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Volume 10 Supplement 7

Genetic Analysis Workshop 19: Sequence, Blood Pressure and Expression Data. Proceedings.


Publication of the proceedings of Genetic Analysis Workshop 19 was supported by National Institutes of Health grant R01 GM031575. Articles have undergone the journal's standard review process for supplements. The Supplement Editors declare that they have no competing interests.

Vienna, Austria24-26 August 2014

Workshop website.

Edited by CMT Greenwood, JW MacCluer and L Almasy.

Page 1 of 2

  1. Genetic Analysis Workshop 19 provided a platform for developing and evaluating statistical methods to analyze whole-genome sequence and gene expression data from a pedigree-based sample, as well as whole-exome...

    Authors: Corinne D. Engelman, Celia M. T. Greenwood, Julia N. Bailey, Rita M. Cantor, Jack W. Kent Jr, Inke R. König, Justo Lorenzo Bermejo, Phillip E. Melton, Stephanie A. Santorico, Arne Schillert, Ellen M. Wijsman, Jean W. MacCluer and Laura Almasy
    Citation: BMC Proceedings 2016 10(Suppl 7):19
  2. The Genetic Analysis Workshops (GAW) are a forum for development, testing, and comparison of statistical genetic methods and software. Each contribution to the workshop includes an application to a specified d...

    Authors: John Blangero, Tanya M. Teslovich, Xueling Sim, Marcio A. Almeida, Goo Jun, Thomas D. Dyer, Matthew Johnson, Juan M. Peralta, Alisa Manning, Andrew R. Wood, Christian Fuchsberger, Jack W. Kent Jr, David A. Aguilar, Jennifer E. Below, Vidya S. Farook, Rector Arya…
    Citation: BMC Proceedings 2016 10(Suppl 7):20
  3. We explore causal relationships between genotype, gene expression and phenotype in the Genetic Analysis Workshop 19 data. We compare the use of structural equation modeling and a Bayesian unified framework app...

    Authors: Holly F. Ainsworth and Heather J. Cordell
    Citation: BMC Proceedings 2016 10(Suppl 7):49
  4. Identifying variants that regulate gene expression and delineating their genetic architecture is a critical next step in our endeavors to better understand the genetic etiology of complex diseases. The appropr...

    Authors: Rita M. Cantor, Calvin Pan and Kimberly Siegmund
    Citation: BMC Proceedings 2016 10(Suppl 7):37
  5. Genome-wide microarray expression is a rich source of functional genomic data. We examined evidence for differences in expression from peripheral blood mononuclear cells between individuals, examined some of f...

    Authors: Michael Gallaugher, Angelo J. Canty and Andrew D. Paterson
    Citation: BMC Proceedings 2016 10(Suppl 7):58
  6. We investigate the possible replication of “known” associated single-nucleotide polymorphisms (SNPs) with blood pressure and expression phenotypes. Previous studies have provided a list of 95 SNPs thought to b...

    Authors: Richard A. J. Howey, Jakris Eu-ahsunthornwattana, Rebecca Darlay and Heather J. Cordell
    Citation: BMC Proceedings 2016 10(Suppl 7):28
  7. We present a novel approach to detect potential cis-acting regulatory loci that combines the functional potential, an empirical DNase-seq based estimate of the allele-specificity of DNase-I hypersensitivity sites...

    Authors: Juan Manuel Peralta, Marcio Almeida, Lawrence J. Abraham, Eric Moses and John Blangero
    Citation: BMC Proceedings 2016 10(Suppl 7):50
  8. Expression quantitative trait locus (eQTL) maps are considered a valuable resource in studying complex diseases. The availability of gene expression data from the Genetic Analysis Workshop 19 (GAW19) provides ...

    Authors: Achilleas N. Pitsillides, Seung-Hoan Choi, John D. Hogan, Jaeyoung Hong and Honghuang Lin
    Citation: BMC Proceedings 2016 10(Suppl 7):31
  9. The main focus of the Genetic Analysis Workshop 19 (GAW19) is identification of genes related to the occurrence of hypertension in the cohort of patients with type 2 diabetes mellitus (T2DM). The aim of our st...

