Clonal mosaicism for large chromosomal anomalies (duplications, deletions and uniparental disomy) was detected using SNP microarray data from over 50,000 subjects recruited for genome-wide association studies. This detection method requires a relatively high frequency of cells (>5–10%) with the same abnormal karyotype (presumably of clonal origin) in the presence of normal cells. The frequency of detectable clonal mosaicism in peripheral blood is low (<0.5%) from birth until 50 years of age, after which it rises rapidly to 2–3% in the elderly. Many of the mosaic anomalies are characteristic of those found in hematological cancers and identify common deleted regions that pinpoint the locations of genes previously associated with hematological cancers. Although only 3% of subjects with detectable clonal mosaicism had any record of hematological cancer prior to DNA sampling, those without a prior diagnosis have an estimated 10-fold higher risk of a subsequent hematological cancer (95% confidence interval = 6–18).

Genome-wide association studies (GWAS) have identified loci reproducibly associated with pulmonary diseases; however, the molecular mechanism underlying these associations are largely unknown. The objectives of this study were to discover genetic variants affecting gene expression in human lung tissue, to refine susceptibility loci for asthma identified in GWAS studies, and to use the genetics of gene expression and network analyses to find key molecular drivers of asthma. We performed a genome-wide search for expression quantitative trait loci (eQTL) in 1,111 human lung samples. The lung eQTL dataset was then used to inform asthma genetic studies reported in the literature. The top ranked lung eQTLs were integrated with the GWAS on asthma reported by the GABRIEL consortium to generate a Bayesian gene expression network for discovery of novel molecular pathways underpinning asthma. We detected 17,178 cis- and 593 trans- lung eQTLs, which can be used to explore the functional consequences of loci associated with lung diseases and traits. Some strong eQTLs are also asthma susceptibility loci. For example, rs3859192 on chr17q21 is robustly associated with the mRNA levels of GSDMA (P = 3.55×10−151). The genetic-gene expression network identified the SOCS3 pathway as one of the key drivers of asthma. The eQTLs and gene networks identified in this study are powerful tools for elucidating the causal mechanisms underlying pulmonary disease. This data resource offers much-needed support to pinpoint the causal genes and characterize the molecular function of gene variants associated with lung diseases.

Author Summary

Recent genome-wide association studies (GWAS) have identified genetic variants associated with lung diseases. The challenge now is to find the causal genes in GWAS–nominated chromosomal regions and to characterize the molecular function of disease-associated genetic variants. In this paper, we describe an international effort to systematically capture the genetic architecture of gene expression regulation in human lung. By studying lung specimens from 1,111 individuals of European ancestry, we found a large number of genetic variants affecting gene expression in the lung, or lung expression quantitative trait loci (eQTL). These lung eQTLs will serve as an important resource to aid in the understanding of the molecular underpinnings of lung biology and its disruption in disease. To demonstrate the utility of this lung eQTL dataset, we integrated our data with previous genetic studies on asthma. Through integrative techniques, we identified causal variants and genes in GWAS–nominated loci and found key molecular drivers for asthma. We feel that sharing our lung eQTLs dataset with the scientific community will leverage the impact of previous large-scale GWAS on lung diseases and function by providing much needed functional information to understand the molecular changes introduced by the susceptibility genetic variants.

Some have suggested that chronic obstructive pulmonary disease (COPD) is a disease of accelerated aging. Aging is characterized by shortening of telomeres. The relationship of telomere length to important clinical outcomes such as mortality, disease progression and cancer in COPD is unknown. Using quantitative polymerase chain reaction (qPCR), we measured telomere length of peripheral leukocytes in 4,271 subjects with mild to moderate COPD who participated in the Lung Health Study (LHS). The subjects were followed for approximately 7.5 years during which time their vital status, FEV1 and smoking status were ascertained. Using multiple regression methods, we determined the relationship of telomere length to cancer and total mortality in these subjects. We also measured telomere length in healthy “mid-life” volunteers and patients with more severe COPD. The LHS subjects had significantly shorter telomeres than those of healthy “mid-life” volunteers (p<.001). Compared to individuals in the 4th quartile of relative telomere length (i.e. longest telomere group), the remaining participants had significantly higher risk of cancer mortality (Hazard ratio, HR, 1.48; p = 0.0324) and total mortality (HR, 1.29; p = 0.0425). Smoking status did not make a significant difference in peripheral blood cells telomere length. In conclusion, COPD patients have short leukocyte telomeres, which are in turn associated increased risk of total and cancer mortality. Accelerated aging is of particular relevance to cancer mortality in COPD.

Background

Non-Hodgkin lymphomas are a heterogeneous group of solid tumours that constitute the 5th highest cause of cancer mortality in the United States and Canada. Poor control of cell death in lymphocytes can lead to autoimmune disease or cancer, making genes involved in programmed cell death of lymphocytes logical candidate genes for lymphoma susceptibility.

