Kari J. Buck

The nature and identification of quantitative trait loci: a community’s view

This white paper by eighty members of the Complex Trait Consortium presents a communitys view on the approaches and statistical analyses that are needed for the identification of genetic loci that determine quantitative traits. Quantitative trait loci (QTLs) can be identified in several ways, but is there a definitive test of whether a candidate locus actually corresponds to a specific QTL?

Identifying genes for alcohol and drug sensitivity: recent progress and future directions

New methods for identifying chromosomal regions containing genes that affect murine responses to alcohol and drugs have been used to identify many provisional quantitative trait loci (QTLs) since 1991. By 1998, 24 QTLs had been definitively mapped (P<5x10(-5)) to specific murine chromosomes, which indicates the presence of a relevant gene or genes at each location. The syntenic (homologous) region of the human genome for these genes is often known. For many mapped QTLs, candidate genes with relevant neurobiological function lie within the mapped region. Data that implicate candidate genes for specific responses include studies of knockout animals. Current strategies for gene identification include the use of congenic strains containing QTL regions introduced from another strain. There is increasing emphasis on gene-gene and gene-environment interactions in such studies.

Evaluating gene expression in C57BL/6J and DBA/2J mouse striatum using RNA-Seq and microarrays.

C57BL/6J (B6) and DBA/2J (D2) are two of the most commonly used inbred mouse strains in neuroscience research. However, the only currently available mouse genome is based entirely on the B6 strain sequence. Subsequently, oligonucleotide microarray probes are based solely on this B6 reference sequence, making their application for gene expression profiling comparisons across mouse strains dubious due to their allelic sequence differences, including single nucleotide polymorphisms (SNPs). The emergence of next-generation sequencing (NGS) and the RNA-Seq application provides a clear alternative to oligonucleotide arrays for detecting differential gene expression without the problems inherent to hybridization-based technologies. Using RNA-Seq, an average of 22 million short sequencing reads were generated per sample for 21 samples (10 B6 and 11 D2), and these reads were aligned to the mouse reference genome, allowing 16,183 Ensembl genes to be queried in striatum for both strains. To determine differential expression, ‘digital mRNA counting’ is applied based on reads that map to exons. The current study compares RNA-Seq (Illumina GA IIx) with two microarray platforms (Illumina MouseRef-8 v2.0 and Affymetrix MOE 430 2.0) to detect differential striatal gene expression between the B6 and D2 inbred mouse strains. We show that by using stringent data processing requirements differential expression as determined by RNA-Seq is concordant with both the Affymetrix and Illumina platforms in more instances than it is concordant with only a single platform, and that instances of discordance with respect to direction of fold change were rare. Finally, we show that additional information is gained from RNA-Seq compared to hybridization-based techniques as RNA-Seq detects more genes than either microarray platform. The majority of genes differentially expressed in RNA-Seq were only detected as present in RNA-Seq, which is important for studies with smaller effect sizes where the sensitivity of hybridization-based techniques could bias interpretation.

Mpdz is a quantitative trait gene for drug withdrawal seizures

Physiological dependence and associated withdrawal episodes can constitute a powerful motivational force that perpetuates drug use and abuse. Using robust behavioral models of drug physiological dependence in mice, positional cloning, and sequence and expression analyses, we identified an addiction-relevant quantitative trait gene, Mpdz. Our findings provide a framework to define the protein interactions and neural circuit by which this genes product (multiple PDZ domain protein) affects drug dependence, withdrawal and relapse.

Genes on mouse Chromosomes 2 and 9 determine variation in ethanol consumption

Abstract. Quantitative trait locus (QTL) mapping efforts in alcohol (ethanol) research are beginning to generate promising data that may ultimately lead to the identification of genes influencing alcohol addiction. Rodents have been extensively utilized to study ethanols rewarding and aversive effects, and to demonstrate the existence of genetic influences on traits such as free-choice ethanol-consumption, ethanol-conditioned place preference and ethanol-conditioned taste aversion. The purpose of the current investigation was to verify or eliminate from further consideration putative QTLs for free-choice ethanol consumption originally identified in BXD Recombinant Inbred (RI) strains and other informative genetic crosses. B6D2F2 mice were utilized in a verification testing strategy to evaluate the viability of putative ethanol consumption QTLs. When data were combined from BXD RI, B6D2F2 and short-term selected line (STSL) mapping studies, verification was obtained for two QTLs, one on Chromosome (Chr) 9 (proximal-mid) and another on Chr 2 (distal), and suggestive verification was obtained for QTLs on Chrs 2 (proximal), 3, 4, 7, and 15. In addition, the possible genetic association of ethanol consumption with conditioned place preference was evaluated. Genetic correlations were estimated from BXD RI strain means, and QTL maps for these traits were compared to evaluate the possibility of a genetic association. The correlational analysis yielded a trend (r = 0.34, p = 0.09), but no statistically significant results. However, comparisons of QTL mapping results between phenotypes suggested some possible genetic overlap for these traits, both putative measures of ethanol reward. These data suggest that the determinants of these two measures are genetically diverse, but may share some common genetic elements.

