bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multivariate sib-pair linkage analysis of longitudinal phenotypes by three step-wise analysis approaches
Authors: Guo Zheng , Li Xia , Rao Shaoqi , Moser Kathy L , Zhang Tianwen , Gong Binsheng , Shen Gongqing , Li Lin , Cannata Ruth , Zirzow Erich , Topol Eric J , Wang Qing 
Published: December 2003

Abstract
Background
Current statistical methods for sib-pair linkage analysis of complex diseases include linear models, generalized linear models, and novel data mining techniques. The purpose of this study was to further investigate the utility and properties of a novel pattern recognition technique (step-wise discriminant analysis) using the chromosome 10 linkage data from the Framingham Heart Study and by comparing it with step-wise logistic regression and linear regression.

Results
The three step-wise approaches were compared in terms of statistical significance and gene localization. Step-wise discriminant linkage analysis approach performed best; next was step-wise logistic regression; and step-wise linear regression was the least efficient because it ignored the categorical nature of disease phenotypes. Nevertheless, all three methods successfully identified the previously reported chromosomal region linked to human hypertension, marker GATA64A09. We also explored the possibility of using the discriminant analysis to detect gene × gene and gene × environment interactions. There was evidence to suggest the existence of gene × environment interactions between markers GATA64A09 or GATA115E01 and hypertension treatment and gene × gene interactions between markers GATA64A09 and GATA115E01. Finally, we answered the theoretical question "Is a trichotomous phenotype more efficient than a binary?" Unlike logistic regression, discriminant sib-pair linkage analysis might have more power to detect linkage to a binary phenotype than a trichotomous one.

Conclusion
We confirmed our previous speculation that step-wise discriminant analysis is useful for genetic mapping of complex diseases. This analysis also supported the possibility of the pattern recognition technique for investigating gene × gene or gene × environment interactions.
Locating disease genes using Bayesian variable selection with the Haseman-Elston method
Authors: Oh Cheongeun , Ye Kenny Q , He Qimei , Mendell Nancy R 
Published: December 2003

Abstract
Background
We applied stochastic search variable selection (SSVS), a Bayesian model selection method, to the simulated data of Genetic Analysis Workshop 13. We used SSVS with the revisited Haseman-Elston method to find the markers linked to the loci determining change in cholesterol over time. To study gene-gene interaction (epistasis) and gene-environment interaction, we adopted prior structures, which incorporate the relationship among the predictors. This allows SSVS to search in the model space more efficiently and avoid the less likely models.

Results
In applying SSVS, instead of looking at the posterior distribution of each of the candidate models, which is sensitive to the setting of the prior, we ranked the candidate variables (markers) according to their marginal posterior probability, which was shown to be more robust to the prior. Compared with traditional methods that consider one marker at a time, our method considers all markers simultaneously and obtains more favorable results.

Conclusions
We showed that SSVS is a powerful method for identifying linked markers using the Haseman-Elston method, even for weak effects. SSVS is very effective because it does a smart search over the entire model space.
Approaches to mapping genetically correlated complex traits
Authors: George Andrew W , Basu Saonli , Li Na , Rothstein Joseph H , Sieberts Solveig K , Stewart William , Wijsman Ellen M , Thompson Elizabeth A 
Published: December 2003

AbstractOur Markov chain Monte Carlo (MCMC) methods were used in linkage analyses of the Framingham Heart Study data using all available pedigrees. Our goal was to detect and map loci associated with covariate-adjusted traits log triglyceride (lnTG) and high-density lipoprotein cholesterol (HDL) using multipoint LOD score analysis, Bayesian oligogenic linkage analysis and identity-by-descent (IBD) scoring methods. Each method used all marker data for all markers on a chromosome. Bayesian linkage analysis detected a linkage signal on chromosome 7 for lnTG and HDL, corroborating previously published results. However, these results were not replicated in a classical linkage analysis of the data or by using IBD scoring methods.We conclude that Bayesian linkage analysis provides a powerful paradigm for mapping trait loci but interpretation of the Bayesian linkage signals is subjective. In the absence of a LOD score method accommodating genetically complex traits and linkage heterogeneity, validation of these signals remains elusive.
On different approximations to multilocus identity-by-descent calculations and the resulting power of variance component-based linkage analysis
Authors: Göring Harald HH , Williams Jeff T , Dyer Thomas D , Blangero John 
Published: December 2003

