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Genetic Variation Across the Human Olfactory Receptor Repertoire Alters Odor Perception
bioRxiv preprint doi: https://doi.org/10.1101/212431; this version posted November 1, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Genetic variation across the human olfactory receptor repertoire alters odor perception Casey Trimmer1,*, Andreas Keller2, Nicolle R. Murphy1, Lindsey L. Snyder1, Jason R. Willer3, Maira Nagai4,5, Nicholas Katsanis3, Leslie B. Vosshall2,6,7, Hiroaki Matsunami4,8, and Joel D. Mainland1,9 1Monell Chemical Senses Center, Philadelphia, Pennsylvania, USA 2Laboratory of Neurogenetics and Behavior, The Rockefeller University, New York, New York, USA 3Center for Human Disease Modeling, Duke University Medical Center, Durham, North Carolina, USA 4Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina, USA 5Department of Biochemistry, University of Sao Paulo, Sao Paulo, Brazil 6Howard Hughes Medical Institute, New York, New York, USA 7Kavli Neural Systems Institute, New York, New York, USA 8Department of Neurobiology and Duke Institute for Brain Sciences, Duke University Medical Center, Durham, North Carolina, USA 9Department of Neuroscience, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA *[email protected] ABSTRACT The human olfactory receptor repertoire is characterized by an abundance of genetic variation that affects receptor response, but the perceptual effects of this variation are unclear. To address this issue, we sequenced the OR repertoire in 332 individuals and examined the relationship between genetic variation and 276 olfactory phenotypes, including the perceived intensity and pleasantness of 68 odorants at two concentrations, detection thresholds of three odorants, and general olfactory acuity. -
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. -
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. -
Systematic Detection of Divergent Brain Proteins in Human Evolution and Their Roles in Cognition
bioRxiv preprint doi: https://doi.org/10.1101/658658; this version posted June 3, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Systematic detection of divergent brain proteins in human evolution and their roles in cognition Guillaume Dumas1,*, Simon Malesys1 and Thomas Bourgeron1 Affiliations: 1 Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, Université de Paris, Paris, (75015) France * Corresponding author: [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/658658; this version posted June 3, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Abstract The human brain differs from that of other primates, but the underlying genetic mechanisms remain unclear. Here we measured the evolutionary pressures acting on all human protein- coding genes (N=17,808) based on their divergence from early hominins such as Neanderthal, and non-human primates. We confirm that genes encoding brain-related proteins are among the most conserved of the human proteome. Conversely, several of the most divergent proteins in humans compared to other primates are associated with brain-associated diseases such as micro/macrocephaly, dyslexia, and autism. We identified specific eXpression profiles of a set of divergent genes in ciliated cells of the cerebellum, that might have contributed to the emergence of fine motor skills and social cognition in humans. -
Inference and Analysis of Population Structure Using Genetic Data and Network Theory
| INVESTIGATION Inference and Analysis of Population Structure Using Genetic Data and Network Theory Gili Greenbaum,*,†,1 Alan R. Templeton,‡,§ and Shirli Bar-David† *Department of Solar Energy and Environmental Physics and †Mitrani Department of Desert Ecology, Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, 84990 Midreshet Ben-Gurion, Israel, ‡Department of Biology, Washington University, St. Louis, Missouri 63130, and §Department of Evolutionary and Environmental Ecology, University of Haifa, 31905 Haifa, Israel ABSTRACT Clustering individuals to subpopulations based on genetic data has become commonplace in many genetic studies. Inference about population structure is most often done by applying model-based approaches, aided by visualization using distance- based approaches such as multidimensional scaling. While existing distance-based approaches suffer