Quantitative trait loci for sensitivity to ethanol intoxication in a C57BL/6J × 129S1/SvImJ inbred mouse cross

Individual variation in sensitivity to acute ethanol (EtOH) challenge is associated with alcohol drinking and is a predictor of alcohol abuse. Previous studies have shown that the C57BL/6J (B6) and 129S1/SvImJ (S1) inbred mouse strains differ in responses on certain measures of acute EtOH intoxication. To gain insight into genetic factors contributing to these differences, we performed quantitative trait locus (QTL) analysis of measures of EtOH-induced ataxia (accelerating rotarod), hypothermia, and loss of righting reflex (LORR) duration in a B6 × S1 F2 population. We confirmed that S1 showed greater EtOH-induced hypothermia (specifically at a high dose) and longer LORR compared to B6. QTL analysis revealed several additive and interacting loci for various phenotypes, as well as examples of genotype interactions with sex. QTLs for different EtOH phenotypes were largely non-overlapping, suggesting separable genetic influences on these behaviors. The most compelling main-effect QTLs were for hypothermia on chromosome 16 and for LORR on chromosomes 4 and 6. Several QTLs overlapped with loci repeatedly linked to EtOH drinking in previous mouse studies. The architecture of the traits we examined was complex but clearly amenable to dissection in future studies. Using integrative genomics strategies, plausible functional and positional candidates may be found. Uncovering candidate genes associated with variation in these phenotypes in this population could ultimately shed light on genetic factors underlying sensitivity to EtOH intoxication and risk for alcoholism in humans

Treatment- and Population-Dependent Activity Patterns of Behavioral and Expression QTLs

Genetic control of gene expression and higher-order phenotypes is almost invariably dependent on environment and experimental conditions. We use two families of recombinant inbred strains of mice (LXS and BXD) to study treatment- and genotype-dependent control of hippocampal gene expression and behavioral phenotypes. We analyzed responses to all combinations of two experimental perturbations, ethanol and restraint stress, in both families, allowing for comparisons across 8 combinations of treatment and population. We introduce the concept of QTL activity patterns to characterize how associations between genomic loci and traits vary across treatments. We identified several significant behavioral QTLs and many expression QTLs (eQTLs). The behavioral QTLs are highly dependent on treatment and population. We classified eQTLs into three groups: cis-eQTLs (expression variation that maps to within 5 Mb of the cognate gene), syntenic trans-eQTLs (the gene and the QTL are on the same chromosome but not within 5 Mb), and non-syntenic trans-eQTLs (the gene and the QTL are on different chromosomes). We found that most non-syntenic trans-eQTLs were treatment-specific whereas both classes of syntenic eQTLs were more conserved across treatments. We also found there was a correlation between regions along the genome enriched for eQTLs and SNPs that were conserved across the LXS and BXD families. Genes with eQTLs that co-localized with the behavioral QTLs and displayed similar QTL activity patterns were identified as potential candidate genes associated with the phenotypes, yielding identification of novel genes as well as genes that have been previously associated with responses to ethanol

Integrative Analysis of Low- and High-Resolution eQTL

The study of expression quantitative trait loci (eQTL) is a powerful way of detecting transcriptional regulators at a genomic scale and for elucidating how natural genetic variation impacts gene expression. Power and genetic resolution are heavily affected by the study population: whereas recombinant inbred (RI) strains yield greater statistical power with low genetic resolution, using diverse inbred or outbred strains improves genetic resolution at the cost of lower power. In order to overcome the limitations of both individual approaches, we combine data from RI strains with genetically more diverse strains and analyze hippocampus eQTL data obtained from mouse RI strains (BXD) and from a panel of diverse inbred strains (Mouse Diversity Panel, MDP). We perform a systematic analysis of the consistency of eQTL independently obtained from these two populations and demonstrate that a significant fraction of eQTL can be replicated. Based on existing knowledge from pathway databases we assess different approaches for using the high-resolution MDP data for fine mapping BXD eQTL. Finally, we apply this framework to an eQTL hotspot on chromosome 1 (Qrr1), which has been implicated in a range of neurological traits. Here we present the first systematic examination of the consistency between eQTL obtained independently from the BXD and MDP populations. Our analysis of fine-mapping approaches is based on ‘real life’ data as opposed to simulated data and it allows us to propose a strategy for using MDP data to fine map BXD eQTL. Application of this framework to Qrr1 reveals that this eQTL hotspot is not caused by just one (or few) ‘master regulators’, but actually by a set of polymorphic genes specific to the central nervous system

