Dissecting direct and indirect genetic effects on chronic obstructive pulmonary disease (COPD) susceptibility.

Cigarette smoking is the major environmental risk factor for chronic obstructive pulmonary disease (COPD). Genome-wide association studies have provided compelling associations for three loci with COPD. In this study, we aimed to estimate direct, i.e., independent from smoking, and indirect effects of those loci on COPD development using mediation analysis. We included a total of 3,424 COPD cases and 1,872 unaffected controls with data on two smoking-related phenotypes: lifetime average smoking intensity and cumulative exposure to tobacco smoke (pack years). Our analysis revealed that effects of two linked variants (rs1051730 and rs8034191) in the AGPHD1/CHRNA3 cluster on COPD development are significantly, yet not entirely, mediated by the smoking-related phenotypes. Approximately 30% of the total effect of variants in the AGPHD1/CHRNA3 cluster on COPD development was mediated by pack years. Simultaneous analysis of modestly (r (2) = 0.21) linked markers in CHRNA3 and IREB2 revealed that an even larger (~42%) proportion of the total effect of the CHRNA3 locus on COPD was mediated by pack years after adjustment for an IREB2 single nucleotide polymorphism. This study confirms the existence of direct effects of the AGPHD1/CHRNA3, IREB2, FAM13A and HHIP loci on COPD development. While the association of the AGPHD1/CHRNA3 locus with COPD is significantly mediated by smoking-related phenotypes, IREB2 appears to affect COPD independently of smoking.

On the analysis of sequence data: testing for disease susceptibility loci using patterns of linkage disequilibrium.

Despite the numerous and successful applications of genome-wide association studies (GWASs), there has been a lot of difficulty in discovering disease susceptibility loci (DSLs). This is due to the fact that the GWAS approach is an indirect mapping technique, often identifying markers. For the identification of DSLs, which is required for the understanding of the genetic pathways for complex diseases, sequencing data that examines every genetic locus directly is necessary. Yet, there is currently a lack of methodology targeted at the identification of the DSLs in sequencing data: existing methods localize the causal variant to a region but not to a single variant, and therefore do not allow one to identify unique loci that cause the phenotype association. Here, we have developed such a method to determine if there is evidence that an individual loci affects case/control status with sequencing data. This methodology differs from other rare variant approaches: rather than testing an entire region comprised of many loci for association with the phenotype, we can identify the individual genetic locus that causes the association between the phenotype and the genetic region. For each variant, the test determines if the pattern of linkage disequilibrium (LD) across the other variants coincides with the pattern expected if that variant were a DSL. Power simulations show that the method successfully detects the causal variant, distinguishing it from other nearby variants (in high LD with the causal variant), and outperforms the standard tests. The efficiency of the method is especially apparent with small samples, which are currently realistic for studies due to sequencing data costs. The practical relevance of the approach is illustrated by an application to a sequencing dataset for nonsyndromic cleft lip with or without cleft palate. The proposed method implicated one variant (P = 0.002, 0.062 after Bonferroni correction), which was not found by standard analyses. Code for implementation is available.

CGene: an R package for implementation of causal genetic analyses.

The excitement over findings from Genome-Wide Association Studies (GWASs) has been tempered by the difficulty in finding the location of the true causal disease susceptibility loci (DSLs), rather than markers that are correlated with the causal variants. In addition, many recent GWASs have studied multiple phenotypes--often highly correlated--making it difficult to understand which associations are causal and which are seemingly causal, induced by phenotypic correlations. In order to identify DSLs, which are required to understand the genetic etiology of the observed associations, statistical methodology has been proposed that distinguishes between a direct effect of a genetic locus on the primary phenotype and an indirect effect induced by the association with the intermediate phenotype that is also correlated with the primary phenotype. However, so far, the application of this important methodology has been challenging, as no user-friendly software implementation exists. The lack of software implementation of this sophisticated methodology has prevented its large-scale use in the genetic community. We have now implemented this statistical approach in a user-friendly and robust R package that has been thoroughly tested. The R package 'CGene' is available for download at http://cran.r-project.org/. The R code is also available at http://people.hsph.harvard.edu/~plipman.

On the follow-up of genome-wide association studies: an overall test for the most promising SNPs.

Even in large-scale genome-wide association studies (GWASs), only a fraction of the true associations are detected at the genome-wide significance level. When few or no associations reach the significance threshold, one strategy is to follow up on the most promising candidates, i.e. the single nucleotide polymorphisms (SNPs) with the smallest association-test P-values, by genotyping them in additional studies. In this communication, we propose an overall test for GWASs that analyzes the SNPs with the most promising P-values simultaneously and therefore allows an early assessment of whether the follow-up of the selected SNPs is likely promising. We theoretically derive the properties of the proposed overall test under the null hypothesis and assess its power based on simulation studies. An application to a GWAS for chronic obstructive pulmonary disease suggests that there are true association signals among the top SNPs and that an additional follow-up study is promising.

Inferring genetic causal effects on survival data with associated endo-phenotypes.

Age-at-onset phenotypes are important traits in genetic association analyses. Often, intermediate phenotypes that are related to the age-at-onset phenotype are also associated with the marker loci that are associated with the age-at-onset phenotype. In order to understand the genetic etiology of the observed associations, statistical methodology is needed to distinguish between a direct genetic effect on the age-at-onset phenotype and an indirect effect induced by the genetic association with the endo-phenotype that is correlated with the age-at-onset phenotype. In this communication, we introduce a new statistical approach to detect causal genetic effects on survival data in the presence of genetic associations with secondary phenotypes that might influence survival as well and thereby induce seemingly causal relationships. Derived using causal inference methodology, the proposed method is based on standard statistical methodology and can be implemented straight-forwardly, using standard software. Using simulation studies, the theoretical properties of the approach are verified and the power is assessed under realistic scenarios. The practical relevance of the approach is illustrated by an application to survival after cardiac surgery, where genetic components of myocardial infarctions are determined to not influence post-surgery hospital duration except through the MI-pathway.

ARLTS1 germline variants and the risk for breast, prostate, and colorectal cancer.

Recently, a nonsense alteration Trp149Stop in the ARLTS1 gene was found more frequently in familial cancer cases versus sporadic cancer patients and healthy controls. Here, the role of Trp149Stop or any other ARLTS1 germline variant was evaluated on breast, prostate, and colorectal cancer risk. The whole gene was screened for germline alterations in 855 familial cancer patients. The five observed variants were further screened in 1169 non-familial cancer patients as well as in 809 healthy population controls. The Trp149Stop was found at low frequencies (0.5-1.2%) in all patient subgroups versus 1.6% in controls, and the mutant allele did not co-segregate with disease status in families with multiple affected individuals. The CC genotype in the Cys148Arg variant was slightly more common among both familial and sporadic breast (odds ratio (OR), 1.48; 95% confidence interval (CI), 1.16-1.87; P=0.001) and prostate cancer patients (OR, 1.50; 95% CI, 1.13-1.99; P=0.005) when compared to controls. A novel ARLTS1 variant Gly65Val was found at higher frequency among familial prostate cancer patients (8 of 164, 4.9%) than in controls (13 of 809, 1.6%; OR, 3.14; 95% CI, 1.28-7.70, P=0.016). However, after adjusting for multiple testing, none of these results were still significant. No association was found with any of the variants and colorectal cancer risk. Our results suggest that Trp149Stop is not a predisposition allele in breast, prostate, or colorectal cancer in the Finnish population, and, while the Gly65Val variant may increase familial prostate cancer risk and the Cys148Arg change may affect both breast and prostate cancer risk, the evidence is not strong in these data.