Fine-mapping analysis of a chromosome 2 region linked to resistance to Mycobacterium tuberculosis infection in Uganda reveals potential regulatory variants.

Tuberculosis (TB) is a major public health burden worldwide, and more effective treatment is sorely needed. Consequently, uncovering causes of resistance to Mycobacterium tuberculosis (Mtb) infection is of special importance for vaccine design. Resistance to Mtb infection can be defined by a persistently negative tuberculin skin test (PTST-) despite living in close and sustained exposure to an active TB case. While susceptibility to Mtb is, in part, genetically determined, relatively little work has been done to uncover genetic factors underlying resistance to Mtb infection. We examined a region on chromosome 2q previously implicated in our genomewide linkage scan by a targeted, high-density association scan for genetic variants enhancing PTST- in two independent Ugandan TB household cohorts (n = 747 and 471). We found association with SNPs in neighboring genes ZEB2 and GTDC1 (peak meta p = 1.9 × 10-5) supported by both samples. Bioinformatic analysis suggests these variants may affect PTST- by regulating the histone deacetylase (HDAC) pathway, supporting previous results from transcriptomic analyses. An apparent protective effect of PTST- against body-mass wasting suggests a link between resistance to Mtb infection and healthy body composition. Our results provide insight into how humans may escape latent Mtb infection despite heavy exposure.

Polymorphisms in TICAM2 and IL1B are associated with TB.

Human genetic susceptibility for tuberculosis (TB) has been demonstrated by several studies, but few have examined the multiple innate and adaptive immunity genes comprehensively, age-specific effects and/or resistance to Mycobacterium tuberculosis (Mtb) infection (resistors (RSTRs)). We hypothesized that RSTRs, defined by a persistently negative tuberculin skin test, may have different genetic influences than Mtb disease. We examined 29 candidate genes in pathways that mediate immune responses to Mtb in subjects in a household contact study in Kampala, Uganda. We genotyped 546 haplotype-tagging single-nucleotide polymorphisms (SNPs) in 835 individuals from 481 families; 28.7% had TB, 10.5% were RSTRs, and the remaining 60.8% had latent Mtb infection. Among our most significant findings were SNPs in TICAM2 (P = 3.6 × 10(-6)) and IL1B (P = 4.3 × 10(-5)) associated with TB. Multiple SNPs in IL4 and TOLLIP were associated with TB (P < 0.05). Age-genotype interaction analysis revealed SNPs in IL18 and TLR6 that were suggestively associated with TB in children aged ⩽ 10 years (P = 2.9 × 10(-3)). By contrast, RSTR was associated with SNPs in NOD2, SLC6A3 and TLR4 (nominal P < 0.05); these genes were not associated with TB, suggesting distinct genetic influences. We report the first association between TICAM2 polymorphisms and TB and between IL18 and pediatric TB.

Differences determined by optical coherence tomography volumetric analysis in non-culprit lesion morphology and inflammation in ST-segment elevation myocardial infarction and stable angina pectoris patients.

BACKGROUND: While the current methodology for determining fibrous cap (FC) thickness of lipid plaques is based on manual measurements of arbitrary points, which could lead to high variability and decreased accuracy, it ignores the three-dimensional (3-D) morphology of coronary artery disease. OBJECTIVE: To compare, utilizing optical coherence tomography (OCT) assessments, volumetric quantification of FC, and macrophage detection using both visual assessment and automated image processing algorithms in non-culprit lesions of STEMI and stable angina pectoris (SAP) patients. METHODS: Lipid plaques were selected from 67 consecutive patients (1 artery/patient). FC was manually delineated by a computer-aided method and automatically classified into three thickness categories: FC < 65 µm (i.e., thin-cap fibroatheroma [TCFA]), 65-150 µm, and >150 µm. Minimum thickness, absolute categorical surface area, and fractional luminal area of FC were analyzed. Automated detection and quantification of macrophage was performed within the segmented FC. RESULTS: A total of 5,503 cross-sections were analyzed. STEMI patients when compared with SAP patients had more absolute categorical surface area for TCFA (0.43 ± 0.45 mm(2) vs. 0.15 ± 0.25 mm(2) ; P = 0.011), thinner minimum FC thickness (31.63 ± 17.09 µm vs. 47.27 ± 26.56 µm, P = 0.012), greater fractional luminal area for TCFA (1.65 ± 1.56% vs. 0.74 ± 1.2%, P = 0.046), and greater macrophage index (0.0217 ± 0.0081% vs. 0.0153 ± 0.0045%, respectively, P < 0.01). CONCLUSION: The novel OCT-based 3-D quantification of the FC and macrophage demonstrated thinner FC thickness and larger areas of TCFA coupled with more inflammation in non-culprit sites of STEMI compared with SAP.

