Sentence examples for marker regression model from inspiring English sources

In the second stage, these residuals were regressed on ROH4Mb using a single marker regression model and a gradient boosted machine (GBM) algorithm.

For the genes involved in immune function the frequency of the favorable allele based on estimated SNP effects from a single marker regression model using the current dataset was not consistently higher in a particular population.

Yield deviations (YD) for cows that were derived from standardized lactation milk, fat and protein yield were weighted according to Garrick et al. [ 28] and a single marker regression model on the subset of SNP was used to describe the association between a trait and SNP.

Grapes et al. [ 3] and Zhao et al. [ 4] demonstrated that the single marker regression model is as powerful and precise as other more sophisticated techniques, such as multiple regression, regression on haplotypes, or the IBD method proposed by Meuwissen and Goddard [ 5].

However, this result was achieved by adding variances contributed by markers from distinct single marker regression models, which may give a misleading picture of the variance captured by markers because LD between them may overemphasize the contribution of significant markers [ 49].

When using DNA polymorphisms, whole-genome-enabled prediction models can be viewed as an extension of the single-marker regression models used in GWAS, where instead of finding genomic regions that may be associated with a complex trait, integrating all marker information for prediction and/or artificial selection is the ultimate goal.

Each animal in an outbreeding population is genetically unique and must be genotyped using a high-density marker set; regression models for mapping must accommodate multiple founder alleles, and complex breeding designs give rise to polygenic covariance among related animals that must be accounted for in mapping analysis.

There are two differences in our current haplotype analysis compared to our earlier single SNP and multi-marker logistic regression models: First, in the present analysis we adjusted for measurement time (inverse relationship) and vaccine groups (six regimes since 1984) only because these factors exert strong effects on vaccine-induced antibody level [2].

In the combined study population (Table 4), multivariate analysis indicated that each explanatory variable alone (model 1) was a significant predictor of HOMA-IR, while HMW adiponectin lost its significance after adjustments for age, BMIz-score and lipids (model 2), or when sex and inflammatory markers entered the regression model (model 3).

Another key feature of ENCAPP is its ability to identify prognostic markers from the regression model itself.

To investigate associations between blood lead, genetic variations of GSTs gene, and inflammatory markers, adjusted logistic regression model was used for further analysis.