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.     

Chromosomal loci that influence oral nicotine consumption in C57BL/6J x C3H/HeJ F2 intercross mice

Several studies have demonstrated that there are genetic influences on free-choice oral nicotine consumption in mice. In order to establish the genetic architecture that underlies individual differences in free-choice nicotine consumption, quantitative trait loci (QTL) mapping was used to identify chromosomal regions that influence free-choice nicotine consumption in male and female F(2) mice derived from a cross between C57BL/6J and C3H/HeJ mice. These two mouse strains were chosen not only because they differ significantly for oral nicotine consumption, but also because they are at or near phenotypic extremes for all measures of nicotine sensitivity that have been reported. A four-bottle choice paradigm was used to assess nicotine consumption over an 8-day period. The four bottles contained water or water supplemented with 25, 50 or 100 microg/ml of nicotine base. Using micrograms of nicotine consumed per milliliter of total fluid consumed per day as the nicotine consumption phenotype, four significant QTL were identified. The QTL with the largest LOD score was located on distal chromosome 1 (peak LOD score = 15.7). Other chromosomes with significant QTL include central chromosome 4 (peak LOD score = 4.1), proximal chromosome 7 (peak LOD score = 6.1) and distal chromosome 15 (peak LOD score = 4.8). These four QTL appear to be responsible for up to 62% of the phenotypic variance in oral nicotine consumption.     

QTL mapping for teat number in an Iberian-by-Meishan pig intercross

The aim of this study was to investigate chromosomal regions affecting the number of teats in pigs and possible epistatic interactions between the identified quantitative trait loci (QTL). An experimental F2 cross between Iberian and Chinese Meishan lines was used for this purpose. A genomic scan was conducted with 117 markers covering the 18 porcine autosomes. Linkage analyses were performed by interval mapping using an animal model to estimate QTL and additive polygenic effects. Complementary analyses with models fitting two QTL were also carried out. The results showed three genomewide significant QTL mapping on chromosomes 5, 10 and 12, whose joint action control up to 30% of the phenotypic variance of the trait. Meishan alleles had a positive additive effect on teat number, and a positive-additive x additive-epistatic interaction was detected between QTL on chromosomes 10 and 12.     

Candidate genes for plasma triglyceride, FFA, and glucose revealed from an intercross between inbred mouse strains NZB/B1NJ and NZW/LacJ

To identify the genes controlling plasma concentrations of triglycerides (TGs), FFAs, and glucose, we carried out a quantitative trait loci (QTL) analysis of the closely related mouse strains New Zealand Black (NZB/B1NJ) and New Zealand White (NZW/LacJ), which share 63% of their genomes. The NZB x NZW F(2) progeny were genotyped and phenotyped to detect QTL, and then comparative genomics, bioinformatics, and sequencing were used to narrow the QTL and reduce the number of candidate genes. Triglyceride concentrations were linked to loci on chromosomes (Chr) 4, 7, 8, 10, and 18. FFA concentrations were affected by a significant locus on Chr 4, a suggestive locus on Chr 16, and two interacting loci on Chr 2 and 15. Plasma glucose concentrations were affected by QTL on Chr 2, 4, 7, 8, 10, 15, 17, and 18. Comparative genomics narrowed the QTL by 31% to 86%; haplotype analysis was usually able to further narrow it by 80%. We suggest several candidate genes: Gba2 on Chr 4, Irs2 on Chr 8, and Ppargc1b on Chr 18 for TG; A2bp1 on Chr 16 for FFA; and G6pc2 on Chr 2 and Timp3 on Chr 10 for glucose.     

Genome-wide association analysis for feed efficiency in Angus cattle

Estimated breeding values for average daily feed intake (AFI; kg/day), residual feed intake (RFI; kg/day) and average daily gain (ADG; kg/day) were generated using a mixed linear model incorporating genomic relationships for 698 Angus steers genotyped with the Illumina BovineSNP50 assay. Association analyses of estimated breeding values (EBVs) were performed for 41,028 single nucleotide polymorphisms (SNPs), and permutation analysis was used to empirically establish the genome-wide significance threshold (P < 0.05) for each trait. SNPs significantly associated with each trait were used in a forward selection algorithm to identify genomic regions putatively harbouring genes with effects on each trait. A total of 53, 66 and 68 SNPs explained 54.12% (24.10%), 62.69% (29.85%) and 55.13% (26.54%) of the additive genetic variation (when accounting for the genomic relationships) in steer breeding values for AFI, RFI and ADG, respectively, within this population. Evaluation by pathway analysis revealed that many of these SNPs are in genomic regions that harbour genes with metabolic functions. The presence of genetic correlations between traits resulted in 13.2% of SNPs selected for AFI and 4.5% of SNPs selected for RFI also being selected for ADG in the analysis of breeding values. While our study identifies panels of SNPs significant for efficiency traits in our population, validation of all SNPs in independent populations will be necessary before commercialization.     

