Methodological aspects of the genetic dissection of gene expression

ADELLE: A global testing method for Trans-eQTL mapping

Understanding the genetic regulatory mechanisms of gene expression is a challenging and ongoing problem. Genetic variants that are associated with expression levels are readily identified when they are proximal to the gene (i.e., cis-eQTLs), but SNPs distant from the gene whose expression levels they are associated with (i.e., trans-eQTLs) have been much more difficult to discover, even though they account for a majority of the heritability in gene expression levels. A major impediment to the identification of more trans-eQTLs is the lack of statistical methods that are powerful enough to overcome the obstacles of small effect sizes and large multiple testing burden of trans-eQTL mapping. Here, we propose ADELLE, a powerful statistical testing framework that requires only summary statistics and is designed to be most sensitive to SNPs that are associated with multiple gene expression levels, a characteristic of many trans-eQTLs. In simulations, we show that ADELLE is more powerful than other methods at detecting SNPs that are associated with 0.2-2% of the traits. We apply ADELLE to a mouse advanced intercross line data set and show its ability to find trans-eQTLs that were not significant under a standard analysis. This demonstrates that ADELLE is a powerful tool at uncovering trans regulators of genetic expression.

Smarce1 and Tensin 4 Are Putative Modulators of Corneoscleral Stiffness

The biomechanical properties of the cornea and sclera are important in the onset and progression of multiple ocular pathologies and vary substantially between individuals, yet the source of this variation remains unknown. Here we identify genes putatively regulating corneoscleral biomechanical tissue properties by conducting high-fidelity ocular compliance measurements across the BXD recombinant inbred mouse set and performing quantitative trait analysis. We find seven cis-eQTLs and non-synonymous SNPs associating with ocular compliance, and show by RT-qPCR and immunolabeling that only two of the candidate genes, Smarce1 and Tns4, showed significant expression in corneal and scleral tissues. Both have mechanistic potential to influence the development and/or regulation of tissue material properties. This work motivates further study of Smarce1 and Tns4 for their role(s) in ocular pathology involving the corneoscleral envelope as well as the development of novel mouse models of ocular pathophysiology, such as myopia and glaucoma.

Longitudinal RNA-Seq Analysis of the Repeatability of Gene Expression and Splicing in Human Platelets Identifies a Platelet SELP Splice QTL

RATIONALE

Longitudinal studies are required to distinguish within versus between-individual variation and repeatability of gene expression. They are uniquely positioned to decipher genetic signal from environmental noise, with potential application to gene variant and expression studies. However, longitudinal analyses of gene expression in healthy individuals-especially with regards to alternative splicing-are lacking for most primary cell types, including platelets.

OBJECTIVE

To assess repeatability of gene expression and splicing in platelets and use repeatability to identify novel platelet expression quantitative trait loci (QTLs) and splice QTLs.

METHODS AND RESULTS

We sequenced the transcriptome of platelets isolated repeatedly up to 4 years from healthy individuals. We examined within and between individual variation and repeatability of platelet RNA expression and exon skipping, a readily measured alternative splicing event. We find that platelet gene expression is generally stable between and within-individuals over time-with the exception of a subset of genes enriched for the inflammation gene ontology. We show an enrichment among repeatable genes for associations with heritable traits, including known and novel platelet expression QTLs. Several exon skipping events were also highly repeatable, suggesting heritable patterns of splicing in platelets. One of the most repeatable was exon 14 skipping of SELP. Accordingly, we identify rs6128 as a platelet splice QTL and define an rs6128-dependent association between SELP exon 14 skipping and race. In vitro experiments demonstrate that this single nucleotide variant directly affects exon 14 skipping and changes the ratio of transmembrane versus soluble P-selectin protein production.

CONCLUSIONS

We conclude that the platelet transcriptome is generally stable over 4 years. We demonstrate the use of repeatability of gene expression and splicing to identify novel platelet expression QTLs and splice QTLs. rs6128 is a platelet splice QTL that alters SELP exon 14 skipping and soluble versus transmembrane P-selectin protein production.

Genomic Locus Modulating IOP in the BXD RI Mouse Strains

Intraocular pressure (IOP) is the primary risk factor for developing glaucoma, yet little is known about the contribution of genomic background to IOP regulation. The present study leverages an array of systems genetics tools to study genomic factors modulating normal IOP in the mouse. The BXD recombinant inbred (RI) strain set was used to identify genomic loci modulating IOP. We measured the IOP in a total of 506 eyes from 38 different strains. Strain averages were subjected to conventional quantitative trait analysis by means of composite interval mapping. Candidate genes were defined, and immunohistochemistry and quantitative PCR (qPCR) were used for validation. Of the 38 BXD strains examined the mean IOP ranged from a low of 13.2mmHg to a high of 17.1mmHg. The means for each strain were used to calculate a genome wide interval map. One significant quantitative trait locus (QTL) was found on Chr.8 (96 to 103 Mb). Within this 7 Mb region only 4 annotated genes were found: Gm15679, Cdh8, Cdh11 and Gm8730. Only two genes (Cdh8 and Cdh11) were candidates for modulating IOP based on the presence of non-synonymous SNPs. Further examination using SIFT (Sorting Intolerant From Tolerant) analysis revealed that the SNPs in Cdh8 (Cadherin 8) were predicted to not change protein function; while the SNPs in Cdh11 (Cadherin 11) would not be tolerated, affecting protein function. Furthermore, immunohistochemistry demonstrated that CDH11 is expressed in the trabecular meshwork of the mouse. We have examined the genomic regulation of IOP in the BXD RI strain set and found one significant QTL on Chr. 8. Within this QTL, there is one good candidate gene, Cdh11.

