Source 1primary paper2014PLoS Genetics
Toolkit/mixed linear model
mixed linear model
Also known as: MLM
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
The mixed linear model (MLM) is a computational genome-wide association study method applied in an enlarged maize association panel. In the cited 2014 study, MLM identified ten loci across five agronomic traits at a Bonferroni-corrected significance threshold of -log10(P) > 5.74.
Usefulness & Problems
Why this is useful
MLM is useful as a statistical association-testing approach for detecting genotype-phenotype associations in GWAS datasets. The supplied evidence shows that it can recover significant loci for agronomic traits in maize, providing a benchmark method within comparative GWAS analyses.
Problem solved
MLM helps address the problem of identifying loci associated with complex agronomic traits in a maize association panel. The evidence does not provide further methodological detail on how the model handles confounding or population structure in this specific study.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete computational method used to design, rank, or analyze an engineered system.
Techniques
Computational DesignTarget processes
No target processes tagged yet.
Implementation Constraints
cofactor dependency: cofactor requirement unknownencoding mode: genetically encodedimplementation constraint: context specific validationoperating role: builder
The documented implementation is computational application in GWAS of an enlarged maize association panel using a Bonferroni-corrected significance threshold of -log10(P) > 5.74. The evidence does not specify software, model parameterization, covariates, kinship formulation, or input genotype format.
Within the supplied comparison, the Anderson-Darling test identified many loci across 17 traits, including loci not observed by MLM. The evidence therefore suggests that MLM may have lower discovery breadth than the alternative method in this dataset, and no additional limitations are described.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
The Anderson-Darling test identified many loci across 17 agronomic traits, including known and new candidate loci that were only observed by the A-D test.
Many loci ranging from one to 34 loci (107 loci for plant height) were identified for 17 traits using the A-D test at the Bonferroni-corrected threshold -log10 (P) >7.05 (α=0.05) using 556809 SNPs. Many known loci and new candidate loci were only observed by the A-D test
Bonferroni-corrected threshold 7.05loci identified for plant height 107SNPs used 556809traits analyzed 17
The Anderson-Darling test identified many loci across 17 agronomic traits, including known and new candidate loci that were only observed by the A-D test.
Many loci ranging from one to 34 loci (107 loci for plant height) were identified for 17 traits using the A-D test at the Bonferroni-corrected threshold -log10 (P) >7.05 (α=0.05) using 556809 SNPs. Many known loci and new candidate loci were only observed by the A-D test
Bonferroni-corrected threshold 7.05loci identified for plant height 107SNPs used 556809traits analyzed 17
The Anderson-Darling test identified many loci across 17 agronomic traits, including known and new candidate loci that were only observed by the A-D test.
Many loci ranging from one to 34 loci (107 loci for plant height) were identified for 17 traits using the A-D test at the Bonferroni-corrected threshold -log10 (P) >7.05 (α=0.05) using 556809 SNPs. Many known loci and new candidate loci were only observed by the A-D test
Bonferroni-corrected threshold 7.05loci identified for plant height 107SNPs used 556809traits analyzed 17
The Anderson-Darling test identified many loci across 17 agronomic traits, including known and new candidate loci that were only observed by the A-D test.
Many loci ranging from one to 34 loci (107 loci for plant height) were identified for 17 traits using the A-D test at the Bonferroni-corrected threshold -log10 (P) >7.05 (α=0.05) using 556809 SNPs. Many known loci and new candidate loci were only observed by the A-D test
Bonferroni-corrected threshold 7.05loci identified for plant height 107SNPs used 556809traits analyzed 17
The Anderson-Darling test identified many loci across 17 agronomic traits, including known and new candidate loci that were only observed by the A-D test.
Many loci ranging from one to 34 loci (107 loci for plant height) were identified for 17 traits using the A-D test at the Bonferroni-corrected threshold -log10 (P) >7.05 (α=0.05) using 556809 SNPs. Many known loci and new candidate loci were only observed by the A-D test
Bonferroni-corrected threshold 7.05loci identified for plant height 107SNPs used 556809traits analyzed 17
The Anderson-Darling test identified many loci across 17 agronomic traits, including known and new candidate loci that were only observed by the A-D test.
