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Associations between genotypes were tested using Pearson's Chi-square test and binomial regression, which was also used to determine whether alleles were geographically clustered.
Distribution was tested for normality using the Shapiro Wilk W test. Differences in anthropometrics and metabolic characteristics between genotypes were tested using ANOVA for the three genotype groups.
Differences in clinical characteristics, age, sex and body mass index between genotypes were tested by Mann-Whitney test, Kruskal-Wallis test, or χ2 test using SPSS 12.0 system software (SPSS Inc., Chicago, IL, USA).
As root hair positioning does not follow a normal distribution, differences in distributions of relative hair position between genotypes were tested for significance using the non-parametric, two-sample Kolmogorov Smirnov (KS -test (http://www.physics.csbsju.edu/stats/KS -testn.plot_form.http) as described (Pietra et al., 2013).
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The difference in the magnitude of the correlations between genotypes was tested using the Fischer z-transformation.
The differences in the clinical information between the genotypes were tested using the Mann-Whitney test, the Kruskal-Wallis test and the chi-squared test.
The associations between binary phenotypes (response/nonresponse) and genotypes were tested using a logistic repression model.
Genotypes were tested additively.
Association between clinical findings and genotypes was tested independently in two groups, defined according to age group (patients aged less than 40 years were grouped in group 1 and those within 40 years and older in group 2).
In the HLC, expression quantitative trait locus (eQTL) mapping is a type of GWAS in which the association between gene expression traits and SNP genotypes are tested.
Residuals containing the probe by genotype effect were tested for difference between genotypes by calculating the d-statistics [ 45] using the Significance Analysis of Microarrays (SAM) procedure implemented in the R package siggenes (Fig. 1B).
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