Quantitative trait locus mapping of acute functional tolerance in the LXS recombinant inbred strains

BACKGROUND

We previously reported that acute functional tolerance (AFT) to the hypnotic effects of alcohol was significantly correlated with drinking in the dark (DID) in the LXS recombinant inbred panel, but only in mice that had been pretreated with alcohol. Here, we have conducted quantitative trait locus (QTL) mapping for AFT. DNA sequencing of the progenitor ILS and ISS strains and microarray analyses were also conducted to identify candidate genes and functional correlates.

METHODS

LXS mice were given either saline or alcohol (5 g/kg) on day 1 and then tested for loss of righting reflex AFT on day 2. QTLs were mapped using standard procedures. Two microarray analyses from brain were conducted: (i) naïve LXS mice and (ii) an alcohol treatment time course in the ILS and ISS. The full genomes of the ILS and ISS were sequenced to a depth of approximately 30×.

RESULTS

A significant QTL for AFT in the alcohol pretreatment group was mapped to distal chromosome 4; numerous suggestive QTLs were also mapped. Preference drinking and DID have previously been mapped to the chromosome 4 locus. The credible interval of the significant chromosome 4 QTL spanned 23 Mb and included 716 annotated genes of which 150 had at least 1 nonsynonymous single nucleotide polymorphism or small indel that differed between the ILS and ISS; expression of 48 of the genes was cis-regulated. Enrichment analysis indicated broad functional categories underlying AFT, including proteolysis, transcription regulation, chromatin modification, protein kinase activity, and apoptosis.

CONCLUSIONS

The chromosome 4 QTL is a key region containing possibly pleiotropic genes for AFT and drinking behavior. Given that the region contains many viable candidates and a large number of the genes in the interval fall into 1 or more of the enriched functional categories, we postulate that many genes of varying effect size contribute to the observed QTL effect.

Genetic mapping of natural variation in schooling tendency in the threespine stickleback

Although there is a heritable basis for many animal behaviors, the genetic architecture of behavioral variation in natural populations remains mostly unknown, particularly in vertebrates. We sought to identify the genetic basis for social affiliation in two populations of threespine sticklebacks (Gasterosteus aculeatus) that differ in their propensity to school. Marine sticklebacks from Japan school strongly whereas benthic sticklebacks from a lake in Canada are more solitary. Here, we expanded on our previous efforts to identify quantitative trait loci (QTL) for differences in schooling tendency. We tested fish multiple times in two assays that test different aspects of schooling tendency: 1) the model school assay, which presents fish with a school of eight model sticklebacks; and 2) the choice assay, in which fish are given a choice between the model school and a stationary artificial plant. We found low-to-moderate levels of repeatability, ranging from 0.1 to 0.5, in schooling phenotypes. To identify the genomic regions that contribute to differences in schooling tendency, we used QTL mapping in two types of crosses: benthic × marine backcrosses and an F2 intercross. We found two QTL for time spent with the school in the model school assay, and one QTL for number of approaches to the school in the choice assay. These QTL were on three different linkage groups, not previously linked to behavioral differences in sticklebacks. Our results highlight the importance of using multiple crosses and robust behavioral assays to uncover the genetic basis of behavioral variation in natural populations.

Is the alcohol deprivation effect genetically mediated? Studies with HXB/BXH recombinant inbred rat strains

BACKGROUND

Two features of alcohol addiction that have been widely studied in animal models are relapse drinking following periods of alcohol abstinence and the escalation of alcohol consumption after chronic continuous or intermittent alcohol exposure. The genetic contribution to these phenotypes has not been systematically investigated.

METHODS

HXB/BXH recombinant inbred (RI) rat strains were given access to alcohol sequentially as follows: alcohol (10%) as the only fluid for 1 week; alcohol (10%) and water in a 2-bottle choice paradigm for 7 weeks ("pre-alcohol deprivation effect [ADE] alcohol consumption"); 2 weeks of access to water only (alcohol deprivation); and 2 weeks of reaccess to 10% alcohol and water ("post-ADE alcohol consumption"). The periods of deprivation and reaccess to alcohol were repeated 3 times. The ADE was defined as the amount of alcohol consumed in the first 24 hours after deprivation minus the average daily amount of alcohol consumed in the week prior to deprivation. Heritability of the phenotypes was determined by analysis of variance, and quantitative trait loci (QTLs) were identified.

RESULTS

All strains showed increased alcohol consumption, compared to the predeprivation period, in the first 24 hours after each deprivation (ADE). Broad-sense heritability of the ADEs was low (ADE1, 9.1%; ADE2, 26.2%; ADE3, 16.3%). Alcohol consumption levels were relatively stable over weeks 2 to 7. Post-ADE alcohol consumption levels consistently increased in some strains and were decreased or unchanged in others. Heritability of pre- and post-ADE alcohol consumption was high and increased over time (week 2, 38.5%; week 7, 51.1%; week 11, 56.8%; week 15, 63.3%). QTLs for pre- and post-ADE alcohol consumption were similar, but the strength of the QTL association with the phenotype decreased over time.

CONCLUSIONS

In the HXB/BXH RI rat strains, genotypic variance does not account for a large proportion of phenotypic variance in the ADE phenotype (low heritability), suggesting a role of environmental factors. In contrast, a large proportion of the variance across the RI strains in pre- and post-ADE alcohol consumption is due to genetically determined variance (high heritability).

