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Volume 19 Supplement 1

Genetic Analysis Workshop 20: envisioning the future of statistical genetics by exploring methods for epigenetic and pharmacogenomic data


Publication of the proceedings of Genetic Analysis Workshop 20 was supported by National Institutes of Health grant R01 GM031575. The articles have undergone the journal's standard peer review process for supplements. The Supplement Editors declare that they were not involved in the peer review process for any article on which they are an author. They declare no other competing interests.

San Diego, CA, USA4-8 March 2017

Edited by Xuexia Wang, Stella W. Aslibekyan, Nathan L. Tintle, Rita Cantor, Saurabh Ghosh, Justo L. Bermejo, Phillip Melton, Mariza de Andrade, and Dave Fardo

Conference website

Articles from this supplement have also been published as a supplement in BMC Proceedings.

  1. Random forest (RF) is a machine-learning method that generally works well with high-dimensional problems and allows for nonlinear relationships between predictors; however, the presence of correlated predictor...

    Authors: Burcu F. Darst, Kristen C. Malecki and Corinne D. Engelman
    Citation: BMC Genetics 2018 19(Suppl 1):65
  2. Transgenerational epigenetic inheritance has been posited as a possible contributor to the observed heritability of metabolic syndrome (MetS). Yet the extent to which estimates of epigenetic inheritance for DN...

    Authors: Lindsay Fernández-Rhodes, Annie Green Howard, Ran Tao, Kristin L. Young, Mariaelisa Graff, Allison E. Aiello, Kari E. North and Anne E. Justice
    Citation: BMC Genetics 2018 19(Suppl 1):69
  3. In studies with multi-omics data available, there is an opportunity to investigate interdependent mechanisms of biological causality. The GAW20 data set includes both DNA genotype and methylation measures befo...

    Authors: Virginia A. Fisher, Lan Wang, Xuan Deng, Chloé Sarnowski, L. Adrienne Cupples and Ching-Ti Liu
    Citation: BMC Genetics 2018 19(Suppl 1):70
  4. An important feature in many genomic studies is quality control and normalization. This is particularly important when analyzing epigenetic data, where the process of obtaining measurements can be bias prone. ...

    Authors: Marissa LeBlanc, Haakon E. Nustad, Manuela Zucknick and Christian M. Page
    Citation: BMC Genetics 2018 19(Suppl 1):66
  5. Single-probe analyses in epigenome-wide association studies (EWAS) have identified associations between DNA methylation and many phenotypes, but do not take into account information from neighboring probes. Me...

    Authors: Samantha Lent, Hanfei Xu, Lan Wang, Zhe Wang, Chloé Sarnowski, Marie-France Hivert and Josée Dupuis
    Citation: BMC Genetics 2018 19(Suppl 1):84
  6. Association studies using a single type of omics data have been successful in identifying disease-associated genetic markers, but the underlying mechanisms are unaddressed. To provide a possible explanation of...

    Authors: Liming Li, Chan Wang, Tianyuan Lu, Shili Lin and Yue-Qing Hu
    Citation: BMC Genetics 2018 19(Suppl 1):67
  7. We propose a gene-level association test that accounts for individual relatedness and population structures in pedigree data in the framework of linear mixed models (LMMs). Our method data-adaptively combines ...

    Authors: Jun Young Park, Chong Wu and Wei Pan
    Citation: BMC Genetics 2018 19(Suppl 1):68
  8. Identification of interactions between epigenetic factors and treatments might lead to personalized intervention of diseases. This paper aims to examine the modification effect of fenofibrate therapy on the as...

    Authors: Runmin Wei and Yanyan Wu
    Citation: BMC Genetics 2018 19(Suppl 1):75
  9. An accumulation of evidence has revealed the important role of epigenetic factors in explaining the etiopathogenesis of human diseases. Several empirical studies have successfully incorporated methylation data...

    Authors: Xiaoxuan Xia, Haoyi Weng, Ruoting Men, Rui Sun, Benny Chung Ying Zee, Ka Chun Chong and Maggie Haitian Wang
    Citation: BMC Genetics 2018 19(Suppl 1):78
  10. Increasingly available multilayered omics data on large populations has opened exciting analytic opportunities and posed unique challenges to robust estimation of causal effects in the setting of complex disea...

    Authors: Jonathan Auerbach, Richard Howey, Lai Jiang, Anne Justice, Liming Li, Karim Oualkacha, Sergi Sayols-Baixeras and Stella W. Aslibekyan
    Citation: BMC Genetics 2018 19(Suppl 1):74
  11. Multiple layers of genetic and epigenetic variability are being simultaneously explored in an increasing number of health studies. We summarize here different approaches applied in the Data Mining and Machine ...

    Authors: Burcu Darst, Corinne D. Engelman, Ye Tian and Justo Lorenzo Bermejo
    Citation: BMC Genetics 2018 19(Suppl 1):76
  12. The rise in popularity and accessibility of DNA methylation data to evaluate epigenetic associations with disease has led to numerous methodological questions. As part of GAW20, our working group of 8 research...

    Authors: Angga M. Fuady, Samantha Lent, Chloé Sarnowski and Nathan L. Tintle
    Citation: BMC Genetics 2018 19(Suppl 1):72
  13. Longitudinal data and repeated measurements in epigenome-wide association studies (EWAS) provide a rich resource for understanding epigenetics. We summarize 7 analytical approaches to the GAW20 data sets that ...

    Authors: Haakon E. Nustad, Marcio Almeida, Angelo J. Canty, Marissa LeBlanc, Christian M. Page and Phillip E. Melton
    Citation: BMC Genetics 2018 19(Suppl 1):77
  14. This paper summarizes the contributions from the Genome-wide Association Study group (GWAS group) of the GAW20. The GWAS group contributions focused on topics such as association tests, phenotype imputation, a...

    Authors: Xuexia Wang, Felix Boekstegers and Regina Brinster
    Citation: BMC Genetics 2018 19(Suppl 1):79
  15. GAW20 working group 5 brought together researchers who contributed 7 papers with the aim of evaluating methods to detect genetic by epigenetic interactions. GAW20 distributed real data from the Genetics of Lip...

    Authors: Mariza de Andrade, E. Warwick Daw, Aldi T. Kraja, Virginia Fisher, Lan Wang, Ke Hu, Jing Li, Razvan Romanescu, Jenna Veenstra, Rui Sun, Haoyi Weng and Wenda Zhou
    Citation: BMC Genetics 2018 19(Suppl 1):81
  16. Fenofibrate (Fb) is a known treatment for elevated triglyceride (TG) levels. The Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study was designed to investigate potential contributors to the effect...

    Authors: Svetlana Cherlin, Maggie Haitian Wang, Heike Bickeböller and Rita M. Cantor
    Citation: BMC Genetics 2018 19(Suppl 1):64

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