Developmental progress and current status of the Animal QTLdb

The Animal QTL Database (QTLdb; http://www.animalgenome.org/QTLdb) has undergone dramatic growth in recent years in terms of new data curated, data downloads and new functions and tools. We have focused our development efforts to cope with challenges arising from rapid growth of newly published data and end users’ data demands, and to optimize data retrieval and analysis to facilitate users’ research. Evidenced by the 27 releases in the past 11 years, the growth of the QTLdb has been phenomenal. Here we report our recent progress which is highlighted by addition of one new species, four new data types, four new user tools, a new API tool set, numerous new functions and capabilities added to the curator tool set, expansion of our data alliance partners and more than 20 other improvements. In this paper we present a summary of our progress to date and an outlook regarding future directions

Research Progress in Genetic Control of Reproductive Performance in Chicken by High-Throughput Sequencing Technology

In chicken, egg production performance is a key trait to the production performance of chickens. Currently, low egg production performance is the major bottleneck, which restraints the development of indigenous chicken industry and blocks the cultivation of new chicken breeds. It has always been the focus of animal genetic breeding in detecting and studying the formation mechanism of complex traits. Due to the egg production is a complex trait determined by multiple genes, and regulated by heredity, environment, and the interaction between them, the mechanism regulating egg-laying performance is yet unknown due to its complexity. With the recent progresses of omics techniques, related researches on it have achieved considerable progress, making it possible to elucidate the molecular mechanism of egg-laying trait now. This article will provide an overall review about the recent research progress in genetic regulation of egg-laying performance in poultry through high-throughput sequencing technology

Building a livestock genetic and genomic information knowledgebase through integrative developments of Animal QTLdb and CorrDB

Successful development of biological databases requires accommodation of the burgeoning amounts of data from high-throughput genomics pipelines. As the volume of curated data in Animal QTLdb (https://www.animalgenome.org/QTLdb) increases exponentially, the resulting challenges must be met with rapid infrastructure development to effectively accommodate abundant data curation and make metadata analysis more powerful. The development of Animal QTLdb and CorrDB for the past 15 years has provided valuable tools for researchers to utilize a wealth of phenotype/genotype data to study the genetic architecture of livestock traits. We have focused our efforts on data curation, improved data quality maintenance, new tool developments, and database co-developments, in order to provide convenient platforms for users to query and analyze data. The database currently has 158 499 QTL/associations, 10 482 correlations and 1977 heritability data as a result of an average 32% data increase per year. In addition, we have made \u3e14 functional improvements or new tool implementations since our last report. Our ultimate goals of database development are to provide infrastructure for data collection, curation, and annotation, and more importantly, to support innovated data structure for new types of data mining, data reanalysis, and networked genetic analysis that lead to the generation of new knowledge

The Genetic Architecture of Economically Important Traits Provides Major Challenges for the Implementation of Gene Editing in Livestock

Gene editing has been hyped as a game-changer in many biological fields including medicine and agriculture. This includes the promise to manipulate the DNA of livestock animals at sufficient throughput, both in terms of number of loci and animals, to consider gene editing as a routine component of livestock breeding programmes. In this essay I will argue that the application of gene editing for complex traits in livestock will prove extremely challenging for a number of reasons: 1) our understanding of the genetic control of complex traits remains sketchy; 2) even with cutting edge ‘omics technologies, the identification of functional mutations remains very challenging; 3) before selecting certain mutations for gene editing, we need to capture the pleiotropic effects of the mutation and test whether its effects are truly additive. With the current understanding of complex traits there is a risk that gene editing will revert to a candidate gene approach without knowledge or understanding of where the important mutations reside. This means that it will be some time before we can really benefit from gene editing for truly complex traits in livestock. In the meantime gene editing could deliver quick wins by ‘repairing’ lethal recessive defects that are present in many elite breeding animals. Furthermore I will outline how gene editing can have an important role in the identification of QTN via in-vitro genetics

Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes

Genetics and “omics” studies designed to uncover genotype to phenotype relationships often identify large numbers of potential candidate genes, among which the causal genes are hidden. Scientists generally lack the time and technical expertise to review all relevant information available from the literature, from key model species and from a potentially wide range of related biological databases in a variety of data formats with variable quality and coverage. Computational tools are needed for the integration and evaluation of heterogeneous information in order to prioritise candidate genes and components of interaction networks that, if perturbed through potential interventions, have a positive impact on the biological outcome in the whole organism without producing negative side effects. Here we review several bioinformatics tools and databases that play an important role in biological knowledge discovery and candidate gene prioritization. We conclude with several key challenges that need to be addressed in order to facilitate biological knowledge discovery in the future.&nbsp

Genome-Wide Association Analysis for Resistance to Infectious Pancreatic Necrosis Virus Identifies Candidate Genes Involved in Viral Replication and Immune Response in Rainbow Trout (Oncorhynchus mykiss)

Infectious pancreatic necrosis (IPN) is a viral disease with considerable negative impact on the rainbow trout (Oncorhynchus mykiss) aquaculture industry. The aim of the present work was to detect genomic regions that explain resistance to infectious pancreatic necrosis virus (IPNV) in rainbow trout. A total of 2,278 fish from 58 full-sib families were challenged with IPNV and 768 individuals were genotyped (488 resistant and 280 susceptible), using a 57K SNP panel Axiom, Affymetrix. A genome-wide association study (GWAS) was performed using the phenotypes time to death (TD) and binary survival (BS), along with the genotypes of the challenged fish using a Bayesian model (Bayes C). Heritabilities for resistance to IPNV estimated using genomic information, were 0.53 and 0.82 for TD and BS, respectively. The Bayesian GWAS detected a SNP located on chromosome 5 explaining 19% of the genetic variance for TD. The proximity of Sentrin-specific protease 5 (SENP5) to this SNP makes it a candidate gene for resistance against IPNV. In case of BS, a SNP located on chromosome 23 was detected explaining 9% of the genetic variance. However, the moderate-low proportion of variance explained by the detected marker leads to the conclusion that the incorporation of all genomic information, through genomic selection, would be the most appropriate approach to accelerate genetic progress for the improvement of resistance against IPNV in rainbow trout

