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      Clinical Implications of Human Population Differences in Genome-Wide Rates of Functional Genotypes

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          There have been a number of recent successes in the use of whole genome sequencing and sophisticated bioinformatics techniques to identify pathogenic DNA sequence variants responsible for individual idiopathic congenital conditions. However, the success of this identification process is heavily influenced by the ancestry or genetic background of a patient with an idiopathic condition. This is so because potential pathogenic variants in a patient’s genome must be contrasted with variants in a reference set of genomes made up of other individuals’ genomes of the same ancestry as the patient. We explored the effect of ignoring the ancestries of both an individual patient and the individuals used to construct reference genomes. We pursued this exploration in two major steps. We first considered variation in the per-genome number and rates of likely functional derived (i.e., non-ancestral, based on the chimp genome) single nucleotide variants and small indels in 52 individual whole human genomes sampled from 10 different global populations. We took advantage of a suite of computational and bioinformatics techniques to predict the functional effect of over 24 million genomic variants, both coding and non-coding, across these genomes. We found that the typical human genome harbors ∼5.5–6.1 million total derived variants, of which ∼12,000 are likely to have a functional effect (∼5000 coding and ∼7000 non-coding). We also found that the rates of functional genotypes per the total number of genotypes in individual whole genomes differ dramatically between human populations. We then created tables showing how the use of comparator or reference genome panels comprised of genomes from individuals that do not have the same ancestral background as a patient can negatively impact pathogenic variant identification. Our results have important implications for clinical sequencing initiatives.

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          Most cited references 66

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          Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes.

          We have conducted a comprehensive search for conserved elements in vertebrate genomes, using genome-wide multiple alignments of five vertebrate species (human, mouse, rat, chicken, and Fugu rubripes). Parallel searches have been performed with multiple alignments of four insect species (three species of Drosophila and Anopheles gambiae), two species of Caenorhabditis, and seven species of Saccharomyces. Conserved elements were identified with a computer program called phastCons, which is based on a two-state phylogenetic hidden Markov model (phylo-HMM). PhastCons works by fitting a phylo-HMM to the data by maximum likelihood, subject to constraints designed to calibrate the model across species groups, and then predicting conserved elements based on this model. The predicted elements cover roughly 3%-8% of the human genome (depending on the details of the calibration procedure) and substantially higher fractions of the more compact Drosophila melanogaster (37%-53%), Caenorhabditis elegans (18%-37%), and Saccharaomyces cerevisiae (47%-68%) genomes. From yeasts to vertebrates, in order of increasing genome size and general biological complexity, increasing fractions of conserved bases are found to lie outside of the exons of known protein-coding genes. In all groups, the most highly conserved elements (HCEs), by log-odds score, are hundreds or thousands of bases long. These elements share certain properties with ultraconserved elements, but they tend to be longer and less perfectly conserved, and they overlap genes of somewhat different functional categories. In vertebrates, HCEs are associated with the 3' UTRs of regulatory genes, stable gene deserts, and megabase-sized regions rich in moderately conserved noncoding sequences. Noncoding HCEs also show strong statistical evidence of an enrichment for RNA secondary structure.
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            SIFT: Predicting amino acid changes that affect protein function.

             P C Ng (2003)
            Single nucleotide polymorphism (SNP) studies and random mutagenesis projects identify amino acid substitutions in protein-coding regions. Each substitution has the potential to affect protein function. SIFT (Sorting Intolerant From Tolerant) is a program that predicts whether an amino acid substitution affects protein function so that users can prioritize substitutions for further study. We have shown that SIFT can distinguish between functionally neutral and deleterious amino acid changes in mutagenesis studies and on human polymorphisms. SIFT is available at http://blocks.fhcrc.org/sift/SIFT.html.
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              Vienna RNA secondary structure server.

               I. Hofacker (2003)
              The Vienna RNA secondary structure server provides a web interface to the most frequently used functions of the Vienna RNA software package for the analysis of RNA secondary structures. It currently offers prediction of secondary structure from a single sequence, prediction of the consensus secondary structure for a set of aligned sequences and the design of sequences that will fold into a predefined structure. All three services can be accessed via the Vienna RNA web server at http://rna.tbi.univie.ac.at/.
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                Author and article information

                Journal
                Front Genet
                Front Genet
                Front. Gene.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                01 November 2012
                2012
                : 3
                Affiliations
                1The Scripps Translational Science La Jolla, CA, USA
                2Scripps Health La Jolla, CA, USA
                3Department of Molecular and Experimental Medicine, The Scripps Research Institute La Jolla, CA, USA
                Author notes

                Edited by: Jill Barnholtz-Sloan, Case Western Reserve University School of Medicine, USA

                Reviewed by: Hemant K. Tiwari, University of Alabama at Birmingham, USA; Indrani Halder, University of Pittsburgh, USA; Paola Raska, Case Western Reserve University, USA

                *Correspondence: Nicholas J. Schork, Department of Molecular and Experimental Medicine, The Scripps Translational Science Institute, The Scripps Research Institute, 3344 North Torrey Pines Court, Suite 300, La Jolla, CA 92037, USA. e-mail: nschork@ 123456scripps.edu

                This article was submitted to Frontiers in Applied Genetic Epidemiology, a specialty of Frontiers in Genetics.

                Article
                10.3389/fgene.2012.00211
                3485509
                23125845
                Copyright © 2012 Torkamani, Pham, Libiger, Bansal, Zhang, Scott-Van Zeeland, Tewhey, Topol and Schork.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.

                Page count
                Figures: 6, Tables: 4, Equations: 0, References: 66, Pages: 19, Words: 13131
                Categories
                Genetics
                Original Research

                Genetics

                whole genome sequencing, congenital disease, clinical sequencing, population genetics

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