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Background Massively parallel cDNA sequencing (RNA-seq) experiments are gradually superseding microarrays in quantitative gene expression profiling. However, many biologists are uncertain about the choice of differentially expressed gene (DEG) analysis methods and the validity of cost-saving sample pooling strategies for their RNA-seq experiments. Hence, we performed experimental validation of DEGs identified by Cuffdiff2, edgeR, DESeq2 and Two-stage Poisson Model (TSPM) in a RNA-seq experiment involving mice amygdalae micro-punches, using high-throughput qPCR on independent biological replicate samples. Moreover, we sequenced RNA-pools and compared their results with sequencing corresponding individual RNA samples. Results False-positivity rate of Cuffdiff2 and false-negativity rates of DESeq2 and TSPM were high. Among the four investigated DEG analysis methods, sensitivity and specificity of edgeR was relatively high. We documented the pooling bias and that the DEGs identified in pooled samples suffered low positive predictive values. Conclusions Our results highlighted the need for combined use of more sensitive DEG analysis methods and high-throughput validation of identified DEGs in future RNA-seq experiments. They indicated limited utility of sample pooling strategies for RNA-seq in similar setups and supported increasing the number of biological replicate samples.

To create surveillance algorithms to detect diabetes and classify type 1 versus type 2 diabetes using structured electronic health record (EHR) data. We extracted 4 years of data from the EHR of a large, multisite, multispecialty ambulatory practice serving ∼700,000 patients. We flagged possible cases of diabetes using laboratory test results, diagnosis codes, and prescriptions. We assessed the sensitivity and positive predictive value of novel combinations of these data to classify type 1 versus type 2 diabetes among 210 individuals. We applied an optimized algorithm to a live, prospective, EHR-based surveillance system and reviewed 100 additional cases for validation. The diabetes algorithm flagged 43,177 patients. All criteria contributed unique cases: 78% had diabetes diagnosis codes, 66% fulfilled laboratory criteria, and 46% had suggestive prescriptions. The sensitivity and positive predictive value of ICD-9 codes for type 1 diabetes were 26% (95% CI 12-49) and 94% (83-100) for type 1 codes alone; 90% (81-95) and 57% (33-86) for two or more type 1 codes plus any number of type 2 codes. An optimized algorithm incorporating the ratio of type 1 versus type 2 codes, plasma C-peptide and autoantibody levels, and suggestive prescriptions flagged 66 of 66 (100% [96-100]) patients with type 1 diabetes. On validation, the optimized algorithm correctly classified 35 of 36 patients with type 1 diabetes (raw sensitivity, 97% [87-100], population-weighted sensitivity, 65% [36-100], and positive predictive value, 88% [78-98]). Algorithms applied to EHR data detect more cases of diabetes than claims codes and reasonably discriminate between type 1 and type 2 diabetes.

Abstract Rationale Animal models demonstrate that aberrant gene expression in utero can result in abnormal pulmonary phenotypes. Objectives We sought to identify genes that are differentially expressed during in utero airway development and test the hypothesis that variants in these genes influence lung function in patients with asthma. Methods Stage 1 (Gene Expression): Differential gene expression analysis across the pseudoglandular (n = 27) and canalicular (n = 9) stages of human lung development was performed using regularized t tests with multiple comparison adjustments. Stage 2 (Genetic Association): Genetic association analyses of lung function (FEV1, FVC, and FEV1/FVC) for variants in five differentially expressed genes were conducted in 403 parent-child trios from the Childhood Asthma Management Program (CAMP). Associations were replicated in 583 parent-child trios from the Genetics of Asthma in Costa Rica study. Measurements and Main Results Of the 1,776 differentially expressed genes between the pseudoglandular (gestational age: 7–16 wk) and the canalicular (gestational age: 17–26 wk) stages, we selected 5 genes in the Wnt pathway for association testing. Thirteen single nucleotide polymorphisms in three genes demonstrated association with lung function in CAMP (P < 0.05), and associations for two of these genes were replicated in the Costa Ricans: Wnt1-inducible signaling pathway protein 1 with FEV1 (combined P = 0.0005) and FVC (combined P = 0.0004), and Wnt inhibitory factor 1 with FVC (combined P = 0.003) and FEV1/FVC (combined P = 0.003). Conclusions Wnt signaling genes are associated with impaired lung function in two childhood asthma cohorts. Furthermore, gene expression profiling of human fetal lung development can be used to identify genes implicated in the pathogenesis of lung function impairment in individuals with asthma.

