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      Discovery and characterization of functional modules associated with body weight in broilers

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          Abstract

          Aim of the present study was to investigate whether body weight (BW) in broilers is associated with functional modular genes. To this end, first a GWAS for BW was conducted using 6,598 broilers and the high density SNP array. The next step was to search for positional candidate genes and QTLs within strong LD genomic regions around the significant SNPs. Using all positional candidate genes, a network was then constructed and community structure analysis was performed. Finally, functional enrichment analysis was applied to infer the functional relevance of modular genes. A total number of 645 positional candidate genes were identified in strong LD genomic regions around 11 genome-wide significant markers. 428 of the positional candidate genes were located within growth related QTLs. Community structure analysis detected 5 modules while functional enrichment analysis showed that 52 modular genes participated in developmental processes such as skeletal system development. An additional number of 14 modular genes ( GABRG1, NGF, APOBEC2, STAT5B, STAT3, SMAD4, MED1, CACNB1, SLAIN2, LEMD2, ZC3H18, TMEM132D, FRYL and SGCB) were also identified as related to body weight. Taken together, current results suggested a total number of 66 genes as most plausible functional candidates for the trait examined.

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          Extended Bayesian information criteria for model selection with large model spaces

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            An efficient multi-locus mixed model approach for genome-wide association studies in structured populations

            Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods, in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying novel associations in known candidates as well as evidence for allelic heterogeneity. We also demonstrate how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large datasets (n > 10000) practicable.
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              Identifying biological themes within lists of genes with EASE.

              EASE is a customizable software application for rapid biological interpretation of gene lists that result from the analysis of microarray, proteomics, SAGE and other high-throughput genomic data. The biological themes returned by EASE recapitulate manually determined themes in previously published gene lists and are robust to varying methods of normalization, intensity calculation and statistical selection of genes. EASE is a powerful tool for rapidly converting the results of functional genomics studies from 'genes' to 'themes'.
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                Author and article information

                Contributors
                etarsani@aua.gr
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                24 June 2019
                24 June 2019
                2019
                : 9
                : 9125
                Affiliations
                [1 ]ISNI 0000 0001 0794 1186, GRID grid.10985.35, Department of Animal Science and Aquaculture, , Agricultural University of Athens, ; Iera Odos 75, 11855 Athens, Greece
                [2 ]ISNI 0000 0004 1776 236X, GRID grid.423101.5, Aviagen Ltd., ; Newbridge, Midlothian EH28 8SZ UK
                [3 ]ISNI 0000 0004 1936 7988, GRID grid.4305.2, The Roslin Institute, , University of Edinburgh, ; EH25 9RG Midlothian, United Kingdom
                Author information
                http://orcid.org/0000-0001-7445-6279
                Article
                45520
                10.1038/s41598-019-45520-5
                6591351
                31235723
                5046be73-d24b-4f2e-9c8c-cbed5d777834
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 16 January 2019
                : 4 June 2019
                Funding
                Funded by: Aviagen Ltd (PhD stipend)
                Categories
                Article
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                © The Author(s) 2019

                Uncategorized
                genomics,animal breeding
                Uncategorized
                genomics, animal breeding

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