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Volume 3 Supplement 7

Genetic Analysis Workshop 16

Proceedings

Genetic Analysis Workshop 16. Go to conference site.

St Louis, MO, USA17-20 September 2008

Page 2 of 3

  1. While recently performed genome-wide association studies have advanced the identification of genetic variants predisposing to type 2 diabetes (T2D), the potential application of these novel findings for diseas...

    Authors: Qing Lu, Yeunjoo Song, Xuefeng Wang, Sungho Won, Yuehua Cui and Robert C Elston

    Citation: BMC Proceedings 2009 3(Suppl 7):S49

    Content type: Proceedings

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  2. The Metabolic Syndrome (MetSyn), which is a clustering of traits including insulin resistance, obesity, hypertension and dyslipidemia, is estimated to have a substantial genetic component, yet few specific gen...

    Authors: Nora L Nock, Xuefeng Wang, Cheryl L Thompson, Yeunjoo Song, Dan Baechle, Paola Raska, Catherine M Stein and Courtney Gray-McGuire

    Citation: BMC Proceedings 2009 3(Suppl 7):S50

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  3. Transmission-ratio distortion (TRD) is a phenomenon in which the segregation of alleles does not obey Mendel's laws. As a simple example, a recessive locus that results in fetal lethality will result in live-b...

    Authors: Andrew D Paterson, Daryl Waggott, Arne Schillert, Claire Infante-Rivard, Shelley B Bull, Yun Joo Yoo and Dushanthi Pinnaduwage

    Citation: BMC Proceedings 2009 3(Suppl 7):S51

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  4. Problems associated with medication use and the consequent effects on genome-wide association analyses were explored using the Genetic Analysis Workshop 16 Problem 3 data. Lipid phenotypes were simulated in th...

    Authors: Treva K Rice, Yun Ju Sung, Gang Shi, C Charles Gu and DC Rao

    Citation: BMC Proceedings 2009 3(Suppl 7):S52

    Content type: Proceedings

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  5. We used data reduction and clustering methods to identify five phenotypically homogeneous groups of study participants with similar profiles for cardiovascular disease risk factors. We constructed both qualita...

    Authors: Marsha Wilcox, Qingqin Li, Yu Sun, Paul Stang, Jesse Berlin and Dai Wang

    Citation: BMC Proceedings 2009 3(Suppl 7):S53

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  6. Genome-wide association is a powerful tool for the identification of genes that underlie common diseases. Genome-wide association studies generate billions of genotypes and pose significant computational chall...

    Authors: Xiang Chen, Meizhuo Zhang, Minghui Wang, Wensheng Zhu, Kelly Cho and Heping Zhang

    Citation: BMC Proceedings 2009 3(Suppl 7):S54

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  7. Results from whole-genome association studies of many common diseases are now available. Increasingly, these are being incorporated into meta-analyses to increase the power to detect weak associations with mea...

    Authors: David Hadley and David P Strachan

    Citation: BMC Proceedings 2009 3(Suppl 7):S55

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  8. We explored five sex-specific quality control filters in North American Rheumatoid Arthritis Consortium's Illumina 550 k datasets. Three X chromosome and three autosomal single-nucleotide polymorphisms flagged...

    Authors: Hua Ling, Kurt Hetrick, Joan E Bailey-Wilson and Elizabeth W Pugh

    Citation: BMC Proceedings 2009 3(Suppl 7):S57

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  9. Genome-wide association studies have become standard in genetic epidemiology. Analyzing hundreds of thousands of markers simultaneously imposes some challenges for statisticians. One issue is the problem of mu...

    Authors: Arne Schillert, Daniel F Schwarz, Maren Vens, Silke Szymczak, Inke R König and Andreas Ziegler

    Citation: BMC Proceedings 2009 3(Suppl 7):S58

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  10. In genome-wide association studies, high-level statistical analyses rely on the validity of the called genotypes, and different genotype calling algorithms (GCAs) have been proposed. We compared the GCAs Bayes...

    Authors: Maren Vens, Arne Schillert, Inke R König and Andreas Ziegler

    Citation: BMC Proceedings 2009 3(Suppl 7):S59

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  11. In this paper, we apply the gradient-boosting machine predictive model to the rheumatoid arthritis data for predicting the case-control status. QQ-plot suggests severe population stratification. In univariate ...

