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At least 1,675 records · Page 93Linked to original sources
PubMed1999

A quantitative trait locus influencing BMI maps to the region of the beta-3 adrenergic receptor.

The beta-3 adrenergic receptor (ADRB3) has been implicated as a regulator of energy expenditure, and a polymorphism in codon 64 of this gene (Trp64Arg) has been associated in some studies with obesity and insulin resistance. However, many studies have failed to detect an effect of this variant, and the importance of the Trp64Arg variant in human obesity remains controversial. We performed a quantitative linkage analysis of the ADRB3 and obesity, using 12 markers (including the intragenic Trp64Arg polymorphism) spanning a 57-cM region of chromosome 8. The study population consisted of 470 individuals from 10 large multigenerational families of Mexican-American ancestry residing in San Antonio, TX. In two-point analysis, logarithm of odds (LOD) scores >1.0 were observed for six markers surrounding ADRB3 in a 33-cM region spanned by markers D8S1477 and D8S1136. The multipoint LOD score was 3.21, occurring between markers D8S1121 and ADRB3, approximately 2-3 cM from ADRB3. Adjusting for the presence of the Arg64 allele or excluding from the analysis the 11 individuals homozygous for the Arg64 allele did not reduce the evidence for linkage. A genome scan was conducted at 10 cM map density to detect other loci influencing variation in BMI. Multipoint LOD scores >1.0 were observed in four other regions, including two on chromosome 17, one on chromosome 6q, and one on chromosome 2p. These data suggest that the ADRB3 should continue to be regarded as a strong candidate gene for obesity even though evidence for an effect of the Trp64Arg polymorphism could not be established. It is also possible that a gene closely linked to ADRB3 may influence susceptibility to obesity.

Adult↗
PubMed2002

Effect of low stressful temperature on genetic variation of five quantitative traits in Drosophila melanogaster.

A half-sib analysis was used to investigate genetic variation for three morphological traits (thorax length, wing length and sternopleural bristle number) and two life-history traits (developmental time and larva-to-adult viability) in Drosophila melanogaster reared at a standard (25 degrees C) and a low stressful (13 degrees C) temperature. Both phenotypic and environmental variation showed a significant increase under stressful conditions in all traits. For estimates of genetic variation, no statistically significant differences were found between the two environments. Narrow heritabilities tended to be higher at 13 degrees C for sternopleural bristle number and viability and at 25 degrees C for wing length and developmental time, whereas thorax length did not show any trend. However, the pattern of genetic variances and evolvability indices (coefficient of genetic variation and evolvability), considered in the context of literature evidence, indicated the possibility of an increase in additive genetic variation for the morphological traits and viability and in nonadditive genetic variation for developmental time. The data suggest that the effect of stressful temperature may be trait-specific and this warns against generalizations about the behaviour of genetic variation under extreme conditions.

Analysis of Variance↗
PubMed2025

From aerial drone to quantitative trait locus: leveraging next-generation phenotyping to reveal the genetics of color and height in field-grown Lactuca sativa.

In recent years, accurate and low-cost variant calling has enabled the genotyping of large diversity panels for genome-wide association studies. As a result, phenotyping rather than genotyping is now the rate-limiting step, especially in field experiments. This has created a strong need for high-throughput, accurate, and low-cost in-field phenotyping. Here, we present a genome-wide association study (GWAS) study on 194 field-grown accessions of lettuce (Lactuca sativa). These accessions were non-destructively phenotyped at two time points 15 days apart using a drone equipped with an RGB and multispectral (MSP) camera. Our high-throughput phenotyping approach integrates an RGB- and MSP camera to measure the color and height of lettuce in this large-scale field experiment. We used the mean and other summary statistics, such as median, quantiles, skewness, kurtosis, minimum, and maximum to quantify different aspects of color and height variation in lettuce from the drone images. Using these summary statistics as traits for GWAS, we confirm several previously described genetic associations, now under field conditions, and identify additional novel associations for color and height traits in lettuce.

Lactuca↗
PubMed1990

Search for faster methods of fitting the regressive models to quantitative traits.

