Brett Kennedy

Phevor Combines Multiple Biomedical Ontologies for Accurate Identification of Disease-Causing Alleles in Single Individuals and Small Nuclear Families

Phevor integrates phenotype, gene function, and disease information with personal genomic data for improved power to identify disease-causing alleles. Phevor works by combining knowledge resident in multiple biomedical ontologies with the outputs of variant-prioritization tools. It does so by using an algorithm that propagates information across and between ontologies. This process enables Phevor to accurately reprioritize potentially damaging alleles identified by variant-prioritization tools in light of gene function, disease, and phenotype knowledge. Phevor is especially useful for single-exome and family-trio-based diagnostic analyses, the most commonly occurring clinical scenarios and ones for which existing personal genome diagnostic tools are most inaccurate and underpowered. Here, we present a series of benchmark analyses illustrating Phevors performance characteristics. Also presented are three recent Utah Genome Project case studies in which Phevor was used to identify disease-causing alleles. Collectively, these results show that Phevor improves diagnostic accuracy not only for individuals presenting with established disease phenotypes but also for those with previously undescribed and atypical disease presentations. Importantly, Phevor is not limited to known diseases or known disease-causing alleles. As we demonstrate, Phevor can also use latent information in ontologies to discover genes and disease-causing alleles not previously associated with disease.

A reappraisal of grandmothering and natural selection

Kachel et al . [[1][1]] conclude from simulations of their agent-based model that fitness benefits from helpful grandmothers do not select for increased longevity. We studied their assumptions and model, ran further simulations and found flaws that are fatal to their test. Here, we explain four

Wham: Identifying Structural Variants of Biological Consequence.

Existing methods for identifying structural variants (SVs) from short read datasets are inaccurate. This complicates disease-gene identification and efforts to understand the consequences of genetic variation. In response, we have created Wham (Whole-genome Alignment Metrics) to provide a single, integrated framework for both structural variant calling and association testing, thereby bypassing many of the difficulties that currently frustrate attempts to employ SVs in association testing. Here we describe Wham, benchmark it against three other widely used SV identification tools–Lumpy, Delly and SoftSearch–and demonstrate Wham’s ability to identify and associate SVs with phenotypes using data from humans, domestic pigeons, and vaccinia virus. Wham and all associated software are covered under the MIT License and can be freely downloaded from github (https://github.com/zeeev/wham), with documentation on a wiki (http://zeeev.github.io/wham/). For community support please post questions to https://www.biostars.org/.

Using VAAST to Identify Disease‐Associated Variants in Next‐Generation Sequencing Data

The VAAST pipeline is specifically designed to identify disease‐associated alleles in next‐generation sequencing data. In the protocols presented in this paper, we outline the best practices for variant prioritization using VAAST. Examples and test data are provided for case‐control, small pedigree, and large pedigree analyses. These protocols will teach users the fundamentals of VAAST, VAAST 2.0, and pVAAST analyses. Curr. Protoc. Hum. Genet. 81:6.14.1‐6.14.25.

Viral pathogen detection by metagenomics and pan-viral group polymerase chain reaction in children with pneumonia lacking identifiable etiology

Summary Two broad-spectrum pathogen detection methods, next-generation sequencing and pan-viral group polymerase chain reaction, detected previously missed, putative pathogens in 34% of children hospitalized with community-acquired pneumonia with no identified etiology.

Shared Segment Analysis and Next-Generation Sequencing Implicates the Retinoic Acid Signaling Pathway in Total Anomalous Pulmonary Venous Return (TAPVR).

Most isolated congenital heart defects are thought to be sporadic and are often ascribed to multifactorial mechanisms with poorly understood genetics. Total Anomalous Pulmonary Venous Return (TAPVR) occurs in 1 in 15,000 live-born infants and occurs either in isolation or as part of a syndrome involving aberrant left-right development. Previously, we reported causative links between TAVPR and the PDGFRA gene. TAPVR has also been linked to the ANKRD1/CARP genes. However, these genes only explain a small fraction of the heritability of the condition. By examination of phased single nucleotide polymorphism genotype data from 5 distantly related TAPVR patients we identified a single 25 cM shared, Identical by Descent genomic segment on the short arm of chromosome 12 shared by 3 of the patients and their obligate-carrier parents. Whole genome sequence (WGS) analysis identified a non-synonymous variant within the shared segment in the retinol binding protein 5 (RBP5) gene. The RBP5 variant is predicted to be deleterious and is overrepresented in the TAPVR population. Gene expression and functional analysis of the zebrafish orthologue, rbp7, supports the notion that RBP5 is a TAPVR susceptibility gene. Additional sequence analysis also uncovered deleterious variants in genes associated with retinoic acid signaling, including NODAL and retinol dehydrogenase 10. These data indicate that genetic variation in the retinoic acid signaling pathway confers, in part, susceptibility to TAPVR.

Human Bocavirus Capsid Messenger RNA Detection in Children With Pneumonia

Summary Human bocavirus messenger RNA detection in nasopharyngeal specimens from children with community-acquired pneumonia (CAP) but not in asymptomatic children undergoing elective outpatient surgery supports the pathogenic role for this virus in CAP and may be a more specific target for diagnostic testing.

Exome analysis of a family with Wolff-Parkinson-White syndrome identifies a novel disease locus.

Wolff–Parkinson–White (WPW) syndrome is a common cause of supraventricular tachycardia that carries a risk of sudden cardiac death. To date, mutations in only one gene, PRKAG2, which encodes the 5′‐AMP‐activated protein kinase subunit γ‐2, have been identified as causative for WPW. DNA samples from five members of a family with WPW were analyzed by exome sequencing. We applied recently designed prioritization strategies (VAAST/pedigree VAAST) coupled with an ontology‐based algorithm (Phevor) that reduced the number of potentially damaging variants to 10: a variant in KCNE2 previously associated with Long QT syndrome was also identified. Of these 11 variants, only MYH6 p.E1885K segregated with the WPW phenotype in all affected individuals and was absent in 10 unaffected family members. This variant was predicted to be damaging by in silico methods and is not present in the 1,000 genome and NHLBI exome sequencing project databases. Screening of a replication cohort of 47 unrelated WPW patients did not identify other likely causative variants in PRKAG2 or MYH6. MYH6 variants have been identified in patients with atrial septal defects, cardiomyopathies, and sick sinus syndrome. Our data highlight the pleiotropic nature of phenotypes associated with defects in this gene.

771Can Human Bocavirus mRNA Detection Differentiate Acute Infection from Viral Shedding

from Viral Shedding? Robert Schlaberg, MD, MPH; Krow Ampofo, MD; Keith Tardif, PhD; Chris Stockmann, MSc; Keith Simmon, MS; Weston Hymas, MS, MB(ASCP); Steven Flygare, MS; Brett Kennedy, PhD; Anne J. Blaschke, MD, PhD; Karen Eilbeck, PhD; Mark Yandell, PhD; Anna M. Bramley, MPH; Seema Jain, MD, MPH; Andrew Pavia, MD, FIDSA, FSHEA; Department of Pathology, University of Utah, Salt Lake City, UT; ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, UT; Department of Pediatrics, Division of Pediatric Infectious Diseases, University of Utah School of Medicine, Salt Lake City, UT; Department of Biomedical Informatics, University of Utah, Salt Lake City, UT; Department of Human Genetics, University of Utah, Salt Lake City, UT; Centers for Disease Control and Prevention, Atlanta, GA; Centers for Disease Control and Prevention, Atlanta, GA