Functional Association Network Research Articles

A simple null model for inferences from network enrichment analysis.

A prevailing technique to infer function from lists of identifications, from molecular biological high-throughput experiments, is over-representation analysis, where the identifications are compared to predefined sets of related genes often referred to as pathways. As at least some pathways are known to be incomplete in their annotation, algorithmic efforts have been made to complement them with information from functional association networks. While the terminology varies in the literature, we will here refer to such methods as Network Enrichment Analysis (NEA). Traditionally, the significance of inferences from NEA has been assigned using a null model constructed from randomizations of the network. Here we instead argue for a null model that more directly relates to the set of genes being studied, and have designed one dynamic programming algorithm that calculates the score distribution of NEA scores that makes it possible to assign unbiased mid p values to inferences. We also implemented a random sampling method, carrying out the same task. We demonstrate that our method obtains a superior statistical calibration as compared to the popular NEA inference engine, BinoX, while also providing statistics that are easier to interpret.

Lonicera japonica extends lifespan and healthspan in Caenorhabditis elegans

Lonicera japonica (LJ) is widely used as the local medicine to improve body and prevent ills in China, but mechanisms of its healthy beneficial effects remain largely unclear. Here, we evaluated the anti-aging and healthspan promoting activities of 75% ethanol extract of LJ (LJ-E) in the animal model Caenorhabditis elegans. Our results showed that LJ-E (500 μg/mL) treatment enhanced the mean lifespan of worms by over 21.87% and significantly improved age-associated physiological functions in C. elegans. The 500 μg/mL concentration of LJ-E enhanced the survival rates under oxidative and thermal stresses, and decreased reactive oxygen species (ROS) levels and fat accumulation in the worms. Gene-specific mutant studies showed that LJ-E-mediated lifespan extension was dependent on mev-1, daf-2, daf-16, and hsf-1, but not eat-2 genes. LJ-E could upregulate stress-inducible genes, viz., hsp-16.2, sod-3 and mtl-1. Moreover, we found that the D1086.10 protein interacted with superoxide dismutase (SOD)–3 by functional protein association networks analysis according to RNA-sequencing results. It was confirmed that D1086.10 was needed to promote longevity, and positively regulated expression of sod-3 by using D1086.10 mutants. Furthermore, LJ-E significantly delayed amyloid β-protein induced paralysis in CL4176 strain. Given the important role of autophagy in aging and protein homeostasis, we observed that LJ-E could remarkably increase the mRNA expression of autophagy gene bec-1 in CL4176 strain, and decrease expression of autophagy substrate p62 protein by more than 40.0% in BC12921 strain. Finally, we found that combination composed of three major compounds (54 μg/mL chlorogenic acid, 15 μg/mL 1,5-dicaffeoylquinic acid and 7.5 μg/mL 1,3-dicaffeoylquinic acid) of 500 μg/mL LJ-E could significantly delay paralysis in CL4176 worms caused by Aβ toxicity, comparable to that of LJ-E. Overall, our study may have important implications in using Lonicera japonica to promote healthy aging and have a potency to design therapeutics for age-related diseases.

Transcriptome profiling of whisker follicles in methamphetamine self-administered rats

Methamphetamine (MA) is a highly addictive psychostimulant that disturbs the central nervous system; therefore, diagnosis of MA addiction is important in clinical and forensic toxicology. In this study, a MA self-administration rat model was used to illustrate the gene expression profiling of the rewarding effect caused by MA. RNA-sequencing was performed to examine changes in gene expression in rat whisker follicles collected before self-administration, after MA self-administration, and after withdrawal sessions. We identified six distinct groups of genes, with statistically significant expression patterns. By constructing the functional association network of these genes and performing the subsequent topological analysis, we identified 43 genes, which have the potential to regulate MA reward and addiction. The gene pathways were then analysed using the Reactome and Knowledgebase for Addiction-Related Gene database, and it was found that genes and pathways associated with Alzheimer’s disease and the heparan sulfate biosynthesis were enriched in MA self-administration rats. The findings suggest that changes of the genes identified in rat whisker follicles may be useful indicators of the rewarding effect of MA. Further studies are needed to provide a comprehensive understanding of MA addiction.

Predicted Arabidopsis Interactome Resource and Gene Set Linkage Analysis: A Transcriptomic Analysis Resource.