QTL Analysis and Genomewide Mutagenesis in Mice: Complementary Genetic Approaches to the Dissection of Complex Traits

Quantitative genetics and quantitative trait locus (QTL) mapping have undergone a revolution in the last decade. Progress in the next decade promises to be at least as rapid, and strategies for fine-mapping QTLs and identifying underlying genes will be radically revised. In this Commentary we address several key issues: first, we revisit a perennial challenge—how to identify individual genes and allelic variants underlying QTLs. We compare current practice and procedures in QTL analysis with novel methods and resources that are just now being introduced. We argue that there is no one standard of proof for showing QTL = gene; rather, evidence from several sources must be carefully assembled until there is only one reasonable conclusion. Second, we compare QTL analysis with whole-genome mutagenesis in mice and point out some of the strengths and weakness of both of these phenotype-driven methods. Finally, we explore the advantages and disadvantages of naturally occurring vs mutagen-induced polymorphisms. We argue that these two complementary genetic methods have much to offer in efforts to highlight genes and pathways most likely to influence the susceptibility and progression of common diseases in human populations.

Harnessing the mouse to unravel the genetics of human disease

Complex traits, i.e. those with multiple genetic and environmental determinants, represent the greatest challenge for genetic analysis, largely due to the difficulty of isolating the effects of any one gene amid the noise of other genetic and environmental influences. Methods exist for detecting and mapping the Quantitative Trait Loci (QTLs) that influence complex traits. However, once mapped, gene identification commonly involves reduction of focus to single candidate genes or isolated chromosomal regions. To reach the next level in unraveling the genetics of human disease will require moving beyond the focus on one gene at a time, to explorations of pleiotropism, epistasis and environment‐dependency of genetic effects. Genetic interactions and unique environmental features must be as carefully scrutinized as are single gene effects. No one genetic approach is likely to possess all the necessary features for comprehensive analysis of a complex disease. Rather, the entire arsenal of behavioral genomic and other approaches will be needed, such as random mutagenesis, QTL analyses, transgenic and knockout models, viral mediated gene transfer, pharmacological analyses, gene expression assays, antisense approaches and importantly, revitalization of classical genetic methods. In our view, classical breeding designs are currently underutilized, and will shorten the distance to the target of understanding the complex genetic and environmental interactions associated with disease. We assert that unique combinations of classical approaches with current behavioral and molecular genomic approaches will more rapidly advance the field.

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 F2 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.

Expression profiling identifies strain‐specific changes associated with ethanol withdrawal in mice

Mice that exhibit characteristics of physical dependence following ethanol exposure serve as useful models of alcoholism in humans. The DBA/2 J and C57BL/6 J inbred strains differ in their behavioral response to ethanol withdrawal. Alterations in gene expression are believed to underlie neuroadaptation to ethanol dependence and tolerance. Therefore, the differences in ethanol withdrawal severity observed between the DBA/2 J and C57BL/6 J strains may be related to differential regulation of gene expression. We have used cDNA microarrays to determine the gene expression profile in the hippocampus of DBA/2 J and C57BL/6 J mice during withdrawal after chronic and acute ethanol exposure. Of the 7634 genes surveyed, approximately 2% were consistently differentially expressed by at least 1.4‐fold in DBA/2 J mice during chronic ethanol withdrawal. Less than 1% of the genes showed altered expression in C57BL/6 J mice under the same conditions, or in DBA/2 J mice during acute ethanol withdrawal. Strain‐ and treatment‐specific patterns of altered expression were observed for multiple genes associated with the Janus kinase/signal transducers and activators of transcription and the mitogen activated protein kinase pathways. Genes associated with both pathways are regulated in DBA/2 J mice during chronic ethanol withdrawal, and to a lesser extent during acute ethanol withdrawal. Only those genes associated with the mitogen‐activated protein kinase (MAPK) pathway exhibited changes in expression in C57BL/6 J mice during ethanol withdrawal. Furthermore, genes associated with retinoic acid‐mediated signaling show differential expression exclusively in C57BL/6 J mice. These findings represent significant differences in cellular adaptation to ethanol between the DBA/2 J and C57BL/6 J strains.

Quantitative trait loci affecting risk for pentobarbital withdrawal map near alcohol withdrawal loci on mouse Chromosomes 1, 4, and 11

Abstract. Barbiturate dependence is associated with the development of physiological dependence (withdrawal), tolerance, or a maladaptive pattern of drug use. Analysis of strain and individual differences with animal models for physiological dependence liability are useful means to identify potential genetic determinants of liability in humans. Behavioral and quantitative trait locus (QTL) mapping analyses were conducted with mice that are resistant versus sensitive to pentobarbital withdrawal. With a multi-stage genetic mapping strategy, a pentobarbital withdrawal QTL (Pbw1) was mapped to the distal region of mouse Chromosome (Chr) 1 and may be identical to an alcohol withdrawal QTL mapped to this chromosomal region. Two suggestive QTLs for pentobarbital withdrawal, both in proximity to QTLs definitely mapped for alcohol withdrawal, were also tentatively identified. These were on Chr 11 in proximity to a gene cluster including several members of the GABAA receptor gene family, and on Chr 4 near a locus associated with β-carboline-induced seizure severity. These data represent the first detection and mapping of loci influencing risk for physiological dependence on barbiturates, and suggest the involvement of common genes in physiological dependence on pentobarbital and alcohol.