Human Identification by Genotyping Single Nucleotide Polymorphisms (Snps) Using an APEX Microarray
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GENOME GENERATION Glossary
GENOME GENERATION Glossary Chromosome An organism’s DNA is packaged into chromosomes. Humans have 23 pairs of chromosomesincluding one pair of sex chromosomes. Women have two X chromosomes and men have one X and one Y chromosome. Dominant (see also recessive) Genes come in pairs. A dominant form of a gene is the “stronger” version that will be expressed. Therefore if someone has one dominant and one recessive form of a gene, only the characteristics of the dominant form will appear. DNA DNA is the long molecule that contains the genetic instructions for nearly all living things. Two strands of DNA are twisted together into a double helix. The DNA code is made up of four chemical letters (A, C, G and T) which are commonly referred to as bases or nucleotides. Gene A gene is a section of DNA that is the code for a specific biological component, usually a protein. Each gene may have several alternative forms. Each of us has two copies of most of our genes, one copy inherited from each parent. Most of our traits are the result of the combined effects of a number of different genes. Very few traits are the result of just one gene. Genetic sequence The precise order of letters (bases) in a section of DNA. Genome A genome is the complete DNA instructions for an organism. The human genome contains 3 billion DNA letters and approximately 23,000 genes. Genomics Genomics is the study of genomes. This includes not only the DNA sequence itself, but also an understanding of the function and regulation of genes both individually and in combination. -
Genome-Wide Human SNP Array 6.0
Data Sheet Genome-Wide Human SNP Array 6.0 Introduction The Genome-Wide Human SNP Array 6.0 contains more than 906,600 single nucleotide polymorphisms (SNPs) and more than 946,000 probes for the detection of copy number variation. SNPs on the array are present on 200 to 1,100 base pairs (bp) Nsp I or Sty I digested fragments in the human genome, and are amplified using the Genome-Wide Human SNP Nsp/Sty Assay Kit 5.0/6.0. This assay, which is also compatible with the SNP Array 5.0, now combines the Nsp and Sty fractions previously assayed on two separate arrays. SNPs on the SNP Array 6.0 were screened in more than 500 distinct samples, including 270 HapMap samples and separate diversity samples. Approximately 482,000 SNPs are derived from the previous-generation Mapping 500K and SNP 5.0 Arrays. The remaining 424,000 SNPs include tag SNP markers derived from the International HapMap Project. These novel markers have better representation of SNPs on chromosomes X and Y, mitochondrial SNPs, SNPs in recombination hotspots, and new SNPs added to the dbSNP database after completion of the GeneChip® Human Mapping 500K Array Set. This array contains a total of 946,000 non-polymorphic copy number probes. These probes—744,000 originally selected for their spacing and 202,000 selected based on known copy number changes reported in the Toronto Database of Genomic Variants (DGV)—enable you to detect de novo copy number changes and perform association studies by genotyping both SNP and known copy number polymorphism (CNP) loci (as The Genome-Wide Human SNP Array 6.0 reported by McCarroll, et al.). -
Pharmacogenetic Testing: a Tool for Personalized Drug Therapy Optimization
pharmaceutics Review Pharmacogenetic Testing: A Tool for Personalized Drug Therapy Optimization Kristina A. Malsagova 1,* , Tatyana V. Butkova 1 , Arthur T. Kopylov 1 , Alexander A. Izotov 1, Natalia V. Potoldykova 2, Dmitry V. Enikeev 2, Vagarshak Grigoryan 2, Alexander Tarasov 3, Alexander A. Stepanov 1 and Anna L. Kaysheva 1 1 Biobanking Group, Branch of Institute of Biomedical Chemistry “Scientific and Education Center”, 109028 Moscow, Russia; [email protected] (T.V.B.); [email protected] (A.T.K.); [email protected] (A.A.I.); [email protected] (A.A.S.); [email protected] (A.L.K.) 2 Institute of Urology and Reproductive Health, Sechenov University, 119992 Moscow, Russia; [email protected] (N.V.P.); [email protected] (D.V.E.); [email protected] (V.G.) 3 Institute of Linguistics and