| Title |
A fast least-squares algorithm for population inference
|
|---|---|
| Published in |
BMC Bioinformatics, January 2013
|
| DOI | 10.1186/1471-2105-14-28 |
| Pubmed ID | |
| Authors |
R Mitchell Parry, May D Wang |
| Abstract |
Population inference is an important problem in genetics used to remove population stratification in genome-wide association studies and to detect migration patterns or shared ancestry. An individual's genotype can be modeled as a probabilistic function of ancestral population memberships, Q, and the allele frequencies in those populations, P. The parameters, P and Q, of this binomial likelihood model can be inferred using slow sampling methods such as Markov Chain Monte Carlo methods or faster gradient based approaches such as sequential quadratic programming. This paper proposes a least-squares simplification of the binomial likelihood model motivated by a Euclidean interpretation of the genotype feature space. This results in a faster algorithm that easily incorporates the degree of admixture within the sample of individuals and improves estimates without requiring trial-and-error tuning. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Norway | 1 | 33% |
| United States | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 5% |
| Unknown | 20 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 43% |
| Student > Ph. D. Student | 4 | 19% |
| Professor | 2 | 10% |
| Student > Bachelor | 1 | 5% |
| Student > Master | 1 | 5% |
| Other | 3 | 14% |
| Unknown | 1 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 10 | 48% |
| Computer Science | 4 | 19% |
| Biochemistry, Genetics and Molecular Biology | 1 | 5% |
| Chemical Engineering | 1 | 5% |
| Decision Sciences | 1 | 5% |
| Other | 3 | 14% |
| Unknown | 1 | 5% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 02 September 2021.
All research outputs
#5,854,969
of 22,693,205 outputs
Outputs from BMC Bioinformatics
#2,166
of 7,254 outputs
Outputs of similar age
#62,740
of 280,489 outputs
Outputs of similar age from BMC Bioinformatics
#49
of 147 outputs
Altmetric has tracked 22,693,205 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 7,254 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 69% of its peers.
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We're also able to compare this research output to 147 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 65% of its contemporaries.