Differential genetic risk for methamphetamine intake confers differential sensitivity to the temperature‐altering effects of other addictive drugs

Mice selectively bred for high methamphetamine (MA) drinking (MAHDR), compared with mice bred for low MA drinking (MALDR), exhibit greater sensitivity to MA reward and insensitivity to aversive and hypothermic effects of MA. Previous work identified the trace amine‐associated receptor 1 gene (Taar1) as a quantitative trait gene for MA intake that also impacts thermal response to MA. All MAHDR mice are homozygous for the mutant Taar1 ^m1J allele, whereas all MALDR mice possess at least one copy of the reference Taar1 ⁺ allele. To determine if their differential sensitivity to MA‐induced hypothermia extends to drugs of similar and different classes, we examined sensitivity to the hypothermic effect of the stimulant cocaine, the amphetamine‐like substance 3,4‐methylenedioxymethamphetamine (MDMA), and the opioid morphine in these lines. The lines did not differ in thermal response to cocaine, only MALDR mice exhibited a hypothermic response to MDMA, and MAHDR mice were more sensitive to the hypothermic effect of morphine than MALDR mice. We speculated that the μ‐opioid receptor gene (Oprm1) impacts morphine response, and genotyped the mice tested for morphine‐induced hypothermia. We report genetic linkage between Taar1 and Oprm1; MAHDR mice more often inherit the Oprm1 ^D2 allele and MALDR mice more often inherit the Oprm1 ^B6 allele. Data from a family of recombinant inbred mouse strains support the influence of Oprm1 genotype, but not Taar1 genotype, on thermal response to morphine. These results nominate Oprm1 as a genetic risk factor for morphine‐induced hypothermia, and provide additional evidence for a connection between drug preference and drug thermal response.     

Morphine intake and the effects of naltrexone and buprenorphine on the acquisition of methamphetamine intake

Some common genetic factors appear to influence risk for drug dependence across multiple drugs of abuse. In previous research, mice that were selectively bred for higher amounts of methamphetamine consumption, using a two‐bottle choice methamphetamine drinking procedure, were found to be less sensitive to the locomotor stimulant effects of morphine and of the more selective μ‐opioid receptor agonist fentanyl, compared to mice that were bred for low methamphetamine consumption. This suggested that μ‐opioid receptor‐mediated pathways may influence genetic risk for methamphetamine consumption. We hypothesized that these differences in opioid sensitivity would impact opioid intake in the methamphetamine drinking lines and that drugs with μ‐opioid receptor activity would impact methamphetamine intake. Consumption of morphine was examined in 2, two‐bottle choice studies, one that compared morphine to quinine consumption and another that used a saccharin fading procedure. Next, naltrexone (0, 0.5, 1, 2, 5, 10 and 20 mg/kg), a μ‐opioid receptor antagonist, and buprenorphine (0, 1, 2 or 4 mg/kg), a μ‐opioid receptor partial agonist, were each examined for their effects on the acquisition of methamphetamine consumption. Low methamphetamine drinking mice consumed more morphine compared to high methamphetamine drinking mice. Naltrexone did not alter methamphetamine consumption in either selected line; however, buprenorphine reduced methamphetamine intake in the high methamphetamine drinking line. These data show that greater sensitivity to opioids is associated with greater opioid intake and warrant further investigation of drugs with μ‐opioid receptor‐specific agonist activity in genetically determined differences in methamphetamine consumption.     

Verification of a genetic locus for methamphetamine intake and the impact of morphine

Mammalian Genome - A quantitative trait locus (QTL) on proximal chromosome (Chr) 10 accounts for >?50% of the genetic variance in methamphetamine (MA) intake in mice selectively bred...     

Localization to chromosome 10 of a locus influencing morphine analgesia in crosses derived from C57BL/ and DBA/2 strains

A quantitative trait locus (QTL) was detected and mapped to proximal chromosome 10 near the markers Mpmv5 and D10Mit51 with a strong influence on morphine-induced analgesia in the BXD recombinant inbred (Rl) strains and in an F₂ cross (B6D2F2) between the BXD progenitor strains, C57BL/6 and DBA/2. A LOD score of 3.9 (p <. 00002) was seen for analgesia using the hot plate assay. Naloxone Bmax was also associated with this chromosome region in BXD RI mice. The mu opioid receptor gene (Oprm) has recently been mapped to this same chromosome region. The observation that several morphine-related traits and naloxone Bmax appear to be partly determined by this presumed single locus is consistent with the hypothesis that the mu opioid receptor gene, or one of its modulators, is the basis for the QTL.     

