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Results for “quantitative trait locus”

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PubMed1999

Association of growth hormone loci with milk yield traits in Holstein bulls.

A pedigree analysis was used to investigate the association of bovine growth hormone loci with milk production traits of Holstein cattle. Holstein bulls were typed for three bovine growth hormone loci located in exon V, intron C, and the 3' region of the gene. Phenotypic data were daughter yield deviations for milk, fat, and protein yields and for fat and protein percentages. Analysis of linkage across families was applied to the data using one or two bovine growth hormone loci as markers linked to a putative biallelic quantitative trait locus. Estimated parameters were allele frequency, genotypic means, within-genotype standard deviation of a putative quantitative trait locus, and recombination fraction between the markers and the quantitative trait locus. Parameters were estimated by maximum likelihood techniques. The estimated frequency of the quantitative trait locus allele that decreased the value of the phenotype ranged from 0.1 for milk yield to 0.6 for protein yield. The estimated effect of an allele substitution at the quantitative trait locus, given in phenotypic standard deviation units, ranged from 0.75 for fat percentage to 1.6 for milk yield. The standard deviation within genotype ranged from 0.67 for fat yield to 0.87 for milk yield. The estimated recombination fraction was close to zero for protein percentage, indicating physical linkage between a quantitative trait locus affecting the trait and the bovine growth hormone loci.

Alleles↗

PubMed1994

Mapping and analysis of dairy cattle quantitative trait loci by maximum likelihood methodology using milk protein genes as genetic markers.

Maximum likelihood methodology was used to estimate effects of both a marker gene and a linked quantitative trait locus (QTL) on quantitative traits in a segregating population. Two alleles were assumed for the QTL. In addition to the effects of genotypes at both loci on the mean of the quantitative trait, recombination frequency between the loci, frequency of the QTL alleles and the residual standard deviation were also estimated. Thus six parameters were estimated in addition to the marker genotype means. The statistical model was tested on simulated data, and used to estimate direct and linked effects of the milk protein genes, beta-lactoglobulin, kappa-casein, and beta-casein, on milk, fat, and protein production and fat and protein percent in the Dutch dairy cattle population. beta-Lactoglobulin had significant direct effects on milk yield and fat percent. kappa-Casein had significant direct effects on milk yield, protein percent and fat yield. beta-Casein had significant direct effects on milk yield, fat and protein percent and fat and protein yield. Linked QTL with significant effects on fat percent were found for kappa-casein and beta-casein. Since the beta-casein and kappa-casein genes are closely linked, it is likely that the same QTL was detected for those two markers. Further, a QTL with a significant effect on fat yield was found to be linked to kappa-casein and a QTL with a significant effect on protein yield was linked to beta-lactoglobulin.

Alleles↗

PubMed2005

Quantitative trait locus-specific genotype x alcoholism interaction on linkage for evoked electroencephalogram oscillations.

We explored the evidence for a quantitative trait locus (QTL)-specific genotype x alcoholism interaction for an evoked electroencephalogram theta band oscillation (ERP) phenotype on a region of chromosome 7 in participants of the US Collaborative Study on the Genetics of Alcoholism. Among 901 participants with both genotype and phenotype data available, we performed variance component linkage analysis (SOLAR version 2.1.2) in the full sample and stratified by DSM-III-R and Feighner-definite alcoholism categories. The heritability of the ERP phenotype after adjusting for age and sex effects in the combined sample and in the alcoholism classification sub-groups ranged from 40% to 66%. Linkage on chromosome 7 was identified at 158 cM (LOD = 3.8) in the full sample and at 108 in the non-alcoholic subgroup (LOD = 3.1). Further, we detected QTL-specific genotype x alcoholism interaction at these loci. This work demonstrates the importance of considering the complexity of common complex traits in our search for genes that predispose to alcoholism.

Alcoholism↗

PubMed2006

Identification of collagen-induced arthritis loci in aged multiparous female mice.