    Authors: Piotr Radkowski, Gracjan Wątor, Jan Skupien, Anna Bogdali and Paweł Wołkow
    Citation: BMC Proceedings 2016 10(Suppl 7):64
  10. For a better understanding of the biological mechanisms involved in complex traits or diseases, networks are often useful tools in genetic studies: coexpression networks based on pairwise correlations between ...

    Authors: Renaud Tissier, Hae-Won Uh, Erik van den Akker, Brunilda Balliu, Spyridoula Tsonaka and Jeanine Houwing-Duistermaat
    Citation: BMC Proceedings 2016 10(Suppl 7):35
  11. With the advance of next-generation sequencing technologies, the study of rare variants in targeted genome regions or even the whole genome becomes feasible. Nevertheless, the massive amount of sequencing data...

    Authors: Xiaoran Tong, Changshuai Wei and Qing Lu
    Citation: BMC Proceedings 2016 10(Suppl 7):36
  12. We propose a new method for identifying disease-related regions of single nucleotide variants in recently admixed populations. We use principal component analysis to derive both global and local ancestry infor...

    Authors: Jonathan Auerbach, Michael Agne, Rachel Fan, Adeline Lo, Shaw-Hwa Lo, Tian Zheng and Pei Wang
    Citation: BMC Proceedings 2016 10(Suppl 7):32
  13. Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best prac...

    Authors: Elizabeth Held, Joshua Cape and Nathan Tintle
    Citation: BMC Proceedings 2016 10(Suppl 7):34
  14. Current findings from genetic studies of complex human traits often do not explain a large proportion of the estimated variation of these traits due to genetic factors. This could be, in part, due to overly st...

    Authors: Emily R. Holzinger, Silke Szymczak, James Malley, Elizabeth W. Pugh, Hua Ling, Sean Griffith, Peng Zhang, Qing Li, Cheryl D. Cropp and Joan E. Bailey-Wilson
    Citation: BMC Proceedings 2016 10(Suppl 7):52
  15. With the development of the next-generation sequencing technology, the influence of rare variants on complex disease has gathered increasing attention. In this paper, we propose a clustering-based approach, th...

    Authors: Rui Sun, Qiao Deng, Inchi Hu, Benny Chung-Ying Zee and Maggie Haitian Wang
    Citation: BMC Proceedings 2016 10(Suppl 7):24
  16. Homozygosity disequilibrium (HD) describes a nonrandom pattern of sizable runs of homozygosity (ROH) that deviated from a random distribution of homozygotes and heterozygotes in the genome. In this study, we d...

    Authors: Hsin-Chou Yang and Yu-Ting Lin
    Citation: BMC Proceedings 2016 10(Suppl 7):27
  17. Current rare-variant, gene-based tests of association often suffer from a lack of statistical power to detect genotype–phenotype associations as a result of a lack of prior knowledge of genetic disease models ...

    Authors: Alden Green, Kaitlyn Cook, Kelsey Grinde, Alessandra Valcarcel and Nathan Tintle
    Citation: BMC Proceedings 2016 10(Suppl 7):23
  18. Genome-wide association studies have made substantial progress in identifying common variants associated with human diseases. Despite such success, a large portion of heritability remains unexplained. Evolutio...

    Authors: Sneha Jadhav, Olga A. Vsevolozhskaya, Xiaoran Tong and Qing Lu
    Citation: BMC Proceedings 2016 10(Suppl 7):60
  19. Several statistical group-based approaches have been proposed to detect effects of variation within a gene for each of the population- and family-based designs. However, unified tests to combine gene-phenotype...

    Authors: Yuriko Katsumata and David W. Fardo
    Citation: BMC Proceedings 2016 10(Suppl 7):21
  20. It has been repeatedly stressed that family-based samples suffer less from genetic heterogeneity and that association analyses with family-based samples are expected to be powerful for detecting susceptibility...

