Enrichment of statistical power for genome-wide association studies

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• 作者：Meng Li (1) (2)
  Xiaolei Liu (2)
  Peter Bradbury (3)
  Jianming Yu (4)
  Yuan-Ming Zhang (5)
  Rory J Todhunter (6)
  Edward S Buckler (2) (3)
  Zhiwu Zhang (2) (7) (8)
  
  1. College of Horticulture ; Nanjing Agricultural University ; Nanjing ; 210095 ; China
  2. Institute for Genomic Diversity ; Cornell University ; Ithaca ; New York ; 14853 ; USA
  3. United States Department of Agriculture (USDA) 鈥?Agricultural Research Service (ARS) ; Ithaca ; New York ; 14853 ; USA
  4. Department of Agronomy ; Kansas State University ; Manhattan ; Kansas ; 66506 ; USA
  5. State Key Laboratory of Crop Genetics and Germplasm Enhancement/National Center for Soybean Improvement ; College of Agriculture ; Nanjing Agricultural University ; Nanjing ; 210095 ; China
  6. Department of Clinical Sciences ; College of Veterinary Medicine ; Cornell University ; Ithaca ; New York ; 14853 ; USA
  7. College of Agronomy ; Northeast Agricultural University ; Harbin ; Heilongjiang ; 150030 ; China
  8. Department of Crop and Soil Science ; Washington State University ; Pullman ; WA ; 99164 ; USA
  
• 关键词：Genome wide association study ; population structure ; kinship ; mixed model ; cluster analysis
• 刊名：BMC Biology
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：12
• 期：1
• 全文大小：1,856 KB
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• 刊物主题：Life Sciences, general;
• 出版者：BioMed Central
• ISSN：1741-7007

文摘

Background The 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. Results This 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. Conclusion Simulations 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.