Searching for genotype-phenotype structure: using hierarchical log-linear models in Crohn disease.

There has been considerable recent success in the detection of gene-disease associations. We consider here the development of tools that facilitate the more detailed characterization of the effect of a genetic variant on disease. We replace the simplistic classification of individuals according to a single binary disease indicator with classification according to a number of subphenotypes.

Analysis of multiple SNPs in a candidate gene or region.

We consider the analysis of multiple single nucleotide polymorphisms (SNPs) within a gene or region. The simplest analysis of such data is based on a series of single SNP hypothesis tests, followed by correction for multiple testing, but it is intuitively plausible that a joint analysis of the SNPs will have higher power, particularly when the causal locus may not have been observed. However, standard tests, such as a likelihood ratio test based on an unrestricted alternative hypothesis, tend to have large numbers of degrees of freedom and hence low power.

Bayesian meta-analysis of genetic association studies with different sets of markers.

Robust assessment of genetic effects on quantitative traits or complex-disease risk requires synthesis of evidence from multiple studies. Frequently, studies have genotyped partially overlapping sets of SNPs within a gene or region of interest, hampering attempts to combine all the available data. By using the example of C-reactive protein (CRP) as a quantitative trait, we show how linkage disequilibrium in and around its gene facilitates use of Bayesian hierarchical models to integrate informative data from all available genetic association studies of this trait, irrespective of the SNP typed.

Detecting association using epistatic information.

Genetic association studies have been less successful than expected in detecting causal genetic variants, with frequent non-replication when such variants are claimed. Numerous possible reasons have been postulated, including inadequate sample size and possible unobserved stratification. Another possibility, and the focus of this paper, is that of epistasis, or gene-gene interaction.

One degree of freedom for dominance in indirect association studies.

Usual tests of association using tag single nucleotide polymorphisms (SNPs) assume that the alleles of the causal locus act additively and that these alleles are then predicted indirectly via a set of tag SNPs. In the presence of strong dominance effects this model is not correct and an extra term needs to be included, which uses the tag SNPs to predict the heterozygosity of the causal locus. Assuming this scenario of a strong dominance effect, we present an appropriate test statistic and investigate how much power, if any, we gain by adding this single degree of freedom for dominance.

Improved power offered by a score test for linkage disequilibrium mapping of quantitative-trait loci by selective genotyping.

Selective genotyping is used to increase efficiency in genetic association studies of quantitative traits by genotyping only those individuals who deviate from the population mean. However, selection distorts the conditional distribution of the trait given genotype, and such data sets are usually analyzed using case-control methods, quantitative analysis within selected groups, or a combination of both. We show that Hotelling's T(2) test, recently proposed for association studies of one or several tagging single-nucleotide polymorphisms in a prospective (i.

No evidence of association or interaction between the IL4RA, IL4, and IL13 genes in type 1 diabetes.

Attempts to identify susceptibility loci that, on their own, have marginal main effects by use of gene-gene interaction tests have increased in popularity. The results obtained from analyses of epistasis are, however, difficult to interpret. Gene-gene interaction, albeit only marginally significant, has recently been reported for the interleukin-4 and interleukin-13 genes (IL4 and IL13) with the interleukin-4 receptor A gene (IL4RA), contributing to the susceptibility of type 1 diabetes (T1D).

Use of unphased multilocus genotype data in indirect association studies.

It is usually assumed that detection of a disease susceptability gene via marker polymorphisms in linkage disequilibrium with it is facilitated by consideration of marker haplotypes. However, capture of the marker haplotype information requires resolution of gametic phase, and this must usually be inferred statistically. Recently, we questioned the value of the marker haplotype information, and suggested that certain analyses of multivariate marker data, not based on haplotypes explicitly and not requiring resolution of gametic phase, are often more powerful than analyses based on haplotypes.

Detecting disease associations due to linkage disequilibrium using haplotype tags: a class of tests and the determinants of statistical power.

In the 'indirect' method of detecting genetic associations between a trait and a DNA variant, we type several markers in a gene or chromosome region of linkage disequilibrium. If there is association between markers and the trait, we presume the existence of one or more causal polymorphisms in the region. In order to obtain a sufficiently dense set of markers it will almost always be necessary to use single nucleotide polymorphisms (SNPs).