from a lack of statistical rigor, model-based approaches entail assumptions of prior conditions such as that the subpopulations are at Hardy-Weinberg equilibria. Here we present a distance-based approach for inference about population structure using genetic data by defining population structure using network theory terminology and methods. A network is constructed from a pairwise genetic-similarity matrix of all sampled individuals. The community partition, a partition of a network to dense subgraphs, is equated with population structure, a partition of the population to genetically related groups. Community-detection algorithms are used to partition the network into communities, interpreted as a partition of the population to subpopulations. The statistical significance of the structure can be estimated by using permutation tests to evaluate the significance of the partition’s modularity, a network theory measure indicating the quality of community partitions. -
Analysis of the Indacaterol-Regulated Transcriptome in Human Airway
Supplemental material to this article can be found at: http://jpet.aspetjournals.org/content/suppl/2018/04/13/jpet.118.249292.DC1 1521-0103/366/1/220–236$35.00 https://doi.org/10.1124/jpet.118.249292 THE JOURNAL OF PHARMACOLOGY AND EXPERIMENTAL THERAPEUTICS J Pharmacol Exp Ther 366:220–236, July 2018 Copyright ª 2018 by The American Society for Pharmacology and Experimental Therapeutics Analysis of the Indacaterol-Regulated Transcriptome in Human Airway Epithelial Cells Implicates Gene Expression Changes in the s Adverse and Therapeutic Effects of b2-Adrenoceptor Agonists Dong Yan, Omar Hamed, Taruna Joshi,1 Mahmoud M. Mostafa, Kyla C. Jamieson, Radhika Joshi, Robert Newton, and Mark A. Giembycz Departments of Physiology and Pharmacology (D.Y., O.H., T.J., K.C.J., R.J., M.A.G.) and Cell Biology and Anatomy (M.M.M., R.N.), Snyder Institute for Chronic Diseases, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada Received March 22, 2018; accepted April 11, 2018 Downloaded from ABSTRACT The contribution of gene expression changes to the adverse and activity, and positive regulation of neutrophil chemotaxis. The therapeutic effects of b2-adrenoceptor agonists in asthma was general enriched GO term extracellular space was also associ- investigated using human airway epithelial cells as a therapeu- ated with indacaterol-induced genes, and many of those, in- tically relevant target. Operational model-fitting established that cluding CRISPLD2, DMBT1, GAS1, and SOCS3, have putative jpet.aspetjournals.org the long-acting b2-adrenoceptor agonists (LABA) indacaterol, anti-inflammatory, antibacterial, and/or antiviral activity. Numer- salmeterol, formoterol, and picumeterol were full agonists on ous indacaterol-regulated genes were also induced or repressed BEAS-2B cells transfected with a cAMP-response element in BEAS-2B cells and human primary bronchial epithelial cells by reporter but differed in efficacy (indacaterol $ formoterol . -
Supplementary Note
1 SUPPLEMENTARY NOTE 2 Table of Contents 3 1 Studies contributing to systolic (SBP) and diastolic blood pressure (DBP) analyses ............ 4 4 2 Consortia and studies providing association results for cardiovascular outcomes ............. 4 5 2.1 CHARGE - Heart Failure Working Group .................................................................................. 4 6 2.2 EchoGen (LM mass and LV weight) .......................................................................................... 4 7 2.3 NEURO-CHARGE (stroke) ........................................................................................................ 4 8 2.4 MetaStroke (stroke) ................................................................................................................ 5 9 2.5 CARDIoGRAMplusC4D (CAD) ................................................................................................... 5 10 2.6 CHARGE CKDgen (CKD, eGFR, microalbuminuria, UACR) ......................................................... 5 11 2.7 KidneyGen (creatinine) ........................................................................................................... 6 12 2.8 CHARGE (cIMT) ....................................................................................................................... 6 13 2.9 CHARGE (mild retinopathy, central retinal artery caliber) ....................................................... 6 14 2.10 SEED (mild retinopathy, central retinal artery caliber) .......................................................... 6 15 -
