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  1. Using the Framingham Heart Study data set provided for Genetic Analysis Workshop 13, we defined the cigarette-use phenotype M for smokers to be the maximum number of cigarettes-per-day (MAXCIG) reported over the ...

    Authors: Nancy L Saccone, Rosalind J Neuman, Scott F Saccone and John P Rice
    Citation: BMC Genetics 2003 4(Suppl 1):S105
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  2. There is substantial evidence for a significant genetic component to the risk for alcoholism. However, susceptibility loci or genes for alcohol dependence remain largely unknown. To identify susceptibility loc...

    Authors: Jennie Z Ma, Dong Zhang, Randolph T Dupont, Michael Dockter, Robert C Elston and Ming D Li
    Citation: BMC Genetics 2003 4(Suppl 1):S104
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  3. Although many years of genetic epidemiological studies have demonstrated that genetics plays a significant role in determining smoking behavior, little information is available on genomic loci or genes affecti...

    Authors: Ming D Li, Jennie Z Ma, Rong Cheng, Randolph T Dupont, Nancy J Williams, Karen M Crews, Thomas J Payne and Robert C Elston
    Citation: BMC Genetics 2003 4(Suppl 1):S103
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  4. Cigarette smoking behavior may have a genetic basis. We assessed evidence for quantitative trait loci (QTLs) affecting the maximum number of cigarettes smoked per day, a trait meant to quantify this behavior, ...

    Authors: Ellen L Goode, Michael D Badzioch, Helen Kim, France Gagnon, Laura S Rozek, Karen L Edwards and Gail P Jarvik
    Citation: BMC Genetics 2003 4(Suppl 1):S102
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  5. Pedigree, demographic, square-root transformed maximum alcohol (SRMAXAPD) and maximum cigarette (MAXCPD) consumption, and genome-wide scan data from the Framingham Heart Study (FHS) were used to investigate ge...

    Authors: Andrew W Bergen, Xiaohong Rose Yang, Yan Bai, Michael B Beerman, Alisa M Goldstein and Lynn R Goldin
    Citation: BMC Genetics 2003 4(Suppl 1):S101
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  6. 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 the...

    Authors: Catherine M Stein, Yeunjoo Song, Robert C Elston, Gyungah Jun, Hemant K Tiwari and Sudha K Iyengar
    Citation: BMC Genetics 2003 4(Suppl 1):S99
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  7. 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 he...

    Authors: Roxana Moslehi, Alisa M Goldstein, Michael Beerman, Lynn Goldin and Andrew W Bergen
    Citation: BMC Genetics 2003 4(Suppl 1):S97
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  8. 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 qua...

    Authors: Benjamin D Horne, Alka Malhotra and Nicola J Camp
    Citation: BMC Genetics 2003 4(Suppl 1):S93
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  9. 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 th...

    Authors: Frank Geller, Astrid Dempfle and Tilman Görg
    Citation: BMC Genetics 2003 4(Suppl 1):S91
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  10. Utilizing a linkage resource for association analysis requires consideration both of the marker data used and correlations among relatives in pedigrees. We previously developed a method for association testing...

    Authors: Kristina Allen-Brady, James M Farnham, Jeff Weiler and Nicola J Camp
    Citation: BMC Genetics 2003 4(Suppl 1):S89
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  11. Basically no methods are available for the analysis of quantitative traits in longitudinal genetic epidemiological studies. We introduce a nonparametric factorial design for longitudinal data on independent si...

    Authors: Bettina Kulle, Karola Köhler, Albert Rosenberger, Sabine Loesgen and Heike Bickeböller
    Citation: BMC Genetics 2003 4(Suppl 1):S85
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  12. The 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 systo...

    Authors: Katherine James, Lindsay-Rae B Weitzel, Corinne D Engelman, Gary Zerbe and Jill M Norris
    Citation: BMC Genetics 2003 4(Suppl 1):S83
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  13. The correlations between systolic blood pressure (SBP) and total cholesterol levels (CHOL) might result from genetic or environmental factors that determine variation in the phenotypes and are shared by family...

    Authors: Jisheng S Cui and Leslie J Sheffield
    Citation: BMC Genetics 2003 4(Suppl 1):S81
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  14. Discrete (qualitative) data segregation analysis may be performed assuming the liability model, which involves an underlying normally distributed quantitative phenotype. The appropriateness of the liability mo...

