Genetic Linkage Map of 46 DNA Markers on Human Chromosome 16
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Population Genetic Considerations for Using Biobanks As International
Carress et al. BMC Genomics (2021) 22:351 https://doi.org/10.1186/s12864-021-07618-x REVIEW Open Access Population genetic considerations for using biobanks as international resources in the pandemic era and beyond Hannah Carress1, Daniel John Lawson2 and Eran Elhaik1,3* Abstract The past years have seen the rise of genomic biobanks and mega-scale meta-analysis of genomic data, which promises to reveal the genetic underpinnings of health and disease. However, the over-representation of Europeans in genomic studies not only limits the global understanding of disease risk but also inhibits viable research into the genomic differences between carriers and patients. Whilst the community has agreed that more diverse samples are required, it is not enough to blindly increase diversity; the diversity must be quantified, compared and annotated to lead to insight. Genetic annotations from separate biobanks need to be comparable and computable and to operate without access to raw data due to privacy concerns. Comparability is key both for regular research and to allow international comparison in response to pandemics. Here, we evaluate the appropriateness of the most common genomic tools used to depict population structure in a standardized and comparable manner. The end goal is to reduce the effects of confounding and learn from genuine variation in genetic effects on phenotypes across populations, which will improve the value of biobanks (locally and internationally), increase the accuracy of association analyses and inform developmental efforts. Keywords: Bioinformatics, Population structure, Population stratification bias, Genomic medicine, Biobanks Background individuals, families, communities and populations, ne- Association studies aim to detect whether genetic vari- cessitated genomic biobanks. -
Gene Linkage and Genetic Mapping 4TH PAGES © Jones & Bartlett Learning, LLC
© Jones & Bartlett Learning, LLC © Jones & Bartlett Learning, LLC NOT FOR SALE OR DISTRIBUTION NOT FOR SALE OR DISTRIBUTION © Jones & Bartlett Learning, LLC © Jones & Bartlett Learning, LLC NOT FOR SALE OR DISTRIBUTION NOT FOR SALE OR DISTRIBUTION © Jones & Bartlett Learning, LLC © Jones & Bartlett Learning, LLC NOT FOR SALE OR DISTRIBUTION NOT FOR SALE OR DISTRIBUTION © Jones & Bartlett Learning, LLC © Jones & Bartlett Learning, LLC NOT FOR SALE OR DISTRIBUTION NOT FOR SALE OR DISTRIBUTION Gene Linkage and © Jones & Bartlett Learning, LLC © Jones & Bartlett Learning, LLC 4NOTGenetic FOR SALE OR DISTRIBUTIONMapping NOT FOR SALE OR DISTRIBUTION CHAPTER ORGANIZATION © Jones & Bartlett Learning, LLC © Jones & Bartlett Learning, LLC NOT FOR4.1 SALELinked OR alleles DISTRIBUTION tend to stay 4.4NOT Polymorphic FOR SALE DNA ORsequences DISTRIBUTION are together in meiosis. 112 used in human genetic mapping. 128 The degree of linkage is measured by the Single-nucleotide polymorphisms (SNPs) frequency of recombination. 113 are abundant in the human genome. 129 The frequency of recombination is the same SNPs in restriction sites yield restriction for coupling and repulsion heterozygotes. 114 fragment length polymorphisms (RFLPs). 130 © Jones & Bartlett Learning,The frequency LLC of recombination differs © Jones & BartlettSimple-sequence Learning, repeats LLC (SSRs) often NOT FOR SALE OR DISTRIBUTIONfrom one gene pair to the next. NOT114 FOR SALEdiffer OR in copyDISTRIBUTION number. 131 Recombination does not occur in Gene dosage can differ owing to copy- Drosophila males. 115 number variation (CNV). 133 4.2 Recombination results from Copy-number variation has helped human populations adapt to a high-starch diet. 134 crossing-over between linked© Jones alleles. & Bartlett Learning,116 LLC 4.5 Tetrads contain© Jonesall & Bartlett Learning, LLC four products of meiosis. -
Human Chromosome‐Specific Aneuploidy Is Influenced by DNA