Many loci ranging from one to 34 loci (107 loci for plant height) were identified for 17 traits using the A-D test at the Bonferroni-corrected threshold -log10 (P) >7.05 (α=0.05) using 556809 SNPs. Many known loci and new candidate loci were only observed by the A-D test
Bonferroni-corrected threshold 7.05loci identified for plant height 107SNPs used 556809traits analyzed 17
The Anderson-Darling test identified many loci across 17 agronomic traits, including known and new candidate loci that were only observed by the A-D test.
Many loci ranging from one to 34 loci (107 loci for plant height) were identified for 17 traits using the A-D test at the Bonferroni-corrected threshold -log10 (P) >7.05 (α=0.05) using 556809 SNPs. Many known loci and new candidate loci were only observed by the A-D test
Bonferroni-corrected threshold 7.05loci identified for plant height 107SNPs used 556809traits analyzed 17
The Anderson-Darling test identified many loci across 17 agronomic traits, including known and new candidate loci that were only observed by the A-D test.
Many loci ranging from one to 34 loci (107 loci for plant height) were identified for 17 traits using the A-D test at the Bonferroni-corrected threshold -log10 (P) >7.05 (α=0.05) using 556809 SNPs. Many known loci and new candidate loci were only observed by the A-D test
Bonferroni-corrected threshold 7.05loci identified for plant height 107SNPs used 556809traits analyzed 17
The Anderson-Darling test identified many loci across 17 agronomic traits, including known and new candidate loci that were only observed by the A-D test.
Many loci ranging from one to 34 loci (107 loci for plant height) were identified for 17 traits using the A-D test at the Bonferroni-corrected threshold -log10 (P) >7.05 (α=0.05) using 556809 SNPs. Many known loci and new candidate loci were only observed by the A-D test
Bonferroni-corrected threshold 7.05loci identified for plant height 107SNPs used 556809traits analyzed 17
The Anderson-Darling test identified many loci across 17 agronomic traits, including known and new candidate loci that were only observed by the A-D test.
Many loci ranging from one to 34 loci (107 loci for plant height) were identified for 17 traits using the A-D test at the Bonferroni-corrected threshold -log10 (P) >7.05 (α=0.05) using 556809 SNPs. Many known loci and new candidate loci were only observed by the A-D test
Bonferroni-corrected threshold 7.05loci identified for plant height 107SNPs used 556809traits analyzed 17
The Anderson-Darling test is a useful complement for GWAS analysis of complex quantitative traits and is especially useful for traits with abnormal phenotype distribution or those controlled by moderate effect loci or rare variations.
we showed that the A-D test is a useful complement for GWAS analysis of complex quantitative traits. Especially for traits with abnormal phenotype distribution, controlled by moderate effect loci or rare variations, the A-D test balances false positives and statistical power.
The Anderson-Darling test is a useful complement for GWAS analysis of complex quantitative traits and is especially useful for traits with abnormal phenotype distribution or those controlled by moderate effect loci or rare variations.
we showed that the A-D test is a useful complement for GWAS analysis of complex quantitative traits. Especially for traits with abnormal phenotype distribution, controlled by moderate effect loci or rare variations, the A-D test balances false positives and statistical power.
The Anderson-Darling test is a useful complement for GWAS analysis of complex quantitative traits and is especially useful for traits with abnormal phenotype distribution or those controlled by moderate effect loci or rare variations.
we showed that the A-D test is a useful complement for GWAS analysis of complex quantitative traits. Especially for traits with abnormal phenotype distribution, controlled by moderate effect loci or rare variations, the A-D test balances false positives and statistical power.
The Anderson-Darling test is a useful complement for GWAS analysis of complex quantitative traits and is especially useful for traits with abnormal phenotype distribution or those controlled by moderate effect loci or rare variations.
we showed that the A-D test is a useful complement for GWAS analysis of complex quantitative traits. Especially for traits with abnormal phenotype distribution, controlled by moderate effect loci or rare variations, the A-D test balances false positives and statistical power.