Collagen-induced arthritis in mice is one of the most commonly used autoimmune experimental models, with many similarities to rheumatoid arthritis. Since collagen-induced arthritis is a complex polygenic disease there is a need for identification of several major disease-controlling genes. Because rheumatoid arthritis particularly affects aged women, we have in the present study identified new genetic regions critical for collagen-induced arthritis by studying aged female mice of a cross between NFR/N and B10.Q (H-2q haplotype). The mice in the present study had different reproductive histories, which did not significantly affect the onset, incidence or severity of the disease. A total of 200 female mice were used in a total genome-wide screening with 125 microsatellite markers. We found one new significant quantitative trait locus affecting the arthritis incidence, severity and day of onset on chromosome 11 (denoted Cia40), which colocalizes with a locus controlling pregnancy failure. Furthermore, a quantitative trait locus of suggestive significance associated with the incidence, severity and day of onset was identified on chromosome 1. Finally, a suggestively significant quantitative trait locus associated with collagen type II antibody titers was identified on chromosome 13. This study indicates that several gene loci control arthritis in aged multiparous females, and that at least one of these loci coincides with pregnancy failure.

Age of Onset↗

PubMed1997

Effect of inaccurate parameter estimates on genetic response to marker-assisted selection in an outbred population.

The effect of inaccurate estimates of variance and of the location of the quantitative trait locus on the genetic response to marker-assisted selection was studied by simulation of an adult multiple ovulation and embryo transfer nucleus breeding scheme. Two genetic models were simulated for the quantitative trait locus: a total of 10 alleles or 2 distinct alleles per base parent. For both models, the locus explained either 5 or 10% of phenotypic variance. A polygenic component was simulated, and the two genetic components were summed to 35% heritability for a trait measured on females. Overestimation of variance of the quantitative trait locus had minimal effect on genetic gain for marker-assisted selection over the short term, but decreased long-term response. The long-term loss was reduced when variance of the quantitative trait locus was reestimated after four generations of marker-assisted selection. Selection for favorable alleles at a nonexistent quantitative trait locus resulted in first generation losses of 3 and 7% for postulated quantitative trait loci, explaining 5 and 10% of variance, respectively. The larger the degree of error in location, the larger was the genetic loss compared with the correct location scenario. For the largest simulated location error of 15 cM, genetic superiority of marker-assisted selection was reduced by 80% in the first generation. We concluded that studies should be undertaken to verify estimates of quantitative trait locus and location to make optimal use of marker-assisted selection.

Alleles↗

PubMed1998

Restricted maximum likelihood analysis of linkage between genetic markers and quantitative trait loci for a granddaughter design.

A REML for the estimation of location and variance of a single quantitative trait locus, together with polygenic and residual variance, is described for the analysis of a granddaughter design. The method is based on a mixed linear model that includes the allelic effects of the quantitative trait locus, which are assumed to be normally distributed. Information from four marker loci situated on a single chromosome was available to derive the covariances at the linked quantitative trait locus. A derivative-free algorithm is described that makes use of the specific structure of the granddaughter design. The procedure has been applied to simulated data for a granddaughter design with 50 grandsire families of 40 sires each. Error variance was 60, and total additive genetic variance equaled 40; the quantitative trait locus explained either 10 or 25% of the latter variance. The size of the marker bracket containing the quantitative trait locus was either 10 or 30 cM. The power of detecting the quantitative trait locus ranged from 19 to 99%. Estimates of total genetic variance and variance explained by the quantitative trait locus were found to be empirically unbiased. A small bias was found in location estimates, especially when markers were not fully informative. The accuracy of parameter estimates was greatly improved by the use of information from individual daughters.

Animals↗

PubMed2001

Linkage analysis of quantitative traits in randomly ascertained pedigrees: comparison of penetrance-based and variance component analysis.

Penetrance-based linkage analysis and variance component linkage analysis are two methods that are widely used to localize genes influencing quantitative traits. Using computer programs PAP and SOLAR as representative software implementations, we have conducted an empirical comparison of both methods' power to map quantitative trait loci in extended, randomly ascertained pedigrees, using simulated data. Two-point linkage analyses were conducted on several quantitative traits of different genetic and environmental etiology using both programs, and the lod scores were compared. The two methods appear to have similar power when the underlying quantitative trait locus is diallelic, with one or the other method being slightly more powerful depending on the characteristics of the quantitative trait and the quantitative trait locus. In the case of a multiallelic quantitative trait locus, however, the variance component approach has much greater power. These findings suggest that one should give careful thought to the likely allelic architecture of the quantitative trait to be analyzed when choosing between these two analytical approaches. It may be the case in general that linkage methods which explicitly or implicitly rely on the assumption of a diallelic trait locus fare poorly when this assumption is incorrect.