Characterization of a major X-linked quantitative trait locus influencing body weight of mice

Growth rate in mice is an archetypal quantitative trait that has long been studied genetically, physiologically, and metabolically, but its genetic basis is still poorly understood due to its complex inheritance and the influence of environment. We measured differences in 17 growth-related traits between a pair of partially congenic lines that differ for a segment of the X chromosome containing a quantitative trait locus (QTL) that we identified in a genomewide QTL scan. The QTL has a large effect on mean body weight of approximately 20% at all ages, and affects early growth rate to a greater extent than late growth rate. Feed is converted to body mass more efficiently in the high chromosome segment-bearing line than the low line. The weights of various internal organs are affected to a somewhat greater extent by the QTL than body weight. The proportional change in body length is smaller than body weight, but this may be an effect of scale. Body weight at late ages appears to allow the most efficient detection of allelic differences at the QTL, although assignment of genotypic state based on phenotype is never unambiguous.     

Multi-trait and multi-environment QTL analysis reveals the impact of seed colour on seed composition traits in <Emphasis Type="Italic">Brassica napus</Emphasis>

Brassica napus seed composition traits (fibre, protein, oil and fatty acid profiles), seed colour and yield-associated traits are regulated by a complex network of genetic factors. Although previous studies have attempted to dissect the underlying genetic basis for these traits, a more complete picture of the available quantitative trait loci (QTL) variation and any interaction between the different traits is required. In this study, QTL mapping for eleven seed composition traits, seed colour and a yield-related trait (TSW) was conducted in a spring-type canola-quality B. napus doubled haploid (DH) population from a cross between black-seeded (DH12075) and yellow-seeded (YN01-429) lines across five environments. A major QTL associated with fibre traits (acid detergent fibre, acid detergent lignin and neutral detergent fibre) and seed colour (whiteness index) was mapped on chromosome N9 across the five environments. Multi-trait analysis identified QTL which had pleiotropic effect for seed colour and other composition traits. Multi-environment analysis revealed genetic (QTL) × environment effects on most QTL. These findings provide a more detailed insight into the complex QTL networks controlling seed composition and yield-associated traits in canola-quality B. napus.     

Using bioinformatics and systems genetics to dissect HDL-cholesterol genetics in an MRL/MpJ x SM/J intercross

A higher incidence of coronary artery disease is associated with a lower level of HDL-cholesterol. We searched for genetic loci influencing HDL-cholesterol in F2 mice from a cross between MRL/MpJ and SM/J mice. Quantitative trait loci (QTL) mapping revealed one significant HDL QTL (Apoa2 locus), four suggestive QTL on chromosomes 10, 11, 13, and 18 and four additional QTL on chromosomes 1 proximal, 3, 4, and 7 after adjusting HDL for the strong Apoa2 locus. A novel nonsynonymous polymorphism supports Lipg as the QTL gene for the chromosome 18 QTL, and a difference in Abca1 expression in liver tissue supports it as the QTL gene for the chromosome 4 QTL. Using weighted gene co-expression network analysis, we identified a module that after adjustment for Apoa2, correlated with HDL, was genetically determined by a QTL on chromosome 11, and overlapped with the HDL QTL. A combination of bioinformatics tools and systems genetics helped identify several candidate genes for both the chromosome 11 HDL and module QTL based on differential expression between the parental strains, cis regulation of expression, and causality modeling. We conclude that integrating systems genetics to a more-traditional genetics approach improves the power of complex trait gene identification.     

Trade-offs in the genetic control of functional and nutritional quality traits in UK winter wheat