The regressive models describe familial patterns of dependence of quantitative measures by specifying regression relationships among a person's phenotype and genotype and the phenotypes and genotypes of antecedents. When the number of sibs in the pattern of dependence increases, as in the class D regressive model, computation of the likelihood becomes time consuming, since the Elston-Stewart algorithm cannot be used generally. On the other hand, the simpler class A regressive model, which imposes a restriction on the sib-sib correlation, may lead to inference of a spurious major gene, as already observed in some instances. A simulation study is performed to explore the robustness of class A model with respect to false inference of a major gene and to search for faster methods of computing the likelihood under class D model. The class A model is not robust against the presence of a sib-sib correlation exceeding that specified by the model, unless tests on transmission probabilities are performed carefully: false detection of a major gene is reduced from a number of 26-30 to between 0 and 4 data sets out of 30 replicates after testing both the Mendelian transmission and the absence of transmission of a major effect against the general transmission model. Among various approximations of the likelihood formulation of the class D model, approximations 6 and 8 are found to work appropriately in terms of both the estimation of all parameters and hypothesis testing, for each generating model. These approximations lessen the computer time by allowing use of the Elston-Stewart algorithm.

Computer Simulation↗
PubMed1974

The effects of r- and K-selection on components of variance for two quantitative traits.

The genetic and environmental components of variance for two quantitative characters were measured in the descendants of Drosophila melanogaster populations which had been grown for several generations at densities of 100, 200, 300, and 400 eggs per vial. Populations subject to intermediate densities had a greater proportion of phenotypic variance available for selection than populations from either extreme. Selection on either character would be least effective under pure r-selection, a frequent attribute of selection programs.

Alleles↗
PubMed1986

Bias of the contribution of single-locus effects to the variance of a quantitative trait.

Advances in our understanding of the physiology of many quantitative phenotypes combined with better measurement abilities is providing a means for pursuing a measured genotype approach to partitioning the phenotypic variance into the contribution of separate loci. The standard estimate of the contribution of a single locus to the phenotypic variance applied recently in the human genetics literature is a biased statistic. We compare the biased estimates from several published studies with biased corrected estimates to illustrate the general problem.

Genetic Markers↗
PubMed2000

Quantitative traits of prion strains are enciphered in the conformation of the prion protein.

Variations in prions, which cause different disease phenotypes, are often referred to as strains. Strains replicate with a high degree of fidelity, which demands a mechanism that can account for this phenomenon. Prion strains differ by qualitative characteristics such as clinical symptoms, brain pathology, topology of accumulated PrP(Sc), and Western blot patterns of glycosylated or deglycosylated PrP(Sc). Since none of these qualitative features can directly explain quantitative strain traits such as incubation time or dose response, we analyzed conformational parameters of PrP(Sc) and the rate of accumulation in different prion strains. Using the conformation-dependent immunoassay (CDI), we were able to discriminate among PrP(Sc) molecules from eight different prion strains propagated in Syrian hamsters. CDI quantifies PrP isoforms by simultaneously following antibody binding to both the denatured and native forms of a protein. In a plot of the ratio of antibody binding to denatured/native PrP graphed as a function of the concentration of PrP(Sc), each strain occupied a unique position, indicating that each strain accumulated different concentrations of particular PrP(Sc) conformers. This conclusion was supported by a unique pattern of equilibrium unfolding of PrP(Sc) found within each strain. By comparing the PrP(Sc) levels before and after limited proteinase K digestion, we found that each strain produces a substantial fraction of protease-sensitive PrP(Sc). We asked whether this fraction of PrP(Sc) might reflect those PrP(Sc) molecules that are most readily cleared by cellular proteases. When the protease-sensitive PrP(Sc) fraction was plotted as a function of the incubation time, a linear relationship was found with an excellent correlation coefficient (r = 0.94). Combined with the data on time courses of prion infection in Tg(MHu2M) and Tg(SHaPrP) mice, the results argue that different incubation times of various prion strains may arise predominantly from distinct rates of PrP(Sc) clearance rather than from different rates of PrP(Sc) formation.

