Assessment of Alternative Single Nucleotide Polymorphism (SNP) Weighting Methods for Single-Step Genomic Prediction of Traits with Different Genetic Architecture
الموضوعات :
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
الکلمات المفتاحية: Weighting, fixation index, genomic prediction, ssGBLUP,
ملخص المقالة :
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.
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