Dataset Information

Genotype imputation in the domestic dog.

ABSTRACT: Application of imputation methods to accurately predict a dense array of SNP genotypes in the dog could provide an important supplement to current analyses of array-based genotyping data. Here, we developed a reference panel of 4,885,283 SNPs in 83 dogs across 15 breeds using whole genome sequencing. We used this panel to predict the genotypes of 268 dogs across three breeds with 84,193 SNP array-derived genotypes as inputs. We then (1) performed breed clustering of the actual and imputed data; (2) evaluated several reference panel breed combinations to determine an optimal reference panel composition; and (3) compared the accuracy of two commonly used software algorithms (Beagle and IMPUTE2). Breed clustering was well preserved in the imputation process across eigenvalues representing 75 % of the variation in the imputed data. Using Beagle with a target panel from a single breed, genotype concordance was highest using a multi-breed reference panel (92.4 %) compared to a breed-specific reference panel (87.0 %) or a reference panel containing no breeds overlapping with the target panel (74.9 %). This finding was confirmed using target panels derived from two other breeds. Additionally, using the multi-breed reference panel, genotype concordance was slightly higher with IMPUTE2 (94.1 %) compared to Beagle; Pearson correlation coefficients were slightly higher for both software packages (0.946 for Beagle, 0.961 for IMPUTE2). Our findings demonstrate that genotype imputation from SNP array-derived data to whole genome-level genotypes is both feasible and accurate in the dog with appropriate breed overlap between the target and reference panels.

SUBMITTER: Friedenberg SG

PROVIDER: S-EPMC5002369 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Publications

Genotype imputation in the domestic dog.

Friedenberg S G SG Meurs K M KM

Mammalian genome : official journal of the International Mammalian Genome Society 20160429 9-10

Application of imputation methods to accurately predict a dense array of SNP genotypes in the dog could provide an important supplement to current analyses of array-based genotyping data. Here, we developed a reference panel of 4,885,283 SNPs in 83 dogs across 15 breeds using whole genome sequencing. We used this panel to predict the genotypes of 268 dogs across three breeds with 84,193 SNP array-derived genotypes as inputs. We then (1) performed breed clustering of the actual and imputed data; ...[more]

PMID: 27129452

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Project description:BackgroundImputation of partially missing or unobserved genotypes is an indispensable tool for SNP data analyses. However, research and understanding of the impact of initial SNP-data quality control on imputation results is still limited. In this paper, we aim to evaluate the effect of different strategies of pre-imputation quality filtering on the performance of the widely used imputation algorithms MaCH and IMPUTE.ResultsWe considered three scenarios: imputation of partially missing genotypes with usage of an external reference panel, without usage of an external reference panel, as well as imputation of completely un-typed SNPs using an external reference panel. We first created various datasets applying different SNP quality filters and masking certain percentages of randomly selected high-quality SNPs. We imputed these SNPs and compared the results between the different filtering scenarios by using established and newly proposed measures of imputation quality. While the established measures assess certainty of imputation results, our newly proposed measures focus on the agreement with true genotypes. These measures showed that pre-imputation SNP-filtering might be detrimental regarding imputation quality. Moreover, the strongest drivers of imputation quality were in general the burden of missingness and the number of SNPs used for imputation. We also found that using a reference panel always improves imputation quality of partially missing genotypes. MaCH performed slightly better than IMPUTE2 in most of our scenarios. Again, these results were more pronounced when using our newly defined measures of imputation quality.ConclusionEven a moderate filtering has a detrimental effect on the imputation quality. Therefore little or no SNP filtering prior to imputation appears to be the best strategy for imputing small to moderately sized datasets. Our results also showed that for these datasets, MaCH performs slightly better than IMPUTE2 in most scenarios at the cost of increased computing time.

Project description:The domestic dog shows a wide range of morphologies, that humans have selected for in the process of creating unique breeds. Recent studies have revealed correlations between changes in morphology and behaviour as reported by owners. For example, as height and weight decrease, many undesirable behaviours (non-social fear, hyperactivity and attention seeking) become more apparent. The current study aimed to explore more of these correlations, but this time used reports from trained observers. Phenotypic measurements were recorded from a range of common dog breeds (n = 45) and included cephalic index (CI: the ratio of skull width to skull length), bodyweight, height and sex. These data were then correlated with results from the Dog Mentality Assessment (DMA), which involves trained observers scoring a dog's reaction to stimuli presented over 10 standardised subtests. Each subtest is designed to evoke a behavioural response. Backward elimination and weighted step-wise regression revealed that shorter dogs demonstrated more aggressive tendencies, reacting defensively toward both assistants dressed as ghosts (p = 0.045), and to a dummy (p = 0.008). Taller dogs were more affectionate when greeting and being handled by humans (p = 0.007, p = <0.001, respectively). Taller dogs were also more cooperative (p = <0.001), and playful (p = 0.001) with humans than shorter dogs. Heavier dogs were more inquisitive toward a dummy (p = 0.011), to the source of a metallic noise (p = 0.010) and to an assistant (p = 0.003). Heavier dogs were also more attentive to the ghosts (p = 0.013). In comparison, lighter dogs were cautious of a dummy (p = <0.001) and fearful of the sound of a gunshot (p = <0.001). Lighter dogs were also cautious of, and demonstrated prolonged fearfulness toward, the source of metallic noise (p = <0.001, p = <0.034, respectively). With a far larger sample and the advantage of third-party reporting (which overcomes potential owner bias), the current findings build on previous studies in this field, further supporting covariance between morphology and behaviour.

Dataset Information

Genotype imputation in the domestic dog.

Publications

Genotype imputation in the domestic dog.

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