Two-marker association tests yield new disease associations for coronary artery disease and hypertension.

It has been postulated that multiple-marker methods may have added ability, over single-marker methods, to detect genetic variants associated with disease. The Wellcome Trust Case Control Consortium (WTCCC) provided the first successful large genome-wide association studies (GWAS) which included single-marker association analyses for seven common complex diseases. Of those signals detected, only one was associated with coronary artery disease (CAD), and none were identified for hypertension (HTN). Our objective was to find additional genetic associations and pathways for cardiovascular disease by examining the WTCCC data for variants associated with CAD and HTN using two-marker testing methods. We applied two-marker association testing to the WTCCC dataset, which includes ~2,000 affected individuals with each disorder, and a shared pool of ~3,000 controls, all genotyped using Affymetrix GeneChip 500 K arrays. For CAD, we detected single nucleotide polymorphisms (SNP) pairs in three genes showing genome-wide significance: HFE2, STK32B, and DIPC2. The most notable SNP pairs in a non-protein-coding region were at 9p21, a known major CAD-associated region. For HTN, we detected SNP pairs in five genes: GPR39, XRCC4, MYO6, ZFAT, and MACROD2. Four further associated SNP pair regions were at least 70 kb from any known gene. We have shown that novel, multiple-marker, statistical methods can be of use in finding variants in GWAS. We describe many new, associated variants for both CAD and HTN and describe their known genetic mechanisms.

Examination of the Structural Validity of the Clinical Learning Environment Inventory Using Exploratory Factor Analysis.

BACKGROUND AND PURPOSE: This study tested the psychometrics of the Clinical Learning Environment Inventory (CLEI-actual version), a tool designed to measure the perceptions of nursing students' clinical learning. The developer of the CLEI did not report structural validity. METHOD: Exploratory factor analysis (EFA) was performed using data from 311 licensure nursing students to assess the CLEI's proposed dimensions or structural validity. RESULTS: The Kaiser-Meyer-Olkin (KMO) test indicated acceptable sampling adequacy. Analysis of four different models, while retaining items with factor loadings >0.35, resulted in a four-factor solution with 32 items. The factors were renamed: Concern for Student Welfare (the highest weighted factor); Organized/Effective Teaching; Enjoyment of Clinical Learning; and Student Decision-Making. CONCLUSIONS: This study suggests that the 32 item four-factor CLEI is sufficiently structurally valid and reliable for further testing.

OCT guidance during stent implantation in primary PCI: A randomized multicenter study with nine months of optical coherence tomography follow-up.

AIMS: To assess the possible merits of optical coherence tomography (OCT) guidance in primary percutaneous coronary intervention (pPCI). METHODS AND RESULTS: 201 patients with ST-elevation myocardial infarction (STEMI) were enrolled in this study. Patients were randomized either to pPCI alone (angio-guided group, n=96) or to pPCI with OCT guidance (OCT-guided group, n=105) and also either to biolimus A9 or to everolimus-eluting stent implantation. All patients were scheduled for nine months of follow-up angiography and OCT study. OCT guidance led to post-pPCI optimization in 29% of cases (59% malapposition and 41% dissections). No complications were found related to the OCT study. OCT analysis at nine months showed significantly less in-segment area of stenosis (6% [-11, 19] versus 18% [3, 33]; p=0.0002) in favor of the OCT-guided group. The rate major adverse cardiovascular events were comparable at nine months in both groups (3% in the OCT group versus 2% in the angio-guided group; p=0.87). CONCLUSIONS: This study demonstrates the safety of OCT guidance during pPCI. The use of OCT optimized stent deployment in 1/3 of patients in this clinical scenario and significantly reduced in-segment area of stenosis at nine months of follow-up. Whether such improvements in OCT endpoints will have a positive impact on late clinical outcomes, they demand both a larger and longer-term follow-up study.

Model-Based Linkage Analysis of a Quantitative Trait.

Linkage Analysis is a family-based method of analysis to examine whether any typed genetic markers cosegregate with a given trait, in this case a quantitative trait. If linkage exists, this is taken as evidence in support of a genetic basis for the trait. Historically, linkage analysis was performed using a binary disease trait, but has been extended to include quantitative disease measures. Quantitative traits are desirable as they provide more information than binary traits. Linkage analysis can be performed using single-marker methods (one marker at a time) or multipoint (using multiple markers simultaneously). In model-based linkage analysis the genetic model for the trait of interest is specified. There are many software options for performing linkage analysis. Here, we use the program package Statistical Analysis for Genetic Epidemiology (S.A.G.E.). S.A.G.E. was chosen because it also includes programs to perform data cleaning procedures and to generate and test genetic models for a quantitative trait, in addition to performing linkage analysis. We demonstrate in detail the process of running the program LODLINK to perform single-marker analysis, and MLOD to perform multipoint analysis using output from SEGREG, where SEGREG was used to determine the best fitting statistical model for the trait.

