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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. 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

  3. 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

  4. Despite strong evidence for a genetic component to variation in high-density lipoprotein cholesterol levels (HDL-C), specific polymorphisms associated with normal variation in HDL-C have not been identified. I...

    Authors: Kari E North, Lisa J Martin, Tom Dyer, Anthony G Comuzzie and Jeff T Williams
    Citation: BMC Genetics 2003 4(Suppl 1):S98
    Content type: Proceedings Published on:

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

  5. 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

  6. 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

  7. Compared to model-based approaches, nonparametric methods for quantitative trait loci mapping are more robust to deviations in distributional assumptions. In this study, we modify a nonparametric regression me...

    Authors: Saurabh Ghosh, Sarah Bertelsen and Theodore Reich
    Citation: BMC Genetics 2003 4(Suppl 1):S92
    Content type: Proceedings Published on:

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

  8. 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

  9. 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

  10. Elevated blood pressure in middle age is a major risk factor for subsequent cardiovascular complications. An important longitudinal characteristic of blood pressure is the "tracking phenomenon". Tracking is de...

    Authors: Tao Wang, Guohua Zhu and Kevin J Keen
    Citation: BMC Genetics 2003 4(Suppl 1):S88
    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. Only one genome scan to date has attempted to make use of the longitudinal data available in the Framingham Heart Study, and this attempt yielded evidence of linkage to a gene for mean systolic blood pressure....

    Authors: Kevin B Jacobs, Courtney Gray-McGuire, Kevin C Cartier and Robert C Elston
    Citation: BMC Genetics 2003 4(Suppl 1):S82
    Content type: Proceedings Published on:

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

  14. 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

  15. Often, multiple measures of a trait are available in a genetic linkage analysis. We compare Monte Carlo Markov chain analysis of two very different measures of hypertension in the simulated Genetic Analysis Wo...

    Authors: E Warwick Daw, Xiaoming Liu and Chih-Chieh Wu
    Citation: BMC Genetics 2003 4(Suppl 1):S80
    Content type: Proceedings Published on:

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

  16. 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

  17. We describe a method for mapping quantitative trait loci that allows for locus heterogeneity. A genome-wide linkage analysis of blood pressure was performed using sib-pair data from the Framingham Heart Study....

    Authors: Xinqun Yang, Kai Wang, Jian Huang and Veronica J Vieland
    Citation: BMC Genetics 2003 4(Suppl 1):S78
    Content type: Proceedings Published on:

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

  18. 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

  19. 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

  20. The Framingham Heart Study was initiated in 1948 as a long-term longitudinal study to identify risk factors associated with cardiovascular disease (CVD). Over the years the scope of the study has expanded to i...

    Authors: Catherine T Falk
    Citation: BMC Genetics 2003 4(Suppl 1):S67
    Content type: Proceedings Published on:

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

  21. 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

  22. Using the genome-wide screening data of the Framingham Heart Study (394 nuclear families, 1328 genotyped subjects, 397 marker loci) we have quantified the underlying genetic diversity through high-dimensional ...

    Authors: Hans H Stassen, Katrin Hoffman and Christian Scharfetter
    Citation: BMC Genetics 2003 4(Suppl 1):S59
    Content type: Proceedings Published on:

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

  23. 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

  24. There are no well accepted criteria for the diagnosis of the metabolic syndrome. However, the metabolic syndrome is identified clinically by the presence of three or more of these five variables: larger waist ...

    Authors: Curtis Olswold and Mariza de Andrade
    Citation: BMC Genetics 2003 4(Suppl 1):S57
    Content type: Proceedings Published on:

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

  25. 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

  26. 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

  27. 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

  28. 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

  29. 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

  30. Plasma triglyceride and high density lipoprotein cholesterol levels are inversely correlated and both are genetically related. Two correlated traits may be influenced both by shared and unshared genes. The pow...

    Authors: Jing-Ping Lin
    Citation: BMC Genetics 2003 4(Suppl 1):S47
    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. 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

  34. 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

  35. 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

  36. The Framingham Heart Study provides a unique source of longitudinal family data related to CVD risk factors. Age-stratified heritability estimates were obtained over three age groups (31–49 years, 50–60 years,...

    Authors: W Mark Brown, Stephanie R Beck, Ethan M Lange, Cralen C Davis, Christine M Kay, Carl D Langefeld and Stephen S Rich
    Citation: BMC Genetics 2003 4(Suppl 1):S32
    Content type: Proceedings Published on:

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

  37. The repeated measures in the Framingham Heart Study in the Genetic Analysis Workshop 13 data set allow us to test for consistency of linkage results within a study across time. We compared regression-based lin...

    Authors: Larry D Atwood, Nancy L Heard-Costa, L Adrienne Cupples and Daniel Levy
    Citation: BMC Genetics 2003 4(Suppl 1):S30
    Content type: Proceedings Published on:

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

  38. The design of appropriate strategies to analyze and interpret linkage results for complex human diseases constitutes a challenge. Parameters such as power, definition of phenotype, and replicability have to be...

    Authors: Neil Shephard, Milena Falcaro, Eleftheria Zeggini, Philip Chapman, Anne Hinks, Anne Barton, Jane Worthington, Andrew Pickles and Sally John
    Citation: BMC Genetics 2003 4(Suppl 1):S26
    Content type: Proceedings Published on:

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

  39. 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

  40. The Framingham Heart Study offspring cohort, a complex data set with irregularly spaced longitudinal phenotype data, was made available as part of Genetic Analysis Workshop 13. To allow an analysis of all of t...

    Authors: Stuart Macgregor, Sara A Knott, Ian White and Peter M Visscher
    Citation: BMC Genetics 2003 4(Suppl 1):S22
    Content type: Proceedings Published on:

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

  41. 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

  42. 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

  43. We investigate the power of heterogeneity LOD test to detect linkage when a trait is determined by several major genes using Genetic Analysis Workshop 13 simulated data. We consider three traits, two of which ...

    Authors: Yun Joo Yoo, Yanling Huo, Yuming Ning, Derek Gordon, Stephen Finch and Nancy R Mendell
    Citation: BMC Genetics 2003 4(Suppl 1):S16
    Content type: Proceedings Published on:

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

  44. 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

  45. 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

  47. 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

  48. One of the great strengths of the Framingham Heart Study data, provided for the Genetic Analysis Workshop 13, is the long-term survey of phenotypic data. We used this unique data to create new phenotypes repre...

    Authors: Astrid Golla, Konstantin Strauch, Johannes Dietter and Max P Baur
    Citation: BMC Genetics 2003 4(Suppl 1):S7
    Content type: Proceedings Published on:

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

  49. We used a random coefficient regression (RCR) model to estimate growth parameters for the time series of observed serum glucose levels in the Replicate 1 of the Genetic Analysis Workshop 13 simulated data. For...

    Authors: Jonathan Corbett, Aldi Kraja, Ingrid B Borecki and Michael A Province
    Citation: BMC Genetics 2003 4(Suppl 1):S5
    Content type: Proceedings Published on:

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

  50. Authors: Laura Almasy, Christopher I Amos, Joan E Bailey-Wilson, Rita M Cantor, Cashell E Jaquish, Maria Martinez, Rosalind J Neuman, Jane M Olson, Lyle J Palmer, Stephen S Rich, M Anne Spence and Jean W MacCluer
    Citation: BMC Genetics 2003 4(Suppl 1):S1
    Content type: Introduction Published on:

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

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