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X Demographics
Mendeley readers
Attention Score in Context
| Title |
How well do whole exome sequencing results correlate with medical findings? A study of 89 Mayo Clinic Biobank samples
|
|---|---|
| Published in |
Frontiers in Genetics, July 2015
|
| DOI | 10.3389/fgene.2015.00244 |
| Pubmed ID | |
| Authors |
Sumit Middha, Noralane M. Lindor, Shannon K. McDonnell, Janet E. Olson, Kiley J. Johnson, Eric D. Wieben, Gianrico Farrugia, James R. Cerhan, Stephen N. Thibodeau |
| Abstract |
Whole exome sequencing (WES) is increasingly being used for diagnosis without adequate information on predictive characteristics of reportable variants typically found on any given individual and correlation with clinical phenotype. In this study, we performed WES on 89 deceased individuals (mean age at death 74 years, range 28-93) from the Mayo Clinic Biobank. Significant clinical diagnoses were abstracted from electronic medical record via chart review. Variants [Single Nucleotide Variant (SNV) and insertion/deletion] were filtered based on quality (accuracy >99%, read-depth >20, alternate-allele read-depth >5, minor-allele-frequency <0.1) and available HGMD/OMIM phenotype information. Variants were defined as Tier-1 (nonsense, splice or frame-shifting) and Tier-2 (missense, predicted-damaging) and evaluated in 56 ACMG-reportable genes, 57 cancer-predisposition genes, along with examining overall genotype-phenotype correlations. Following variant filtering, 7046 total variants were identified (~79/person, 644 Tier-1, 6402 Tier-2), 161 among 56 ACMG-reportable genes (~1.8/person, 13 Tier-1, 148 Tier-2), and 115 among 57 cancer-predisposition genes (~1.3/person, 3 Tier-1, 112 Tier-2). The number of variants across 57 cancer-predisposition genes did not differentiate individuals with/without invasive cancer history (P > 0.19). Evaluating genotype-phenotype correlations across the exome, 202(3%) of 7046 filtered variants had some evidence for phenotypic correlation in medical records, while 3710(53%) variants had no phenotypic correlation. The phenotype associated with the remaining 44% could not be assessed from a typical medical record review. These data highlight significant continued challenges in the ability to extract medically meaningful predictive results from WES. |
X Demographics
The data shown below were collected from the profiles of 27 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 States | 6 | 22% |
| United Kingdom | 3 | 11% |
| Spain | 1 | 4% |
| Germany | 1 | 4% |
| Italy | 1 | 4% |
| Switzerland | 1 | 4% |
| Montenegro | 1 | 4% |
| Unknown | 13 | 48% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 17 | 63% |
| Scientists | 9 | 33% |
| Practitioners (doctors, other healthcare professionals) | 1 | 4% |
Mendeley readers
The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 3% |
| Netherlands | 1 | 3% |
| Italy | 1 | 3% |
| Unknown | 37 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 8 | 20% |
| Other | 6 | 15% |
| Researcher | 6 | 15% |
| Unspecified | 4 | 10% |
| Student > Ph. D. Student | 4 | 10% |
| Other | 8 | 20% |
| Unknown | 4 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 9 | 23% |
| Medicine and Dentistry | 9 | 23% |
| Biochemistry, Genetics and Molecular Biology | 7 | 18% |
| Unspecified | 4 | 10% |
| Engineering | 3 | 8% |
| Other | 4 | 10% |
| Unknown | 4 | 10% |
Attention Score in Context
This research output has an Altmetric Attention Score of 29. 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 13 June 2017.
All research outputs
#1,305,472
of 24,666,614 outputs
Outputs from Frontiers in Genetics
#243
of 13,298 outputs
Outputs of similar age
#16,585
of 268,544 outputs
Outputs of similar age from Frontiers in Genetics
#5
of 72 outputs
Altmetric has tracked 24,666,614 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,298 research outputs from this source. They receive a mean Attention Score of 3.8. This one has done particularly well, scoring higher than 98% 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 268,544 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 72 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.