New insight to estimate under-expansion after stent implantation on bifurcation lesions using optical coherence tomography.

Optical coherence tomography (OCT) allows full volumetric segmentation of the lumen. However, for the estimation of stent under-expansion we still rely on the conventional method (CM) of single cross-sectional narrowing compared with reference vessel, likely masking true lesion significance, especially for bifurcations and tapered vessels. We, therefore, suggest a novel concept of volumetric metrics that take into account vessel tapering and major side branches and is capable of obtaining ideal lumen area for every frame of the stent by OCT. Forty-four patients with bifurcation lesions were enrolled. In volumetric metrics, expansion index was calculated as [(actual lumen area/ideal lumen area) × 100] in all frames. While minimum expansion index (MEI) was often located in the proximal segment to the major side branch, minimum stent area (MSA) by CM was frequently located in the distal segment (p < 0.001). Furthermore, the frequency of the under-expansion was significantly greater in newly metrics compared with CM [21 (47.7%) and 11 (25.0%), p = 0.045]. New metrics changed the presence of the under-expansion in 40.9% (18/44) of patients and the locations of MEI and MSA were different in 72.7% (32/44) of cases. Volumetric assessment enables to more accurately assess stent under-expansion.

Extended use of percutaneous edge-to-edge mitral valve repair beyond EVEREST (Endovascular Valve Edge-to-Edge Repair) criteria: 30-day and 12-month clinical and echocardiographic outcomes from the GRASP (Getting Reduction of Mitral Insufficiency by Percutaneous Clip Implantation) registry.

OBJECTIVES: This study sought to compare, in high-risk patients with 3+ to 4+ mitral regurgitation (MR) dichotomized by baseline echocardiographic features, acute, 30-day, and 12-month outcomes following percutaneous mitral valve repair using the MitraClip. BACKGROUND: The feasibility and mid-term outcomes after MitraClip implantation in patients with echocardiographic features different from the EVEREST (Endovascular Valve Edge-to-Edge Repair) I and II trials have been scarcely studied. METHODS: Clinical and echocardiographic outcomes through 12-month follow-up of consecutive patients who underwent MitraClip implantation were obtained from an ongoing prospective registry. Two different groups, divided according to baseline echocardiographic criteria (investigational group [EVERESTOFF] and control group [EVERESTON]), were compared. RESULTS: Seventy-eight patients were included in EVERESTOFF and 93 patients in EVERESTON groups. Important and comparable acute reductions in MR and no clip-related complications were revealed. The primary safety endpoint at 30 days was comparable between groups (2.6% vs. 6.5%, respectively, p = 0.204); in addition, MR reduction was mostly sustained, whereas equivalent improvement in New York Heart Association functional class were demonstrated. Kaplan-Meier freedom from death, surgery for mitral valve dysfunction, or grade ≥3+ MR at 12 months was demonstrated in 71.4% and 76.2%, respectively, in the EVERESTOFF and EVERESTON groups (log rank p = 0.378). Significant improvements in ejection fraction and reduction in left ventricle volumes were demonstrated in both groups over time, but the baseline between-group differences were sustained. CONCLUSIONS: MitraClip implantation in patients with expanded baseline echocardiographic features, compared with the control group, was associated with similar rates of safety and efficacy through 12-month follow-up. Further validation of our findings is warranted.

Melanocortin-3 receptor gene variants in a Maori kindred with obesity and early onset type 2 diabetes.

Genetic studies suggest a diabetes susceptibility locus on human chromosome 20, near the melanocortin receptor-3 (MC3-R) gene. We examined the MC3-R as a candidate gene for type 2 diabetes in 12 members of a large Maori kindred with multiple affected members. The coding region of the MC3-R gene was sequenced for both diabetic and non-diabetic individuals. Two separate single base pair substitutions were found in the MC3-R coding sequence and these resulted in amino acid changes, Lysine6Threonine and Isoleucine81Valine. Neither of these MC3-R variants tracked with the presence of diabetes. Furthermore, the variant and wild type MC3-R showed similar functional coupling to adenylyl cyclase. A polymorphic marker (D20S32e) close to the human MC3-R (hMC3-R) coding sequence was investigated in a 60-member pedigree for association with diabetes and other metabolic parameters. There was an association between D20S32e genotype and fasting insulin (P=0.0085) and the insulin resistance index, HOMA-R (P=0.0042). An association was also found between genotype and HDL levels during oral glucose tolerance testing (P=0.0037). These associations were independent of BMI, age, gender and diabetes. Our data do not support a role for variations in the coding region of the hMC3-R in the development of type 2 diabetes in this Maori kindred, but suggest that a locus on chromosome 20 q, close to D20S32e, may contribute to both insulin secretion and action in the Maori kindred.

