Toolkit/Anderson-Darling test

Anderson-Darling test

Computational Method·Research·Since 2014

Also known as: A-D test

Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.

Summary

The Anderson-Darling test is a nonparametric computational method applied in genome-wide association studies of complex quantitative traits. In an enlarged maize association panel, it identified loci across 17 agronomic traits, including both previously known loci and additional candidate loci detected only by this test.

Usefulness & Problems

Why this is useful

This method is useful as a complement to GWAS analysis for complex quantitative traits. The cited study states that it is especially useful for traits with abnormal phenotype distributions and for traits influenced by moderate-effect loci or rare variations.

Problem solved

It addresses limitations of GWAS approaches when phenotype distributions are abnormal or when genetic architecture includes moderate-effect loci or rare variants. In the cited maize study, it enabled detection of candidate loci that were not observed by other compared analyses.

Taxonomy & Function

Primary hierarchy

Technique Branch

Method: A concrete computational method used to design, rank, or analyze an engineered system.

Target processes

No target processes tagged yet.

Implementation Constraints

The available evidence supports use as a computational method within genome-wide association studies in an enlarged maize association panel. No implementation details such as software package, parameterization, input formatting, or integration with specific GWAS pipelines are described in the supplied evidence.

The supplied evidence is limited to a single 2014 maize GWAS study. No quantitative performance metrics, false-positive behavior, computational cost, or validation in other organisms or study designs are provided in the supplied evidence.

Validation

Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication

Supporting Sources

Ranked Claims

Claim 1method comparisonsupports2014Source 1needs review

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
Claim 2method comparisonsupports2014Source 1needs review

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
Claim 3method comparisonsupports2014Source 1needs review

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
Claim 4method comparisonsupports2014Source 1needs review

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
Claim 5method comparisonsupports2014Source 1needs review

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
Claim 6method comparisonsupports2014Source 1needs review

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
Claim 7method comparisonsupports2014Source 1needs review

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
Claim 8method comparisonsupports2014Source 1needs review

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.
Claim 9method comparisonsupports2014Source 1needs review

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.
Claim 10method comparisonsupports2014Source 1needs review

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.
Claim 11method comparisonsupports2014Source 1needs review

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.
Claim 12method comparisonsupports2014Source 1needs review

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.
Claim 13method comparisonsupports2014Source 1needs review

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.
Claim 14method comparisonsupports2014Source 1needs review

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.
Claim 15result summarysupports2014Source 1needs review

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
Claim 16result summarysupports2014Source 1needs review

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
Claim 17result summarysupports2014Source 1needs review

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
Claim 18result summarysupports2014Source 1needs review

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
Claim 19result summarysupports2014Source 1needs review

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
Claim 20result summarysupports2014Source 1needs review

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
Claim 21result summarysupports2014Source 1needs review

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 source2 linked approval claimsfirst-pass slug anderson-darling-test
a new method, the Anderson-Darling (A-D) test

Source:

method comparisonsupports

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

Source:

method comparisonsupports

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.

Source:

Comparisons

Source-backed strengths

In the reported application, the Anderson-Darling test detected many loci across 17 agronomic traits. Its strengths, as supported by the source, are complementary locus discovery in GWAS and sensitivity to known loci as well as additional candidate loci uniquely identified by the A-D test.

Ranked Citations

  1. 1.
    StructuralSource 1PLoS Genetics2014Claim 1Claim 2Claim 3

    Extracted from this source document.