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Volume 8 Supplement 1

Genetic Analysis Workshop 18: Human sequence data in extended pedigrees

Proceedings

Edited by H Bickeböller, JN Bailey, J Beyene, RM Cantor, HJ Cordell, RC Culverhouse, CD Engelman, DW Fardo, S Ghosh, IR König, J Lorenzo Bermejo, PE Melton, SA Santorico, GA Satten, L Sun, NL Tintle, A Ziegler, JW MacCluer and L Almasy

Publication charges for this supplement were funded by the Texas Biomedical Research Institute. Articles have undergone the journal's standard review process for supplements. AZ received intramural funding from the University of Lübeck, Germany. The remaining Supplement Editors declare that they have no competing interests.

Genetic Analysis Workshop 18. Go to conference site.

Stevenson, WA, USA13-17 October 2012

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  2. Imprinting effects can lead to parent-of-origin patterns in many complex human diseases. For hypertension, previous studies revealed the possible involvement of imprinted genes. Genetic Analysis Workshop 18 re...

    Authors: Fangyuan Zhang and Shili Lin
    Citation: BMC Proceedings 2014 8(Suppl 1):S52
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  3. Rare variants have been proposed to play a significant role in the onset and development of common diseases. However, traditional analysis methods have difficulties in detecting association signals for rare ca...

    Authors: Tian-Xiao Zhang, Yi-Ran Xie and John P Rice
    Citation: BMC Proceedings 2014 8(Suppl 1):S53
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  7. We applied a gene-based haplotype approach for the genome-wide association analysis on hypertension using Genetic Analysis Workshop 18 data for unrelated individuals. Association of single-nucleotide polymorph...

    Authors: Xiaowei Shen, Osvaldo Espin-Garcia, Xin Qiu, Yonathan Brhane, Geoffrey Liu and Wei Xu
    Citation: BMC Proceedings 2014 8(Suppl 1):S57
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  9. Testing rare variants directly is possible with next-generation sequencing technology. In this article, we propose a sliding-window-based optimal-weighted approach to test for the effects of both rare and comm...

    Authors: Xingwang Zhao, Qiuying Sha, Shuanglin Zhang and Xuexia Wang
    Citation: BMC Proceedings 2014 8(Suppl 1):S59
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  10. It is believed that almost all common diseases are the consequence of complex interactions between genetic markers and environmental factors. However, few such interactions have been documented to date. Conven...

    Authors: Ruixue Fan, Chien-Hsun Huang, Inchi Hu, Haitian Wang, Tian Zheng and Shaw-Hwa Lo
    Citation: BMC Proceedings 2014 8(Suppl 1):S60
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  11. The genetic basis of blood pressure often involves multiple genetic factors and their interactions with environmental factors. Gene-environment interaction is assumed to play an important role in determining i...

    Authors: Honglang Wang, Tao He, Cen Wu, Ping-Shou Zhong and Yuehua Cui
    Citation: BMC Proceedings 2014 8(Suppl 1):S61
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  12. Environment has long been known to play an important part in disease etiology. However, not many genome-wide association studies take environmental factors into consideration. There is also a need for new meth...

    Authors: Maggie Haitian Wang, Chien-Hsun Huang, Tian Zheng, Shaw-Hwa Lo and Inchi Hu
    Citation: BMC Proceedings 2014 8(Suppl 1):S62
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  13. Genome-wide association studies have proven successful but they remain underpowered for detecting variants of weaker effect. Alternative methods propose to test for association by using an aggregate score that...

    Authors: Nora Bohossian, Mohamad Saad, Andrés Legarra and Maria Martinez
    Citation: BMC Proceedings 2014 8(Suppl 1):S63
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  15. Logistic regression is usually applied to investigate the association between inherited genetic variants and a binary disease phenotype. A limitation of standard methods used to estimate the parameters of logi...

    Authors: Miriam Kesselmeier, Carine Legrand, Barbara Peil, Maria Kabisch, Christine Fischer, Ute Hamann and Justo Lorenzo Bermejo
    Citation: BMC Proceedings 2014 8(Suppl 1):S65
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  16. The concept of breeding values, an individual's phenotypic deviation from the population mean as a result of the sum of the average effects of the genes they carry, is of great importance in livestock, aquacul...

