Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix
Open accessAbstract
Correlated multiple testing is widely performed in genetic research, particularly in multilocus analyses of complex diseases. Failure to control appropriately for the effect of multiple testing will either result in a flood of false-positive claims or in true hits being overlooked. Cheverud proposed the idea of adjusting correlated tests as if they were independent, according to an 'effective number' (M(eff)) of independent tests. However, our experience has indicated that Cheverud's estimate of the Meff is overly large and will lead to excessively conservative results. We propose a more accurate estimate of the M(eff), and design M(eff)-based procedures to control the experiment-wise significant level and the false discovery rate. In an evaluation, based on both real and simulated data, the M(eff)-based procedures were able to control the error rate accurately and consequently resulted in a power increase, especially in multilocus analyses. The results confirm that the M(eff) is a useful concept in the error-rate control of correlated tests. With its efficiency and accuracy, the M(eff) method provides an alternative to computationally intensive methods such as the permutation test.
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Modifications to the Bonferroni correction in the context of QTL mapping have been proposed by Cheverud (2001), and further modifications proposed by Li and Ji (2005).
“Modifications to the Bonferroni correction in the context of QTL mapping have been proposed by Cheverud (2001), and further modifications proposed by Li and Ji (2005).”
From The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis (2013) · cites this paper as Li and Ji (2005)
The Introduction says Li and Ji proposed a more accurate estimate of Cheverud’s effective number of tests and new procedures for controlling multiple testing. Cheverud’s work concerned interval-mapping genome scans, but Li and Ji describe their own extension in the context of multilocus analyses, not specifically QTL mapping. (LLM, conf=0.85)
Experimental — accuracy not yet measuredFor the maize data, the Li and Ji (2005) approach produced an effective number of tests n* = 81, which gives a larger threshold P value than the Bonferroni correction (0.05/81 instead of 0.05/246).
“For the maize data, the Li and Ji (2005) approach produced a value of n* = 81, which gives a larger threshold P value than the Bonferroni correction (divide 0.05 by 81, instead of dividing by 246).”
From The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis (2013) · cites this paper as Li and Ji (2005)
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- Authors
- Li, J., & Ji, L.
- Affiliations
- Tsinghua University (CN)
- ORCID
- Li, J
- Venue
- Heredity
- Published
- 2005-08-03
- Volume
- 95
- Issue
- 3
- Pages
- 221-227
- ISSN
- 0018-067X (ISSN-L)
- 1365-2540
- OpenAlex
- W2107755054
- Cited by
- 1,458
- References
- 3
- Citations by year
- 2026: 73 · 2025: 78 · 2024: 97 · 2023: 67 · 2022: 62 · 2021: 101 · 2020: 86 · 2019: 81 · 2018: 82 · 2017: 78 · 2016: 93 · 2015: 72 · 2014: 74 · 2013: 76 · 2012: 88
- FWCI
- 14.43
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Heredity (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Ministry of Science and Technology of the People's Republic of China
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Keywords
- multiple testing correction, multilocus analysis, complex diseases, false discovery rate, statistical power
- MeSH terms
- Data Interpretation, Statistical, Genetic Testing, Genetics, Medical, Models, Genetic, Research Design, Genetic Predisposition to Disease
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