Skip to main content

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

Page 2 of 3

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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

Annual Journal Metrics

  • For BMC Genetics (former title)

    Citation Impact
    2.759 - 2-year Impact Factor (2021)
    3.295 - 5-year Impact Factor (2021)
    1.126 - SNIP (Source Normalized Impact per Paper)
    0.585 - SJR (SCImago Journal Rank)

    50 days to first decision for all manuscripts (Median)
    83 days to first decision for reviewed manuscripts only (Median)

    852,595 Downloads (2021)
    325 Altmetric mentions (2021)

Sign up for article alerts and news from this journal