bluuebunny/crossref_metadata_2025_split huggingface dataset:
Use of sibling risk ratios and components of genetic variance in the characterization of a simulated oligogenic disease
Authors: Almasy L. , Tierney C. , Risch N. 
Published: June 2005

AbstractSibling risk ratios are used to quantify the genetic effect of two simulated disease loci, identified through TDT and association methods and characterized via components of variance derived from a relative penetrance matrix. Inconsistencies between the data set and the simulating model are also discussed. ©1995 Wiley‐Liss, Inc.
Systematic search of susceptibility loci with methods using gametic disequilibrium
Authors: Bickeböller H. , Margaritte‐Jeannin P. , Babron M.‐C. , Clerget‐Darpoux F. 
Published: June 2005

AbstractSusceptibility genes are identified for a simulated complex trait by a systematic genome search for linkage between disease and a genetic marker in the presence of gametic disequilibrium. The transmission/disequilibrium tests TDTa or TDTg for multiallelic markers compare transmitted and nontransmitted alleles or the genotypes formed by the two transmitted alleles and the genotypes formed by the two nontransmitted alleles, respectively. With these two tests we were able to identify the two markers D1G31 and D5G23. Under the simulating model these are in fact two susceptibility genes involved in the disease. ©1995 Wiley‐Liss, Inc.
Modeling the role of two susceptibility loci by the MASC method
Authors: Dizier Marie‐Hélène , Babron Marie‐Claude , Clerget‐Darpoux Françoise 
Published: June 2005

AbstractTwo susceptibility genes, in linkage disequilibrium with alleles of the markers D1G31 and D5G23, have been identified for the disease in the simulated data set of Problem 1. Here we apply the MASC (marker association segregation chi‐square) method to model the joint effect of these two genes, by testing two‐locus models. The model we obtain, that is the most parsimonious and that best fits the data, corresponds to a direct involvement of the alleles D1G31‐8 and D5G23‐7, with a nonmultiplicative effect of the two alleles. This was indeed assumed in the true model used for simulating the data. ©1995 Wiley‐Liss, Inc.
Screening for linkage and association in nuclear families
Authors: Knapp M. , Durner M. , Baur M. P. 
Published: June 2005

AbstractWe applied linkage analysis with a sib‐pair method, which also takes into account information on unaffected siblings, and family‐based methods of association analysis to determine the disease affecting loci in Problem 1. Whereas the first two disease loci were correctly identified by association analysis, the sib‐pair linkage method failed to detect the disease loci 3 and 4. We therefore determined the data structure and sample size necessary for demonstrating linkage to these loci. ©1995 Wiley‐Liss, Inc.
Detection of vulnerability loci by association and sib‐pair methods
Authors: Levinson Douglas F. 
Published: June 2005

AbstractA haplotype‐based haplotype relative risk (HHRR) analysis of simulated data for 200 affected offspring and their parents (Genetic Analysis Workshop 9, Problem 1) detected linkage disequilibrium at 2 of 360 marker loci. An additive model was suggested but not proven by haplotypes of affected vs. unaffected offspring. These findings were consistent with the generating model. Affected sib pair analysis failed to detect additional loci. Discussion among workshop participants suggested that the chi‐square test used here (2 [transmitted vs. nontransmitted] x n [alleles] for each locus) was invalid because of the nonindependence of proportions of transmitted alleles. In post‐workshop analyses, transmission disequilibrium tests (TDTs) for each allele at each locus detected only the true associations if p values were corrected by one of two methods: Bonferroni correction for 2,035 TDTs, or correcting each test for n (number of tests at the locus) minus 1 and then for the number of loci tested. Screening loci for linkage disequilibrium requires careful attention to correction for multiple comparisons. ©1995 Wiley‐Liss, Inc.
Genome scanning for complex disease genes using the transmission/disequilibrium test and haplotype‐based haplotype relative risk
Authors: Matise Tara Cox 
Published: June 2005