Article Human chromosome-specific aneuploidy is influenced by DNA-dependent centromeric features Marie Dumont1,†, Riccardo Gamba1,†, Pierre Gestraud1,2,3, Sjoerd Klaasen4, Joseph T Worrall5, Sippe G De Vries6, Vincent Boudreau7, Catalina Salinas-Luypaert1, Paul S Maddox7, Susanne MA Lens6, Geert JPL Kops4 , Sarah E McClelland5, Karen H Miga8 & Daniele Fachinetti1,* Abstract Introduction Intrinsic genomic features of individual chromosomes can contri- Defects during cell division can lead to loss or gain of chromosomes bute to chromosome-specific aneuploidy. Centromeres are key in the daughter cells, a phenomenon called aneuploidy. This alters elements for the maintenance of chromosome segregation fidelity gene copy number and cell homeostasis, leading to genomic instabil- via a specialized chromatin marked by CENP-A wrapped by repeti- ity and pathological conditions including genetic diseases and various tive DNA. These long stretches of repetitive DNA vary in length types of cancers (Gordon et al, 2012; Santaguida & Amon, 2015). among human chromosomes. Using CENP-A genetic inactivation in While it is known that selection is a key process in maintaining aneu- human cells, we directly interrogate if differences in the centro- ploidy in cancer, a preceding mis-segregation event is required. It was mere length reflect the heterogeneity of centromeric DNA-depen- shown that chromosome-specific aneuploidy occurs under conditions dent features and whether this, in turn, affects the genesis of that compromise genome stability, such as treatments with micro- chromosome-specific aneuploidy. Using three distinct approaches, tubule poisons (Caria et al, 1996; Worrall et al, 2018), heterochro- we show that mis-segregation rates vary among different chromo- matin hypomethylation (Fauth & Scherthan, 1998), or following somes under conditions that compromise centromere function. -
Searching the Genomes of Inbred Mouse Strains for Incompatibilities That Reproductively Isolate Their Wild Relatives
Journal of Heredity 2007:98(2):115–122 ª The American Genetic Association. 2007. All rights reserved. doi:10.1093/jhered/esl064 For permissions, please email: [email protected]. Advance Access publication January 5, 2007 Searching the Genomes of Inbred Mouse Strains for Incompatibilities That Reproductively Isolate Their Wild Relatives BRET A. PAYSEUR AND MICHAEL PLACE From the Laboratory of Genetics, University of Wisconsin, Madison, WI 53706. Address correspondence to the author at the address above, or e-mail: [email protected]. Abstract Identification of the genes that underlie reproductive isolation provides important insights into the process of speciation. According to the Dobzhansky–Muller model, these genes suffer disrupted interactions in hybrids due to independent di- vergence in separate populations. In hybrid populations, natural selection acts to remove the deleterious heterospecific com- binations that cause these functional disruptions. When selection is strong, this process can maintain multilocus associations, primarily between conspecific alleles, providing a signature that can be used to locate incompatibilities. We applied this logic to populations of house mice that were formed by hybridization involving two species that show partial reproductive isolation, Mus domesticus and Mus musculus. Using molecular markers likely to be informative about species ancestry, we scanned the genomes of 1) classical inbred strains and 2) recombinant inbred lines for pairs of loci that showed extreme linkage disequi- libria. By using the same set of markers, we identified a list of locus pairs that displayed similar patterns in both scans. These genomic regions may contain genes that contribute to reproductive isolation between M. domesticus and M. -
Development of Novel SNP Assays for Genetic Analysis of Rare Minnow (Gobiocypris Rarus) in a Successive Generation Closed Colony
diversity Article Development of Novel SNP Assays for Genetic Analysis of Rare Minnow (Gobiocypris rarus) in a Successive Generation Closed Colony Lei Cai 1,2,3, Miaomiao Hou 1,2, Chunsen Xu 1,2, Zhijun Xia 1,2 and Jianwei Wang 1,4,* 1 The Key Laboratory of Aquatic Biodiversity and Conservation of Chinese Academy of Sciences, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430070, China; [email protected] (L.C.); [email protected] (M.H.); [email protected] (C.X.); [email protected] (Z.X.) 2 University of Chinese Academy of Sciences, Beijing 100049, China 3 Guangdong Provincial Key Laboratory of Laboratory Animals, Guangdong Laboratory Animals Monitoring Institute, Guangzhou 510663, China 4 National Aquatic Biological Resource Center, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430070, China * Correspondence: [email protected] Received: 10 November 2020; Accepted: 11 December 2020; Published: 18 December 2020 Abstract: The complex genetic architecture of closed colonies during successive passages poses a significant challenge in the understanding of the genetic background. Research on the dynamic changes in genetic structure for the establishment of a new closed colony is limited. In this study, we developed 51 single nucleotide polymorphism (SNP) markers for the rare minnow (Gobiocypris rarus) and conducted genetic diversity and structure analyses in five successive generations of a closed colony using 20 SNPs. The range of mean Ho and He in five generations was 0.4547–0.4983 and 0.4445–0.4644, respectively. No significant differences were observed in the Ne, Ho, and He (p > 0.05) between the five closed colony generations, indicating well-maintained heterozygosity. -
