Ross Lazarus

Genome-wide Association Analysis Identifies PDE4D as an Asthma-Susceptibility Gene

Asthma, a chronic airway disease with known heritability, affects more than 300 million people around the world. A genome-wide association (GWA) study of asthma with 359 cases from the Childhood Asthma Management Program (CAMP) and 846 genetically matched controls from the Illumina ICONdb public resource was performed. The strongest region of association seen was on chromosome 5q12 in PDE4D. The phosphodiesterase 4D, cAMP-specific (phosphodiesterase E3 dunce homolog, Drosophila) gene (PDE4D) is a regulator of airway smooth-muscle contractility, and PDE4 inhibitors have been developed as medications for asthma. Allelic p values for top SNPs in this region were 4.3 x 10(-07) for rs1588265 and 9.7 x 10(-07) for rs1544791. Replications were investigated in ten independent populations with different ethnicities, study designs, and definitions of asthma. In seven white and Hispanic replication populations, two PDE4D SNPs had significant results with p values less than 0.05, and five had results in the same direction as the original population but had p values greater than 0.05. Combined p values for 18,891 white and Hispanic individuals (4,342 cases) in our replication populations were 4.1 x 10(-04) for rs1588265 and 9.2 x 10(-04) for rs1544791. In three black replication populations, which had different linkage disequilibrium patterns than the other populations, original findings were not replicated. Further study of PDE4D variants might lead to improved understanding of the role of PDE4D in asthma pathophysiology and the efficacy of PDE4 inhibitor medications.

Single nucleotide polymorphisms in innate immunity genes: abundant variation and potential role in complex human disease.

Summary: Under selective pressure from infectious microorganisms, multicellular organisms have evolved immunological defense mechanisms, broadly categorized as innate or adaptive. Recent insights into the complex mechanisms of human innate immunity suggest that genetic variability in genes encoding its components may play a role in the development of asthma and related diseases. As part of a systematic assessment of genetic variability in innate immunity genes, we have thus far have examined 16 genes by resequencing 93 unrelated subjects from three ethnic samples (European American, African American and Hispanic American) and a sample of European American asthmatics. Approaches to discovering and understanding variation and the subsequent implementation of disease association studies are described and illustrated. Although highly conserved across a wide range of species, the innate immune genes we have sequenced demonstrate substantial interindividual variability predominantly in the form of single nucleotide polymorphisms (SNPs). Genetic variation in these genes may play a role in determining susceptibility to a range of common, chronic human diseases which have an inflammatory component. Differences in population history have produced distinctive patterns of SNP allele frequencies, linkage disequilibrium and haplotypes when ethnic groups are compared. These and other factors must be taken into account in the design and analysis of disease association studies.

Single-nucleotide polymorphisms in the Toll-like receptor 9 gene (TLR9): frequencies, pairwise linkage disequilibrium, and haplotypes in three U.S. ethnic groups and exploratory case-control disease association studies.

TLR9 is a mammalian Toll-like receptor homologue that appears to function as an innate immune pattern recognition protein for motifs that are far more common in bacterial than in mammalian DNA. The gene was sequenced in 71 subjects from three self-identified U.S. ethnic groups to identify single-nucleotide polymorphisms (SNPs). A total of 20 SNPs were found of which only 20% were in the public dbSNP database. Four SNPs were relatively common in all three ethnic samples. Using these four SNPs, seven distinct haplotypes were statistically inferred, of which four accounted for 75% or more chromosomes. These four haplotypes could be distinguished from each other by the alleles of two SNPs (-1237 and 2848). Five exploratory nested case-control disease-association studies (asthma, DVT, MI, and COPD in European Americans and asthma in African Americans) were performed by genotyping DNA collected from four ongoing cohort studies. There was evidence suggesting increased risk for asthma with a C allele at -1237 (odds ratio 1.85, 95%CI 1.05 to 3.25) among European Americans (genotypes available from 67 cases and 152 controls). No other significant disease associations were detected. Replication of this finding in other, larger samples is needed. This study suggests that there is substantial diversity in human TLR9, possibly associated with asthma in Europeans but not African Americans. No association was detected with three other diseases potentially related to innate immunity.