An advanced functional understanding of omics data is important for elucidating the design logic of physiological processes in plants and effectively controlling desired traits in plants. We present the latest versions of the Predicted Arabidopsis Interactome Resource (PAIR) and of the gene set linkage analysis (GSLA) tool, which enable the interpretation of an observed transcriptomic change (differentially expressed genes [DEGs]) in Arabidopsis (Arabidopsis thaliana) with respect to its functional impact for biological processes. PAIR version 5.0 integrates functional association data between genes in multiple forms and infers 335,301 putative functional interactions. GSLA relies on this high-confidence inferred functional association network to expand our perception of the functional impacts of an observed transcriptomic change. GSLA then interprets the biological significance of the observed DEGs using established biological concepts (annotation terms), describing not only the DEGs themselves but also their potential functional impacts. This unique analytical capability can help researchers gain deeper insights into their experimental results and highlight prospective directions for further investigation. We demonstrate the utility of GSLA with two case studies in which GSLA uncovered how molecular events may have caused physiological changes through their collective functional influence on biological processes. Furthermore, we showed that typical annotation-enrichment tools were unable to produce similar insights to PAIR/GSLA. The PAIR version 5.0-inferred interactome and GSLA Web tool both can be accessed at http://public.synergylab.cn/pair/.

Non-small-cell lung cancer pathological subtype-related gene selection and bioinformatics analysis based on gene expression profiles.

Lung cancer is one of the most common malignant diseases and a major threat to public health on a global scale. Non-small-cell lung cancer (NSCLC) has a higher degree of malignancy and a lower 5-year survival rate compared with that of small-cell lung cancer. NSCLC may be mainly divided into two pathological subtypes, adenocarcinoma and squamous cell carcinoma. The aim of the present study was to identify disease genes based on the gene expression profile and the shortest path analysis of weighted functional protein association networks with the existing protein-protein interaction data from the Search Tool for the Retrieval of Interacting Genes. The gene expression profile (GSE10245) was downloaded from the National Center for Biotechnology Information Gene Expression Omnibus database, including 40 lung adenocarcinoma and 18 lung squamous cell carcinoma tissues. A total of 8 disease genes were identified using Naïve Bayesian Classifier based on the Maximum Relevance Minimum Redundancy feature selection method following preprocessing. An additional 21 candidate genes were selected using the shortest path analysis with Dijkstra's algorithm. The AURKA and SLC7A2 genes were selected three and two times in the shortest path analysis, respectively. All those genes participate in a number of important pathways, such as oocyte meiosis, cell cycle and cancer pathways with Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. The present findings may provide novel insights into the pathogenesis of NSCLC and enable the development of novel therapeutic strategies. However, further investigation is required to confirm these findings.

FunCoup 4: new species, data, and visualization.

This release of the FunCoup database (http://funcoup.sbc.su.se) is the fourth generation of one of the most comprehensive databases for genome-wide functional association networks. These functional associations are inferred via integrating various data types using a naive Bayesian algorithm and orthology based information transfer across different species. This approach provides high coverage of the included genomes as well as high quality of inferred interactions. In this update of FunCoup we introduce four new eukaryotic species: Schizosaccharomyces pombe, Plasmodium falciparum, Bos taurus, Oryza sativa and open the database to the prokaryotic domain by including networks for Escherichia coli and Bacillus subtilis. The latter allows us to also introduce a new class of functional association between genes - co-occurrence in the same operon. We also supplemented the existing classes of functional association: metabolic, signaling, complex and physical protein interaction with up-to-date information. In this release we switched to InParanoid v8 as the source of orthology and base for calculation of phylogenetic profiles. While populating all other evidence types with new data we introduce a new evidence type based on quantitative mass spectrometry data. Finally, the new JavaScript based network viewer provides the user an intuitive and responsive platform to further evaluate the results.

Evolutionarily Conserved Alternative Splicing Across Monocots.