Genome-wide association study of birth weight in sheep

Birth weight is the earliest available growth trait with considerable impacts on lamb survivability and growth performance traits. This study was conducted to perform a genome-wide association study of birth weight in a meat-type sheep. A total of 132 Lori-Bakhtiari sheep were selected based on estimated of breeding values (EBVs) for BW analyses. The selected animals were genotyped using Illumina Ovine SNP50 Bead Chip. After quality control, a total of 41 323 single-nucleotide polymorphisms (SNPs) and 130 sheep were used for subsequent analyses. Plink 1.90 beta software was used for the analyses. Seven SNPs on chromosomes 1, 16, 19 and 22 were detected based on genome-wide unadjusted P-values (P <10⁻⁶), which jointly accounted for 1.2% of total genetic variation. However, based on Bonferroni-adjusted P-values, only three SNPs on chromosome 1 had significant associations with EBVs for birth weight (P <0.05), which jointly explained 0.8% of total genetic variation. A total of seven genes were found in 50 kb intervals from the three significant SNPs on chromosome 1, but only three genes, including RAB6B (a member of RAS oncogene family), Tf serotransferrin and GIGYF2 (a GRB10 interacting GYF protein 2), could be considered as candidate genes for birth weight in future studies. The results of this study may facilitate potential use of the genes involving in growth and production traits for genetic improvement of productivity in sheep.     

Mapping quantitative trait loci for egg production traits in an F2 intercross of Oh-Shamo and White Leghorn chickens

We performed quantitative trait locus (QTL) analyses for egg production traits, including age at first egg (AFE) and egg production rates (EPR) measured every 4 weeks from 22 to 62 weeks of hen age, in a population of 421 F₂ hens derived from an intercross between the Oh‐Shamo (Japanese Large Game) and White Leghorn breeds of chickens. Simple interval mapping revealed a main‐effect QTL for AFE on chromosome 1 and four main‐effect QTL for EPR on chromosomes 1 and 11 (three on chromosome 1 and one on chromosome 11) at the genome‐wide 5% levels. Among the three EPR QTL on chromosome 1, two were identified at the early stage of egg laying (26–34 weeks of hen age) and the remaining one was discovered at the late stage (54–58 weeks). The alleles at the two EPR QTL derived from the Oh‐Shamo breed unexpectedly increased the trait values, irrespective of the Oh‐Shamo being inferior to the White Leghorn in the trait. This suggests that the Oh‐Shamo, one of the indigenous Japanese breeds, is an untapped resource that is important for further improvement of current elite commercial laying chickens. In addition, six epistatic QTL were identified on chromosomes 2, 4, 7, 8, 17 and 19, where none of the above main‐effect QTL were located. This is the first example of detection of epistatic QTL affecting egg production traits. The main and epistatic QTL identified accounted for 4–8% of the phenotypic variance. The total contribution of all QTL detected for each trait to the phenotypic and genetic variances ranged from 4.1% to 16.9% and from 11.5% to 58.5%, respectively.     

RiceQTLPro: an integrated database for quantitative trait loci marker mapping in rice plant

The National Agricultural Biotechnology Information Center (NABIC) in South Korea reconstructed a RiceQTLPro database for gene positional analysis and structure prediction of the chromosomes. This database is an integrated web-based system providing information about quantitative trait loci (QTL) markers in rice plant. The RiceQTLPro has the three main features namely, (1) QTL markers list, (2) searching of markers using keyword, and (3) searching of marker position on the rice chromosomes. This updated database provides 112 QTL markers information with 817 polymorphic markers on each of the 12 chromosomes in rice.

