Quantitative Genomics and Computational Systems Biology in Agricultural Species
A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".
Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 9181
Special Issue Editor
Interests: quantitative genomics; statistical genetics; computational biology; animal genetics; integrative systems genomics
Special Issue Information
Dear colleagues,
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.
Guest Editor
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Keywords
- 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
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