The genetics of gene expression in complex mouse crosses as a tool to study the molecular underpinnings of behavior traits

Complex Mus musculus crosses provide increased resolution to examine the relationships between gene expression and behavior. While the advantages are clear, there are numerous analytical and technological concerns that arise from the increased genetic complexity that must be considered. Each of these issues is discussed, providing an initial framework for complex cross study design and planning.

Genetic variation within the Chrna7 gene modulates nicotine reward-like phenotypes in mice

Mortality from tobacco smoking remains the leading cause of preventable death in the world, yet current cessation therapies are only modestly successful, suggesting new molecular targets are needed. Genetic analysis of gene expression and behavior identified Chrna7 as potentially modulating nicotine place conditioning in the BXD panel of inbred mice. We used gene targeting and pharmacological tools to confirm the role of Chrna7 in nicotine conditioned place preference (CPP). To identify molecular events downstream of Chrna7 that may modulate nicotine preference, we performed microarray analysis of α7 knock-out (KO) and wild-type (WT) nucleus accumbens (NAc) tissue, followed by confirmation with quantitative polymerase chain reaction (PCR) and immunoblotting. In the BXD panel, we found a putative cis expression quantitative trait loci (eQTL) for Chrna7 in NAc that correlated inversely to nicotine CPP. We observed that gain-of-function α7 mice did not display nicotine preference at any dose tested, whereas conversely, α7 KO mice demonstrated nicotine place preference at a dose below that routinely required to produce preference. In B6 mice, the α7 nicotinic acetylcholine receptor (nAChR)-selective agonist, PHA-543613, dose-dependently blocked nicotine CPP, which was restored using the α7 nAChR-selective antagonist, methyllycaconitine citrate (MLA). Our genomic studies implicated a messenger RNA (mRNA) co-expression network regulated by Chrna7 in NAc. Mice lacking Chrna7 demonstrate increased insulin signaling in the NAc, which may modulate nicotine place preference. Our studies provide novel targets for future work on development of more effective therapeutic approaches to counteract the rewarding properties of nicotine for smoking cessation.

An effective framework for reconstructing gene regulatory networks from genetical genomics data

MOTIVATION

Systems Genetics approaches, in particular those relying on genetical genomics data, put forward a new paradigm of large-scale genome and network analysis. These methods use naturally occurring multi-factorial perturbations (e.g. polymorphisms) in properly controlled and screened genetic crosses to elucidate causal relationships in biological networks. However, although genetical genomics data contain rich information, a clear dissection of causes and effects as required for reconstructing gene regulatory networks is not easily possible.

RESULTS

We present a framework for reconstructing gene regulatory networks from genetical genomics data where genotype and phenotype correlation measures are used to derive an initial graph which is subsequently reduced by pruning strategies to minimize false positive predictions. Applied to realistic simulated genetic data from a recent DREAM challenge, we demonstrate that our approach is simple yet effective and outperforms more complex methods (including the best performer) with respect to (i) reconstruction quality (especially for small sample sizes) and (ii) applicability to large data sets due to relatively low computational costs. We also present reconstruction results from real genetical genomics data of yeast.

AVAILABILITY

A MATLAB implementation (script) of the reconstruction framework is available at www.mpi-magdeburg.mpg.de/projects/cna/etcdownloads.html

CONTACT

klamt@mpi-magdeburg.mpg.de.

QTL analysis of soybean seed weight across multi-genetic backgrounds and environments

Seed weight, measured as mass per seed, is an important yield component of soybean and is generally positively correlated with seed yield (Burton et al, Crop Sci 27:1093, 1987). In previous reports, quantitative trait loci (QTL) associated with seed weight, were identified in single genetic background. The objective of the present study was to identify QTL and epistatic QTL underlying soybean seed weight in three RIL populations (with one common male parent 'Hefeng25') and across three different environments. Overall, 18, 11, and 17 seed weight QTL were identified in HC ('Hefeng25' × 'Conrad'), HM ('Hefeng25' × 'Maple Arrow'), and HB ('Hefeng25' × 'Bayfield') populations, respectively. The amount of phenotypic variation explained by a single QTL underlying seed weight was usually less than 10 %. The environment and background-independent QTL often had higher additive (a) effects. In contrast, the environment or background-dependent QTL were probably due to weak expression of QTL. QTL by environment interaction effects were in the opposite direction of a effects and/or epistasis effects. Four QTL and one QTL could be identified (2.0 < LOD < 9.06) in the HC and HB populations, respectively, across three environments (swHCA2-1, swHCC2-1, swHCD1b-1, swHCA2-2 (linked to Satt233, Satt424, Satt460, Satt428, respectively) and swHBA1-1(Satt449). Seven QTL could be identified in all three RIL populations in at least one location. Two QTL could be identified in the three RIL populations across three environments. These two QTL may have greater potential for use in marker-assisted selection of seed weight in soybean.

Evolutionary conservation of metabolism explains howDrosophila nutrigenomics can help us understand human nutrigenomics

While large populations in the third world are enduring famine, much of the developed world is undergoing an obesity epidemic. In addition to reflecting an unbalanced distribution of food, the "epidemic of overabundance" is ironically leading to a decrease in the health and longevity of the obese and improperly nourished in the first world. International consortia, such as the European Nutrigenomics Organization (NuGO), are increasing our knowledge of nutrientgene interactions and the effects of diet and obesity on human health. In this review, we summarize both previous and ongoing nutrigenomics studies in Drosophila and we explain how these studies can be used to provide insights into molecular mechanisms underlying nutrigenomics in humans. We will discuss how quantitative trait locus (QTL) experiments have identified genes that affect triglyceride levels in Drosophila, and how microarray analyses show that hundreds of genes have altered gene expression under different dietary conditions. Finally, we will discuss ongoing combined microarray-QTL studies, termed "genetical genomics," that promise to identify "master modulatory loci" that regulate global responses of potentially hundreds of genes under different dietary conditions. When "master modulatory loci" are identified in Drosophila, then experiments in mammalian models can be used to determine the relevance of these genes to human nutrition and health.

