ArticleGenetics1997
Hardy-Weinberg testing for continuous data.
Article in Genetics, 1997. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Insights into the coupling of duplication events and macroevolution from an age profile of animal transmembrane gene families.PLoS computational biology · 2006Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Estimation of allelic and genotypic distributions for continuous data using kernel density estimation is discussed and illustrated for some variable number of tandem repeat data. These kernel density estimates provide a useful representation of data when only some of the many variants at a locus are present in a sample. Two Hardy-Weinberg test procedures are introduced for continuous data: a continuous chi-square test with test statistic T(CCS) and a test based on Hellinger's distance with test statistic T(HD). Simulations are used to compare the powers of these tests to each other and to the powers of a test of intraclass correlation T(IC), as well as to the power of Fisher's exact test T(FET) applied to discretized data. Results indicate that the power of T(CCS) is better than that of T(HD), but neither is as powerful as T(FET). The intraclass correlation test does not perform as well as the other tests examined in this article.
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Registered trials
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