Accession Number : ADA555235


Title :   Genome-Wide Association Mapping for Intelligence in Military Working Dogs: Canine Cohort, Canine Intelligence Assessment Regimen, Genome-Wide Single Nucleotide Polymorphism (SNP) Typing, and Unsupervised Classification Algorithm for Genome-Wide Association Data Analysis


Descriptive Note : Final rept. Apr 2009-Sep 2011


Corporate Author : AIR FORCE RESEARCH LAB WRIGHT-PATTERSON AFB OH HUMAN PERFORMANCE WING (711TH) BIOSCIENCES AND PERFORMANCE DIV/APPLIED BIOTECHNOLOGY BRANCH


Personal Author(s) : Chan, Victor T ; Mauzy, Camilla A ; Soto, Armando ; Wagner, Jessica A ; Walters, Amy D ; Frey, Jeanette S ; Hill, Tiffany M ; Overall, Karen L ; Juarbe-Diaz, Soraya ; Dyer, Donna


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a555235.pdf


Report Date : Sep 2011


Pagination or Media Count : 39


Abstract : This seedling project aimed to genetically map intelligence in the military working dog (MWD) population. A total of 199 canine subjects were recruited from United States working dog contractors. Of the recruited subjects, 153 were tested using the Canine Intelligence Testing Protocol (CITP), developed by Dr. Karen Overall (UPENN) to specifically analyze canine intelligence. CITP allows quantitative assessment of intelligence in individual dogs using a scoring system based on the latency to response, success-in-effort time, attentiveness, interest in novelty exploration, response to signaling and showing, observational learning, problem solving/boldness, and handedness. Blood samples were collected from the canines, and genomic DNA prepared. A total of 117 dogs, belonging to three breeds (German Shepherds, Belgian Malinois, Labrador Retrievers) were down-selected and successfully genotyped for whole genome (WG) single nucleotide polymorphism (SNP) markers by means of the Affymetrix Canine SNP Array v2. A proof-of-concept advanced data mining algorithm for unsupervised analysis of the genome-wide association study (GWAS) dataset was successfully developed. Using this algorithm, canine subjects were successfully clustered into the correct breeds with an accuracy ranging from 89% - 100%, solely based on the WG SNP profiles. The details of the algorithm are described in the Technical Report AFRL-RH-WP-TR-2011-0081, entitled Development of Advanced Classification Algorithm for Genome-Wide Single Nucleotide Polymorphism (SNP) Data Analysis. While not initially part of the seedling proposal, this project did receive IACUC permission to test DoD MWDs for follow-on studies, a unique and significant accomplishment.


Descriptors :   *BEHAVIORAL GENETICS , *CLASSIFICATION , *GENETIC MAPPING , *GENETIC MARKERS , *GENOME , *MILITARY DOGS , *POLYMORPHISM , ALGORITHMS , ATTENTION , CLUSTERING , DATA MINING , DEOXYRIBONUCLEIC ACIDS , INTELLIGENCE TESTS , LEARNING , NUCLEOTIDES , PROBLEM SOLVING , REACTION TIME , RESPONSE(BIOLOGY)


Subject Categories : Psychology
      Genetic Engineering and Molecular Biology
      Biology


Distribution Statement : APPROVED FOR PUBLIC RELEASE