A systems genetic analysis of alcohol drinking by mice, rats and men: influence of brain GABAergic transmission

Genetic influences on the predisposition to complex behavioral or physiological traits can reflect genetic polymorphisms that lead to altered gene product function, and/or variations in gene expression levels. We have explored quantitative variations in an animal's alcohol consumption, using a genetical genomic/phenomic approach. In our studies, gene expression is correlated with amount of alcohol consumed, and genomic regions that regulate the alcohol consumption behavior and the quantitative levels of gene expression (behavioral and expression quantitative trait loci [QTL]) are determined and used as a filter to identify candidate genes predisposing the behavior. We determined QTLs for alcohol consumption using the LXS panel of recombinant inbred mice. We then identified genes that were: 1) differentially expressed between five high and five low alcohol-consuming lines or strains of mice; and 2) were physically located in, or had an expression QTL (eQTL) within the alcohol consumption QTLs. Comparison of mRNA and protein levels in brains of high and low alcohol consuming mice led us to a bioinformatic examination of potential regulation by microRNAs of an identified candidate transcript, Gnb1 (G protein beta subunit 1). We combined our current analysis with our earlier work identifying candidate genes for the alcohol consumption trait in mice, rats and humans. Our overall analysis leads us to postulate that the activity of the GABAergic system, and in particular GABA release and GABA receptor trafficking and signaling, which involves G protein function, contributes significantly to genetic variation in the predisposition to varying levels of alcohol consumption. This article is part of a Special Issue entitled 'Trends in neuropharmacology: in memory of Erminio Costa'.

Functional genomics complements quantitative genetics in identifying disease-gene associations

An ultimate goal of genetic research is to understand the connection between genotype and phenotype in order to improve the diagnosis and treatment of diseases. The quantitative genetics field has developed a suite of statistical methods to associate genetic loci with diseases and phenotypes, including quantitative trait loci (QTL) linkage mapping and genome-wide association studies (GWAS). However, each of these approaches have technical and biological shortcomings. For example, the amount of heritable variation explained by GWAS is often surprisingly small and the resolution of many QTL linkage mapping studies is poor. The predictive power and interpretation of QTL and GWAS results are consequently limited. In this study, we propose a complementary approach to quantitative genetics by interrogating the vast amount of high-throughput genomic data in model organisms to functionally associate genes with phenotypes and diseases. Our algorithm combines the genome-wide functional relationship network for the laboratory mouse and a state-of-the-art machine learning method. We demonstrate the superior accuracy of this algorithm through predicting genes associated with each of 1157 diverse phenotype ontology terms. Comparison between our prediction results and a meta-analysis of quantitative genetic studies reveals both overlapping candidates and distinct, accurate predictions uniquely identified by our approach. Focusing on bone mineral density (BMD), a phenotype related to osteoporotic fracture, we experimentally validated two of our novel predictions (not observed in any previous GWAS/QTL studies) and found significant bone density defects for both Timp2 and Abcg8 deficient mice. Our results suggest that the integration of functional genomics data into networks, which itself is informative of protein function and interactions, can successfully be utilized as a complementary approach to quantitative genetics to predict disease risks. All supplementary material is available at http://cbfg.jax.org/phenotype.

Many recent efforts to understand the genetic origins of complex diseases utilize statistical approaches to analyze phenotypic traits measured in genetically well-characterized populations. While these quantitative genetics methods are powerful, their success is limited by sampling biases and other confounding factors, and the biological interpretation of results can be challenging since these methods are not based on any functional information for candidate loci. On the other hand, the functional genomics field has greatly expanded in past years, both in terms of experimental approaches and analytical algorithms. However, functional approaches have been applied to understanding phenotypes in only the most basic ways. In this study, we demonstrate that functional genomics can complement traditional quantitative genetics by analytically extracting protein function information from large collections of high throughput data, which can then be used to predict genotype-phenotype associations. We applied our prediction methodology to the laboratory mouse, and we experimentally confirmed a role in osteoporosis for two of our predictions that were not candidates from any previous quantitative genetics study. The ability of our approach to produce accurate and unique predictions implies that functional genomics can complement quantitative genetics and can help address previous limitations in identifying disease genes.

Expression levels of uridine 5'-diphospho-glucuronosyltransferase genes in breast tissue from healthy women are associated with mammographic density

INTRODUCTION

Mammographic density (MD), as assessed from film screen mammograms, is determined by the relative content of adipose, connective and epithelial tissue in the female breast. In epidemiological studies, a high percentage of MD confers a four to six fold risk elevation of developing breast cancer, even after adjustment for other known breast cancer risk factors. However, the biologic correlates of density are little known.

METHODS

Gene expression analysis using whole genome arrays was performed on breast biopsies from 143 women; 79 women with no malignancy (healthy women) and 64 newly diagnosed breast cancer patients, both included from mammographic centres. Percent MD was determined using a previously validated, computerized method on scanned mammograms. Significance analysis of microarrays (SAM) was performed to identify genes influencing MD and a linear regression model was used to assess the independent contribution from different variables to MD.