AbstractTwo tests for allelic association were applied to a simulated complex disease for the Genetic Analysis Workshop 9. The transmission/disequilibrium test [Spielman et al., 1993] and the haplotype‐based haplotype relative risk approach [Terwilliger and Ott, 1992] were used to detect disease‐associated alleles in a set of 360 computer‐simulated markers located on six chromosomes and genotyped in 200 nuclear families. This analysis emulates a genome‐based search for linked markers. Computer simulations were also performed to clarify statistical properties of the TDT. ©1995 Wiley‐Liss, Inc.
Interval mapping of quantitative trait loci using a sib‐pair linkage method
Authors: Borecki Ingrid B. , Vogler George P. 
Published: June 2005

AbstractFulker and Cardon's interval mapping extension of Haseman and Elston's sib‐pair linkage method was used to map loci affecting the quantitative phenotypes presented as part of Problem 2, both adjusted and not adjusted for covariates: Q1 was adjusted for age and the environmental factor (EF); Q2 and Q3 for EF; and Q4 for age, sex, and EF. Adjusted Q2 and Q4 were also log‐transformed. The effect of candidate locus C5 (D5G28) on Q1 was detected by a test of association – apparently, allele 1 of C5 is protective (leading to lower values of Q1), allele 2 has no effect, and allele 3 contributes to elevated levels of Q1. C5 accounted for 5.2% of the variation in Q1; it was included as an additional concomitant in the adjustment procedure. Analysis of the correlational structure among the variables revealed that Q4 was not associated with either affected status or Q1 after controlling for the effects of age, and we concluded that Q4 probably does not itself play a role in the etiology of the disease. Mapping studies using a significance level of 0.05 lead to the detection of all the genes, but also resulted in a high frequency of false positive results. On the other hand, using a 0.0005 significance level resulted in the detection of D2G10‐11 for both Q1 and Q3, and D1G2 was detected for Q2. One false positive was detected using this significance level and the effects of D1G2 on Q1 and D5G22‐23 on Q4 were missed. There was no systematic effect of adjustment for covariates on the detection of loci, although in general, analysis of adjusted phenotypes yielded substantially higher rates of false positives. Finally, this mapping approach correctly located D1G2 and D2G10‐11 for Q1 using the nonadjusted phenotype, and D1G10‐11 for Q3 using the adjusted phenotype. The maximum difference between the estimated map location from the true location was 1.5 cM. It would be important to estimate the error interval around these inferred locations in order to assess the utility of this method for fine mapping over small (e.g., 2 cM) intervals. © 1995 Wiley‐Liss, Inc.
A genetic analysis of common disease data
Authors: Cantor Rita M. 
Published: June 2005

AbstractSimulated common disease data have been analyzed to identify a model of disease expression that involves the complex interaction of quantitative traits controlled by major genes and an environmental factor. Correlation and multiple regression analyses in conjunction with quantitative sib‐pair linkage analyses revealed a portion of the model involved. © 1995 Wiley‐Liss, Inc.
Weighted pairwise correlation and transmission disequilibrium in a common oligogenic disease
Authors: Corder Elizabeth H. 
Published: June 2005

AbstractWeighted pairwise correlation (WPC) linkage analysis and the transmission disequilibrium test (TDT) for allelic association were applied to simulated family data for a common oligogenic disorder in order to localize genes that increase levels of Q1. Preliminary linear models indicated that Q1 increases with age, Q2, Q3, and the occurrence of D5G28 allele 3 (D5G28(3). WPC provided evidence for linkage of Q1 to Major Gene 1, D5G28 (p < 0.0001), and to other loci when ranks based on age and affection status (Q1 < or > 87.5) were used to define the phenotype. D5G28 was the only locus linked to Q1 (p = 0.01) when the phenotype was defined as Q1 adjusted for age, Q2, Q3, and D5G28(3). The TDT indicated linkage disequilibirum between a gene for elevated Q1 (> 87.5) and alleles at loci adjacent to D5G28: D5G29(2) was apparently protective (p = 0.008) while allele 8 was associated with risk (p = 0.007); D5G30(7) was also apparently protective (p = 0.02). However, significant disequilibirum was not found for D5G28(3), possibly due to lack of adjustment of Q1 and small sample size. These results imply that the combined use of WPC and the TDT may be an effective strategy for genomic screens in complex disorders defined by quantitative traits, especially when the trait can be adjusted for age and other relevant factors. © 1995 Wiley‐Liss, Inc.
Analysis of quantitative risk factors for a common oligogenic disease
Authors: Goddard K. A. B. , Jarvik G. P. , Graham J. , McNeney B. , Hsu L. , Siegmund K. , Grosser S. , Olson J. , Wijsman E. M. 
Published: June 2005

