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Volume 4 Supplement 1

Genetic Analysis Workshop 13: Analysis of Longitudinal Family Data for Complex Diseases and Related Risk Factors


Edited by 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, Jean W MacCluer

Genetic Analysis Workshop 13: Analysis of Longitudinal Family Data for Complex Diseases and Related Risk Factors. Go to conference site.

New Orleans, LA, USANovember 11-14, 2002

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  1. Content type: Proceedings

    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

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  2. Content type: Proceedings

    A standard multivariate principal components (PCs) method was utilized to identify clusters of variables that may be controlled by a common gene or genes (pleiotropy). Heritability estimates were obtained and ...

    Authors: Jeannette T Bensen, Leslie A Lange, Carl D Langefeld, Bao-Li Chang, Eugene R Bleecker, Deborah A Meyers and Jianfeng Xu

    Citation: BMC Genetics 2003 4(Suppl 1):S53

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  3. Content type: Proceedings

    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

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  4. Content type: Proceedings

    Multivariate variance-components analysis provides several advantages over univariate analysis when studying correlated traits. It can test for pleiotropy or (in the longitudinal context) gene × age interactio...

    Authors: Peter Kraft, Lara Bauman, Jin Ying Yuan and Steve Horvath

    Citation: BMC Genetics 2003 4(Suppl 1):S55

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  5. Content type: Proceedings

    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

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  6. Content type: Proceedings

    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

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  7. Content type: Proceedings

    We report tree-based association analysis as applied to the two Framingham cohorts and to the first replication of the simulated data obtained from the Genetic Analysis Workshop 13. For this analysis, familial...

    Authors: Elizabeth J Atkinson and Mariza de Andrade

    Citation: BMC Genetics 2003 4(Suppl 1):S63

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  8. Content type: Proceedings

    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

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  9. Content type: Proceedings

    In the analysis of complex traits such as fasting plasma glucose levels, researchers often adjust the trait for some important covariates before assessing gene susceptibility, and may at times encounter confou...

    Authors: Chien-Hsiun Chen, Chee Jen Chang, Wei-Shiung Yang, Chun-Liang Chen and Cathy SJ Fann

    Citation: BMC Genetics 2003 4(Suppl 1):S65

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  10. Content type: Proceedings

    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

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  11. Content type: Proceedings

    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

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  12. Content type: Proceedings

    Our 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-adjuste...

    Authors: Andrew W George, Saonli Basu, Na Li, Joseph H Rothstein, Solveig K Sieberts, William Stewart, Ellen M Wijsman and Elizabeth A Thompson

    Citation: BMC Genetics 2003 4(Suppl 1):S71

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  13. Content type: Proceedings

    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

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  14. Content type: Proceedings

    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

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  15. Content type: Proceedings

    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

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  16. Content type: Proceedings

    This Genetic Analysis Workshop 13 contribution presents a linkage analysis of hypertension in the Framingham data based on the posterior probability of linkage, or PPL. We dichotomized the phenotype, coding in...

    Authors: Mark W Logue, Rhinda J Goedken and Veronica J Vieland

    Citation: BMC Genetics 2003 4(Suppl 1):S75

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  17. Content type: Proceedings

    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

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  18. Content type: Proceedings

    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

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  19. Content type: Proceedings

    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

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  20. Content type: Proceedings

    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

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  21. Content type: Proceedings

    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

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  22. Content type: Proceedings

    Genetic studies of complex disorders such as hypertension often utilize families selected for this outcome, usually with information obtained at a single time point. Since age-at-onset for diagnosed hypertensi...

    Authors: Karen A Kopciuk, Laurent Briollais, Florence Demenais and Shelley B Bull

    Citation: BMC Genetics 2003 4(Suppl 1):S84

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  23. Content type: Proceedings

    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

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  24. Content type: Proceedings

    Systolic 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 ...

    Authors: Dushanthi Pinnaduwage, Joseph Beyene and Shafagh Fallah

    Citation: BMC Genetics 2003 4(Suppl 1):S86

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  25. Content type: Proceedings

    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

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  26. Content type: Proceedings

    Genes have been found to influence the age of onset of several diseases and traits. The occurrence of many chronic diseases, obesity included, appears to be strongly age-dependent. However, an analysis of pote...

    Authors: Corinne D Engelman, Heather L Brady, Anna E Baron and Jill M Norris

    Citation: BMC Genetics 2003 4(Suppl 1):S90

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  27. Content type: Proceedings

    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

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  28. Content type: Proceedings

    The multiple metabolic syndrome is defined by a clustering of risk factors for cardiovascular disease. We sought to evaluate the familial correlations of the components of the syndrome using data from the Fram...

    Authors: Kristine E Lee, Barbara EK Klein and Ronald Klein

    Citation: BMC Genetics 2003 4(Suppl 1):S94

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  29. Content type: Proceedings

    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 underst...

    Authors: Lisa J Martin, Kari E North, Tom Dyer, John Blangero, Anthony G Comuzzie and Jeff Williams

    Citation: BMC Genetics 2003 4(Suppl 1):S95

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  30. Content type: Proceedings

    Because high blood pressure, altered lipid levels, obesity, and diabetes so frequently occur together, they are sometimes collectively referred to as the metabolic syndrome. While there have been many studies ...

    Authors: Matthew B McQueen, Lars Bertram, Eric B Rimm, Deborah Blacker and Susan L Santangelo

    Citation: BMC Genetics 2003 4(Suppl 1):S96

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  31. Content type: Proceedings

    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

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  32. Content type: Proceedings

    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

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  33. Content type: Proceedings

    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

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  34. Content type: Proceedings

    Atherogenic dyslipidemia (AD) is a common feature in persons with premature coronary heart disease. While several linkage studies have been carried out to dissect the genetic etiology of lipid levels, few have...

    Authors: Agustin G Yip, Qianli Ma, Marsha Wilcox, Carolien I Panhuysen, John Farrell, Lindsay A Farrer and Diego F Wyszynski

    Citation: BMC Genetics 2003 4(Suppl 1):S100

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