RESULTS

SAM-analysis identified 24 genes differentially expressed between samples from breasts with high and low MD. These genes included three uridine 5'-diphospho-glucuronosyltransferase (UGT) genes and the oestrogen receptor gene (ESR1). These genes were down-regulated in samples with high MD compared to those with low MD. The UGT gene products, which are known to inactivate oestrogen metabolites, were also down-regulated in tumour samples compared to samples from healthy individuals. Several single nucleotide polymorphisms (SNPs) in the UGT genes associated with the expression of UGT and other genes in their vicinity were identified.

CONCLUSIONS

Three UGT enzymes were lower expressed both in breast tissue biopsies from healthy women with high MD and in biopsies from newly diagnosed breast cancers. The association was strongest amongst young women and women using hormonal therapy. UGT2B10 predicts MD independently of age, hormone therapy and parity. Our results indicate that down-regulation of UGT genes in women exposed to female sex hormones is associated with high MD and might increase the risk of breast cancer.

Genetics of the hippocampal transcriptome in mouse: a systematic survey and online neurogenomics resource

Differences in gene expression in the CNS influence behavior and disease susceptibility. To systematically explore the role of normal variation in expression on hippocampal structure and function, we generated an online microarray database for a diverse panel of strains of mice, including most common inbred strains and numerous recombinant inbred lines (www.genenetwork.org). Using this resource, coexpression networks for families of genes can be generated rapidly to test causal models related to function. The data set is optimized for quantitative trait locus (QTL) mapping and was used to identify over 5500 QTLs that modulate mRNA levels. We describe a wide variety of analyses and novel synthetic approaches that take advantage of this resource, and demonstrate how both the data and associated tools can be applied to the study of gene regulation in the hippocampus and relations to structure and function.

Genetical genomic determinants of alcohol consumption in rats and humans

BACKGROUND

We have used a genetical genomic approach, in conjunction with phenotypic analysis of alcohol consumption, to identify candidate genes that predispose to varying levels of alcohol intake by HXB/BXH recombinant inbred rat strains. In addition, in two populations of humans, we assessed genetic polymorphisms associated with alcohol consumption using a custom genotyping array for 1,350 single nucleotide polymorphisms (SNPs). Our goal was to ascertain whether our approach, which relies on statistical and informatics techniques, and non-human animal models of alcohol drinking behavior, could inform interpretation of genetic association studies with human populations.

RESULTS

In the HXB/BXH recombinant inbred (RI) rats, correlation analysis of brain gene expression levels with alcohol consumption in a two-bottle choice paradigm, and filtering based on behavioral and gene expression quantitative trait locus (QTL) analyses, generated a list of candidate genes. A literature-based, functional analysis of the interactions of the products of these candidate genes defined pathways linked to presynaptic GABA release, activation of dopamine neurons, and postsynaptic GABA receptor trafficking, in brain regions including the hypothalamus, ventral tegmentum and amygdala. The analysis also implicated energy metabolism and caloric intake control as potential influences on alcohol consumption by the recombinant inbred rats. In the human populations, polymorphisms in genes associated with GABA synthesis and GABA receptors, as well as genes related to dopaminergic transmission, were associated with alcohol consumption.

CONCLUSION

Our results emphasize the importance of the signaling pathways identified using the non-human animal models, rather than single gene products, in identifying factors responsible for complex traits such as alcohol consumption. The results suggest cross-species similarities in pathways that influence predisposition to consume alcohol by rats and humans. The importance of a well-defined phenotype is also illustrated. Our results also suggest that different genetic factors predispose alcohol dependence versus the phenotype of alcohol consumption.

Mapping complex disease traits with global gene expression

Variation in gene expression is an important mechanism underlying susceptibility to complex disease. The simultaneous genome-wide assay of gene expression and genetic variation allows the mapping of the genetic factors that underpin individual differences in quantitative levels of expression (expression QTLs; eQTLs). The availability of systematically generated eQTL information could provide immediate insight into a biological basis for disease associations identified through genome-wide association (GWA) studies, and can help to identify networks of genes involved in disease pathogenesis. Although there are limitations to current eQTL maps, understanding of disease will be enhanced with novel technologies and international efforts that extend to a wide range of new samples and tissues.

Reverse engineering the genotype-phenotype map with natural genetic variation

The genetic variation that occurs naturally in a population is a powerful resource for studying how genotype affects phenotype. Each allele is a perturbation of the biological system, and genetic crosses, through the processes of recombination and segregation, randomize the distribution of these alleles among the progeny of a cross. The randomized genetic perturbations affect traits directly and indirectly, and the similarities and differences between traits in their responses to common perturbations allow inferences about whether variation in a trait is a cause of a phenotype (such as disease) or whether the trait variation is, instead, an effect of that phenotype. It is then possible to use this information about causes and effects to build models of probabilistic 'causal networks'. These networks are beginning to define the outlines of the 'genotype-phenotype map'.

FastMap: fast eQTL mapping in homozygous populations

Motivation: Gene expression Quantitative Trait Locus (eQTL) mapping measures the association between transcript expression and genotype in order to find genomic locations likely to regulate transcript expression. The availability of both gene expression and high-density genotype data has improved our ability to perform eQTL mapping in inbred mouse and other homozygous populations. However, existing eQTL mapping software does not scale well when the number of transcripts and markers are on the order of 10⁵ and 10⁵–10⁶, respectively.

