bluuebunny/crossref_metadata_2025_split huggingface dataset:
Segregation analysis of plasma lipoprotein(a) levels in pedigrees with molecularly defined familial hypercholesterolemia
Authors: Friedlander Yechiel , Leitersdorf Eran 
Published: June 2005

AbstractThe role of genetic and environmental factors in determining the variability in plasma lipoprotein(a) [Lp(a)] levels was investigated in 220 members of 14 families with familial hypercholesterolemia (FH) whose plasma Lp(a) levels were previously reported [Leitersdorf et al. (1991) J Lipid Res 32:1513–1519]. One hundred four subjects harbored a mutant low density lipoprotein (LDL) receptor allele as confirmed by the identification of the specific mutations in addition to the haplo‐type analysis reported before. Four different mutant alleles were identified, each in a defined genetic group—Druze, Christian‐Arabs, Ashkenazi, and Sephardic Jews. Sex‐ and age‐adjusted mean plasma Lp(a) levels were significantly higher in FH family members (34.0 mg/dl) than in non‐FH family members (21.1 mg/dl). Lp(a) levels were further adjusted for lipid levels and apo(a) isoforms. A mixture of two normal distributions fitted the adjusted Lp(a) levels better than did a single normal distribution. Segregation analysis indicated that a major effect of a non‐transmitted environmental factor explained the mixture of distributions in addition to polygenic loci which influenced Lp(a) levels within each distribution. The major environmental factor and the polygenic loci accounted for 45% and 20% of the adjusted Lp(a) variation, respectively. Furthermore, sex, age, lipid levels, apo(a) isoform, the major environmental effect, and the unmeasured polygenes could account for 80% of the unadjusted variation of plasma Lp(a) in these families. © 1995 Wiley‐Liss, Inc.
Guess LOD approach: Sufficient conditions for robustness
Authors: Williamson John A. , Amos Christopher I. 
Published: June 2005

AbstractAnalysis of genetic linkage between a disease and a marker locus requires specifying a genetic model describing both the inheritance pattern and the gene frequencies of the marker and trait loci. Misspecification of the genetic model is likely for etiologically complex diseases. In previous work we have shown through analytic studies that misspecifying the genetic model for disease inheritance does not lead to excess false‐positive evidence for genetic linkage provided the genetic marker alleles of all pedigree members are known, or can be inferred without bias from the data. Here, under various selection or ascertainment schemes we extend these previous results to situations in which the genetic model for the marker locus may be incorrect. We provide sufficient conditions for the asymptotic unbiased estimation of the recombination fraction under the null hypothesis of no linkage, and also conditions for the limiting distribution of the likelihood ratio test for no linkage to be chi‐squared. Through simulation studies we document some situations under which asymptotic bias can result when the genetic model is misspecified. Among those situations under which an excess of false‐positive evidence for genetic linkage can be generated, the most common is failure to provide accurate estimates of the marker allele frequencies. We show that in most cases false‐positive evidence for genetic linkage is unlikely to result solely from the misspecification of the genetic model for disease or trait inheritance. © 1995 Wiley‐Liss, Inc.
General purpose model and a computer program for combined segregation and path analysis (SEGPATH): Automatically creating computer programs from symbolic language model specifications
Authors: Province Michael A. , Rao D. C. 
Published: June 2005

AbstractA general purpose model and a flexible computer program, called SEGPATH, have been developed to assist in the creation and implementation of a variety of genetic epidemiological models. SEGPATH is a computer program which can be used to generate programs to implement linear models for pedigree data, based upon a flexible, model‐specification syntax. SEGPATH models can perform segregation analysis, path analysis, or combined segregation and path analysis using any user‐specified path model and can be structured to analyze any number of multivariate phenotypes, environmental indices, and/or measured covariate fixed effects (including measured genotypes). Population heterogeneity models, repeated‐measures models, longitudinal models, auto‐regressive models, developmental models, and gene‐by‐environment interaction models can all be created under SEGPATH. Pedigree structures can be defined to be arbitrarily complex, and the data analyzed with programs generated by SEGPATH can have any missing value structure, with entire individuals missing, or missing on one or more measurements. Corrections for ascertainment can be done on a vector of phenotypes and/or other measures. Because the model specification syntax is general, SEGPATH can also be used in non‐genetic applications where there is a hierarchical structure, such as longitudinal, repeated‐measures, time series, or nested models. A variety of applications are demonstrated. © 1995 Wiley‐Liss, Inc.
Using loss of heterozygosity data in affected pedigree member linkage tests
Authors: Lustbader Edward D. , Rebbeck Timothy R. , Buetow Kenneth H. 
Published: June 2005