    Authors: Niloofar Arshadi, Billy Chang and Rafal Kustra

    Citation: BMC Proceedings 2009 3(Suppl 7):S60

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  12. Variable selection in genome-wide association studies can be a daunting task and statistically challenging because there are more variables than subjects. We propose an approach that uses principal-component a...

    Authors: Gina M D'Angelo, DC Rao and C Charles Gu

    Citation: BMC Proceedings 2009 3(Suppl 7):S62

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  13. The objective of this study was to detect interactions between relevant single-nucleotide polymorphisms (SNPs) associated with rheumatoid arthritis (RA). Data from Problem 1 of the Genetic Analysis Workshop 16...

    Authors: Oscar González-Recio, Evangelina López de Maturana, Andrés T Vega, Corinne D Engelman and Karl W Broman

    Citation: BMC Proceedings 2009 3(Suppl 7):S63

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  14. Random forests (RF) is one of a broad class of machine learning methods that are able to deal with large-scale data without model specification, which makes it an attractive method for genome-wide association ...

    Authors: Yoonhee Kim, Robert Wojciechowski, Heejong Sung, Rasika A Mathias, Li Wang, Alison P Klein, Rhoshel K Lenroot, James Malley and Joan E Bailey-Wilson

    Citation: BMC Proceedings 2009 3(Suppl 7):S64

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  15. Using the North American Rheumatoid Arthritis Consortium genome-wide association dataset, we applied ridged, multiple least-squares regression to identify genetic variants with apparent unique contributions to...

    Authors: Yan V Sun, Kerby A Shedden, Ji Zhu, Nam-Hee Choi and Sharon LR Kardia

    Citation: BMC Proceedings 2009 3(Suppl 7):S67

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  16. Random forest (RF) analysis of genetic data does not require specification of the mode of inheritance, and provides measures of variable importance that incorporate interaction effects. In this paper we descri...

    Authors: Rui Tang, Jason P Sinnwell, Jia Li, David N Rider, Mariza de Andrade and Joanna M Biernacka

    Citation: BMC Proceedings 2009 3(Suppl 7):S68

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  17. Random forest is an efficient approach for investigating not only the effects of individual markers on a trait but also the effect of the interactions among the markers in genetic association studies. This app...

    Authors: Minghui Wang, Xiang Chen, Meizhuo Zhang, Wensheng Zhu, Kelly Cho and Heping Zhang

    Citation: BMC Proceedings 2009 3(Suppl 7):S69

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  18. Fifteen known type 2 diabetes (T2D) gene variants were assessed for their associations with T2D status in 228 T2D families from the Framingham Heart Study (FHS) Original, Offspring, and Children Cohorts. Bayes...

    Authors: Ping An, Mary Feitosa, Shamika Ketkar, Avril Adelman, Shiow Lin, Ingrid Borecki and Michael Province

    Citation: BMC Proceedings 2009 3(Suppl 7):S71

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  19. Gene × gene interactions play important roles in the etiology of complex multi-factorial diseases like rheumatoid arthritis (RA). In this paper, we describe our use of a two-stage search strategy consisting of...

    Authors: Pritam Chanda, Aidong Zhang, Lara Sucheston and Murali Ramanathan

    Citation: BMC Proceedings 2009 3(Suppl 7):S72

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  20. We compare and contrast case-only designs for detecting gene × gene (G × G) interaction in rheumatoid arthritis (RA) using the genome-wide data provided by Genetic Analysis Workshop 16 Problem 1. Logistic as w...

    Authors: Geraldine M Clarke, Fredrik H Pettersson and Andrew P Morris

    Citation: BMC Proceedings 2009 3(Suppl 7):S73

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  21. Many phenotypes of public health importance (e.g., diabetes, coronary artery disease, major depression, obesity, and addictions to alcohol and nicotine) involve complex pathways of action. Interactions between...

    Authors: Robert Culverhouse, Wu Jin, Carol H Jin, Anthony L Hinrichs and Brian K Suarez

    Citation: BMC Proceedings 2009 3(Suppl 7):S74

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  22. Rheumatoid arthritis (RA, MIM 180300) is a chronic and complex autoimmune disease. Using the North American Rheumatoid Arthritis Consortium (NARAC) data set provided in Genetic Analysis Workshop 16 (GAW16), we...