Balancing Selection Is the Main Force Shaping the Evolution of Innate Immunity Genes

The evolutionarily recent geographic expansion of humans, and the even more recent development of large, relatively dense human settlements, has exposed our species to new pathogenic environments. Potentially lethal pathogens are likely to have exerted important selective pressures on our genome, so immunity genes can be expected to show molecular signatures of the adaptation of human populations to these recent conditions. While genes related to the acquired immunity system have indeed been reported to show traces of local adaptation, little is known about the response of the innate immunity system. In this study, we analyze the variability patterns in different human populations of fifteen genes related to innate immunity. We have used both single nucleotide polymorphism and sequence data, and through the analysis of interpopulation differentiation, the linkage disequilibrium pattern, and intrapopulation diversity, we have discovered some signatures of positive and especially balancing selection in these genes, thus confirming the importance of the immune system genetic plasticity in the evolutionary adaptive process. Interestingly, the strongest evidence is found in three TLR genes and CD14. These innate immunity genes play a pivotal role, being involved in the primary recognition of pathogens. In general, more evidences of selection appear in the European populations, in some case possibly related to severe population specific pressures. However, we also describe evidence from African populations, which may reflect parallel or long-term selective forces acting in different geographic areas.

Distributed health data networks: a practical and preferred approach to multi-institutional evaluations of comparative effectiveness, safety, and quality of care.

Background:Comparative effectiveness research, medical product safety evaluation, and quality measurement will require the ability to use electronic health data held by multiple organizations. There is no consensus about whether to create regional or national combined (eg, “all payer”) databases for these purposes, or distributed data networks that leave most Protected Health Information and proprietary data in the possession of the original data holders. Objectives:Demonstrate functions of a distributed research network that supports research needs and also address data holders concerns about participation. Key design functions included strong local control of data uses and a centralized web-based querying interface. Research Design:We implemented a pilot distributed research network and evaluated the design considerations, utility for research, and the acceptability to data holders of methods for menu-driven querying. We developed and tested a central, web-based interface with supporting network software. Specific functions assessed include query formation and distribution, query execution and review, and aggregation of results. Results:This pilot successfully evaluated temporal trends in medication use and diagnoses at 5 separate sites, demonstrating some of the possibilities of using a distributed research network. The pilot demonstrated the potential utility of the design, which addressed the major concerns of both users and data holders. No serious obstacles were identified that would prevent development of a fully functional, scalable network. Conclusions:Distributed networks are capable of addressing nearly all anticipated uses of routinely collected electronic healthcare data. Distributed networks would obviate the need for centralized databases, thus avoiding numerous obstacles.

Use of Automated Ambulatory-Care Encounter Records for Detection of Acute Illness Clusters, Including Potential Bioterrorism Events

The advent of domestic bioterrorism has emphasized the need for enhanced detection of clusters of acute illness. We describe a monitoring system operational in eastern Massachusetts, based on diagnoses obtained from electronic records of ambulatory-care encounters. Within 24 hours, ambulatory and telephone encounters recording patients with diagnoses of interest are identified and merged into major syndrome groups. Counts of new episodes of illness, rates calculated from health insurance records, and estimates of the probability of observing at least this number of new episodes are reported for syndrome surveillance. Census tracts with unusually large counts are identified by comparing observed with expected syndrome frequencies. During 1996–1999, weekly counts of new cases of lower respiratory syndrome were highly correlated with weekly hospital admissions. This system complements emergency room- and hospital-based surveillance by adding the capacity to rapidly identify clusters of illness, including potential bioterrorism events.