Model-based linkage analysis of a quantitative trait.

Linkage analysis is a family-based method of analysis to examine whether any typed genetic markers co-segregate with a given trait, in this case a quantitative trait. If linkage exists, this is taken as evidence in support of a genetic basis for the trait. Historically, linkage analysis was performed using a binary disease trait, but has been extended to include quantitative disease measures. Quantitative traits are desirable as they provide more information than binary traits. Linkage analysis can be performed using single marker methods (one marker at a time) or multipoint (using multiple markers simultaneously). In model-based linkage analysis, the genetic model for the trait of interest is specified. There are many software options for performing linkage analysis. Here, we use the program package Statistical Analysis for Genetic Epidemiology (S.A.G.E.). S.A.G.E. was chosen because it includes programs to perform data cleaning procedures and to generate and test genetic models for a quantitative trait, in addition to performing linkage analysis. We demonstrate in detail the process of running the program LODLINK to perform single marker analysis and MLOD to perform multipoint analysis using output from SEGREG, where SEGREG was used to determine the best fitting statistical model for the trait.

Comparison of requirements and capabilities of major multipurpose software packages.

The aim of this chapter is to introduce the reader to commonly used software packages and illustrate their input requirements, analysis options, strengths, and limitations. We focus on packages that perform more than one function and include a program for quality control, linkage, and association analyses. Additional inclusion criteria were (1) programs that are free to academic users and (2) currently supported, maintained, and developed. Using those criteria, we chose to review three programs: Statistical Analysis for Genetic Epidemiology (S.A.G.E.), PLINK, and Merlin. We will describe the required input format and analysis options. We will not go into detail about every possible program in the packages, but we will give an overview of the packages requirements and capabilities.

Model-free tests for genetic linkage.

This unit covers statistical methods of linkage analysis that do not require the assumption of a detailed genetic model, as is required for standard lod score analysis. The unit has been updated to include the latest methods in sib-pair analysis, including updates to using the software program SIBPAL as well as the relative-pair analysis software applications GENEHUNTER, GENEHUNTER PLUS, and Merlin.

Introduction to Genetic Analysis Workshop 15 summaries.

The 15th biennial Genetic Analysis Workshop (GAW15) took place November 11-15, 2006 in St. Pete Beach, Florida. The workshop's primary focus was on the appropriate linkage, association, and other analyses of the increasingly large datasets generated by genetics research. A record number of participants (N=350) contributed 252 papers to GAW15. These contributions were organized into 17 presentation groups, with a range of 11 to 18 papers in each group (median of 15 papers per group). The data sets--or "problems"--for GAW15 included information from two real data sets and a simulated data set. The first problem utilizing real data included gene expression as the phenotype and genome-wide markers for linkage and association studies. The second problem allowed for detecting and characterizing genetic effects for rheumatoid arthritis. And the simulated problem was generated to reflect the data structure underlying the rheumatoid arthritis study. Further details on GAW15 are provided here, and the primary findings from the workshop are highlighted in the following group summary papers.

Segregation analysis of 389 Icelandic pedigrees with Breast and prostate cancer.

Breast cancer and prostate cancer are the most commonly occurring cancers in females and males, respectively. The objective of this project was to test the hypothesis that breast cancer in females and prostate cancer in males represent homologous cancers that may be controlled by one or more common unidentified genes that may explain some of the observed familial aggregation. We modeled the transmission of a breast-prostate cancer phenotype in 389 pedigrees ascertained through a breast cancer proband drawn from the Icelandic Cancer Registry. Assuming that age at diagnosis of this combined phenotype followed a logistic distribution, segregation analyses were performed to evaluate residual parental effects, a sibship covariate, and a dichotomous cohort effect. The most parsimonious model was a Mendelian codominant model, which could partly explain the familial aggregation of both cancers. Inheritance of a putative high-risk allele (A) predicted gender-specific mean ages of onset for females as 53.8 years, 59.7 years, and 65.6 years for the putative AA, AB, and BB genotypes, respectively. Similarly, the predicted means were 73.7 years, 75.6 years, and 78.3 years, respectively, among males. Under this codominant model, the lifetime risk of a woman being affected was 19% by age 80 years. This implies that when prostate cancer among male relatives of breast cancer probands (unselected for family history or early-onset disease) is considered a pleiotrophic effect of the same gene that increases the risk for breast cancer, women are predicted to have a less than 1 in 5 risk of developing breast cancer when they carry the putative high-risk allele. However, this is a higher risk than in the general Icelandic population. Our results suggest that BRCA2 mutations alone are inadequate to explain all of the excess clustering of prostate cancer cases in families of breast cancer probands, and that additional genes conferring excess risk to both breast and prostate cancer may exist in this population.