Phenotype Data: A Neglected Resource in Biomedical Research?

  title={Phenotype Data: A Neglected Resource in Biomedical Research?},
  author={Philip Weiss},
  journal={Current Bioinformatics},
  • P. Weiss
  • Published 31 July 2006
  • Biology
  • Current Bioinformatics
To a great extent, our phenotype is determined by our genetic material. Many genotypic modifications may ultimately become manifest in more or less pronounced changes in phenotype. Despite the importance of how specific genetic alterations contribute to the development of diseases, surprisingly little effort has been made towards exploiting systematically the current knowledge of genotype-phenotype relationships. In the past, genes were characterized with the help of so-called "forward genetics… 

Tables from this paper

PhenomicDB: a new cross-species genotype/phenotype resource
PhenomicDB is a multi-species genotype/phenotype database, which shows phenotypes associated with their corresponding genes and grouped by gene orthologies across a variety of species, and envision that integration of classical phenotypes with high-throughput data will bring new momentum and insights to the authors' understanding.
Phenotype mining for functional genomics and gene discovery.
This chapter reviews the use of phenotype data in the biomedical field and gives an overview of phenotype resources, focusing on PhenomicDB--a cross-species genotype-phenotype database--which is the largest available collection of phenotype descriptions across species and experimental methods.
Mining phenotypes for gene function prediction
The intrinsic nature of phenotypes to visibly reflect genetic activity underlines their usefulness in inferring new gene functions and it is shown that text clustering can play an important role in this process.
Ontologies improve cross-species phenotype analysis
How matching terms from textual phenotype descriptions to biomedical ontologies like the Medical Subject Headings, the Gene Ontology and the Mammalian Phenotype Ontology can significantly help to overcome heterogeneity in speciesspecific terminology is described.
Computational tools for comparative phenomics: the role and promise of ontologies
Recent computational approaches that facilitate the integration of experimental data from model organisms with clinical observations in humans are reviewed, thereby enabling comparative phenomics and leading to the potential of translating basic discoveries from the model systems into diagnostic and therapeutic advances at the clinical level.
Phenoclustering: online mining of cross-species phenotypes
An online system in which more than 300 000 phenotypes from a wide variety of sources and screening methods can be analyzed together, providing the world's largest cross-species phenotype data collection with a tool to mine its wealth of information.
Co-clustering phenome–genome for phenotype classification and disease gene discovery
A regularized non-negative matrix tri-factorization (R-NMTF) algorithm is introduced to co-cluster phenotypes and genes, and simultaneously detect associations between the detected phenotype clusters and gene clusters to significantly improve the classification of disease phenotype and disease pathway genes.
Integrating phenotype and gene expression data for predicting gene function
It is suggested that augmenting standard gene expression data sets with publicly-available textual phenotype data can help generate more precise functional annotation predictions while mitigating the weaknesses of a standard textual phenotype approach.
Hypothesis building using the Animal Trait Ontology
The Animal Trait Ontology project is aimed at the development of a standardized trait ontology for farm animals and software tools to assist the research community in collaborative creation, editing, maintenance, and use of such an ontology.
Measuring selection in contemporary human populations
Methods to predict evolutionary change and attempts to measure selection and inheritance in humans are reviewed and it is suggested that the authors' nature is dynamic, not static.


From genotype to phenotype: genetics and medical practice in the new millennium.
  • D. Weatherall
  • Biology, Medicine
    Philosophical transactions of the Royal Society of London. Series B, Biological sciences
  • 1999
This information provides some indication of the difficulties that will be met when trying to define the genes that are involved in common multigenic disorders and, in particular, in trying to relate disease phenotypes to the complex interactions between many genes and multiple environmental factors.
Mining OMIM for insight into complex diseases.
These methods provide a formal approach to analyzing phenotypes among diverse diseases, and may help indicate fruitful areas for further research into their underlying genetic causes.
HGVbase: a curated resource describing human DNA variation and phenotype relationships
The evolving features of HGVbase are described, and the technological choices made to enable efficient storage and data mining of increasingly large and complex data sets are covered in detail.
Systematic Association of Genes to Phenotypes by Genome and Literature Mining
An unsupervised, systematic approach for associating genes and phenotypic characteristics that combines literature mining with comparative genome analysis is used, suggesting that the approach reveals many novel genes likely to play a role in infectious diseases.
Terminological Mapping for High Throughput Comparative Biology of Phenotypes
The results suggest that additional strategies are required for combining terminologies, as observed in other domains, for high-throughput comparative phenomics.
SNPs and haplotypes: genetic markers for disease and drug response (review).
  • B. Shastry
  • Medicine, Biology
    International journal of molecular medicine
  • 2003
The genetic make-up of an individual not only determines disease susceptibility but also response to drug treatment, and the availability of the human DNA sequence, its variation between individuals and the functional understanding of genetic determinants between individuals may enable pharmaceutical companies to discover safer and effective drugs.
A new paradigm for drug discovery: integrating clinical, genetic, genomic and molecular phenotype data to identify drug targets.
The many possibilities that exist in employing a more comprehensive genetics and functional genomics approach to the functional annotation of genomes are explored, and in applying such methods to the validation of targets for complex traits in the drug discovery process are explored.
Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease
The distribution of types of mutation in mendelian disease genes argues for serious consideration of the early application of a genomic-scale sequence-based approach to association studies and against complete reliance on a positional cloning approach based on a map of anonymous single nucleotide polymorphism haplotypes.
The challenge of documenting mutation across the genome: The human genome variation society approach
Through their interactions, a number of members of the Human Genome Variation Society have developed nomenclature, standard software to curate mutations in gene specific databases, a WayStation to collect and review new mutations from research and diagnostic laboratories, and central databases to store and display these mutations and their associated phenotypes.
Bridging the gap between molecular genetics and metabolic medicine: access to genetic information
  • S. Aymé
  • Biology
    European Journal of Pediatrics
  • 2000
Even if clinicians do not have as many services at their disposal as the molecular geneticists, various useful databases already exist and should no longer be ignored in practice.