Mapping of QTL Influencing Saccharin Consumption in the Selectively Bred Alcohol-Preferring and -Nonpreferring Rat Lines

The inbred preferring (iP) and nonpreferring (iNP) rat strains were derived from the selectively bred alcohol-preferring (P) and alcohol-nonpreferring (NP) lines. Previously, 381 iP × iNP F2 progeny were generated to identify quantitative trait loci (QTLs) influencing alcohol consumption and preference. Saccharin consumption (ml/48 h) and saccharin intake (ml/kg/day) were also measured in the F2 sample and were significantly correlated with both alcohol consumption and preference (all r .20, p < .0001), suggesting that there might be some QTLs influencing both saccharin and alcohol phenotypes. We have performed a genome screen using F2 animals with extreme saccharin or alcohol consumption to identify QTLs contributing to saccharin-related phenotypes. Lod scores greater than 2.0 were found on chromosomes 3, 16 and 18 in this sample. Additional genotyping was performed in these regions in the full sample of 381 F2 progeny to further characterize these putative QTLs. On chromosome 3, the maximum lod score in the full sample was 2.7 with saccharin consumption. This QTL appears to overlap with a QTL identified for alcohol consumption in the iP and iNP lines and has pleiotropic effects on both phenotypes. Interestingly, this region of rat chromosome 3 is syntenic with mouse chromosome 2, where a QTL influencing alcohol preference has been previously reported. The QTL on chromosome 16 has a maximum lod score of 4.0 with saccharin intake and 2.6 with saccharin consumption. The QTL on chromosome 18 has a maximum lod score of 2.7 with saccharin consumption. Taken together, these data provide the first results of a genome screen for QTLs contributing to saccharin phenotypes in the rat.     

A Simple Method to Calculate Resolving Power and Confidence Interval of QTL Map Location

Resolving power is defined as the 95% confidence interval for quantitative trait locus (QTL) map location that would be obtained when scoring an infinite number of markers in a given constellation of a marker-QTL mapping experiment. Resolving power can serve as a close estimate of the confidence interval of QTL map location, as well as a guide to the lower efficient limit of marker spacing in an initial marker-QTL mapping experiment. In the present study, an extensive series of simulations was carried out to provide estimates of resolving power, for backcross (BC) and F₂ designs, over a wide range of experimental sizes and of gene effects and dominance at the QTL. From the simulation results, the remarkably simple expressions, 3000/(mNd ²) (where m = 1 for BC and m = 2 for F₂; N = population size, and d = allele substitution effect) and 530/N (in terms of , the proportion of variance explained), were obtained for estimating resolving power. These expressions can provide a convenient guide to planning marker spacing in BC and F₂ marker-QTL linkage experiments and for placing confidence intervals about QTL map location obtained in such experiments.     

Identification of Quantitative Trait Loci Influencing Alcohol Consumption in the High Alcohol Drinking and Low Alcohol Drinking Rat Lines

Selective breeding has been employed to develop high-alcohol-drinking (HAD) and low-alcohol-drinking (LAD) rat lines from the heterogeneous N/Nih rat. Within-family selection and a rotational breeding design were used to discourage inbreeding (Li et al, 1993). To identify quantitative trait loci (QTLs) contributing to alcohol consumption, reciprocal HAD and LAD matings in conjunction with F1 intercrosses were used to create 459 F2 progeny. Using selective genotyping of 151 F2 progeny with extreme alcohol consumption scores and a novel least squares method developed by Haley et al (1994), five chromosomal regions (1,5,10,12, and 16) were identified with lod scores greater than 2.0. Genotyping of the entire sample of 459 F2 progeny produced maximum lod scores of 3.5 on chromosome 5, 2.4 on chromosome 10, 4.7 on chromosome 12 and 2.9 on chromosome 16. The evidence of linkage to chromosome 1 diminished substantially to a maximum lod score of 0.5 when all F2 progeny were genotyped. This study is the first genome-wide study for QTLs underlying alcohol consumption that has employed noninbred lines. Further localization of these QTLs will likely provide insight and candidate genes for the study of human alcoholism.     