A complex network of trade-offs exists between wheat quality and nutritional traits. We investigated the correlated relationships among several milling and baking traits as well as mineral density in refined white and whole grain flour. Our aim was to determine their pleiotropic genetic control in a multi-parent population over two trial years with direct application to practical breeding. Co-location of major quantitative trait loci (QTL) and principal component based multi-trait QTL mapping increased the power to detect QTL and revealed pleiotropic effects explaining many complementary and antagonistic trait relationships. High molecular weight glutenin subunit genes explained much of the heritable variation in important dough rheology traits, although additional QTL were detected. Several QTL, including one linked to the TaGW2 gene, controlled grain size and increased flour extraction rate. The semi-dwarf Rht-D1b allele had a positive effect on Hagberg falling number, but reduced grain size, specific weight, grain protein content and flour water absorption. Mineral nutrient concentrations were lower in Rht-D1b lines for many elements, in wholemeal and white flour, but potassium concentration was higher in Rht-D1b lines. The presence of awns increased calcium content without decreasing extraction rate, despite the negative correlation between these traits. QTL were also found that affect the relative concentrations of key mineral nutrients compared to phosphorus which may help increase bioavailability without associated anti-nutritional effects of phytic acid. Taken together these results demonstrate the potential for marker-based selection to optimise trait trade-offs and enhance wheat nutritional value by considering pleiotropic genetic effects across multiple traits.Subject terms: Plant breeding, Quantitative trait, Genetic variation     

Construction of Chromosome Segment Substitution Lines in Peanut (Arachis hypogaea L.) Using a Wild Synthetic and QTL Mapping for Plant Morphology

Chromosome segment substitution lines (CSSLs) are powerful QTL mapping populations that have been used to elucidate the molecular basis of interesting traits of wild species. Cultivated peanut is an allotetraploid with limited genetic diversity. Capturing the genetic diversity from peanut wild relatives is an important objective in many peanut breeding programs. In this study, we used a marker-assisted backcrossing strategy to produce a population of 122 CSSLs from the cross between the wild synthetic allotetraploid (A. ipaënsis×A. duranensis)^4x and the cultivated Fleur11 variety. The 122 CSSLs offered a broad coverage of the peanut genome, with target wild chromosome segments averaging 39.2 cM in length. As a demonstration of the utility of these lines, four traits were evaluated in a subset of 80 CSSLs. A total of 28 lines showed significant differences from Fleur11. The line×trait significant associations were assigned to 42 QTLs: 14 for plant growth habit, 15 for height of the main stem, 12 for plant spread and one for flower color. Among the 42 QTLs, 37 were assigned to genomic regions and three QTL positions were considered putative. One important finding arising from this QTL analysis is that peanut growth habit is a complex trait that is governed by several QTLs with different effects. The CSSL population developed in this study has proved efficient for deciphering the molecular basis of trait variations and will be useful to the peanut scientific community for future QTL mapping studies.     

Environment-dependent survival of Drosophila melanogaster: a quantitative genetic analysis

Summary Survival under starvation conditions was investigated in relationship to survival when food was present because these traits could be linked by evolutionary history. Recombinant inbred lines derived from natural populations of Drosophila melanogaster were used to test genetic correlations and architecture of these survival traits. Sexes were genetically correlated within traits and there was significant correlation between survival traits. A number of quantitative trait loci (QTLs) were present for starvation survival and/or survival on food. In general, the QTL effects were consistent for sexes and environments. QTL effects were found on each major chromosome, but the major effects were largely localized on the second chromosome. Importantly, the 'four-allele' progenitor of the recombinant inbred lines used in the present study allowed the sign and magnitude of effects to be assigned to linkage groups. One such linkage group on the second chromosome conferred starvation resistance and longevity, supporting the hypothesis of an association between starvation resistance and lifespan.     

A quantitative trait variant in Gabra2 underlies increased methamphetamine stimulant sensitivity

Psychostimulant (methamphetamine, cocaine) use disorders have a genetic component that remains mostly unknown. We conducted genome-wide quantitative trait locus (QTL) analysis of methamphetamine stimulant sensitivity. To facilitate gene identification, we employed a Reduced Complexity Cross between closely related C57BL/6 mouse substrains and examined maximum speed and distance traveled over 30 min following methamphetamine (2 mg/kg, i.p.). For maximum methamphetamine-induced speed following the second and third administration, we identified a single genome-wide significant QTL on chromosome 11 that peaked near the Cyfip2 locus (LOD = 3.5, 4.2; peak = 21 cM [36 Mb]). For methamphetamine-induced distance traveled following the first and second administration, we identified a genome-wide significant QTL on chromosome 5 that peaked near a functional intronic indel in Gabra2 coding for the alpha-2 subunit of the GABA-A receptor (LOD = 3.6–5.2; peak = 34–35 cM [66–67 Mb]). Striatal cis-expression QTL mapping corroborated Gabra2 as a functional candidate gene underlying methamphetamine-induced distance traveled. CRISPR/Cas9-mediated correction of the mutant intronic deletion on the C57BL/6J background to the wild-type C57BL/6NJ allele was sufficient to reduce methamphetamine-induced locomotor activity toward the wild-type C57BL/6NJ-like level, thus validating the quantitative trait variant (QTV). These studies show the power and efficiency of Reduced Complexity Crosses in identifying causal variants underlying complex traits. Functionally restoring Gabra2 expression decreased methamphetamine stimulant sensitivity and supports preclinical and human genetic studies implicating the GABA-A receptor in psychostimulant addiction-relevant traits. Importantly, our findings have major implications for studying psychostimulants in the C57BL/6J strain—the gold standard strain in biomedical research.     

