Traceability of Plant Contribution in Olive Oil by Amplified Fragment Length Polymorphisms

2005 ◽  
Vol 53 (18) ◽  
pp. 6995-7002 ◽  
Author(s):  
Simona Pafundo ◽  
Caterina Agrimonti ◽  
Nelson Marmiroli
Keyword(s):  
Olive Oil ◽  
Fragment Length ◽  
Length Polymorphisms ◽  
Amplified Fragment Length Polymorphisms

scholarly journals Amplified fragment length polymorphisms (AFLP's)

2017 ◽  
pp. 119 ◽  
Author(s):  
June Simpson
Keyword(s):  
Molecular Marker ◽  
Restriction Fragment ◽  
Restriction Enzyme ◽  
Fragment Length ◽  
Polymorphic Loci ◽  
Length Polymorphisms ◽  
Amplified Fragment Length Polymorphisms ◽  
Pcr Conditions

AFLP is a combination restriction fragment/PCR molecular marker technique which detects polymorphisms due to changes at or in the vicinity of restriction enzyme sites. The technique detects multiple polymorphic loci throughout the genome and may be used for fingerprinting and mapping purposes. The main advantages of the method are the consistency and reliability of the technique due to stringent PCR conditions and the ability to rapidly detect many polymorphic loci.

Selection criteria for scoring amplified fragment length polymorphisms (AFLPs) positively affect the reliability of population genetic parameter estimates

Genome ◽  
2010 ◽  
Vol 53 (4) ◽  
pp. 302-310 ◽  
Author(s):  
Doris Herrmann ◽  
Bénédicte N. Poncet ◽  
Stéphanie Manel ◽  
Delphine Rioux ◽  
Ludovic Gielly ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Fragment Length ◽  
Population Genetic ◽  
Selection Criteria ◽  
Genetic Parameters ◽  
Data Set ◽  
Marker Selection ◽  
Length Polymorphisms ◽  
Amplified Fragment Length Polymorphisms ◽  
Population Genetic Parameters

A reliable data set is a fundamental prerequisite for consistent results and conclusions in population genetic studies. However, marker scoring of genetic fingerprints such as amplified fragment length polymorphisms (AFLPs) is a highly subjective procedure, inducing inconsistencies owing to personal or laboratory-specific criteria. We applied two alternative marker selection algorithms, the newly developed script scanAFLP and the recently published AFLPScore, to a large AFLP genome scan to test how population genetic parameters and error rates were affected. These results were confronted with replicated random selections of marker subsets. We show that the newly developed marker selection criteria reduced the mismatch error rate and had a notable influence on estimates of genetic diversity and differentiation. Both effects are likely to influence biological inference. For example, genetic diversity (HS) was 29% lower while genetic differentiation (FST) was 8% higher when applying scanAFLP compared with AFLPScore. Likewise, random selections of markers resulted in substantial deviations of population genetic parameters compared with the data sets including specific selection criteria. These randomly selected marker sets showed surprisingly low variance among replicates. We conclude that stringent marker selection and phenotype calling reduces noise in the data set while retaining patterns of population genetic structure.

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