Availability

The database is available for free at http://nabic.rda.go.kr/gere/rice/geneticMap/     

Genome-wide association mapping in Norwegian Red cattle identifies quantitative trait loci for fertility and milk production on BTA12

Reproductive performance is a critical trait in dairy cattle. Poor reproductive performance leads to prolonged calving intervals, higher culling rates and extra expenses related to multiple inseminations, veterinary treatments and replacements. Genetic gain for improved reproduction through traditional selection is often slow because of low heritability and negative correlations with production traits. Detection of DNA markers associated with improved reproductive performance through genome-wide association studies could lead to genetic gain that is more balanced between fertility and production. Norwegian Red cattle are well suited for such studies, as very large numbers of detailed reproduction records are available. We conducted a genome-wide association study for non-return rate, fertility treatments and retained placenta using almost 1 million records on these traits and 17 343 genome-wide single-nucleotide polymorphisms. Genotyping costs were minimized by genotyping the sires of the cows recorded and by using daughter averages as phenotypes. The genotyped sires were assigned to either a discovery or a validation population. Associations were only considered to be validated if they were significant in both groups. Strong associations were found and validated on chromosomes 1, 5, 8, 9, 11 and 12. Several of these were highly supported by findings in other studies. The most important result was an association for non-return rate in heifers in a region of BTA12 where several associations for milk production traits have previously been found. Subsequent fine-mapping verified the presence of a quantitative trait loci (QTL) having opposing effects on non-return rate and milk production at 18 Mb. The other reproduction QTL did not have pleiotropic effects on milk production, and these are therefore of considerable interest for use in marker-assisted selection.     

Linkage disequilibrium clustering‐based approach for association mapping with tightly linked genomewide data

Genomewide association studies (GWAS) aim to identify genetic markers strongly associated with quantitative traits by utilizing linkage disequilibrium (LD) between candidate genes and markers. However, because of LD between nearby genetic markers, the standard GWAS approaches typically detect a number of correlated SNPs covering long genomic regions, making corrections for multiple testing overly conservative. Additionally, the high dimensionality of modern GWAS data poses considerable challenges for GWAS procedures such as permutation tests, which are computationally intensive. We propose a cluster‐based GWAS approach that first divides the genome into many large nonoverlapping windows and uses linkage disequilibrium network analysis in combination with principal component (PC) analysis as dimensional reduction tools to summarize the SNP data to independent PCs within clusters of loci connected by high LD. We then introduce single‐ and multilocus models that can efficiently conduct the association tests on such high‐dimensional data. The methods can be adapted to different model structures and used to analyse samples collected from the wild or from biparental F₂ populations, which are commonly used in ecological genetics mapping studies. We demonstrate the performance of our approaches with two publicly available data sets from a plant (Arabidopsis thaliana) and a fish (Pungitius pungitius), as well as with simulated data.     

Genome-wide scan of plasma cholecystokinin in baboons shows linkage to human chromosome 17

Objective: Cholecystokinin (CCK) is known to inhibit food intake and is an important signal for controlling meal volume, indicating a possible role in weight regulation. Our objective was to investigate genetic influences on plasma CCK in baboons. Research Methods and Procedures: Subjects were 376 baboons (males = 113, females = 263) from the Southwest National Primate Research Center, housed at the Southwest Foundation for Biomedical Research, San Antonio, Texas. Anthropometric and biochemical parameters were analyzed. Genetic effects on plasma CCK were estimated by the maximum likelihood‐based variance components method implemented in the software program SOLAR (Sequential Oligogenic Linkage Analysis Routines). Results: Male baboons (32.7 ± 6 kg) were much heavier than females (20.2 ± 4 kg). Similarly, mean (± standard deviation) plasma CCK values were also higher in male baboons (13.8 ± 6 pM) than female baboons (12.5 ± 4 pM). Significant heritabilities were observed for plasma CCK (0.14 ± 0.1, p < 0.05), body weight (h² = 0.62 ± 0.15, p < 10^?8), and glucose (h² = 0.68 ± 0.17, p < 10^?7). A genome‐wide scan of plasma CCK detected a strong signal for a quantitative trait locus (QTL) on chromosome 17p12–13 [logarithm of the odds (LOD) = 3.1] near marker D17S804. Suggestive evidence of a second QTL was observed on chromosome 4q34–35 (LOD = 2.3) near marker D4S2374. Discussion: A substantial contribution of additive genetic effects to the variation in plasma levels of CCK was demonstrated in baboons. The identification of a QTL for plasma CCK on chromosome 17p is significant, as several obesity‐related traits such as BMI, leptin, adiponectin, and acylation stimulating protein have already been mapped to this region.     

