Effects of selection for ethanol preference on gene expression in the nucleus accumbens of HS-CC mice

Previous studies on changes in murine brain gene expression associated with the selection for ethanol preference have used F₂ intercross or heterogeneous stock (HS) founders, derived from standard laboratory strains. However, these populations represent only a small proportion of the genetic variance available in Mus musculus. To investigate a wider range of genetic diversity, we selected mice for ethanol preference using an HS derived from the eight strains of the collaborative cross. These HS mice were selectively bred (four generations) for high and low ethanol preference. The nucleus accumbens shell of naive S₄ mice was interrogated using RNA sequencing (RNA-Seq). Gene networks were constructed using the weighted gene coexpression network analysis assessing both coexpression and cosplicing. Selection targeted one of the network coexpression modules (greenyellow) that was significantly enriched in genes associated with receptor signaling activity including Chrna7, Grin2a, Htr2a and Oprd1. Connectivity in the module as measured by changes in the hub nodes was significantly reduced in the low preference line. Of particular interest was the observation that selection had marked effects on a large number of cell adhesion molecules, including cadherins and protocadherins. In addition, the coexpression data showed that selection had marked effects on long non-coding RNA hub nodes. Analysis of the cosplicing network data showed a significant effect of selection on a large cluster of Ras GTPase-binding genes including Cdkl5, Cyfip1, Ndrg1, Sod1 and Stxbp5. These data in part support the earlier observation that preference is linked to Ras/Mapk pathways.

Genes, behavior and next-generation RNA sequencing

Advances in next-generation sequencing suggest that RNA-Seq is poised to supplant microarray-based approaches for transcriptome analysis. This article briefly reviews the use of microarrays in the brain-behavior context and then illustrates why RNA-Seq is a superior strategy. Compared with microarrays, RNA-Seq has a greater dynamic range, detects both coding and noncoding RNAs, is superior for gene network construction, detects alternative spliced transcripts, detects allele specific expression and can be used to extract genotype information, e.g. nonsynonymous coding single nucleotide polymorphisms. Examples of where RNA-Seq has been used to assess brain gene expression are provided. Despite the advantages of RNA-Seq, some disadvantages remain. These include the high cost of RNA-Seq and the computational complexities associated with data analysis. RNA-Seq embraces the complexity of the transcriptome and provides a mechanism to understand the underlying regulatory code; the potential to inform the brain-behavior relationship is substantial.

Gene networks and haloperidol-induced catalepsy

The current study examined the changes in striatal gene network structure induced by short-term selective breeding from a heterogeneous stock for haloperidol response. Brain (striatum) gene expression data were obtained using the Illumina WG 8.2 array, and the datasets from responding and non-responding selected lines were independently interrogated using a weighted gene coexpression network analysis (WGCNA). We detected several gene modules (groups of coexpressed genes) in each dataset; the membership of the modules was found to be largely concordant, and a consensus network was constructed. Further validation of the network topology showed that using approximately 35 samples is sufficient to reliably infer the transcriptome network. An in-depth analysis showed significant changes in network structure and gene connectivity associated with the selected lines; these changes were validated using a bootstrapping procedure. The most dramatic changes were associated with a gene module richly annotated with neurobehavioral traits. The changes in network connectivity were concentrated in the links between this module and the rest of the network, in addition to changes within the module; this observation is consistent with recent results in protein and metabolic networks. These results suggest that a network-based strategy will help identify the genetic factors associated with haloperidol response.

Hardy-Weinberg testing for HLA class II (DRB1, DQA1, DQB1, and DPB1) loci in 26 human ethnic groups

Testing the fit of population data to Hardy-Weinberg proportions is crucial in the validation of many current approaches in population genetic studies. In this paper, we tested fit to Hardy-Weinberg proportions using exact approaches for both the overall and individual heterozygote genotype data of four HLA Class II loci: DRB1, DQA1, DQB1, and DPB1, from 26 human populations. Eighty of 99 overall tests fit the Hardy-Weinberg expectation (73% for DRB1, 89% for DQA1, 81% for DQB1 and 81% for DPB1). Deviations from Hardy-Weinberg proportions were both locus and group specific. Although we could not rule out other mechanisms at work, the individual test results indicated that the departure was possibly partly due to recent admixture. Evidence for selection and other sources of deviation are also discussed.

HLA class II DR-DQ amino acids and insulin-dependent diabetes mellitus: application of the haplotype method

Insulin-dependent diabetes mellitus (IDDM) HLA class II DRB1-DQA1-DQB1 data from four populations (Norwegian, Sardinian, Mexican American, and Taiwanese) have been analyzed to detect the amino acids involved in the disease process. The combination of sites DRB1#67 and 86; DQA1#47; and DQB1#9, 26, 57, and 70 predicts the IDDM component in these four populations, when the results and criteria of the haplotype method for amino acids, developed in the companion paper in this issue of the Journal, are used. The following sites, either individually, or in various combinations, previously have been suggested as IDDM components: DRB1#57, 70, 71, and 86; DQA1#52; and DQB1#13, 45, and 57 (DQB1#13 and 45 correlates 100% with DQB1#9 and 26). We propose that DQA1#47 is a better predictor of IDDM than is the previously suggested DQA1#52, and we add DRB1#67 and DQB1#70 to the HLA DR-DQ IDDM amino acids. We do not claim to have identified all HLA DR-DQ amino acids-or highly correlated sites-involved in IDDM. The frequencies and predisposing/protective effects of the haplotypes defined by these seven sites have been compared, and the effects on IDDM are consistent across the populations. The strongest susceptible effects came from haplotypes DRB1 *0301/DQA1 *0501/ DQB1*0201 and DRB1*0401-5-7-8/DQA1*0301/ DQB1*0302. The number of strong protective haplotypes observed was larger than the number of susceptible ones; some of the predisposing haplotypes were present in only one or two populations. Although the sites under consideration do not necessarily have a functional involvement in IDDM, they should be highly associated with such sites and should prove to be useful in risk assessment.