ArticleHeredity2019
Application of partial least squares in exploring the genome selection signatures between populations.
Article in Heredity, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
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Who cites it
2 citing papers in PubMed, 5 citations in OpenAlex.
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Natural and artificial selection have led to substantial variation in the phenotypic traits of different populations. Therefore, there is a need to develop methods that are based on cross-population comparisons to discover loci related to specific traits. Here, we suggested a strategy to detect the genome selection signatures between populations based on the partial least squares (PLS) theory. Using the binary population indicator as the response variable in the PLS analysis, alleles under selection between populations were identified from the first PLS component. We explored the theory behind the PLS analysis to reveal its usefulness in detecting the loci under selection. Through the simulation study, the results showed that the PLS method had a better performance than the F
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.