Sentence examples for multiple correlated association from inspiring English sources

Exact(1)

In particular, we consider: 1) conditional tests to determine the causal variant among multiple correlated association signals; 2) analysis of trauma timing, type, and severity; 3) a gene-based approach to detecting epistasis.

Similar(59)

An additional issue is to determine the critical threshold with multiple correlated SNPs in genome-wide association studies.

We detail the necessary steps for conducting Mendelian randomization investigations using published data, and present novel statistical methods for combining data on the associations of multiple (correlated or uncorrelated) genetic variants with the risk factor and outcome into a single causal effect estimate.

Inouye et al. (15) showed that association testing of multiple correlated phenotypes offers better power than univariate analysis of single traits.

Structured association mapping methods are powerful computational methods that directly map the quantitative-trait (gene-expression) network to genotypes, explicitly combining information across multiple correlated traits to increase the power of detecting association.

Since our primary goal is to measure the performance of the association methods in the case of multiple correlated phenotypes, we sampled 50 SNPs randomly from the 697 SNPs in the region in order to reduce the correlation among SNPs from the linkage disequilibrium.

In addition to combining samples, another approach to increasing association test power is to perform joint analysis of multiple correlated phenotypes.

This is particularly an issue in cases with multiple correlated exposures, where the direction of bias is uncertain.

The proposed metrics are applicable to validation for models with multiple correlated responses.

We consider regression models with multiple correlated responses for each design point.

The KLE for multiple correlated random excitations relies on expansions in terms of correlated sets of random variables reflecting the cross-covariance of the random processes.

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