Biology subjects

Naisha Shah

Publications and source records attributed to Naisha Shah.

3 recordsLinked to original sources

bioRxiv2016

Identification of misclassified ClinVar variants using disease population prevalence

There is a significant interest in the standardized classification of human genetic variants. The availability of new large datasets generated through genome sequencing initiatives provides a ground for the computational evaluation of the supporting evidence. We used whole genome sequence data from 8,102 unrelated individuals to analyze the adequacy of estimated rates of disease on the basis of genetic risk and the expected population prevalence of the disease. Analyses included the ACMG recommended 56 gene-condition sets for incidental findings and 631 genes associated with 348 OrphaNet conditions. A total of 21,004 variants were used to identify patterns of inflation (i.e. excess genetic risk). Inflation, i.e., misclassification, increases as the level of evidence in ClinVar supporting the pathogenic nature of the variant decreases. The burden of rare variants was a main contributing factor of the observed inflation indicating misclassified benign private mutations. We also analyzed the dynamics of re-classification of variant pathogenicity in ClinVar over time. The study strongly suggests that ClinVar includes a significant proportion of wrongly ascertained variants, and underscores the critical role of ClinVar to contrast claims, and foster validation across submitters.

Genomics↗

bioRxiv2016

Deep Sequencing of 10,000 Human Genomes

We report on the sequencing of 10,545 human genomes at 30-40x coverage with an emphasis on quality metrics and novel variant and sequence discovery. We find that 84% of an individual human genome can be sequenced confidently. This high confidence region includes 91.5% of exon sequence and 95.2% of known pathogenic variant positions. We present thedistribution of over 150 million single nucleotide variants in the coding and non-coding genome. Each newly sequenced genome contributes an average of 8,579 novel variants. In addition, each genome carries in average 0.7 Mb of sequence that is not found in the main build of the hg38 reference genome. The density of this catalog of variation allowed us to construct highresolution profiles that define genomic sites that are highly intolerant of genetic variation. These results indicate that the data generated by deep genome sequencing is of the quality necessary for clinical use.\n\nSignificance statementDeclining sequencing costs and new large-scale initiatives towards personalized medicine are driving a massive expansion in the number of human genomes being sequenced. Therefore, there is an urgent need to define quality standards for clinical use. This includes deep coverage and sequencing accuracy of an individuals genome, rather than aggregated coverage of data across a cohort or population. Our work represents the largest effort to date in sequencing human genomes at deep coverage with these new standards. This study identifies over 150 million human variants, a majority of them rare and unknown. Moreover, these data identify sites in the genome that are highly intolerant to variation - possibly essential for life or health. We conclude that high coverage genome sequencing provides accurate detail on human variation for discovery and for clinical applications.

Genomics↗

bioRxiv2015

Building Genomic Analysis Pipelines in a Hackathon Setting with Bioinformatician Teams: DNA-seq, Epigenomics, Metagenomics and RNA-seq

We assembled teams of genomics professionals to assess whether we could rapidly develop pipelines to answer biological questions commonly asked by biologists and others new to bioinformatics by facilitating analysis of high-throughput sequencing data. In January 2015, teams were assembled on the National Institutes of Health (NIH) campus to address questions in the DNA-seq, epigenomics, metagenomics and RNA-seq subfields of genomics. The only two rules for this hackathon were that either the data used were housed at the National Center for Biotechnology Information (NCBI) or would be submitted there by a participant in the next six months, and that all software going into the pipeline was open-source or open-use. Questions proposed by organizers, as well as suggested tools and approaches, were distributed to participants a few days before the event and were refined during the event. Pipelines were published on GitHub, a web service providing publicly available, free-usage tiers for collaborative software development (https://github.com/features/). The code was published at https://github.com/DCGenomics/ with separate repositories for each team, starting with hackathon_v001.

Genomics↗