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Exploring the Genotype–Phenotype Map to Explain Complex Traits
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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".

Deadline for manuscript submissions: closed (30 June 2017) | Viewed by 37689

Special Issue Editor

Prof. Dr. Klaus Wimmers

E-Mail Website
Guest Editor
Competence Area Genetics and Genomics, Research Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
Interests: animal genomics; transcriptome; epigenome; animal health; animal welfare
Special Issues, Collections and Topics in MDPI journals

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 submissions that pass pre-check are 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 semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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 (6 papers)

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Research

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973 KiB  
Article
Patterns of Novel Alleles and Genotype/Phenotype Correlations Resulting from the Analysis of 108 Previously Undetected Mutations in Patients Affected by Neurofibromatosis Type I
by Francesco Bonatti, Alessia Adorni, Annalisa Matichecchia, Paola Mozzoni, Vera Uliana, Francesco Pisani, Livia Garavelli, Claudio Graziano, Maria Gnoli, Diana Carli, Stefania Bigoni, Elena Boschi, Davide Martorana and Antonio Percesepe
Int. J. Mol. Sci. 2017, 18(10), 2071; https://doi.org/10.3390/ijms18102071 - 29 Sep 2017
Cited by 11 | Viewed by 4150
Abstract
Neurofibromatosis type I, a genetic disorder due to mutations in the NF1 gene, is characterized by a high mutation rate (about 50% of the cases are de novo) but, with the exception of whole gene deletions associated with a more severe phenotype, no [...] Read more.
Neurofibromatosis type I, a genetic disorder due to mutations in the NF1 gene, is characterized by a high mutation rate (about 50% of the cases are de novo) but, with the exception of whole gene deletions associated with a more severe phenotype, no specific hotspots and few solid genotype/phenotype correlations. After retrospectively re-evaluating all NF1 gene variants found in the diagnostic activity, we studied 108 patients affected by neurofibromatosis type I who harbored mutations that had not been previously reported in the international databases, with the aim of analyzing their type and distribution along the gene and of correlating them with the phenotypic features of the affected patients. Out of the 108 previously unreported variants, 14 were inherited by one of the affected parents and 94 were de novo. Twenty-nine (26.9%) mutations were of uncertain significance, whereas 79 (73.2%) were predicted as pathogenic or probably pathogenic. No differential distribution in the exons or in the protein domains was observed and no statistically significant genotype/phenotype correlation was found, confirming previous evidences. Full article
(This article belongs to the Special Issue Exploring the Genotype–Phenotype Map to Explain Complex Traits)
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274 KiB  
Article
Strategies towards Improved Feed Efficiency in Pigs Comprise Molecular Shifts in Hepatic Lipid and Carbohydrate Metabolism
by Henry Reyer, Michael Oster, Elizabeth Magowan, Dirk Dannenberger, Siriluck Ponsuksili and Klaus Wimmers
Int. J. Mol. Sci. 2017, 18(8), 1674; https://doi.org/10.3390/ijms18081674 - 01 Aug 2017
Cited by 21 | Viewed by 6872
Abstract
Due to the central role of liver tissue in partitioning and metabolizing of nutrients, molecular liver-specific alterations are of considerable interest to characterize an efficient conversion and usage of feed in livestock. To deduce tissue-specific and systemic effects on nutrient metabolism and feed [...] Read more.
Due to the central role of liver tissue in partitioning and metabolizing of nutrients, molecular liver-specific alterations are of considerable interest to characterize an efficient conversion and usage of feed in livestock. To deduce tissue-specific and systemic effects on nutrient metabolism and feed efficiency (FE) twenty-four animals with extreme phenotypes regarding residual feed intake (RFI) were analyzed. Transcriptome and fatty acid profiles of liver tissue were complemented with measurements on blood parameters and thyroid hormone levels. Based on 803 differentially-abundant probe sets between low- and high-FE animals, canonical pathways like integrin signaling and lipid and carbohydrate metabolism, were shown to be affected. Molecular alterations of lipid metabolism show a pattern of a reduced hepatic usage of fatty acids in high-FE animals. Complementary analyses at the systemic level exclusively pointed to increased circulating triglycerides which were, however, accompanied by considerably lower concentrations of saturated and polyunsaturated fatty acids in the liver of high-FE pigs. These results are in accordance with altered muscle-to-fat ratios usually ascribed to FE animals. It is concluded that strategies to improve FE might favor a metabolic shift from energy storage towards energy utilization and mobilization. Full article
(This article belongs to the Special Issue Exploring the Genotype–Phenotype Map to Explain Complex Traits)
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3930 KiB  
Article
Characterization of Copy Number Variation’s Potential Role in Marek’s Disease
by Lingyang Xu, Yanghua He, Yi Ding, Guirong Sun, Jose Adrian Carrillo, Yaokun Li, Mona M. Ghaly, Li Ma, Huanmin Zhang, George E. Liu and Jiuzhou Song
Int. J. Mol. Sci. 2017, 18(5), 1020; https://doi.org/10.3390/ijms18051020 - 09 May 2017
Cited by 8 | Viewed by 5310
Abstract
Marek’s Disease (MD) is a highly contagious pathogenic and oncogenic disease primarily affecting chickens. Chicken Lines 63 and 72, as well as their recombinant congenic strains (RCS) with varied susceptibility to MD, are ideal models to study the complex mechanisms of genetic resistance [...] Read more.
Marek’s Disease (MD) is a highly contagious pathogenic and oncogenic disease primarily affecting chickens. Chicken Lines 63 and 72, as well as their recombinant congenic strains (RCS) with varied susceptibility to MD, are ideal models to study the complex mechanisms of genetic resistance to MD. In this study, we investigated copy number variation (CNV) in these inbred chicken lines using the Affymetrix Axiom HD 600 K SNP genotyping array. We detected 393 CNV segments across all ten chicken lines, of which 12 CNVs were specifically identified in Line 72. We then assessed genetic structure based on CNV and observed markedly different patterns. Finally, we validated two deletion events in Line 72 and correlated them with genes expression using qPCR and RNA-seq, respectively. Our combined results indicated that these two CNV deletions were likely to contribute to MD susceptibility. Full article
(This article belongs to the Special Issue Exploring the Genotype–Phenotype Map to Explain Complex Traits)
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1177 KiB  
Article
Integrative Analysis of Metabolomic, Proteomic and Genomic Data to Reveal Functional Pathways and Candidate Genes for Drip Loss in Pigs
by Julia Welzenbach, Christiane Neuhoff, Hanna Heidt, Mehmet Ulas Cinar, Christian Looft, Karl Schellander, Ernst Tholen and Christine Große-Brinkhaus
Int. J. Mol. Sci. 2016, 17(9), 1426; https://doi.org/10.3390/ijms17091426 - 30 Aug 2016
Cited by 24 | Viewed by 7177
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

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1064 KiB  
Review
The Importance of Endophenotypes to Evaluate the Relationship between Genotype and External Phenotype
by Marinus F. W. Te Pas, Ole Madsen, Mario P. L. Calus and Mari A. Smits
Int. J. Mol. Sci. 2017, 18(2), 472; https://doi.org/10.3390/ijms18020472 - 22 Feb 2017
Cited by 25 | Viewed by 7858
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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269 KiB  
Review
Genetic Marker Discovery in Complex Traits: A Field Example on Fat Content and Composition in Pigs
by Ramona Natacha Pena, Roger Ros-Freixedes, Marc Tor and Joan Estany
Int. J. Mol. Sci. 2016, 17(12), 2100; https://doi.org/10.3390/ijms17122100 - 14 Dec 2016
Cited by 33 | Viewed by 5531
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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