Animals↗
PubMed2002

Mapping of quantitative trait loci for body weight at three, six, and nine weeks of age in a broiler layer cross.

An F2 chicken population was established from a cross of a broiler sire-line and an egg laying (White Leghorn) line. There were two males and two females from both lines in the base population. The F1 progeny consisted of 8 males and 32 females. Over 500 F2 offspring from five hatches were reared to slaughter at a live weight of 2 kg at 9 wk of age. Body weights at 3, 6, and 9 wk were recorded. The DNA was extracted from blood samples, and genotypes for 101 microsatellite markers were determined. Data of 466 individuals from 30 families were available for analysis. Interval mapping QTL analyses were carried out. The QTL significant at the genome wide level that affected body weight at two ages were identified on chromosomes 1, 2, 4, 7, and 8 and a QTL on Chromosome 13 influenced body weight at all three ages. Genetic effects were generally additive, and the broiler allele increased body weight in all cases. The effects for significant individual QTL accounted for between 0.2 and 1.0 phenotypic standard deviations and the sum of the additive effects accounted for approximately 0.75 of the line difference in body weight at 6 wk of age. The largest single additive effect was on chromosome 4, and the effect of substituting one copy of the gene was an increase in weight of 249 g. Interactions of the QTL with sex or family were unimportant. There was no evidence for imprinting or of two or more QTL at the same location for any of the traits.

Aging↗
PubMed2004

Quantitative trait loci affecting life span in replicated populations of Drosophila melanogaster. II. Response to selection.

Three selection experiments were used to identify chromosome regions that contain QTL affecting late-life and early-life fitness in Drosophila melanogaster. The selection experiments were initiated by crossing pairs of inbred lines that had been derived from outbred laboratory populations that had different mean life spans. QTL regions were located by association with microsatellite markers that showed significant selection responses. Regions between recombination map positions 54 and 81 on chromosome 2, between 0 and 30 on chromosome 3, and near locations 49 and 81 on chromosome 3 had the strongest support as locations of life-span QTL. There was good general agreement between the life-span QTL regions that were identified by selection and those that were identified in a companion recombination mapping experiment that used the same fly stocks. Many marker loci responded in opposite directions to selection for late- and early-life fitness, indicating negative genetic correlations or trade-offs between those traits. Indirect evidence suggested that some negative genetic correlations were due to antagonistic pleiotropy.

Animals↗
PubMed1997

Effects of genotype-by-sex interaction on quantitative trait linkage analysis.

Genotype-by-sex (G x S) interaction refers to the interaction of autosomal genes with male or female physiological "environments." G x S interaction has been demonstrated in quantitative genetic analysis of a variety of traits including serum lipid concentrations and anthropometrics, and the importance of considering sex-specific major gene effects in segregation analyses also has been demonstrated. The goal of this study was to examine the effects of G x S interaction on the power to detect linkage. Trait Q3 in GAW10 Problem 2 was analyzed because it was modeled to have G x S interaction at the major gene locus MG3. All 200 nuclear family and 200 extended pedigree replicates were first screened for the presence of G x S interaction in Q3 using a quantitative genetic method. More than half of both the nuclear family and extended pedigree replicates evidenced significant G x S interaction. Variance components linkage analysis was then performed using all markers on GAW10 chromosome 4 in all 200 nuclear family and 200 extended pedigree replicates. A peak lod score of 1.92 at the correct chromosomal location was obtained using the extended pedigree data and incorporating G x S interaction effects. Not incorporating G x S interaction lowered the peak lod score from the analyses of the extended pedigrees to 1.53. Incorporation of G x S interaction effects also increased the power to detect linkage in the nuclear family replicates, although the nuclear families had considerably less power than the extended pedigrees to detect linkage, whether or not G x S interaction was modeled. Incorporation of G x S interaction effects can increase the power to detect linkage, even when the G x S interaction effects are modest.

Environment↗
PubMed2005

Quantitative trait Loci analysis using the false discovery rate.

