Identification of polymorphisms explaining a linkage signal: application to the GAW14 simulated data

We applied three approaches for the identification of polymorphisms explaining the linkage evidence to the Genetic Analysis Workshop 14 simulated data: 1) the genotype-IBD sharing test (GIST); 2) an approach suggested by Horikawa and colleagues; and 3) the homozygote sharing test (HST). These tests were compared with a family-based association test. Two linked regions with highest nonparametric linkage scores were selected to apply these methods. In the first region, Horikawa's method identified the most SNPs within the region containing the disease susceptibility locus, while HST performed best in the second region. However, Horikawa's method also had the most type I errors. These methods show potential as additional tools to complement family-based association tests for the identification of disease susceptibility variants

Screening large-scale association study data: exploiting interactions using random forests

BACKGROUND: Genome-wide association studies for complex diseases will produce genotypes on hundreds of thousands of single nucleotide polymorphisms (SNPs). A logical first approach to dealing with massive numbers of SNPs is to use some test to screen the SNPs, retaining only those that meet some criterion for futher study. For example, SNPs can be ranked by p-value, and those with the lowest p-values retained. When SNPs have large interaction effects but small marginal effects in a population, they are unlikely to be retained when univariate tests are used for screening. However, model-based screens that pre-specify interactions are impractical for data sets with thousands of SNPs. Random forest analysis is an alternative method that produces a single measure of importance for each predictor variable that takes into account interactions among variables without requiring model specification. Interactions increase the importance for the individual interacting variables, making them more likely to be given high importance relative to other variables. We test the performance of random forests as a screening procedure to identify small numbers of risk-associated SNPs from among large numbers of unassociated SNPs using complex disease models with up to 32 loci, incorporating both genetic heterogeneity and multi-locus interaction. RESULTS: Keeping other factors constant, if risk SNPs interact, the random forest importance measure significantly outperforms the Fisher Exact test as a screening tool. As the number of interacting SNPs increases, the improvement in performance of random forest analysis relative to Fisher Exact test for screening also increases. Random forests perform similarly to the univariate Fisher Exact test as a screening tool when SNPs in the analysis do not interact. CONCLUSIONS: In the context of large-scale genetic association studies where unknown interactions exist among true risk-associated SNPs or SNPs and environmental covariates, screening SNPs using random forest analyses can significantly reduce the number of SNPs that need to be retained for further study compared to standard univariate screening methods

Mapping complex traits using Random Forests

Random Forest is a prediction technique based on growing trees on bootstrap samples of data, in conjunction with a random selection of explanatory variables to define the best split at each node. In the case of a quantitative outcome, the tree predictor takes on a numerical value. We applied Random Forest to the first replicate of the Genetic Analysis Workshop 13 simulated data set, with the sibling pairs as our units of analysis and identity by descent (IBD) at selected loci as our explanatory variables. With the knowledge of the true model, we performed two sets of analyses on three phenotypes: HDL, triglycerides, and glucose. The goal was to approach the mapping of complex traits from a multivariate perspective. The first set of analyses mimics a candidate gene approach with a high proportion of true genes among the predictors while the second set represents a genome scan analysis using microsatellite markers. Random Forest was able to identify a few of the major genes influencing the phenotypes, such as baseline HDL and triglycerides, but failed to identify the major genes regulating baseline glucose levels

Joint modeling of linkage and association using affected sib-pair data

There has been a growing interest in developing strategies for identifying single-nucleotide polymorphisms (SNPs) that explain a linkage signal by joint modeling of linkage and association. We compare several existing methods and propose a new method called the homozygote sharing transmission-disequilibrium test (HSTDT) to detect linkage and association or to identify SNPs explaining the linkage signal on chromosome 6 for rheumatoid arthritis using 100 replicates of the Genetic Analysis Workshop (GAW) 15 simulated affected sib-pair data. Existing methods considered included the family-based tests of association implemented in FBAT, a transmission-disequilibrium test, a conditional logistic regression approach, a likelihood-based approach implemented in LAMP, and the homozygote sharing test (HST). We compared the type I error rates and power for tests classified into three categories according to their null hypotheses: 1) no association in the presence of linkage (i.e., a SNP explains none of the linkage evidence), 2) no linkage adjusting for the association (i.e., a SNP explains all linkage evidence), and 3) no linkage and no association. For testing association in the presence of linkage, we found similar power among all tests except for the homozygote sharing test that had lower power. When testing linkage adjusting for association, similar power was observed between LAMP and HST, but lower power for the conditional logistic regression method. When testing linkage or association, the conditional logistic regression method was more powerful than FBAT

Joint Modeling of Linkage and Association Using Affected Sib-pair Data

There has been a growing interest in developing strategies for identifying single-nucleotide polymorphisms (SNPs) that explain a linkage signal by joint modeling of linkage and association. We compare several existing methods and propose a new method called the homozygote sharing transmission-disequilibrium test (HSTDT) to detect linkage and association or to identify SNPs explaining the linkage signal on chromosome 6 for rheumatoid arthritis using 100 replicates of the Genetic Analysis Workshop (GAW) 15 simulated affected sib-pair data. Existing methods considered included the family-based tests of association implemented in FBAT, a transmission-disequilibrium test, a conditional logistic regression approach, a likelihood-based approach implemented in LAMP, and the homozygote sharing test (HST). We compared the type I error rates and power for tests classified into three categories according to their null hypotheses: 1) no association in the presence of linkage (i.e., a SNP explains none of the linkage evidence), 2) no linkage adjusting for the association (i.e., a SNP explains all linkage evidence), and 3) no linkage and no association. For testing association in the presence of linkage, we found similar power among all tests except for the homozygote sharing test that had lower power. When testing linkage adjusting for association, similar power was observed between LAMP and HST, but lower power for the conditional logistic regression method. When testing linkage or association, the conditional logistic regression method was more powerful than FBAT