Sequencing of 50 Human Exomes Reveals Adaptation to High Altitude
REPORTS Digestive and Kidney Diseases) and The University of Omnibus, with accession code GSE21661. These data, as Figs. S1 to S6 Luxembourg–Institute for Systems Biology Program to well as phenotype data, are also available on our Tables S1 to S12 C.D.H. T.S.S. was supported by NIH Genetics Training laboratory Web site, http://jorde-lab.genetics.utah. References Grant T32. All studies have been performed with edu. Please contact R.L.G. for access to DNA samples. informed consent approved by the Institutional Board of 10 March 2010; accepted 6 May 2010 Qinghai Medical College of Qinghai University in Supporting Online Material Published online 13 May 2010; Xining, Qinghai Province, People’s Republic of China. All www.sciencemag.org/cgi/content/full/science.1189406/DC1 10.1126/science.1189406 SNP genoptypes are deposited in Gene Expression Materials and Methods Include this information when citing this paper. also estimated single-nucleotide polymorphism Sequencing of 50 Human Exomes (SNP) probabilities and population allele frequen- cies for each site. A total of 151,825 SNPs were Reveals Adaptation to High Altitude inferred to have >50% probability of being var- iable within the Tibetan sample, and 101,668 had Xin Yi,1,2* Yu Liang,1,2* Emilia Huerta-Sanchez,3* Xin Jin,1,4* Zha Xi Ping Cuo,2,5* John E. Pool,3,6* >99% SNP probability (table S2). Sanger se- Xun Xu,1 Hui Jiang,1 Nicolas Vinckenbosch,3 Thorfinn Sand Korneliussen,7 Hancheng Zheng,1,4 quencing validated 53 of 56 SNPs that had at least Tao Liu,1 Weiming He,1,8 Kui Li,2,5 Ruibang Luo,1,4 Xifang Nie,1 Honglong Wu,1,9 Meiru Zhao,1 95% SNP probability and minor allele frequencies Hongzhi Cao,1,9 Jing Zou,1 Ying Shan,1,4 Shuzheng Li,1 Qi Yang,1 Asan,1,2 Peixiang Ni,1 Geng Tian,1,2 between 3% and 50%. -
Cancer Genomics Terminology
Cancer Genomics Terminology Acquired Susceptibility Mutation: A mutation in a gene that occurs after birth from a carcinogenic insult. Allele: One of the variant forms of a gene. Different alleles may produce variation in inherited characteristics. Allele Heterogeneity: A phenotype that can be produced by different genetic mechanisms. Amino Acid: The building blocks of protein, for which DNA carries the genetic code. Analytical Sensitivity: The proportion of positive test results correctly reported by the laboratory among samples with a mutation(s) that the laboratory’s test is designed to detect. Analytic Specificity: The proportion of negative test results correctly reported among samples when no detectable mutation is present. Analytic Validity: A test’s ability to accurately and reliably measure the genotype of interest. Apoptosis: Programmed cell death. Ashkenazi: Individuals of Eastern European Jewish ancestry/decent (For example-Germany and Poland). Non-assortative mating occurred in this population. Association: When significant differences in allele frequencies are found between a disease and control population, the disease and allele are said to be in association. Assortative Mating: In population genetics, selective mating in a population between individuals that are genetically related or have similar characteristics. Autosome: Any chromosome other than a sex chromosome. Humans have 22 pairs numbered 1-22. Base Excision Repair Gene: The gene responsible for the removal of a damaged base and replacing it with the correct nucleotide. Base Pair: Two bases, which form a “rung on the DNA ladder”. Bases are the “letters” (Adenine, Thymine, Cytosine, Guanine) that spell out the genetic code. Normally adenine pairs with thymine and cytosine pairs with guanine. -
Linkage & Genetic Mapping in Eukaryotes