    Authors: Ellen E Quillen, V Saroja Voruganti, Geetha Chittoor, Rohina Rubicz, Juan M Peralta, Marcio AA Almeida, Jack W Kent Jr, Vincent P Diego, Thomas D Dyer, Anthony G Comuzzie, Harald HH Göring, Ravindranath Duggirala, Laura Almasy and John Blangero
    Citation: BMC Proceedings 2014 8(Suppl 1):S66
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  18. Although markers identified by genome-wide association studies have individually strong statistical significance, their performance in prediction remains limited. Our goal was to use animal breeding genomic pr...

    Authors: Chen Yao, Ning Leng, Kent A Weigel, Kristine E Lee, Corinne D Engelman and Kristin J Meyers
    Citation: BMC Proceedings 2014 8(Suppl 1):S68
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  19. Genetic studies often collect data on multiple traits. Most genetic association analyses, however, consider traits separately and ignore potential correlation among traits, partially because of difficulties in...

    Authors: Lili Ding, Brad G Kurowski, Hua He, Eileen S Alexander, Tesfaye B Mersha, David W Fardo, Xue Zhang, Valentina V Pilipenko, Leah Kottyan and Lisa J Martin
    Citation: BMC Proceedings 2014 8(Suppl 1):S69
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  20. We propose a genetic association analysis using Dirichlet regression to analyze the Genetic Analysis Workshop 18 data. Clinical variables, arranged in a longitudinal data structure, are employed to fit a multi...

    Authors: Osvaldo Espin-Garcia, Xiaowei Shen, Xin Qiu, Yonathan Brhane, Geoffrey Liu and Wei Xu
    Citation: BMC Proceedings 2014 8(Suppl 1):S70
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  21. Unlike case-control studies, family-based tests for association are protected against population stratification. Complex genetic traits are often governed by quantitative precursors and it has been argued that...

    Authors: Tanushree Haldar, Indranil Mukhopadhyay and Saurabh Ghosh
    Citation: BMC Proceedings 2014 8(Suppl 1):S71
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  25. Genetic variants that predispose adults and the elderly to high blood pressure are largely unknown. We used a bivariate linear mixed model approach to jointly test the associations of common single-nucleotide ...

    Authors: Binod Neupane and Joseph Beyene
    Citation: BMC Proceedings 2014 8(Suppl 1):S75
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  26. In a genome-wide association study, association between disease trait and hundreds of thousands of genetic markers are tested. Several methods have been proposed to control the false discovery rate in such hig...

    Authors: Xin Qiu, Xiaowei Shen, Osvaldo Espin-Garcia, Abul Kalam Azad, Geoffrey Liu and Wei Xu
    Citation: BMC Proceedings 2014 8(Suppl 1):S76
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  27. Pleiotropy, which occurs when a single genetic factor influences multiple phenotypes, is present in many genetic studies of complex human traits. Longitudinal family data, such as the Genetic Analysis Workshop...

    Authors: Lizhen Xu, Radu V Craiu, Andriy Derkach, Andrew D Paterson and Lei Sun
    Citation: BMC Proceedings 2014 8(Suppl 1):S77
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  28. For the analysis of the longitudinal hypertension family data, we focused on modeling binary traits of hypertension measured repeatedly over time. Our primary objective is to examine predictive abilities of lo...

    Authors: Yun-Hee Choi, Rafiqul Chowdhury and Balakumar Swaminathan
    Citation: BMC Proceedings 2014 8(Suppl 1):S78
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  29. In the last few years, a bewildering variety of methods/software packages that use linear mixed models to account for sample relatedness on the basis of genome-wide genomic information have been proposed. We c...

    Authors: Jakris Eu-ahsunthornwattana, Richard AJ Howey and Heather J Cordell
    Citation: BMC Proceedings 2014 8(Suppl 1):S79
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  31. Most association studies focus on disease risk, with less attention paid to disease progression or severity. These phenotypes require longitudinal data. This paper presents a new method for analyzing longitudi...

