Novel genotyping algorithms for rare variants significantly improve the accuracy of Applied Biosystems™ Axiom™ array genotyping calls: Retrospective evaluation of UK Biobank array data.

The UK Biobank genotyped about 500k participants using Applied Biosystems Axiom microarrays. Participants were subsequently sequenced by the UK Biobank Exome Sequencing Consortium. Axiom genotyping was highly accurate in comparison to sequencing results, for almost 100,000 variants both directly genotyped on the UK Biobank Axiom array and via whole exome sequencing. However, in a study using the exome sequencing results of the first 50k individuals as reference (truth), it was observed that the positive predictive value (PPV) decreased along with the number of heterozygous array calls per variant. We developed a novel addition to the genotyping algorithm, Rare Heterozygous Adjusted (RHA), to significantly improve PPV in variants with minor allele frequency below 0.01%. The improvement in PPV was roughly equal when comparing to the exome sequencing of 50k individuals, or to the more recent ~200k individuals. Sensitivity was higher in the 200k data. The improved calling algorithm, along with enhanced quality control of array probesets, significantly improved the positive predictive value and the sensitivity of array data, making it suitable for the detection of ultra-rare variants.

Genotyping Array Design and Data Quality Control in the Million Veteran Program.

The Million Veteran Program (MVP), initiated by the Department of Veterans Affairs (VA), aims to collect biosamples with consent from at least one million veterans. Presently, blood samples have been collected from over 800,000 enrolled participants. The size and diversity of the MVP cohort, as well as the availability of extensive VA electronic health records, make it a promising resource for precision medicine. MVP is conducting array-based genotyping to provide a genome-wide scan of the entire cohort, in parallel with whole-genome sequencing, methylation, and other 'omics assays. Here, we present the design and performance of the MVP 1.0 custom Axiom array, which was designed and developed as a single assay to be used across the multi-ethnic MVP cohort. A unified genetic quality-control analysis was developed and conducted on an initial tranche of 485,856 individuals, leading to a high-quality dataset of 459,777 unique individuals. 668,418 genetic markers passed quality control and showed high-quality genotypes not only on common variants but also on rare variants. We confirmed that, with non-European individuals making up nearly 30%, MVP's substantial ancestral diversity surpasses that of other large biobanks. We also demonstrated the quality of the MVP dataset by replicating established genetic associations with height in European Americans and African Americans ancestries. This current dataset has been made available to approved MVP researchers for genome-wide association studies and other downstream analyses. Further data releases will be available for analysis as recruitment at the VA continues and the cohort expands both in size and diversity.

Clonal composition of human ovarian cancer based on copy number analysis reveals a reciprocal relation with oncogenic mutation status.

Intratumoral heterogeneity of cancer cells remains largely unexplored. Here we investigated the composition of ovarian cancer and its biological relevance. A whole-genome single nucleotide polymorphism array was applied to detect the clonal composition of 24 formalin-fixed, paraffin-embedded samples of human ovarian cancer. Genome-wide segmentation data consisting of the log2 ratio (log2R) and B allele frequency (BAF) were used to calculate an estimate of the clonal composition number (CC number) for each tumor. Somatic mutation profiles of cancer-related genes were also determined for the same 24 samples by next-generation sequencing. The CC number was estimated successfully for 23 of the 24 cancer samples. The mean ± SD value for the CC number was 1.7 ± 1.1 (range of 0-4). A somatic mutation in at least one gene was identified in 22 of the 24 ovarian cancer samples, with the mutations including those in the oncogenes KRAS (29.2%), PIK3CA (12.5%), BRAF (8.3%), FGFR2 (4.2%), and JAK2 (4.2%) as well as those in the tumor suppressor genes TP53 (54.2%), FBXW7 (8.3%), PTEN (4.2%), and RB1 (4.2%). Tumors with one or more oncogenic mutations had a significantly lower CC number than did those without such a mutation (1.0 ± 0.8 versus 2.3 ± 0.9, P = 0.0027), suggesting that cancers with driver oncogene mutations are less heterogeneous than those with other mutations. Our results thus reveal a reciprocal relation between oncogenic mutation status and clonal composition in ovarian cancer using the established method for the estimation of the CC number.

iTools: a framework for classification, categorization and integration of computational biology resources.

