Genetic Epidemiology
Springer New York
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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Genome-Wide Association Studies
|
|---|---|
| Chapter number | 4 |
| Book title |
Genetic Epidemiology
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-7868-7_4 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7867-0, 978-1-4939-7868-7
|
| Authors |
Abbas Dehghan, Dehghan, Abbas |
| Abstract |
Genetic association studies have made a major contribution to our understanding of the genetics of complex disorders over the last 10 years through genome-wide association studies (GWAS). In this chapter, we review the key concepts that underlie the GWAS approach. We will describe the "common disease, common variant" theory, and will review how we finally afforded to capture the common variance in genome to make GWAS possible. Finally, we will go over technical aspects of GWAS such as genotype imputation, epidemiologic designs, analysis methods, and considerations such as genomic inflation, multiple testing, and replication. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 50% |
| Spain | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 50% |
| Members of the public | 2 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 127 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 127 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 26 | 20% |
| Student > Master | 18 | 14% |
| Student > Bachelor | 15 | 12% |
| Researcher | 14 | 11% |
| Student > Doctoral Student | 5 | 4% |
| Other | 12 | 9% |
| Unknown | 37 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 36 | 28% |
| Agricultural and Biological Sciences | 17 | 13% |
| Medicine and Dentistry | 12 | 9% |
| Social Sciences | 4 | 3% |
| Neuroscience | 4 | 3% |
| Other | 14 | 11% |
| Unknown | 40 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 31 January 2020.
All research outputs
#12,805,885
of 23,090,520 outputs
Outputs from Methods in molecular biology
#3,185
of 13,206 outputs
Outputs of similar age
#199,728
of 442,629 outputs
Outputs of similar age from Methods in molecular biology
#258
of 1,499 outputs
Altmetric has tracked 23,090,520 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,206 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 75% 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 442,629 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 1,499 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.