Imputation from SNP chip to sequence: a case study in a Chinese indigenous chicken population

Abstract

BackgroundGenome-wide association studies and genomic predictions are thought to be optimized by using whole-genome sequence (WGS) data. However, sequencing thousands of individuals of interest is expensive. Imputation from SNP panels to WGS data is an attractive and less expensive approach to obtain WGS data. The aims of this study were to investigate the accuracy of imputation and to provide insight into the design and execution of genotype imputation.ResultsWe genotyped 450 chickens with a 600 K SNP array, and sequenced 24 key individuals by whole genome re-sequencing. Accuracy of imputation from putative 60 K and 600 K array data to WGS data was 0.620 and 0.812 for Beagle, and 0.810 and 0.914 for FImpute, respectively. By increasing the sequencing cost from 24X to 144X, the imputation accuracy increased from 0.525 to 0.698 for Beagle and from 0.654 to 0.823 for FImpute. With fixed sequence depth (12X), increasing the number of sequenced animals from 1 to 24, improved accuracy from 0.421 to 0.897 for FImpute and from 0.396 to 0.777 for Beagle. Using optimally selected key individuals resulted in a higher imputation accuracy compared with using randomly selected individuals as a reference population for re-sequencing. With fixed reference population size (24), imputation accuracy increased from 0.654 to 0.875 for FImpute and from 0.512 to 0.762 for Beagle as the sequencing depth increased from 1X to 12X. With a given total cost of genotyping, accuracy increased with the size of the reference population for FImpute, but the pattern was not valid for Beagle, which showed the highest accuracy at six fold coverage for the scenarios used in this study.ConclusionsIn conclusion, we comprehensively investigated the impacts of several key factors on genotype imputation. Generally, increasing sequencing cost gave a higher imputation accuracy. But with a fixed sequencing cost, the optimal imputation enhance the performance of WGP and GWAS. An optimal imputation strategy should take size of reference population, imputation algorithms, marker density, and population structure of the target population and methods to select key individuals into consideration comprehensively. This work sheds additional light on how to design and execute genotype imputation for livestock populations.Electronic supplementary materialThe online version of this article (10.1186/s40104-018-0241-5) contains supplementary material, which is available to authorized users.

Sequence Imputation from Low Density Single Nucleotide Polymorphism Panel in a Black Poplar Breeding population

“…Simultaneously, we attained imputation qualities as high as 0.84. This imputation quality is similar to the one obtained on horses [34] with Impute2 software or in cattle [33], and higher than the one obtained on chickens [35]. The study is based on a subset of a breeding population in black poplar, with a relatively low effective number of contributing parents, which could explain partly the success of the imputation.…”

“…We used the FImpute software (v 2.2) [11], as many studies have already pinpointed its good performance for imputation when compared to many other alternatives [16,35,43,44]. FImpute can use different sizes of rolling windows with a given overlap to scan the genomes of target and reference datasets.…”

“…Both studies suggest that a good compromise is sequencing as many individuals as possible but at medium coverage (x8). To our knowledge, only three studies in animals have tried to impute successfully from low and medium densities (13 K and 50-60K) to real sequence data (350K and 13 millions) [33][34][35]. These studies show that inferring whole sequences from low-density marker panels with good accuracy is possible under certain conditions, notably with high levels of relatedness and persistence of LD between the markers across populations.…”

Genome wide association analysis on semen volume and milk yield using different strategies of imputation to whole genome sequence in French dairy goats

“…4), and thus implies that an imputation error would have a major impact on the final correlation. Nonetheless, the correlations that we obtained even for high MAF were low in comparison with other studies in livestock [3,29,30] or humans [31] where the correlation between true and imputed genotypes could reach 0.8. As our imputation study involved very few sequenced individuals (33 and 40), a single imputation error would drastically reduce this correlation.…”

“…Even though the CRs obtained in this study were similar to those involving equivalent reference population sizes in other species, the correlations were significantly lower than in dairy cattle [28] or poultry [29]. Indeed, CRs ranged from 0.75 to 0.85 in Li et al [28] and the genotype CR was around 0.8, depending on the chromosome, in Ye et al [29]. However, the squared correlations for cattle breeds with similar population sizes ranged from 0.63 to 0.76 in Li et al [28].…”