Intercultural Communication, Sechenov University, 119992 Moscow, Russia; [email protected] * Correspondence: [email protected]; Tel.: +7-499-764-9878 Received: 2 November 2020; Accepted: 17 December 2020; Published: 19 December 2020 Abstract: Pharmacogenomics is a study of how the genome background is associated with drug resistance and how therapy strategy can be modified for a certain person to achieve benefit. The pharmacogenomics (PGx) testing becomes of great opportunity for physicians to make the proper decision regarding each non-trivial patient that does not respond to therapy. Although pharmacogenomics has become of growing interest to the healthcare market during the past five to ten years the exact mechanisms linking the genetic polymorphisms and observable responses to drug therapy are not always clear. Therefore, the success of PGx testing depends on the physician’s ability to understand the obtained results in a standardized way for each particular patient. -
HIV Genotyping and Phenotyping AHS – M2093
Corporate Medical Policy HIV Genotyping and Phenotyping AHS – M2093 File Name: HIV_genotyping_and_phenotyping Origination: 1/2019 Last CAP Review: 2/2021 Next CAP Review: 2/2022 Last Review: 2/2021 Description of Procedure or Service Description Human immunodeficiency virus (HIV) is an RNA retrovirus that infects human immune cells (specifically CD4 cells) causing progressive deterioration of the immune system ultimately leading to acquired immune deficiency syndrome (AIDS) characterized by susceptibility to opportunistic infections and HIV-related cancers (CDC, 2014). Related Policies Plasma HIV-1 and HIV-2 RNA Quantification for HIV Infection Scientific Background Human immunodeficiency virus (HIV) targets the immune system, eventually hindering the body’s ability to fight infections and diseases. If not treated, an HIV infection may lead to acquired immunodeficiency syndrome (AIDS) which is a condition caused by the virus. There are two main types of HIV: HIV-1 and HIV-2; both are genetically different. HIV-1 is more common and widespread than HIV-2. HIV replicates rapidly; a replication cycle rate of approximately one to two days ensures that after a single year, the virus in an infected individual may be 200 to 300 generations removed from the initial infection-causing virus (Coffin & Swanstrom, 2013). This leads to great genetic diversity of each HIV infection in a single individual. As an RNA retrovirus, HIV requires the use of a reverse transcriptase for replication purposes. A reverse transcriptase is an enzyme which generates complimentary DNA from an RNA template. This enzyme is error-prone with the overall single-step point mutation rate reaching ∼3.4 × 10−5 mutations per base per replication cycle (Mansky & Temin, 1995), leading to approximately one genome in three containing a mutation after each round of replication (some of which confer drug resistance). -
Review of the Current State of Genetic Testing - a Living Resource
Review of the Current State of Genetic Testing - A Living Resource Prepared by Liza Gershony, DVM, PhD and Anita Oberbauer, PhD of the University of California, Davis Editorial input by Leigh Anne Clark, PhD of Clemson University July, 2020 Contents Introduction .................................................................................................................................................. 1 I. The Basics ......................................................................................................................................... 2 II. Modes of Inheritance ....................................................................................................................... 7 a. Mendelian Inheritance and Punnett Squares ................................................................................. 7 b. Non-Mendelian Inheritance ........................................................................................................... 10 III. Genetic Selection and Populations ................................................................................................ 13 IV. Dog Breeds as Populations ............................................................................................................. 15 V. Canine Genetic Tests ...................................................................................................................... 16 a. Direct and Indirect Tests ................................................................................................................ 17 b. Single -