AbstractAn empirical comparison between three different methods for estimation of pair-wise identity-by-descent (IBD) sharing at marker loci was conducted in order to quantify the resulting differences in power and localization precision in variance components-based linkage analysis. On the examined simulated, error-free data set, it was found that an increase in accuracy of allele sharing calculation resulted in an increase in power to detect linkage. Linkage analysis based on approximate multi-marker IBD matrices computed by a Markov chain Monte Carlo approach was much more powerful than linkage analysis based on exact single-marker IBD probabilities. A "multiple two-point" approximation to true "multipoint" IBD computation was found to be roughly intermediate in power. Both multi-marker approaches were similar to each other in accuracy of localization of the quantitative trait locus and far superior to the single-marker approach. The overall conclusions of this study with respect to power are expected to also hold for different data structures and situations, even though the degree of superiority of one approach over another depends on the specific circumstances. It should be kept in mind, however, that an increase in computational accuracy is expected to go hand in hand with a decrease in robustness to various sources of errors.
Importance sampling method of correction for multiple testing in affected sib-pair linkage analysis
Authors: Klein Alison P , Kovac Ilija , Sorant Alexa JM , Baffoe-Bonnie Agnes , Doan Betty Q , Ibay Grace , Lockwood Erica , Mandal Diptasri , Santhosh Lekshmi , Weissbecker Karen , Woo Jessica , Zambelli-Weiner April , Zhang Jie , Naiman Daniel Q , Malley James , Bailey-Wilson Joan E 
Published: December 2003

AbstractUsing the Genetic Analysis Workshop 13 simulated data set, we compared the technique of importance sampling to several other methods designed to adjust p-values for multiple testing: the Bonferroni correction, the method proposed by Feingold et al., and naïve Monte Carlo simulation. We performed affected sib-pair linkage analysis for each of the 100 replicates for each of five binary traits and adjusted the derived p-values using each of the correction methods. The type I error rates for each correction method and the ability of each of the methods to detect loci known to influence trait values were compared. All of the methods considered were conservative with respect to type I error, especially the Bonferroni method. The ability of these methods to detect trait loci was also low. However, this may be partially due to a limitation inherent in our binary trait definitions.
An autosome-wide search using longitudinal data for loci linked to type 2 diabetes progression
Authors: Jun Gyungah , Song Yeunjoo , Stein Catherine M , Iyengar Sudha K 
Published: December 2003

AbstractA genome-wide screen was conducted for type 2 diabetes progression genes using measures of elevated fasting glucose levels as quantitative traits from the offspring enrolled in the Framingham Heart Study. We analyzed young (20–34 years) and old (≥ 35 years) subjects separately, using single-point and multipoint sibpair analysis, because of the possible differential impact of progression on the groups of interest. We observed significant linkage with change in fasting glucose levels on 1q25-32 (p = 5.21 × 10-8), 3p26.3-21.31 (p = 1 × 10-11), 8q23.1-24.13 (p = 2.94 × 10-6), 9p24.1-21.3 (p = 7 × 10-7), and 18p11.31-q22.1 (p < 10-11). The evidence for linkage on chromosomes 8 and 18 was consistent for the subset of study participants aged 43 through 55 years.
Genome scan linkage results for longitudinal systolic blood pressure phenotypes in subjects from the Framingham Heart Study
Authors: James Katherine , Weitzel Lindsay-Rae B , Engelman Corinne D , Zerbe Gary , Norris Jill M 
Published: December 2003