Mechanistic role of a disease-associated genetic variant within the ADAM33 asthma susceptibility gene

<p>Abstract</p> <p>Background</p> <p>ADAM33 has been identified as an asthma-associated gene in an out-bred population. Genetic studies suggested that the functional role of this metalloprotease was in airway remodeling. However, the mechanistic roles of the disease-associated SNPs have yet to be elucidated especially in the context of the pathophysiology of asthma. One disease-associated SNP, BC+1, which resides in intron BC toward the 5' end of ADAM33, is highly associated with the disease.</p> <p>Methods</p> <p>The region surrounding this genetic variant was cloned into a model system to determine if there is a regulatory element within this intron that influences transcription.</p> <p>Results</p> <p>The BC+1 protective allele did not impose any affect on the transcription of the reporter gene. However, the at-risk allele enforced such a repressive affect on the promoter that no protein product from the reporter gene was detected. These results indicated that there exists within intron BC a regulatory element that acts as a repressor for gene expression. Moreover, since SNP BC+1 is a common genetic variant, this region may interact with other undefined regulatory elements within ADAM33 to provide a rheostat effect, which modulates pre-mRNA processing. Thus, SNP BC+1 may have an important role in the modulation of ADAM33 gene expression.</p> <p>Conclusion</p> <p>These data provide for the first time a functional role for a disease-associated SNP in ADAM33 and begin to shed light on the deregulation of this gene in the pathophysiology of asthma.</p

Pharmacogenetics of asthma in children

Allergic diseases such as bronchial asthma and atopic dermatitis develop by a combination of genetic and environmental factors. Several candidate causative genes of asthma and atopy have been reported as the genetic factors. The clinical features of patients and causes of diseases vary. Therefore, personalized medicine (tailor-made medicine) is necessary for the improvement of quality of life (QOL) and for asthma cure. Pharmacogenetics is very important for personalized medicine. Here, we present the genetics and pharmacogenetics of asthma in children. Finally, we show the guideline for personalized medicine for asthma, particularly in childhood, including the pharmacogenetics of anti-asthmatic drugs, preliminarily produced by the authors

Adam33 polymorphisms are associated with COPD and lung function in long-term tobacco smokers

<p>Abstract</p> <p>Background</p> <p>Variation in ADAM33 has been shown to be important in the development of asthma and altered lung function. This relationship however, has not been investigated in the population susceptible to COPD; long term tobacco smokers. We evaluated the association between polymorphisms in ADAM33 gene with COPD and lung function in long term tobacco smokers.</p> <p>Methods</p> <p>Caucasian subjects, at least 50 year old, who smoked ≥ 20 pack-years (n = 880) were genotyped for 25 single nucleotide polymorphisms (SNPs) in ADAM33. COPD was defined as an FEV1/FVC ratio < 70% and percent-predicted (pp)FEV1 < 75% (n = 287). The control group had an FEV1/FVC ratio ≥ 70% and ppFEV₁≥ 80% (n = 311) despite ≥ 20 pack years of smoking. Logistic and linear regressions were used for the analysis. Age, sex, and smoking status were considered as potential confounders.</p> <p>Results</p> <p>Five SNPs in ADAM33 were associated with COPD (Q-1, intronic: p < 0.003; S1, Ile → Val: p < 0.003; S2, Gly → Gly: p < 0.04; V-1 intronic: p < 0.002; V4, in 3' untranslated region: p < 0.007). Q-1, S1 and V-1 were also associated with ppFEV1, FEV1/FVC ratio and ppFEF25–75 (p values 0.001 – 0.02). S2 was associated with FEV1/FVC ratio (p < 0.05). The association between S1 and residual volume revealed a trend toward significance (p value < 0.07). Linkage disequilibrium and haplotype analyses suggested that S1 had the strongest degree of association with COPD and pulmonary function abnormalities.</p> <p>Conclusion</p> <p>Five SNPs in ADAM33 were associated with COPD and lung function in long-term smokers. Functional studies will be needed to evaluate the biologic significance of these polymorphisms in the pathogenesis of COPD.</p

The Search for Genetic Variants and Epigenetics Related to Asthma

For the past two decades, a huge number of genetic studies have been conducted to identify the genetic variants responsible for asthma risk. Several types of genetic and genomic approaches, including linkage analysis, candidate gene single nucleotide polymorphism studies, and whole genome-wide association studies have been applied. In this review article, the results of these approaches are summarized, and their limitations are discussed. Additionally, perspectives for applying upcoming new epigenetic or genomic technologies, such as copy number variation, are introduced to increase our understanding of new omic approaches to asthma genetics