Special Issue "Quantitative Genomics and Computational Systems Biology in Agricultural Species"
Deadline for manuscript submissions: 31 October 2020.
Interests: quantitative genomics; statistical genetics; computational biology; animal genetics; integrative systems genomics
Quantitative genetics and epigenetics has seen a paradigm shift moving from microarray-based technologies to next generation sequencing (NGS)-based genomics/epigenomics in studying (epi)genetic variation in quantitative traits and complex diseases. Furthermore, the phenotypic data collected in farms/breeding herds go well beyond conventional traits included in breeding goals. They include highly dense observations on, for example, green house gas emissions, feeding/eating behavior, metabolic health, resource use efficiency, including feed efficiency, antimicrobial resistsance, and other sustainability traits. Thus, there is increasing need for introducing big data analysis methods that can handle massively parallel phenotypic and epigenomics/genomics data while studying (epi)genetic variation. It is also increasingly emphasised to include functionally relevant targets/features that explain large proporion of (epi)genetic variance. Current statistical–quantitative geneticists have begun to adapt to Artificial Intelligence (AI) and Machine Learning (ML) methods in tackling these challenges.
By virtue of NGS-based omics data and phenomics, it is essential that researchers and practitioners in this field also be well aquainted with bioinformatics and computational systems biology approaches.
The current Special Issue calls for original articles, review papers, perspectives and/or opinion articles. The topic that covers may include:
- Genome-wide association studies (GWAS) using NGS based (epi)genomic data with phenotype/disease data for quantitative traits and diseases;
- Genomic selection in any agricultural species (animal, plant, fish and poultry) with a focus on using high throughput phenotyping;
- AI/machine learning methods for analysis of genomic/epigenomic datasets in any agricultural species (animal, plant, fish and poultry);
- Computational methods and tools for multiomics data integration and multiomics prediction models for quantitative traits and diseases;
- Network biology/systems biology for quantitative traits and diseases.
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Quantitative (epi)genetics
- Genetic traits
- (Epi)genetic variation
- Phenotypic data
- Computational systems biology
- Genome wide association studies (GWAS)
- Next-generation sequencing (NGS)
- Machine learning (ML) and artificial intelligence (AI)
- Network biology
- Multiomics data analysis and integration
- High throughput phenotyping