    Authors: GP Crockford, DT Bishop and JH Barrett
    Citation: BMC Genetics 2003 4(Suppl 1):S79
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  15. Current linkage analysis methods for quantitative traits do not usually incorporate imprinting effects. Here, we carried out genome-wide linkage analysis for loci influencing adult height in the Framingham Hea...

    Authors: Nandita Mukhopadhyay and Daniel E Weeks
    Citation: BMC Genetics 2003 4(Suppl 1):S76
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  16. This paper presents a method of performing model-free LOD-score based linkage analysis on quantitative traits. It is implemented in the QMFLINK program. The method is used to perform a genome screen on the Fra...

    Authors: Jo Knight, Bernard V North, Pak C Sham and David Curtis
    Citation: BMC Genetics 2003 4(Suppl 1):S74
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  17. Using 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,...

    Authors: Alison P Klein, Ilija Kovac, Alexa JM Sorant, Agnes Baffoe-Bonnie, Betty Q Doan, Grace Ibay, Erica Lockwood, Diptasri Mandal, Lekshmi Santhosh, Karen Weissbecker, Jessica Woo, April Zambelli-Weiner, Jie Zhang, Daniel Q Naiman, James Malley and Joan E Bailey-Wilson
    Citation: BMC Genetics 2003 4(Suppl 1):S73
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  18. An 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 lo...

    Authors: Harald HH Göring, Jeff T Williams, Thomas D Dyer and John Blangero
    Citation: BMC Genetics 2003 4(Suppl 1):S72
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  19. The goal of this study is to evaluate, compare, and contrast several standard and new linkage analysis methods. First, we compare a recently proposed confidence set approach with MAPMAKER/SIBS. Then, we evalua...

    Authors: Swati Biswas, Charalampos Papachristou, Mark E Irwin and Shili Lin
    Citation: BMC Genetics 2003 4(Suppl 1):S70
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  20. 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 ...

    Authors: Cheongeun Oh, Kenny Q Ye, Qimei He and Nancy R Mendell
    Citation: BMC Genetics 2003 4(Suppl 1):S69
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  21. 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 invest...

    Authors: Zheng Guo, Xia Li, Shaoqi Rao, Kathy L Moser, Tianwen Zhang, Binsheng Gong, Gongqing Shen, Lin Li, Ruth Cannata, Erich Zirzow, Eric J Topol and Qing Wang
    Citation: BMC Genetics 2003 4(Suppl 1):S68
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  22. Our goal was to identify subgroups of sib pairs from the Framingham Heart Study data set that provided higher evidence of linkage to particular candidate regions for cardiovascular disease traits. The focus of...

    Authors: Tracy Jennifer Costello, Michael David Swartz, Mahyar Sabripour, Xiangjun Gu, Rishika Sharma and Carol Jean Etzel
    Citation: BMC Genetics 2003 4(Suppl 1):S66
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  23. Random Forest is a prediction technique based on growing trees on bootstrap samples of data, in conjunction with a random selection of explanatory variables to define the best split at each node. In the case o...

    Authors: Alexandre Bureau, Josée Dupuis, Brooke Hayward, Kathleen Falls and Paul Van Eerdewegh
    Citation: BMC Genetics 2003 4(Suppl 1):S64
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  24. We address the question of whether statistical correlations among quantitative traits lead to correlation of linkage results of these traits. Five measured quantitative traits (total cholesterol, fasting gluco...

    Authors: Ayse Ulgen, Zhihua Han and Wentian Li
    Citation: BMC Genetics 2003 4(Suppl 1):S60
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  25. Using the simulated data set from Genetic Analysis Workshop 13, we explored the advantages of using longitudinal data in genetic analyses. The weighted average of the longitudinal data for each of seven quanti...

    Authors: Nathan Pankratz, Nitai Mukhopadhyay, Shuguang Huang, Tatiana Foroud and Sandra Close Kirkwood
    Citation: BMC Genetics 2003 4(Suppl 1):S58
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  26. Cardiovascular disease-related traits, such as body mass index (BMI), systolic blood pressure (SBP), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL), and glucose levels (...

    Authors: Xiao-Qing Liu, Anthony JG Hanley and Andrew D Paterson
    Citation: BMC Genetics 2003 4(Suppl 1):S56
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  27. We used an approach for detecting genotype × environment interactions to detect and characterize genotype × age interaction in longitudinal measures of three well known cardiovascular risk factors: total plasm...

