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Twitter Demographics
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Attention Score in Context
| Title |
Replicated analysis of the genetic architecture of quantitative traits in two wild great tit populations
|
|---|---|
| Published in |
Molecular Ecology, December 2015
|
| DOI | 10.1111/mec.13452 |
| Pubmed ID | |
| Authors |
Anna W. Santure, Jocelyn Poissant, Isabelle De Cauwer, Kees van Oers, Matthew R. Robinson, John L. Quinn, Martien A. M. Groenen, Marcel E. Visser, Ben C. Sheldon, Jon Slate |
| Abstract |
Currently, there is much debate on the genetic architecture of quantitative traits in wild populations. Is trait variation influenced by many genes of small effect or by a few genes of major effect? Where is additive genetic variation located in the genome? Do the same loci cause similar phenotypic variation in different populations? Great tits (Parus major) have been studied extensively in long-term studies across Europe and consequently are considered an ecological 'model organism'. Recently, genomic resources have been developed for the great tit, including a custom SNP chip and genetic linkage map. In this study, we used a suite of approaches to investigate the genetic architecture of eight quantitative traits in two long-term study populations of great tits-one in the Netherlands and the other in the United Kingdom. Overall, we found little evidence for the presence of genes of large effects in either population. Instead, traits appeared to be influenced by many genes of small effect, with conservative estimates of the number of contributing loci ranging from 31 to 310. Despite concordance between population-specific heritabilities, we found no evidence for the presence of loci having similar effects in both populations. While population-specific genetic architectures are possible, an undetected shared architecture cannot be rejected because of limited power to map loci of small and moderate effects. This study is one of few examples of genetic architecture analysis in replicated wild populations and highlights some of the challenges and limitations researchers will face when attempting similar molecular quantitative genetic studies in free-living populations. |
Twitter Demographics
The data shown below were collected from the profiles of 21 tweeters who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 14% |
| Canada | 2 | 10% |
| Netherlands | 2 | 10% |
| Australia | 1 | 5% |
| Finland | 1 | 5% |
| Japan | 1 | 5% |
| Germany | 1 | 5% |
| United Kingdom | 1 | 5% |
| Unknown | 9 | 43% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 14 | 67% |
| Members of the public | 6 | 29% |
| Science communicators (journalists, bloggers, editors) | 1 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 101 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 2% |
| Finland | 1 | <1% |
| Canada | 1 | <1% |
| New Zealand | 1 | <1% |
| United States | 1 | <1% |
| Lebanon | 1 | <1% |
| Unknown | 94 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 31 | 31% |
| Student > Ph. D. Student | 20 | 20% |
| Student > Master | 11 | 11% |
| Student > Bachelor | 9 | 9% |
| Professor | 4 | 4% |
| Other | 14 | 14% |
| Unknown | 12 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 63 | 62% |
| Biochemistry, Genetics and Molecular Biology | 13 | 13% |
| Environmental Science | 2 | 2% |
| Philosophy | 1 | <1% |
| Arts and Humanities | 1 | <1% |
| Other | 7 | 7% |
| Unknown | 14 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 28 July 2016.
All research outputs
#3,094,475
of 24,471,305 outputs
Outputs from Molecular Ecology
#1,702
of 6,568 outputs
Outputs of similar age
#50,938
of 398,487 outputs
Outputs of similar age from Molecular Ecology
#34
of 111 outputs
Altmetric has tracked 24,471,305 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,568 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.5. This one has gotten more attention than average, scoring higher than 74% 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 398,487 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 111 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.