AbstractAll three simulated loci influencing the quantitative variables Q1, Q2, and Q3 were successfully mapped by using a strategy of covariate adjustment and segregation analysis, coupled with association analyses and lod‐score analyses. © 1995 Wiley‐Liss, Inc.
Association and linkage with quantitative traits
Authors: King Terri M. , Zhu Dakai , Amos Christopher I. 
Published: June 2005

AbstractThis paper examines two approaches for the analysis of quantitative traits: (1) association studies and (2) linkage studies. The trait studied was Q1 from simulated Problem 2 data set in Genetic Analysis Workshop 9. Our purpose was to evaluate associations present in the data, to identify nongenetic and genetic predictors of the trait, and to explore the simulated genome for linkage. Through the association study, we found evidence for the primary major gene associated with this trait. The linkage study found evidence of residual genetic effect acting through other traits. Adjustments of Q1 for Q2 and Q3 led to a failure to find significant effects of MG2 and MG3. This supports the suggestion that adjustment for genetically influenced traits for effects of other genetic traits can reduce the power to detect major gene effects. In summary, we detected the major gene directly associated with the trait of interest through association studies. Linkage analysis detected evidence for two other genes associated to a lesser degree with the trait. © 1995 Wiley‐Liss, Inc.
Screening for linkage using a multipoint identity‐by‐descent method
Authors: Lewis Cathryn M. , Goldgar David E. 
Published: June 2005

AbstractThe multipoint identity‐by‐descent method (MIM) was used to analyze simulated data for quantitative traits from GAW9. A two‐stage method of implementation was used. First, polymorphic markers spaced 6‐12 cM apart were used to identify chromosomes of interest for each trait Q1‐Q4; and second, for each of these chromosomes, markers spaced 2 cM apart were used to confirm the linkage detected and refine the region for the susceptibility loci. MIM performed well at both levels of mapping, correctly detecting major genes for trait Q3 on chromosome 2, trait Q2 on chromosome 1, and trait Q4 on chromosome 5. © 1995 Wiley‐Liss, Inc.
How can maximum likelihood methods reveal candidate gene effects on a quantitative trait?
Authors: Martinez M. , Abel L. , Demenais F. 
Published: June 2005

AbstractDifferent maximum likelihood approaches were used to explore the role of candidate genes in the variability of quantitative trait Q1 while accounting for the effects of age, Q2, and Q3. Segregation analysis, under the class D regressive model, provides evidence for a Mendelian gene effect on the adjusted trait Q1. Results of gene mapping through lod‐score analyses remain puzzling. Pairwise lod scores indicate a possible linkage with the candidate gene C5 which is excluded when using tightly linked informative marker loci. Finally, our combined segregation and linkage analysis clearly shows that a C5 linked gene is involved in Q1 variability. However, given the lod‐score results within the C5 region, we postulate a more complex mechanism for Q1 than a single di‐allelic C5 linked gene. The knowledge of the true model (C5 is MG1 and has three alleles) permits a partial explanation of our results. This study demonstrates the advantages of using complementary approaches to reveal the role of candidate genes in complex traits, and the value of simultaneous estimation of linkage and segregation parameters. © 1995 Wiley‐Liss, Inc.
Multivariate genetic analysis of an oligogenic disease
Authors: Moldin Steven O. , Van Eerdewegh Paul 
Published: June 2005

