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Plant Genetics and Molecular Biology

Overview of attention for book
Attention for Chapter 48: Genetic Mapping Populations for Conducting High-Resolution Trait Mapping in Plants
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#7 of 198)
  • High Attention Score compared to outputs of the same age (87th percentile)

Mentioned by

31 tweeters


6 Dimensions

Readers on

41 Mendeley
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Chapter title
Genetic Mapping Populations for Conducting High-Resolution Trait Mapping in Plants
Chapter number 48
Book title
Plant Genetics and Molecular Biology
Published in
Advances in biochemical engineering biotechnology, January 2018
DOI 10.1007/10_2017_48
Pubmed ID
Book ISBNs
978-3-31-991312-4, 978-3-31-991313-1

James Cockram, Ian Mackay, Cockram, James, Mackay, Ian


Fine mapping of quantitative trait loci (QTL) is the route to more detailed molecular characterization and functional studies of the relationship between polymorphism and trait variation. It is also of direct relevance to breeding since it makes QTL more easily integrated into marker-assisted breeding and into genomic selection. Fine mapping requires that marker-trait associations are tested in populations in which large numbers of recombinations have occurred. This can be achieved by increasing the size of mapping populations or by increasing the number of generations of crossing required to create the population. We review the factors affecting the precision and power of fine mapping experiments and describe some contemporary experimental approaches, focusing on the use of multi-parental or multi-founder populations such as the multi-parent advanced generation intercross (MAGIC) and nested association mapping (NAM). We favor approaches such as MAGIC since these focus explicitly on increasing the amount of recombination that occurs within the population. Whatever approaches are used, we believe the days of mapping QTL in small populations must come to an end. In our own work in MAGIC wheat populations, we started with a target of developing 1,000 lines per population: that number now looks to be on the low side. Graphical Abstract.

Twitter Demographics

The data shown below were collected from the profiles of 31 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 41 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 34%
Student > Master 10 24%
Researcher 8 20%
Student > Doctoral Student 2 5%
Student > Bachelor 1 2%
Other 4 10%
Unknown 2 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 71%
Biochemistry, Genetics and Molecular Biology 5 12%
Unspecified 1 2%
Psychology 1 2%
Engineering 1 2%
Other 0 0%
Unknown 4 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 18. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 May 2020.
All research outputs
of 16,218,785 outputs
Outputs from Advances in biochemical engineering biotechnology
of 198 outputs
Outputs of similar age
of 279,160 outputs
Outputs of similar age from Advances in biochemical engineering biotechnology
of 1 outputs
Altmetric has tracked 16,218,785 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 198 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 279,160 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them