Sentence examples for traits for analysis from inspiring English sources

Studies of clinical disease can also benefit from judicious selection of endophenotypes and related quantitative traits for analysis.

For example, in morphological analyses, variable traits are often preferentially selected over fixed traits for analysis.

The tool can be used for analysis of architecture (relationship between different morphological traits), for analysis of genetics (both forward and reverse approaches) and for breeding programs.

We therefore focused on ecological traits related to where and how tadpoles forage in the stream, and as a proxy for these we selected adequate morphological traits for analysis.

In this pilot PheWAS, we accessed the African American samples in EAGLE BioVU and selected eight cardiovascular-related traits for analysis, including: body mass index (BMI), total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides, mean corpuscular hemoglobin (MCH), T2D, and hypertension.

In Australia, calving interval is available on more cows than measures of fertility calculated from insemination and pregnancy diagnosis data [ 20], which is why calving interval was chosen as the fertility trait for analysis.

Traits selected for analysis were average daily gain and the following immune traits, total and differential WBC count, proportions of PBML subsets and APP levels.

Due to the fact that some people had available data on only one of the six glycaemic traits used for analysis, the final number of participants for the individual glycaemic traits is lower than of the total population (n = 5,979): 5,849 for FG, 5,608 for PLG, 5,591 for FINS, 5,596 for PLINS and 5,344 for HOMA2-%S and HOMA2-%B (Fig.  1).

Application of this approach is particularly useful for characterization of quantitative traits, or for analysis of modifying loci that affect the phenotype of a sensitizing mutation.

All traits used here were estimated by the same individual (EG) to eliminate inter-observer variation and % measurement error for all traits retained for analysis was estimated to be < 1% [ 32].

Note that rotation is based upon the robust covariance estimates and pays no attention to the ultimate goal of finding cohesive clusters of comparable individuals - the implicit assumption is that the key distinctions among groups are present and identifiable in the traits used for analysis.