Mapping of quantitative trait loci affecting eggshell quality on chromosome 9 in an F2 intercross between two chicken lines divergently selected for eggshell strength

Broken and cracked eggshells are major causes of significant economic losses to the egg production industry. The quantitative trait loci (QTL) on chromosome 9 influencing the quality of eggshells were identified by analysing an intercross between two parent lines developed from the same founder population by a two-way selection for eggshell strength with non-destructive deformation conducted over 14 generations. Chromosome-wide highly significant ( P  <   0.01) QTL associated with egg weight (EW), short length of egg (SLE), long length of egg (LLE) and eggshell weight were mapped to the distal region of chromosome 9. Among the QTL affecting EW, SLE and LLE, ovocalyxin-32 was identified as a potential candidate gene influencing eggshell traits. Marker-assisted selection based on these QTL could be used to develop strategies for reducing the breakage and cracking of eggs in commercial layer houses.     

Quantitative trait Loci for regional adiposity in mouse lines divergently selected for food intake

Objective: Obesity is thought to result from an interaction between genotype and environment. Excessive adiposity is associated with a number of important comorbidities; however, the risk of obesity‐related disease varies with the distribution of fat throughout the body. The aim of this study was to map quantitative trait loci (QTLs) associated with regional fat depots in mouse lines divergently selected for food intake corrected for body mass. Research Methods and Procedures: Using an F₂ intercross design (n = 457), the dry mass of regional white (subcutaneous, gonadal, retroperitoneal, and mesenteric) adipose tissue (WAT) and brown adipose tissue (BAT) depots were analyzed to map QTLs. Results: The total variance explained by the mapped QTL varied between 12% and 39% for BAT and gonadal fat depots, respectively. Using the genome‐wide significance threshold, nine QTLs were associated with multiple fat depots. Chromosomes 4 and 19 were associated with WAT and BAT and chromosome 9 with WAT depots. Significant sex × QTL interactions were identified for gonadal fat on chromosomes 9, 16, and 19. The pattern of QTLs identified for the regional deposits showed the most similarity between retroperitoneal and gonadal fat, whereas BAT showed the least similarity to the WAT depots. Analysis of total fat mass explained in excess of 40% of total variance. Discussion: There was limited concordance between the QTLs mapped in our study and those reported previously. This is likely to reflect the unique nature of the mouse lines used. Results provide an insight into the genetic basis of regional fat distribution.     

Transmission distortion and genetic incompatibilities between alleles in a multigenerational mouse advanced intercross line

While direct additive and dominance effects on complex traits have been mapped repeatedly, additional genetic factors contributing to the heterogeneity of complex traits have been scarcely investigated. To assess genetic background effects, we investigated transmission ratio distortions (TRDs) of alleles from parent to offspring using an advanced intercross line (AIL) of an initial cross between the mouse inbred strains C57BL/6NCrl (B6N) and BFMI860-12 [Berlin Fat Mouse Inbred (BFMI)]. A total of 341 males of generation 28 and their respective 61 parents and 66 grandparents were genotyped using Mega Mouse Universal Genotyping Arrays. TRDs were investigated using allele transmission asymmetry tests, and pathway overrepresentation analysis was performed. Sequencing data were used to test for overrepresentation of nonsynonymous SNPs (nsSNPs) in TRD regions. Genetic incompatibilities were tested using the Bateson–Dobzhansky–Muller two-locus model. A total of 62 TRD regions were detected, many in close proximity to the telocentric centromere. TRD regions contained 44.5% more nsSNPs than randomly selected regions (182 vs 125.9 ± 17.0, P < 1 × 10⁻⁴). Testing for genetic incompatibilities between TRD regions identified 29 genome-wide significant incompatibilities between TRD regions [P_(BF) < 0.05]. Pathway overrepresentation analysis of genes in TRD regions showed that DNA methylation, epigenetic regulation of RNA, and meiotic/meiosis regulation pathways were affected independent of the parental origin of the TRD. Paternal BFMI TRD regions showed overrepresentation in the small interfering RNA biogenesis and in the metabolism of lipids and lipoproteins. Maternal B6N TRD regions harbored genes involved in meiotic recombination, cell death, and apoptosis pathways. The analysis of genes in TRD regions suggests the potential distortion of protein–protein interactions influencing obesity and diabetic retinopathy as a result of disadvantageous combinations of allelic variants in Aass, Pgx6, and Nme8. Using an AIL significantly improves the resolution at which we can investigate TRD. Our analysis implicates distortion of protein–protein interactions as well as meiotic drive as the underlying mechanisms leading to the observed TRD in our AIL. Furthermore, genes with large amounts of nsSNPs located in TRD regions are more likely to be involved in pathways that are related to the phenotypic differences between the parental strains. Genes in these TRD regions provide new targets for investigating genetic adaptation, protein–protein interactions, and determinants of complex traits such as obesity.     

