Assessment of Alternative Single Nucleotide Polymorphism (SNP) Weighting Methods for Single-Step Genomic Prediction of Traits with Different Genetic Architecture

Subject Areas : Camel

S. Moghaddaszadeh-Ahrabi ¹ , M. Bazrafshan ²

1 - Department of Animal Science, Faculty of Agriculture and Natural Resources, Islamic Azad University, Tabriz Branch, Tabriz, Iran
2 - Department of Animal and Poultry Science, College of Aburaihan, University of Tehran, Pakdasht, Tehran, Iran

Received: 2022-07-23 Accepted : 2022-12-08 Published : 2023-06-01

Keywords: Weighting, fixation index, genomic prediction, ssGBLUP,

Abstract :

We investigated the prediction accuracy and bias of single-step genomic BLUP (ssGBLUP) with or without weights for single-nucleotide polymorphisms (SNPs). The SNP weights were calculated using population Fixation Index (WssGBLUPFST) and a nonlinear method called nonlinearA (WssGBLUPNLA). The results of these two weighted methods were compared with a non-weighted method. The individuals of the reference population were sorted based on their estimated breeding values and the top 5% and bottom 5% of individuals based on their estimated breeding values (EBVs) were considered as subpopulations 1 and 2. The FST values for all SNPs between subpopulations 1 and 2 were scaled between zero and one and used as weights. The prediction accuracy and bias of predictions in WssGBLUPFST, WssGBLUPNLA and ssGBLUP methods were compared considering varying the numbers of quantitative trait locus (QTL) (10, 50 and 500), heritability (0.1 and 0.4) and size of reference population (1500, 5000 and 12500). In 10 and 50 QTL, both weighting methods outperformed regular ssGBLUP and with simulation, WssGBLUPFST outperformed WssGBLUPNLA. By increasing the number of QTL to 500 QTL, the WssGBLUPFST was no longer superior to WssGBLUPNLA and ssGBLUP. Our results suggest usefulness of weighting genomic relationship matrix by using FST, especially when the trait is affected by a few numbers of QTL. The prediction accuracy of WssGBLUP methods is expected to increase by identifying and giving appropriate weight to QTL with major effects. Combining different test statistics into a single framework such as decomposition of multiple signals may help reduce false positives and pinpoint the QTL position with more precision.

References:

Aguilar I., Misztal I., Johnson D., Legarra A., Tsuruta S. and Lawlor T. (2010). Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. J. Dairy Sci. 93(2), 743-752.

Chang C.C., Chow C.C., Tellier L.C., Vattikuti S., Purcell S.M. and Lee J.J. (2015). Second-generation PLINK: Rising to the challenge of larger and richer datasets. Gigascience. 4(1), 7-15.

Chang L.Y., Toghiani S., Hay E.H., Aggrey S.E. and Rekaya R. (2019). A weighted genomic relationship matrix based on Fixation Index (Fst) prioritized SNPs for genomic selection. Genes. 10(11), 922-931.

Chang L.Y., Toghiani S., Ling A., Aggrey S.E. and Rekaya R. (2018). High density marker panels, SNPs prioritizing and accuracy of genomic selection. BMC Genet. 19(1), 1-10.

Ghoreishifar S.M., Eriksson S., Johansson A.M., Khansefid M., Moghaddaszadeh-Ahrabi S., Parna N., Davoudi P. and Javanmard A. (2020a). Signatures of selection reveal candidate genes involved in economic traits and cold acclimation in five Swedish cattle breeds. Genet. Sel. Evol. 52(1), 1-15.

Ghoreishifar S.M., Moradi-Shahrbabak H., Fallahi M.H., Jalil Sarghale A., Moradi-Shahrbabak M., Abdollahi-Arpanahi R. and Khansefid M. (2020b). Genomic measures of inbreeding coefficients and genome-wide scan for runs of homozygosity islands in Iranian river buffalo, Bubalus bubalis. BMC Genet. 21(1), 16-22.

Ghoreishifar S.M., Rochus C.M., Moghaddaszadeh-Ahrabi S., Davoudi P., Ardestani S.S., Zinovieva N.A., Deniskova T.E. and Johansson A.M. (2021). Shared ancestry and signatures of recent selection in gotland sheep. Genes. 12(3), 433-442.

Kemper K.E., Saxton S.J., Bolormaa S., Hayes B.J. and Goddard M.E. (2014). Selection for complex traits leaves little or no classic signatures of selection. BMC Genom. 15(1), 246-254.

Khansefid M., Goddard M.E., Haile-Mariam M., Konstantinov K.V., Schrooten C., de Jong G., Jewell E.G. O’Connor E. Pryce J.E. Daetwyler H.D. and MacLeod I.M. (2020). Improving Genomic Prediction of Crossbred and Purebred Dairy Cattle. Fron. Genet. 11, 1-14.

Lourenco D., Fragomeni B., Bradford H., Menezes I., Ferraz J., Aguilar I., Lourenco S. and Misztal I. (2017). Implications of SNP weighting on single‐step genomic predictions for different reference population sizes. J. Anim. Breed. Genet. 134(6), 463-471.

Ma Y., Ding X., Qanbari S., Weigend S., Zhang Q. and Simianer H. (2015). Properties of different selection signature statistics and a new strategy for combining them. Heredity. 115(5), 426-436.

Mehrban H., Naserkheil M., Lee D.H., Cho C., Choi T., Park M. and Ibáñez-Escriche N. (2021). Genomic prediction using alternative strategies of weighted single-step genomic BLUP for yearling weight and carcass traits in Hanwoo beef cattle. Genes. 12(2), 266-275.

Oget C., Teissier M., Astruc J.M., Tosser-Klopp G. and Rupp R. (2019). Alternative methods improve the accuracy of genomic prediction using information from a causal point mutation in a dairy sheep model. BMC Genom. 20(1), 1-14.

Salek Ardestani S., Aminafshar M., Zandi Baghche M., Banabazi M.H., Sargolzaei M. and Miar Y. (2020b). Whole-genome signatures of selection in sport horses revealed selection footprints related to musculoskeletal system development processes. Animals. 10(1), 53-64.

Salek Ardestani S., Jafarikia M., Sargolzaei M., Sullivan B. and Miar Y. (2021). Genomic prediction of average daily gain, back-fat thickness, and loin muscle depth using different genomic tools in Canadian Swine populations. Front. Genet. 12, 735-743.

Sargolzaei M. and Schenkel F.S. (2009). QMSim: A large-scale genome simulator for livestock. Bioinformatics. 25(5), 680-681.

Teissier M., Larroque H. and Robert-Granie C. (2019). Accuracy of genomic evaluation with weighted single-step genomic best linear unbiased prediction for milk production traits, udder type traits, and somatic cell scores in French dairy goats. J. Dairy Sci. 102(4), 3142-3154.

VanRaden P.M. (2008). Efficient methods to compute genomic predictions. J. Dairy Sci. 91(11), 4414-4423.

Walsh J.B. (2021). Genomic selection signatures and animal breeding. J. Anim. Breed. Genet. 138, 1-3.

Wang H., Misztal I., Aguilar I., Legarra A. and Muir W. (2012). Genome-wide association mapping including phenotypes from relatives without genotypes. Genet. Res. 94(2), 73-83.

Weir B.S. and Cockerham C.C. (1984). Estimating F-statistics for the analysis of population structure. Evolution. 38(6), 1358-1370.

Zhang X., Lourenco D., Aguilar I., Legarra A. and Misztal I. (2016). Weighting strategies for single-step genomic BLUP: An iterative approach for accurate calculation of GEBV and GWAS. Front. Genet. 7, 151-162.