Literature DB >> 23756890

Overview of Statistical Methods for Genome-Wide Association Studies (GWAS).

Ben Hayes1.   

Abstract

This chapter provides an overview of statistical methods for genome-wide association studies (GWAS) in animals, plants, and humans. The simplest form of GWAS, a marker-by-marker analysis, is illustrated with a simple example. The problem of selecting a significance threshold that accounts for the large amount of multiple testing that occurs in GWAS is discussed. Population structure causes false positive associations in GWAS if not accounted for, and methods to deal with this are presented. Methodology for more complex models for GWAS, including haplotype-based approaches, accounting for identical by descent versus identical by state, and fitting all markers simultaneously are described and illustrated with examples.

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Year:  2013        PMID: 23756890     DOI: 10.1007/978-1-62703-447-0_6

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  38 in total

Review 1.  Genetics, environment, and gene-environment interactions in the development of systemic rheumatic diseases.

Authors:  Jeffrey A Sparks; Karen H Costenbader
Journal:  Rheum Dis Clin North Am       Date:  2014-09-02       Impact factor: 2.670

Review 2.  The overdue promise of short tandem repeat variation for heritability.

Authors:  Maximilian O Press; Keisha D Carlson; Christine Queitsch
Journal:  Trends Genet       Date:  2014-08-30       Impact factor: 11.639

3.  Genome-Wide Association Study of Cerebral Microbleeds on MRI.

Authors:  Hong-Qi Li; Wen-Jie Cai; Xiao-He Hou; Mei Cui; Lan Tan; Jin-Tai Yu; Qiang Dong
Journal:  Neurotox Res       Date:  2019-06-18       Impact factor: 3.911

4.  Genetic architecture of variation in Arabidopsis thaliana rosettes.

Authors:  Odín Morón-García; Gina A Garzón-Martínez; M J Pilar Martínez-Martín; Jason Brook; Fiona M K Corke; John H Doonan; Anyela V Camargo Rodríguez
Journal:  PLoS One       Date:  2022-02-16       Impact factor: 3.240

5.  Exploring Machine Learning Algorithms to Unveil Genomic Regions Associated With Resistance to Southern Root-Knot Nematode in Soybeans.

Authors:  Caio Canella Vieira; Jing Zhou; Mariola Usovsky; Tri Vuong; Amanda D Howland; Dongho Lee; Zenglu Li; Jianfeng Zhou; Grover Shannon; Henry T Nguyen; Pengyin Chen
Journal:  Front Plant Sci       Date:  2022-05-03       Impact factor: 6.627

6.  Identification of Genetic Factors Controlling the Formation of Multiple Flowers Per Node in Pepper (Capsicum spp.).

Authors:  Youngin Kim; Geon Woo Kim; Koeun Han; Hea-Young Lee; Jinkwan Jo; Jin-Kyung Kwon; Zachary Lemmon; Zachary Lippman; Byoung-Cheorl Kang
Journal:  Front Plant Sci       Date:  2022-05-09       Impact factor: 6.627

Review 7.  Genetics and Epigenetics of Spontaneous Intracerebral Hemorrhage.

Authors:  Eva Giralt-Steinhauer; Joan Jiménez-Balado; Isabel Fernández-Pérez; Lucía Rey Álvarez; Ana Rodríguez-Campello; Ángel Ois; Elisa Cuadrado-Godia; Jordi Jiménez-Conde; Jaume Roquer
Journal:  Int J Mol Sci       Date:  2022-06-09       Impact factor: 6.208

Review 8.  Genetics of Spontaneous Intracerebral Hemorrhage.

Authors:  Guido J Falcone; Daniel Woo
Journal:  Stroke       Date:  2017-11-07       Impact factor: 7.914

9.  A comprehensive map of disease networks and molecular drug discoveries for glaucoma.

Authors:  Haixin Wang; Yanhui Deng; Ling Wan; Lulin Huang
Journal:  Sci Rep       Date:  2020-06-16       Impact factor: 4.379

10.  Emerging issues in genomic selection.

Authors:  Ignacy Misztal; Ignacio Aguilar; Daniela Lourenco; Li Ma; Juan Pedro Steibel; Miguel Toro
Journal:  J Anim Sci       Date:  2021-06-01       Impact factor: 3.159

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