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FImpute uses an overlapping sliding window method to detect the relationship between study and reference set, while IMPUTE2 apples a hidden Markov model.
Our approximation is based on the idea that, within a limited genomic region, allelic consistency between study and reference individuals can be used to quickly rule out unhelpful reference haplotypes, thereby making imputation faster without sacrificing accuracy.
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In this situation, differences in the pattern of LD between the study and reference populations may affect imputation accuracy.
Distributions of gender, age (<40, 40 49, 50 59, and ⩾60 years), and comorbidity were compared between the study and reference cohorts, and examined using the χ-test.
**p: comparison between study group and reference group (age < 30 years).
There were no significant differences in parity and smoking habits between mothers in the study and reference groups.
We compared the capsule locus between genes tex and galE from study and reference strains by using MUSCLE.
Therefore, if the number of samples is limited, such a characteristic difference may cause inconsistent results between studies, and the lack of reference data to set up treatment thresholds.
For each sample, the expression ratio between the study case and reference pool was determined for each transcript on the microarray.
The imputation quality, which is often related to imputation quality thresholds employed to clean the dataset, imputation algorithms and even matching degree between the study sample and reference panels, may affect the estimation of Me in an imputed genotype dataset.
A comparison of the frequency of HF hospital admissions (considering recurrent admissions) within the first year after CRT implantation showed an excellent fit between the CLEAR study and this reference.
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