One difficulty when identifying alternative splicing (AS) events in plants is distinguishing functional AS from splicing noise. One way to add confidence to the validity of a splice isoform is to observe that it is conserved across evolutionarily related species. We use a high throughput method to identify junction-based conserved AS events from RNA-Seq data across nine plant species, including five grass monocots (maize, sorghum, rice, Brachpodium, and foxtail millet), plus two nongrass monocots (banana and African oil palm), the eudicot Arabidopsis, and the basal angiosperm Amborella In total, 9804 AS events were found to be conserved between two or more species studied. In grasses containing large regions of conserved synteny, the frequency of conserved AS events is twice that observed for genes outside of conserved synteny blocks. In plant-specific RS and RS2Z subfamilies of the serine/arginine (SR) splice-factor proteins, we observe both conservation and divergence of AS events after the whole genome duplication in maize. In addition, plant-specific RS and RS2Z splice-factor subfamilies are highly connected with R2R3-MYB in STRING functional protein association networks built using genes exhibiting conserved AS. Furthermore, we discovered that functional protein association networks constructed around genes harboring conserved AS events are enriched for phosphatases, kinases, and ubiquitylation genes, which suggests that AS may participate in regulating signaling pathways. These data lay the foundation for identifying and studying conserved AS events in the monocots, particularly across grass species, and this conserved AS resource identifies an additional layer between genotype to phenotype that may impact future crop improvement efforts.

Use of Microarray Datasets to generate Caco-2-dedicated Networks and to identify Reporter Genes of Specific Pathway Activity

Intestinal epithelial cells, like Caco-2, are commonly used to study the interaction between food, other luminal factors and the host, often supported by microarray analysis to study the changes in gene expression as a result of the exposure. However, no compiled dataset for Caco-2 has ever been initiated and Caco-2-dedicated gene expression networks are barely available. Here, 341 Caco-2-specific microarray samples were collected from public databases and from in-house experiments pertaining to Caco-2 cells exposed to pathogens, probiotics and several food compounds. Using these datasets, a gene functional association network specific for Caco-2 was generated containing 8937 nodes 129711 edges. Two in silico methods, a modified version of biclustering and the new Differential Expression Correlation Analysis, were developed to identify Caco-2-specific gene targets within a pathway of interest. These methods were subsequently applied to the AhR and Nrf2 signalling pathways and altered expression of the predicted target genes was validated by qPCR in Caco-2 cells exposed to coffee extracts, known to activate both AhR and Nrf2 pathways. The datasets and in silico method(s) to identify and predict responsive target genes can be used to more efficiently design experiments to study Caco-2/intestinal epithelial-relevant biological processes.

Enhancing Efficiency of Protein Functional Prediction Through Association Network Using Greedy Weighting Method

Background: In spite of the significant data surrounding complex gene networks including gene function, the occurrence of huge redundancy affects the efficiency. Objective: This work proposes a mining method to reduce the number of redundant nodes in a composite weighted network. Method: The idea is to eliminate the redundancies of nodes via a hybrid approach, i.e. the integration of multiple functional association networks using a Greedy Algorithm. This is achieved by mining the gene function from weighted gene co-expression networks based on neighbor similarity, as per the available datasets. Subsequently, Linear Regression and Greedy Algorithm are applied simultaneously for exclusion of the redundant nodes. Then, assigning the indexing rates for the remaining nodes in the dataset further assists the process. Results and Conclusion: In comparison with other well-known algorithms, this method is 93% more efficient, as per three selected benchmarks.

GenePANDA\u2014a novel network-based gene prioritizing tool for complex diseases

Here we describe GenePANDA, a novel network-based tool for prioritizing candidate disease genes. GenePANDA assesses whether a gene is likely a candidate disease gene based on its relative distance to known disease genes in a functional association network. A unique feature of GenePANDA is the introduction of adjusted network distance derived by normalizing the raw network distance between two genes with their respective mean raw network distance to all other genes in the network. The use of adjusted network distance significantly improves GenePANDA’s performance on prioritizing complex disease genes. GenePANDA achieves superior performance over five previously published algorithms for prioritizing disease genes. Finally, GenePANDA can assist in prioritizing functionally important SNPs identified by GWAS.

Empirical and bioinformatic characterization of buffalo (Bubalus bubalis) colostrum whey peptides & their angiotensin I-converting enzyme inhibition.

Whey based peptides are well known for their nutritional and multifunctional properties. In this context, whey proteins from buffalo colostrum & milk were digested by in vitro simulation digestion and analyzed by nano-LC-MS/MS. Functional protein association networks, gene annotations and localization of identified proteins were carried out. An ACE inhibitory peptide sorted from the library was custom synthesized and an in vitro ACE assay was performed. The study led to the identification of 74 small peptides which were clustered into 5 gene functional groups and majority of them were secretory proteins. Among the identified peptides, majority of them were found identical to angiotensin I-converting enzyme (ACE) inhibitors, antioxidant, antimicrobial, immunomodulatory and opioidal peptides. An octapeptide (m/z - 902.51, IQKVAGTW) synthesized was found to inhibit ACE with an IC50 of 300±2µM. The present investigation thus establishes newer vista for food derived peptides having ACE inhibitory potential for nutraceutical or therapeutic applications.