Genetic Linkage Analysis
BASIC SCIENCE SEMINARS IN NEUROLOGY SECTION EDITOR: HASSAN M. FATHALLAH-SHAYKH, MD Genetic Linkage Analysis Stefan M. Pulst, MD enetic linkage analysis is a powerful tool to detect the chromosomal location of dis- ease genes. It is based on the observation that genes that reside physically close on a chromosome remain linked during meiosis. For most neurologic diseases for which the underlying biochemical defect was not known, the identification of the chromo- Gsomal location of the disease gene was the first step in its eventual isolation. By now, genes that have been isolated in this way include examples from all types of neurologic diseases, from neu- rodegenerative diseases such as Alzheimer, Parkinson, or ataxias, to diseases of ion channels lead- ing to periodic paralysis or hemiplegic migraine, to tumor syndromes such as neurofibromatosis types 1 and 2. Arch Neurol. 1999;56:667-672 With the advent of new genetic markers tin gene, diagnosis using flanking mark- and automated genotyping, genetic map- ers requires the analysis of several family ping can be conducted extremely rap- members. idly. Genetic linkage maps have been gen- erated for the human genome and for LINKAGE OF GENES model organisms and have provided the basis for the construction of physical maps When Mendel observed an “independent that permit the rapid mapping of disease assortment of traits” (Mendel’s second traits. law), he was fortunate to have chosen traits As soon as a chromosomal location that were not localized close to one an- for a disease phenotype has been estab- other on the same chromosome.1 Subse- lished, genetic linkage analysis helps quent studies revealed that many genes determine whether the disease pheno- were indeed linked, ie, that traits did not type is only caused by mutation in a assort or segregate independently, but that single gene or mutations in other genes traits encoded by these linked genes were can give rise to an identical or similar inherited together. -
Descriptors for Genetic Markers Technologies
• Descriptors for genetic markers technologies Version 1.0, February 2004 Carmen De Vicente, Thomas Metz and Adriana Alercia <www.futureharvest.org> IPGRI is a Future Harvest Centre supported by the Consultative Group on International Agricultural Research (CGIAR) ii DESCRIPTORS FOR GENETIC MARKERS TECHNOLOGIES The International Plant Genetic Resources Institute (IPGRI) is an independent international scientific organization that seeks to advance the conservation and use of plant genetic diversity for the well-being of present and future generations. It is one of 16 Future Harvest Centres supported by the Consultative Group on International Agricultural Research (CGIAR), an association of public and private members who support efforts to mobilize cutting-edge science to reduce hunger and poverty, improve human nutrition and health, and protect the environment. IPGRI has its headquarters in Maccarese, near Rome, Italy, with offices in more than 20 other countries worldwide. The Institute operates through three programmes: (1) the Plant Genetic Resources Programme, (2) the CGIAR Genetic Resources Support Programme and (3) the International Network for the Improvement of Banana and Plantain (INIBAP). The international status of IPGRI is conferred under an Establishment Agreement which, by January 2003, had been signed by the Governments of Algeria, Australia, Belgium, Benin, Bolivia, Brazil, Burkina Faso, Cameroon, Chile, China, Congo, Costa Rica, Côte d’Ivoire, Cyprus, Czech Republic, Denmark, Ecuador, Egypt, Greece, Guinea, Hungary, India, Indonesia, Iran, Israel, Italy, Jordan, Kenya, Malaysia, Mauritania, Morocco, Norway, Pakistan, Panama, Peru, Poland, Portugal, Romania, Russia, Senegal, Slovakia, Sudan, Switzerland, Syria, Tunisia, Turkey, Uganda and Ukraine. Financial support for IPGRI’s research is provided by more than 150 donors, including governments, private foundations and international organizations. -