    Authors: Anthony Musolf, Alejandro Q Nato Jr, Douglas Londono, Lisheng Zhou, Tara C Matise and Derek Gordon
    Citation: BMC Proceedings 2014 8(Suppl 1):S81
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  32. Genetic association analysis on complex phenotypes under a longitudinal design involving pedigrees encounters the problem of correlation within pedigrees, which could affect statistical assessment of the genet...

    Authors: Qihua Tan, Jacob V B Hjelmborg, Mads Thomassen, Andreas Kryger Jensen, Lene Christiansen, Kaare Christensen, Jing Hua Zhao and Torben A Kruse
    Citation: BMC Proceedings 2014 8(Suppl 1):S82
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  33. We present a genome-wide association study of a quantitative trait, "progression of systolic blood pressure in time," in which 142 unrelated individuals of the Genetic Analysis Workshop 18 real genotype data w...

    Authors: Tatsiana Vaitsiakhovich, Dmitriy Drichel, Marina Angisch, Tim Becker, Christine Herold and André Lacour
    Citation: BMC Proceedings 2014 8(Suppl 1):S83
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  34. In recent years, longitudinal family-based studies have had success in identifying genetic variants that influence complex traits in genome-wide association studies. In this paper, we suggest that longitudinal...

    Authors: Shuai Wang, Wei Gao, Julius Ngwa, Catherine Allard, Ching-Ti Liu and L Adrienne Cupples
    Citation: BMC Proceedings 2014 8(Suppl 1):S84
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  38. We have extended our recently developed 2-step approach for gene-based analysis to the family design and to the analysis of rare variants. The goal of this approach is to study the joint effect of multiple sin...

    Authors: Jeanine J Houwing-Duistermaat, Quinta Helmer, Bruna Balliu, Erik van den Akker, Roula Tsonaka and Hae-Won Uh
    Citation: BMC Proceedings 2014 8(Suppl 1):S88
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  41. Increasing evidence shows that complex diseases are caused by both common and rare variants. Recently, several statistical methods for detecting associations of rare variants have been developed, including the...

    Authors: Shuaicheng Wang, Shurong Fang, Qiuying Sha and Shuanglin Zhang
    Citation: BMC Proceedings 2014 8(Suppl 1):S91
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  44. Every known link between a genetic variant and blood pressure improves the understanding and potentially the risk assessment of related diseases such as hypertension. Genetic data have become increasingly comp...

    Authors: Erin Austin, Wei Pan and Xiaotong Shen
    Citation: BMC Proceedings 2014 8(Suppl 1):S94
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  45. Background: Hypertension is a prevalent condition linked to major cardiovascular conditions and multiple other comorbidities. Genetic information can offer a deeper understanding about susceptibility and the unde...

    Authors: Ashley Bonner, Binod Neupane and Joseph Beyene
    Citation: BMC Proceedings 2014 8(Suppl 1):S95
    Content type: Proceedings Published on:

  46. In this paper, we compare logistic regression and 2 other classification methods in predicting hypertension given the genotype information. We use logistic regression analysis in the first step to detect signi...

    Authors: Hsin-Hsiung Huang, Tu Xu and Jie Yang
    Citation: BMC Proceedings 2014 8(Suppl 1):S96
    Content type: Proceedings Published on:

  47. Many complex diseases are related to genetics, and it is of great interest to evaluate the association between single-nucleotide polymorphisms (SNPs) and disease outcome. The association of genetics with outco...

    Authors: Jun Liu and Joseph Beyene
    Citation: BMC Proceedings 2014 8(Suppl 1):S97
    Content type: Proceedings Published on:

  49. Graphical models are increasingly used in genetic analyses to take into account the complex relationships between genetic and nongenetic factors influencing the phenotypes. We propose a model for determining t...

    Authors: Rajesh Talluri and Sanjay Shete
    Citation: BMC Proceedings 2014 8(Suppl 1):S99
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  50. The new generation of sequencing platforms opens new horizons in the genetics field. It is possible to exhaustively assay all genetic variants in an individual and search for phenotypic associations. The whole...

    Authors: Marcio Almeida, Juan M Peralta, Vidya Farook, Sobha Puppala, John W Kent Jr, Ravindranath Duggirala and John Blangero
    Citation: BMC Proceedings 2014 8(Suppl 1):S100
    Content type: Proceedings Published on:

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