Genome composition and phylogeny of microbes predict their co-occurrence in the environment.

The genomic information of microbes is a major determinant of their phenotypic properties, yet it is largely unknown to what extent ecological associations between different species can be explained by their genome composition. To bridge this gap, this study introduces two new genome-wide pairwise measures of microbe-microbe interaction. The first (genome content similarity index) quantifies similarity in genome composition between two microbes, while the second (microbe-microbe functional association index) summarizes the topology of a protein functional association network built for a given pair of microbes and quantifies the fraction of network edges crossing organismal boundaries. These new indices are then used to predict co-occurrence between reference genomes from two 16S-based ecological datasets, accounting for phylogenetic relatedness of the taxa. Phylogenetic relatedness was found to be a strong predictor of ecological associations between microbes which explains about 10% of variance in co-occurrence data, but genome composition was found to be a strong predictor as well, it explains up to 4% the variance in co-occurrence when all genomic-based indices are used in combination, even after accounting for evolutionary relationships between the species. On their own, the metrics proposed here explain a larger proportion of variance than previously reported more complex methods that rely on metabolic network comparisons. In summary, results of this study indicate that microbial genomes do indeed contain detectable signal of organismal ecology, and the methods described in the paper can be used to improve mechanistic understanding of microbe-microbe interactions.

Structure-function analysis of DNA helicase HELQ: A new diagnostic marker in ovarian cancer

It has been previously reported that a deficiency of the helicase, POLQ-like (HELQ) gene increases the risk of ovarian cancer. The present study aimed to explore the structure-function association of HELQ and discuss the effect of molecular structure on the occurrence of tumors. ExPASy tools were employed to analyze the physicochemical properties and secondary structure of the genes. PHYRE2 Protein Fold Recognition Server was used to construct the three-dimensional model and find the ligand-binding sites of HELQ. In addition, the potential functions corresponding to these structures were excavated by comparing and analyzing protein domains. The HELQ protein is located in the cytoplasm (56.5%) and nucleus (21.7%). HELQ has 4 conserved domains, consisting of DEXDc, HELICc, HHH_5 and PRK02362, which contain the adenosine triphosphate (ATP) binding site, nucleotide binding region and putative Mg2+ binding site. In the secondary structure, it was found that HELQ was mainly composed of α helix (46.68%) and random coils (43.05%), with only 10.26% extended strand. According to 3DLigandSite Server, the ligand binding sites appeared in ILE333, LYS335, TYR337, SER362, LEU367, LYS397, GLN340, GLY363, GLY364 and ASN678 of the amino acid sequence. Among the functional protein association networks, regulator of telomere elongation helicase 1, family with sequence similarity 175 member A, small ubiquitin-like modifier 1, DNA polymerase ν and coiled-coil domain containing 158 were involved and co-expressed with HELQ. PredictProtein analysis indicated that the dominant functions of HELQ were ATP-dependent helicase activity and participation in the DNA repair process. Characteristics of the HELQ protein were obtained by bioinformatics analysis, based on which the role of HELQ in DNA replication, DNA repair and maintenance of genomic stability was explored. It was concluded that modulation the function of HELQ helicase may be used in the treatment of ovarian cancer.

A novel method for crosstalk analysis of biological networks: improving accuracy of pathway annotation.

Analyzing gene expression patterns is a mainstay to gain functional insights of biological systems. A plethora of tools exist to identify significant enrichment of pathways for a set of differentially expressed genes. Most tools analyze gene overlap between gene sets and are therefore severely hampered by the current state of pathway annotation, yet at the same time they run a high risk of false assignments. A way to improve both true positive and false positive rates (FPRs) is to use a functional association network and instead look for enrichment of network connections between gene sets. We present a new network crosstalk analysis method BinoX that determines the statistical significance of network link enrichment or depletion between gene sets, using the binomial distribution. This is a much more appropriate statistical model than previous methods have employed, and as a result BinoX yields substantially better true positive and FPRs than was possible before. A number of benchmarks were performed to assess the accuracy of BinoX and competing methods. We demonstrate examples of how BinoX finds many biologically meaningful pathway annotations for gene sets from cancer and other diseases, which are not found by other methods. BinoX is available at http://sonnhammer.org/BinoX.