Fine mapping of QTL for prepulse inhibition in LG/J and SM/J mice using F2 and advanced intercross lines

Prepulse inhibition (PPI) of the startle response is a measure of sensorimotor gating, a process that filters out extraneous sensory, motor and cognitive information. Humans with neurological and psychiatric disorders, including schizophrenia, obsessive‐compulsive disorder and Huntington's disease, exhibit a reduction in PPI. Habituation of the startle response is also disrupted in schizophrenic patients. In order to elucidate the genes involved in sensorimotor gating, we phenotyped 472 mice from an F₂ cross between LG/J × SM/J for PPI and genotyped these mice genome‐wide using 162 single nucleotide polymorphism (SNP) markers. We used prepulse intensity levels that were 3, 6 and 12 dB above background (PPI3, PPI6 and PPI12, respectively). We identified a significant quantitative trait locus (QTL) on chromosome 12 for all three prepulse intensities as well as a significant QTL for both PPI6 and PPI12 on chromosome 11. We identified QTLs on chromosomes 7 and 17 for the startle response when sex was included as an interactive covariate and found a QTL for habituation of the startle response on chromosome 4. We also phenotyped 135 mice from an F₃₄ advanced intercross line (AIL) between LG/J × SM/J for PPI and genotyped them at more than 3000 SNP markers. Inclusions of data from the AIL mice reduced the size of several of these QTLs to less than 5 cM. These results will be useful for identifying genes that influence sensorimotor gaiting and show the power of AIL for fine mapping of QTLs.     

The mouse QTL map helps interpret human genome-wide association studies for HDL cholesterol

Genome-wide association (GWA) studies represent a powerful strategy for identifying susceptibility genes for complex diseases in human populations but results must be confirmed and replicated. Because of the close homology between mouse and human genomes, the mouse can be used to add evidence to genes suggested by human studies. We used the mouse quantitative trait loci (QTL) map to interpret results from a GWA study for genes associated with plasma HDL cholesterol levels. We first positioned single nucleotide polymorphisms (SNPs) from a human GWA study on the genomic map for mouse HDL QTL. We then used mouse bioinformatics, sequencing, and expression studies to add evidence for one well-known HDL gene (Abca1) and three newly identified genes (Galnt2, Wwox, and Cdh13), thus supporting the results of the human study. For GWA peaks that occur in human haplotype blocks with multiple genes, we examined the homologous regions in the mouse to prioritize the genes using expression, sequencing, and bioinformatics from the mouse model, showing that some genes were unlikely candidates and adding evidence for candidate genes Mvk and Mmab in one haplotype block and Fads1 and Fads2 in the second haplotype block. Our study highlights the value of mouse genetics for evaluating genes found in human GWA studies.     

Genome-wide scan for bovine twinning rate QTL using linkage disequilibrium

Twinning is a complex trait with negative impacts on health and reproduction, which cause economic loss in dairy production. Several twinning rate quantitative trait loci (QTL) have been detected in previous studies, but confidence intervals for QTL location are broad and many QTL are unreplicated. To identify genomic regions or genes associated with twinning rate, QTL analysis based on linkage combined with linkage disequilibrium (LLD) and individual marker associations was conducted across the genome using high-throughput single nucleotide polymorphism (SNP) genotypes. A total of 9919 SNP markers were genotyped with 200 sires and sons in 19 half-sib North American Holstein dairy cattle families. After SNPs were genotyped, informative markers were selected for genome-wide association tests and QTL searches. Evidence for twinning rate QTL was found throughout the genome. Thirteen markers significantly associated with twinning rate were detected on chromosomes 2, 5 and 14 ( P  < 2.3 × 10⁻⁵). Twenty-six regions on fourteen chromosomes were identified by LLD analysis at P  < 0.0007. Seven previously reported ovulation or twinning rate QTL were supported by results of single marker association or LLD analyses. Single marker association analysis and LLD mapping were complementary tools for the identification of putative QTL in this genome scan.     

