Using linkage analysis to detect gene-gene interaction by stratifying family data on known disease, or disease-associated, alleles

Identifying genetic risk loci for diabetic complications and showing evidence for heterogeneity of type 1 diabetes based on complications risk

There is a growing body of evidence suggesting that type 1 diabetes (T1D) is a genetically heterogeneous disease. However, the extent of this heterogeneity, and what observations may distinguish different forms, is unclear. One indicator may be T1D-related microvascular complications (MVCs), which are familial, but occur in some families, and not others. We tested the hypothesis that T1D plus MVC is genetically distinct from T1D without MCV. We studied 415 families (2,462 individuals, 896 with T1D) using genome-wide linkage analysis, comparing families with and without MVC. We also tested for interaction between identified loci and alleles at the HLA-DRB1 locus. We found significant linkage scores at 1p36.12, 1q32.1, 8q21.3, 12p11.21 and 22q11.21. In all regions except 1p36.12, linkage scores differed between MVC-based phenotype groups, suggesting that families with MVCs express different genetic influences than those without. Our linkage results also suggested gene-gene interaction between the above putative loci and the HLA region; HLA-based strata produced significantly increased linkage scores in some strata, but not others within a phenotype group. We conclude that families with type 1 diabetes plus MVCs are genetically different from those with diabetes alone.

Using propensity score adjustment method in genetic association studies

BACKGROUND

The statistical tests for single locus disease association are mostly under-powered. If a disease associated causal single nucleotide polymorphism (SNP) operates essentially through a complex mechanism that involves multiple SNPs or possible environmental factors, its effect might be missed if the causal SNP is studied in isolation without accounting for these unknown genetic influences. In this study, we attempt to address the issue of reduced power that is inherent in single point association studies by accounting for genetic influences that negatively impact the detection of causal variant in single point association analysis. In our method we use propensity score (PS) to adjust for the effect of SNPs that influence the marginal association of a candidate marker. These SNPs might be in linkage disequilibrium (LD) and/or epistatic with the target-SNP and have a joint interactive influence on the disease under study. We therefore propose a propensity score adjustment method (PSAM) as a tool for dimension reduction to improve the power for single locus studies through an estimated PS to adjust for influence from these SNPs while regressing disease status on the target-genetic locus. The degree of freedom of such a test is therefore always restricted to 1.

RESULTS

We assess PSAM under the null hypothesis of no disease association to affirm that it correctly controls for the type-I-error rate (<0.05). PSAM displays reasonable power (>70%) and shows an average of 15% improvement in power as compared with commonly-used logistic regression method and PLINK under most simulated scenarios. Using the open-access multifactor dimensionality reduction dataset, PSAM displays improved significance for all disease loci. Through a whole genome study, PSAM was able to identify 21 SNPs from the GAW16 NARAC dataset by reducing their original trend-test p-values from within 0.001 and 0.05 to p-values less than 0.0009, and among which 6 SNPs were further found to be associated with immunity and inflammation.

CONCLUSIONS

PSAM improves the significance of single-locus association of causal SNPs which have had marginal single point association by adjusting for influence from other SNPs in a dataset. This would explain part of the missing heritability without increasing the complexity of the model due to huge multiple testing scenarios. The newly reported SNPs from GAW16 data would provide evidences for further research to elucidate the etiology of rheumatoid arthritis. PSAM is proposed as an exploratory tool that would be complementary to other existing methods. A downloadable user friendly program, PSAM, written in SAS, is available for public use.

Using Linkage Analysis to Detect Gene-Gene Interactions. 2. Improved Reliability and Extension to More-Complex Models

Detecting gene-gene interaction in complex diseases has become an important priority for common disease genetics, but most current approaches to detecting interaction start with disease-marker associations. These approaches are based on population allele frequency correlations, not genetic inheritance, and therefore cannot exploit the rich information about inheritance contained within families. They are also hampered by issues of rigorous phenotype definition, multiple test correction, and allelic and locus heterogeneity. We recently developed, tested, and published a powerful gene-gene interaction detection strategy based on conditioning family data on a known disease-causing allele or a disease-associated marker allele⁴. We successfully applied the method to disease data and used computer simulation to exhaustively test the method for some epistatic models. We knew that the statistic we developed to indicate interaction was less reliable when applied to more-complex interaction models. Here, we improve the statistic and expand the testing procedure. We computer-simulated multipoint linkage data for a disease caused by two interacting loci. We examined epistatic as well as additive models and compared them with heterogeneity models. In all our models, the at-risk genotypes are “major” in the sense that among affected individuals, a substantial proportion has a disease-related genotype. One of the loci (A) has a known disease-related allele (as would have been determined from a previous analysis). We removed (pruned) family members who did not carry this allele; the resultant dataset is referred to as “stratified.” This elimination step has the effect of raising the “penetrance” and detectability at the second locus (B). We used the lod scores for the stratified and unstratified data sets to calculate a statistic that either indicated the presence of interaction or indicated that no interaction was detectable. We show that the new method is robust and reliable for a wide range of parameters. Our statistic performs well both with the epistatic models (false negative rates, i.e., failing to detect interaction, ranging from 0 to 2.5%) and with the heterogeneity models (false positive rates, i.e., falsely detecting interaction, ≤1%). It works well with the additive model except when allele frequencies at the two loci differ widely. We explore those features of the additive model that make detecting interaction more difficult. All testing of this method suggests that it provides a reliable approach to detecting gene-gene interaction.

Linkage Analysis of Genomic Regions Contributing to the Expression of Type 1 Diabetes Microvascular Complications and Interaction with HLA

We conducted linkage analysis to follow up earlier work on microvascular complications of type 1 diabetes (T1D). We analyzed 415 families (2,008 individuals) previously genotyped for 402 SNP markers spanning chromosome 6. We did linkage analysis for the phenotypes of retinopathy and nephropathy. For retinopathy, two linkage peaks were mapped: one located at the HLA region and another novel locus telomeric to HLA. For nephropathy, a linkage peak centromeric to HLA was mapped, but the linkage peak telomeric to HLA seen in retinopathy was absent. Because of the strong association of T1D with DRB1*03:01 and DRB1*04:01, we stratified our analyses based on families whose probands were positive for DRB1*03:01 or DRB1*04:01. When analyzing the DRB1*03:01-positive retinopathy families, in addition to the novel telomeric locus, one centromeric to HLA was identified at the same location as the nephropathy peak. When we stratified on DRB1*04:01-positive families, the HLA telomeric peak strengthened but the centromeric peak disappeared. Our findings showed that HLA and non-HLA loci on chromosome 6 are involved in T1D complications' expression. While the HLA region is a major contributor to the expression of T1D, our results suggest an interaction between specific HLA alleles and other loci that influence complications' expression.