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Special Issue "Exploring the Genotype–Phenotype Map to Explain Complex Traits"

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Biochemistry, Molecular Biology and Biophysics".

Deadline for manuscript submissions: 30 June 2017

Special Issue Editor

Guest Editor
Prof. Klaus Wimmers

Leibniz Institute for Farm Animal Biology (FBN), Institute of Genome, Biology, Genomics Unit, Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
E-Mail
Phone: +49-38208-68700
Fax: +49-38208-68702
Interests: animal genomics; transcriptome; epigenome; animal health; animal welfare

Special Issue Information

Dear Colleagues,

This Special Issue addresses studies along the genotype–phenotype map, aiming at unraveling the genetic foundation of complex quantitative traits.

The appearance of higher organisms is the result of the interaction of environmental factors and many genes. In fact, most properties of plants, animals, and humans depend on the activity of many genes and different modes of interaction along the genotype–phenotype map. The emergence of holistic approaches to analyze the genome, epigenome, transcriptome, proteome, and metabolome provide new opportunities to follow the transition of the genetic potential, which is detailed in the genome, into the final organismal phenotype. The integration of ‘omics’ data and systems biological approaches enable the detection of molecular pathways that are responsible for complex traits and that mediate the interaction of the genotype and the environment. The Special Issue, “Exploring the Genotype–Phenotype Map to Explain Complex Traits”, covers research papers and reviews of studies in man, model organisms, plants, and animals, which are driven by the goal of explaining the determination of a trait based on ‘omics’ data and complementary bioinformatics and systems biology strategies.

Prof. Klaus Wimmers
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. International Journal of Molecular Sciences 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

  • functional networks
  • pathway analysis
  • quantitative trait
  • microarray
  • next generation (exome) sequencing
  • gene polymorphisms
  • genetic causation
  • epigenetics
  • gene expression
  • non-coding RNAs
  • proteomics
  • metabolomics

Published Papers (3 papers)

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Research

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Open AccessArticle Integrative Analysis of Metabolomic, Proteomic and Genomic Data to Reveal Functional Pathways and Candidate Genes for Drip Loss in Pigs
Int. J. Mol. Sci. 2016, 17(9), 1426; doi:10.3390/ijms17091426
Received: 27 June 2016 / Revised: 12 August 2016 / Accepted: 22 August 2016 / Published: 30 August 2016
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Abstract
The aim of this study was to integrate multi omics data to characterize underlying functional pathways and candidate genes for drip loss in pigs. The consideration of different omics levels allows elucidating the black box of phenotype expression. Metabolite and protein profiling was
[...] Read more.
The aim of this study was to integrate multi omics data to characterize underlying functional pathways and candidate genes for drip loss in pigs. The consideration of different omics levels allows elucidating the black box of phenotype expression. Metabolite and protein profiling was applied in Musculus longissimus dorsi samples of 97 Duroc × Pietrain pigs. In total, 126 and 35 annotated metabolites and proteins were quantified, respectively. In addition, all animals were genotyped with the porcine 60 k Illumina beadchip. An enrichment analysis resulted in 10 pathways, amongst others, sphingolipid metabolism and glycolysis/gluconeogenesis, with significant influence on drip loss. Drip loss and 22 metabolic components were analyzed as intermediate phenotypes within a genome-wide association study (GWAS). We detected significantly associated genetic markers and candidate genes for drip loss and for most of the metabolic components. On chromosome 18, a region with promising candidate genes was identified based on SNPs associated with drip loss, the protein “phosphoglycerate mutase 2” and the metabolite glycine. We hypothesize that association studies based on intermediate phenotypes are able to provide comprehensive insights in the genetic variation of genes directly involved in the metabolism of performance traits. In this way, the analyses contribute to identify reliable candidate genes. Full article
(This article belongs to the Special Issue Exploring the Genotype–Phenotype Map to Explain Complex Traits)
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Review

