Special Issue "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: 31 October 2020.

Special Issue Editor

Prof. Haja N. Kadarmideen
Website
Guest Editor
Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
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.
Prof. Haja N. Kadarmideen
Guest Editor

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.

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

Published Papers (1 paper)

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Research

Open AccessFeature PaperArticle
Gene Networks Driving Genetic Variation in Milk and Cheese-Making Traits of Spanish Assaf Sheep
Genes 2020, 11(7), 715; https://doi.org/10.3390/genes11070715 - 27 Jun 2020
Abstract
Most of the milk produced by sheep is used for the production of high-quality cheese. Consequently, traits related to milk coagulation properties and cheese yield are economically important to the Spanish dairy industry. The present study aims to identify candidate genes and their [...] Read more.
Most of the milk produced by sheep is used for the production of high-quality cheese. Consequently, traits related to milk coagulation properties and cheese yield are economically important to the Spanish dairy industry. The present study aims to identify candidate genes and their regulators related to 14 milk and cheese-making traits and to develop a low-density panel of markers that could be used to predict an individual’s genetic potential for cheese-making efficiency. In this study, we performed a combination of the classical genome-wide association study (GWAS) with a stepwise regression method and a pleiotropy analysis to determine the best combination of the variants located within the confidence intervals of the potential candidate genes that may explain the greatest genetic variance for milk and cheese-making traits. Two gene networks related to milk and cheese-making traits were created using the genomic relationship matrices built through a stepwise multiple regression approach. Several co-associated genes in these networks are involved in biological processes previously found to be associated with milk synthesis and cheese-making efficiency. The methodology applied in this study enabled the selection of a co-association network comprised of 374 variants located in the surrounding of genes showing a potential influence on milk synthesis and cheese-making efficiency. Full article
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