Imputation Accuracy of DogsLife Labrador Retrievers Sequenced at Varying Read Depth
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PROVIDER: PRJEB59274 | ENA |
REPOSITORIES: ENA
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Project description:The dog is a valuable model species for the genetic analysis of complex traits, and the use of genotype imputation in dogs will be an important tool for future studies. It is of particular interest to analyse the effect of factors like single nucleotide polymorphism (SNP) density of genotyping arrays and relatedness between dogs on imputation accuracy due to the acknowledged genetic and pedigree structure of dog breeds. In this study, we simulated different genotyping strategies based on data from 1179 Labrador Retriever dogs. The study involved 5826 SNPs on chromosome 1 representing the high density (HighD) array; the low-density (LowD) array was simulated by masking different proportions of SNPs on the HighD array. The correlations between true and imputed genotypes for a realistic masking level of 87.5% ranged from 0.92 to 0.97, depending on the scenario used. A correlation of 0.92 was found for a likely scenario (10% of dogs genotyped using HighD, 87.5% of HighD SNPs masked in the LowD array), which indicates that genotype imputation in Labrador Retrievers can be a valuable tool to reduce experimental costs while increasing sample size. Furthermore, we show that genotype imputation can be performed successfully even without pedigree information and with low relatedness between dogs in the reference and validation sets. Based on these results, the impact of genotype imputation was evaluated in a genome-wide association analysis and genomic prediction in Labrador Retrievers.
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Project description:Missing values in proteomic data sets have real consequences on downstream data analysis and reproducibility. Although several imputation methods exist to handle missing values, no single imputation method is best suited for a diverse range of data sets, and no clear strategy exists for evaluating imputation methods for large-scale DIA-MS data sets, especially at different levels of protein quantification. To navigate through the different imputation strategies available in the literature, we have established a workflow to assess imputation methods on large-scale label-free DIA-MS data sets. We used three DIA-MS data sets with real missing values to evaluate eight different imputation methods with multiple parameters at different levels of protein quantification; dilution series data set, a small pilot data set, and a larger proteomic data set.
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Project description:Missing values in proteomic data sets have real consequences on downstream data analysis and reproducibility. Although several imputation methods exist to handle missing values, no single imputation method is best suited for a diverse range of data sets, and no clear strategy exists for evaluating imputation methods for large-scale DIA-MS data sets, especially at different levels of protein quantification. To navigate through the different imputation strategies available in the literature, we have established a workflow to assess imputation methods on large-scale label-free DIA-MS data sets. We used three DIA-MS data sets with real missing values to evaluate eight different imputation methods with multiple parameters at different levels of protein quantification; dilution series data set, a small pilot data set, and a larger proteomic data set of clinical ovarian cancer patient samples.
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Project description:The intent of the experiment was to identify genes that were differentially expressed between dogs affected with anterior cruciate ligament (ACL) rupture and breed-matched controls. Anterior cruciate ligament and knee synovial tissue biopsies were collected from 4 ACL rupture affected cases and 4 unaffected control dogs. Cases and controls were matched as closely as possible based on breed, sex, neutered status, age, and weight. Medications that the dogs were taking at the time of sample collection were also considered. We prioritized sample size and quality above all other variables, therefore, two matched pairs of Golden Retrievers were chosen with two matched pairs of Labrador Retrievers for this analysis. Tissues from cases were collected during knee stabilization surgery. Tissues from unaffected control dogs were collected from dogs undergoing pelvic limb amputation or euthanasia for reasons unrelated to this study. Illumina TruSeq RNA libraries were constructed and 150bp paired-end sequencing was performed using the Illumina Hi-Seq 2500 platform. Table 1. Breed, sex, age, and weight of matched case and control pairs chosen for RNA sequencing analysis Cases Matched Controls Breed Sex Age (yr) Weight (kg) Breed Sex Age (yr) Weight (kg) GR1 CM 8.8 30.5 GR2 CM 14.9 N/A GR3 CM 5.6 44.0 GR4 CM 3.9 34.0 LR1 CM 9.7 36.0 LR2 CM 12.7 28.5 LR3 CM 13.3 36.0 LR4 CM 13.5 35.0 GR = Golden Retriever. LR = Labrador Retriever. CM= castrated male. Weight at the time of death was not available for one dog.
2021-02-26 | E-MTAB-10119 | biostudies-arrayexpress
Project description:2013 TSA Labrador Retriever Cohort
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