Using mouse models to dissect the genetics of obesity

Mice have proved to be powerful models for understanding obesity in humans and farm animals. Single-gene mutants and genetically modified mice have been used successfully to discover genes and pathways that can regulate body weight. For polygenic obesity, the most common pattern of inheritance, many quantitative trait loci (QTLs) have been mapped in crosses between selected and inbred mouse lines. Most QTL effects are additive, and diet, age and gender modify the genetic effects. Congenic, recombinant inbred, advanced intercross, and chromosome substitution strains are needed to map QTLs finely, to identify the genes underlying the traits, and to examine interactions between them.     

Both additivity and epistasis control the genetic variation for fruit quality traits in tomato

The effect of a gene involved in the variation of a quantitative trait may change due to epistatic interactions with the overall genetic background or with other genes through digenic interactions. The classical populations used to map quantitative trait loci (QTL) are poorly efficient to detect epistasis. To assess the importance of epistasis in the genetic control of fruit quality traits, we compared 13 tomato lines having the same genetic background except for one to five chromosome fragments introgressed from a distant line. Six traits were assessed: fruit soluble solid content, sugar content and titratable acidity, fruit weight, locule number and fruit firmness. Except for firmness, a large part of the variation of the six traits was under additive control, but interactions between QTL leading to epistasis effects were common. In the lines cumulating several QTL regions, all the significant epistatic interactions had a sign opposite to the additive effects, suggesting less than additive epistasis. Finally the re-examination of the segregating population initially used to map the QTL confirmed the extent of epistasis, which frequently involved a region where main effect QTL have been detected in this progeny or in other studies.     

Small- and Large-Effect Quantitative Trait Locus Interactions Underlie Variation in Yeast Sporulation Efficiency

Quantitative trait loci (QTL) with small effects on phenotypic variation can be difficult to detect and analyze. Because of this a large fraction of the genetic architecture of many complex traits is not well understood. Here we use sporulation efficiency in Saccharomyces cerevisiae as a model complex trait to identify and study small-effect QTL. In crosses where the large-effect quantitative trait nucleotides (QTN) have been genetically fixed we identify small-effect QTL that explain approximately half of the remaining variation not explained by the major effects. We find that small-effect QTL are often physically linked to large-effect QTL and that there are extensive genetic interactions between small- and large-effect QTL. A more complete understanding of quantitative traits will require a better understanding of the numbers, effect sizes, and genetic interactions of small-effect QTL.     

Integration of QTL and bioinformatic tools to identify candidate genes for triglycerides in mice

To identify genetic loci influencing lipid levels, we performed quantitative trait loci (QTL) analysis between inbred mouse strains MRL/MpJ and SM/J, measuring triglyceride levels at 8 weeks of age in F2 mice fed a chow diet. We identified one significant QTL on chromosome (Chr) 15 and three suggestive QTL on Chrs 2, 7, and 17. We also carried out microarray analysis on the livers of parental strains of 282 F2 mice and used these data to find cis-regulated expression QTL. We then narrowed the list of candidate genes under significant QTL using a "toolbox" of bioinformatic resources, including haplotype analysis; parental strain comparison for gene expression differences and nonsynonymous coding single nucleotide polymorphisms (SNP); cis-regulated eQTL in livers of F2 mice; correlation between gene expression and phenotype; and conditioning of expression on the phenotype. We suggest Slc25a7 as a candidate gene for the Chr 7 QTL and, based on expression differences, five genes (Polr3 h, Cyp2d22, Cyp2d26, Tspo, and Ttll12) as candidate genes for Chr 15 QTL. This study shows how bioinformatics can be used effectively to reduce candidate gene lists for QTL related to complex traits.     

