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Regional-based association analysis instead of individual testing of each SNP was introduced in genome-wide association studies to increase the power of gene mapping, especially for rare genetic variants. For regional association tests, the kernel machine-based regression approach was recently proposed as a more powerful alternative to collapsing-based methods. However, the vast majority of existing algorithms and software for the kernel machine-based regression are applicable only to unrelated samples. In this paper, we present a new method for the kernel machine-based regression association analysis of quantitative traits in samples of related individuals. The method is based on the GRAMMAR+ transformation of phenotypes of related individuals, followed by use of existing kernel machine-based regression software for unrelated samples. We compared the performance of kernel-based association analysis on the material of the Genetic Analysis Workshop 17 family sample and real human data by using our transformation, the original untransformed trait, and environmental residuals. We demonstrated that only the GRAMMAR+ transformation produced type I errors close to the nominal value and that this method had the highest empirical power. The new method can be applied to analysis of related samples by using existing software for kernel-based association analysis developed for unrelated samples.

Background: Despite extensive research on the genetic determinants of glaucoma, the genes identified to date explain only a small proportion of cases in the general population. Methods: Genome-wide linkage and association analyses of quantitative traits related to glaucoma were performed: intraocular pressure, size and morphology of the optic disc (individual and combined by method of principal components) and thickness of the retinal nerve fibre layer (RNFL), in a large pedigree from a genetically isolated Dutch population. Results: For the size of the optic disc, the study demonstrated a significant linkage signal (logarithm of odds (LOD)=3.6) at the LRP1B region on chromosome 2q21.2-q22.2 and significant association (p=8.953×10 -12) with the previously reported CDC7/TGFBR3 locus at 1p22. For parameters describing morphology of the optic disc, the study obtaine

In the Victorian era, Sir Francis Galton showed that 'when dealing with the transmission of stature from parents to children, the average height of the two parents, ... is all we need care to know about them' (1886). One hundred and twenty-two years after Galton's work was published, 54 loci showing strong statistical evidence for association to human height were described, providing us with potential genomic means of human height prediction. In a population-based study of 5748 people, we find that a 54-loci genomic profile explained 4-6% of the sex- and age-adjusted height variance, and had limited ability to discriminate tall/short people, as characterized by the area under the receiver-operating characteristic curve (AUC). In a family-based study of 550 people, with both parents having height measurements, we find that the Galtonia