    Authors: Chien-Hsun Huang, Lei Cong, Jun Xie, Bo Qiao, Shaw-Hwa Lo and Tian Zheng

    Citation: BMC Proceedings 2009 3(Suppl 7):S75

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  23. The detection of gene-gene interaction is an important approach to understand the etiology of rheumatoid arthritis (RA). The goal of this study is to identify gene-gene interaction of SNPs at the allelic level...

    Authors: Jeesun Jung, Joon Jin Song and Deukwoo Kwon

    Citation: BMC Proceedings 2009 3(Suppl 7):S76

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  24. Knowledge of simulated genetic effects facilitates interpretation of methodological studies. Genetic interactions for common disorders are likely numerous and weak. Using the 200 replicates of the Genetic Anal...

    Authors: Ilija P Kovac and Marie-Pierre Dubé

    Citation: BMC Proceedings 2009 3(Suppl 7):S77

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  25. After more than 200 genome-wide association studies, there have been some successful identifications of a single novel locus. Thus, the identification of single-nucleotide polymorphisms (SNP) with interaction ...

    Authors: Jia Li, Rui Tang, Joanna M Biernacka and Mariza de Andrade

    Citation: BMC Proceedings 2009 3(Suppl 7):S78

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  26. Although several genes (including a strong effect in the human leukocyte antigen (HLA) region) and some environmental factors have been implicated to cause susceptibility to rheumatoid arthritis (RA), the etio...

    Authors: Xueying Liang, Ying Gao, Tram K Lam, Qizhai Li, Cathy Falk, Xiaohong R Yang, Alisa M Goldstein and Lynn R Goldin

    Citation: BMC Proceedings 2009 3(Suppl 7):S79

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  27. For the Framingham Heart Study (FHS) and simulated FHS (FHSsim) data, we tested for gene-gene interaction in quantitative traits employing a longitudinal nonparametric association test (LNPT) and, for comparis...

    Authors: Dörthe Malzahn, Yesilda Balavarca, Jingky P Lozano and Heike Bickeböller

    Citation: BMC Proceedings 2009 3(Suppl 7):S80

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  28. We sought to find significant gene × gene interaction in a genome-wide association analysis of rheumatoid arthritis (RA) by performing pair-wise tests of interaction among collections of single-nucleotide poly...

    Authors: Alisa K Manning, Julius Suh Ngwa, Audrey E Hendricks, Ching-Ti Liu, Andrew D Johnson, Josée Dupuis and L Adrienne Cupples

    Citation: BMC Proceedings 2009 3(Suppl 7):S81

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  29. The aim of this study was to detect the effect of interactions between single-nucleotide polymorphisms (SNPs) on incidence of heart diseases. For this purpose, 2912 subjects with 350,160 SNPs from the Framingh...

    Authors: Li Yao, Wenjun Zhong, Zhumin Zhang, Matthew J Maenner and Corinne D Engelman

    Citation: BMC Proceedings 2009 3(Suppl 7):S83

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  30. While genetic and environmental factors and their interactions influence susceptibility to rheumatoid arthritis (RA), causative genetic variants have not been identified. The purpose of the present study was t...

    Authors: Rector Arya, Elizabeth Hare, Inmaculada del Rincon, Christopher P Jenkinson, Ravindranath Duggirala, Laura Almasy and Agustin Escalante

    Citation: BMC Proceedings 2009 3(Suppl 7):S84

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  31. The HLA region is considered to be the main genetic risk factor for rheumatoid arthritis. Previous research demonstrated that HLA-DRB1 alleles encoding the shared epitope are specific for disease that is characte...

    Authors: Yen-Feng Chiu, Hui-Yi Kao, Yi-Shin Chen, Fang-Chi Hsu and Hsin-Chou Yang

    Citation: BMC Proceedings 2009 3(Suppl 7):S85

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  32. Studies of complex diseases collect panels of disease-related traits, also known as secondary phenotypes or endophenotypes. They reflect intermediate responses to environment exposures, and as such, are likely...

    Authors: C Charles Gu, Wei (Will) Yang, Aldi T Kraja, Lisa de las Fuentes and Victor G Dávila-Román

    Citation: BMC Proceedings 2009 3(Suppl 7):S86

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  33. Age-dependent genetic effects on susceptibility to hypertension have been documented. We present a novel variance-component method for the estimation of age-dependent genetic effects on longitudinal systolic b...