AbstractLinkage analysis can be used to test the hypothesis that a marker locus of known location segregates independently from a presumed disease gene. One way to test this hypothesis is to measure the similarity of marker alleles among pairs of relatives affected with the disease. When the disease under consideration is cancer, it is possible to take advantage of the marker alleles in tumors to revise the similarity measure obtained from the observations made in constitutional tissue. Only cancers that arise through the model of recessive oncogenesis are amenable to this revised analysis. This model postulates that cancer is caused by somatic genetic changes which result in the loss of one or both copies of a normal allele at a tumor suppressor locus. If an individual's inherited genotype is heterozygous at the marker locus, the model of recessive oncogenesis suggests that we may observe loss of constitutional heterozygosity at the marker locus in the tumor. In this report, we show how to incorporate this loss of heterozygosity data into affected pedigree member linkage tests. The revised procedure is illustrated using data obtained from relatives with breast cancer. Substantial improvement in the power to reject the different chromosome hypothesis is obtained when loss of heterozygosity is observed in multiple relatives with the same marker alleles retained in the tumors. ©1995 Wiley‐Liss, Inc.
Simulation study comparing interval estimates for the recombination fraction
Authors: Nemesure Barbara Berger , Greenberg David A. , Mendell Nancy R. 
Published: June 2005

AbstractThree interval estimation procedures were evaluated to determine the method which provides the most accurate estimates for the recombination fraction, θ. The lod–0.83 support interval, the jackknife confidence interval, and the confidence interval based on estimated asymptotic standard error were compared by calculating the coverage probabilities of each. Family data that were simulated under the model of a single fully penetrant, dominant disease locus at some distance, θ, from fully informative matings were used. Comparisons were based on 1,000 random samples of size 20, 60, and 100 families. In addition, a methodology for obtaining prediction intervals for θ was developed. This procedure is of practical use and does not require asymptotic assumptions based on large sample theory. The results provide an a priori idea about precision of the estimates, as well as empirical interval estimates of θ. Graphs of the authors' Monte Carlo intervals are presented for these simulations. Investigators studying different traits, however, could condition specifically on the family structure and distribution of the disease they are investigating and obtain similar graphs. ©1995 Wiley‐Liss, Inc.
Angiotensin‐converting enzyme genotypes in the high‐ and low‐risk area for coronary heart disease in Finland
Authors: Perola Markus , Sajantila Antti , Sarti Cinzia , Stengård Jari , Tamminen Markku , Puska Pekka , Huttunen Jussi , Tuomilehto Jaakko , Peltonen Leena 
Published: June 2005

AbstractThe deletion/deletion genotype of the insertion (I)/deletion (D) polymorphism of the angiotensin‐converting enzyme (ACE) gene has been suggested to be a risk factor for myocardial infarction (MI). The objective of this study was to evaluate whether genotypic distributions of the I/D polymorphism of the ACE gene are different between individuals from high‐risk and low‐risk areas for coronary heart disease in the genetically isolated population of Finland and to assess the impact of this genetic risk factor by comparing individuals with different parental histories of MI. Representative population‐based samples of middle‐aged men (n = 363) and women (n = 358) from two areas of Finland were used. The area had a borderline significant effect on the prevalence of the genotype DD (β = 0.35, SE = 0.16, χ2 = 4.70, df = 1, P = 0.03), the DD genotype being more prevalent in eastern Finland (the high‐risk area). The II genotype was more prevalent in women with parental history of MI, so we could not replicate the previous findings of the risk‐increasing effect of DD genotype in this sample. Although the observed difference in the ACE DD genotype between the high‐risk and low‐risk areas for coronary heart disease might represent one of the genetic factors contributing to the difference in risk of coronary heart disease between eastern and southwestern Finland, the data emphasize the fact that also other risk factors, including other genes, contribute to this difference and the high incidence of MI in Finland. ©1995 Wiley‐Liss, Inc.
Variation in HLA‐associated risks of childhood insulin‐dependent diabetes in the finnish population: I. Allele effects at A, B, and DR Loci
Authors: Langholz Bryan , Tuomilento‐Wolf Eva , Thomas Duncan , Pitkäniemi Janne , Tuomilehto Jaakko ,   DiMe Study Group
Published: June 2005