Jump to: Research

Open AccessReview The Importance of Endophenotypes to Evaluate the Relationship between Genotype and External Phenotype
Int. J. Mol. Sci. 2017, 18(2), 472; doi:10.3390/ijms18020472
Received: 24 October 2016 / Revised: 2 February 2017 / Accepted: 13 February 2017 / Published: 22 February 2017
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Abstract
With the exception of a few Mendelian traits, almost all phenotypes (traits) in livestock science are quantitative or complex traits regulated by the expression of many genes. For most of the complex traits, differential expression of genes, rather than genomic variation in the
[...] Read more.
With the exception of a few Mendelian traits, almost all phenotypes (traits) in livestock science are quantitative or complex traits regulated by the expression of many genes. For most of the complex traits, differential expression of genes, rather than genomic variation in the gene coding sequences, is associated with the genotype of a trait. The expression profiles of the animal’s transcriptome, proteome and metabolome represent endophenotypes that influence/regulate the externally-observed phenotype. These expression profiles are generated by interactions between the animal’s genome and its environment that range from the cellular, up to the husbandry environment. Thus, understanding complex traits requires knowledge about not only genomic variation, but also environmental effects that affect genome expression. Gene products act together in physiological pathways and interaction networks (of pathways). Due to the lack of annotation of the functional genome and ontologies of genes, our knowledge about the various biological systems that contribute to the development of external phenotypes is sparse. Furthermore, interaction with the animals’ microbiome, especially in the gut, greatly influences the external phenotype. We conclude that a detailed understanding of complex traits requires not only understanding of variation in the genome, but also its expression at all functional levels. Full article
(This article belongs to the Special Issue Exploring the Genotype–Phenotype Map to Explain Complex Traits)
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Open AccessReview Genetic Marker Discovery in Complex Traits: A Field Example on Fat Content and Composition in Pigs
Int. J. Mol. Sci. 2016, 17(12), 2100; doi:10.3390/ijms17122100
Received: 3 November 2016 / Revised: 6 December 2016 / Accepted: 7 December 2016 / Published: 14 December 2016
PDF Full-text (269 KB) | HTML Full-text | XML Full-text
Abstract
Among the large number of attributes that define pork quality, fat content and composition have attracted the attention of breeders in the recent years due to their interaction with human health and technological and sensorial properties of meat. In livestock species, fat accumulates
[...] Read more.
Among the large number of attributes that define pork quality, fat content and composition have attracted the attention of breeders in the recent years due to their interaction with human health and technological and sensorial properties of meat. In livestock species, fat accumulates in different depots following a temporal pattern that is also recognized in humans. Intramuscular fat deposition rate and fatty acid composition change with life. Despite indication that it might be possible to select for intramuscular fat without affecting other fat depots, to date only one depot-specific genetic marker (PCK1 c.2456C>A) has been reported. In contrast, identification of polymorphisms related to fat composition has been more successful. For instance, our group has described a variant in the stearoyl-coA desaturase (SCD) gene that improves the desaturation index of fat without affecting overall fatness or growth. Identification of mutations in candidate genes can be a tedious and costly process. Genome-wide association studies can help in narrowing down the number of candidate genes by highlighting those which contribute most to the genetic variation of the trait. Results from our group and others indicate that fat content and composition are highly polygenic and that very few genes explain more than 5% of the variance of the trait. Moreover, as the complexity of the genome emerges, the role of non-coding genes and regulatory elements cannot be disregarded. Prediction of breeding values from genomic data is discussed in comparison with conventional best linear predictors of breeding values. An example based on real data is given, and the implications in phenotype prediction are discussed in detail. The benefits and limitations of using large SNP sets versus a few very informative markers as predictors of genetic merit of breeding candidates are evaluated using field data as an example. Full article
(This article belongs to the Special Issue Exploring the Genotype–Phenotype Map to Explain Complex Traits)

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