Mapping genetic loci that regulate lipid levels in a NZB/B1NJxRF/J intercross and a combined intercross involving NZB/B1NJ, RF/J, MRL/MpJ, and SJL/J mouse strains

The NZB/B1NJ (NZB) mouse strain exhibits high cholesterol and HDL levels in blood compared with several other strains of mice. To study the genetic regulation of blood lipid levels, we performed a genome-wide linkage analysis in 542 chow-fed F2 female mice from an NZBxRF/J (RF) intercross and in a combined data set that included NZBxRF and MRL/MpJxSJL/J intercrosses. In the NZBxRF F2 mice, the cholesterol and HDL concentrations were influenced by quantitative trait loci (QTL) on chromosome (Chr) 5 [logarithm of odds (LOD) 17-19; D5Mit10] that was in the region identified earlier in crosses involving NZB mice, but two QTLs on Chr 12 (LOD 4.7; D12Mit182) and Chr 19 (LOD 5.7; D19Mit1) were specific to the NZBxRF intercross. Triglyceride levels were affected by two novel QTLs at D12Mit182 (LOD 8.7) and D15Mit13 (LOD 3.5). The combined-cross linkage analysis (1,054 mice, 231 markers) 1) identified four shared QTLs (Chrs 5, 7, 14, and 17) that were not detected in one of the parental crosses and 2) improved the resolution of two shared QTLs. In summary, we report additional loci regulating lipid levels in NZB mice that had not been identified earlier in crosses involving the NZB strain of mice. The identification of shared loci from multiple crosses increases confidence toward finding the QTL gene.     

Congenic dissection of a major QTL for methamphetamine sensitivity implicates epistasis

We previously used the C57BL/6J (B6) × A/J mouse chromosome substitution strain (CSS) panel to identify a major quantitative trait locus (QTL) on chromosome 11 influencing methamphetamine (MA)‐induced locomotor activity. We then made an F₂ cross between CSS‐11 and B6 and narrowed the locus (Bayes credible interval: 79–109 Mb) which was inherited dominantly and accounted for 14% of the phenotypic variance in the CSS panel. In the present study, we created congenic and subcongenic lines possessing heterozygous portions of this QTL to narrow the interval. We identified one line (84–96 Mb) that recapitulated the QTL, thus narrowing the region to 12 Mb. This interval also produced a small decrease in locomotor activity following prior saline treatment. When we generated subcongenic lines spanning the entire 12‐Mb region, the phenotypic difference in MA sensitivity abruptly disappeared, suggesting an epistatic mechanism. We also evaluated the rewarding properties of MA (2 mg/kg, i.p.) in the 84‐ to 96‐Mb congenic line using the conditioned place preference (CPP) test. We replicated the locomotor difference in the MA‐paired CPP chamber yet observed no effect of genotype on MA‐CPP, supporting the specificity of this QTL for MA‐induced locomotor activity under these conditions. Lastly, to aid in prioritizing candidate genes responsible for this QTL, we used the Affymetrix GeneChip® Mouse Gene 1.0ST Array to identify genes containing expression QTLs (eQTL) in the striatum of drug‐naÏve, congenic mice. These findings highlight the difficulty of using congenic lines to fine map QTLs and illustrate how epistasis may thwart such efforts.     

Identification of chromosomal regions associated with growth and carcass traits in an F3 full sib intercross line originating from a cross of chicken lines divergently selected on body weight

An F₃ resource population originating from a cross between two divergently selected lines for high (D+ line) or low (D− line) body weight at 8-weeks of age (BW55) was generated and used for Quantitative Trait Locus (QTL) mapping. From an initial cross of two founder F₀ animals from D(+) and D(−) lines, progeny were randomly intercrossed over two generations following a full sib intercross line (FSIL) design. One hundred and seventy-five genome-wide polymorphic markers were employed in the DNA pooling and selective genotyping of F₃ to identify markers with significant effects on BW55. Fifty-three markers on GGA2, 5 and 11 were then genotyped in the whole F₃ population of 503 birds, where interval mapping with GridQTL software was employed. Eighteen QTL for body weight, carcass traits and some internal organ weights were identified. On GGA2, a comparison between 2-QTL vs. 1-QTL analysis revealed two separate QTL regions for body, feet, breast muscle and carcass weight. Given co-localization of QTL for some highly correlated traits, we concluded that there were 11 distinct QTL mapped. Four QTL localized to already mapped QTL from other studies, but seven QTL have not been previously reported and are hence novel and unique to our selection line. This study provides a low resolution QTL map for various traits and establishes a genetic resource for future fine-mapping and positional cloning in the advanced FSIL generations.