    Authors: LM Havill and MC Mahaney
    Citation: BMC Genetics 2003 4(Suppl 1):S54
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  28. Epidemiological studies have indicated that obesity and low high-density lipoprotein (HDL) levels are strong cardiovascular risk factors, and that these traits are inversely correlated. Despite the belief that...

    Authors: Rector Arya, Donna Lehman, Kelly J Hunt, Jennifer Schneider, Laura Almasy, John Blangero, Michael P Stern and Ravindranath Duggirala
    Citation: BMC Genetics 2003 4(Suppl 1):S52
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  29. We analyzed the Genetic Analysis Workshop 13 (GAW13) simulated data to contrast and compare different methods for the genetic linkage analysis of hypertension and change in blood pressure over time. We also ex...

    Authors: Evadnie Rampersaud, Andrew Allen, Yi-Ju Li, Yujun Shao, Meredyth Bass, Carol Haynes, Allison Ashley-Koch, Eden R Martin, Silke Schmidt and Elizabeth R Hauser
    Citation: BMC Genetics 2003 4(Suppl 1):S50
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  30. One implicit assumption in most linkage analysis is that live-born siblings unselected for a phenotype do not share alleles greater than the Mendelian expectation at any particular locus. However, since most f...

    Authors: Andrew D Paterson, Lei Sun and Xiao-Qing Liu
    Citation: BMC Genetics 2003 4(Suppl 1):S48
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  31. We compare two new software packages for linkage analysis, LODPAL and GENEFINDER. Both allow for covariate adjustment. Replicates 1 to 3 of Genetic Analysis Workshop 13 simulated data sets were used for the an...

    Authors: Fang-Chi Hsu, Jacqueline B Hetmanski, Lan Li, Diane Markakis, Kevin Jacobs and Yin Yao Shugart
    Citation: BMC Genetics 2003 4(Suppl 1):S46
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  32. Observational cohort studies have been little used in linkage analyses due to their general lack of large, disease-specific pedigrees. Nevertheless, the longitudinal nature of such studies makes them potential...

    Authors: Chao Xing, Fredrick R Schumacher, David V Conti and John S Witte
    Citation: BMC Genetics 2003 4(Suppl 1):S44
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  33. Missing data are a great concern in longitudinal studies, because few subjects will have complete data and missingness could be an indicator of an adverse outcome. Analyses that exclude potentially informative...

    Authors: Terri Kang, Peter Kraft, W James Gauderman and Duncan Thomas
    Citation: BMC Genetics 2003 4(Suppl 1):S43
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  34. This investigation was undertaken to assess the sensitivity and specificity of the genotyping error detection function of the computer program SIMWALK2. We chose to examine chromosome 22, which had 7 microsate...

    Authors: Michael D Badzioch, Hawkins B DeFrance and Gail P Jarvik
    Citation: BMC Genetics 2003 4(Suppl 1):S40
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  35. Informative missingness of parental genotype data occurs when the genotype of a parent influences the probability of the parent's genotype data being observed. Informative missingness can occur in a number of ...

    Authors: Andrew S Allen, Julianne S Collins, Paul J Rathouz, Craig L Selander and Glen A Satten
    Citation: BMC Genetics 2003 4(Suppl 1):S39
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  36. Exploratory data-driven multivariate analysis provides a means of investigating underlying structure in complex data. To explore the stability of multivariate data modeling, we have applied a common method of ...

    Authors: Haydar Sengul and M Michael Barmada
    Citation: BMC Genetics 2003 4(Suppl 1):S38
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  37. Using the longitudinal Framingham Heart Study data on blood pressure, we analyzed the reproducibility of linkage measures from serial cross-sectional surveys of a defined population by performing genome-wide m...

    Authors: Sanjay R Patel, Juan C Celedon, Scott T Weiss and Lyle J Palmer
    Citation: BMC Genetics 2003 4(Suppl 1):S37
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  38. To evaluate linkage evidence for body mass index (BMI) using both cross-sectional and longitudinal data, we performed genome-wide multipoint linkage analyses on subjects who had complete data at four selected ...

    Authors: Xiaohui Li, Dai Wang, Kai Yang, Xiuqing Guo, Ying-chao Lin, Carlos G Samayoa and Huiying Yang
    Citation: BMC Genetics 2003 4(Suppl 1):S35
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  39. Using univariate and multivariate variance components linkage analysis methods, we studied possible genotype × age interaction in cardiovascular phenotypes related to the aging process from the Framingham Hear...

