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Background Whether the clinical or pathophysiologic significance of the “treatable trait” high blood eosinophil count in COPD is the same as for asthma remains controversial. We sought to determine the relationship between the blood eosinophil count, clinical characteristics and gene expression from bronchial brushings in COPD and asthma. Methods Subjects were recruited into a COPD (emphysema versus airway disease [EvA]) or asthma cohort (Unbiased BIOmarkers in PREDiction of respiratory disease outcomes, U‐BIOPRED). We determined gene expression using RNAseq in EvA (n = 283) and Affymetrix microarrays in U‐BIOPRED (n = 85). We ran linear regression analysis of the bronchial brushings transcriptional signal versus blood eosinophil counts as well as differential expression using a blood eosinophil > 200 cells/μL as a cut‐off. The false discovery rate was controlled at 1% (with continuous values) and 5% (with dichotomized values). Results There were no differences in age, gender, lung function, exercise capacity and quantitative computed tomography between eosinophilic versus noneosinophilic COPD cases. Total serum IgE was increased in eosinophilic asthma and COPD. In EvA, there were 12 genes with a statistically significant positive association with the linear blood eosinophil count, whereas in U‐BIOPRED, 1197 genes showed significant associations (266 positive and 931 negative). The transcriptome showed little overlap between genes and pathways associated with blood eosinophil counts in asthma versus COPD. Only CST1 was common to eosinophilic asthma and COPD and was replicated in independent cohorts. Conclusion Despite shared “treatable traits” between asthma and COPD, the molecular mechanisms underlying these clinical entities are predominately different., In a chronic obstructive pulmonary disease (COPD) cohort (EvA, n = 283), 12 genes, whereas in asthma cohort (UBIOPRED, n = 85), 1197 genes in bronchial epithelial brushes were correlated with a blood eosinophil count. The gene CST1 was common to eosinophilic asthma and COPD and was replicated in independent cohorts. Despite shared “treatable traits” between asthma and COPD, the molecular mechanisms underlying these clinical entities are predominately different.

The Markov Chain Monte Carlo linkage package Loki was used to perform a genome scan under realistic conditions (using a 10-cM marker map without marker data on unsampled individuals, analyzing each chromosome separately, and without knowing the answers) for traits Q1 and Q2 on general population replicate 1. Using this approach we detected and correctly localized MG1 for Q1 and MG3 for Q2. We then repeated the analyses on replicate 1 and the "best replicate" (42) adding more information (using marker data on everyone, fitting a polygenic effect, and analyzing multiple chromosomes jointly) to see the effect on the detection of trait loci. We found that adding more data often improves the quality of the linkage signal, and reduces the false positive rate, but did not allow the detection of trait loci missed by the initial analysis. We also investigated the convergence of the sampler by repeating one multi-chromosome analysis six times with different random number seeds. We concluded that a strategy of performing a single chromosome scan using a moderate number of sampling iterations, followed by a multi-chromosome analysis of all chromosomes with linkage signals detected in the first scan using a longer sampling run, was an effective way of performing a genome scan on this data set.

Spina bifida cystica (SB) is one of the most common and disabling of birth defects. Folic acid supplementation in mothers during the periconceptional period has been shown to prevent more than 70% of neural tube defects (NTD) including SB. However, the mechanism is unknown. We tested a series of multicase SB families in which 224 individuals were genotyped and a group of 215 unrelated unaffected (external) control individuals for association of SB with the T allele of methylenetetrahydrofolate reductase (MTHFR) C677T polymorphism that produces a heat-labile enzyme protein. The data were analyzed using first the transmission/disequilibrium test (TDT) and second a modified case-control study design with Monte Carlo sampling methods. No association of SB with the MTHFR T allele was found by either method. Presently, association between SB and the T allele has been found in four studies, a Dutch study, an Irish study, a North American study, and an Italian study. But no association was found in four other studies, a British study, a French study, a Turkish study, and a German study. A California population-based study found only modestly increased risk of SB with this allele that was not significant at the P < 0.05 level. The present study finds no evidence of the association. Only one other study, the German study, has used TDT analysis. The present study is the first to use a modified case-control study design with Monte Carlo sampling methods to test this association. Thus, it appears that the MTHFR T allele is a risk factor for SB in some populations but not others. Major genetic risk factors for folate-related SB remain to be found.

Markov chain Monte Carlo (MCMC) methods offer a rapid parametric approach that can test for linkage throughout the entire genome. It has an advantage similar to nonparametric methods in that the model does not have to be completely specified a priori. However, unlike nonparametric methods, there are no limitations on pedigree size and MCMC methods can also handle relatively complex pedigree structures. In addition MCMC methods can be used to carry segregation analysis in order to answer questions on the genetic components of a disease phenotype. Segregation analysis gave evidence for between two and eight alcoholism susceptibility loci, each having a modest effect on the phenotype. MCMC methods were used to map alcoholism loci using the phenotypes ALDX1 (DSM-III-R and Feighner criteria) and ALDX2 (World Health Organization diagnosis ICD-10 criteria). There was mild evidence for quantitative trait loci on chromosomes 2, 10, and 11.

Many researchers are considering the use of transmission/disequilibrium tests (TDT) for trios of genotypes (father, mother, child) as a method for localizing genes associated with complex diseases. We evaluate the effect of random errors (allele changes) in trios on the power to detect linkage. For a marker in the simulated data set, one allele is associated with the fictitious disease in a certain subpopulation. For the data as given (no errors), our power to detect linkage using the multiallelic TDT (TDTmhet) is 68% (critical p-value set at 0.0001). We introduce errors into trios at various rates (1%, 5%, or 10%), remove only trios displaying mendelian inconsistencies, and recalculate power to detect linkage. Our principal finding is that there is power loss to detect linkage with the TDTmhet when errors are introduced. We observe power losses of 8%, 16%, and 48% for error rates of 1%, 5%, and 10%, respectively. To determine the source of the power loss, we perform Monte Carlo simulations. At the 1% and 5% rates, we conclude that power loss is due primarily to loss in sample size. At the 10% rate, we observe substantial power loss due to error introduction in addition to sample size reduction. We also determine, given a particular error rate, the probability that we detect errors if we use only mendelian consistency as a check. We find that the mean detection rates for the data sets with 1%, 5%, or 10% error rates are 58%, 60%, and 62%, respectively. As a result, the apparent error rate appears to be almost half the true error rate. Based on these results, we recommend that researchers maintain error rates below 5% when using the TDTmhet for linkage, use additional methods beyond mendelian consistency checks when searching for errors in their data, and modify sample size calculations when accounting for errors in their genotype data.

Our objective was to infer the genetic model for the quantitative traits using a variety of methods developed in our group. Only a single data set was analyzed in any one analysis, although some comparison between data sets was made. In addition, the simulated model was not known during the course of the analysis. Basic modeling and segregation analyses for the five quantitative traits was followed by several simple genome scans to indicate areas of interest. A Markov chain Monte Carlo (MCMC) multipoint quantitative trait locus (QTL) mapping approach was then used to estimate the posterior probabilities of linkage of QTL to each chromosome simultaneously with trait model parameters, and to further localize the genes. Comparisons between the nuclear family and pedigree data sets indicated a greater power for QTL detection and mapping with the pedigree data sets. Even with the pedigree data, however, precise localization of the QTL did not appear to be possible using single replicate data sets. Two of the three genes with effects on trait Q1 were detected by the MCMC method.