Short-Term Selective Breeding as a Tool for QTL Mapping: Ethanol Preference Drinking in Mice

Short-term selective breeding starting from an F₂ intercross of two inbred strains is a largely unexploited but potentially useful tool for quantitative trait locus (QTL) mapping. The selection lines can also serve as a valuable confirmation test of recornbinant inbred (RI) QTL results when the same two progenitor strains are used. Starting from an F₂ from a C57BL/6J (B6) × DBA/2J (D2) cross (B6D2F2), this approach was used in a population of ~72 mice per generation bidirectionally selected for two-bottle choice 10% ethanol (alcohol) preference for four generations. The high-preference line diverged significantly from the low line in the first generation with a realized heritabittty of .32. By generation 4, the preference ratios in the high line were double those seen in the low line. Regions of the genome previously implicated by BXD RI QTL analysis as containing QTLs were searched using microsatellite markers. The test for the presence of QTLs was based on the divergence of marker allele frequencies in the two oppositely selected lines significantly exceeding that expected from random (genetic) drift and allele frequency estimation error. Combining the BXD and two-way selection line results, the most probable QTL was found on chromosome 3 (near the AdhI locus; LOD ~2.9), other probable QTLs were found with LOD 2.4–2.6.     

Optimal Selection of Sib Pairs from Random Samples for Linkage Analysis of a QTL Using the EDAC Test

Percentages of extremely concordant and extremely discordant sib pairs are calculated that maximize the power to detect a quantitative trait locus (QTL) under a variety of circumstances using the EDAC test. We assume a large fixed number of randomly sampled sib pairs, such as one would hope to find in the large twin registries, and limited resources to genotype a certain number of selected sib pairs. Our aim is to investigate whether optimal selection can be achieved when prior knowledge concerning the QTL gene action, QTL allele frequency, QTL effect size, and background (residual) sib correlation is limited or absent. To this end we calculate the best selection percentages for a large number of models, which differ in QTL gene action allele frequency, background correlation, and QTL effect size. By averaging these percentages over gene action, over allele frequency, over gene action, and over allele frequencies, we arrive at general recommendations concerning selection percentages. The soundness of these recommendations is subsequently in a number of test cases.     

Quantitative trait loci (QTL) applications to substances of abuse: Physical dependence studies with nitrous oxide and ethanol in BXD mice

Recombinant inbred (RI) mouse strains were developed primarily as a tool to detect and provisionally map major gene loci—those with effects large enough to cause a bimodal distribution in the trait of interest. This implied that progress toward gene mapping was possible only for gene loci accounting for at least half of the genetic variance. More recently, QTL (quantitative trait loci) approaches have been advanced that do not require bimodal distributions and are thus applicable to a much wider range of phenotypes. They offer the prospect of meaningful progress toward detecting and mapping minor as well as major gene loci affecting any trait of interest, provided there is a significant degree of genetic determination among the RI strains. This paper presents a review of RI gene mapping efforts concerning phenotypes related to drug abuse and presents new data for studies now in progress for nitrous oxide and acute ethanol withdrawal intensity. These two studies exemplify several strengths and limitations of the RI QTL approach.     

Genetic influences on behavioral and neuroendocrine responses to predator-odor stress in rats

The exposure of animals to a variety of stressful events can induce behavioral and physiological responses, which can be modulated by anxiety levels. It is well recognized that genetic factors play a substantial role in both anxiety and stress reactivity. The present study examined the effect of exposure to 2,4,5-trimethylthiazoline (TMT), a component of fox feces, on nociception and corticosterone levels in Lewis (LEW) and Spontaneously Hypertensive (SHR) inbred rat strains (which display genetic differences in anxiety models such as the elevated plus-maze and open-field). The influence of two quantitative trait loci (QTL), named Ofil1 and Ofil2, which are known to affect emotionality in LEW versus SHR intercrosses on the responses to TMT was also investigated. LEW and SHR rats of both sexes displayed similar levels of behavioral and neuroendocrine responses after TMT exposure. As expected, TMT odor stress produced analgesia and enhanced corticosterone levels. Ofil1 on chromosome 4 affected stress-induced analgesia in males only. Ofil2 on chromosome 7 had no effect. The results suggest that behaviors measured in classical models of generalized anxiety and reactivity to stress produced by predator odors can be genetically dissociated. Finding a locus with an effect on the behavioral responses to stress represents the starting point in the search for genes responsible for stress-related traits.     

Approaches toward the genetic analysis of complex traits: asthma and atopy

With the challenges emerging from the analysis and interpretation of the human genome, and the specific issues pertinent to pursuing the Genome Project itself, it is truly an exciting time in the development of the biological sciences. The occasion is certainly ripe for the emergence of new concepts and ideas, as the theories of complexity, natural selection, and reductionism become integrated into a new whole. We need to learn how to approach the analyses of the complex data sets that will be generated by the Genome Project and address, more generally, the problems inherent in the analysis of the complex diseases such as asthma. Finally, we need to consider how the recent advances in genetics and genomics will affect biomedical research reaching into the next millennium and beyond.     

