What does imputation mean in genetics?
statistical inference of unobserved genotypes
Imputation in genetics refers to the statistical inference of unobserved genotypes.
What is imputation quality score?
Basically, IMPUTE2 reports an information metric (info score). This metric typically takes values between 0 and 1, where values near 1 indicate that a SNP has been imputed with high certainty. The info metric is often used to remove poorly imputed SNPs from the association testing results.
What is imputation SNP?
Genotype imputation is a process of estimating missing genotypes from the haplotype or genotype reference panel. It can effectively boost the power of detecting single nucleotide polymorphisms (SNPs) in genome-wide association studies, integrate multi-studies for meta-analysis, and be applied in fine-mapping studies.
What is imputed genotype data?
Genotype imputation is the term used to describe the process of inferring unobserved genotypes in a sample of individuals. It is a key step prior to a genome-wide association study (GWAS) or genomic prediction. The imputation accuracy will directly influence the results from subsequent analyses.
What is imputation in data science?
In statistics, imputation is the process of replacing missing data with substituted values. When substituting for a data point, it is known as “unit imputation”; when substituting for a component of a data point, it is known as “item imputation”.
How accurate is imputation?
Imputation accuracy has previously been assessed for African populations (Huang et al., 2009; Hancock et al., 2012; Roshyara et al., 2016) and for populations with two- or three-way admixture, with results reaching over 75% accuracy (Nelson et al., 2016).
How do you assess imputation?
To assess an imputation model using PPC, one or more test quantities are selected; these test quantities are generally parameters of scientific interest. For example, if the analysis model were a regression model, the test quantities could be regression coefficients, standard errors and p-values.
How do you measure imputation accuracy?
Accuracy was calculated by comparing the proportion of SNPs in the overlap that were complete (or flipped) matches to the number of overlapping SNPs. This provided an indication of accuracy and error rate within the overlapping region and should be a good indication of overall imputation accuracy.
What is imputation research?
Imputation, also called ascription, is a statistical process that statisticians, survey researchers, and other scientists use to replace data that are missing from a data set due to item nonresponse. Researchers do imputation to improve the accuracy of their data sets.
Why is data imputation important?
Because missing data can create problems for analyzing data, imputation is seen as a way to avoid pitfalls involved with listwise deletion of cases that have missing values.
How do you validate imputation?
To check it, you can do some cross-validation: randomly remove 1/5 (say) of the observations for your variable of interest, run the algorithm, then compare the held-out values to the random imputations.
What is phasing and imputation?
Most imputation methods include two steps, a phasing step that involves resolving the haplotypes of high-density genotyped animals, and an imputation step that involves identifying which combination of these haplotypes match the low-density genotyped animals or ungenotyped animals that have allele probabilities.
Why is imputation used?
Imputation preserves all cases by replacing missing data with an estimated value based on other available information. Once all missing values have been imputed, the data set can then be analysed using standard techniques for complete data.
How do you report imputed data?
Describe the Nature and Structure of Any Missing Data
- Recommendation 1—Report Rates of Missing Data.
- Recommendation 2—Report Reasons Data are Missing.
- Recommendation 3—Report Evidence of Ignorable Patterns or Assumptions.
- Recommendation 4—Report Variables Used in the Imputation Phase.
What is genotype phasing?
Phasing is the process of inferring haplotypes from genotype data. Efficient algorithms and associated software for accurate phasing in pedigrees are needed, especially for populations lacking reference panels of sequenced individuals.
How do you do imputation?
Another common approach among those who are paying attention is imputation. Imputation simply means replacing the missing values with an estimate, then analyzing the full data set as if the imputed values were actual observed values.
How does mean imputation work?
Mean imputation (MI) is one such method in which the mean of the observed values for each variable is computed and the missing values for that variable are imputed by this mean. This method can lead into severely biased estimates even if data are MCAR (see, e.g., Jamshidian and Bentler, 1999).
Can genome-wide association studies be performed by imputation of genotypes?
A new multipoint method for genome-wide association studies by imputation of genotypes. Nature Genet. 39, 906–913 (2007). Stephens, M. & Donnelly, P. Inference in molecular population genetics. J. R. Statist. Soc.
How is genotype imputation used in genetic testing?
One obvious use of genotype imputation based analysis is to accelerate fine-mapping studies. Once an association signal has been identified and confirmed, genotype imputation can be used to evaluate the evidence for association at each of several nearby SNPs and help focus the search for potential causal variants.
What do we know about quality control of imputed genotype data?
We review and compare the information metrics that are commonly used when carrying out quality control of imputed genotype data. In the past few years genome-wide association (GWA) studies have uncovered a large number of convincingly replicated associations for many complex human diseases.
How does genetic diversity affect imputation accuracy?
These results indicate that differences in genetic diversity between the study population and the reference panel also influence imputation accuracy. Huang et al. 29 also found that imputation-based mixtures of at least 2 HapMap panels reduced imputation error rates in 25 of the populations.