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Volume 4 Supplement 1

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

Proceedings

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 1 of 3

  1. The Genetic Analysis Workshop 13 simulated data aimed to mimic the major features of the real Framingham Heart Study data that formed Problem 1, but under a known inheritance model and with 100 replicates, so ...

    Authors: E Warwick Daw, John Morrison, Xiaojun Zhou and Duncan C Thomas
    Citation: BMC Genetics 2003 4(Suppl 1):S3
  2. We performed variance components linkage analysis in nuclear families from the Framingham Heart Study on nine phenotypes derived from systolic blood pressure (SBP). The phenotypes were the maximum and mean SBP...

    Authors: Martyn C Byng, Sheila A Fisher, Cathryn M Lewis and John C Whittaker
    Citation: BMC Genetics 2003 4(Suppl 1):S4
  3. 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
  4. 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
  5. The study of change in intermediate phenotypes over time is important in genetics. In this paper we explore a new approach to phenotype definition in the genetic analysis of longitudinal phenotypes. We utilize...

    Authors: Lyle J Palmer, Katrina J Scurrah, Martin Tobin, Sanjay R Patel, Juan C Celedon, Paul R Burton and Scott T Weiss
    Citation: BMC Genetics 2003 4(Suppl 1):S12
  6. 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
  7. 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
  8. We compare two methods to detect genetic linkage by using serial observations of systolic blood pressure in pedigree data from the Framingham Heart Study focusing on chromosome 17. The first method is a varian...

    Authors: Mariza de Andrade and Curtis Olswold
    Citation: BMC Genetics 2003 4(Suppl 1):S17
  9. Gene × environment models are widely used to assess genetic and environmental risks and their association with a phenotype of interest for many complex diseases. Mixed generalized linear models were used to as...

    Authors: Jill S Barnholtz-Sloan, Laila M Poisson, Steven W Coon, Gary A Chase and Benjamin A Rybicki
    Citation: BMC Genetics 2003 4(Suppl 1):S18
  10. Family studies are often conducted in a cross-sectional manner without long-term follow-up data. The relative contribution of a gene to a specific trait could change over the lifetime. The Framingham Heart Stu...

    Authors: Rong Cheng, Naeun Park, Susan E Hodge and Suh-Hang Hank Juo
    Citation: BMC Genetics 2003 4(Suppl 1):S20
  11. 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
  12. 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
  13. Longitudinal data often have multiple (repeated) measures recorded along a time trajectory. For example, the two cohorts from the Framingham Heart Study (GAW13 Problem 1) contain 21 and 5 repeated measures for...

    Authors: Shaoqi Rao, Lin Li, Xia Li, Kathy L Moser, Zheng Guo, Gongqing Shen, Ruth Cannata, Erich Zirzow, Eric J Topol and Qing Wang
    Citation: BMC Genetics 2003 4(Suppl 1):S24
  14. 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
  15. 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
  16. The Framingham Heart Study is a very successful longitudinal research for cardiovascular diseases. The completion of a 10-cM genome scan in Framingham families provided an opportunity to evaluate linkage using...

    Authors: Dai Wang, Xiaohui Li, Ying-Chao Lin, Kai Yang, Xiuqing Guo and Huiying Yang
    Citation: BMC Genetics 2003 4(Suppl 1):S28
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. Several different approaches can be used to examine generational and temporal trends in family studies. The measurement of offspring and parents can be made over a short period of time with parents and offspri...

    Authors: Rasika A Mathias, Marie-Hélène Roy-Gagnon, Cristina M Justice, George J Papanicolaou, Yu Ti Fan, Elizabeth W Pugh and Alexander F Wilson
    Citation: BMC Genetics 2003 4(Suppl 1):S36
  24. 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
  25. The pedigree and genotype data from the Framingham Heart Study were examined for errors. Errors in 21 of 329 pedigrees were detected with the program PREST, and of these the errors in 16 pedigrees were resolve...

    Authors: Gerry Brush and Laura Almasy
    Citation: BMC Genetics 2003 4(Suppl 1):S41
  26. Methods to handle missing data have been an area of statistical research for many years. Little has been done within the context of pedigree analysis. In this paper we present two methods for imputing missing ...

    Authors: Brooke Fridley, Kari Rabe and Mariza de Andrade
    Citation: BMC Genetics 2003 4(Suppl 1):S42
  27. 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
  28. 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
  29. Genetic heterogeneity and complex biologic mechanisms of blood pressure regulation pose significant challenges to the identification of susceptibility loci influencing hypertension. Previous linkage studies ha...

    Authors: Denise Daley, Shannon R Edwards, Yeunjoo Song, Dan Baechle, Sobha Puppala, JH Schick, Jane M Olson and Katrina AB Goddard
    Citation: BMC Genetics 2003 4(Suppl 1):S45
  30. 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
  31. 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
  32. Family-based association testing is an important part of genetic epidemiology. Tests are available to include multiple siblings, unaffected offspring, and to adjust for environmental covariates. We explore a s...

    Authors: Laila M Poisson, Benjamin A Rybicki, Steven W Coon, Jill S Barnholtz-Sloan and Gary A Chase
    Citation: BMC Genetics 2003 4(Suppl 1):S49
  33. 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

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