Sentence examples for multiple causal SNPs from inspiring English sources

Both approaches do not consider the joint effects among multiple causal SNPs.

If effects of multiple causal SNPs are allowed, the number of both the RR and the δ parameters increases substantially.

This result supports that multivariate analysis should be preferred over single variate analysis such as single SNP when there are multiple causal SNPs.

Our simulation study has shown that PCA regression is more powerful than the best scoring SNP when including the possibility of multiple causal SNPs of varying allele frequencies [ 8].

Although explicit models that allow for multiple causal SNPs that explain all the linkage in a region could be considered, difficulty arises if genotype effects and LD are modelled explicitly via the RR and δ parameters.

Using a correlation metric constructed for a dominant/recessive causal SNP (see Methods) when the causal SNP really is dominant/recessive tends to perform slightly better than using one constructed for a multiplicative causal SNP.

As illustrated in Figure 1, the MPGL (Fig. 1a) can detect causal SNPs in multiple populations jointly, unlike standard regression techniques applied on individuals in each population separately to infer associations in a population-specific manner (Fig. 1b).

We first plot the true association strengths in red for Populations 1 and 2 in Figure 3a and 3b, with a blue marker representing causal SNPs shared across multiple populations.

Single SNP analysis may not be adequate for identifying multiple causal variants and predicting risk of disease [ 14].

Specifically, multiple causal variants in a QTL region can be tested using a multiple SNP association model in this region.

We examined the effect of averaging over either all two SNP or all three SNP models in the context of the above multiple causal variant simulations.