Multiple cross mapping (MCM) markedly improves the localization of a QTL for ethanol-induced activation.

This study examines the use of multiple cross mapping (MCM) to reduce the interval for an ethanol response QTL on mouse chromosome 1. The phenotype is the acute locomotor response to a 1.5-g/kg i.p. dose of ethanol. The MCM panel consisted of the six unique intercrosses that can be obtained from the C57BL/6J (B6), DBA/2J (D2), BALB/cJ (C) and LP/J (LP) inbred mouse strains (N > or = 600/cross). Ethanol response QTL were detected only with the B6xD2 and B6xC intercrosses. For both crosses, the D2 and C alleles were dominant and decreased ethanol response. The QTL information was used to develop an algorithm for sorting and editing the chromosome 1 Mit microsatellite marker set (http://www.jax.org). This process yielded a cluster of markers between 82 and 85cM (MGI). Evidence that the QTL was localized in or near this interval was obtained by the analysis of a sample (n = 550) of advanced cross heterogenous stock animals. In addition, it was observed that one of the BXD recombinant inbred strains (BXD-32) had a recombination in the interval of interest which produced the expected change in behavior. Overall, the data obtained suggest that the information available within existing genetic maps coupled with MCM data can be used to reduce the QTL interval. In addition, the MCM data set can be used to interrogate gene expression data to estimate which polymorphisms within the interval of interest are relevant to the QTL

A strategy for the integration of QTL, gene expression, and sequence analyses.

Although hundreds if not thousands of quantitative trait loci (QTL) have been described for a wide variety of complex traits, only a very small number of these QTLs have been reduced to quantitative trait genes (QTGs) and quantitative trait nucleotides (QTNs). A strategy, Multiple Cross Mapping (MCM), is described for detecting QTGs and QTNs that is based on leveraging the information contained within the haplotype structure of the mouse genome. As described in the current report, the strategy utilizes the six F(2) intercrosses that can be formed from the C57BL/6J (B6), DBA/2J (D2), BALB/cJ (C), and LP/J (LP) inbred mouse strains. Focusing on the phenotype of basal locomotor activity, it was found that in all three B6 intercrosses, a QTL was detected on distal Chromosome (Chr) 1; no QTL was detected in the other three intercrosses, and thus, it was assumed that at the QTL, the C, D2, and LP strains had functionally identical alleles. These intercross data were used to form a simple algorithm for interrogating microsatellite, single nucleotide polymorphism (SNP), brain gene expression, and sequence databases. The results obtained point to Kcnj9 (which has a markedly lower expression in the B6 strain) as being the likely QTG. Further, it is suggested that the lower expression in the B6 strain results from a polymorphism in the 5'-UTR that disrupts the binding of at least three transcription factors. Overall, the method described should be widely applicable to the analysis of QTLs

Resources for Systems Genetics.

A key characteristic of systems genetics is its reliance on populations that vary to a greater or lesser degree in genetic complexity-from highly admixed populations such as the Collaborative Cross and Diversity Outcross to relatively simple crosses such as sets of consomic strains and reduced complexity crosses. This protocol is intended to help investigators make more informed decisions about choices of resources given different types of questions. We consider factors such as costs, availability, and ease of breeding for common scenarios. In general, we recommend using complementary resources and minimizing depth of resampling of any given genome or strain