Results: We propose a new method, FastMap, for fast and efficient eQTL mapping in homozygous inbred populations with binary allele calls. FastMap exploits the discrete nature and structure of the measured single nucleotide polymorphisms (SNPs). In particular, SNPs are organized into a Hamming distance-based tree that minimizes the number of arithmetic operations required to calculate the association of a SNP by making use of the association of its parent SNP in the tree. FastMap's tree can be used to perform both single marker mapping and haplotype association mapping over an m-SNP window. These performance enhancements also permit permutation-based significance testing.

Availability: The FastMap program and source code are available at the website: http://cebc.unc.edu/fastmap86.html

Contact:iir@unc.edu; nobel@email.unc.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

[Integrating genetic and gene expression data: methods and applications of eQTL mapping]

The availability of high-throughput genotyping technologies and microarray assays has allowed researchers to investigate genetic variations that influence levels of gene expression. Expression Quantitative Trait Locus (eQTL) mapping methods have been used to identify the genetic basis of gene expression. Similar to traditional QTL studies, the main goal of eQTL is to identify the genomic locations to which the expression traits are linked. Although microarrays provide the expression data of thousands of transcripts, standard QTL mapping methods, which are able to handle at most tens of traits, cannot be applied directly. As a result, it is necessary to consider the statistical principles involved in the design and analysis of these experiments. In this paper, we reviewed individual selection, experimental design of microarray, normalization of gene expression data, mapping methods, and explaining of results and proposed potential methodological problems for such analyses. Finally, we discussed the applications of this integrative genomic approach to estimate heritability of transcripts, identify candidate genes, construct gene networks, and understand interactions between genes, genes and environments.

Fine mapping of regulatory loci for mammalian gene expression using radiation hybrids

We mapped regulatory loci for nearly all protein-coding genes in mammals using comparative genomic hybridization and expression array measurements from a panel of mouse-hamster radiation hybrid cell lines. The large number of breaks in the mouse chromosomes and the dense genotyping of the panel allowed extremely sharp mapping of loci. As the regulatory loci result from extra gene dosage, we call them copy number expression quantitative trait loci, or ceQTLs. The -2log10P support interval for the ceQTLs was <150 kb, containing an average of <2-3 genes. We identified 29,769 trans ceQTLs with -log10P > 4, including 13 hotspots each regulating >100 genes in trans. Further, this work identifies 2,761 trans ceQTLs harboring no known genes, and provides evidence for a mode of gene expression autoregulation specific to the X chromosome.

Semiparametric methods for genome-wide linkage analysis of human gene expression data

With the availability of high-throughput microarray technologies, investigators can simultaneously measure the expression levels of many thousands of genes in a short period. Although there are rich statistical methods for analyzing microarray data in the literature, limited work has been done in mapping expression quantitative trait loci (eQTL) that influence the variation in levels of gene expression. Most existing eQTL mapping methods assume that the expression phenotypes follow a normal distribution and violation of the normality assumption may lead to inflated type I error and reduced power. QTL analysis of expression data involves the mapping of many expression phenotypes at thousands or hundreds of thousands of marker loci across the whole genome. An appropriate procedure to adjust for multiple testing is essential for guarding against an abundance of false positive results. In this study, we applied a semiparametric quantitative trait loci (SQTL) mapping method to human gene expression data. The SQTL mapping method is rank-based and therefore robust to non-normality and outliers. Furthermore, we apply an efficient Monte Carlo procedure to account for multiple testing and assess the genome-wide significance level. Particularly, we apply the SQTL mapping method and the Monte-Carlo approach to the gene expression data provided by Genetic Analysis Workshop 15.

Genetic complexity and quantitative trait loci mapping of yeast morphological traits

Functional genomics relies on two essential parameters: the sensitivity of phenotypic measures and the power to detect genomic perturbations that cause phenotypic variations. In model organisms, two types of perturbations are widely used. Artificial mutations can be introduced in virtually any gene and allow the systematic analysis of gene function via mutants fitness. Alternatively, natural genetic variations can be associated to particular phenotypes via genetic mapping. However, the access to genome manipulation and breeding provided by model organisms is sometimes counterbalanced by phenotyping limitations. Here we investigated the natural genetic diversity of Saccharomyces cerevisiae cellular morphology using a very sensitive high-throughput imaging platform. We quantified 501 morphological parameters in over 50,000 yeast cells from a cross between two wild-type divergent backgrounds. Extensive morphological differences were found between these backgrounds. The genetic architecture of the traits was complex, with evidence of both epistasis and transgressive segregation. We mapped quantitative trait loci (QTL) for 67 traits and discovered 364 correlations between traits segregation and inheritance of gene expression levels. We validated one QTL by the replacement of a single base in the genome. This study illustrates the natural diversity and complexity of cellular traits among natural yeast strains and provides an ideal framework for a genetical genomics dissection of multiple traits. Our results did not overlap with results previously obtained from systematic deletion strains, showing that both approaches are necessary for the functional exploration of genomes.

Proximity model for expression quantitative trait loci (eQTL) detection

Expression quantitative trait loci (eQTL) are loci or markers on the genomes that are associated with gene expression. It is well known to biologists that some (cis) genetic influences on expression occur over short distances on the genome while some (trans) influences can operate remotely. We use a log-linear model to place structure on the prior probability for genetic control of a transcript by a marker locus so that the loci that are closest to a transcript are given a higher prior probability of controlling that transcript to reflect the important role that genomic proximity can play in the regulation of expression. This proximity model is an extension of the mixture over marker (MOM) model for the simultaneous detection of cis and trans eQTL of Kendziorski (Kendziorski et al., 2006, Biometrics62(1), 19-27). The genomic locations of the transcripts are used to improve the accuracy of the posterior distribution for the location of the eQTL. We compare the MOM method to our extension with both simulated data and data sets of recombinant inbred mouse lines. We also discuss an extension of the MOM method to model multiple eQTLs, and find that many transcripts are likely associated with more than one eQTL.

