Biology subjects

Pontikos, N.

Publications and source records attributed to Pontikos, N..

6 recordsLinked to original sources

bioRxiv2018

Phenogenon: Gene to Phenotype Associations for Rare Genetic Diseases

As genome sequencing is increasingly applied to molecular diagnosis of rare Mendelian disorders, large number of patients with diverse phenotypes have their genomic and phenotypic data pooled together to uncover new genotype - phenotype relations. We introduce Phenogenon, a method that combines: the power of Human Phenotype Ontology for describing patient phenotypes, gnomAD for estimating rare variant population frequency, and CADD for variant pathogenicity prediction. By using a divide and conquer approach, we demonstrate here that Phenogenon is able to uncover true gene to phenotype relations, such as \"ABCA4 - Macular dystrophy\" and \"SCN1A - Seizures\". Additionally, it accurately infers mode of inheritance, such as a recessive mode of inheritance in the case of the \"ABCA4 - Macular dystrophy\" relationship and a dominant mode of inheritance with the \"SCN1A - Seizures\" relationship. We also found that CADD has more power to detect early-onset rare genetic diseases than late-onset diseases. In this study, we ran Phenogenon against a diverse cohort of 3288 patients. Among the top 13 gene-phenotype relations, seven were previously known. We also highlight four potentially novel gene - phenotype relations such as \"SIPA1L3 - Abnormal electroretinogram\".

bioinformatics↗

bioRxiv2018

PGP-UK: a research and citizen science hybrid project in support of personalized medicine

Molecular analyses such as whole-genome sequencing have become routine and are expected to be transformational for future healthcare and lifestyle decisions. Population-wide implementation of such analyses is, however, not without challenges, and multiple studies are ongoing to identify what these are and explore how they can be addressed. Defined as a research project, the Personal Genome Project UK (PGP-UK) is part of the global PGP network and focuses on open data sharing and citizen science to advance and accelerate personalized genomics and medicine. Here we report our findings on using an open consent recruitment protocol, active participant involvement, open access release of personal genome, methylome and transcriptome data and associated analyses, including 47 new variants predicted to affect gene function and innovative reports based on the analysis of genetic and epigenetic variants. For this pilot study, we recruited ten participants willing to actively engage as citizen scientists with the project. In addition, we introduce Genome Donation as a novel mechanism for openly sharing previously restricted data and discuss the first three donations received. Lastly, we present GenoME, a free, open-source educational app suitable for the lay public to allow exploration of personal genomes. Our findings demonstrate that citizen science-based approaches like PGP-UK have an important role to play in the public awareness, acceptance and implementation of genomics and personalized medicine.

genomics↗

bioRxiv2017

Pheno4J: A Gene To Phenotype Graph Database

SummaryEfficient storage and querying of large amounts of genetic and phenotypic data is crucial to contemporary clinical genetic research. This introduces computational challenges for classical relational databases, due to the sparsity and sheer volume of the data. Our Java based solution loads annotated genetic variants and well phenotyped patients into a graph database to allow fast efficient storage and querying of large volumes of structured genetic and phenotypic data. This abstracts technical problems away and lets researchers focus on the science rather than the implementation. We have also developed an accompanying webserver with end-points to facilitate querying of the database.\n\nAvailability and ImplementationThe Java code and python code is available at https://github.com/phenopolis/pheno4i\n\nContactn.pontikos@ucl.ac.uk

bioinformatics↗

bioRxiv2017

The human oral microbiome is shaped by shared environment rather than genetics: evidence from a large family of closely-related individuals

The human microbiome is affected by multiple factors, including the environment and host genetics. In this study, we analyzed the oral microbiome of an extended family of Ashkenazi Jewish individuals living in several cities and investigated associations with both shared household and host genetic similarities. We found that environmental effects dominated over genetic ones. While there was weak evidence of geographic structuring at the level of cities, we observed a large and significant effect of shared household on microbiome composition, supporting the role of immediate shared environment in dictating the presence or absence of taxa. This effect was also seen when including adults who had grown up in the same household but moved out prior to the time of sampling, suggesting that the establishment of the oral microbiome earlier in life may affect its long-term composition. We found weak associations between host genetic relatedness and microbiome dissimilarity when using family pedigrees as proxies for genetic similarity. However this association disappeared when using more accurate measures of kinship based on genome-wide genetic markers, indicating that environment rather than host genetics is the dominant factor affecting the composition of the oral microbiome in closely-related individuals. Our results support the concept that there is a consistent core microbiome conserved across global scales, but that small-scale effects due to shared living environment significantly affect microbial community composition.\n\nIMPORTANCEPrevious research shows that relatives have a more similar oral microbiome composition than non-relatives, but it remains difficult to distinguish the effects of relatedness and shared household environment. Furthermore, pedigree measures may not accurately measure host genetic similarity. In this study, we include genetic relatedness based on genome-wide SNPs (rather than pedigree measures) and shared environment in the same analysis. We quantify the relative importance of these factors by studying the oral microbiome in members of a large extended Ashkenazi Jewish family who share a similar diet and lifestyle despite living in different locations. We find that host genetics plays no significant role and that the dominant factor is shared environment at the household level. We also find that this effect appears to persist in individuals who have moved out of the parental household, suggesting that the oral microbiome established earlier in life persists long-term.

microbiology↗

bioRxiv2017

Duplication events downstream of IRX1 cause North Carolina macular dystrophy at the MCDR3 locus

Autosomal dominant North Carolina macular dystrophy (NCMD) is believed to represent a failure of macular development. The disorder has been assigned by linkage to two loci, MCDR1 on chromosome 6q16 and MCDR3 on chromosome 5p15-p13. Recently, noncoding variants upstream of PRDM13 and a large duplication including IRX1 have been identified. However, the underlying disease-causing mechanism remains uncertain. Through a combination of sequencing studies, we report two novel overlapping duplications at the MCDR3 locus, in a gene desert downstream of IRX1 and upstream of ADAMTS16. One duplication of 43 kb was identified in nine NCMD families (with evidence for a shared ancestral haplotype), and another one of 45 kb was found in a single family. The MCDR3 locus is thus refined to a shared region of 39 kb that contains DNAse hypersensitive sites active at a restricted time window during retinal development. Publicly available data confirmed expression of IRX1 and ADAMTS16 in human fetal retina, with IRX1 preferentially expressed in fetal macula. These findings represent a major advance in our understanding of the molecular genetics of NCMD at the MCDR3 locus and provide insights into the genetic pathways involved in human macular development.\n\nAbbreviations list

genomics↗

bioRxiv2016

Phenopolis: an open platform for harmonization and analysis of sequencing and phenotype data

SummaryPhenopolis is an open-source web server which provides an intuitive interface to genetic and phenotypic databases. It integrates analysis tools which include variant filtering and gene prioritisation based on phenotype. The Phenopolis platform will accelerate clinical diagnosis, gene discovery and encourage wider adoption of the Human Phenotype Ontology in the study of rare disease.\n\nAvailability and ImplementationA demo of the website is available at http://phenopolis.github.io (username: demo, password: demo123). If you wish to install a local copy, souce code and installation instruction are available at https://github.com/pontikos/phenopolis. The software is implemented using Python, MongoDB, HTML/Javascript and various bash shell scripts.\n\nContactn.pontikos@ucl.ac.uk\n\nSupplementary informationhttp://phenopolis.github.io

bioinformatics↗