    Authors: Vincent P Diego, Laura Almasy, Thomas D Dyer, Júlia MP Soler and John Blangero
    Citation: BMC Genetics 2003 4(Suppl 1):S34
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  40. The purpose of this study was to estimate both cross-sectional sibling recurrence risk ratio (λ s ) and lifetime λ s for the metabolic s...

    Authors: Wei J Chen, Pi-Hua Liu, Yen-Yi Ho, Kuo-Liong Chien, Min-Tzu Lo, Wei-Liang Shih, Yu-Chun Yen and Wen-Chung Lee
    Citation: BMC Genetics 2003 4(Suppl 1):S33
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  41. With the availability of longitudinal data, age-specific (stratified) or age-adjusted genetic analyses have the potential to localize different putative trait influencing loci. If age does not influence the lo...

    Authors: Stephanie R Beck, W Mark Brown, Adrienne H Williams, June Pierce, Stephen S Rich and Carl D Langefeld
    Citation: BMC Genetics 2003 4(Suppl 1):S31
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  42. We explored three approaches to heritability and linkage analyses of longitudinal total cholesterol levels (CHOL) in the Genetic Analysis Workshop 13 simulated data without knowing the answers. The first two w...

    Authors: Qiong Yang, Irmarie Chazaro, Jing Cui, Chao-Yu Guo, Serkalem Demissie, Martin Larson, Larry D Atwood, L Adrienne Cupples and Anita L DeStefano
    Citation: BMC Genetics 2003 4(Suppl 1):S29
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  43. We propose a statistical model for linkage analysis of the longitudinal data. The proposed model is a mixed model based on the new Haseman and Elston model and allows several random effects. Specifically, the ...

    Authors: Young Ju Suh, Taesung Park and Soo Yeon Cheong
    Citation: BMC Genetics 2003 4(Suppl 1):S27
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  44. This paper describes an analysis of systolic blood pressure (SBP) in the Genetic Analysis Workshop 13 (GAW13) simulated data. The main aim was to assess evidence for both general and specific genetic effects o...

    Authors: Katrina J Scurrah, Martin D Tobin and Paul R Burton
    Citation: BMC Genetics 2003 4(Suppl 1):S25
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  45. To compare different strategies for linkage analyses of longitudinal quantitative trait measures, we applied the "revisited" Haseman-Elston (RHE) regression model (the cross product of centered sib-pair trait ...

    Authors: Lucia Mirea, Shelley B Bull and James Stafford
    Citation: BMC Genetics 2003 4(Suppl 1):S23
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  46. We present a method for using slopes and intercepts from a linear regression of a quantitative trait as outcomes in segregation and linkage analyses. We apply the method to the analysis of longitudinal systoli...

    Authors: Conway Gee, John L Morrison, Duncan C Thomas and W James Gauderman
    Citation: BMC Genetics 2003 4(Suppl 1):S21
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  47. The data arising from a longitudinal familial study have a complex correlation structure that cannot be modeled using classical methods for the analysis of familial data at a single time point.

    Authors: Laurent Briollais, Anjela Tzontcheva and Shelley Bull
    Citation: BMC Genetics 2003 4(Suppl 1):S19
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  48. The Framingham Heart Study has contributed a great deal to advances in medicine. Most of the phenotypes investigated have been univariate traits (quantitative or qualitative). The aims of this study are to der...

    Authors: Marsha A Wilcox, Diego F Wyszynski, Carolien I Panhuysen, Qianli Ma, Agustin Yip, John Farrell and Lindsay A Farrer
    Citation: BMC Genetics 2003 4(Suppl 1):S15
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  49. The genetic regulation of variation in intra-individual fluctuations in systolic blood pressure over time is poorly understood. Analysis of the magnitude of the average fluctuation of a person's systolic blood...

    Authors: Jennifer Lin, Anthony Hinrichs and Brian K Suarez
    Citation: BMC Genetics 2003 4(Suppl 1):S11
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

  50. A 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 a...

    Authors: Gyungah Jun, Yeunjoo Song, Catherine M Stein and Sudha K Iyengar
    Citation: BMC Genetics 2003 4(Suppl 1):S8
    Content type: Proceedings Published on:

    This article is part of a Supplement: Volume 4 Supplement 1

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