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Mendeley readers
Attention Score in Context
| Title |
TDT-HET: A new transmission disequilibrium test that incorporates locus heterogeneity into the analysis of family-based association data
|
|---|---|
| Published in |
BMC Bioinformatics, January 2012
|
| DOI | 10.1186/1471-2105-13-13 |
| Pubmed ID | |
| Authors |
Douglas Londono, Steven Buyske, Stephen J Finch, Swarkar Sharma, Carol A Wise, Derek Gordon |
| Abstract |
Locus heterogeneity is one of the most documented phenomena in genetics. To date, relatively little work had been done on the development of methods to address locus heterogeneity in genetic association analysis. Motivated by Zhou and Pan's work, we present a mixture model of linked and unlinked trios and develop a statistical method to estimate the probability that a heterozygous parent transmits the disease allele at a di-allelic locus, and the probability that any trio is in the linked group. The purpose here is the development of a test that extends the classic transmission disequilibrium test (TDT) to one that accounts for locus heterogeneity. |
X Demographics
The data shown below were collected from the profiles of 3 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 | 2 | 67% |
| India | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 4% |
| Unknown | 25 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 23% |
| Student > Ph. D. Student | 3 | 12% |
| Professor > Associate Professor | 3 | 12% |
| Student > Bachelor | 2 | 8% |
| Student > Doctoral Student | 2 | 8% |
| Other | 6 | 23% |
| Unknown | 4 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 6 | 23% |
| Medicine and Dentistry | 5 | 19% |
| Biochemistry, Genetics and Molecular Biology | 2 | 8% |
| Psychology | 2 | 8% |
| Social Sciences | 2 | 8% |
| Other | 3 | 12% |
| Unknown | 6 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 06 March 2013.
All research outputs
#14,431,034
of 23,891,012 outputs
Outputs from BMC Bioinformatics
#4,546
of 7,455 outputs
Outputs of similar age
#154,924
of 251,080 outputs
Outputs of similar age from BMC Bioinformatics
#46
of 71 outputs
Altmetric has tracked 23,891,012 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,455 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 38th percentile – i.e., 38% of its peers scored the same or lower than it.
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 251,080 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 71 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.