The advancement of the computational biology field hinges on progress in three fundamental directions--the development of new computational algorithms, the availability of informatics resource management infrastructures and the capability of tools to interoperate and synergize. There is an explosion in algorithms and tools for computational biology, which makes it difficult for biologists to find, compare and integrate such resources. We describe a new infrastructure, iTools, for managing the query, traversal and comparison of diverse computational biology resources. Specifically, iTools stores information about three types of resources--data, software tools and web-services. The iTools design, implementation and resource meta-data content reflect the broad research, computational, applied and scientific expertise available at the seven National Centers for Biomedical Computing. iTools provides a system for classification, categorization and integration of different computational biology resources across space-and-time scales, biomedical problems, computational infrastructures and mathematical foundations. A large number of resources are already iTools-accessible to the community and this infrastructure is rapidly growing. iTools includes human and machine interfaces to its resource meta-data repository. Investigators or computer programs may utilize these interfaces to search, compare, expand, revise and mine meta-data descriptions of existent computational biology resources. We propose two ways to browse and display the iTools dynamic collection of resources. The first one is based on an ontology of computational biology resources, and the second one is derived from hyperbolic projections of manifolds or complex structures onto planar discs. iTools is an open source project both in terms of the source code development as well as its meta-data content. iTools employs a decentralized, portable, scalable and lightweight framework for long-term resource management. We demonstrate several applications of iTools as a framework for integrated bioinformatics. iTools and the complete details about its specifications, usage and interfaces are available at the iTools web page http://iTools.ccb.ucla.edu.

The Simbios National Center: Systems Biology in Motion.

Physics-based simulation is needed to understand the function of biological structures and can be applied across a wide range of scales, from molecules to organisms. Simbios (the National Center for Physics-Based Simulation of Biological Structures, http://www.simbios.stanford.edu/) is one of seven NIH-supported National Centers for Biomedical Computation. This article provides an overview of the mission and achievements of Simbios, and describes its place within systems biology. Understanding the interactions between various parts of a biological system and integrating this information to understand how biological systems function is the goal of systems biology. Many important biological systems comprise complex structural systems whose components interact through the exchange of physical forces, and whose movement and function is dictated by those forces. In particular, systems that are made of multiple identifiable components that move relative to one another in a constrained manner are multibody systems. Simbios' focus is creating methods for their simulation. Simbios is also investigating the biomechanical forces that govern fluid flow through deformable vessels, a central problem in cardiovascular dynamics. In this application, the system is governed by the interplay of classical forces, but the motion is distributed smoothly through the materials and fluids, requiring the use of continuum methods. In addition to the research aims, Simbios is working to disseminate information, software and other resources relevant to biological systems in motion.

Predicting allosteric communication in myosin via a pathway of conserved residues.

We present a computational method that predicts a pathway of residues that mediate protein allosteric communication. The pathway is predicted using only a combination of distance constraints between contiguous residues and evolutionary data. We applied this analysis to find pathways of conserved residues connecting the myosin ATP binding site to the lever arm. These pathway residues may mediate the allosteric communication that couples ATP hydrolysis to the lever arm recovery stroke. Having examined pre-stroke conformations of Dictyostelium, scallop, and chicken myosin II as well as Dictyostelium myosin I, we observed a conserved pathway traversing switch II and the relay helix, which is consistent with the understood need for allosteric communication in this conformation. We also examined post-rigor and rigor conformations across several myosin species. Although initial residues of these paths are more heterogeneous, all but one of these paths traverse a consistent set of relay helix residues to reach the beginning of the lever arm. We discuss our results in the context of structural elements and reported mutational experiments, which substantiate the significance of the pre-stroke pathways. Our method provides a simple, computationally efficient means of predicting a set of residues that mediate allosteric communication. We provide a refined, downloadable application and source code (on https://simtk.org) to share this tool with the wider community (https://simtk.org/home/allopathfinder).

Identification of candidate disease genes by EST alignments, synteny, and expression and verification of Ensembl genes on rat chromosome 1q43-54.

We aligned Incyte ESTs and publicly available sequences to the rat genome and analyzed rat chromosome 1q43-54, a region in which several quantitative trait loci (QTLs) have been identified, including renal disease, diabetes, hypertension, body weight, and encephalomyelitis. Within this region, which contains 255 Ensembl gene predictions, the aligned sequences clustered into 568 Incyte genes and gene fragments. Of the Incyte genes, 261 (46%) overlapped 184 (72%) of the Ensembl gene predictions, whereas 307 were unique to Incyte. The rat-to-human syntenic map displays rearrangement of this region on rat chr. 1 onto human chromosomes 9 and 10. The mapping of corresponding human disease phenotypes to either one of these chromosomes has allowed us to focus in on genes associated with disease phenotypes. As an example, we have used the syntenic information for the rat Rf-1 disease region and the orthologous human ESRD disease region to reduce the size of the original rat QTL to only 11.5 Mb. Using the syntenic information in combination with expression data from ESTs and microarrays, we have selected a set of 66 candidate disease genes for Rf-1. The combination of the results from these different analyses represents a powerful approach for narrowing the number of genes that could play a role in the development of complex diseases.