    Authors: Longfei Wang, Sungkyoung Choi, Sungyoung Lee, Taesung Park and Sungho Won
    Citation: BMC Proceedings 2016 10(Suppl 7):25
  21. Meta-analysis has been widely used in genetic association studies to increase sample size and to improve power, both in the context of single-variant analysis, as well as for gene-based tests. Meta-analysis ap...

    Authors: Shuai Wang, Virginia A. Fisher, Yuning Chen and Josée Dupuis
    Citation: BMC Proceedings 2016 10(Suppl 7):51
  22. A statistical departure from Mendel’s law of segregation is known as transmission ratio distortion. Although well documented in many other organisms, the extent of transmission ratio distortion and its influen...

    Authors: Sahir R. Bhatnagar, Celia M. T. Greenwood and Aurélie Labbe
    Citation: BMC Proceedings 2016 10(Suppl 7):12
  23. Advances in whole genome sequencing have enabled the investigation of rare variants, which could explain some of the missing heritability that genome-wide association studies are unable to detect. Most methods...

    Authors: Burcu F. Darst and Corinne D. Engelman
    Citation: BMC Proceedings 2016 10(Suppl 7):46
  24. Recent focus on studying rare variants makes imputation accuracy of rare variants an important issue. Many approaches have been proposed to increase imputation accuracy among rare variants, from reference pane...

    Authors: Samantha Lent, Xuan Deng, L. Adrienne Cupples, Kathryn L. Lunetta, CT Liu and Yanhua Zhou
    Citation: BMC Proceedings 2016 10(Suppl 7):48
  25. We propose a novel LASSO (least absolute shrinkage and selection operator) penalized regression method used to analyze samples consisting of (potentially) related individuals. Developed in the context of linea...

    Authors: Charalampos Papachristou, Carole Ober and Mark Abney
    Citation: BMC Proceedings 2016 10(Suppl 7):53
  26. Pedigree genome-wide association studies (GWAS) (Option 29) in the current version of the Mendel software is an optimized subroutine for performing large-scale genome-wide quantitative trait locus (QTL) analys...

    Authors: Hua Zhou, Jin Zhou, Tao Hu, Eric M. Sobel and Kenneth Lange
    Citation: BMC Proceedings 2016 10(Suppl 7):10
  27. The new generation of whole genome sequencing platforms offers great possibilities and challenges for dissecting the genetic basis of complex traits. With a very high number of sequence variants, a naïve multi...

    Authors: Marcio Almeida, Lucy Blondell, Juan M. Peralta, Jack W. Kent Jr, Goo Jun, Tanya M. Teslovich, Christian Fuchsberger, Andrew R. Wood, Alisa K. Manning, Timothy M. Frayling, Pablo E. Cingolani, Robert Sladek, Thomas D. Dyer, Goncalo Abecasis, Ravindranath Duggirala and John Blangero
    Citation: BMC Proceedings 2016 10(Suppl 7):63
  28. Genetic association studies aim to test for disease or trait association with genetic variants, either throughout the human genome or in regions of interest. However, for most diseases and traits, the combined...

    Authors: Yen-Yi Ho, Weihua Guan, Michael O’Connell and Saonli Basu
    Citation: BMC Proceedings 2016 10(Suppl 7):26
  29. Population-based identity by descent (IBD) mapping is a statistical method for detection of genetic loci that share an ancestral segment among “unrelated” pairs of individuals for a disease. As a complementary...

    Authors: Xiao-Qing Liu, Jillian Fazio, Pingzhao Hu and Andrew D. Paterson
    Citation: BMC Proceedings 2016 10(Suppl 7):8
  30. We used our extension of the kernel score test to family data to analyze real and simulated baseline systolic blood pressure in extended pedigrees. We compared the power for different kernels and for different...

    Authors: Dörthe Malzahn, Stefanie Friedrichs and Heike Bickeböller
    Citation: BMC Proceedings 2016 10(Suppl 7):17
  31. Although many genes have been implicated as hypertension candidates, to date, few studies have integrated different types of genomic data for the purpose of biomarker selection.

