A new score statistic to test for association given linkage in affected sibling pair-control designs

Genome-wide linkage scan in affected sibling pairs identifies novel susceptibility region for venous thromboembolism: Genetics In Familial Thrombosis study

BACKGROUND

Venous thromboembolism (VTE) is a multicausal disorder involving environmental and genetic risk factors. In many thrombophilic families the clustering of thrombotic events cannot be explained by known genetic risk factors, indicating that some remain to be discovered.

OBJECTIVES

We aimed to identify novel thrombosis susceptibility alleles in a large panel of small thrombophilic families: the Genetics In Familial Thrombosis (GIFT) study.

PATIENTS/METHODS

In the GIFT study, 201 families were recruited consisting of 438 siblings with an objectively confirmed VTE at a young age. Multipoint linkage analysis (402 SSR markers) and fine mapping were performed, followed by genotyping of tagging SNPs in positional candidate genes.

RESULTS

Established genetic risk factors such as factor V Leiden, ABO blood group non-O, prothrombin 20210A, fibrinogen gamma 10034T and deficiencies of antithrombin, protein C and protein S were more frequent in GIFT patients than in unselected VTE patients. Linkage supported the presence of novel thrombosis susceptibility loci on 7p21.3-22.2 (LOD score = 3.23) and Xq24-27.3 (LOD score = 1.95). Simulation analysis showed that the chr7 signal was genome-wide statistically significant (P = 0.022). Tagging SNPs (n = 157) in eight positional candidate genes (LOD drop 1.5 regions) were genotyped in GIFT patients and 332 healthy controls. Five chr7 SNPs associated with VTE. SNP THSD7A rs2074597 was responsible for part of the chr7 signal.

CONCLUSIONS

The GIFT panel is rich in established genetic risk factors for VTE, but genetic factors remain unidentified in many families. Genome-wide linkage failed to identify the previously established genetic risk factors for VTE, but identified a novel VTE susceptibility locus on chr7.

Determination of the real effect of genes identified in GWAS: the example of IL2RA in multiple sclerosis

Genome-wide association studies (GWAS), although efficient to detect genes involved in complex diseases, are not designed to measure the real effect of the genes. This is illustrated here by the example of IL2RA in multiple sclerosis (MS). Association between IL2RA and MS is clearly established, although the functional variation is still unknown: the effect of IL2RA might be better described by several SNPs than by a single one. This study investigates whether a pair of SNPs better explains the observed linkage and association data than a single SNP. In total, 522 trio families and 244 affected sib-pairs were typed for 26 IL2RA SNPs. For each SNP and pairs of SNPs, the phased genotypes of patients and controls were compared to determine the SNP set offering the best risk discrimination. Consistency between the genotype risks provided by the retained set and the identical by descent allele sharing in affected sib-pairs was assessed. After controlling for multiple testing, the set of SNPs rs2256774 and rs3118470, provides the best discrimination between the case and control genotype distributions (P-corrected=0.009). The relative risk between the least and most at-risk genotypes is 3.54 with a 95% confidence interval of [2.14-5.94]. Furthermore, the linkage information provided by the allele sharing between affected sibs is consistent with the retained set (P=0.80) but rejects the SNP reported in the literature (P=0.006). Establishing a valid modeling of a disease gene is essential to test its potential interaction with other genes and to reconstruct the pathophysiological pathways.

Efficient evaluation of ranking procedures when the number of units is large, with application to SNP identification

Simulation-based assessment is a popular and frequently necessary approach for evaluating statistical procedures. Sometimes overlooked is the ability to take advantage of underlying mathematical relations and we focus on this aspect. We show how to take advantage of large-sample theory when conducting a simulation using the analysis of genomic data as a motivating example. The approach uses convergence results to provide an approximation to smaller-sample results, results that are available only by simulation. We consider evaluating and comparing various ranking-based methods for identifying the most highly associated SNPs in a genome-wide association study, derive integral equation representations of the pre-posterior distribution of percentiles produced by three ranking methods, and provide examples comparing performance. These results are of interest in their own right and set the framework for a more extensive set of comparisons.

Testing for genetic association in the presence of linkage and gene-covariate interactions

In order to study family-based association in the presence of linkage, we extend a generalized linear mixed model proposed for genetic linkage analysis (Lebrec and van Houwelingen (2007), Human Heredity 64, 5-15) by adding a genotypic effect to the mean. The corresponding score test is a weighted family-based association tests statistic, where the weight depends on the linkage effect and on other genetic and shared environmental effects. For testing of genetic association in the presence of gene-covariate interaction, we propose a linear regression method where the family-specific score statistic is regressed on family-specific covariates. Both statistics are straightforward to compute. Simulation results show that adjusting the weight for the within-family variance structure may be a powerful approach in the presence of environmental effects. The test statistic for genetic association in the presence of gene-covariate interaction improved the power for detecting association. For illustration, we analyze the rheumatoid arthritis data from GAW15. Adjusting for smoking and anti-cyclic citrullinated peptide increased the significance of the association with the DR locus.

Using linkage and association to identify and model genetic effects: summary of GAW15 Group 4

Group 4 at Genetic Analysis Workshop 15 focused on methods that exploited both linkage and association information to map disease loci. All contributions considered the dichotomous trait of rheumatoid arthritis, using either affected sibpairs and/or unrelated controls. While one contribution investigated linkage and association approaches separately in genome-wide analyses, the remaining others focused on joint linkage and association methods in specific genomic regions. The latter contributions proposed new methods and/or examined existing methods that addressed whether one or more polymorphisms partially or fully explained a linkage signal, particularly the methods proposed by Li et al. that are implemented in the computer program Linkage and Association Modeling in Pedigrees (LAMP). Using simulated SNP data under linkage peaks, several contributions found that existing family-based association approaches such as those of Martin et al. and Lake et al. had power similar to LAMP and to several methods proposed by the contributors for testing that a single nucleotide polymorphism partially explains a linkage peak. In evaluating methods for identifying if a polymorphism or a set of polymorphisms fully accounted for a linkage signal, several contributions found that it was important to understand that these methods may be subject to low power in some situations and thus, a non-significant result was not necessarily indicative of the polymorphism(s) being fully responsible for the linkage signal. Finally, modeling the disease using association evidence conditional on linkage may improve understanding of the etiology of disease.