Family-based association analysis of β2-adrenergic receptor polymorphisms in the childhood asthma management program☆

BACKGROUND Beta2-adrenergic receptor (B2AR) polymorphisms have been associated with a variety of asthma-related phenotypes, but association results have been inconsistent across different studies. OBJECTIVE We sought to apply family-based association methods to individual single nucleotide polymorphisms (SNPs) and haplotypes of SNPs in B2AR to define the relationship of these genetic variants to asthma-related phenotypes. METHODS DNA samples were obtained from 707 Childhood Asthma Management Program participants, representing 650 sibships, as well as their parents. Genotyping was performed at 8 B2AR SNPs. Qualitative asthma-related phenotypes were analyzed with single SNPs and haplotypes by using TRANSMIT; quantitative asthma-related phenotypes were analyzed with the Family-Based Association Test. RESULTS Several SNPs, including SNP -654 and SNP +46, demonstrated significant associations (P <.05) to postbronchodilator FEV1 as both a qualitative (<80% of predicted value) and quantitative phenotype. Quantitative phenotypic association analysis demonstrated significant evidence for association of SNP +523 with bronchodilator responsiveness expressed as a percentage of baseline FEV1 (P =.012) or a percentage of predicted FEV1 (P =.008). Similar evidence for association between the +523 SNP and qualitative bronchodilator responsiveness phenotypes was also found. Analysis of haplotypes supported an association of B2AR variants with spirometric values and bronchodilator responsiveness. CONCLUSION B2AR variants are associated with spirometric values and bronchodilator responsiveness, but different regions of the gene provide evidence for association with these phenotypes.

Design of a national distributed health data network.

Key Summary Points: Attributes of a National Distributed Health Data Network Supports both observational and intervention studies. Local data holder control over access and uses of data. Mitigates need to share or exchange protected health information. Singular, multipurpose, multi-institutional infrastructure. A distributed health data network is a system that allows secure remote analysis of separate data sets, each derived from a different medical organizations or health plans records. Such networks allow data holders to retain physical control over use of their data, thereby avoiding many obstacles related to confidentiality, regulation, and proprietary interests. They can be used for observational studies, particularly public health surveillance, and can also provide baseline and follow-up data to support clinical trials, including those that use cluster randomization. In addition, a network can monitor use, adoption, and diffusion of new technologies and clinical evidence. Such networks are critical elements of the learning health care system recommended by the Institute of Medicine (1), which supports the use of routinely collected health care data to improve our understanding of the comparative benefits and harms of medical technologies. The United States will soon be able to analyze data from millions of individuals. Congress has mandated that the U.S. Food and Drug Administration develop a postmarket risk identification and analysis system that covers 100 million persons (2). In addition, the expansion of comparative effectiveness research envisioned by Congress requires access to health care information for large, diverse populations in real-world settings (3). Large, centralized data repositories could support these functions, but we and others (4, 5) believe that a distributed health data network has many practical advantages. First, a distributed network allows data holders to retain physical and logical control of their data. Second, it mitigates many security, proprietary, legal, and privacy concerns, including those regulated by the Privacy and Security Rules of the Health Insurance Portability and Accountability Act (6). Third, it eliminates the need to create, maintain, and secure access to central data repositories. Fourth, it minimizes the need to disclose protected health information outside the data-owning entity. Finally, a distributed network allows data holders to assess, track, and authorize requests for all data uses. Several public agencies have supported the development of single-purpose distributed data networks, either directly or in principle (711). These networks are limited in scope and do not support the broad range of public and private needs filled by the network we describe. We favor a single distributed network with multiple usesfor example, one that could be used to study comparative clinical effectiveness and the diffusion of medical technologiesover multiple independent and single-purpose networks. A multipurpose network would reduce the burden on data holders of participating in multiple networks, as well as that on network developers of creating and maintaining redundant infrastructure. The framework that we describe suggests how we could develop a national network with broad capabilities. How Would a National Distributed Health Data Network Work? In the simplest national distributed health data network, each data holder creates a copy of their data (a network datamart) that adheres to a common data model, thus ensuring identical file structures, data fields, and coding systems. Several common data models already exist (10, 1217). The Figure illustrates the basic flow of network operations. Authorized users submit queries by means of a secure Web site. Data holders set authorization policies for each user and query type and can require approvals from privacy boards and institutional review boards. The network interface allows nontechnical users to ask simple questions without assistance (for example, a report on the uptake of a given treatment by age, sex, and geographic region). It also allows sophisticated users to perform complex analyses (for example, comparing the rates of serious cardiovascular outcomes among patients who receive different second-line antihypertensive treatments). For many questions, transferring protected health information will not be necessary. However, it may be necessary to aggregate relatively small amounts of data for analysis. Using the network, data holders may provide limited access to full-text medical records for validation and additional details. It is usually necessary to review only a small proportion of records to confirm diagnoses or to obtain risk factor data that are not coded (such as smoking status). Figure. System operations in a distributed health network. An authorized user accesses the secure network Web site to submit queries (computer programs) to run against data in the network datamarts. The boxes at the far right depict areas under control of the data holder (data holders A through D are shown). Authorization to execute a query is under control of the data holder and can be limited to specific users and uses. Data holders retrieve queries for execution, which eliminates the need for data holders to monitor incoming requests. Query results are encrypted and returned to the central Web site, where they are processed and presented to the requester. Details of each step are recorded for auditing. Example of the Use of a Distributed Network Some research programs already use a distributed network model (10, 14, 18), which provides a relevant starting point to implement a national network. The HMO Research Network Center for Education and Research on Therapeutics has conducted many multisite studies by distributing computer programs that each site applied to a local copy of their data. The outputs are then combined to provide aggregate results. Examples of studies performed in this way include the evaluation of laboratory monitoring practices for medications (1825), the use of medications during pregnancy (2628), and the use of medications that carry a black box warning (29). Such studies provide an important evidence development function that feeds back to providers, payers, and patients. Policy Issues Development and implementation of a multipurpose, multi-institutional distributed health data network requires substantial stakeholder engagement and dedicated software development. On the basis of the previously described research studies, we recommend incremental implementation with a limited set of data holders and data types. Begin with information about eligibility for health care (such as health plan enrollment data); this would allow identification of defined populations, which are important for many uses. Initial data should also include demographic characteristics; diagnosis, procedure, and pharmacy dispensing data (30); and, potentially, electronic health record data, such as vital signs. During initial implementation, pilot testing is needed to assess network design, software development, and development and implementation of the common data model. A distributed networks viability depends on both its governance mechanisms and sustained funding. A governance institution is needed to develop and oversee procedures for requesting use of the network; to set priorities; and to audit use for compliance with various security, privacy, human subject research, and proprietary concerns. Such an institution should also monitor research integrity, data integrity, conflict of interest policies, transparency of activity and results, policies related to access and use, reproducibility, publishing rights, and dispute resolution. Annual development and maintenance costs would probably be several tens of millions of dollars for an initial system that covers up to 100 million persons. This would be similar to the 3-year startup cost for the National Cancer Institutes Cancer Biomedical Informatics Grid, which totaled

