Genetic variations in nitric oxide synthase 1 adaptor protein are associated with sudden cardiac death in US white community-based populations.

Background: The ECG QT interval is associated with risk of sudden cardiac death (SCD). A previous genome-wide association study demonstrated that allelic variants (rs10494366 and rs4657139) in the nitric oxide synthase 1 adaptor protein (NOS1AP), which encodes a carboxy-terminal PDZ ligand of neuronal nitric oxide synthase, are associated with the QT interval in white adults. The present analysis was conducted to validate the association between NOS1AP variants and the QT interval and to examine the association with SCD in a combined population of 19 295 black and white adults from the Atherosclerosis Risk In Communities Study and the Cardiovascular Health Study.

Normalization of microarray expression data using within-pedigree pool and its effect on linkage analysis.

"Genetical genomics", the study of natural genetic variation combining data from genetic marker-based studies with gene expression analyses, has exploded with the recent development of advanced microarray technologies. To account for systematic variation known to exist in microarray data, it is critical to properly normalize gene expression traits before performing genetic linkage analyses. However, imposing equal means and variances across pedigrees can over-correct for the true biological variation by ignoring familial correlations in expression values.

Covariate-based linkage analysis: application of a propensity score as the single covariate consistently improves power to detect linkage.

Successful identification of genetic risk loci for complex diseases has relied on the ability to minimize disease and genetic heterogeneity to increase the power to detect linkage. One means to account for disease heterogeneity is by incorporating covariate data. However, the inclusion of each covariate will add one degree of freedom to the allele sharing based linkage test, which may in fact decrease power.

Identification of tag single-nucleotide polymorphisms in regions with varying linkage disequilibrium.

We compared seven different tagging single-nucleotide polymorphism (SNP) programs in 10 regions with varied amounts of linkage disequilibrium (LD) and physical distance. We used the Collaborative Studies on the Genetics of Alcoholism dataset, part of the Genetic Analysis Workshop 14. We show that in regions with moderate to strong LD these programs are relatively consistent, despite different parameters and methods.

Application of the propensity score in a covariate-based linkage analysis of the Collaborative Study on the Genetics of Alcoholism.

Background: Covariate-based linkage analyses using a conditional logistic model as implemented in LODPAL can increase the power to detect linkage by minimizing disease heterogeneity. However, each additional covariate analyzed will increase the degrees of freedom for the linkage test, and therefore can also increase the type I error rate. Use of a propensity score (PS) has been shown to improve consistently the statistical power to detect linkage in simulation studies.

Linkage mapping methods applied to the COGA data set: presentation Group 4 of Genetic Analysis Workshop 14.

Presentation Group 4 participants analyzed the Collaborative Study on the Genetics of Alcoholism data provided for Genetic Analysis Workshop 14. This group examined various aspects of linkage analysis and related issues. Seven papers included linkage analyses, while the eighth calculated identity-by-descent (IBD) probabilities.

Importance sampling method of correction for multiple testing in affected sib-pair linkage analysis.

Using the Genetic Analysis Workshop 13 simulated data set, we compared the technique of importance sampling to several other methods designed to adjust p-values for multiple testing: the Bonferroni correction, the method proposed by Feingold et al., and naïve Monte Carlo simulation. We performed affected sib-pair linkage analysis for each of the 100 replicates for each of five binary traits and adjusted the derived p-values using each of the correction methods.