    Authors: Bonnie R Joubert, Guoqing Diao, Danyu Lin, Kari E North and Nora Franceschini

    Citation: BMC Proceedings 2009 3(Suppl 7):S87

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  34. Genome-wide association studies are often limited in their ability to attain their full potential due to the sheer volume of information created. We sought to use the random forest algorithm to identify single...

    Authors: Matthew J Maenner, Loren C Denlinger, Asher Langton, Kristin J Meyers, Corinne D Engelman and Halcyon G Skinner

    Citation: BMC Proceedings 2009 3(Suppl 7):S88

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  35. Longitudinal studies that collect repeated measurements on the same subjects over time have long been considered as being more powerful and providing much better information on individual changes than cross-se...

    Authors: Gang Shi, Treva K Rice, Chi Charles Gu and Debeeru C Rao

    Citation: BMC Proceedings 2009 3(Suppl 7):S89

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  36. The identification of several hundred genomic regions affecting disease risk has proven the ability of genome-wide association studies have proven their ability to identify genetic contributors to disease. Cur...

    Authors: David H Ballard, Chatchawit Aporntewan, Ji Young Lee, Joon Sang Lee, Zheyang Wu and Hongyu Zhao

    Citation: BMC Proceedings 2009 3(Suppl 7):S91

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  37. Gene identification using linkage, association, or genome-wide expression is often underpowered. We propose that formal combination of information from multiple gene-identification approaches may lead to the i...

    Authors: Jac C Charlesworth, Juan M Peralta, Eugene Drigalenko, Harald HH Göring, Laura Almasy, Thomas D Dyer and John Blangero

    Citation: BMC Proceedings 2009 3(Suppl 7):S92

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  38. Genome-wide association studies (GWAS) test hundreds of thousands of single-nucleotide polymorphisms (SNPs) for association to a trait, treating each marker equally and ignoring prior evidence of association t...

    Authors: Francesca Lantieri, Min A Jhun, Jungsun Park, Taesung Park and Marcella Devoto

    Citation: BMC Proceedings 2009 3(Suppl 7):S93

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  39. We describe an empirical Bayesian linear model for integration of functional gene annotation data with genome-wide association data. Using case-control study data from the North American Rheumatoid Arthritis C...

    Authors: Jérémie JP Lebrec, Tom WJ Huizinga, René EM Toes, Jeanine J Houwing-Duistermaat and Hans C van Houwelingen

    Citation: BMC Proceedings 2009 3(Suppl 7):S94

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  40. Recently, gene set analysis (GSA) has been extended from use on gene expression data to use on single-nucleotide polymorphism (SNP) data in genome-wide association studies. When GSA has been demonstrated on SN...

    Authors: Nathan L Tintle, Bryce Borchers, Marshall Brown and Airat Bekmetjev

    Citation: BMC Proceedings 2009 3(Suppl 7):S96

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  41. Our aim is to develop methods for mapping genes related to age at onset in general pedigrees. We propose two score tests, one derived from a gamma frailty model with pairwise likelihood and one derived from a ...

    Authors: Andrea Callegaro, Hae-Won Uh, Quinta Helmer and Jeanine J Houwing-Duistermaat

    Citation: BMC Proceedings 2009 3(Suppl 7):S97

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  42. We examine a Bayesian Markov-chain Monte Carlo framework for simultaneous segregation and linkage analysis in the simulated single-nucleotide polymorphism data provided for Genetic Analysis Workshop 16. We con...

    Authors: E Warwick Daw, Jevon Plunkett, Mary Feitosa, Xiaoyi Gao, Andrew Van Brunt, Duanduan Ma, Jacek Czajkowski, Michael A Province and Ingrid Borecki

    Citation: BMC Proceedings 2009 3(Suppl 7):S98

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  43. Complex traits are often manifested by multiple correlated traits. One example of this is hypertension (HTN), which is measured on a continuous scale by systolic blood pressure (SBP). Predisposition to HTN is ...

    Authors: Courtney Gray-McGuire, Yeunjoo Song, Nathan J Morris and Catherine M Stein

    Citation: BMC Proceedings 2009 3(Suppl 7):S99

    Content type: Proceedings

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