AbstractJoint multivariate segregation and linkage analysis provides a method for simultaneously analyzing data on affection status, correlated phenotypic traits, environmental risk factors, and other covariates. The power of this approach for mapping disease susceptibility loci of small effect (oligogenes) was evaluated by analyzing the GAW9 Problem 2 data set. The program REGRESS, which assumes a pleiotropy model in which one locus influences both affection status (AF) and a quantitative trait, was used to conduct joint segregation and linkage analysis of bivariate phenotypes, each comprising AF and one quantitative trait (Q2,Q3,Q4). A genome‐wide search using markers spaced approximately 10 cM apart was conducted and regions on chromosomes 1, 2, and 5 were identified as demonstrating linkage with three respective bivariate phenotypes at the following markers: AF/Q2 ‐ D1G2; AF/Q3 ‐ D2G10; and AF/Q4 ‐ D5G18. The effects of other loci were included in a general model by specifying the quantitative traits they influenced as covariates along with age, sex, and an environmental effect. Use of covariate and quantitative trait data in each analysis resulted in respective χ2 values with 1 df of 38.4, 65.4, and 22.0 to reject the no linkage hypothesis at \documentclass{article}\pagestyle{empty}\begin{document}$ {\rm \hat \theta } $\end{document} = 0, with respective equivalent lod scores of 8.3, 14.2, and 4.8. Rejection at p < 0.0002 occurred using markers as far away as 20 cM. These loci were not detected when AF alone was analyzed. © 1995 Wiley‐Liss, Inc.
An investigation to determine whether clusters of cancer types exist in nine breast‐ovary families
Authors: Bansal A. , Bishop D. T. , Cannings C. 
Published: June 2005

AbstractMonte‐Carlo simulations were carried out on nine BRCA1‐linked families selected from the 1993 Breast Cancer Linkage Consortium. These families all displayed a prevalence of both breast and ovarian cancers, but little evidence was found to conclude that sufferers of either type formed clusters in particular branches of families. It was noted that the application of these methods to larger pedigrees with known probands would be useful in further clarifying the issue. © 1995 Wiley‐Liss, Inc.
Variability of genotype‐specific penetrance probabilities in the calculation of risk support intervals
Authors: Leal Suzanne M. , Ott Jurg 
Published: June 2005

AbstractPreviously, a maximum likelihood method was described to construct a support interval for the risk. This method is extended to incorporate genotype specific penetrance probabilities in the calculation of a risk support interval. As an empirical example, the support interval for the risk is calculated for a member of a published breast‐ovarian cancer kindred. © 1995 Wiley‐Liss, Inc.
Gain in efficiency from using generalized least squares in the Haseman‐Elston test
Authors: Single R. M. , Finch S. J. 
Published: June 2005

AbstractTechniques that test for linkage between a marker and a trait locus based on the regression methods proposed by Haseman and Elston [1972] involve testing a null hypothesis of no linkage by examination of the regression coefficient. Modified Haseman‐Elston methods accomplish this using ordinary least squares (OLS), weighted least squares (WLS), in which weights are reciprocals of estimated variances, and generalized estimating equations (GEE). Methods implementing the WLS and GEE currently use a diagonal covariance matrix, thus incorrectly treating the squared trait differences of two sib pairs within a family as uncorrelated. Correctly specifying the correlations between sib pairs in a family yields the best linear unbiased estimator of the regression coefficient [Scheffe, 1959]. This estimator will be referred to as the generalized least squares (GLS) estimator. We determined the null variance of the GLS estimator and the null variance of the WLS/OLS estimator. The correct null variance of the WLS/OLS estimate of the Haseman‐Elston (H‐E) regression coefficient may be either larger or smaller than the variance of the WLS/OLS estimate calculated assuming that the squared sib‐pair differences are uncorrelated. For a fully informative marker locus, the gain in efficiency using GLS rather than WLS/OLS under the null hypothesis is approximately 11% in a large multifamily study with three siblings per family and 25% for families with four siblings each. © 1995 Wiley‐Liss, Inc.
Germ layer formation and the role of the primitive streak in the chick. I. Basic architecture and morphogenetic tissue movements
Authors: Spratt Nelson T. , Haas Hermann 
Published: June 2005

AbstractHistological studies, carbon, carmine and vital dye marking of in ovo and explanted blastoderms have provided evidence for the following: (1) The cellular precursors of all three primary germ layers in the unincubated blastoderm are topographically represented by a disc‐shaped, epithelial‐like and complete uppermost layer; and ring‐shaped, incomplete and congruent middle and lowest layers below the uppermost layer. (2) There is a gradient in cell population density decreasing from posterior to anterior. (3) Centripetal extension of a sheet of lowest and adjacent middle layer cells, primarily from the posterior portion of the ring but also from all points on its circumference, results in closing of the ring and completion of the middle and lowest layers as coherent discs. (4) No movement (invagination or involution) of uppermost, surface areas as coherent sheets into either the middle or lowest layers could be demonstrated in any region, including the primitive streak. (5) Embryonic germ layers arise in situ by proliferation from the streak. (6) The streak in its morphology and function is more like a blastema than a blastopore. (7) As an elongated growth center, the streak has its origin from the marginal zone ring, itself a circular blastema with multiple streak‐ and embryo body‐forming capacities.
Studies on the development of mouse embyros in vitro. III. The effect of fixed‐nitrogen source
Authors: Brinster Ralph L. 
Published: June 2005