AbstractVariation in the risk of insulin‐dependent diabetes mellitus (IDDM) across alleles at HLA‐A, B, and DR loci was investigated in a population‐based study of 801 families of children with newly diagnosed IDDM in Finland nationwide. Parallel analyses assessed the relative frequencies of alleles in IDDM children compared with age‐matched sibling controls and with the four possible genotypes which could have been inherited from the parents. The joint effects of DR3 and DR4 alleles were investigated under dominant, recessive, and additive models of gene expression. The additive model gave the best fit, though the relative risk for DR4 homozygotes was smaller than predicted. To investigate other alleles, we fitted the standard multiplicative model for alleles at each locus. After controlling for the correlation among alleles, significantly elevated risks were found for B13, DR3, DR4, and DR14. Subjects with these alleles have more than twice the risk of IDDM as those without. Alleles A24 and B62 incurred relative risks between one and two. DR2 and DR5 were significantly negatively associated with IDDM, incurring less than half the risk. These findings support an independent role of class I antigens in the etiology of IDDM. © 1995 Wiley‐Liss, Inc.
Apolipoprotein B gene DNA polymorphisms (EcoRI and MspI) and serum lipid levels in the serbian healthy population: Interaction of rare alleles and smoking and cholesterol levels
Authors: Glišić S. , Savić I. , Alavantić D. 
Published: June 2005

AbstractThe frequency of restriction fragment length polymorphisms (RFLPs) of the apolipoprotein B (apo B) gene, detected by EcoRI and MspI, and their influence on serum lipids were studied in a total of 239 healthy subjects from the Belgrade area. The influence of interaction between different genotypes and smoking was also studied. The relative frequency of both rare R2 and M2 alleles (lacking the cutting site) was similar to that reported in other groups of Caucasians (0.16 and 0.11, respectively). No association was observed between the apo B genotypes and serum lipid levels adjusted for age, body mass index, and blood pressure either in the whole sample or in either women or men. When smokers and non‐smokers were considered separately, smoking had a significant impact on total cholesterol variability in all individuals with genotype M1M2 and high density lipoprotein (HDL) cholesterol variability in women with genotype R1R2. The presence of the rare alleles of these two polymorphisms in smokers was associated with lower lipid levels in the whole sample and in both women and men analyzed separately, except for an increase of HDL cholesterol level in male smokers, heterozygous for EcoRI polymorphism (R1R2). ©1995 Wiley‐Liss, Inc.
Genome scan for association and linkage
Authors: Holmans P. , McGuffin P. , Clayton D. 
Published: June 2005

AbstractIn this paper, a genome search is performed on the GAW Problem 1 data, in an attempt to determine which, if any, of the marker loci are associated and/or linked with the disease. Since there was no clear indication from the data of the likely mode of inheritance, methods were used which did not require such assumptions to be made. A two‐stage procedure was used to test for association. Firstly a standard unmatched case‐control test was applied to all the loci. The family‐based method of Self et al. [1991] was then applied to those loci which gave a positive result in the first stage. This procedure correctly detected loci 1 and 2, and that disease risk was increased for homozygote carriers of the disease allele at each locus, although a false positive result was also found. The affected sib pair method of Holmans [1993] was also applied to the data, although the sample contained far too few sib‐pairs for such an analysis to be effective. This analysis failed to find any of the disease loci. ©1995 Wiley‐Liss, Inc.
The TDT reveals linkage and linkage disequilibrium in a rare disease
Authors: McGinnis Ralph E. , Ewens Warren J. , Spielman Richard S. 
Published: June 2005

AbstractWe used the TDT as the basis for our analysis of data from Problem 1 of GAW9. Among the 360 marker loci on six chromosomes, we searched for any that might show both linkage and allelic association with the disease. We applied the TDT to each allele at every marker locus and found strong evidence for linkage in two regions: one on chromosome 1, another on chromosome 5. ©1995 Wiley‐Liss, Inc.
Evaluation of screening strategies to detect an oligogenic disease
Authors: Rogus John J. , Haines Jonathan L. 
Published: June 2005

