Dual-trait selection for ethanol consumption and withdrawal: genetic and transcriptional network effects.

Abstract

Data from C57BL/6J (B6)×DBA/2J (D2) F2 intercrosses (B6xD2F2 ), standard and recombinant inbred strains, and heterogeneous stock mice indicate that a reciprocal (or inverse) genetic relationship exists between alcohol consumption and withdrawal severity. Furthermore, some genetic studies have detected reciprocal quantitative trait loci (QTLs) for these traits. We used a novel mouse model developed by simultaneous selection for both high alcohol consumption/low withdrawal and low alcohol consumption/high withdrawal and analyzed the gene expression and genome-wide genotypic differences. Randomly chosen third selected generation (S3 ) mice (N=24/sex/line), bred from a B6xD2F2 , were genotyped using the Mouse Universal Genotyping Array, which provided 2,760 informative markers. QTL analysis used a marker-by-marker strategy with the threshold for a significant log of the odds (LOD) set at 10. Gene expression in the ventral striatum was measured using the Illumina Mouse 8.2 array. Differential gene expression and the weighted gene co-expression network analysis (WGCNA) were implemented. Significant QTLs for consumption/withdrawal were detected on chromosomes (Chr) 2, 4, 9, and 12. A suggestive QTL mapped to Chr 6. Some of the QTLs overlapped with known QTLs mapped for 1 of the traits individually. One thousand seven hundred and forty-five transcripts were detected as being differentially expressed between the lines; there was some overlap with known withdrawal genes (e.g., Mpdz) located within QTL regions. WGCNA revealed several modules of co-expressed genes showing significant effects in both differential expression and intramodular connectivity; a module richly annotated with kinase-related annotations was most affected. Marked effects of selection on expression and network structure were detected. QTLs overlapping with differentially expressed genes on Chr 2 (distal) and 4 suggest that these are cis-eQTLs (Chr 2: Kif3b, Kcnq2; Chr 4: Mpdz, Snapc3). Other QTLs identified were on Chr 2 (proximal), 9, and 12. Network results point to involvement of kinase-related mechanisms and outline the need for further efforts such as interrogation of noncoding RNAs.