Evaluation of Openarray™ As a Genotyping Method for Forensic DNA Phenotyping and Human Identification
G C A T T A C G G C A T genes Article Evaluation of OpenArray™ as a Genotyping Method for Forensic DNA Phenotyping and Human Identification Michele Ragazzo 1, Giulio Puleri 1, Valeria Errichiello 1, Laura Manzo 1, Laura Luzzi 1 , Saverio Potenza 2 , Claudia Strafella 1,3 , Cristina Peconi 3, Fabio Nicastro 4 , Valerio Caputo 1 and Emiliano Giardina 1,3,* 1 Department of Biomedicine and Prevention, Tor Vergata University of Rome, 00133 Rome, Italy; [email protected] (M.R.); [email protected] (G.P.); [email protected] (V.E.); [email protected] (L.M.); [email protected] (L.L.); [email protected] (C.S.); [email protected] (V.C.) 2 Department of Biomedicine and Prevention, Section of Legal Medicine, Social Security and Forensic Toxicology, University of Rome Tor Vergata, 00133 Rome, Italy; [email protected] 3 Genomic Medicine Laboratory UILDM, IRCCS Santa Lucia Foundation, 00179 Rome, Italy; [email protected] 4 Austech, 00153 Rome, Italy; [email protected] * Correspondence: [email protected] Abstract: A custom plate of OpenArray™ technology was evaluated to test 60 single-nucleotide polymorphisms (SNPs) validated for the prediction of eye color, hair color, and skin pigmentation, and for personal identification. The SNPs were selected from already validated subsets (Hirisplex-s, Precision ID Identity SNP Panel, and ForenSeq DNA Signature Prep Kit). The concordance rate and call rate for every SNP were calculated by analyzing 314 sequenced DNA samples. The sensitivity of the assay was assessed by preparing a dilution series of 10.0, 5.0, 1.0, and 0.5 ng. -
Genotyping-By-Sequencing to Unlock Genetic Diversity and Population Structure in White Yam (Dioscorea Rotundata Poir.)
agronomy Article Genotyping-by-Sequencing to Unlock Genetic Diversity and Population Structure in White Yam (Dioscorea rotundata Poir.) Ranjana Bhattacharjee 1,* , Paterne Agre 1 , Guillaume Bauchet 2, David De Koeyer 3, Antonio Lopez-Montes 1,4, P. Lava Kumar 1 , Michael Abberton 1, Patrick Adebola 5, Asrat Asfaw 5 and Robert Asiedu 1 1 International Institute of Tropical Agriculture, PMB 5320, Ibadan 200001, Nigeria; [email protected] (P.A.); [email protected] (A.L.-M.); [email protected] (P.L.K.); [email protected] (M.A.); [email protected] (R.A.) 2 Boyce Thompson Institute, Cornell University, Ithaca, NY 14853, USA; [email protected] 3 Agriculture and Agri-Food Canada, Fredericton, NB E3B 4Z7, Canada; [email protected] 4 International Trade Center, 29 Independence Avenue, Accra 00233, Ghana 5 International Institute of Tropical Agriculture, PMB 82, Kubuwa, Abuja 901101, Nigeria; [email protected] (P.A.); [email protected] (A.A.) * Correspondence: [email protected] Received: 7 August 2020; Accepted: 14 September 2020; Published: 22 September 2020 Abstract: White yam (Dioscorea rotundata Poir.) is one of the most important tuber crops in West Africa, where it is indigenous and represents the largest repository of biodiversity through several years of domestication, production, consumption, and trade. In this study, the genotyping-by-sequencing (GBS) approach was used to sequence 814 genotypes consisting of genebank landraces, breeding lines, and market varieties to understand the level of genetic diversity and pattern of the population structure among them. The genetic diversity among different genotypes was assessed using three complementary clustering methods, the model-based admixture, discriminant analysis of principal components (DAPC), and phylogenetic tree. -
An Integrated Map of Genetic Variation from 1,092 Human Genomes