The Anderson-Darling test is a useful complement for GWAS analysis of complex quantitative traits and is especially useful for traits with abnormal phenotype distribution or those controlled by moderate effect loci or rare variations.
we showed that the A-D test is a useful complement for GWAS analysis of complex quantitative traits. Especially for traits with abnormal phenotype distribution, controlled by moderate effect loci or rare variations, the A-D test balances false positives and statistical power.
The Anderson-Darling test is a useful complement for GWAS analysis of complex quantitative traits and is especially useful for traits with abnormal phenotype distribution or those controlled by moderate effect loci or rare variations.
we showed that the A-D test is a useful complement for GWAS analysis of complex quantitative traits. Especially for traits with abnormal phenotype distribution, controlled by moderate effect loci or rare variations, the A-D test balances false positives and statistical power.
The Anderson-Darling test is a useful complement for GWAS analysis of complex quantitative traits and is especially useful for traits with abnormal phenotype distribution or those controlled by moderate effect loci or rare variations.
we showed that the A-D test is a useful complement for GWAS analysis of complex quantitative traits. Especially for traits with abnormal phenotype distribution, controlled by moderate effect loci or rare variations, the A-D test balances false positives and statistical power.
The Anderson-Darling test is a useful complement for GWAS analysis of complex quantitative traits and is especially useful for traits with abnormal phenotype distribution or those controlled by moderate effect loci or rare variations.
we showed that the A-D test is a useful complement for GWAS analysis of complex quantitative traits. Especially for traits with abnormal phenotype distribution, controlled by moderate effect loci or rare variations, the A-D test balances false positives and statistical power.
The Anderson-Darling test is a useful complement for GWAS analysis of complex quantitative traits and is especially useful for traits with abnormal phenotype distribution or those controlled by moderate effect loci or rare variations.
we showed that the A-D test is a useful complement for GWAS analysis of complex quantitative traits. Especially for traits with abnormal phenotype distribution, controlled by moderate effect loci or rare variations, the A-D test balances false positives and statistical power.
The Anderson-Darling test is a useful complement for GWAS analysis of complex quantitative traits and is especially useful for traits with abnormal phenotype distribution or those controlled by moderate effect loci or rare variations.
we showed that the A-D test is a useful complement for GWAS analysis of complex quantitative traits. Especially for traits with abnormal phenotype distribution, controlled by moderate effect loci or rare variations, the A-D test balances false positives and statistical power.
Using the mixed linear model, ten loci for five traits were identified at a Bonferroni-corrected threshold of -log10(P) greater than 5.74.
Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold -log10 (P) >5.74 (α=1).
Bonferroni-corrected threshold 5.74loci identified 10traits with identified loci 5
Using the mixed linear model, ten loci for five traits were identified at a Bonferroni-corrected threshold of -log10(P) greater than 5.74.
Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold -log10 (P) >5.74 (α=1).
Bonferroni-corrected threshold 5.74loci identified 10traits with identified loci 5
Using the mixed linear model, ten loci for five traits were identified at a Bonferroni-corrected threshold of -log10(P) greater than 5.74.
Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold -log10 (P) >5.74 (α=1).
Bonferroni-corrected threshold 5.74loci identified 10traits with identified loci 5
Using the mixed linear model, ten loci for five traits were identified at a Bonferroni-corrected threshold of -log10(P) greater than 5.74.
Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold -log10 (P) >5.74 (α=1).
Bonferroni-corrected threshold 5.74loci identified 10traits with identified loci 5
Using the mixed linear model, ten loci for five traits were identified at a Bonferroni-corrected threshold of -log10(P) greater than 5.74.
Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold -log10 (P) >5.74 (α=1).
Bonferroni-corrected threshold 5.74loci identified 10traits with identified loci 5
Using the mixed linear model, ten loci for five traits were identified at a Bonferroni-corrected threshold of -log10(P) greater than 5.74.
Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold -log10 (P) >5.74 (α=1).
