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      Genome-wide association study of delay discounting in Heterogenous Stock rats

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          Abstract

          Delay discounting refers to the behavioral tendency to devalue rewards as a function of their delay in receipt. Heightened delay discounting has been associated with substance use disorders, as well as multiple co-occurring psychopathologies. Genetic studies in humans and animal models have established that delay discounting is a heritable trait, but only a few specific genes have been associated with delay discounting. Here, we aimed to identify novel genetic loci associated with delay discounting through a genome-wide association study (GWAS) using Heterogenous Stock rats, a genetically diverse outbred population derived from eight inbred founder strains. We assessed delay discounting in 650 male and female rats using an adjusting amount procedure in which rats chose between smaller immediate sucrose rewards or a larger reward at variable delays. Preference switch points were calculated for each rat and both exponential and hyperbolic functions were fitted to these indifference points. Area under the curve (AUC) and the discounting parameter k of both functions were used as delay discounting measures. GWAS for AUC, exponential k, and indifference points for a short delay identified significant loci on chromosomes 20 and 14. The gene Slc35f1, which encodes a member of the solute carrier family of nucleoside sugar transporters, was the only gene within the chromosome 20 locus. That locus also contained an eQTL for Slc35f1, suggesting that heritable differences in the expression of that gene might be responsible for the association with behavior. The gene Adgrl3, which encodes a member of the latrophilin family of G-protein coupled receptors, was the only gene within the chromosome 14 locus. These findings implicate novel genes in delay discounting and highlight the need for further exploration.

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          Fast and accurate short read alignment with Burrows–Wheeler transform

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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            Cutadapt removes adapter sequences from high-throughput sequencing reads

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              Proteomics. Tissue-based map of the human proteome.

              Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. Copyright © 2015, American Association for the Advancement of Science.
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                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                13 December 2023
                : 2023.12.12.570851
                Affiliations
                [1 ]Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
                [2 ]Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
                [3 ]Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, 92093, USA
                [4 ]Departments of Behavioral Neuroscience, Psychiatry, the Oregon Institute of Occupational Health Sciences, Oregon Health & Science University, Portland, OR, 97239 USA
                Author notes

                Author Contributions

                SHM and AAP designed the study. SHM and her lab members performed the behavioral phenotyping, and SHM and MKL completed the analyses of the behavior and quantified the phenotypes of the individual subjects. SHM, MKL, and AAP wrote the manuscript. AAP and OP oversaw the genotyping. DC, KN, KC performed the genotyping. ASC, AAP, OP, TS, and BBJ developed and oversaw the genetic analysis. SZ, SAM, and BL prepared the phenotypic data for genetic analysis. LSW and AB produced the HS rats used for this study.

                [* ]Corresponding author mitchesu@ 123456ohsu.edu
                Author information
                http://orcid.org/0000-0001-5810-4425
                http://orcid.org/0000-0003-1709-9214
                http://orcid.org/0000-0002-6541-3418
                http://orcid.org/0000-0001-7269-5145
                http://orcid.org/0000-0003-3939-7773
                http://orcid.org/0009-0002-0717-0507
                http://orcid.org/0009-0005-6006-2996
                http://orcid.org/0000-0002-4388-5983
                http://orcid.org/0000-0002-7943-798X
                http://orcid.org/0000-0003-3024-114X
                http://orcid.org/0000-0003-3634-0747
                http://orcid.org/0000-0002-0225-7200
                Article
                10.1101/2023.12.12.570851
                10760013
                38168347
                5054d2ed-39e7-4a14-bfa9-bc3a85958f7d

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.

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                Funding
                Funding was provided by DHHS U01DA046077, P60AA010760, P50DA037844, and U01DA051234
                Categories
                Article

                delay discounting,adjusting amount,gwas,heterogenous stock rats

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