Genetical genomics: use all data

Background

Genetical genomics is a very powerful tool to elucidate the basis of complex traits and disease susceptibility. Despite its relevance, however, statistical modeling of expression quantitative trait loci (eQTL) has not received the attention it deserves. Based on two reasonable assertions (i) a good model should consider all available variables as potential effects, and (ii) gene expressions are highly interconnected, we suggest that an eQTL model should consider the rest of expression levels as potential regressors, in addition to the markers.

Results

It is shown that power can be increased with this strategy. We also show, using classical statistical and support vector machines techniques in a reanalysis of public data, that the external transcripts, i.e., transcripts other than the one being analysed, explain on average much more variability than the markers themselves. The presence of eQTL hotspots is reassessed in the light of these results.

Conclusion

Model choice is a critical yet neglected issue in genetical genomics studies. Although we are far from having a general strategy for model choice in this area, we can at least propose that any transcript level is scanned not only for the markers genotyped but also for the rest of gene expression levels. Some sort of stepwise regression strategy can be used to select the final model.

Genetics of global gene expression

A new field of genetic analysis of global gene expression has emerged in recent years, driven by the realization that traditional techniques of linkage and association analysis can be applied to thousands of transcript levels measured by microarrays. Genetic dissection of transcript abundance has shed light on the architecture of quantitative traits, provided a new approach for connecting DNA sequence variation with phenotypic variation, and improved our understanding of transcriptional regulation and regulatory variation.

Review of microarray experimental design strategies for genetical genomics studies

Genetical genomics approaches provide a powerful tool for studying the genetic mechanisms governing variation in complex traits. By combining information on phenotypic traits, pedigree structure, molecular markers, and gene expression, such studies can be used for estimating heritability of mRNA transcript abundances, for mapping expression quantitative trait loci (eQTL), and for inferring regulatory gene networks. Microarray experiments, however, can be extremely costly and time consuming, which may limit sample sizes and statistical power. Thus it is crucial to optimize experimental designs by carefully choosing the subjects to be assayed, within a selective profiling approach, and by cautiously controlling systematic factors affecting the system. Also, a rigorous strategy should be used for allocating mRNA samples across assay batches, slides, and dye labeling, so that effects of interest are not confounded with nuisance factors. In this presentation, we review some selective profiling strategies for genetical genomics studies, including the selection of individuals for increased genetic dissimilarity and for a higher number of recombination events. Efficient designs for studying epistasis are also discussed, as well as experiments for inferring heritability of transcriptional levels. It is shown that solving an optimal design problem generally requires a numerical implementation and that the optimality criteria should be intimately related to the goals of the experiment, such as the estimation of additive, dominance, and interacting effects, localizing putative eQTL, or inferring genetic and environmental variance components associated with transcriptional abundances.

Genetical genomics in livestock: potentials and pitfalls

Genetical genomics combines gene mapping and gene expression approaches to identify loci controlling gene expression (eQTLs) that may underlie functional trait variation. The combination of genomic tools has great potential to facilitate dissection of complex traits, but studies need careful design and interpretation. Here we explore both the potential and the pitfalls of this approach with illustrations from actual studies. There are now an appreciable number of studies in model species and even humans demonstrating the feasibility of genetical genomics. However, most studies are too limited in size and design to unlock the full potential of the approach. Limited statistical power of studies exacerbates the problem of detection of false-positive eQTL and some reported results should be interpreted with caution. As one approach to more successful implementation of genetical genomics, we propose to combine expression studies with fine mapping of functional trait loci. This synergistic approach facilitates the implementation of genetical genomics for species without inbred resources but is equally applicable to model species. These properties make it particularly suitable for livestock populations where many QTL are already in the public domain and potentially very large pedigreed populations can be accessed.

Integration of structural and functional genomics

This paper introduces a special issue of Animal Genetics, which is devoted to the recent symposium held at Iowa State University entitled 'Integration of Structural and Functional Genomics'. We describe issues and needs that confront the animal genomics community, and describe how this symposium was structured to address these issues by improving communication and collaboration across species and disciplines. The session topics and oral presentations are briefly described for all invited speakers.

From genetical genomics to systems genetics: potential applications in quantitative genomics and animal breeding

This article reviews methods of integration of transcriptomics (and equally proteomics and metabolomics), genetics, and genomics in the form of systems genetics into existing genome analyses and their potential use in animal breeding and quantitative genomic modeling of complex traits. Genetical genomics or the expression quantitative trait loci (eQTL) mapping method and key findings in this research are reviewed. Various procedures and potential uses of eQTL mapping, global linkage clustering, and systems genetics are illustrated using actual analysis on recombinant inbred lines of mice with data on gene expression (for diabetes- and obesity-related genes), pathway, and single nucleotide polymorphism (SNP) linkage maps. Experimental and bioinformatics difficulties and possible solutions are discussed. The main uses of this systems genetics approach in quantitative genomics were shown to be in refinement of the identified QTL, candidate gene and SNP discovery, understanding gene-environment and gene-gene interactions, detection of candidate regulator genes/eQTL, discriminating multiple QTL/eQTL, and detection of pleiotropic QTL/eQTL, in addition to its use in reconstructing regulatory networks. The potential uses in animal breeding are direct selection on heritable gene expression measures, termed “expression assisted selection,” and genetical genomic selection of both QTL and eQTL based on breeding values of the respective genes, termed “expression-assisted evaluation.”

