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CC BY V4.0, Open Access
Journal article
Enrichment of statistical power for genome-wide association studies
BMC biology, Vol.12(1), pp.73-73
10/17/2014
Handle:
https://hdl.handle.net/2376/104037
PMCID: PMC4210555
PMID: 25322753
Abstract
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.
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Details
- Title
- Enrichment of statistical power for genome-wide association studies
- Creators
- Meng Li - College of Horticulture, Nanjing Agricultural University, Nanjing, 210095 ChinaXiaolei Liu - Huazhong Agricultural UniversityPeter Bradbury - United States Department of Agriculture (USDA) – Agricultural Research Service (ARS), Ithaca, New York 14853 USAJianming Yu - Department of Agronomy, Kansas State University, Manhattan, Kansas 66506 USAYuan-Ming Zhang - State Key Laboratory of Crop Genetics and Germplasm Enhancement/National Center for Soybean Improvement, College of Agriculture, Nanjing Agricultural University, Nanjing, 210095 ChinaRory J Todhunter - Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York 14853 USAEdward S Buckler - Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853 USAZhiwu Zhang - Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853 USA
- Publication Details
- BMC biology, Vol.12(1), pp.73-73
- Academic Unit
- Department of Crop and Soil Sciences
- Publisher
- BioMed Central; London
- Identifiers
- 99900546713601842
- Language
- English
- Resource Type
- Journal article