QTL analysis of a red junglefowl x White Leghorn intercross reveals trade-off in resource allocation between behavior and production traits

Behaviors with high energetic costs may decrease in frequency in domestic animals as a response to selection for increased production. The aim of this study was to quantify production traits, foraging behavior, and social motivation in F₂ progeny from a White Leghorn × red junglefowl intercross (n = 751–1046) and to perform QTL analyses on the behavioral traits. A foraging-social maze was used for behavioral testing, which consisted of four identical arms and a central box. In two arms there was ad libitum access to the birds' usual food, and in the other two there was novel food (sunflower seeds) mixed with cat litter. In one arm with each of the two food sources, social stimuli were simulated by the presence of a mirror. Each bird could therefore feed on novel or well known food either alone or in the perceived company of a conspecific. Egg production, sexual maturity (females), food intake, and growth were measured individually, and residual food intake and metabolic body weight were estimated using standard methods. A genome scan using 104 microsatellite markers was carried out to identify QTLs affecting behavioral traits. Phenotypic growth rates at different ages showed weak associations in both sexes. Sexual maturity and egg weight were not strongly correlated to growth, indicating that these traits are not genetically linked. Time spent in each arm and in the central part of the maze was analyzed using principal component analyses. Four principal components (PC) were extracted, each reflecting a pattern of behavior in the maze. Females with early onset of sexual maturity scored higher on the PC1 reflecting preference for free food without social stimuli, and females with higher egg production scored higher on the PC2 reflecting exploration. Males with an overall higher growth rate and higher residual food intake scored higher on the PC3, which possibly reflected fear of the test situation, and tended to score higher on the PC4 reflecting low contrafreeloading. Significant QTLs were found for PC1 and PC4 scores on chromosomes 27 and 7, respectively. The location of the QTLs coincided with known QTLs for growth rate and body weight. The results suggest a trade-off between energy-demanding behavior and high production and that some of this may be caused by genetic linkage or pleiotropic gene effects.     

A novel method,the Variant Impact On Linkage Effect Test (VIOLET), leads to improved identification of causal variants in linkage regions

The Human Genome Project was expected to individualize medicine by rapidly advancing knowledge of common complex disease through discovery of disease-causing genetic variants. However, this has proved challenging. Although linkage analysis has identified replicated chromosomal regions, subsequent detection of causal variants for complex traits has been limited. One explanation for this difficulty is that utilization of association to follow up linkage is problematic given that linkage and association are not required to co-occur. Indeed, co-occurrence is likely to occur only in special circumstances, such as Mendelian inheritance, but cannot be universally expected. To overcome this problem, we propose a novel method, the Variant Impact On Linkage Effect Test (VIOLET), which differs from other quantitative methods in that it is designed to follow up linkage by identifying variants that influence the variance explained by a quantitative trait locus. VIOLET''s performance was compared with measured genotype and combined linkage association in two data sets with quantitative traits. Using simulated data, VIOLET had high power to detect the causal variant and reduced false positives compared with standard methods. Using real data, VIOLET identified a single variant, which explained 24% of linkage; this variant exhibited only nominal association (P=0.04) using measured genotype and was not identified by combined linkage association. These results demonstrate that VIOLET is highly specific while retaining low false-negative results. In summary, VIOLET overcomes a barrier to gene discovery and thus may be broadly applicable to identify underlying genetic etiology for traits exhibiting linkage.     

Genetic dissection of the olfactory bulbs of mice: QTLs on four chromosomes modulate bulb size

Olfaction is influenced by a complex mix of environmental and genetic factors that modulate the production, migration, and maturation of cells in the olfactory bulbs. In this study we analyzed effects of sex, age, body weight, and brain weight on olfactory bulb size in sexually mature mice. We then used regression corrected values (residuals) to map quantitative trait loci (QTLs) that selectively modulate bulb weight. This biometric analysis has relied on an F₂ intercross between C57BL/6J (B6) and DBA/2J (D2) inbred strains and a large sample of 35 BXD recombinant inbred (RI) strains. Bilateral bulb weight in adult mice ranges from 10 to 30 mg. Half of this remarkable variation can be predicted from differences in brain weight, sex, body weight, and age. A 100-mg difference in brain weight is associated with a 4.4-mg difference in bulb weight. Bulbs gain in weight by 0.2 mg/week—a 1% increase that continues until at least 300 days of age. Males tend to have slightly larger bulbs than females. By combining data from both related crosses (F₂ and RI) we identified four QTLs with selective effects on bulb size (genomewide p < .05). Bulb4a is located on chromosome 4 (Chr 4) and Bulb6a is located on Chr 6. Alleles inherited from B6 at both of these loci increase bulb weight by 0.5-1.0 mg. Bulb11a is located on proximal Chr 11 and Bulb17a is located on the proximal part of Chr 17. In contrast to the first two QTLs, B6 alleles at these two loci decrease bulb weight by 0.5-1.0 mg. Collectively, the four loci account for 20% of the phenotypic variance in bulb weight.     