Materials and Methods

We tested for genetic association with NHL and NHL subtypes, of SNPs in lymphocyte cell death genes using an established population-based study. 17 candidate genes were chosen based on biological function, with 123 SNPs tested. These included tagSNPs from HapMap and novel SNPs discovered by re-sequencing 47 cases in genes for which SNP representation was judged to be low. The main analysis, which estimated odds ratios by fitting data to an additive logistic regression model, used European ancestry samples that passed quality control measures (569 cases and 547 controls). A two-tiered approach for multiple testing correction was used: correction for number of tests within each gene by permutation-based methodology, followed by correction for the number of genes tested using the false discovery rate.

Results

Variant rs928883, near miR-155, showed an association (OR per A-allele: 2.80 [95% CI: 1.63–4.82]; pF = 0.027) with marginal zone lymphoma that is significant after correction for multiple testing.

Conclusions

This is the first reported association between a germline polymorphism at a miRNA locus and lymphoma.

We describe a statistical approach to predict gender-labeling errors in candidate-gene association studies, when Y-chromosome markers have not been included in the genotyping set. The approach adds value to methods that consider only the heterozygosity of X-chromosome SNPs, by incorporating available information about the intensity of X-chromosome SNPs in candidate genes relative to autosomal SNPs from the same individual. To our knowledge, no published methods formalize a framework in which heterozygosity and relative intensity are simultaneously taken into account. Our method offers the advantage that, in the genotyping set, no additional space is required beyond that already assigned to X-chromosome SNPs in the candidate genes. We also show how the predictions can be used in a two-phase sampling design to estimate the gender-labeling error rates for an entire study, at a fraction of the cost of a conventional design.

Summary: We present the Sample-based Laboratory Information Management System (SLIMS), a powerful and user-friendly open source web application that provides all members of a laboratory with an interface to view, edit and create sample information. SLIMS aims to simplify common laboratory tasks with tools such as a user-friendly shopping cart for subjects, samples and containers that easily generates reports, shareable lists and plate designs for genotyping. Further key features include customizable data views, database change-logging and dynamically filled pre-formatted reports. Along with being feature-rich, SLIMS' power comes from being able to handle longitudinal data from multiple time-points and biological sources. This type of data is increasingly common from studies searching for susceptibility genes for common complex diseases that collect thousands of samples generating millions of genotypes and overwhelming amounts of data. LIMSs provide an efficient way to deal with this data while increasing accessibility and reducing laboratory errors; however, professional LIMS are often too costly to be practical. SLIMS gives labs a feasible alternative that is easily accessible, user-centrically designed and feature-rich. To facilitate system customization, and utilization for other groups, manuals have been written for users and developers.

Availability: Documentation, source code and manuals are available at http://genapha.icapture.ubc.ca/SLIMS/index.jsp. SLIMS was developed using Java 1.6.0, JSPs, Hibernate 3.3.1.GA, DB2 and mySQL, Apache Tomcat 6.0.18, NetBeans IDE 6.5, Jasper Reports 3.5.1 and JasperSoft's iReport 3.5.1.

Contact: denise.daley@hli.ubc.ca

Background

Genetic variants at the vitamin D receptor (VDR) locus are associated with asthma and atopy. We hypothesized that polymorphisms in other genes of the vitamin D pathway are associated with asthma or atopy.

Methods

Eleven candidate genes were chosen for this study, five of which code for proteins in the vitamin D metabolism pathway (CYP27A1, CYP27B1, CYP2R1, CYP24A1, GC) and six that are known to be transcriptionally regulated by vitamin D (IL10, IL1RL1, CD28, CD86, IL8, SKIIP). For each gene, we selected a maximally informative set of common SNPs (tagSNPs) using the European-derived (CEU) HapMap dataset. A total of 87 SNPs were genotyped in a French-Canadian family sample ascertained through asthmatic probands (388 nuclear families, 1064 individuals) and evaluated using the Family Based Association Test (FBAT) program. We then sought to replicate the positive findings in four independent samples: two from Western Canada, one from Australia and one from the USA (CAMP).

Results

A number of SNPs in the IL10, CYP24A1, CYP2R1, IL1RL1 and CD86 genes were modestly associated with asthma and atopy (p < 0.05). Two-gene models testing for both main effects and the interaction were then performed using conditional logistic regression. Two-gene models implicating functional variants in the IL10 and VDR genes as well as in the IL10 and IL1RL1 genes were associated with asthma (p < 0.0002). In the replicate samples, SNPs in the IL10 and CYP24A1 genes were again modestly associated with asthma and atopy (p < 0.05). However, the SNPs or the orientation of the risk alleles were different between populations. A two-gene model involving IL10 and VDR was replicated in CAMP, but not in the other populations.