Chromosome Mapping↗

PubMed2005

Chromosomal mapping of hyperserum IgA and glomerular IgA deposition in a high IgA (HIGA) strain of DdY mice.

BACKGROUND: The high IgA (HIGA) strain of ddY mice is an inbred model of IgA nephropathy (IgAN), established by selective mating of outbred ddY mice. HIGA mice show high levels of serum IgA and glomerulonephritis with mesangial IgA deposition. To identify the genetic loci responsible for hyperserum IgA and glomerular IgA deposition in this strain, quantitative trait loci analysis was carried out. METHODS: By crossing HIGA with BALB/c mice, 244 F2 generations were produced. Serum IgA levels and glomerular IgA deposition were examined at 40 weeks of age. Genetic markers were typed at 105 microsatellites and the quantitative trait loci of hyperserum IgA and glomerular IgA deposition were confirmed using Map Manager QTX software. RESULTS: Two significant quantitative trait loci of hyperserum IgA were identified on chromosome 2 [logarithm of odds (LOD) = 5.01] and chromsome 4 (LOD = 4.45), and a suggestive quantitative trait locus of hyperserum IgA was located on chromosome 1 (LOD = 3.49). On chromosome 15, a significant quantitative trait locus of glomerular IgA deposition was identified (LOD = 4.40) without the hyperserum IgA locus. Serum IgA level was weakly correlated with the intensity of glomerular IgA in 244 F2 mice; however, the quantitative trait loci of hyperserum IgA were not significantly associated with glomerular IgA deposition. CONCLUSION: These findings indicate that, in HIGA mice, glomerular IgA deposition is mainly regulated by a quantitative trait locus on chromosome 15, and hyperserum IgA synergistically but weakly affect glomerular IgA deposition. The immune disturbance similar to IgAN was revealed to be under multigenic control in HIGA mice.

Animals↗

PubMed2002

[Theoretical studies of QTL analysis of complex genetic diseases in humans].

Some genes controlling human diseases have been located on the regions less than 1 cM using linkage disequilibrium, and a few of them have been cloned. Z. W. Luo developed a method that can detect and estimate the coefficient of linkage disequilibrium between a marker locus and quantitative trait locus (QTL), and raised the theoretical strategies for high-resolution mapping of complex genetic disorders in humans. Based on the data mentioned above, a method for linkage test between a marker locus and QTL was set up, and a method for linkage test between two marker loci and epistatic QTL was suggested for the first time.

Chromosome Mapping↗

PubMed2001

Transmission/disequilibrium test analysis of total serum IgE levels in the Hutterite and Collaborative Study on the Genetics of Asthma data sets.

The Hutterite and Collaborative Study on the Genetics of Asthma data sets provided by Genetic Analysis Workshop 12 were analyzed using a regression-based transmission/disequilibrium test that assesses linkage between a marker locus and quantitative trait locus when allelic association is present, as proposed by George et al. [Am J Hum Genet 65:236-45, 1999]. Because the same marker set and analytical technique was used, the results from these data sets are amenable for comparison. Statistically significant results common to both data sets were found on chromosomes 1 and 3. A noteworthy result, significant at p < 10(-4), was detected on chromosome 18 in the Hutterites.

Adult↗

PubMed2002

Genetic and physiological analysis of germination efficiency in maize in relation to nitrogen metabolism reveals the importance of cytosolic glutamine synthetase.

