Volume 10 Supplement 7

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

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.

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Association of rare haplotypes on ULK4 and MAP4 genes with hypertension

Several variants have been implicated earlier on ULK4 and MAP4 genes on chromosome 3 to be associated with hypertension. As a natural follow-up step, we explore association of haplotypes in those genes. We consid...

Authors: Ananda S. Datta, Yuan Zhang, Lei Zhang and Swati Biswas

Citation: BMC Proceedings 2016 10(Suppl 7):44

Content type: Proceedings Published on: 15 November 2016
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Comparison of 2 models for gene–environment interactions: an example of simulated gene–medication interactions on systolic blood pressure in family-based data

Nearly half of adults in the United States who are diagnosed with hypertension use blood-pressure-lowering medications. Yet there is a large interindividual variability in the response to these medications. Tw...

Authors: Lindsay Fernández-Rhodes, Chani J. Hodonsky, Mariaelisa Graff, Shelly-Ann M. Love, Annie Green Howard, Amanda A. Seyerle, Christy L. Avery, Geetha Chittoor, Nora Franceschini, V. Saroja Voruganti, Kristin Young, Jeffrey R. O’Connell, Kari E. North and Anne E. Justice

Citation: BMC Proceedings 2016 10(Suppl 7):65

Content type: Proceedings Published on: 18 October 2016
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Identifying rare and common variants with Bayesian variable selection

Recent advances in next-generation sequencing technologies have made it possible to generate large amounts of sequence data with rare variants in a cost-effective way. Yet, the statistical aspect of testing di...

Authors: Cheongeun Oh

Citation: BMC Proceedings 2016 10(Suppl 7):45

Content type: Proceedings Published on: 18 October 2016
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Type I error rates of rare single nucleotide variants are inflated in tests of association with non–normally distributed traits using simple linear regression methods

In this study, the effects of (a) the minor allele frequency of the single nucleotide variant (SNV), (b) the degree of departure from normality of the trait, and (c) the position of the SNVs on type I error ra...

Authors: Tae-Hwi Schwantes-An, Heejong Sung, Jeremy A. Sabourin, Cristina M. Justice, Alexa J. M. Sorant and Alexander F. Wilson

Citation: BMC Proceedings 2016 10(Suppl 7):62

Content type: Proceedings Published on: 18 October 2016
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Identification of low frequency and rare variants for hypertension using sparse-data methods

Availability of genomic sequence data provides opportunities to study the role of low-frequency and rare variants in the etiology of complex disease. In this study, we conduct association analyses of hypertens...

Authors: Ji-Hyung Shin, Ruiyang Yi and Shelley B. Bull

Citation: BMC Proceedings 2016 10(Suppl 7):39

Content type: Proceedings Published on: 11 October 2016
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Using next-generation DNA sequence data for genetic association tests based on allele counts with and without consideration of zero inflation

The relationship between genetic variability and individual phenotypes is usually investigated by testing for association relying on called genotypes. Allele counts obtained from next-generation sequence data ...

Authors: Rosa González Silos, Özge Karadag, Barbara Peil, Christine Fischer, Maria Kabisch, Carine Legrand and Justo Lorenzo Bermejo

Citation: BMC Proceedings 2016 10(Suppl 7):41

Content type: Proceedings Published on: 18 October 2016
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Comparing performance of non–tree-based and tree-based association mapping methods

A central goal in the biomedical and biological sciences is to link variation in quantitative traits to locations along the genome (single nucleotide polymorphisms). Sequencing technology has rapidly advanced ...

Authors: Katherine L. Thompson and David W. Fardo

Citation: BMC Proceedings 2016 10(Suppl 7):43

Content type: Proceedings Published on: 18 October 2016
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Causal effect estimation in sequencing studies: a Bayesian method to account for confounder adjustment uncertainty

Estimating the causal effect of a single nucleotide variant (SNV) on clinical phenotypes is of interest in many genetic studies. The effect estimation may be confounded by other SNVs as a result of linkage dis...

Authors: Chi Wang, Jinpeng Liu and David W. Fardo

Citation: BMC Proceedings 2016 10(Suppl 7):38

Content type: Proceedings Published on: 18 October 2016
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Genetic Analysis Workshop 19: Sequence, Blood Pressure and Expression Data. Proceedings.

Proceedings

Association of rare haplotypes on ULK4 and MAP4 genes with hypertension

Comparison of 2 models for gene–environment interactions: an example of simulated gene–medication interactions on systolic blood pressure in family-based data

Identifying rare and common variants with Bayesian variable selection

Type I error rates of rare single nucleotide variants are inflated in tests of association with non–normally distributed traits using simple linear regression methods

Identification of low frequency and rare variants for hypertension using sparse-data methods

Using next-generation DNA sequence data for genetic association tests based on allele counts with and without consideration of zero inflation

Comparing performance of non–tree-based and tree-based association mapping methods

Causal effect estimation in sequencing studies: a Bayesian method to account for confounder adjustment uncertainty

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