Skip to main content

Volume 17 Supplement 2

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


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

 Edited by CMT Greenwood, JW MacCluer and L Almasy.

  1. This paper summarizes the contributions from the Population-Based Association group at the Genetic Analysis Workshop 19. It provides an overview of the new statistical approaches tried out by group members in ...

    Authors: Justo Lorenzo Bermejo
    Citation: BMC Genetics 2016 17(Suppl 2):S2
  2. We currently have the ability to quantify transcript abundance of messenger RNA (mRNA), genome-wide, using microarray technologies. Analyzing genotype, phenotype and expression data from 20 pedigrees, the memb...

    Authors: Rita M. Cantor and Heather J. Cordell
    Citation: BMC Genetics 2016 17(Suppl 2):S3
  3. Longitudinal phenotypic data provides a rich potential resource for genetic studies which may allow for greater understanding of variants and their covariates over time. Herein, we review 3 longitudinal analyt...

    Authors: Yen-Feng Chiu, Anne E. Justice and Phillip E. Melton
    Citation: BMC Genetics 2016 17(Suppl 2):S4
  4. High-density genetic marker data, especially sequence data, imply an immense multiple testing burden. This can be ameliorated by filtering genetic variants, exploiting or accounting for correlations between va...

    Authors: Stefanie Friedrichs, Dörthe Malzahn, Elizabeth W. Pugh, Marcio Almeida, Xiao Qing Liu and Julia N. Bailey
    Citation: BMC Genetics 2016 17(Suppl 2):S8
  5. New technologies for acquisition of genomic data, while offering unprecedented opportunities for genetic discovery, also impose severe burdens of interpretation andpenalties for multiple testing.

    Authors: Jack W. Kent Jr
    Citation: BMC Genetics 2016 17(Suppl 2):S5
  6. In the analysis of current genomic data, application of machine learning and data mining techniques has become more attractive given the rising complexity of the projects. As part of the Genetic Analysis Works...

    Authors: Inke R. König, Jonathan Auerbach, Damian Gola, Elizabeth Held, Emily R. Holzinger, Marc-André Legault, Rui Sun, Nathan Tintle and Hsin-Chou Yang
    Citation: BMC Genetics 2016 17(Suppl 2):S1
  7. Empirical studies and evolutionary theory support a role for rare variants in the etiology of complex traits. Given this motivation and increasing affordability of whole-exome and whole-genome sequencing, meth...

    Authors: Stephanie A. Santorico and Audrey E. Hendricks
    Citation: BMC Genetics 2016 17(Suppl 2):S6

Annual Journal Metrics

  • For BMC Genetics (former title)

    2022 Citation Impact
    2.9 - 2-year Impact Factor
    3.2 - 5-year Impact Factor
    0.904 - SNIP (Source Normalized Impact per Paper)
    0.642 - SJR (SCImago Journal Rank)

    2022 Speed
    30 days submission to first editorial decision for all manuscripts (Median)
    157 days submission to accept (Median)

    2022 Usage  
    211 Altmetric mentions

Sign up for article alerts and news from this journal