Tuberous sclerosis (TSC) is an autosomal dominant disorder caused by mutations in either of two genes, TSC1 and TSC2. Point mutations and small indels account for most TSC1 and TSC2 mutations. We examined 261 TSC DNA samples (209 small-mutation-negative and 52 unscreened) for large deletion/duplication mutations using multiplex ligation-dependent probe amplification (MLPA) probe sets designed to permit interrogation of all TSC1/2 exons, as well as 15-50 kb of flanking sequence. Large deletion/duplication mutations in TSC1 and TSC2 were identified in 54 patients, of which 50 were in TSC2, and 4 were in TSC1. All but two mutations were deletions. Only 13 deletions were intragenic in TSC2, and one in TSC1, so that 39 (73%) deletions extended beyond the 5', 3' or both ends of TSC1 or TSC2. Mutations were identified in 24% of small-mutation-negative and 8% of unscreened samples. Eight of 54 (15%) mutations were mosaic, affecting 34-62% of cells. All intragenic mutations were confirmed by LR-PCR. Genotype/phenotype analysis showed that all (21 of 21) patients with TSC2 deletions extending 3' into the PKD1 gene had kidney cysts. Breakpoints of intragenic deletions were randomly distributed along the TSC2 sequence, and did not preferentially involve repeat sequence elements. Our own 20-plex probe sets gave more robust performance than the 40-plex probe sets from MRC-Holland. We conclude that large deletions in TSC1 and TSC2 account for about 0.5 and 6% of mutations seen in TSC patients, respectively, and MLPA is a highly sensitive and accurate detection method, including for mosaicism.

Automatic identification of notifiable diseases from electronic medical records can potentially improve the timeliness and completeness of public health surveillance. We describe the development and implementation of an algorithm for prospective surveillance of patients with acute hepatitis B using electronic medical record data. Initial algorithms were created by adapting Centers for Disease Control and Prevention diagnostic criteria for acute hepatitis B into electronic terms. The algorithms were tested by applying them to ambulatory electronic medical record data spanning 1990 to May 2006. A physician reviewer classified each case identified as acute or chronic infection. Additional criteria were added to algorithms in serial fashion to improve accuracy. The best algorithm was validated by applying it to prospective electronic medical record data from June 2006 through April 2008. Completeness of case capture was assessed by comparison with state health department records. A final algorithm including a positive hepatitis B specific test, elevated transaminases and bilirubin, absence of prior positive hepatitis B tests, and absence of an ICD9 code for chronic hepatitis B identified 112/113 patients with acute hepatitis B (sensitivity 97.4%, 95% confidence interval 94-100%; specificity 93.8%, 95% confidence interval 87-100%). Application of this algorithm to prospective electronic medical record data identified 8 cases without false positives. These included 4 patients that had not been reported to the health department. There were no known cases of acute hepatitis B missed by the algorithm. An algorithm using codified electronic medical record data can reliably detect acute hepatitis B. The completeness of public health surveillance may be improved by automatically identifying notifiable diseases from electronic medical record data.

Abstract Rationale Association studies have implicated many genes in asthma pathogenesis, with replicated associations between single-nucleotide polymorphisms (SNPs) and asthma reported for more than 30 genes. Genome-wide genotyping enables simultaneous evaluation of most of this variation, and facilitates more comprehensive analysis of other common genetic variation around these candidate genes for association with asthma. Objectives To use available genome-wide genotypic data to assess the reproducibility of previously reported associations with asthma and to evaluate the contribution of additional common genetic variation surrounding these loci to asthma susceptibility. Methods Illumina Human Hap 550Kv3 BeadChip (Illumina, San Diego, CA) SNP arrays were genotyped in 422 nuclear families participating in the Childhood Asthma Management Program. Genes with at least one SNP demonstrating prior association with asthma in two or more populations were tested for evidence of association with asthma, using family-based association testing. Measurements and Main Results We identified 39 candidate genes from the literature, using prespecified criteria. Of the 160 SNPs previously genotyped in these 39 genes, 10 SNPs in 6 genes were significantly associated with asthma (including the first independent replication for asthma-associated integrin β3 [ITGB3]). Evaluation of 619 additional common variants included in the Illumina 550K array revealed additional evidence of asthma association for 15 genes, although none were significant after adjustment for multiple comparisons. Conclusions We replicated asthma associations for a minority of candidate genes. Pooling genome-wide association study results from multiple studies will increase the power to appreciate marginal effects of genes and further clarify which candidates are true “asthma genes.”

Abstract Rationale Computed tomography (CT) scanning of the lung may reduce phenotypic heterogeneity in defining subjects with chronic obstructive pulmonary disease (COPD), and allow identification of genetic determinants of emphysema severity and distribution. Objectives We sought to identify genes associated with CT scan distribution of emphysema in individuals without α1-antitrypsin deficiency but with severe COPD. Methods We evaluated baseline CT densitometry phenotypes in 282 individuals with emphysema enrolled in the Genetics Ancillary Study of the National Emphysema Treatment Trial, and used regression models to identify genetic variants associated with emphysema distribution. Measurements and Main Results Emphysema distribution was assessed by two methods—assessment by radiologists and by computerized density mask quantitation, using a threshold of −950 Hounsfield units. A total of 77 polymorphisms in 20 candidate genes were analyzed for association with distribution of emphysema. GSTP1, EPHX1, and MMP1 polymorphisms were associated with the densitometric, apical-predominant distribution of emphysema (p value range = 0.001–0.050). When an apical-predominant phenotype was defined by the radiologist scoring method, GSTP1 and EPHX1 single-nucleotide polymorphisms were found to be significantly associated. In a case–control analysis of COPD susceptibility limited to cases with densitometric upper-lobe–predominant cases, the EPHX1 His139Arg single-nucleotide polymorphism was associated with COPD (p = 0.005). Conclusions Apical and basal emphysematous destruction appears to be influenced by different genes. Polymorphisms in the xenobiotic enzymes, GSTP1 and EPHX1, are associated with apical-predominant emphysema. Altered detoxification of cigarette smoke metabolites may contribute to emphysema distribution, and these findings may lead to further insight into genetic determinants of emphysema.