AbstractUsing data simulated to reflect an oligogenic disease, we evaluated screening strategies based on lod‐score and weighted pairwise correlation (WPC) analysis with respect to their ability to efficiently identify regions near disease loci. Lod‐score analysis was done twice, once assuming a near‐recessive mode of inheritance with a high penetrance and again assuming a semidominant mode of inheritance with lower penetrance. Under the near‐recessive model, no disease loci were correctly identified, while there was one false positive result. Under the semidominant model, D1G31 was correctly identified, and there were two false positive results. Due to the lack of highly informative families and possible sensitivity to parameter misspecification, this poor performance was not unexpected. WPC, on the other hand, is assumption‐free and thus potentially more powerful than a misspecified parametric model, but almost certainly less powerful than a well‐specified parametric model. Using WPC modified to handle binary phenotypes with no age‐of‐onset, we found results closely resembling those under the semidominant model, although no markers near disease loci exceeded the theoretical critical value for WPC. © 1995 Wiley‐Liss, Inc.
Precision of marker heterozygosity estimates
Authors: Shugart Yin Y. 
Published: June 2005

AbstractSample sizes may greatly affect the accuracy of estimates of marker heterozygosities. Therefore, indicating the precision of these estimates is strongly recommended. A computer program HETMAX was written and used to obtain unbiased estimates of heterozygosity, their standard errors and unit support intervals, based on a subset of GAW9 data. The accuracy of the estimates and the associated sample sizes are presented. © 1995 Wiley‐Liss, Inc.Sample sizes may greatly affect the accuracy of estimates of marker heterozygosities. Therefore, indicating the precision of these estimates is strongly recommended. A computer program HETMAX was written and used to obtain unbiased estimates of heterozygosity, their standard errors and unit support intervals, based on a subset of GAW9 data. The accuracy of the estimates and the associated sample sizes are presented. © 1995 Wiley‐Liss, Inc.
Sib‐based detection of QTLs
Authors: Suarez Brian K. , Hampe Carol L. , O'rourke Dennis , Van Eerdewegh Paul , Reich Theodore 
Published: June 2005

AbstractAssociation and transmission/nontransmission analyses were used in a split sample design to identify disease susceptibility alleles at two loci. Sib‐pair analysis on various subsets of the data identified an additional four regions that yielded signals of disease predisposing quantitative trait loci (QTLs). Three of these four regions represented Type I errors. A new simulation indicates that a multiplex sampling strategy would substantially improve QTL detection for this oligogenic transmission model. © 1995 Wiley‐Liss, Inc.
Measuring gene‐disease association using a general pair method
Authors: Ward Patrick J. , Bonaïti‐Pellié Catherine 
Published: June 2005

AbstractThe general‐pair‐method (GPM) was applied to the analysis of the Problem 1 data set. GPM is a nonparametric, identity‐by‐state method for associating variation at a chromosomal locus with disease status. It can accommodate information from all pairs of individuals across any degree of genetic relatedness, including genetically unrelated individuals, within a single test statistic. Pairwise contrasts between members of different pedigrees, including “control” pedigrees containing no affected individuals, can therefore be exploited in order to increase the power of the test statistic. Such a strategy makes sense within the context of a candidate gene (direct association) study, where the alleles or genotypes of high disease risk are assumed to be the same ones within all families. Applied to the Problem 1 data set in this way, the GPM successfully identified the effect on disease status of locus 31 on chromosome 1, and of locus 23 on chromosome 5. The observed strength of gene‐disease association at the other chromosomal loci was compatible with the null hypothesis. © 1995 Wiley‐Liss, Inc.
Simulation of a common oligogenic disease with quantitative risk factors. GAW9 problem 2: The answers
Authors: MacCluer Jean W. , Blangero John , Dyer Thomas D. , Kammerer Candace M. 
Published: June 2005