QTL affecting conformation traits in Angora goats

A genomic screen for quantitative trait loci (QTL) affecting conformation traits was performed by genotyping 288 Angora goats offspring from 8 half-sub families with 76 microsatellite markers. The following traits were recorded: weaning weight (WW, Kg); stature (S, cm); chest depth (CD, cm); shoulder width (SW, cm); rump length (RL, cm); rump width (RW, cm); head length (HL, cm); head width (HW, cm); shin circumference (SC, cm); chest circumference (CC, cm) and body length (BL, cm). Data were analyzed using the QTL Express program. A total of 5 QTL were detected in five chromosomes with chromosome wide significance level. For the 11 analysed traits the results were: evidence of two possible QTL for HL were found in chromosomes 1 and 4, a putative QTL for trait CD was found in chromosome 2, evidence for BL was found in chromosome 8 and a possible QTL was found for trait CC in chromosome 9. The results reported here show the existence of chromosomal regions in Angora goats involved in conformation traits and represent the first in depth search in some specific-genome sections in order to identify and characterize the genetic variability involved in these traits.     

Quantitative Trait Loci for Lipid Content in Drosophila melanogaster

Recombinant inbred lines derived from a natural population were used to investigate natural genetic variation for lipid abundance, protein abundance, and weight of Drosophila melanogaster. Females were heavier and contained more lipid and soluble protein than males. Lipid and protein abundance were genetically correlated with female weight, but male weight was not correlated with lipid or protein. Lipid and protein abundance were genetically correlated in males, but not in females. Quantitative trait loci (QTLs) for weight and protein abundance were predominantly on the X chromosome, whereas QTLs for lipid abundance were found on the second and third chromosomes. QTLs for lipid proportion (lipid abundance normalized by weight or protein abundance) were present on all chromosomes; a lipid proportion QTL on the third chromosome correlated with a QTL for starvation resistance observed in a previous study using the same set of recombinant inbred lines, suggesting that it might underlie both traits. Candidate genes are discussed in relationship to lipid abundance, lipid proportion, and starvation resistance.     

High-density linkage mapping of yield components and epistatic interactions in maize with doubled haploid lines from four crosses

Grain yield (GY) is a genetically complex and physiologically multiplicative trait which can be decomposed into the components kernel number (KN) and 100-kernel weight (HKW). Genetic analysis of these less complex yield component traits may give insights into the genetic architecture and predictive ability of complex traits. Here, we investigated how the incorporation of component traits and epistasis in quantitative trait locus (QTL) mapping approaches influences the accuracy of GY prediction. High-density genetic maps with 7,000–10,000 polymorphic single nucleotide polymorphisms were constructed for four biparental populations. The populations comprised between 99 and 227 doubled haploid maize lines which were phenotyped in field trials in two environments. Heritability was highest for HKW (88–89 %), intermediate for KN (72–80 %), and lowest for GY (64–83 %). Mapped QTL explained in total 21–55 %, 22–67 %, and 24–75 % of the genotypic variance for GY, KN, and HKW, respectively. Support intervals of QTL were short, indicating that QTL were located with high precision. Co-located QTLs with same parental origin of favorable alleles were detected within populations for different traits and between populations for the same traits. Using GY predictions based on the detected QTL, prediction accuracies (r) determined by cross validation ranged from 0.18 to 0.52. Epistatic models did not outperform the corresponding additive models. In conclusion, models based on QTL positions of component traits support the identification of favorable alleles for multiplicative traits and provide a basis to select superior inbred lines by marker-assisted breeding.     

A region on chicken chromosome 2 affects both egg white thinning and egg weight

We describe the results from genetic dissection of a QTL region on chicken chromosome 2, shown to affect egg weight and quality in an earlier genome scan of an F₂ intercross between two divergent egg layer lines. As the 90% confidence intervals for the detected QTL covered tens of centiMorgans, new analyses were needed. The datasets were reanalysed with denser marker intervals to characterise the QTL region. Analysis of a candidate gene from the original QTL region, vimentin, did not support its role in controlling egg white thinning. Even after reanalysis with additional seven markers in the QTL area, the 90% confidence intervals remained large or even increased, suggesting the presence of multiple linked QTL for the traits. A grid search fitting two QTL on chromosome 2 for each trait suggested that there are two distinct QTL areas affecting egg white thinning in both production periods and egg weight in the late production period. The results indicate possible pleiotropic effects of some of the QTL on egg quality and egg weight. However, it was not possible to make a distinction between close linkage versus pleiotropic effects.