A Novel and Fast Approach for Population Structure Inference Using Kernel-PCA and Optimization (PSIKO)
- Submitting institution
-
The University of East Anglia
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 182619458
- Type
- D - Journal article
- DOI
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10.1534/genetics.114.171314
- Title of journal
- Genetics
- Article number
- -
- First page
- 1421
- Volume
- 198
- Issue
- 4
- ISSN
- 1943-2631
- Open access status
- Out of scope for open access requirements
- Month of publication
- December
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- 13
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- We develop a novel, kernel-PCA-based algorithm to detect population structure in datasets, an issue that affects genome-wide association studies. It is freely available in the PSIKO software and resulted via collaborative work with Professor Bancroft’s group, then at the John-Innes Centre, supported in part by his BBSRC and Defra grants and a Norwich Research Park PhD-studentship to Popescu. Amongst other applications, PSIKO has been applied to topical issues such as ash dieback (Sollars et al, Nature, 2017), the genetic diversity of oilseed rape (Havlickova et al, The Plant Journal, 2018), and biofuels (Nguyen et al, Biotechnology for Biofuels, 2020).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -