Skip to main content

Articles

Page 40 of 42

  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. 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

  16. 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

  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. 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

  19. 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

  20. 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

  21. 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

  22. 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

  23. 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

  24. 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

  25. 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

  26. 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

  27. 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

  28. 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

  29. 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

  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

    Content type: Proceedings

    Published on:

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

  31. 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

  32. 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

  33. 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

  34. 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

  35. 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

  36. 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

  37. 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

  38. 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

  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

  46. 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

  47. 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

  48. 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

  49. 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

2018 Journal Metrics

Announcement

As a result of the significant disruption that is being caused by the COVID-19 pandemic we are very aware that many researchers will have difficulty in meeting the timelines associated with our peer review process during normal times.  Please do let us know if you need additional time. Our systems will continue to remind you of the original timelines but we intend to be highly flexible at this time.

Portable Peer Review

The editors of BMC Genetics support initiatives that expedite the peer review process and are happy to consider manuscripts that have been reviewed in Peerage of Science. Please indicate in your cover letter if this applies to your manuscript.