Enrichment of statistical power for genome-wide association studies

BMC Biology

Methodology Article

Meng Li¹  Jianming Yu²  Rory J Todhunter³  Xiaolei Liu⁴  Zhiwu Zhang⁵  Edward S Buckler⁶  Yuan-Ming Zhang⁷  Peter Bradbury⁸ 

[1] College of Horticulture, Nanjing Agricultural University, 210095, Nanjing, China;Institute for Genomic Diversity, Cornell University, 14853, IthacaNew York, USA;Department of Agronomy, Kansas State University, 66506, Manhattan, Kansas, USA;Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, 14853, IthacaNew York, USA;Institute for Genomic Diversity, Cornell University, 14853, IthacaNew York, USA;Institute for Genomic Diversity, Cornell University, 14853, IthacaNew York, USA;College of Agronomy, Northeast Agricultural University, 150030, Harbin, Heilongjiang, China;Department of Crop and Soil Science, Washington State University, 99164, Pullman, WA, USA;Institute for Genomic Diversity, Cornell University, 14853, IthacaNew York, USA;United States Department of Agriculture (USDA) – Agricultural Research Service (ARS), 14853, IthacaNew York, USA;State Key Laboratory of Crop Genetics and Germplasm Enhancement/National Center for Soybean Improvement, College of Agriculture, Nanjing Agricultural University, 210095, Nanjing, China;United States Department of Agriculture (USDA) – Agricultural Research Service (ARS), 14853, IthacaNew York, USA;

关键词: Genome wide association study;    population structure;    kinship;    mixed model;    cluster analysis;   

DOI  :  10.1186/s12915-014-0073-5

 received in 2014-06-30, accepted in 2014-09-09,  发布年份 2014

来源: Springer

【 摘 要 】

BackgroundThe inheritance of most human diseases and agriculturally important traits is controlled by many genes with small effects. Identifying these genes, while simultaneously controlling false positives, is challenging. Among available statistical methods, the mixed linear model (MLM) has been the most flexible and powerful for controlling population structure and individual unequal relatedness (kinship), the two common causes of spurious associations. The introduction of the compressed MLM (CMLM) method provided additional opportunities for optimization by adding two new model parameters: grouping algorithms and number of groups.ResultsThis study introduces another model parameter to develop an enriched CMLM (ECMLM). The parameter involves algorithms to define kinship between groups (that is, kinship algorithms). The ECMLM calculates kinship using several different algorithms and then chooses the best combination between kinship algorithms and grouping algorithms.ConclusionSimulations show that the ECMLM increases statistical power. In some cases, the magnitude of power gained by using ECMLM instead of CMLM is larger than the improvement found by using CMLM instead of MLM.

【 授权许可】

Unknown   
© Li et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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.