AbstractThere has been considerable discussion as to whether the developing mouse ova requires an exogenous protein supply. A series of experiments were performed to determine the effect of various fixed‐nitrogen sources on the development in vitro of two‐cell mouse ova into blastocysts. It was found that two‐cell mouse ova would not develop into blastocysts in medium that did not contain a fixed‐nitrogen source. Crystalline bovine serum albumin (BSA) at 1 mg/ml or, alternatively, the constituent amino acids of BSA could supply this requirement for fixed‐nitrogen. In the presence of single amino acids at 10−3 M or 8 × 10−3 M, only an occasional two‐cell ova developed into a blastocyst.Single amino acids were omitted from the medium containing the constituent amino acids of BSA in an attempt to demonstrate an essential amino acid requirement of two‐cell mouse ova. The omission of no single amino acid from the medium, completely prevented the development of some two‐cell ova into blastocysts. The only amino acid whose omission resulted in a significant decrease in development was cystine. In the medium where amino acids were used in place of BSA, a non‐protein polymer was used to replace the physical properties of albumin. The non‐protein polymers which appeared best were Polyvinylpyrrolidone (MW = 150,000), Acacia, Dextran, and Ficoll.
Adipose tissue of Drosophila melanogaster. I. An experimental study of larval fat body
Authors: Butterworth Francis M. , Bodenstein Dietrich , King Robert C. 
Published: June 2005

AbstractMorphological, cytochemical and developmental studies were undertaken of the adipose cells present in third instar larvae and in adult females of Drosophila melanogaster. The adipose cells of the adult type in the abdomens of recently emerged and seven‐day‐old adult females contain deposits of lipid and glycogen. Although the cells increase in size during this period, their nuclei do not. During the third instar the larval adipose cells and their nuclei undergo a five‐ and twofold increase in average cross‐sectional area, respectively. Deposits of lipid and glycogen are present throughout this period. Proteinaceous globules first appear in the middle of the third instar. They increase in diameter and number until just before pupation they comprise one fourth of the cytoplasm of the cells. In the young adult the surviving larval adipose cells and their nuclei have decreased in average cross‐sectional area by two and three times, respectively, from the late third instar values. The surviving larval adipose cells contain droplets of unsaturated lipids, protein globules, and deposits of glycogen which completely fill the remaining cytoplasmic area. By the third day of adult life these cells histolyze. When late second instar larvae are starved for three days, the average cross‐sectional area of their fat body cells decreases ten times. Despite the absence of glycogen and a huge decrease in lipid, protein globules form, and by the third day of starvation comprise one fourth of the cytoplasm. Transplantation experiments demonstrate that a hormone produced by the ring gland during the late third instar induces the formation of these protein globules.
Oogenesis in rabbits
Authors: Peters Hannah , Levy Emilia , Crone Monna 
Published: June 2005

AbstractOogenesis in rabbits was investigated by examining the development of germ cells after birth. The transformation of oogonia to oocytes begins in the one day old animal. The appearance of the different stages of meiotic prophase was noted and their differential distribution in the germ cell population determined. The early stages of meiotic prophase develop within the first two weeks of life, however, they are transitory and have transformed into the “stationary” phase of prophase by the end of the third week of life, when all nuclei of oocytes are in diplotene.DNA synthesis in germ cells was studied with the aid of H3‐thymidine, which was injected into animals within the first ten days of life. The number of labeled germ cells varies with the age of the animal at the time of injection. Injection between the day of birth and the age of four days results in the labeling of 20 to 35% of the germ cells. After this age the labeling index falls, reaching 3% in the ten day old animal.The synthesis of the oocyte DNA takes place in the premeiotic interphase and may extend into the early phase of prophase, i.e. leptotene.