We have developed an approach combining physiology and quantitative genetics to enhance our understanding of nitrogen (N) metabolism during kernel germination. The physiological study highlighted the central role of glutamine (Gln) synthetase (GS) and Gln synthesis during this developmental process because a concomitant increase of both the enzyme activity and the amino acid content was observed. This result suggests that Gln is acting either as a sink for ammonium released during both storage protein degradation and amino acid deamination or as a source for amino acid de novo synthesis by transamination. In the two parental lines used for the quantitative genetics approach, we found that the increase in Gln occurred earlier in Io compared with F(2), a result consistent with its faster germinating capacity. The genetic study was carried out on 140 F6 recombinant inbred lines derived from the cross between F(2) and Io. Quantitative trait locus mapping identified three quantitative trait loci (QTLs) related to germination trait (T50, time at which 50% of the kernels germinated) that explain 18.2% of the phenotypic variance; three QTLs related to a trait linked to germination performance, kernel size/weight (thousand kernels weight), that explain 17% of the phenotypic variance; two QTLs related to GS activity at early stages of germination that explain 17.7% of the phenotypic variance; and one QTL related to GS activity at late stages of germination that explains 7.3% of the phenotypic variance. Coincidences of QTL for germination efficiency and its components with genes encoding cytosolic GS (GS1) and the corresponding enzyme activity were detected, confirming the important role of the enzyme during the germination process. A triple colocalization on chromosome 4 between gln3 (a structural gene encoding GS1) and a QTL for GS activity and T50 was found; whereas on chromosome 5, a QTL for GS activity and thousand kernels weight colocalized with gln4, another structural gene encoding GS1. This observation suggests that for each gene, the corresponding enzyme activity is of major importance for germination efficiency either through the size of the grain or through its faster germinating capacity. Consistent with the possible nonoverlapping function of the two GS1 genes, we found that in the parental line Io, the expression of Gln3 was transiently enhanced during the first hours of germination, whereas that of gln4 was constitutive.

Chromosome Mapping↗

PubMed2004

Obesity in BSB mice is correlated with expression of genes for iron homeostasis and leptin.

OBJECTIVE: We searched for genes whose alleles cause obesity and novel pathways correlated with obesity. RESEARCH METHODS AND PROCEDURES: BSB mice are a model of complex obesity due to interactions among genes from C57BL/6J (B) and Mus spretus (SPRET) in (B x SPRET) x B backcross mice. Stringent criteria identified 50 genes differentially expressed in epididymal adipose tissue from 7 pairs of lean vs. obese BSB mice. Quantitative reverse transcription-polymerase chain reaction of adipose tissue RNA from 48 BSB mice with a range of obesity was assayed. Leptin was evaluated in inbred (SPRET/Ei) and outbred (SPRET/Pt) BSB mice. RESULTS: Leptin (Lep) and adipsin expressions had the greatest fold differences between obese and lean mice. Four genes involved in iron homeostasis were included in the 50 differentially expressed genes [hemochromatosis (Hfe), diaphorase 1, transferrin receptor (Trfr) 2, and protoporphyrinogen oxidase] and two additional iron-related genes did not quite meet the stringent criteria for differential expression (Trfr and lactotransferrin). Hfe and Trfr mRNA levels and liver iron were negatively correlated with fat mass. Variation in obesity phenotypes explained 49%, 40%, and 37%, respectively, of the variance in Hfe, Lep, and Trfr mRNA levels. Leptin differed by haplotype at the Lep locus in outbred BSB. The quantitative trait locus identified in the outbred cross did not occur in inbred BSB. DISCUSSION: Our results suggest that iron homeostasis in BSB mice is coordinately regulated in vivo in adipose depots in response to obesity. Lep alleles derived from outbred, but not inbred, SPRET are a positional candidate for the chromosome 6 quantitative trait locus in BSB mice.

Alleles↗

PubMed1999

Pharmacogenetic evidence for the involvement of 5-hydroxytryptamine (Serotonin)-1B receptors in the mediation of morphine antinociceptive sensitivity.