Efficient,robust, and unified method for mapping complex traits (I): Two-point linkage analysis

The completion of a preliminary human genome map and development of molecular methods have enabled researchers to assay a large number of polymorphic markers that are evenly spaced along the entire human genome. Among many applications, marker data are valuable for mapping complex traits through linkage or linkage-disequilibrium analysis, the former of which is the focus of this paper, the first in a series on this subject. Formalizing the concept and computation for linkage analysis, Elston and Stewart [1971; Human Heredity 21:523–542] introduced a likelihood function to capture relevant genetic information and a recursive algorithm for computing the likelihood function. However, the computing burden is prohibitive in processing complex pedigrees. Since that fundamental development, improving the computational algorithm and extending the method has been a dynamic area of research. The primary objective of this communication is to introduce a semiparametric method for linkage analysis. It is a particularly suitable approach with desirable properties for mapping complex traits that may be binary, continuous, and partially observed (i.e., censored). It incorporates candidate genes, environmental factors, and their interactions with the putative gene and is expected to be robust and efficient in comparison with likelihood-based methods. The properties of the estimates have been studied in finite samples with a limited simulation study. This method is illustrated with an application to family data contributed to the Breast Cancer Consortium. Am. J. Med. Genet. 77:366–383, 1998. © 1998 Wiley-Liss, Inc.     

Serum Lipids in the GENECARD Study of Coronary Artery Disease Identify Quantitative Trait Loci and Phenotypic Subsets on Chromosomes 3q and 5q

Coronary artery disease (CAD) and dyslipidemia have strong genetic components. Heterogeneity complicates evaluating genetics of complex diseases such as CAD; incorporating disease-related phenotypes may help reduce heterogeneity. We hypothesized that incorporating lipoproteins in a study of CAD would increase the power to map genes, narrow linkage peaks, identify phenotypic subsets, and elucidate the contribution of established risk factors to genetic results.
We performed ordered subset analysis (OSA) and quantitative trait linkage (QTL) using serum lipoproteins and microsatellite markers in 346 families with early-onset CAD. OSA defined homogeneous subsets and calculated lod scores across a chromosome after ranking families by mean lipoprotein values. QTL used variance components analysis. We found significantly increased linkage to chromosome 3q13 (LOD 5.10, p = 0.008) in families with higher HDL cholesterol, lower LDL and total cholesterol, lower triglycerides, and fewer CAD risk factors, possibly due to a concentrated non-lipoprotein-related genetic effect. OSA identified linkage on chromosome 5q34 in families with higher cholesterol, possibly representing a hereditary lipoprotein phenotype. Multiple QTLs were identified, with the strongest for: total cholesterol on chromosome 5q14 (LOD 4.3); LDL on 20p12 (LOD 3.97); HDL on 3p14 (LOD 1.65); triglycerides on 18q22 (LOD 1.43); and HDL/TC ratio on 3q27-28 (LOD 2.06).
Our findings suggest the presence of etiologic heterogeneity in families with early-onset CAD, potentially due to differential effects of lipoprotein phenotypes. Candidate genes are under investigation.     

Testing genetic models for multiple symptoms: An application to the genetic analysis of liability to depression

A model is presented which allows for the contribution of genes and environment to categorical data on multiple symptoms. The model distinguishes between parameters needed to express the relationship between a latent trait and observed responses and the parameters required to represent the causes of variation in the latent trait. The regression of the latent trait on covariates may also be specified. The model is applied to symptoms of depression in 1983 pairs of adult female monozygotic and dizygotic twins. A model which allows only for polygenic variation in the latent trait is supported as well as the mixed model, which also allows for the effects of a major gene. The likelihood is significantly lower when all genetic effects are ascribed to a single gene. Practical limitations of the method are discussed.This research is supported by Grants AG04954, AA06781, GM32782, GM30250, and MH40828 from the National Institutes of Health. We are indebted to Dr. Greg Carey for his incisive discussion.     

