Comparison of genotype imputation strategies using a combined reference panel for chicken population

Abstract

Using whole-genome sequence (WGS) data are supposed to be optimal for genome-wide association studies and genomic predictions. However, sequencing thousands of individuals of interest is expensive. Imputation from single nucleotide polymorphisms panels to WGS data is an attractive approach to obtain highly reliable WGS data at low cost. Here, we conducted a genotype imputation study with a combined reference panel in yellow-feather dwarf broiler population. The combined reference panel was assembled by sequencing 24 key individuals of a yellow-feather dwarf broiler population (internal reference panel) and WGS data from 311 chickens in public databases (external reference panel). Three scenarios were investigated to determine how different factors affect the accuracy of imputation from 600 K array data to WGS data, including: genotype imputation with internal, external and combined reference panels; the number of internal reference individuals in the combined reference panel; and different reference sizes and selection strategies of an external reference panel. Results showed that imputation accuracy from 600 K to WGS data were 0.834±0.012, 0.920±0.007 and 0.982±0.003 for the internal, external and combined reference panels, respectively. Increasing the reference size from 50 to 250 improved the accuracy of genotype imputation from 0.848 to 0.974 for the combined reference panel and from 0.647 to 0.917 for the external reference panel. The selection strategies for the external reference panel had no impact on the accuracy of imputation using the combined reference panel. However, if only an external reference panel with reference size >50 was used, the selection strategy of minimizing the average distance to the closest leaf had the greatest imputation accuracy compared with other methods. Generally, using a combined reference panel provided greater imputation accuracy, especially for low-frequency variants. In conclusion, the optimal imputation strategy with a combined reference panel should comprehensively consider genetic diversity of the study population, availability and properties of external reference panels, sequencing and computing costs, and frequency of imputed variants. This work sheds light on how to design and execute genotype imputation with a combined external reference panel in a livestock population.

Strategies for Obtaining and Pruning Imputed Whole-Genome Sequence Data for Genomic Prediction

“…An obvious improvement in imputation accuracy was observed using a combined reference panel for genotype imputation ( Figure 1 ). Similar results were found in a previous study (Mitt et al, 2017; Ye et al, 2018a). Higher imputation accuracy was expected lead to higher prediction accuracy of WGP (Moghaddar et al, 2015).…”

“…For all scenarios, we randomly masked 2% of SNPs from the target panel as the validation set before imputation, and then we calculated the correlation between the imputed genotypes and masked genotypes per individual. Detailed information about these reference panels is in our previous study (Ye et al, 2018a).…”

“…Currently, the imputation reference panels are always divided into three types: the internal reference panel, the external reference panel, and the combined reference panel. The combined reference panel has greater imputation accuracy, especially for low-frequency variants (Ye et al, 2018a). The imputed WGS data also include long-range LD within a breed, which may prevent the precise localization of quantitative trait nucleotides (QTN) (van Binsbergen et al, 2015).…”

New Insights From Imputed Whole-Genome Sequence-Based Genome-Wide Association Analysis and Transcriptome Analysis: The Genetic Mechanisms Underlying Residual Feed Intake in Chickens

“…The combined reference panels contained 36,840,795 SNPs probes across 28 autosomes and a sex chromosome (chrZ). More detailed information could be found in our previous study (Ye et al, 2019).…”

“…This high imputation accuracy was full enough to ensure the confidence of the GWAS results. This high imputation accuracy obtained may due to performing the imputation with a two-step approach (Kreiner-Moller et al, 2015) and using a combined reference panel (Ye et al, 2019). Compared with our previous study, which performed GWAS for RFI using 426 chickens with 600 K array data (Xu et al, 2016), new significant SNPs and genes were identified because more individuals (626 birds) and higher marker density (imputed WGS data) were utilized to perform GWAS.…”