Morphine antinociception has been shown to be influenced significantly by genetic factors, now beginning to be identified in mice. A recent quantitative trait locus analysis revealed a significant statistical association between morphine antinociceptive magnitude and a region of mouse chromosome 9. This region contains the Htr1b gene, which encodes the 5-hydroxytryptamine (serotonin)-1B (5-HT(1B)) receptor subtype. To investigate the possibility that Htr1b represents the quantitative trait locus, C57BL/6 and DBA/2 inbred strains, the progenitors of the original quantitative trait locus mapping populations, were administered a novel 5-HT(1B) receptor antagonist (GR127935) concomitant with morphine. These mice are known to differ in morphine antinociceptive sensitivity on thermal pain assays (DBA/2 high; C57BL/6 low). GR127935 caused a dose-dependent antagonism (both reversal and prevention) of morphine antinociception in DBA/2 mice but had no effect in C57BL/6 mice. However, a 5-hydroxytryptamine-1A subtype (5-HT(1A)) receptor agonist, 8-hydroxydipropylaminotetralin, reversed morphine antinociception equally in the two strains. DBA/2 mice also exhibited significantly greater antinociception than did C57BL/6 mice from the administration of a 5-HT(1B) agonist, CGS12066. These data collectively support a role for 5-HT(1B) receptors in the mediation of morphine antinociception and support the contention that polymorphisms in the Htr1b gene may underlie individual differences in morphine sensitivity.

8-Hydroxy-2-(di-n-propylamino)tetralin↗

PubMed2026

Exploring genomic regions regulating the liver transcriptome and energy homeostasis in pigs.

In pigs, energy homeostasis has an impact on meat quality and health. In a Duroc pig population, 30 quantitative trait locus (QTL) regions associated with fatty acid (FA) composition in adipose tissue, plasma, liver and muscle were previously identified. Mapping of expression quantitative trait locus (eQTL) regions will provide a molecular hypothesis for genotype-phenotype interactions and may allow the identification of shared causal variants, key to increasing our understanding of the genetic regulation of FA composition and energy homeostasis. However, gene expression is impacted by environmental factors, while individual-level allelic imbalance (AI) can be more reliable and can be surveyed via allelic-specific expression (ASE) analysis. Furthermore, treatment of ASE as a quantitative trait allows the identification of allele-specific expression quantitative trait loci (aseQTLs), which are variants whose heterozygosity is linked to the AI of a nearby single-nucleotide polymorphism (SNP), pointing to regulatory elements. In this study, liver was selected as a key metabolic hub with an important role in the regulation of energy homeostasis, and 310 liver RNA sequencing samples were analysed using a combination of (1) eQTL mapping, (2) ASE analysis, and (3) aseQTL mapping methods. A total of 2 188 eQTL regions were identified, mostly cis-eQTL regions (73.17%). ASE analysis reported 1 964 ASE SNPs, associated with 633 genes. Finally, aseQTL mapping reported 64 172 aseQTL, associated with the AI of 31 genes. Colocalisation analysis combined with ASE analysis showed that the expression of FADS1 and FADS2 genes is associated with the polyunsaturated FA composition in several tissues, where microRNA regulation may be present. Finally, in the DGAT2 gene, annotated in a QTL region associated with multiple FAs in adipose tissue, ASE revealed allelic imbalance in the 3' untranslated region (UTR) of this gene. Allelic differential expression can be caused by a 13-bp insertion affecting messenger RNA stability, previously described, exemplifying how allelic imbalance is caused by a post-transcriptional regulatory mechanism undetectable by eQTL mapping. Furthermore, aseQTLs were associated with this gene, linked to a previously identified copy number variant not yet associated with DGAT2 expression. These results demonstrate how ASE analysis and aseQTL mapping can complement eQTL mapping, as they resolved a complex region affected by allelic heterogeneity, a main confounding effect of QTL mapping. In conclusion, the combination of eQTL mapping, ASE analysis and aseQTL mapping allowed the characterisation of the regulation of liver gene expression, improving our understanding of the genetic determinism of energy homeostasis.

Allele-specific expression↗

PubMed2003

Mapping viability loci using molecular markers.

In genetic mapping experiments, some molecular markers often show distorted segregation ratios. We hypothesize that these markers are linked to some viability loci that cause the observed segregation ratios to deviate from Mendelian expectations. Although statistical methods for mapping viability loci have been developed for line-crossing experiments, methods for viability mapping in outbred populations have not been developed yet. In this study, we develop a method for mapping viability loci in outbred populations using a full-sib family as an example. We develop a maximum likelihood (ML) method that uses the observed marker genotypes as data and the proportions of the genotypes of the viability locus as parameters. The ML solutions are obtained via the expectation-maximization algorithm. Application and efficiencies of the method are demonstrated and tested using a set of simulated data. We conclude that mapping viability loci can be accomplished using similar statistical techniques used in quantitative trait locus mapping for quantitative traits.

