A unified local objective function for optimally selecting SNPs on arrays for agricultural genomics applications. (31st January 2020)
- Record Type:
- Journal Article
- Title:
- A unified local objective function for optimally selecting SNPs on arrays for agricultural genomics applications. (31st January 2020)
- Main Title:
- A unified local objective function for optimally selecting SNPs on arrays for agricultural genomics applications
- Authors:
- Wu, X‐L.
Li, H.
Ferretti, R.
Simpson, B.
Walker, J.
Parham, J.
Mastro, L.
Qiu, J.
Schultz, T.
Tait, R. G.
Bauck, S. - Abstract:
- Summary: Over the years, ad‐hoc procedures were used for designing SNP arrays, but the procedures and strategies varied considerably case by case. Recently, a multiple‐objective, local optimization (MOLO) algorithm was proposed to select SNPs for SNP arrays, which maximizes the adjusted SNP information ( E score) under multiple constraints, e.g. on MAF, uniformness of SNP locations ( U score), the inclusion of obligatory SNPs and the number and size of gaps. In the MOLO, each chromosome is split into equally spaced segments and local optima are selected as the SNPs having the highest adjusted E score within each segment, conditional on the presence of obligatory SNPs. The computation of the adjusted E score, however, is empirical, and it does not scale well between the uniformness of SNP locations and SNP informativeness. In addition, the MOLO objective function does not accommodate the selection of uniformly distributed SNPs. In the present study, we proposed a unified local function for optimally selecting SNPs, as an amendment to the MOLO algorithm. This new local function takes scalable weights between the uniformness and informativeness of SNPs, which allows the selection of SNPs under varied scenarios. The results showed that the weighting between the U and the E scores led to a higher imputation concordance rate than the U score or E score alone. The results from the evaluation of six commercial bovine SNP chips further confirmed this conclusion.
- Is Part Of:
- Animal genetics. Volume 51:Number 2(2020)
- Journal:
- Animal genetics
- Issue:
- Volume 51:Number 2(2020)
- Issue Display:
- Volume 51, Issue 2 (2020)
- Year:
- 2020
- Volume:
- 51
- Issue:
- 2
- Issue Sort Value:
- 2020-0051-0002-0000
- Page Start:
- 306
- Page End:
- 310
- Publication Date:
- 2020-01-31
- Subjects:
- cattle -- imputation concordance -- local optimization -- Shannon entropy -- SNP array
Animal genetics -- Periodicals
572.8 - Journal URLs:
- http://www.blackwell-synergy.com/member/institutions/issuelist.asp?journal=age ↗
http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1365-2052 ↗
http://onlinelibrary.wiley.com/ ↗
http://firstsearch.oclc.org ↗
http://firstsearch.oclc.org/journal=0268-9146;screen=info;ECOIP ↗ - DOI:
- 10.1111/age.12916 ↗
- Languages:
- English
- ISSNs:
- 0268-9146
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 0903.572000
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British Library STI - ELD Digital store - Ingest File:
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