Genetic dissection of gene regulation in multiple mouse tissues

The analysis of the influence of genetic variation on regulation of gene expression at a near-genome-wide level has become the focus of much recent interest. It is widely appreciated that many genes are expressed in a tissue-specific manner and that others are more ubiquitously expressed but relatively little is known about how genetic variation might influence these tissue patterns of gene expression. In this review we discuss what is known about the tissue specificity of the influence of genetic variation and review the challenges that we face in combining hugely parallel, microarray-based gene analysis with equally expensive genetic analysis. We conclude that the available data suggest that genetic variation is essentially tissue specific in its effects upon gene expression and this has important implications for experimental analysis.

Candidate genes and their regulatory elements: alcohol preference and tolerance

QTL analysis of behavioral traits and mouse brain gene expression studies were combined to identify candidate genes involved in the traits of alcohol preference and acute functional alcohol tolerance. The systematic application of normalization and statistical analysis of differential gene expression, behavioral and expression QTL location, and informatics methodologies resulted in identification of 8 candidate genes for the trait of alcohol preference and 22 candidate genes for acute functional tolerance. Pathway analysis, combined with clustering by ontology, indicated the importance of transcriptional regulation and DNA and protein binding elements in the acute functional tolerance trait, and protein kinases and intracellular signal transduction elements in the alcohol preference trait. A rudimentary search for transcription control elements that could indicate coregulation of the panels of candidate genes produced modest results, implicating SMAD-3 in the regulation of four of the eight candidate genes for alcohol preference. However, the realization of the many caveats related to transcription factor binding site analysis, and attempts to correlate between transcription factor binding and function, forestalled any definitive global analysis of transcriptional control of differentially expressed candidate genes.

Genetic and genomic analysis of a fat mass trait with complex inheritance reveals marked sex specificity

The integration of expression profiling with linkage analysis has increasingly been used to identify genes underlying complex phenotypes. The effects of gender on the regulation of many physiological traits are well documented; however, “genetical genomic” analyses have not yet addressed the degree to which their conclusions are affected by sex. We constructed and densely genotyped a large F2 intercross derived from the inbred mouse strains C57BL/6J and C3H/HeJ on an apolipoprotein E null (ApoE^−/−) background. This BXH.ApoE^−/− population recapitulates several “metabolic syndrome” phenotypes. The cross consists of 334 animals of both sexes, allowing us to specifically test for the dependence of linkage on sex. We detected several thousand liver gene expression quantitative trait loci, a significant proportion of which are sex-biased. We used these analyses to dissect the genetics of gonadal fat mass, a complex trait with sex-specific regulation. We present evidence for a remarkably high degree of sex-dependence on both the cis and trans regulation of gene expression. We demonstrate how these analyses can be applied to the study of the genetics underlying gonadal fat mass, a complex trait showing significantly female-biased heritability. These data have implications on the potential effects of sex on the genetic regulation of other complex traits.

Although their genomes are nearly identical, the males and females of a species exhibit striking differences in many traits, including complex traits such as obesity. This study combines genetic and genomic tools to identify in parallel quantitative trait loci (QTLs) for a measure of gonadal fat mass and for expression of transcripts in the liver. The results are used to explore the relationship between genetic variation, sexual differentiation, and obesity in the mouse model. Using over 300 intercross progeny of two inbred mouse strains, five loci in the genome were found to be highly correlated with abdominal fat mass. Four of the five loci exhibited opposite effects on obesity in the two sexes, a phenomenon known as sexual antagonism. To identify candidate genes that may be involved in obesity through their expression in the liver, global gene expression analysis was employed using microarrays. Many of these expression QTLs also show sex-specific effects on transcription. A hotspot for trans-acting QTLs regulating the expression of transcripts whose abundance is correlated with gonadal fat mass was identified on Chromosome 19. This region of the genome colocalizes with a clinical QTL for gonadal fat mass, suggesting that it harbors a good candidate gene for obesity.

An integrative genomics approach to infer causal associations between gene expression and disease

A key goal of biomedical research is to elucidate the complex network of gene interactions underlying complex traits such as common human diseases. Here we detail a multistep procedure for identifying potential key drivers of complex traits that integrates DNA-variation and gene-expression data with other complex trait data in segregating mouse populations. Ordering gene expression traits relative to one another and relative to other complex traits is achieved by systematically testing whether variations in DNA that lead to variations in relative transcript abundances statistically support an independent, causative or reactive function relative to the complex traits under consideration. We show that this approach can predict transcriptional responses to single gene-perturbation experiments using gene-expression data in the context of a segregating mouse population. We also demonstrate the utility of this approach by identifying and experimentally validating the involvement of three new genes in susceptibility to obesity.

Weighting by heritability for detection of quantitative trait loci with microarray estimates of gene expression

The use of recombinant inbred lines allows an estimate of the heritability of expression measured by individual probes. By testing heritability-weighted averages to define expression of a transcript, more QTLs can be detected than with previously described methods.

Heritable differences in transcribed RNA levels can be mapped as quantitative trait loci (QTLs). Transcribed RNA levels are often measured by hybridization to microarrays of oligonucleotide probes, in which each transcript is represented by multiple probes. The use of recombinant inbred lines allows an estimate of the heritability of expression measured by individual probes. This heritability varies greatly. We have tested heritability-weighted averages to define expression of a transcript and found that these allow detection of more QTLs than previously described methods.