Biology subjects

Birch, C. L.

Publications and source records attributed to Birch, C. L..

3 recordsLinked to original sources

bioRxiv2019

Identification of Pathogenic Structural Variants in Rare Disease Patients through Genome Sequencing

PurposeClinical whole genome sequencing is becoming more common for determining the molecular diagnosis of rare disease. However, standard clinical practice often focuses on small variants such as single nucleotide variants and small insertions/deletions. This leaves a wide range of larger \"structural variants\" that are not commonly analyzed in patients.\n\nMethodsWe developed a pipeline for processing structural variants for patients who received whole genome sequencing through the Undiagnosed Diseases Network (UDN). This pipeline called structural variants, stored them in an internal database, and filtered the variants based on internal frequencies and external annotations. The remaining variants were manually inspected and then interesting findings were reported as research variants to clinical sites in the UDN.\n\nResultsOf 477 analyzed UDN cases, 286 cases ({approx} 60%) received at least one structural variant as a research finding. The variants in 16 cases ({approx} 4%) are considered \"Certain\" or \"Highly likely\" molecularly diagnosed and another 4 cases are currently in review. Of those 20 cases, at least 13 were identified originally through our pipeline with one finding leading to identification of a new disease. As part of this paper, we have also released the collection of variant calls identified in our cohort along with heterozygous and homozygous call counts. This data is available at https://github.com/HudsonAlpha/UDN_SV_export.\n\nConclusionStructural variants are key genetic features that should be analyzed during routine clinical genomic analysis. For our UDN patients, structural variants helped solve {approx} 4% of the total number of cases ({approx} 13% of all genome sequencing solves), a success rate we expect to improve with better tools and greater understanding of the human genome.

genomics↗

bioRxiv2019

VarSight: Prioritizing Clinically Reported Variants with Binary Classification Algorithms

MotivationIn genomic medicine for rare disease patients, the primary goal is to identify one or more variants that cause their disease. Typically, this is done through filtering and then prioritization of variants for manual curation. However, prioritization of variants in rare disease patients remains a challenging task due to the high degree of variability in phenotype presentation and molecular source of disease. Thus, methods that can identify and/or prioritize variants to be clinically reported in the presence of such variability are of critical importance. ResultsWe tested the application of classification algorithms that ingest variant predictions along with phenotype information for predicting whether a variant will ultimately be clinically reported and returned to a patient. To test the classifiers, we performed a retrospective study on variants that were clinically reported to 237 patients in the Undiagnosed Diseases Network. We treated the classifiers as variant prioritization systems and compared them to another variant prioritization algorithm and two single-measure controls. We showed that these classifiers outperformed the other methods with the best classifier ranking 73% of all reported variants and 97% of reported pathogenic variants in the top 20. AvailabilityThe scripts used to generate results presented in this paper are available at https://github.com/HudsonAlpha/VarSight. Contactjholt@hudsonalpha.org

bioinformatics↗

bioRxiv2018

Programmatic Detection of Diploid-Triploid Mixoploidy via Whole Genome Sequencing

PurposeMixoploidy is a type of mosaicism where an organism is a mixture of cells with different numbers of chromosomes. There are a broad range of phenotypes associated with mixoploidy that vary greatly depending on the fraction of cells that are non-diploid, their chromosome number, their distribution, and presumably the specific variation present in the patient. Clinical detection of mixoploidy is important for diagnosis.\n\nMethodsWe developed a method to detect mixoploidy from clinical whole genome sequencing (WGS) data through the identification of excess of variant calls centered on unusual B-allele frequencies. Our method isolates the signal from these variants using trio calls and then solves a basic linear equation to estimate levels of diploid-triploid mixoploidy within the sample.\n\nResultsWe show that our method reflects the results from a cytogenetic test. We provide examples detailing how our method has been used to identify diploid-triploid mixoploid individuals from within the NIH Undiagnosed Diseases Network. We present confirmatory findings obtained by clinical cytogenetic testing and show that our method can be used to identify the diploid-triploid ratio in these cases.\n\nConclusionWGS data from patients with rare diseases can be used to identify mixoploid individuals. Individuals with certain characteristics as discussed should be tested for mixoploidy as part of standard clinical pipeline procedures. Scripts that perform this calculation are publicly available at https://github.com/HudsonAlpha/mixoviz.

genomics↗