AbstractThe relationship between elevated blood pressure and cardiovascular and cerebrovascular disease risk is well accepted. Both systolic and diastolic hypertension are associated with this risk increase, but systolic blood pressure appears to be a more important determinant of cardiovascular risk than diastolic blood pressure. Subjects for this study are derived from the Framingham Heart Study data set. Each subject had five records of clinical data of which systolic blood pressure, age, height, gender, weight, and hypertension treatment were selected to characterize the phenotype in this analysis.We modeled systolic blood pressure as a function of age using a mixed modeling methodology that enabled us to characterize the phenotype for each individual as the individual's deviation from the population average rate of change in systolic blood pressure for each year of age while controlling for gender, body mass index, and hypertension treatment. Significant (p = 0.00002) evidence for linkage was found between this normalized phenotype and a region on chromosome 1. Similar linkage results were obtained when we estimated the phenotype while excluding values obtained during hypertension treatment. The use of linear mixed models to define phenotypes is a methodology that allows for the adjustment of the main factor by covariates. Future work should be done in the area of combining this phenotype estimation directly with the linkage analysis so that the error in estimating the phenotype can be properly incorporated into the genetic analysis, which, at present, assumes that the phenotype is measured (or estimated) without error.
Genome-wide linkage analysis of systolic blood pressure slope using the Genetic Analysis Workshop 13 data sets
Authors: Pinnaduwage Dushanthi , Beyene Joseph , Fallah Shafagh 
Published: December 2003

AbstractSystolic blood pressure (SBP) is an age-dependent complex trait for which both environmental and genetic factors may play a role in explaining variability among individuals. We performed a genome-wide scan of the rate of change in SBP over time on the Framingham Heart Study data and one randomly selected replicate of the simulated data from the Genetic Analysis Workshop 13. We used a variance-component model to carry out linkage analysis and a Markov chain Monte Carlo-based multiple imputation approach to recover missing information. Furthermore, we adopted two selection strategies along with the multiple imputation to deal with subjects taking antihypertensive treatment. The simulated data were used to compare these two strategies, to explore the effectiveness of the multiple imputation in recovering varying degrees of missing information, and its impact on linkage analysis results. For the Framingham data, the marker with the highest LOD score for SBP slope was found on chromosome 7. Interestingly, we found that SBP slopes were not heritable in males but were for females; the marker with the highest LOD score was found on chromosome 18. Using the simulated data, we found that handling treated subjects using the multiple imputation improved the linkage results. We conclude that multiple imputation is a promising approach in recovering missing information in longitudinal genetic studies and hence in improving subsequent linkage analyses.
Genome scan for body mass index and height in the Framingham Heart Study
Authors: Geller Frank , Dempfle Astrid , Görg Tilman 
Published: December 2003

Abstract
Background
Body mass index (BMI) and adult height are moderately and highly heritable traits, respectively. To investigate the genetic background of these quantitative phenotypes, we performed a linkage genome scan in the extended pedigrees of the Framingham Heart Study. Two variance-components approaches (SOLAR and MERLIN-VC) and one regression method (MERLIN-REGRESS) were applied to the data.

Results
Evidence for linkage to BMI was found on chromosomes 16 and 6 with maximum LOD scores of 3.2 and 2.7, respectively. For height, all markers showing a LOD score greater than 1 in our analysis correspond to previously reported linkage regions, including chromosome 6q with a maximum LOD score of 2.45 and chromosomes 9, 12, 14, 18, and 22. Regarding the analysis, the three applied methods gave very similar results in this unselected sample with approximately normally distributed traits.

Conclusion
Our analysis resulted in the successful identification of linked regions. In particular, we consider the regions on chromosomes 6 and 16 for BMI and the regions on chromosomes 6, 9, and 12 for stature interesting for fine mapping and candidate gene studies.
Comparison of linkage analysis methods for genome-wide scanning of extended pedigrees, with application to the TG/HDL-C ratio in the Framingham Heart Study
Authors: Horne Benjamin D , Malhotra Alka , Camp Nicola J 
Published: December 2003

Abstract
Background
High triglycerides (TG) and low high-density lipoprotein cholesterol (HDL-C) jointly increase coronary disease risk. We performed linkage analysis for TG/HDL-C ratio in the Framingham Heart Study data as a quantitative trait, using methods implemented in LINKAGE, GENEHUNTER (GH), MCLINK, and SOLAR. Results were compared to each other and to those from a previous evaluation using SOLAR for TG/HDL-C ratio on this sample. We also investigated linked pedigrees in each region using by-pedigree analysis.

Results
Fourteen regions with at least suggestive linkage evidence were identified, including some that may increase and some that may decrease coronary risk. Ten of the 14 regions were identified by more than one analysis, and several of these regions were not previously detected. The best regions identified for each method were on chromosomes 2 (LOD = 2.29, MCLINK), 5 (LOD = 2.65, GH), 7 (LOD = 2.67, SOLAR), and 22 (LOD = 3.37, LINKAGE). By-pedigree multi-point LOD values in MCLINK showed linked pedigrees for all five regions, ranging from 3 linked pedigrees (chromosome 5) to 14 linked pedigrees (chromosome 7), and suggested localizations of between 9 cM and 27 cM in size.