Identification of growth trait related genes in a Yorkshire purebred pig population by genome-wide association studies

Objective The aim of this study is to identify genomic regions or genes controlling growth traits in pigs. Methods Using a panel of 54,148 single nucleotide polymorphisms (SNPs), we performed a genome-wide Association (GWA) study in 562 pure Yorshire pigs with four growth traits: average daily gain from 30 kg to 100 kg or 115 kg, and days to 100 kg or 115 kg. Fixed and random model Circulating Probability Unification method was used to identify the associations between 54,148 SNPs and these four traits. SNP annotations were performed through the Sus scrofa data set from Ensembl. Bioinformatics analysis, including gene ontology analysis, pathway analysis and network analysis, was used to identify the candidate genes. Results We detected 6 significant and 12 suggestive SNPs, and identified 9 candidate genes in close proximity to them (suppressor of glucose by autophagy [SOGA1], R-Spondin 2 [RSPO2], mitogen activated protein kinase kinase 6 [MAP2K6], phospholipase C beta 1 [PLCB1], rho GTPASE activating protein 24 [ARHGAP24], cytoplasmic polyadenylation element binding protein 4 [CPEB4], GLI family zinc finger 2 [GLI2], neuronal tyrosine-phosphorylated phosphoinositide-3-kinase adaptor 2 [NYAP2], and zinc finger protein multitype 2 [ZFPM2]). Gene ontology analysis and literature mining indicated that the candidate genes are involved in bone, muscle, fat, and lung development. Pathway analysis revealed that PLCB1 and MAP2K6 participate in the gonadotropin signaling pathway and suggests that these two genes contribute to growth at the onset of puberty. Conclusion Our results provide new clues for understanding the genetic mechanisms underlying growth traits, and may help improve these traits in future breeding programs

Genetic variability in a Holstein population using SNP markers and their use for monitoring mating strategies

As genotyping costs continue to decrease, the demand for genotyping has increased among farmers. In most livestock herds, an important issue is controlling the increase in inbreeding coefficient. While this remains a large motive to genotype, producers are often unaware of the other benefits that genotyping could bring. The aim of this study was to demonstrate that SNP chips could be used as an effective herd management tool by utilizing a population of Italian Holstein-Friesian cattle. After filtering, the total number of animals and SNPs retained for analyses were 44 and 27,365, respectively. The principal component analyses (PCA) were able to identify a sire and origin-of-sire effect within the herd, while determining that sires do not influence individual genomic selection index values. The inbreeding coefficients calculated from genotypes (FIS) provided a glimpse into the herd\u2019s heterozygosity and determined that the genetic variability is being well maintained. On the other hand, inbreeding coefficients on the genomic level were deduced from runs of homozygosity (FROH) and were compared to the inbreeding coefficients based on pedigree (FPED). Furthermore, 1,950 runs of homozygosity (ROH) were identified with the average length of ROH being 4.66 Mb. Genes and QTL within the genomic regions most commonly associated (top 1% and top 5% of SNP) with ROH were characterized. These results indicate that genotyping small herds, albeit at low-density, provides insights to the genetic variability within the herd and thus allows producers the ability to manage their stock from a genetic standpoint

Genomic Selection Signatures In Sheep From The Western Pyrenees

Background: The current large spectrum of sheep phenotypic diversity results from the combined product of sheep selection for different production traits such as wool, milk and meat, and its natural adaptation to new environments. In this study, we scanned the genome of 25 Sasi Ardi and 75 Latxa sheep from the Western Pyrenees for three types of regions under selection: (1) regions underlying local adaptation of Sasi Ardi semi-feral sheep, (2) regions related to a long traditional dairy selection pressure in Latxa sheep, and (3) regions experiencing the specific effect of the modern genetic improvement program established for the Latxa breed during the last three decades. Results: Thirty-two selected candidate regions including 147 annotated genes were detected by using three statistical parameters: pooled heterozygosity H, Tajima's D, and Wright's fixation index F-st. For Sasi Ardi sheep, chromosomes Ovis aries (OAR) 4, 6, and 22 showed the strongest signals and harbored several candidate genes related to energy metabolism and morphology (BBS9, ELOVL3 and LDB1), immunity (NFKB2), and reproduction (H2AFZ). The major genomic difference between Sasi Ardi and Latxa sheep was on OAR6, which is known to affect milk production, with highly selected regions around the ABCG2, SPP1, LAP3, NCAPG, LCORL, and MEPE genes in Latxa sheep. The effect of the modern genetic improvement program on Latxa sheep was also evident on OAR15, on which several olfactory genes are located. We also detected several genes involved in reproduction such as ESR1 and ZNF366 that were affected by this selection program. Conclusions: Natural and artificial selection have shaped the genome of both Sasi Ardi and Latxa sheep. Our results suggest that Sasi Ardi traits related to energy metabolism, morphological, reproductive, and immunological features have been under positive selection to adapt this semi-feral sheep to its particular environment. The highly selected Latxa sheep for dairy production showed clear signatures of selection in genomic regions related to milk production. Furthermore, our data indicate that the selection criteria applied in the modern genetic improvement program affect immunity and reproduction traits.The authors gratefully acknowledge support from the University of the Basque Country (UPV/EHU) and the Conservatoire des Races d'Aquitaine (US13/29