A risk allele for focal segmental glomerulosclerosis in African Americans is located within a region containing APOL1 and MYH9

60 million for fiscal years 2004 to 2006 (31). The National Cancer Institute fiscal year 2010 budget requests

Effects of body fat on ventilatory function in children and adolescents: Cross-sectional findings from a random population sample of school children

100 million for these efforts in addition to the current funding level (32). The total annual cost of developing and maintaining a network is in line with that of individual clinical trials routinely performed to evaluate new pharmaceuticals. Although initial implementation costs are sizeable, the expected marginal costs to use the system would be small for any particular study. Various funding mechanisms are possible. Initially, we expect costs to be borne by the federal entities, whose current needs would drive network implementation. Ultimately, we believe the costs should be amortized over the systems multiple users and should support the networks expansion, functionality, and use. For example, methods could be developed for linking to the National Death Index or identifying individuals for whom multiple data holders possess different kinds of information (such as pharmacy data held by one source and clinical encounter data held by another). Advances in technologies designed to link individual records over time (such as anonymous identity resolution) without exposing protected health information are especially desirable (33). Conclusion A national distributed health data network can become an important asset to improving health and health care. A common core network would offer considerable advantages that would better support the needs of multiple users, such as the U.S. Food and Drug Administration (for their Sentinel System) and the Agency for Healthcare Research and Quality (for their comparative effectiveness network), than would building individual networks for each of these uses. The similarities in data needs and uses, coupled with potential savings of time and effort, favor a single, multipurpose network. In addition, local data holder control over use and access would encourage particip