AbstractFor Problem 2, simulated family data were provided for a common oligogenic disease that was defined by imposing a threshold, T, on a quantitative trait, Q1. Every individual with a value of Q1 > T (where T = 87.5) was defined as affected. Participants were told that three other quantitative traits (Q2, Q3, and Q4) and an environmental factor (EF) also were thought to be associated with the disease as intervening variables, and that 12 candidate loci (C1 ‐ C12) were postulated to influence traits associated with the disease. The tasks for GAW9 were to characterize the genetic and environmental contribution to Q1 (and the disease), Q2, Q3, and Q4, and to localize any single genes that had detectable effects. © 1995 Wiley‐Liss, Inc.
Evaluation of genetic and environmental effects using GEE and APM methods
Authors: Bull S. B. , Chapman N. H. , Greenwood C. M. T. , Darlington G. A. 
Published: June 2005

AbstractTwo analytic methods were used in the Problem 2 data set. First, generalized estimating equations (GEE) modelling was developed to adjust for familial correlation in regressions evaluating candidate genes and an environmental factor. Second, the affected‐pedigree‐member (APM) method was used to identify chromosomal regions of interest and linkage of candidate genes with disease affection status. The GEE method identified C5 (MG1) as important for the quantitative trait Q1 and the corresponding affection status DIS, but the APM method was only suggestive. The GEE method identified C2 (MG2) as important for Q2 but only marginally important for Q1 and not important for DIS. © 1995 Wiley‐Liss, Inc.
Statistical genetics of normal variation in family data for oligogenic diseases
Authors: Mahaney Michael C. , Jaquish Cashell E. , Comuzzie Anthony G. 
Published: June 2005

AbstractA quadrivariate quantitative genetic analysis detected significant heritabilities for four simulated quantitative traits (Q1‐Q4) with additive genetic pleiotropy between traits Q1, Q2, and Q3. Using univariate segregation analysis, we tentatively detected five major loci: one each for Q2, Q3, and Q4 and two, at different maxima, for Q1. Bivariate one‐locus segregation analysis identified significant major locus pleiotropy for Q1, Q2, and Q3 only; and suggested identity between one of Q1's major genes and that for Q2, and between the second Q1 major gene and that for Q3. Patterns of linkage, supportive of inferences from the bivariate segregation analyses, were detected between three candidate genes and the major genes for Q1, Q2 and Q4. © 1995 Wiley‐Liss, Inc.
The influence of response bias on segregation and linkage analysis
Authors: Mitchell Braxton D. , Atwood Larry D. , Reinhart Laurie 
Published: June 2005

AbstractResponse bias in epidemiologic studies can occur if affected individuals are more (or less) likely to participate in a survey than their unaffected counterparts. To examine the effect of response bias in the context of a family study, we conducted segregation and linkage analysis in all 1,000 individuals in the Problem 2 data set, and in two different 65% samples: one sample consisting of 648 randomly selected individuals, and the other sample nonrandomly constructed so that individuals with high levels of Q1 were oversampled. In this simulation the ability to detect major genes for Q1‐Q4 in segregation analysis and to link these putative major genes to genetic markers in linkage analysis was not markedly different between the 65% random and the 65% enriched samples. © 1995 Wiley‐Liss, Inc.
Statistical properties of the allelic and genotypic transmission/disequilibrium test for multiallelic markers
Authors: Bickeböller Heike , Clerget‐Darpoux FrançOise 
Published: June 2005

AbstractThe transmission/disequilibrium test (TDT) is extended in two ways for a multiallelic marker: (1) to compare transmitted and nontransmitted alleles from a single heterozygous parent and (2) to compare genotypes formed by the two transmitted alleles and genotypes formed by the two nontransmitted alleles using the information on both parents, heterozygous or not, simultaneously. © 1995 Wiley‐Liss, Inc.
Computing conditional recombination probabilities given marker information
Authors: Miller Michael B. 
Published: June 2005

AbstractWhen a parent is homozygous, it is not possible to determine directly whether offspring share an allele identical by descent (IBD) from that parent. However, an IBD probability can be computed using information from linked loci where the parent is heterozygous. To solve this problem for the phase‐known case, probability equations and a computer program were developed for efficient computation of conditional probabilities under a Sturt model of chiasma interference. Data are analyzed and contrasted with and without taking interference into account. The effect of failing to account for interference is very small, but the computational cost is also small. © 1995 Wiley‐Liss, Inc.