Biology subjects

van der Werf, J. H. J.

Publications and source records attributed to van der Werf, J. H. J..

3 recordsLinked to original sources

bioRxiv2022

A novel hyper-parameter can increase the prediction accuracy in a single-step genetic evaluation

The H-matrix best linear unbiased prediction (HBLUP) method has been widely used in livestock breeding programs. It can integrate all information, including pedigree, genotypes, and phenotypes on both genotyped and non-genotyped individuals into one single evaluation that can provide reliable predictions of breeding values. The existing HBLUP method (e.g., that implemented in BLUPf90 software) requires hyper-parameters that should be adequately optimised as otherwise the genomic prediction accuracy may decrease. In this study, we assess the performance of HBLUP using various hyper-parameters such as blending, tuning and scale factor in simulated as well as real data on Hanwoo cattle. In both simulated and cattle data, we show that blending is not necessary, indicating that the prediction accuracy decreases when using a blending hyper-parameter < 1. The tuning process (adjusting genomic relationships accounting for base allele frequencies) improves prediction accuracy in the simulated data, confirming previous studies, although the improvement is not statistically significant in the Hanwoo cattle data. We also demonstrate that a scale factor, , which determines the relationship between allele frequency and per-allele effect size, can improve the HBLUP accuracy in both simulated and real data. Our findings suggest that an optimal scale factor should be considered to increase the prediction accuracy, in addition to blending and tuning processes, when using HBLUP. Author SummaryDespite significant advancements in genotyping technologies, the capability to predict the phenotypes of complex traits is still limited. H-matrix best linear unbiased prediction (HBLUP) method has been used to tackle this limitation to demonstrate a promising prediction accuracy. However, the performance of HBLUP depends heavily on the optimisation of hyper-parameters (e.g. blending and tuning). In this study, we introduce a scale factor (), as a new hyper-parameter in HBLUP, which accounts for the relationship between allele frequency and per-allele effect size. Using simulation and real data analysis, we investigate the impact of the hyper-parameters (blending, tuning, and scale factor) on the performance of HBLUP. In general, the blending process may not improve the prediction accuracy for simulation and cattle data although a marginally improved prediction accuracy is observed with a blending hyper-parameter = 0.86 for one of carcass traits in the cattle data. In contrast, the tuning process can increase the HBLUP accuracy particularly in simulated data. Furthermore, we observe that an optimal scale factor plays a significant role in improving the prediction accuracy in both simulated and real data, and the improvement is relatively large compared with blending and tuning processes. In this context, we propose considering the scale factor as a hyper-parameter to increase the predictive performance of HBLUP.

genomics↗

bioRxiv2022

Effects of Experimental Design, Genetic Architecture and Threshold on Power and False Positive Rate of Genome-Wide Association Studies

Genome-Wide Association Studies are an important tool for identifying genetic markers associated with a trait, but it has been plagued by the multiple testing problem, which necessitates a multiple testing correction method. While many multiple testing methods have been suggested, e.g. Bonferroni and Benjamini-Hochbergs False Discovery Rate, the quality of the adjusted threshold based on these methods is not as well investigated. The aim of this study was to evaluate the balance between power and false positive rate of a Genome-Wide Association Studies experiment with Bonferroni and Benjamini-Hochbergs False Discovery Rate multiple testing correction methods and to test the effects of various experimental design and genetic architecture parameters on this balance. Our results suggest that when the markers are independent the threshold from Benjamini-Hochbergs False Discovery Rate provides a better balance between power and false positive rate in an experiment. However, with correlations between markers the threshold of Benjamini-Hochbergs False Discovery Rate becomes too lenient with an excessive number of false positives. Experimental design parameters such as sample size and number of markers used, as well as genetic architecture of a trait affect the balance between power and false positive rate. This experiment provided guidance in selecting an appropriate experimental design and multiple testing correction method when conducting an experiment.

genetics↗

bioRxiv2018

Effect of selection on bias and accuracy in genomic prediction of breeding values

Reference populations for genomic selection (GS) usually involve highly selected individuals, which may result in biased prediction of estimated genomic breeding values (GEBV). In the present study, bias and accuracy of GEBV were explored for various genetic models and prediction methods when using selected individuals for a reference. Data were simulated for an animal breeding program to compare Best Linear Unbiased Prediction of breeding values using pedigree based relationships (PBLUP), genomic relationships for genotyped animals only (GBLUP) and a Single Step approach (SSGBLUP), where information on genotyped individuals was used to infer a matrix H with relationships among all available genotyped and non-genotyped individuals that were linked through pedigree. In SSGBLUP, various weights (=0.95, 0.80, 0.50) for the genomic relationship matrix (G) relative to the numerator relationship matrix (A) were applied to construct H and in another version (SSGBLUP_F), inbreeding was accounted for while computing A-1. With GBLUP, accuracy of GEBV prediction increased linearly with an increase in the number of animals selected in reference. For the scenario with no-selection and random mating (RR) prediction was unbiased. For GBLUP, lower accuracy and bias observed in the scenarios with selection and random mating (SR) or selection and positive assortative mating (SA), in which prediction bias increased when a smaller and highly selected proportion genotyped. Bias disappeared when all individuals were genotyped. SSGBLUP_F showed higher accuracy compared to GBLUP and bias of prediction was negligible even with selective genotyping. However, PBLUP and SSGBLUP showed bias in SA owing to not fully accounting for allele frequency changes because of selection of quantitative trait loci (QTL) with larger effects and also due to high inbreeding rate. In genetic models with fewer QTL but each with larger effect, predictions were less accurate and more biased for selection scenarios. Results suggest that prediction accuracy and bias is affected by the genetic architecture of the trait. Selective genotyping lead to significant bias in GEBV prediction. SSGBLUP with appropriate scaling of A and G matrices can provide accurate and less biased prediction but scaling requires careful consideration in populations under selection and with high levels of inbreeding.

genomics↗