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      Above and beyond state-of-the-art approaches to investigate sequence data: summary of methods and results from the population-based association group at the Genetic Analysis Workshop 19

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      BMC Genetics
      BioMed Central
      Genetic Analysis Workshop 19
      24-26 August 2014

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          Abstract

          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 order to take best advantage of population-based sequence data.

          Although contributions were highly heterogeneous regarding the applied quality control criteria and the number of investigated variants, several technical issues were identified, leading to practical recommendations. Preliminary analyses revealed that Hurdle-negative binomial regression is a promising approach to investigate the distribution of allele counts instead of called genotypes from sequence data. Convergence problems, however, limited the use of this approach, creating a technical challenge shared by environment-stratified models used to investigate rare variant-environment interactions, as well as by rare variant haplotype analyses using well-established public software. Estimates of relatedness and population structure strongly depended on the allele frequency of selected variants for inference. Another practical recommendation was that dissenting probability values from standard and small-sample tests of a particular hypothesis may reflect a lack of validity of large-sample approximations. Novel statistical approaches that integrate evolutionary information showed some advantage to detect weak genetic signals, and Bayesian adjustment for confounding was able to efficiently estimate causal genetic effects. Haplotype association methods may constitute a valuable complement of collapsing approaches for sequence data. This paper reports on the experience of members of the Population-Based Association group with several novel, promising approaches to preprocessing and analyzing sequence data, and to following up identified association signals.

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          Most cited references9

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

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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

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              Identifying rare and common variants with Bayesian variable selection

              C Oh (2015)
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                Author and article information

                Contributors
                0049/6221/56-4180 , Justo.Lorenzo@imbi.uni-heidelberg.de
                Conference
                BMC Genet
                BMC Genet
                BMC Genetics
                BioMed Central (London )
                1471-2156
                3 February 2016
                3 February 2016
                2016
                : 17
                Issue : Suppl 2 Issue sponsor : 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.
                : 2
                Affiliations
                Statistical Genetics Group, Institute of Medical Biometry and Informatics, University of Heidelberg, Im Neuenheimer Feld 305, 69120 Heidelberg, Germany
                Article
                310
                10.1186/s12863-015-0310-0
                4895250
                26866664
                1213f0ac-5176-429e-bd44-73582b77425e
                © Lorenzo Bermejo. 2015

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                Genetic Analysis Workshop 19
                Vienna, Austria
                24-26 August 2014
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                © The Author(s) 2016

                Genetics
                Genetics

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