Biology subjects

McCarthy, S. E.

Publications and source records attributed to McCarthy, S. E..

4 recordsLinked to original sources

bioRxiv2018

Detection of rare disease-related genetic variants using the birthday model

MotivationExome sequencing is a powerful technique for the identification of disease-causing genes. A number of Mendelian inherited disease genes have been identified through this method. However, it remains a challenge to leverage exome sequencing for the study of complex disorders, such as schizophrenia and bipolar disorder, due to the genetic and phenotypic heterogeneity of these disorders. Although not feasible for many studies, sequencing large sample sizes (>10,000) may improve statistical power to associate more variants, while the aggregation of distinct rare variants associated with a given disease can make the identification of causal genes statistically challenging. Therefore, new methods for rare variant association are imperative to identify causative genes of complex disorders.\n\nResultsHere we propose a method to predict causative rare variants using a popular probabilistic problem: The Birthday Model, which estimates the probability that multiple individuals in a group share the same birthday. We consider the probability and coincidence of samples sharing a variant akin to the chance of individuals sharing the same birthday. We investigated the parameter effects of our model, providing guidelines for its use and interpretation of the results. Using published data on autism spectrum disorder, hypertriglyceridemia in addition to a current case-control study on bipolar disorder, we evaluated this probabilistic method to identify potential causative variants. Several genes in the top results of the case-control study were associated with autism spectrum and bipolar disorder. Given that the core probability based on the birthday model is very sensitive to low recurrence, the method successfully tests the association of rare variants, which generally do not provide enough signal in commonly used statistical tests. Importantly, the simplicity of the model allows quick interpretation of genomic data, enabling users to select gene candidates for further biological validation of specific mutations and downstream functional or other studies.\n\nAvailabilityhttps://github.com/yberstein/Birthday-Alqorithm http://labshare.cshl.edu/shares/mccombielab/www-data/Birthday-Algorithm/Birthday-Alqorithm.html\n\nContactyberstei@cshl.edu (or yaelberstein@gmail.com)\n\nSupplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2017

KaryoScan: abnormal karyotype detection from whole-exome sequence

MotivationDetection of abnormal karyotypes from whole-exome sequencing has significant clinical potential, enabling a primary screen for chromosomal anomalies among samples undergoing short-read sequencing for nucleotide resolution genomic characterization.\n\nResultsWe present KaryoScan, a high-throughput method for detecting chromosomal anomalies within large cohort exome sequencing studies. We detect and validate autosomal and sex chromosomal aneuploidies in a large exome sequencing cohort, and demonstrate detection of smaller and complex events (partial chromosome, mosaic, copy neutral, and complex rearrangements), representing the range of anomalies that can be uncovered from the exome.\n\nAvailabilityhttps://github.com/rgcgithub/karyoscan

bioinformatics↗

bioRxiv2017

Profiling and leveraging relatedness in a precision medicine cohort of 92,455 exomes

Large-scale human genetics studies are ascertaining increasing proportions of populations as they continue growing in both number and scale. As a result, the amount of cryptic relatedness within these study cohorts is growing rapidly and has significant implications on downstream analyses. We demonstrate this growth empirically among the first 92,455 exomes from the DiscovEHR cohort and, via a custom simulation framework we developed called SimProgeny, show that these measures are in-line with expectations given the underlying population and ascertainment approach. For example, we identified [~]66,000 close (first- and second-degree) relationships within DiscovEHR involving 55.6% of study participants. Our simulation results project that >70% of the cohort will be involved in these close relationships as DiscovEHR scales to 250,000 recruited individuals. We reconstructed 12,574 pedigrees using these relationships (including 2,192 nuclear families) and leveraged them for multiple applications. The pedigrees substantially improved the phasing accuracy of 20,947 rare, deleterious compound heterozygous mutations. Reconstructed nuclear families were critical for identifying 3,415 de novo mutations in [~]1,783 genes. Finally, we demonstrate the segregation of known and suspected disease-causing mutations through reconstructed pedigrees, including a tandem duplication in LDLR causing familial hypercholesterolemia. In summary, this work highlights the prevalence of cryptic relatedness expected among large healthcare population genomic studies and demonstrates several analyses that are uniquely enabled by large amounts of cryptic relatedness.

genomics↗

bioRxiv2017

Profiling copy number variation and disease associations from 50,726 DiscovEHR Study exomes

Copy number variants (CNVs) are a substantial source of genomic variation and contribute to a wide range of human disorders. Gene-disrupting exonic CNVs have important clinical implications as they can underlie variability in disease presentation and susceptibility. The relationship between exonic CNVs and clinical traits has not been broadly explored at the population level, primarily due to technical challenges. We surveyed common and rare CNVs in the exome sequences of 50,726 adult DiscovEHR study participants with linked electronic health records (EHRs). We evaluated the diagnostic yield and clinical expressivity of known pathogenic CNVs, and performed tests of association with EHR-derived serum lipids, thereby evaluating the relationship between CNVs and complex traits and phenotypes in an unbiased, real-world clinical context. We identified CNVs from megabase to exon-level resolution, demonstrating reliable, high-throughput detection of clinically relevant exonic CNVs. In doing so, we created a catalog of high-confidence common and rare CNVs and refined population frequency estimates of known and novel gene-disrupting CNVs. Our survey among an unselected clinical population provides further evidence that neuropathy-associated duplications and deletions in 17p12 have similar population prevalence but are clinically under-diagnosed. Similarly, adults who harbor 22q11.2 deletions frequently had EHR documentation of neurodevelopmental/neuropsychiatric disorders and congenital anomalies, but not a formal genetic diagnosis (i.e., deletion). In an exome-wide association study of lipid levels, we identified a novel five-exon duplication within LDLR segregating in a large kindred with features of familial hypercholesterolemia. Exonic CNVs provide new opportunities to understand and diagnose human disease.

genomics↗