Bonferroni-corrected threshold 5.74loci identified 10traits with identified loci 5
Using the mixed linear model, ten loci for five traits were identified at a Bonferroni-corrected threshold of -log10(P) greater than 5.74.
Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold -log10 (P) >5.74 (α=1).
Bonferroni-corrected threshold 5.74loci identified 10traits with identified loci 5
Using the mixed linear model, ten loci for five traits were identified at a Bonferroni-corrected threshold of -log10(P) greater than 5.74.
Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold -log10 (P) >5.74 (α=1).
Bonferroni-corrected threshold 5.74loci identified 10traits with identified loci 5
Using the mixed linear model, ten loci for five traits were identified at a Bonferroni-corrected threshold of -log10(P) greater than 5.74.
Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold -log10 (P) >5.74 (α=1).
Bonferroni-corrected threshold 5.74loci identified 10traits with identified loci 5
Using the mixed linear model, ten loci for five traits were identified at a Bonferroni-corrected threshold of -log10(P) greater than 5.74.
Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold -log10 (P) >5.74 (α=1).
Bonferroni-corrected threshold 5.74loci identified 10traits with identified loci 5
Using the mixed linear model, ten loci for five traits were identified at a Bonferroni-corrected threshold of -log10(P) greater than 5.74.
Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold -log10 (P) >5.74 (α=1).
Bonferroni-corrected threshold 5.74loci identified 10traits with identified loci 5
Using the mixed linear model, ten loci for five traits were identified at a Bonferroni-corrected threshold of -log10(P) greater than 5.74.
Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold -log10 (P) >5.74 (α=1).
Bonferroni-corrected threshold 5.74loci identified 10traits with identified loci 5
Using the mixed linear model, ten loci for five traits were identified at a Bonferroni-corrected threshold of -log10(P) greater than 5.74.
Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold -log10 (P) >5.74 (α=1).
Bonferroni-corrected threshold 5.74loci identified 10traits with identified loci 5
Using the mixed linear model, ten loci for five traits were identified at a Bonferroni-corrected threshold of -log10(P) greater than 5.74.
Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold -log10 (P) >5.74 (α=1).
Bonferroni-corrected threshold 5.74loci identified 10traits with identified loci 5
Using the mixed linear model, ten loci for five traits were identified at a Bonferroni-corrected threshold of -log10(P) greater than 5.74.
Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold -log10 (P) >5.74 (α=1).
Bonferroni-corrected threshold 5.74loci identified 10traits with identified loci 5
Using the mixed linear model, ten loci for five traits were identified at a Bonferroni-corrected threshold of -log10(P) greater than 5.74.
Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold -log10 (P) >5.74 (α=1).
Bonferroni-corrected threshold 5.74loci identified 10traits with identified loci 5
Using the mixed linear model, ten loci for five traits were identified at a Bonferroni-corrected threshold of -log10(P) greater than 5.74.
Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold -log10 (P) >5.74 (α=1).
Bonferroni-corrected threshold 5.74loci identified 10traits with identified loci 5
Approval Evidence
1 source1 linked approval claimfirst-pass slug mixed-linear-model
applying both mixed linear model (MLM)
Source:
result summarysupports
Using the mixed linear model, ten loci for five traits were identified at a Bonferroni-corrected threshold of -log10(P) greater than 5.74.
Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold -log10 (P) >5.74 (α=1).
Source:
Comparisons
Source-backed strengths
In the cited application, MLM detected ten loci for five traits under a Bonferroni-corrected threshold of -log10(P) > 5.74. The evidence supports that MLM produced statistically significant GWAS hits in maize, but does not provide broader performance metrics beyond this result.
Compared with free-energy calculations
mixed linear model and free-energy calculations address a similar problem space.
Shared frame: same top-level item type
Compared with mathematical model
mixed linear model and mathematical model address a similar problem space.
Shared frame: same top-level item type
Strengths here: looks easier to implement in practice.
Compared with SwiftLib
mixed linear model and SwiftLib address a similar problem space.
Shared frame: same top-level item type
Ranked Citations
- 1.