16P11.2 Deletion Syndrome Guidebook
16p11.2 Deletion Syndrome Guidebook Page 1 Version 1.0, 12/11/2015 16p11.2 Deletion Syndrome Guidebook This guidebook was developed by the Simons VIP Connect Study Team to help you learn important information about living with the 16p11.2 deletion syndrome. Inside, you will find that we review everything from basic genetics and features of 16p11.2 deletion syndrome, to a description of clinical care and management considerations. - Simons VIP Connect Page 2 Version 1.0, 12/11/2015 Table of Contents How Did We Collect All of this Information? ................................................................................................ 4 What is Simons VIP Connect? ................................................................................................................... 4 What is the Simons Variation in Individuals Project (Simons VIP)? .......................................................... 5 Simons VIP Connect Research................................................................................................................... 5 Definition of 16p11.2 Deletion ..................................................................................................................... 6 What is a Copy Number Variant (CNV)? ................................................................................................... 7 Inheritance ................................................................................................................................................ 8 How is a 16p11.2 Deletion Found? .......................................................................................................... -
Single Nucleotide Polymorphism (SNP) Discovery in Mammals
Molecular Ecology (2004) 13, 1423–1431 doi: 10.1111/j.1365-294X.2004.02159.x SingleBlackwell Publishing, Ltd. nucleotide polymorphism (SNP) discovery in mammals: a targeted-gene approach NICOLA AITKEN,* STEVEN SMITH,† CARSTEN SCHWARZ‡ and PHILLIP A. MORIN§ Laboratory for Conservation Genetics, Max Planck Institute for Evolutionary Anthropology, Inselstrasse 22, D-04103, Leipzig, Germany Abstract Single nucleotide polymorphisms (SNPs) have rarely been exploited in nonhuman and nonmodel organism genetic studies. This is due partly to difficulties in finding SNPs in species where little DNA sequence data exist, as well as to a lack of robust and inexpensive genotyping methods. We have explored one SNP discovery method for molecular ecology, evolution, and conservation studies to evaluate the method and its limitations for population genetics in mammals. We made use of ‘CATS’ (or ‘EPIC’) primers to screen for novel SNPs in mammals. Most of these primer sets were designed from primates and/or rodents, for amplifying intron regions from conserved genes. We have screened 202 loci in 16 repres- entatives of the major mammalian clades. Polymerase chain reaction (PCR) success correlated with phylogenetic distance from the human and mouse sequences used to design most primers; for example, specific PCR products from primates and the mouse amplified the most con- sistently and the marsupial and armadillo amplifications were least successful. Approxi- mately 24% (opossum) to 65% (chimpanzee) of primers produced usable PCR product(s) in the mammals tested. Products produced generally high but variable levels of readable sequence and similarity to the expected genes. In a preliminary screen of chimpanzee DNA, 12 SNPs were identified from six (of 11) sequenced regions, yielding a SNP on average every 400 base pairs (bp). -
Genetic Linkage of the Huntington's Disease Gene to a DNA Marker James F
LE JOURNAL CANADIEN DES SCIENCES NEUROLOG1QUES SPECIAL FEATURE Genetic Linkage of the Huntington's Disease Gene to a DNA Marker James F. Gusella ABSTRACT: Recombinant DNA techniques have provided the means to generate large numbers of new genetic linkage markers. This technology has been used to identify a DNA marker that coinherits with the Huntington's Disease (HD) gene in family studies. The HD locus has thereby been mapped to human chromosome 4. The discovery of a genetic marker for the inheritance of HD has implications both for patient care and future research. The same approach holds considerable promise for the investigation of other genetic diseases, including Dystonia Musculorum Deformans. RESUME: Les techniques d'ADN recombine ont fourni le moyen de g£nerer un grand nombre de nouveux marqueurs a liason g6n£tique. Cette