    Authors: Hongbao Cao, Wei Guo, Haide Qin, Mengyuan Xu, Benjamin Lehrman, Yu Tao and Yin-Yao Shugart
    Citation: BMC Proceedings 2016 10(Suppl 7):40
  32. In the past few years, imputation approaches have been mainly used in population-based designs of genome-wide association studies, although both family- and population-based imputation methods have been propos...

    Authors: Mohamad Saad, Alejandro Q. Nato Jr, Fiona L. Grimson, Steven M. Lewis, Lisa A. Brown, Elizabeth M. Blue, Timothy A. Thornton, Elizabeth A. Thompson and Ellen M. Wijsman
    Citation: BMC Proceedings 2016 10(Suppl 7):7
  33. Structural equation modeling (SEM) has been used in a wide range of applied sciences including genetic analysis. The recently developed R package, strum, implements a framework for SEM for general pedigree data. ...

    Authors: Yeunjoo E. Song, Nathan J. Morris and Catherine M. Stein
    Citation: BMC Proceedings 2016 10(Suppl 7):55
  34. Large-scale sequencing studies often measure many related phenotypes in addition to the genetic variants. Joint analysis of multiple phenotypes in genetic association studies may increase power to detect disea...

    Authors: Jianping Sun, Sahir R. Bhatnagar, Karim Oualkacha, Antonio Ciampi and Celia M. T. Greenwood
    Citation: BMC Proceedings 2016 10(Suppl 7):14
  35. There is great interindividual variation in systolic blood pressure (SBP) as a result of the influences of several factors, including sex, ancestry, smoking status, medication use, and, especially, age. The ma...

    Authors: Anne E. Justice, Annie Green Howard, Geetha Chittoor, Lindsay Fernandez-Rhodes, Misa Graff, V. Saroja Voruganti, Guoqing Diao, Shelly-Ann M. Love, Nora Franceschini, Jeffrey R. O’Connell, Christy L. Avery, Kristin L. Young and Kari E. North
    Citation: BMC Proceedings 2016 10(Suppl 7):56
  36. The incorporation of longitudinal data into genetic epidemiological studies has the potential to provide valuable information regarding the effect of time on complex disease etiology. Yet, the majority of rese...

    Authors: Phillip E. Melton, Juan M. Peralta and Laura Almasy
    Citation: BMC Proceedings 2016 10(Suppl 7):30
  37. Interactions between genes are an important part of the genetic architecture of complex diseases. In this paper, we use literature-guided individual genes known to be associated with type 2 diabetes (referred ...

    Authors: Adeline Lo, Michael Agne, Jonathan Auerbach, Rachel Fan, Shaw-Hwa Lo, Pei Wang and Tian Zheng
    Citation: BMC Proceedings 2016 10(Suppl 7):13
  38. Whereas genome-wide association study (GWAS) has proven to be an important tool for discovery of variants influencing many human diseases and traits, unfortunately its performance has not been much of all-arou...

    Authors: Bamidele O. Tayo, Liping Tong and Richard S. Cooper
    Citation: BMC Proceedings 2016 10(Suppl 7):15
  39. The aggregation of functionally associated variants given a priori biological information can aid in the discovery of rare variants associated with complex diseases. Many methods exist that aggregate rare vari...

    Authors: Alessandra Valcarcel, Kelsey Grinde, Kaitlyn Cook, Alden Green and Nathan Tintle
    Citation: BMC Proceedings 2016 10(Suppl 7):16
  40. Estimating relationships among subjects in a sample, within family structures or caused by population substructure, is complicated in admixed populations. Inaccurate allele frequencies can bias both kinship es...

    Authors: Elizabeth M. Blue, Lisa A. Brown, Matthew P. Conomos, Jennifer L. Kirk, Alejandro Q. Nato Jr, Alice B. Popejoy, Jesse Raffa, John Ranola, Ellen M. Wijsman and Timothy Thornton
    Citation: BMC Proceedings 2016 10(Suppl 7):42

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