Conclusion
Reasonable concordance was found across analysis methods. No single method identified all regions, either by full sample LOD or with by-pedigree analysis. Concordance across methods appeared better at the pedigree level, with many regions showing by-pedigree support in MCLINK when no evidence was observed in the full sample. Thus, investigating by-pedigree linkage evidence may provide a useful tool for evaluating linkage regions.
Phenotypic, genetic, and genome-wide structure in the metabolic syndrome
Authors: Martin Lisa J , North Kari E , Dyer Tom , Blangero John , Comuzzie Anthony G , Williams Jeff 
Published: December 2003

Abstract
Background
Insulin resistance, obesity, dyslipidemia, and high blood pressure characterize the metabolic syndrome. In an effort to explore the utility of different multivariate methods of data reduction to better understand the genetic influences on the aggregation of metabolic syndrome phenotypes, we calculated phenotypic, genetic, and genome-wide LOD score correlation matrices using five traits (total cholesterol, high density lipoprotein cholesterol, triglycerides, systolic blood pressure, and body mass index) from the Framingham Heart Study data set prepared for the Genetic Analysis Workshop 13, clinic visits 10 and 1 for the original and offspring cohorts, respectively. We next applied factor analysis to summarize the relationship between these phenotypes.

Results
Factors generated from the genetic correlation matrix explained the most variation. Factors extracted using the other matrices followed a different pattern and suggest distinct effects.

Conclusions
Given these results, different methods of multivariate data reduction may provide unique clues on the clustering of this complex syndrome.
A genome-wide linkage scan for body mass index on Framingham Heart Study families
Authors: Moslehi Roxana , Goldstein Alisa M , Beerman Michael , Goldin Lynn , Bergen Andrew W 
Published: December 2003

Abstract
Background
Genome-wide scan data from a community-based sample was used to identify the genetic factors that affect body mass index (BMI). BMI was defined as weight (kg) over the square of height (m), where weight and height were obtained from the first measurement available between the ages of 40 and 50 years.

Results
Significant familial correlations were observed in mother:father (spouse) relative pairs and in all relative pairs examined except parent:daughter pairs. Single-point sib-pair regression analysis provided nominal evidence for linkage (p < 0.05) of loci to BMI at 23 markers. Multi-point sib-pair regression analysis provided nominal evidence for linkage to BMI at 42 loci on 12 chromosomes. Empirical p-values showed results consistent with the multi-point results; all but three of the loci identified by multi-point analysis were also significant.

Conclusion
The largest regions of nominally significant linkage were found on chromosomes 2, 3, and 11. The most significant evidence for linkage was obtained with markers D2S1788, D2S1356, D2S1352, D3S1744, and D11S912 from multi-point sib-pair single-trait regression analysis. Our results are in agreement with some of the recently published reports on BMI using various data sets including the Framingham Heart Study data.
Structural equation model-based genome scan for the metabolic syndrome
Authors: Stein Catherine M , Song Yeunjoo , Elston Robert C , Jun Gyungah , Tiwari Hemant K , Iyengar Sudha K 
Published: December 2003

Abstract
Background
The metabolic syndrome is characterized by the clustering of several traits, including obesity, hypertension, decreased levels of HDL cholesterol, and increased levels of glucose and triglycerides. Because these traits cluster, there are likely common genetic factors involved.

Results
We used a multivariate structural equation model (SEM) approach to scan the genome for loci involved in the metabolic syndrome. We found moderate evidence for linkage on chromosomes 2, 3, 11, 13, and 15, and these loci appear to have different relative effects on the component traits of the metabolic syndrome.