Algorithms↗

PubMed1986

Maximum likelihood techniques for the mapping and analysis of quantitative trait loci with the aid of genetic markers.

A method is presented to estimate the biometric parameters of a quantitative trait locus linked to a genetic marker when both loci are segregating in the F-2 generation of a cross between two inbred lines. The method, which assumes underlying normal distributions, is a combination of maximum likelihood and moments methods and uses the statistics of the genetic marker genotype samples for the quantitative trait to estimate the recombination frequency between the two loci and the means and variances of the genotypes of the quantitative trait locus. With this method, the genetic parameters of a locus affecting plant height linked to an electrophoretic marker for esterase were accurately estimated from a sample of 1596 F-2 progeny of a cross between two species of Lycopersicon (tomato). Linkage distance between the two loci was 38 map units and the effect of the quantitative trait locus was 1.6 phenotypic standard deviation units. Accurate estimates of the genetic parameters and linkage distance for populations of 2000 individuals simulated with a segregating codominant locus with an effect of 1.63 standard deviations linked to a genetic marker with .2 recombination were also derived by this method. The method is not effective in distinguishing between complete and partial linkage in samples of only 500 individuals or for quantitative loci with effects less than a phenotypic standard deviation. The method is more effective for codominant than for dominant loci.

Algorithms↗

PubMed2001

The genetic basis of plasma variation in adiponectin, a global endophenotype for obesity and the metabolic syndrome.

Here we present the first genetic analysis of adiponectin levels, a newly identified adipocyte-derived protein. Recent work has suggested that adiponectin may play a role in mediating the effects of body weight as a risk factor for coronary artery disease. For this analysis we assayed serum levels of adiponectin in 1100 adults of predominantly northern European ancestry distributed across 170 families. Quantitative genetic analysis of adiponectin levels detected an additive genetic heritability of 46%. The maximum LOD score detected in a genome wide scan for adiponectin levels was 4.06 (P = 7.7 x 10(-6)), 35 cM from pter on chromosome 5. The second largest LOD score (LOD = 3.2; P = 6.2 x 10(-5)) was detected on chromosome 14, 29 cM from pter. The detection of a significant linkage with a quantitative trait locus on chromosome 5 provides strong evidence for a replication of a previously reported quantitative trait locus for obesity-related phenotypes. In addition, several secondary signals offer potential evidence of replications for additional previously reported obesity-related quantitative trait loci on chromosomes 2 and 10. Not only do these results identify quantitative trait loci with significant effects on a newly described, and potentially very important, adipocyte-derived protein, they also reveal the emergence of a consistent pattern of linkage results for obesity-related traits across a number of human populations.

Adiponectin↗

PubMed2005

Genome-wide search for new genes controlling plasma lipid concentrations in mice and humans.

PURPOSE OF REVIEW: Quantitative trait locus analysis has been used in both humans and mice for the purpose of finding new genes regulating plasma lipid levels. We review these methods and discuss new approaches that can help find quantitative trait locus genes. RECENT FINDINGS: Many quantitative trait loci have been found that regulate plasma levels for HDL cholesterol (37 in mice and 30 in humans), LDL cholesterol (25 in mice and 20 in humans) and triglycerides (19 in mice and 30 in humans). Most of the human quantitative trait loci have concordant mouse quantitative trait loci mapping to homologous regions (93% for HDL cholesterol, 100% for LDL cholesterol and 80% for triglycerides), suggesting that many genes identified in mice may also regulate the same traits in humans. New approaches based on recently developed genomic and bioinformatic technologies and resources should greatly facilitate finding these genes. SUMMARY: New genes regulating plasma lipid levels can be found in mice and then tested in humans. Some of these genes could be potential therapeutic targets for human atherosclerosis.

Animals↗