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      Whole-genome sequencing of Atacama skeleton shows novel mutations linked with dysplasia

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          Abstract

          Over a decade ago, the Atacama humanoid skeleton (Ata) was discovered in the Atacama region of Chile. The Ata specimen carried a strange phenotype—6-in stature, fewer than expected ribs, elongated cranium, and accelerated bone age—leading to speculation that this was a preserved nonhuman primate, human fetus harboring genetic mutations, or even an extraterrestrial. We previously reported that it was human by DNA analysis with an estimated bone age of about 6–8 yr at the time of demise. To determine the possible genetic drivers of the observed morphology, DNA from the specimen was subjected to whole-genome sequencing using the Illumina HiSeq platform with an average 11.5× coverage of 101-bp, paired-end reads. In total, 3,356,569 single nucleotide variations (SNVs) were found as compared to the human reference genome, 518,365 insertions and deletions (indels), and 1047 structural variations (SVs) were detected. Here, we present the detailed whole-genome analysis showing that Ata is a female of human origin, likely of Chilean descent, and its genome harbors mutations in genes ( COL1A1, COL2A1, KMT2D, FLNB, ATR, TRIP11, PCNT) previously linked with diseases of small stature, rib anomalies, cranial malformations, premature joint fusion, and osteochondrodysplasia (also known as skeletal dysplasia). Together, these findings provide a molecular characterization of Ata's peculiar phenotype, which likely results from multiple known and novel putative gene mutations affecting bone development and ossification.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

            Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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              PLINK: a tool set for whole-genome association and population-based linkage analyses.

              Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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                Author and article information

                Journal
                Genome Res
                Genome Res
                genome
                genome
                GENOME
                Genome Research
                Cold Spring Harbor Laboratory Press
                1088-9051
                1549-5469
                April 2018
                April 2018
                : 28
                : 4
                : 423-431
                Affiliations
                [1 ]Institute for Computational Health Sciences, University of California San Francisco, San Francisco, California 94158, USA;
                [2 ]Roche Sequencing Solutions, Belmont, California 94002, USA;
                [3 ]Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA;
                [4 ]Baxter Laboratory for Stem Cell Biology, Department of Microbiology and Immunology, Stanford University, Stanford, California 94305, USA;
                [5 ]International Laboratory for Human Genome Research, National Autonomous University of Mexico (UNAM) Santiago de Querétaro, Querétaro 76230, Mexico;
                [6 ]Human Immune Monitoring Center and Functional Genomics Facility, Stanford University, Stanford, California 94305, USA;
                [7 ]Ultra Intelligence Corporation, Boulder, Colorado 80301, USA;
                [8 ]Department of Pediatric Radiology, Stanford University School of Medicine, Stanford, California 94305, USA
                Author notes
                [9]

                These authors have equal senior authorship.

                Article
                9509184
                10.1101/gr.223693.117
                5880234
                29567674
                8f470e65-fb81-4d55-a66a-0ffe193211d5
                © 2018 Bhattacharya et al.; Published by Cold Spring Harbor Laboratory Press

                This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.

                History
                : 21 April 2017
                : 21 February 2018
                Page count
                Pages: 9
                Funding
                Funded by: Human Frontier Science Program Long-Term Fellowship , open-funder-registry 10.13039/501100000854;
                Funded by: Rachford and Carlota A. Harris Professorship
                Categories
                Research

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