Model-Based Multifactor Dimensionality Reduction to detect epistasis for quantitative traits in the presence of error-free and noisy data

Detecting gene-gene interactions or epistasis in studies of human complex diseases is a big challenge in the area of epidemiology. To address this problem, several methods have been developed, mainly in the context of data dimensionality reduction. One of these methods, Model-Based Multifactor Dimensionality Reduction, has so far mainly been applied to case-control studies. In this study, we evaluate the power of Model-Based Multifactor Dimensionality Reduction for quantitative traits to detect gene-gene interactions (epistasis) in the presence of error-free and noisy data. Considered sources of error are genotyping errors, missing genotypes, phenotypic mixtures and genetic heterogeneity. Our simulation study encompasses a variety of settings with varying minor allele frequencies and genetic variance for different epistasis models. On each simulated data, we have performed Model-Based Multifactor Dimensionality Reduction in two ways: with and without adjustment for main effects of (known) functional SNPs. In line with binary trait counterparts, our simulations show that the power is lowest in the presence of phenotypic mixtures or genetic heterogeneity compared to scenarios with missing genotypes or genotyping errors. In addition, empirical power estimates reduce even further with main effects corrections, but at the same time, false-positive percentages are reduced as well. In conclusion, phenotypic mixtures and genetic heterogeneity remain challenging for epistasis detection, and careful thought must be given to the way important lower-order effects are accounted for in the analysis.     

Silver-Russell syndrome: a dissection of the genetic aetiology and candidate chromosomal regions

The main features of Silver-Russell syndrome (SRS) are pre- and postnatal growth restriction and a characteristic small, triangular face. SRS is also accompanied by other dysmorphic features including fifth finger clinodactyly and skeletal asymmetry. The disorder is clinically and genetically heterogeneous, and various modes of inheritance and abnormalities involving chromosomes 7, 8, 15, 17, and 18 have been associated with SRS and SRS-like cases. However, only chromosomes 7 and 17 have been consistently implicated in patients with a strict clinical diagnosis of SRS. Two cases of balanced translocations with breakpoints in 17q23.3-q25 and two cases with a hemizygous deletion of the chorionic somatomammatropin gene (CSH1) on 17q24.1 have been associated with SRS, strongly implicating this region. Maternal uniparental disomy for chromosome 7 (mUPD(7)) occurs in up to 10% of SRS patients, with disruption of genomic imprinting underlying the disease status in these cases. Recently, two SRS patients with a maternal duplication of 7p11.2-p13, and a single proband with segmental mUPD for the region 7q31-qter, were described. These key patients define two separate candidate regions for SRS on both the p and q arms of chromosome 7. Both the 7p11.2-p13 and 7q31-qter regions are subject to genomic imprinting and the homologous regions in the mouse are associated with imprinted growth phenotypes. This review provides an overview of the genetics of SRS, and focuses on the newly defined candidate regions on chromosome 7. The analyses of imprinted candidate genes within 7p11.2-p13 and 7q31-qter, and gene candidates on distal 17q, are discussed.


Keywords: Silver-Russell syndrome; imprinting; mUPD(7); candidates     

Identification of Informative Strains and Provisional QTL Mapping of Amphetamine (AMPH)-Induced Locomotion in Recombinant Congenic Strains (RCS) of Mice

Amphetamine (AMPH)-induced locomotor activity is a rodent behavioral trait that reflects mesolimbic dopaminergic activity. To identify potential quantitative trait loci (QTL) associated with this behavior, we used 34 recombinant congenic strains (RCSs) of mice derived from A/J (A strains) and C57BL/6J (B strains) and measured AMPH-induced total distance traveled (AMPH-TDIST). Two strains in the A panel (A52 and A63) showed significantly elevated AMPH-TDIST compared to the parental A/J strain and behaved similarly to C57BL/6J. Simple sequence length polymorphism (SSLP) markers on chromosomes 1, 2, 3, 5, 6, 8, 9, 10 and 20 were significantly associated with AMPH-TDIST in the A strains. Within the B panel, two strains (B81 and B74) had significantly higher and two strains (B69 and B75) had significantly lower AMPH-TDIST than C57BL/6J. Markers associated with AMPH-TDIST in the B strains appeared on chromosomes 5, 17 and 20. Combining data from this approach and other genetic (mapping data in humans) and functional (cDNA expression) sources may help to identify suitable candidate genes relevant to human disorders where mesolimbic dopamine dysregulation has been postulated.Edited by Stephen Maxson