The high frequency of single-nucleotide polymorphisms (SNPs) in the human genome presents an unparalleled opportunity to track down the genetic basis of common diseases. At the same time, the sheer number of SNPs also makes unfeasible genomewide disease association studies. The haplotypic nature of the human genome, however, lends itself to the selection of a parsimonious set of SNPs, called haplotype tagging SNPs (htSNPs), able to distinguish the haplotypic variations in a population. Current approaches rely on statistical analysis of transmission rates to identify htSNPs. In contrast to these approximate methods, this contribution describes an exact, analytical, and lossless method, called BEST (Best Enumeration of SNP Tags), able to identify the minimum set of SNPs tagging an arbitrary set of haplotypes from either pedigree or independent samples. Our results confirm that a small proportion of SNPs is sufficient to capture the haplotypic variations in a population and that this proportion decreases exponentially as the haplotype length increases. We used BEST to tag the haplotypes of 105 genes in an African-American and a European-American sample. An interesting finding of this analysis is that the vast majority (95%) of the htSNPs in the European-American sample is a subset of the htSNPs of the African-American sample. This result seems to provide further evidence that a severe bottleneck occurred during the founding of Europe and the conjectured \"Out of Africa\" event.

Abstract Rationale Patients with severe chronic obstructive pulmonary disease (COPD) may have varying levels of disability despite similar levels of lung function. This variation may reflect different COPD subtypes, which may have different genetic predispositions. Objectives To identify genetic associations for COPD-related phenotypes, including measures of exercise capacity, pulmonary function, and respiratory symptoms. Methods In 304 subjects from the National Emphysema Treatment Trial, we genotyped 80 markers in 22 positional and/or biologically plausible candidate genes. Regression models were used to test for association, using a test–replication approach to guard against false-positive results. For significant associations, effect estimates were recalculated using the entire cohort. Positive associations with dyspnea were confirmed in families from the Boston Early-Onset COPD Study. Results The test–replication approach identified four genes—microsomal epoxide hydrolase (EPHX1), latent transforming growth factor-β binding protein-4 (LTBP4), surfactant protein B (SFTPB), and transforming growth factor-β1 (TGFB1)—that were associated with COPD-related phenotypes. In all subjects, single-nucleotide polymorphisms (SNPs) in EPHX1 (p ⩽ 0.03) and in LTBP4 (p ⩽ 0.03) were associated with maximal output on cardiopulmonary exercise testing. Markers in LTBP4 (p ⩽ 0.05) and SFTPB (p = 0.005) were associated with 6-min walk test distance. SNPs in EPHX1 were associated with carbon monoxide diffusing capacity (p ⩽ 0.04). Three SNPs in TGFB1 were associated with dyspnea (p ⩽ 0.002), one of which replicated in the family study (p = 0.02). Conclusions Polymorphisms in several genes seem to be associated with COPD-related traits other than FEV1. These associations may identify genes in pathways important for COPD pathogenesis.

Objective. Electronic health records (EHRs) have the potential to improve completeness and timeliness of tuberculosis (TB) surveillance relative to traditional reporting, particularly for culture-negative disease. We report on the development and validation of a TB detection algorithm for EHR data followed by implementation in a live surveillance and reporting system. Methods. We used structured electronic data from an ambulatory practice in eastern Massachusetts to develop a screening algorithm aimed at achieving 100% sensitivity for confirmed active with the highest possible positive predictive value (PPV) for physician-suspected disease. We validated the algorithm in 16 years of retrospective electronic data and then implemented it in a realtime EHR-based surveillance system. We assessed PPV and the completeness of case capture relative to conventional reporting in 18 months of prospective surveillance. Results. The final algorithm required a prescription for pyrazinamide, an International Classification of Diseases, Ninth Revision (ICD-9) code for and prescriptions for two antituberculous medications, or an ICD-9 code for PPV of 84% (95% confidence interval 78, 88) for physician-suspected disease. One-third of confirmed cases were culture-negative. All false-positives were instances of latent TB. In 18 months of prospective EHR-based surveillance with this algorithm, seven additional cases of physician-suspected active were detected, including two patients with culture-negative disease. A review of state health department records revealed no cases missed by the algorithm. Conclusions. Live, prospective surveillance using EHR data is feasible and promising.