Sentence examples for multilocus association studies from inspiring English sources
Because of combining the genetic information of multiple loci, multilocus association studies (MLAS) are expected to be more powerful than single locus association studies (SLAS) in disease genes mapping.
This simulation is mainly designed to see the influence of non-normally distributed random effects in multilocus association studies.
The simulations show that the score test can control the type I error rate but is sometimes conservative, especially for small sample sizes; the similar result has also been observed by Wu, et al. [ 31] and Li, et al. [ 46] under the context of multilocus association studies.
To correspond to the motivating example of multilocus association studies described before and to mimic the true genetic data, in this paper we use the additive genetic model so that Z = 0, 1 and 2 represents the count of the minor allele [ 31].
Large-scale, multilocus genetic association studies require powerful and appropriate statistical-analysis tools that are designed to relate genotype and haplotype information to phenotypes of interest.
To present the permutation-based LRT, we now give an example motivating from genetic statistics concerning multilocus association analyses in case control studies.
Finally we apply the permutation-based LRT to multilocus association analysis in the case control study, where the problem can be investigated under the framework of logistic mixed effects model.
The impact of multilocus inheritance on the power of candidate gene association studies and the parameters derived therefrom is considered.
In most current large-scale genetic association studies, data are collected as unphased multilocus genotypes (that is, which alleles are aligned together on particular segments of chromosome is unknown).
We also wanted to use these markers to study the pairwise linkage disequilibrium and multilocus association in order to obtain knowledge of the long-term and background LD in each population, because this information can be useful for studies of population genetics and the genetics of complex diseases.
Because the number of genetic markers is usually orders of magnitude greater than the number of individuals available for the study, the selection or regularization of the predictors is the key factor of a multilocus association model.
