GeNeViSTA

Mutation detection is the gold standard for the diagnosis of monogenic disorders. The advent of next generation sequencing strategies has revolutionized diagnostics in genetics. The whole genome and whole exome can now be sequenced with ease and at an affordable cost. For the disorders caused by large sized genes or with etiological heterogeneity, this is a great boon. Enormous data is generated which needs expertise for correct interpretation. The challenge is to correctly identify the causative sequence variation from the thousands of variations identified in each individual.

There are various methods which are employed for filtering the variants obtained and these include a series of filters designed to remove the low quality and common variants and non-pathogenic variants which are defined as the variants in non-coding, non-splice site regions and synonymous or missense mutations. Subsequently the best candidate gene correlating with the disease is chosen. But this strategy of identification of a deleterious variant purely based on the sequence variant pathogenicity will not help in correct identification of the disease-causing mutant gene in very rare diseases and diseases with unknown etiology. This is why the idea of using phenotypic data for prioritizing the variants has emerged. In this article we review the various phenotype-based tools used to analyze data of genetic variants obtained by next generation sequencing based technology and we wish to stress upon the importance of correct delineation and description of phenotype as the first step in genomic analysis.

1 Human Phenotype Ontology (HPO)

The phenome is defined as the set of all phenotypic traits of an organism. The variation in phenotype needs to be described in a systematic human and computer interpretable form. Standard measures for capturing phenotypic abnormalities are needed.¹ With this aim, the Human Phenotype Ontology (HPO) was initiated in 2007, with over 8000 terms, representing human phenotypic abnormalities and annotating all clinical entries in the Online Mendelian Inheritance in Man (OMIM) database with the terms of HPO.² It has 10,088 classes and 13,326 subclass relationships between these classes.³ The annotated terms in HPO follow the true path rule by which a disease directly annotated to a term implicitly implies annotation to all the previous terms.

The HPO has three independent sub ontologies which include the mode of inheritance, the onset and clinical course and phenotypic abnormalities. Each of the HPO classes has a unique identifier, a label and a list of synonyms. More than 65% of the classes have a textual definition. HPO classes have cross references to other resources like Disease Ontology, Unified Medical Language System, Medical Subject Headings and International Classification of Diseases 10^th revision.

The Human Phenotype Ontology includes a wide range of phenotypic abnormalities including morphological abnormalities, abnormal processes and abnormal investigations. (Table1)³

2 Tools using HPO

The important tools used in differential diagnosis and exome analysis which uses HPO are Phenomizer and Exomiser. Other clinical databases and analysis tools like PhenoTips, DECIPHER and Cartagenia also use HPO.