ARTICLE doi:10.1038/nature11632 An integrated map of genetic variation from 1,092 human genomes The 1000 Genomes Project Consortium* By characterizing the geographic and functional spectrum of human genetic variation, the 1000 Genomes Project aims to build a resource to help to understand the genetic contribution to disease. Here we describe the genomes of 1,092 individuals from 14 populations, constructed using a combination of low-coverage whole-genome and exome sequencing. By developing methods to integrate information across several algorithms and diverse data sources, we provide a validated haplotype map of 38 million single nucleotide polymorphisms, 1.4 million short insertions and deletions, and more than 14,000 larger deletions. We show that individuals from different populations carry different profiles of rare and common variants, and that low-frequency variants show substantial geographic differentiation, which is further increased by the action of purifying selection. We show that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways, and that each individual contains hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites. This resource, which captures up to 98% of accessible single nucleotide polymorphisms at a frequency of 1% in related populations, enables analysis of common and low-frequency variants in individuals from diverse, including admixed, populations. Recent efforts to map human genetic variation by sequencing exomes1 individual genome sequences, to help separate shared variants from and whole genomes2–4 have characterized the vast majority of com- those private to families, for example. -
Chromosome SNP Microarray a New High-Density Allele-Specific Diagnostic Platform
Chromosome SNP Microarray A New High-density Allele-specific Diagnostic Platform Analysis of submicroscopic genomic changes can pair (allele) targets that have two different forms, revealing which form is present at that locus as well as the number of copies of that detect the cause of congenital anomalies and/or DNA segment. CGH-based arrays cannot detect polymorphic allele learning disabilities. targets (only dosage), resulting in a significant advantage for the SNP array. This advantage is based both on added confirmation of Introduction dosage changes through allele comparisons and the identification of Genetic imbalances are often associated with multiple birth defects, syndrome-associated “copy neutral” contiguous stretches of allele developmental delay, growth retardation, and dysmorphic features. homozygosity. The presence of the latter allows for detection of Standard cytogenetic analysis can identify visible chromosomal uniparental disomy for all chromosomes and, when consanguinity alterations, such as an extra chromosome band, but small deletions is present, it will provide the degree as well as the resulting genomic or duplications in the genome cannot be reliably detected. location of regions of recessive allele risk.5,6 Submicroscopic unbalanced rearrangements have been found in approximately 3% of patients with learning disabilities and mental Increase in Genomic Targets retardation of unknown cause using a set of FISH (fluorescence The initial 262,000 SNP microarray has been upgraded to offer a in situ hybridization) probes that can only target the ends of the much more dense array of 1.8 million genomic targets (marker every chromosomes.1 700 bp).7 The ultra dense array is much more sensitive in identifying extremely small genomic variations and more statistically reliable Advances in molecular cytogenetics further improved the sensitivity due to the large increase in markers through which each variation is of testing through the application of microarray-based comparative detected. -
DNA Phenotyping and Kinship Determination
DNA Phenotyping and Kinship Determination Ellen McRae Greytak, PhD Director of Bioinformatics Parabon NanoLabs, Inc. ©©2 2015015 ParabonParabon NanoLabs,NanoLabs Inc.Inc All rightsrights reserved. reserved Forensic Applications of DNA Phenotyping Predict a person’s ancestry and/or appearance (“phenotype”) from his or her DNA Generate investigative leads when DNA doesn’t match a database (e.g., CODIS) Gain additional information (e.g., pigmentation, detailed ancestry) about unidentified remains Main value is in excluding non-matching individuals to help narrow a suspect list Without information on age, weight, lifestyle, etc., phenotyping currently is not targeted toward individual identification Snapshot