DMAK: A curated pan-cancer DNA methylation annotation knowledgebase

ABSTRACTPan-cancer analysis can identify cell- and tissue-specific genomic loci and regions with underlying biological functions. Here we present an online curated DNA Methylation Annotation Knowledgebase, DMAK, which includes the pan-cancer analysis results for differentially-methylated loci and regions by the Reduced Representation Bisulfite Sequencing profiling technology. DMAK contains 3 modules of curated information and analysis results on 688,445 CpG sites across 19 cancer and embryonic stem cell lines from ENCODE, and further analysis of survival associations with clinical sources retrieved from TCGA. The knowledgebase covers all identified differentially-methylated CpG sites and regions of interest, further annotated genomic information, together with tumor suppressor genes information and calculated methylation level. DMAK provides meaningful clues for deriving functional association network and related clinical association results based on protein-coding genes, including tumor suppressor genes, identified from differentially methylated regions of interest. Thus DMAK constitutes a comprehensive reference source for the current epigenetic research and clinical study.

Genome-wide association studies and gene expression profiles of rheumatoid arthritis: An analysis.

ObjectivesThe molecular mechanism of rheumatoid arthritis (RA) remains elusive. We conducted a protein-protein interaction network-based integrative analysis of genome-wide association studies (GWAS) and gene expression profiles of RA.MethodsWe first performed a dense search of RA-associated gene modules by integrating a large GWAS meta-analysis dataset (containing 5539 RA patients and 20 169 healthy controls), protein interaction network and gene expression profiles of RA synovium and peripheral blood mononuclear cells (PBMCs). Gene ontology (GO) enrichment analysis was conducted by DAVID. The protein association networks of gene modules were generated by STRING.ResultsFor RA synovium, the top-ranked gene module is HLA-A, containing TAP2, HLA-A, HLA-C, TAPBP and LILRB1 genes. For RA PBMCs, the top-ranked gene module is GRB7, consisting of HLA-DRB5, HLA-DRA, GRB7, CD63 and KIT genes. Functional enrichment analysis identified three significant GO terms for RA synovium, including antigen processing and presentation of peptide antigen via major histocompatibility complex class I (false discovery rate (FDR) = 4.86 × 10 – 4), antigen processing and presentation of peptide antigen (FDR = 2.33 × 10 – 3) and eukaryotic translation initiation factor 4F complex (FDR = 2.52 × 10 – 2).ConclusionThis study reported several RA-associated gene modules and their functional association networks.Cite this article: X. Xiao, J. Hao, Y. Wen, W. Wang, X. Guo, F. Zhang. Genome-wide association studies and gene expression profiles of rheumatoid arthritis: an analysis. Bone Joint Res 2016;5:314–319. DOI: 10.1302/2046-3758.57.2000502.

Functional network analysis of genes differentially expressed during xylogenesis in soc1ful woody Arabidopsis plants.

Many plant genes are known to be involved in the development of cambium and wood, but how the expression and functional interaction of these genes determine the unique biology of wood remains largely unknown. We used the soc1ful loss of function mutant - the woodiest genotype known in the otherwise herbaceous model plant Arabidopsis - to investigate the expression and interactions of genes involved in secondary growth (wood formation). Detailed anatomical observations of the stem in combination with mRNA sequencing were used to assess transcriptome remodeling during xylogenesis in wild-type and woody soc1ful plants. To interpret the transcriptome changes, we constructed functional gene association networks of differentially expressed genes using the STRING database. This analysis revealed functionally enriched gene association hubs that are differentially expressed in herbaceous and woody tissues. In particular, we observed the differential expression of genes related to mechanical stress and jasmonate biosynthesis/signaling during wood formation in soc1ful plants that may be an effect of greater tension within woody tissues. Our results suggest that habit shifts from herbaceous to woody life forms observed in many angiosperm lineages could have evolved convergently by genetic changes that modulate the gene expression and interaction network, and thereby redeploy the conserved wood developmental program.

