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Aims/hypothesisWe investigated the risk associated with HLA-B*39 alleles in the context of specific HLA-DR/DQ haplotypes.MethodsWe studied a readily available dataset from the Type 1 Diabetes Genetics Consortium that consists of 2,300 affected sibling pair families genotyped for both HLA alleles and 2,837 single nucleotide polymorphisms across the major histocompatibility complex region.ResultsThe B*3906 allele significantly enhanced the risk of type 1 diabetes when present on specific HLA-DR/DQ haplotypes (DRB1*0801-DQB1*0402: p = 1.6 × 10−6, OR 25.4; DRB1*0101-DQB1*0501: p = 4.9 × 10−5, OR 10.3) but did not enhance the risk of DRB1*0401-DQB1*0302 haplotypes. In addition, the B*3901 allele enhanced risk on the DRB1*1601-DQB1*0502 haplotype (p = 3.7 × 10−3, OR 7.2).Conclusions/interpretationThese associations indicate that the B*39 alleles significantly increase risk when present on specific HLA-DR/DQ haplotypes, and HLA-B typing in concert with specific HLA-DR/DQ genotypes should facilitate genetic prediction of type 1 diabetes, particularly in a research setting.Electronic supplementary materialThe online version of this article (doi:10.1007/s00125-011-2161-1) contains supplementary material, which is available to authorised users.

Prenatal and postnatal cigarette smoke exposure enhances the risk of developing asthma. Despite this as well as other smoking related risks, 11% of women still smoke during pregnancy. We hypothesized that cigarette smoke exposure during prenatal development generates long lasting differential methylation altering transcriptional activity that correlates with disease. In a house dust mite (HDM) model of allergic airway disease, we measured airway hyperresponsiveness (AHR) and airway inflammation between mice exposed prenatally to cigarette smoke (CS) or filtered air (FA). DNA methylation and gene expression were then measured in lung tissue. We demonstrate that HDM-treated CS mice develop a more severe allergic airway disease compared to HDM-treated FA mice including increased AHR and airway inflammation. While DNA methylation changes between the two HDM-treated groups failed to reach genome-wide significance, 99 DMRs had an uncorrected p-value < 0.001. 6 of these 99 DMRs were selected for validation, based on the immune function of adjacent genes, and only 2 of the 6 DMRs confirmed the bisulfite sequencing data. Additionally, genes near these 6 DMRs (Lif, Il27ra, Tle4, Ptk7, Nfatc2, and Runx3) are differentially expressed between HDM-treated CS mice and HDM-treated FA mice. Our findings confirm that prenatal exposure to cigarette smoke is sufficient to modify allergic airway disease; however, it is unlikely that specific methylation changes account for the exposure-response relationship. These findings highlight the important role in utero cigarette smoke exposure plays in the development of allergic airway disease.

The clinical use of genomic analysis has expanded rapidly resulting in an increased availability and utility of genomic information in clinical care. We have developed an infrastructure utilizing informatics tools and clinical processes to facilitate the use of whole genome sequencing data for population health management across the healthcare system. Our resulting framework scaled well to multiple clinical domains in both pediatric and adult care, although there were domain specific challenges that arose. Our infrastructure was complementary to existing clinical processes and well-received by care providers and patients. Informatics solutions were critical to the successful deployment and scaling of this program. Implementation of genomics at the scale of population health utilizes complicated technologies and processes that for many health systems are not supported by current information systems or in existing clinical workflows. To scale such a system requires a substantial clinical framework backed by informatics tools to facilitate the flow and management of data. Our work represents an early model that has been successful in scaling to 29 different genes with associated genetic conditions in four clinical domains. Work is ongoing to optimize informatics tools; and to identify best practices for translation to smaller healthcare systems.

Comprehensive genetic profiling of tumors using next‐generation sequencing (NGS) is gaining acceptance for guiding treatment decisions in cancer care. We designed a cancer profiling test combining both deep sequencing and immunohistochemistry (IHC) of relevant cancer targets to aid therapy choices in both standard‐of‐care (SOC) and advanced‐stage treatments for solid tumors. The SOC report is provided in a short turnaround time for four tumors, namely lung, breast, colon, and melanoma, followed by an investigational report. For other tumor types, an investigational report is provided. The NGS assay reports single‐nucleotide variants (SNVs), copy number variations (CNVs), and translocations in 152 cancer‐related genes. The tissue‐specific IHC tests include routine and less common markers associated with drugs used in SOC settings. We describe the standardization, validation, and clinical utility of the StrandAdvantage test (SA test) using more than 250 solid tumor formalin‐fixed paraffin‐embedded (FFPE) samples and control cell line samples. The NGS test showed high reproducibility and accuracy of >99%. The test provided relevant clinical information for SOC treatment as well as more information related to investigational options and clinical trials for >95% of advanced‐stage patients. In conclusion, the SA test comprising a robust and accurate NGS assay combined with clinically relevant IHC tests can detect somatic changes of clinical significance for strategic cancer management in all the stages.