BACKGROUND: A strong predictor for the development of alcohol use disorder (AUD) is altered sensitivity to the intoxicating effects of alcohol. Individual differences in the initial sensitivity to alcohol are controlled in part by genetic factors. Mice offer a powerful tool to elucidate the genetic basis of behavioral and physiological traits relevant to AUD, but conventional experimental crosses have only been able to identify large chromosomal regions rather than specific genes. Genetically diverse, highly recombinant mouse populations make it possible to observe a wider range of phenotypic variation, offer greater mapping precision, and thus increase the potential for efficient gene identification. METHODS: We have taken advantage of the Diversity Outbred (DO) mouse population to identify and precisely map quantitative trait loci (QTL) associated with ethanol sensitivity. We phenotyped 798 male J:DO mice for three measures of ethanol sensitivity: ataxia, hypothermia, and loss of the righting response. We used high-density MegaMUGA and GigaMUGA to obtain genotypes ranging from 77,808 to 143,259 SNPs. We also performed RNA sequencing in striatum to map expression QTLs and identify gene expression-trait correlations. We then applied a systems genetic strategy to identify narrow QTLs and construct the network of correlations that exists between DNA sequence, gene expression values, and ethanol-related phenotypes to prioritize our list of positional candidate genes. RESULTS: We observed large amounts of phenotypic variation with the DO population and identified suggestive and significant QTLs associated with ethanol sensitivity on chromosomes 1, 2, and 16. The implicated regions were narrow (4.5-6.9 Mb in size) and each QTL explained ~4-5% of the variance. CONCLUSIONS: Our results can be used to identify alleles that contribute to AUD in humans, elucidate causative biological mechanisms, or assist in the development of novel therapeutic interventions.
BACKGROUND: A strong predictor for the development of alcohol use disorder (AUD) is altered sensitivity to the intoxicating effects of alcohol. Individual differences in the initial sensitivity to alcohol are controlled in part by genetic factors. Mice offer a powerful tool to elucidate the genetic basis of behavioral and physiological traits relevant to AUD, but conventional experimental crosses have only been able to identify large chromosomal regions rather than specific genes. Genetically diverse, highly recombinant mouse populations make it possible to observe a wider range of phenotypic variation, offer greater mapping precision, and thus increase the potential for efficient gene identification. METHODS: We have taken advantage of the Diversity Outbred (DO) mouse population to identify and precisely map quantitative trait loci (QTL) associated with ethanol sensitivity. We phenotyped 798 male J:DO mice for three measures of ethanol sensitivity: ataxia, hypothermia, and loss of the righting response. We used high-density MegaMUGA and GigaMUGA to obtain genotypes ranging from 77,808 to 143,259 SNPs. We also performed RNA sequencing in striatum to map expression QTLs and identify gene expression-trait correlations. We then applied a systems genetic strategy to identify narrow QTLs and construct the network of correlations that exists between DNA sequence, gene expression values, and ethanol-related phenotypes to prioritize our list of positional candidate genes. RESULTS: We observed large amounts of phenotypic variation with the DO population and identified suggestive and significant QTLs associated with ethanol sensitivity on chromosomes 1, 2, and 16. The implicated regions were narrow (4.5-6.9 Mb in size) and each QTL explained ~4-5% of the variance. CONCLUSIONS: Our results can be used to identify alleles that contribute to AUD in humans, elucidate causative biological mechanisms, or assist in the development of novel therapeutic interventions.
Authors: Daniel M Gatti; Karen L Svenson; Andrey Shabalin; Long-Yang Wu; William Valdar; Petr Simecek; Neal Goodwin; Riyan Cheng; Daniel Pomp; Abraham Palmer; Elissa J Chesler; Karl W Broman; Gary A Churchill Journal: G3 (Bethesda) Date: 2014-09-18 Impact factor: 3.154
Authors: Alexandre M Colville; Ovidiu D Iancu; Denesa R Lockwood; Priscila Darakjian; Shannon K McWeeney; Robert Searles; Christina Zheng; Robert Hitzemann Journal: Front Genet Date: 2018-08-28 Impact factor: 4.599
Authors: Wayne Xu; Vichithra R B Liyanage; Aaron MacAulay; Romina D Levy; Kyle Curtis; Carl O Olson; Robby M Zachariah; Shayan Amiri; Marjorie Buist; Geoffrey G Hicks; James R Davie; Mojgan Rastegar Journal: Sci Rep Date: 2019-01-18 Impact factor: 4.379
Authors: Henry R Kranzler; Hang Zhou; Rachel L Kember; Rachel Vickers Smith; Amy C Justice; Scott Damrauer; Philip S Tsao; Derek Klarin; Aris Baras; Jeffrey Reid; John Overton; Daniel J Rader; Zhongshan Cheng; Janet P Tate; William C Becker; John Concato; Ke Xu; Renato Polimanti; Hongyu Zhao; Joel Gelernter Journal: Nat Commun Date: 2019-04-02 Impact factor: 14.919