Title |
Enrichment of statistical power for genome-wide association studies
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Published in |
BMC Biology, October 2014
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DOI | 10.1186/s12915-014-0073-5 |
Pubmed ID | |
Authors |
Meng Li, Xiaolei Liu, Peter Bradbury, Jianming Yu, Yuan-Ming Zhang, Rory J Todhunter, Edward S Buckler, Zhiwu Zhang |
Abstract |
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. |
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Geographical breakdown
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Unknown | 2 | 100% |
Demographic breakdown
Type | Count | As % |
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Scientists | 1 | 50% |
Science communicators (journalists, bloggers, editors) | 1 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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France | 2 | <1% |
United States | 2 | <1% |
Brazil | 1 | <1% |
Spain | 1 | <1% |
United Kingdom | 1 | <1% |
Unknown | 207 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 54 | 25% |
Researcher | 36 | 17% |
Student > Master | 24 | 11% |
Student > Doctoral Student | 22 | 10% |
Other | 12 | 6% |
Other | 23 | 11% |
Unknown | 43 | 20% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 127 | 59% |
Biochemistry, Genetics and Molecular Biology | 16 | 7% |
Medicine and Dentistry | 5 | 2% |
Veterinary Science and Veterinary Medicine | 4 | 2% |
Computer Science | 3 | 1% |
Other | 12 | 6% |
Unknown | 47 | 22% |