VAAST (the Variant Annotation, Analysis & Search Tool) is a probabilistic search tool for identifying damaged genes and their disease-causing variants in personal genome sequences. VAAST builds on existing amino acid substitution (AAS) and aggregative approaches to variant prioritization, combining elements of both into a single unified likelihood framework that allows users to identify damaged genes and deleterious variants with greater accuracy, and in an easy-to-use fashion. VAAST can score both coding and noncoding variants, evaluating the cumulative impact of both types of variants simultaneously. VAAST can identify rare variants causing rare genetic diseases, and it can also use both rare and common variants to identify genes responsible for common diseases. VAAST thus has a much greater scope of use than any existing methodology. Here we demonstrate its ability to identify damaged genes using small cohorts (n = 3) of unrelated individuals, wherein no two share the same deleterious variants, and for common, multigenic diseases using as few as 150 cases.

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 Phevor's 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.

Prognostic testing for AIS has the potential to reduce psychological trauma, serial exposure to diagnostic radiation, unnecessary treatments, and direct and indirect costs-of-care related to scoliosis monitoring in low-risk patients. Further improvements in test performance are expected as the optimal markers for each locus are identified and the underlying biologic pathways are better understood. The validity of the test applies only to white AIS patients; versions of the test optimized for AIS patients of other races have yet to be developed.

Background Understanding how sequence variants within healthy genomes are distributed with respect to ethnicity and disease-implicated genes is an essential first step toward establishing baselines for personalized genomic medicine. Methods In this study, we present an analysis of 10 genomes from healthy individuals of various ethnicities, produced using six different sequencing technologies. In total, these genomes contain more than 34 million single-nucleotide variants. Results We have analyzed these variants from a clinical perspective, assaying the influence of sequencing technology and ethnicity on prognosis. We have also examined the utility of OMIM and the disease-gene literature for determining the impact of rare, personal variants on an individual’s health. Conclusions Our analyses demonstrate that clinical prognoses are complicated by sequencing platform-specific errors and ethnicity. We show that disease-causing alleles are globally distributed along ethnic lines, with alleles known to be disease causing in Eurasians being significantly more likely to be homozygous in Africans.

Sanger sequencing of multigenic disorders can be technically challenging, time consuming, and prohibitively expensive. High-throughput next-generation sequencing (NGS) can provide a cost-effective method for sequencing targeted genes associated with multigenic disorders. We have developed a NGS clinical targeted gene assay for the mitochondrial genome and for 108 selected nuclear genes associated with mitochondrial disorders. Mitochondrial disorders have a reported incidence of 1 in 5000 live births, encompass a broad range of phenotypes, and are attributed to mutations in the mitochondrial and nuclear genomes. Approximately 20% of mitochondrial disorders result from mutations in mtDNA, with the remaining 80% found in nuclear genes that affect mtDNA levels or mitochondrion protein assembly. In our NGS approach, the 16,569-bp mtDNA is enriched by long-range PCR and the 108 nuclear genes (which represent 1301 amplicons and 680 kb) are enriched by RainDance emulsion PCR. Sequencing is performed on Illumina HiSeq 2000 or MiSeq platforms, and bioinformatics analysis is performed using commercial and in-house developed bioinformatics pipelines. A total of 16 validation and 13 clinical samples were examined. All previously reported variants associated with mitochondrial disorders were found in validation samples, and 5 of the 13 clinical samples were found to have mutations associated with mitochondrial disorders in either the mitochondrial genome or the 108 nuclear genes. All variants were confirmed by Sanger sequencing.

Suicidal behavior is a complex disorder, with evidence for genetic risk independent of other genetic risk factors including psychiatric disorders. Since 1996, over 3000 DNA samples from Utah suicide decedents have been collected and banked for research use through the Utah Medical Examiner. In addition, over 12 000 Utah suicides were identified through examination of death certificates back to 1904. By linking this data with the Utah Population Database, we have identified multiple extended pedigrees with increased risk for suicide completion. A number of medical conditions co-occur with suicide, including asthma, and this study was undertaken to identify genetic risk common to asthma and suicide. This study tests the hypothesis that a particular comorbid condition may identify a more homogeneous genetic subgroup, facilitating the identification of specific genetic risk factors in that group. From pedigrees at increased risk for suicide, we identified three pedigrees also at significantly increased familial risk for asthma. Five suicide decedents from each of these pedigrees, plus an additional three decedents not from these pedigrees with diagnosed asthma, and 10 decedents with close relatives with asthma were genotyped. Results were compared with 183 publicly available unaffected control exomes from 1000 Genomes and CEPH (Centre d'etude du polymorphisme humain) samples genotyped on the same platform. A further 432 suicide decedents were also genotyped as non-asthma suicide controls. Genotyping was done using the Infinium HumanExome BeadChip. For analysis, we used the pedigree extension of Variant Annotation, Analysis and Search Tool (pVAAST) to calculate the disease burden of each gene. The Phenotype Driven Variant Ontological Re-ranking tool (Phevor) then re-ranked our pVAAST results in context of the phenotype. Using asthma as a seed phenotype, Phevor traversed biomedical ontologies and identified genes with similar biological properties to those known to result in asthma. Our top associated genes included those related to neurodevelopment or neural signaling (brain-derived neurotrophic factor (BDNF), neutral sphingomyelinase 2 (SMPD2), homeobox b2 (HOXB2), neural cell adhesion molecule (NCAM2), heterogeneous nuclear ribonucleoprotein A0 (HNRNPA0)), inflammation (free fatty acid receptor 2 (FFAR2)) and inflammation with additional evidence of neuronal involvement (oxidized low density lipoprotein receptor 1 (OLR1), toll-like receptor 3 (TLR3)). Of particular interest, BDNF has been previously implicated in both psychiatric disorders and asthma. Our results demonstrate the utility of combining pedigree and co-occurring phenotypes to identify rare variants associated with suicide risk in conjunction with specific co-occurring conditions.