A unified local objective function for optimally selecting SNPs on arrays for agricultural genomics applications.
SNP array
Shannon entropy
cattle
imputation concordance
local optimization
Journal
Animal genetics
ISSN: 1365-2052
Titre abrégé: Anim Genet
Pays: England
ID NLM: 8605704
Informations de publication
Date de publication:
Mar 2020
Mar 2020
Historique:
accepted:
09
01
2020
pubmed:
1
2
2020
medline:
29
9
2020
entrez:
1
2
2020
Statut:
ppublish
Résumé
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.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
306-310Informations de copyright
© 2020 Stichting International Foundation for Animal Genetics.
Références
Boerner V., Johnston D., Wu X.L. & Bauck S. (2015) Accuracy of Igenity genomically estimated breeding values for predicting Australian Angus BREEDPLAN traits. Journal of Animal Science 93, 513-21.
Boichard D., Chung H., Dassonneville R. et al. (2012) Design of a bovine low-density SNP array optimized for imputation. PLoS ONE 7, e34130.
Bolormaa S., Gore K., van der Werf J.H., Hayes B.J. & Daetwyler H.D. (2015) Design of a low-density SNP chip for the main Australian sheep breeds and its effect on imputation and genomic prediction accuracy. Animal Genetics 46, 544-56.
Chan E.K., Hawken R. & Reverter A. (2009) The combined effect of SNP-marker and phenotype attributes in genome-wide association studies. Animal Genetics 40, 149-56.
Habier D., Fernando R.L. & Dekkers J.C.M. (2009) Genomic selection using low-density marker panels. Genetics 182, 343-53.
He J., Guo Y., Xu J., Li H., Fuller A., Tait R.G. Jr, Wu X.L. & Bauck S. (2018) Comparing SNP panels and statistical methods for estimating genomic breed composition of individual animals in ten cattle breeds. BMC Genetics 19, 56.
Höglund J.K., Sahana G., Brøndum R.F., Guldbrandtsen B., Buitenhuis B. & Lund M.S. (2014) Fine mapping QTL for female fertility on BTA04 and BTA13 in dairy cattle using HD SNP and sequence data. BMC Genomics 15, 790.
Keating B.J., Tischfield S., Murray S.S. et al. (2008) Concept, design and implementation of a cardiovascular gene-centric 50 k SNP array for large-scale genomic association studies. PLoS ONE 3, e3583.
Kim J.G., Nonneman D., Rohrer G.A., Vallet J.L. & Christenson R.K. (2003) Linkage mapping of a SNP in the porcine MADH1 gene to a region of chromosome 8 that contains QTL for uterine capacity. Animal Genetics 34, 310-1.
Kranis A., Gheyas A.A., Boschiero C. et al. (2013) Development of a high density 600K SNP genotyping array for chicken. BMC Genomics 14, 59.
Lopes F.B., Wu X.L., Li H., Xu J., Perkins T., Genho J., Ferretti R., Tait R.G. Jr, Bauck S. & Rosa G.J.M. (2018) Improving accuracy of genomic prediction in Brangus cattle by adding animals with imputed low-density SNP genotypes. Journal of Animal Breeding and Genetics 135, 14-27.
Macciotta N.P., Gaspa G., Bomba L., Vicario D., Dimauro C., Cellesi M. & Ajmone-Marsan P. (2015) Genome-wide association analysis in Italian Simmental cows for lactation curve traits using a low-density (7K) SNP panel. Journal of Dairy Science 98, 8175-85.
Mao X., Kadri N.K., Thomasen J.R., De Koning D.J., Sahana G. & Guldbrandtsen B. (2016) Fine mapping of a calving QTL on Bos taurus autosome 18 in Holstein cattle. Journal of Animal Breeding and Genetics 133, 207-18.
Matukumalli L.K., Lawley C.T. & Schnabel R.D. et al. (2009) Development and characterization of a high density SNP genotyping assay for cattle. PLoS ONE 4, e5350.
Moser G., Khatkar M.S., Hayes B.J. & Raadsma H.W. (2010) Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNPs. Genetics Selection Evolution 42, 37.
Okut H., Wu X.L., Rosa G.J., Bauck S., Woodward B.W., Schnabel R.D., Taylor J.F. & Gianola D. (2013) Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models. Genetics Selection Evolution 45, 34.
Schweer K.R., Kachman S.D., Kuehn L.A., Freetly H.C., Pollak J.E. & Spangler M.L. (2018) Genome-wide association study for feed efficiency traits using SNP and haplotype models. Journal of Animal Science 96, 2086-98.
Weigel K.A., de los Campos G., González-Recio O., Naya H., Wu X.L., Long N., Rosa G.J. & Gianola D. (2009) Predicting ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers. Journal of Dairy Science 92, 5248-57.
Wiggans G.R., Cooper T.A., VanRaden P.M., Olson K.M. & Tooker M.E. (2012) Use of the Illumina Bovine3K BeadChip in dairy genomic evaluation. Journal of Dairy Science 95, 1552-8.
Wu X.L., Xu J., Feng G. et al. (2016) Optimal design of low-density SNP arrays for genomic prediction: algorithm and applications. PLoS ONE 11, e0161719.
Zhang Z. & Druet T. (2010) Marker imputation with low-density marker panels in Dutch Holstein cattle. Journal of Dairy Science 93, 5487-94.