technologie a He employe" afin d'identifier un marqueur d'ADN qui co-herite avec le gene de la maladie de Huntington (MH) dans les etudes familiales. Le lieu du gene de la MH a ainsi 6te localise sur le chromosome humain numero 4. La d6couverte d'un marqueur g6n6tique pour I'her6dit6 de MH a des implications pour la soin de patients ainsi que pour la recherche dans le futur. La meme approche semble pleine de promesses pour l'investigation d'autres maladies g6ndtiques, incluant la dystonie musculaire deTormante. Can. J. Neurol. Sci. 1984; 11:421-425 Huntington's Disease currently no effective therapy to cure this devastating disease, Huntington's disease (HD) is a genetic neurodegenerative or to slow its inexorable progression. disorder first described by George Huntington in 1873 (Hunting ton, 1972;Hayden, 1981;Chaseetal., 1979). -
Genome-Wide Mapping and Characterization of Microsatellites In
www.nature.com/scientificreports OPEN Genome-wide mapping and characterization of microsatellites in the swamp eel genome Received: 14 February 2017 Zhigang Li, Feng Chen, Chunhua Huang, Weixin Zheng, Chunlai Yu, Hanhua Cheng & Accepted: 26 April 2017 Rongjia Zhou Published: xx xx xxxx We described genome-wide screening and characterization of microsatellites in the swamp eel genome. A total of 99,293 microsatellite loci were identified in the genome with an overall density of 179 microsatellites per megabase of genomic sequences. The dinucleotide microsatellites were the most abundant type representing 71% of the total microsatellite loci and the AC-rich motifs were the most recurrent in all repeat types. Microsatellite frequency decreased as numbers of repeat units increased, which was more obvious in long than short microsatellite motifs. Most of microsatellites were located in non-coding regions, whereas only approximately 1% of the microsatellites were detected in coding regions. Trinucleotide repeats were most abundant microsatellites in the coding regions, which represented amino acid repeats in proteins. There was a chromosome-biased distribution of microsatellites in non-coding regions, with the highest density of 203.95/Mb on chromosome 8 and the least on chromosome 7 (164.06/Mb). The most abundant dinucleotides (AC)n was mainly located on chromosome 8. Notably, genomic mapping showed that there was a chromosome-biased association of genomic distributions between microsatellites and transposon elements. Thus, the novel dataset of microsatellites in swamp eel provides a valuable resource for further studies on QTL-based selection breeding, genetic resource conservation and evolutionary genetics. Swamp eel (Monopterus albus) taxonomically belongs to teleosts, the family Synbranchidae of the order Synbranchiformes (Neoteleostei, Teleostei, and Vertebrata). -
Genetic Markers and Plant Genetic Resource Management P
NCRPIS Publications and Papers North Central Regional Plant Introduction Station 1995 Genetic Markers and Plant Genetic Resource Management P. K. Bretting United States Department of Agriculture Mark P. Widrlechner Iowa State University, [email protected] Follow this and additional works at: http://lib.dr.iastate.edu/ncrpis_pubs Part of the Agricultural Science Commons, Agriculture Commons, and the Plant Breeding and Genetics Commons The ompc lete bibliographic information for this item can be found at http://lib.dr.iastate.edu/ ncrpis_pubs/75. For information on how to cite this item, please visit http://lib.dr.iastate.edu/ howtocite.html. This Book Chapter is brought to you for free and open access by the North Central Regional Plant Introduction Station at Iowa State University Digital Repository. It has been accepted for inclusion in NCRPIS Publications and Papers by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Plant Breeding Reviews, Volume 13 Edited by Jules Janick © 1995 John Wiley & Sons, Inc. ISBN: 978-0-471-57343-2 12 P. K. BRETTING AND M. P. WIDRLECHNER C. Maintenance 1. Maintaining Trueness-to-Type a. Morphological Traits b. Secondary Metabolites c. Isozymes, Seed Proteins, and DNA Markers d. Comparative Studies e. Pollination Control Methods 2. Monitoring Shifts in Population Genetic Structure in Heterogeneous Germplasm a. Deviations from Random Mating b. Regeneration of Autogamous Species 3. Monitoring Genetic Shifts Caused by Differential Viability in Storage 4. Monitoring Genetic Shifts Caused by In Vitro Culture 5. Monitoring Germplasm Viability and Health D. Utilization 1. Developing Optimal Utilization Strategies from Genetic Marker Data 2.