Conclusion
Our results suggest that the metabolic syndrome components, diabetes, obesity, and hypertension, are under the pleiotropic control of several loci.
Risperidone long-acting injection: the first 50 patients
Authors: Paton Carol , Okocha Chike 
Published: December 2003

Aims and MethodRisperidone long-acting injection (RLAI) is the first atypical antipsychotic drug to be available in a ‘depot’ formulation. The evidence base underpinning its use is small. We sought to evaluate its early use in clinical practice by a naturalistic follow-up study of the first 50 patients to be prescribed RLAI in one National Health Service Trust.ResultsAt 6 months, 54% of patients had achieved at least minimal improvement, 4% were unchanged, 24% failed to comply, and 18% fared poorly and were switched to alternative antipsychotics. The attrition rate at 6 months was 42%. Supplementation with oral antipsychotics was often required for longer than 3 weeks. Only half of those who had a good clinical outcome at 6 months had achieved this by 3 months.Clinical ImplicationsSome patients responded well to RLAI, but the overall attrition rate was high. Although RLAI provides additional choice in the range of treatments available for people with schizophrenia, we have much to learn about how to optimise its use in practice.
Experience of prison psychiatry: a gap in psychiatrists' basic professional training
Authors: Reiss David , Famoroti Olumuyiwa John 
Published: December 2003

Aims and MethodTo survey the forensic psychiatry training and experience received by a sample of successful MRCPsych candidates. A questionnaire was sent to all 208 candidates who passed the Spring 2001 MRCPsych Part II examination.ResultsA total of 99 candidates replied. Of the 42 who had not trained in forensic psychiatry posts, two-thirds had never visited a prison.Clinical ImplicationsMore supervised visits to prisons should be provided for basic specialist trainees.
Impaired hypothalamus‐pituitary‐adrenal axis activity and more severe multiple sclerosis with hypothalamic lesions
Authors: Huitinga Inge , Erkut Zeynel A. , van Beurden Denis , Swaab Dick F. 
Published: December 2003

AbstractIn this postmortem study, we investigated the relationship between multiple sclerosis (MS) lesions in the hypothalamus and the state of activity of corticotropin‐releasing hormone (CRH)–producing neurons that control the hypothalamus‐pituitary‐adrenal (HPA) axis. A high incidence (15/16) of MS lesions was found in the hypothalamus, of which more than 50% was active, that is, contained activated macrophages. MS patients have increased numbers of CRH‐immunoreactive neurons coexpressing vasopressin (CRH/VP neurons), a sign of chronic activation of CRH neurons and increased CRH mRNA expression. Active MS lesions correlated with a low number of hyperactive CRH/VP neurons. High human leukocyte antigen (HLA)‐DR, ‐DP, ‐DQ expression, a measure for macrophage and microglial activation, correlated with low CRH mRNA expression. The nearer the HLA expression was situated to the CRH neurons, the stronger the inhibiting effect, suggesting that activated microglial cells or macrophages suppress these neurons. The more active MS lesions were present in the hypothalamus, the shorter was the disease duration until the moment of death, indicating an unfavorable course of the disease. Thus, MS patients have a chronically activated CRH system, but, in the subgroup of patients with active MS lesions in the hypothalamus, this activation is impaired and the disease course is worse.
Early magnetic resonance imaging findings in patients receiving tissue plasminogen activator predict outcome: Insights into the pathophysiology of acute stroke in the thrombolysis era
Authors: Chalela Julio A. , Kang Dong‐Wha , Luby Marie , Ezzeddine Mustapha , Latour Lawrence L. , Todd Jason W. , Dunn Billy , Warach Steven 
Published: December 2003

AbstractWe measured ischemic brain changes with diffusion and perfusion MRI in 42 ischemic stroke patients before and 2 hours (range approximately 1.5 to 4.5 hours) after standard intravenous tissue plasminogen activator (tPA) therapy. The median time from stroke onset to tPA was 131 minutes. Clinical and MRI variables (change in perfusion and/or diffusion weighted lesion volume) were compared between those with excellent outcome defined as 3‐month modified Rankin score (mRS) of 0 to 1 and those with incomplete recovery (mRS >1). In multivariate logististic regression analysis, the most powerful independent predictor for excellent outcome was improved brain perfusion: hypoperfusion volume on mean transit time (MTT) map decrease >30% from baseline to 2‐hour post tPA scan (p=0.009; odds ratio [95% confidence interval], 20.7 [2.1‐203.9]). Except for age < 70 years, no other baseline clinical or imaging variable was an independent predictor of outcome. We propose MTT lesion volume decrease more than 30% 2 hours after tPA as an early marker of long‐term clinical benefit of thrombolytic therapy.