Genes into Animal Model Evaluations when only a Small Fraction of the Population Has Been Genotyped

An efficient algorithm is described for marker-assisted selection appropriate for large populations, even though only a small fraction of the population is genotyped. Genotype probabilities for specific loci are computed for all animals without genotypes. Effects of the quantitative trait loci (QTL) are then estimated by a “cow model” and the appropriate effects are subtracted from the cows’ records. Selection is based on genetic evaluations computed from the adjusted records after addition of each animal's QTL genotype effect. The proposed scheme was applied to 10 simulated populations of 37,000 cows generated over 30 yr and compared with a selection scheme based on a standard animal model. Two diallelic QTL with substitution effects of 0.5 and 0.32 phenotypic standard deviations were simulated with initial frequencies of 0.5 for both alleles. Means and standard errors of estimates of the QTL effects at yr 30 were 0.498±0.011 and 0.347±0.008. Thus, estimation of the larger QTL was nearly exact, whereas the smaller QTL was slightly overestimated. At yr 9 through 12 after the beginning of the breeding program, genetic gain in the marker-assisted selection scheme was 0.17 standard deviations greater than the standard scheme. This corresponds to nearly 2 yr of genetic progress relative to the standard scheme, or more than 40% of the total genetic gain obtained by the standard scheme at yr 9. Although genetic gain of the 2 schemes was nearly equal by yr 30, the Gibson effect of eventual greater progress by trait-based selection was not observed. Extension of the methods proposed in the current study could be applied to rank sires accurately including both marker and pedigree information for the large number of segregating QTL that will be detected by whole-genome single nucleotide polymorphism scans.

Animal QTLdb: beyond a repository

Over the past ten years there have been a large number of publications that have described hundreds of quantitative trait loci (QTL) in livestock species. To facilitate the comparison of QTL results across experiments, the Animal QTL database (QTLdb) was developed to house all published QTL information as a public repository. The QTLdb was originally developed to serve the porcine genomics community (previously known as PigQTLdb). We have further developed the QTLdb to house QTL data from multiple species, including but not limited to cattle, chickens, and pigs. In addition, tools have been developed to allow QTL map alignments against consensus linkage maps, radiation hybrid (RH) maps, BAC fingerprinted contig (FPC) maps, single nucleotide polymorphism (SNP) location maps, and human maps. In addition, we have expanded the capabilities of the database such that research tools were developed where "private" preliminary QTL data could be entered and compared against all public data. This allows researchers to visualize data before publication and compare it with published results to aid in data interpretation. To serve this purpose, the database curator/editor tools also include functions that allow registered users to enter their own QTL data, make use of the QTLdb tools for data analysis, and use the QTLdb as a publishing tool (URL: http://www.animalgenome.org).

AnimalQTLdb: a livestock QTL database tool set for positional QTL information mining and beyond

The Animal Quantitative Trait Loci (QTL) database (AnimalQTLdb) is designed to house all publicly available QTL data on livestock animal species from which researchers can easily locate and compare QTL within species. The database tools are also added to link the QTL data to other types of genomic information, such as radiation hybrid (RH) maps, finger printed contig (FPC) physical maps, linkage maps and comparative maps to the human genome, etc. Currently, this database contains data on 1287 pig, 630 cattle and 657 chicken QTL, which are dynamically linked to respective RH, FPC and human comparative maps. We plan to apply the tool to other animal species, and add more structural genome information for alignment, in an attempt to aid comparative structural genome studies ().