Conclusion

A number of genes involved in the vitamin D pathway demonstrate modest levels of association with asthma and atopy. Multilocus models testing genes in the same pathway are potentially more effective to evaluate the risk of asthma, but the effects are not uniform across populations.

Summary: Traditional methods of genetic study design and analysis work well under the scenario that a handful of single nucleotide polymorphisms (SNPs) independently contribute to the risk of disease. For complex diseases, susceptibility may be determined not by a single SNP, but rather a complex interplay between SNPs. For large studies involving hundreds of thousands of SNPs, a brute force search of all possible combinations of SNPs associated with disease is not only inefficient, but also results in a multiple testing paradigm, whereby larger and larger sample sizes are needed to maintain statistical power. Pathway-based methods are an example of one of the many approaches in identifying a subset of SNPs to test for interaction. To help determine which SNP–SNP interactions to test, we developed Path, a software application designed to help researchers interface their data with biological information from several bioinformatics resources. To this end, our application brings together currently available information from nine online bioinformatics resources including the National Center for Biotechnology Information (NCBI), Online Mendelian Inheritance in Man (OMIM), Kyoto Encyclopedia of Genes and Genomes (KEGG), UCSC Genome Browser, Seattle SNPs, PharmGKB, Genetic Association Database, the Single Nucleotide Polymorphism database (dbSNP) and the Innate Immune Database (IIDB).

Availability: The software, example datasets and tutorials are freely available from http://genapha.icapture.ubc.ca/PathTutorial.

Contact: ddaley@mrl.ubc.ca

Background

T-cell immunoglobulin mucin-3 (TIM3) is a TH1-specific type 1 membrane protein that regulates TH1 proliferation and the development of immunological tolerance. TIM3 and its genetic variants have been suggested to play a role in regulating allergic diseases. Polymorphisms in the TIM3 promoter region have been reported to be associated with allergic phenotypes in several populations. The aims of this study were to examine whether genetic variation in the promoter region of TIM3 influenced transcription of the gene and risk for allergic phenotypes.

Methods

We performed 5' rapid amplification of cDNA ends and reverse transcription-polymerase chain reaction. We screened for polymorphisms in the promoter region. Deletion analysis was used to localize the promoter region of TIM3. Genotyping was performed by TaqMan assays in three asthma/allergy population samples.

Results

We found two regions with promoter activity in TIM3. One region was from -214 bp to +58 bp and the other from -1.6 kb to -914 bp relative to the transcription start site. None of the single nucleotide polymorphisms (SNPs) or haplotypes affected the transcriptional activity in reporter gene assays. No association between the SNPs and any phenotype was observed in the study cohorts.

Conclusion

Our findings indicate that SNPs and haplotypes in the TIM3 promoter region do not have a functional effect in vitro and are not associated with allergic diseases. These data suggest that polymorphisms in the TIM3 promoter region are unlikely to play an important role in susceptibility to allergic diseases.

Our goal was to compare methods for tagging single-nucleotide polymorphisms (tagSNPs) with respect to the power to detect disease association under differing haplotype-disease association models. We were also interested in the effect that SNP selection samples, consisting of either cases, controls, or a mixture, would have on power. We investigated five previously described algorithms for choosing tagSNPS: two that picked SNPs based on haplotype structure (Chapman-haplotypic and Stram), two that picked SNPs based on pair-wise allelic association (Chapman-allelic and Cousin), and one control method that chose equally spaced SNPs (Zhai). In two disease-associated regions from the Genetic Analysis Workshop 14 simulated data, we tested the association between tagSNP genotype and disease over the tagSNP sets chosen by each method for each sampling scheme. This was repeated for 100 replicates to estimate power. The two allelic methods chose essentially all SNPs in the region and had nearly optimal power. The two haplotypic methods chose about half as many SNPs. The haplotypic methods had poor performance compared to the allelic methods in both regions. We expected an improvement in power when the selection sample contained cases; however, there was only moderate variation in power between the sampling approaches for each method. Finally, when compared to the haplotypic methods, the reference method performed as well or worse in the region with ancestral disease haplotype structure.

Background

Genetic heterogeneity and complex biologic mechanisms of blood pressure regulation pose significant challenges to the identification of susceptibility loci influencing hypertension. Previous linkage studies have reported regions of interest, but lack consistency across studies. Incorporation of covariates, in particular the interaction between two independent risk factors (gender and BMI) greatly improved our ability to detect linkage.

Results

We report a highly significant signal for linkage to chromosome 2p, a region that has been implicated in previous linkage studies, along with several suggestive linkage regions.

Conclusion

We demonstrate the importance of including covariates in the linkage analysis when the phenotype is complex.