Parkin Somatic Mutations Link Melanoma and Parkinson's Disease

Epidemiological studies suggest a direct link between melanoma and Parkinson's disease (PD); however, the underlying molecular basis is unknown. Since mutations in Parkin are the major driver of early-onset PD and Parkin was recently reported to play a role in cancer development, we hypothesized that Parkin links melanoma and PD. By analyzing whole exome/genome sequencing of Parkin from 246 melanoma patients, we identified five non-synonymous mutations, three synonymous mutations, and one splice region variant in Parkin in 3.6% of the samples. In vitro analysis showed that wild-type Parkin plays a tumor suppressive role in melanoma development resulting in cell-cycle arrest, reduction of metabolic activity, and apoptosis. Using a mass spectrometry-based analysis, we identified potential Parkin substrates in melanoma and generated a functional protein association network. The activity of mutated Parkin was assessed by protein structure modeling and examination of Parkin E3 ligase activity. The Parkin-E28K mutation impairs Parkin ubiquitination activity and abolishes its tumor suppressive effect. Taken together, our analysis of genomic sequence and in vitro data indicate that Parkin is a potential link between melanoma and Parkinson's disease. Our findings suggest new approaches for early diagnosis and treatment against both diseases.

Cross-cell DNA methylation annotation and analysis for pan-cancer study

Pan-cancer study can uncover cell- and tissue-specific genomic loci and regions with underlying biological functions, as one of fundamental procedures toward precision medicine. We utilized the online curated resource of DNA methylation annotation knowledgebase, to implement the cross-cell interrogation of pan-cancer study of breast cancer. The study revealed genome-wide differentially-methylated loci and regions by the reduced representation bisulfite sequencing profiling. The knowledgebase contains three level of curated information across multiple cancer and normal cells from the ENCODE Consortium. The reference base covers all identified differentially-methylation CpG sites and regions of interest, further annotated gene information, together with tumor suppressor gene and methylation level. Lastly, it includes the inferred functional association network and related Gene Ontology analysis results based on all the tumor suppressor genes identified from the differentially-methylated regions of interest. Our knowledgebase and analysis results provide a thorough reference source for biomedical researchers and clinicians. The cross-cell analysis results are deposited at: http://github.com/gladex/DMAK.

Characterization and refinement of growth related quantitative trait loci in European sea bass (Dicentrarchus labrax) using a comparative approach

The identification of genetic markers for traits of interest for aquaculture, such as growth, is an important step for the establishment of breeding programmes. As more genomic information becomes available the possibility of applying comparative genomics to identify and refine quantitative trait locus (QTLs) and potentially identify candidate genes responsible for the QTL effect may accelerate genetic improvement in established and new aquaculture species. Here we report such an approach on growth related traits in the European sea bass (Dicentrarchus labrax), an important species for European aquaculture. A genetic map was generated with markers targeted to previously identified QTL for growth which reduced distance and improved resolution in these regions. A total of 36 significant QTLs were identified when morphometric traits were considered individually in maternal half sibs, paternal half sibs and sib-pair analysis. Twenty seven new markers targeted to the growth QTLs, obtained by comparative mapping, reduced the average distance between markers from 23.4, 9.1, and 5.8cM in the previous map to 3.4, 2.2, and 5.2cM, on linkage group (LG) LG4, LG6 and LG15 respectively. Lists of genes embedded in the QTL – 591 genes in LG4, 234 genes in LG6 and 450 genes in LG15 – were obtained from the European sea bass genome. Comparative mapping revealed conserved gene synteny across teleost fishes. Functional protein association network analysis with the gene products of the 3 linkage groups revealed a large global association network including 42 gene products. Strikingly the association network was populated with genes of known biological importance for growth and body weight in terrestrial farm animals, such as elements of the signaling pathways for Jak–STAT, MAPK, adipocytokine and insulin, growth hormone, IGFI and II. This study demonstrates the feasibility of a comparative genomics combined with functional gene annotation to refine the resolution of QTL and the establishment of hypothesis to accelerate discovery of putative responsible genes. Statement of relevanceThis study demonstrates the feasibility of a comparative genomics approach, combined with functional annotation to refine the resolution of QTL and establishment of hypothesis to accelerate discovery of candidate genes. As production of genomic data is becoming more accessible, the implementation of this strategy will rapidly and efficiently provide the tools required for genetic selection in new candidate aquaculture species.