Workflow Workflow of a Parabon® Snapshot™ Investigation Unidentified Remains DNA Evidence Is Collected and Sent to Crime Lab DNA Evidence DNA Crime Lab CCrime Lab Extracts DNA And Produces STR Profile Checked STR Profile (a.k.a. “DNA Fingerprint”) AAgainst DNA Database(s) Yes Match No Found? SnapshotS Composite Ordered Extracted DNA ™ D N A PH E N O T Y P I N G DNA Service Labs Unidentified DNA Is Genotype Data Is Genotyping Lab Produces SNP Sent To Service Lab Sent To Parabon Profile (a.k.a. “DNA Blueprint”) (DNA Extracted If Needed) 50pg – 2ng DNA Evidence — or — Extracted DNA NOTE: STR Profiles Do Not Contain Sufficient Genetic Information to Produce A SNP Genotype Parabon NanoLabs PParabonb AnalyzesAl PParabon Predicts Physical Traits Investigator Uses Genotype Data and Produces Snapshot Report -
Genotyping of Single Nucleotide Polymorphisms Using Allele
diagnostics Article Genotyping of Single Nucleotide Polymorphisms Using Allele-Specific qPCR Producing Amplicons of Small Sizes Directly from Crude Serum Isolated from Capillary Blood by a Hand-Powered Paper Centrifuge Gustavo Barcelos Barra 1,2,*,† , Ticiane Henriques Santa Rita 1,2,†, Daniella Paniago Jardim 1,2,†, Pedro Góes Mesquita 1, Camila Santos Nobre 1,2, Rafael Henriques Jácomo 1 and Lídia Freire Abdalla Nery 1 1 Sabin Laboratory, Brasília 70632-300, Brazil; [email protected] (T.H.S.R.); [email protected] (D.P.J.); [email protected] (P.G.M.); [email protected] (C.S.N.); [email protected] (R.H.J.); [email protected] (L.F.A.N.) 2 Post-Graduation of Health Science, University of Brasília, Brasilia 70910-900, Brazil * Correspondence: [email protected] † These authors contributed equally to the study. Received: 15 November 2018; Accepted: 29 December 2018; Published: 11 January 2019 Abstract: The cell-free genomic DNA (gDNA) concentration in serum ranges from 1500 to 7500 copies/mL within 2 h after phlebotomy (6–24 times the concentration observed in plasma). Here, we aimed to evaluate the gDNA size distribution in serum with time after coagulation and to test if crude serum can be directly used as a source of gDNA for qPCR. Next, we investigated if single nucleotide polymorphisms (SNPs) could be genotyped directly from the crude serum isolated from capillary blood using a hand-powered paper centrifuge. All tested PCR targets (65, 100, 202 and 688 base pairs) could be successfully amplified from DNA extracted from serum, irrespective of their amplicon size. -
Identity-By-Descent Detection Across 487,409 British Samples Reveals Fine Scale Population Structure and Ultra-Rare Variant Asso
ARTICLE https://doi.org/10.1038/s41467-020-19588-x OPEN Identity-by-descent detection across 487,409 British samples reveals fine scale population structure and ultra-rare variant associations ✉ Juba Nait Saada 1 , Georgios Kalantzis 1, Derek Shyr 2, Fergus Cooper 3, Martin Robinson 3, ✉ Alexander Gusev 4,5,7 & Pier Francesco Palamara 1,6,7 1234567890():,; Detection of Identical-By-Descent (IBD) segments provides a fundamental measure of genetic relatedness and plays a key role in a wide range of analyses. We develop FastSMC, an IBD detection algorithm that combines a fast heuristic search with accurate coalescent-based likelihood calculations. FastSMC enables biobank-scale detection and dating of IBD segments within several thousands of years in the past. We apply FastSMC to 487,409 UK Biobank samples and detect ~214 billion IBD segments transmitted by shared ancestors within the past 1500 years, obtaining a fine-grained picture of genetic relatedness in the UK. Sharing of common ancestors strongly correlates with geographic distance, enabling the use of genomic data to localize a sample’s birth coordinates with a median error of 45 km. We seek evidence of recent positive selection by identifying loci with unusually strong shared ancestry and detect 12 genome-wide significant signals. We devise an IBD-based test for association between phenotype and ultra-rare loss-of-function variation, identifying 29 association sig- nals in 7 blood-related traits. 1 Department of Statistics, University of Oxford, Oxford, UK. 2 Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA. 3 Department of Computer Science, University of Oxford, Oxford, UK.