LinLinkkaaggee && GGeenneetticic MMaappppiningg inin EEuukkaarryyootteess CChh.. 66 1 LLIINNKKAAGGEE AANNDD CCRROOSSSSIINNGG OOVVEERR ! IInn eeuukkaarryyoottiicc ssppeecciieess,, eeaacchh lliinneeaarr cchhrroommoossoommee ccoonnttaaiinnss aa lloonngg ppiieeccee ooff DDNNAA – A typical chromosome contains many hundred or even a few thousand different genes ! TThhee tteerrmm lliinnkkaaggee hhaass ttwwoo rreellaatteedd mmeeaanniinnggss – 1. Two or more genes can be located on the same chromosome – 2. Genes that are close together tend to be transmitted as a unit Copyright ©The McGraw-Hill Companies, Inc. Permission required for reproduction or display 2 LinkageLinkage GroupsGroups ! Chromosomes are called linkage groups – They contain a group of genes that are linked together ! The number of linkage groups is the number of types of chromosomes of the species – For example, in humans " 22 autosomal linkage groups " An X chromosome linkage group " A Y chromosome linkage group ! Genes that are far apart on the same chromosome can independently assort from each other – This is due to crossing-over or recombination Copyright ©The McGraw-Hill Companies, Inc. Permission required for reproduction or display 3 LLiinnkkaaggee aanndd RRecombinationecombination Genes nearby on the same chromosome tend to stay together during the formation of gametes; this is linkage. The breakage of the chromosome, the separation of the genes, and the exchange of genes between chromatids is known as recombination. (we call it crossing over) 4 IndependentIndependent assortment:assortment: -
Genetic Distance
NBER WORKING PAPER SERIES THE DIFFUSION OF DEVELOPMENT Enrico Spolaore Romain Wacziarg Working Paper 12153 http://www.nber.org/papers/w12153 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 March 2006 Spolaore: Department of Economics, Tufts University, 315 Braker Hall, 8 Upper Campus Rd., Medford, MA 02155-6722, [email protected]. Wacziarg: Graduate School of Business, Stanford University, Stanford CA 94305-5015, USA, [email protected]. We thank Alberto Alesina, Robert Barro, Francesco Caselli, Steven Durlauf, Jim Fearon, Oded Galor, Yannis Ioannides, Pete Klenow, Andros Kourtellos, Ed Kutsoati, Peter Lorentzen, Paolo Mauro, Sharun Mukand, Gérard Roland, Antonio Spilimbergo, Chih Ming Tan, David Weil, Bruce Weinberg, Ivo Welch, and seminar participants at Boston College, Fundação Getulio Vargas, Harvard University, the IMF, INSEAD, London Business School, Northwestern University, Stanford University, Tufts University, UC Berkeley, UC San Diego, the University of British Columbia, the University of Connecticut and the University of Wisconsin-Madison for comments. We also thank Robert Barro and Jim Fearon for providing some data. The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research. ©2006 by Enrico Spolaore and Romain Wacziarg. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source. The Diffusion of Development Enrico Spolaore and Romain Wacziarg NBER Working Paper No. 12153 March 2006 JEL No. O11, O57 ABSTRACT This paper studies the barriers to the diffusion of development across countries over the very long-run. -
Differentially Methylated Genes
10/30/2013 Disclosures Key Rheumatoid Arthritis-Associated Pathogenic Pathways Revealed by Integrative Analysis of RA Omics Datasets Consultant: IGNYTA Funding: Rheumatology Research Foundation By John W. Whitaker, Wei Wang and Gary S. Firestein DNA methylation and gene regulation The RA methylation signature in FLS DNA methylation – DNMT1 (maintaining methylation) OA – DNMT3a, 3b (de novo methylation) RA % of CpG methylation: 0% 100% Nakano et al. 2013 ARD AA06 AANAT AARS ABCA6 ABCC12 ABCG1 ABHD8 ABL2 ABR ABRA ACACA ACAN ACAP3 ACCSL ACN9 ACOT7 ACOX2 ACP5 ACP6 ACPP ACSL1 ACSL3 ACSM5 ACVRL1 ADAM10 ADAM32 ADAM33 ADAMTS12 ADAMTS15 ADAMTS19 ADAMTS4 ADAT3 ADCK4 ADCK5 ADCY2 ADCY3 ADCY6 ADORA1 ADPGK ADPRHL1 ADTRP AFAP1 AFAP1L2 AFF3 AFG3L1P AGAP11 AGER AGTR1 AGXT AIF1L AIM2 AIRE AJUBA AK4 AKAP12 AKAP2 AKR1C2 AKR1E2 AKT2 ALAS1 ALDH1L1-AS1 ALDH3A1 ALDH3B1 ALDH8A1 ALDOB ALDOC ALOX12 ALPK3 ALS2CL ALX4 AMBRA1 AMPD2 AMPD3 ANGPT1 ANGPT2 ANGPTL5 ANGPTL6 ANK1 ANKMY2 ANKRD29 ANKRD37 ANKRD53 ANO3 ANO6 ANO7 ANP32C ANXA6 ANXA8L2 AP1G1 AP2A2 AP2M1 AP5B1 APBA2 APC APCDD1 APOBEC3B APOBEC3G APOC1 APOH APOL6 APOLD1 APOM AQP1 AQP10 AQP6 AQP9 ARAP1 ARHGAP24 ARHGAP42 ARHGEF19 ARHGEF25 ARHGEF3 ARHGEF37 ARHGEF7 ARL4C ARL6IP 5 ARL8B ARMC3 ARNTL2 ARPP21 ARRB1 ARSI ASAH2B ASB10 ASB2 ASCL2 ASIC4 ASPH ATF3 ATF7 ATL1 ATL3 ATP10A ATP1A1 ATP1A4 ATP2C1 ATP5A1 ATP5EP2 ATP5L2 ATP6V0CP3 ATP6V1C1 ATP6V1E2 ATXN7L1 ATXN7L2 AVPI1 AXIN2 B3GNT7 B3GNT8 B3GNTL1 BACH1 BAG3 Differential methylated genes in RA FLS BAIAP2L2 BANP BATF BATF2 BBS2 BCAS4 BCAT1 BCL7C BDKRB2 BEGAIN BEST1 BEST3