False discovery rate control has become an essential tool in any study that has a very large multiplicity problem. False discovery rate-controlling procedures have also been found to be very effective in QTL analysis, ensuring reproducible results with few falsely discovered linkages and offering increased power to discover QTL, although their acceptance has been slower than in microarray analysis, for example. The reason is partly because the methodological aspects of applying the false discovery rate to QTL mapping are not well developed. Our aim in this work is to lay a solid foundation for the use of the false discovery rate in QTL mapping. We review the false discovery rate criterion, the appropriate interpretation of the FDR, and alternative formulations of the FDR that appeared in the statistical and genetics literature. We discuss important features of the FDR approach, some stemming from new developments in FDR theory and methodology, which deem it especially useful in linkage analysis. We review false discovery rate-controlling procedures--the BH, the resampling procedure, and the adaptive two-stage procedure-and discuss the validity of these procedures in single- and multiple-trait QTL mapping. Finally we argue that the control of the false discovery rate has an important role in suggesting, indicating the significance of, and confirming QTL and present guidelines for its use.

Chromosome Mapping↗
PubMed1992

Comparing evolvability and variability of quantitative traits.

There are two distinct reasons for making comparisons of genetic variation for quantitative characters. The first is to compare evolvabilities, or ability to respond to selection, and the second is to make inferences about the forces that maintain genetic variability. Measures of variation that are standardized by the trait mean, such as the additive genetic coefficient of variation, are appropriate for both purposes. Variation has usually been compared as narrow sense heritabilities, but this is almost always an inappropriate comparative measure of evolvability and variability. Coefficients of variation were calculated from 842 estimates of trait means, variances and heritabilities in the literature. Traits closely related to fitness have higher additive genetic and nongenetic variability by the coefficient of variation criterion than characters under weak selection. This is the reverse of the accepted conclusion based on comparisons of heritability. The low heritability of fitness components is best explained by their high residual variation. The high additive genetic and residual variability of fitness traits might be explained by the great number of genetic and environmental events they are affected by, or by a lack of stabilizing selection to reduce their phenotypic variance. Over one-third of the quantitative genetics papers reviewed did not report trait means or variances. Researchers should always report these statistics, so that measures of variation appropriate to a variety of situations may be calculated.

Animals↗
PubMed2001

Hierarchical modeling of the relation between sequence variants and a quantitative trait: addressing multiple comparison and population stratification issues.

When analyzing the relation between genetic sequence information and disease traits, false-positive associations can arise due to multiple comparisons and population stratification. In an attempt to address these issues, we incorporate into a conventional analytic model higher-level--or "prior"--models that use additional information to improve estimates while allowing for differing population structures. We apply this hierarchical model to simulated data from the Genetic Analysis Workshop 12. We focus on the effects of common candidate gene sequence variants on quantitative risk factor 5 (Q5) levels. In particular, we compare the regression coefficients (and 95% confidence intervals) obtained from conventional (one-stage) analyses versus the corresponding results from the hierarchical analyses. When examining either the marry-ins or all subjects in the general and isolate populations, the conventional model detected numerous sites in candidate genes 1-5 and 7 that had statistically significant regression coefficients (alpha level = 0.05). In contrast, our hierarchical model primarily only detected associations for variants in candidate gene 2, which is the casual gene for Q5.

Chromosome Mapping↗
PubMed2003

Genome-wide linkage scan on estimated breeding values for a quantitative trait.

BACKGROUND: A genome-wide linkage scan was performed on Replicate 1 of the simulated data for fasting triglyceride levels. The aim of this study was to implement mixed-model methodology to estimate breeding values for each individual for this trait and to assess the merit of these breeding values in linkage analysis. These breeding values utilize all the pedigree information, and the genetic and phenotypic correlations with other measured traits across the two cohorts. A genome-wide linkage scan was run on both the new breeding value traits and the original traits. RESULTS: Using breeding values, a maximum LOD of 7.78 was found on chromosome 5 at a position very close to a gene underlying the triglyceride levels. This effect was not detected using the original trait. CONCLUSION: The results imply that estimating breeding values may be a suitable method of deriving traits for use in genome-wide scans.

Chromosome Mapping↗