Longitudinal Multi-omics Analyses Identify Responses of Megakaryocytes, Erythroid Cells, and Plasmablasts as Hallmarks of Severe COVID-19.

Temporal resolution of cellular features associated with a severe COVID-19 disease trajectory is needed for understanding skewed immune responses and defining predictors of outcome. Here, we performed a longitudinal multi-omics study using a two-center cohort of 14 patients. We analyzed the bulk transcriptome, bulk DNA methylome, and single-cell transcriptome (>358,000 cells, including BCR profiles) of peripheral blood samples harvested from up to 5 time points. Validation was performed in two independent cohorts of COVID-19 patients. Severe COVID-19 was characterized by an increase of proliferating, metabolically hyperactive plasmablasts. Coinciding with critical illness, we also identified an expansion of interferon-activated circulating megakaryocytes and increased erythropoiesis with features of hypoxic signaling. Megakaryocyte- and erythroid-cell-derived co-expression modules were predictive of fatal disease outcome. The study demonstrates broad cellular effects of SARS-CoV-2 infection beyond adaptive immune cells and provides an entry point toward developing biomarkers and targeted treatments of patients with COVID-19.

Prediction of disease-free survival for precision medicine using cooperative learning on multi-omic data.

In precision medicine, both predicting the disease susceptibility of an individual and forecasting its disease-free survival are areas of key research. Besides the classical epidemiological predictor variables, data from multiple (omic) platforms are increasingly available. To integrate this wealth of information, we propose new methodology to combine both cooperative learning, a recent approach to leverage the predictive power of several datasets, and polygenic hazard score models. Polygenic hazard score models provide a practitioner with a more differentiated view of the predicted disease-free survival than the one given by merely a point estimate, for instance computed with a polygenic risk score. Our aim is to leverage the advantages of cooperative learning for the computation of polygenic hazard score models via Cox's proportional hazard model, thereby improving the prediction of the disease-free survival. In our experimental study, we apply our methodology to forecast the disease-free survival for Alzheimer's disease (AD) using three layers of data. One layer contains epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status and 10 leading principal components. Another layer contains selected genomic loci, and the last layer contains methylation data for selected CpG sites. We demonstrate that the survival curves computed via cooperative learning yield an AUC of around $0.7$, above the state-of-the-art performance of its competitors. Importantly, the proposed methodology returns (1) a linear score that can be easily interpreted (in contrast to machine learning approaches), and (2) a weighting of the predictive power of the involved data layers, allowing for an assessment of the importance of each omic (or other) platform. Similarly to polygenic hazard score models, our methodology also allows one to compute individual survival curves for each patient.

Polygenic hazard score models for the prediction of Alzheimer's free survival using the lasso for Cox's proportional hazards model.

The prediction of the susceptibility of an individual to a certain disease is an important and timely research area. An established technique is to estimate the risk of an individual with the help of an integrated risk model, that is, a polygenic risk score with added epidemiological covariates. However, integrated risk models do not capture any time dependence, and may provide a point estimate of the relative risk with respect to a reference population. The aim of this work is twofold. First, we explore and advocate the idea of predicting the time-dependent hazard and survival (defined as disease-free time) of an individual for the onset of a disease. This provides a practitioner with a much more differentiated view of absolute survival as a function of time. Second, to compute the time-dependent risk of an individual, we use published methodology to fit a Cox's proportional hazard model to data from a genetic SNP study of time to Alzheimer's disease (AD) onset, using the lasso to incorporate further epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status, 10 leading principal components, and selected genomic loci. We apply the lasso for Cox's proportional hazards to a data set of 6792 AD patients (composed of 4102 cases and 2690 controls) and 87 covariates. We demonstrate that fitting a lasso model for Cox's proportional hazards allows one to obtain more accurate survival curves than with state-of-the-art (likelihood-based) methods. Moreover, the methodology allows one to obtain personalized survival curves for a patient, thus giving a much more differentiated view of the expected progression of a disease than the view offered by integrated risk models. The runtime to compute personalized survival curves is under a minute for the entire data set of AD patients, thus enabling it to handle datasets with 60,000-100,000 subjects in less than 1 h.

FGF20 and PGM2 variants are associated with childhood asthma in family-based whole-genome sequencing studies.

BACKGROUND: Asthma is a heterogeneous common respiratory disease that remains poorly understood. The established genetic associations fail to explain the high estimated heritability, and the prevalence of asthma differs between populations and geographic regions. Robust association analyses incorporating different genetic ancestries and whole-genome sequencing data may identify novel genetic associations. METHODS: We performed family-based genome-wide association analyses of childhood-onset asthma based on whole-genome sequencing (WGS) data for the 'The Genetic Epidemiology of Asthma in Costa Rica' study (GACRS) and the Childhood Asthma Management Program (CAMP). Based on parent-child trios with children diagnosed with asthma, we performed a single variant analysis using an additive and a recessive genetic model and a region-based association analysis of low-frequency and rare variants. RESULTS: Based on 1180 asthmatic trios (894 GACRS trios and 286 CAMP trios, a total of 3540 samples with WGS data), we identified three novel genetic loci associated with childhood-onset asthma: rs4832738 on 4p14 ($P=1.72\ast{10}^{-9}$, recessive model), rs1581479 on 8p22 ($P=1.47\ast{10}^{-8}$, additive model) and rs73367537 on 10q26 ($P=1.21\ast{10}^{-8}$, additive model in GACRS only). Integrative analyses suggested potential novel candidate genes underlying these associations: PGM2 on 4p14 and FGF20 on 8p22. CONCLUSION: Our family-based whole-genome sequencing analysis identified three novel genetic loci for childhood-onset asthma. Gene expression data and integrative analyses point to PGM2 on 4p14 and FGF20 on 8p22 as linked genes. Furthermore, region-based analyses suggest independent potential low-frequency/rare variant associations on 8p22. Follow-up analyses are needed to understand the functional mechanisms and generalizability of these associations.

Explainable AI for Bioinformatics: Methods, Tools and Applications.

Artificial intelligence (AI) systems utilizing deep neural networks and machine learning (ML) algorithms are widely used for solving critical problems in bioinformatics, biomedical informatics and precision medicine. However, complex ML models that are often perceived as opaque and black-box methods make it difficult to understand the reasoning behind their decisions. This lack of transparency can be a challenge for both end-users and decision-makers, as well as AI developers. In sensitive areas such as healthcare, explainability and accountability are not only desirable properties but also legally required for AI systems that can have a significant impact on human lives. Fairness is another growing concern, as algorithmic decisions should not show bias or discrimination towards certain groups or individuals based on sensitive attributes. Explainable AI (XAI) aims to overcome the opaqueness of black-box models and to provide transparency in how AI systems make decisions. Interpretable ML models can explain how they make predictions and identify factors that influence their outcomes. However, the majority of the state-of-the-art interpretable ML methods are domain-agnostic and have evolved from fields such as computer vision, automated reasoning or statistics, making direct application to bioinformatics problems challenging without customization and domain adaptation. In this paper, we discuss the importance of explainability and algorithmic transparency in the context of bioinformatics. We provide an overview of model-specific and model-agnostic interpretable ML methods and tools and outline their potential limitations. We discuss how existing interpretable ML methods can be customized and fit to bioinformatics research problems. Further, through case studies in bioimaging, cancer genomics and text mining, we demonstrate how XAI methods can improve transparency and decision fairness. Our review aims at providing valuable insights and serving as a starting point for researchers wanting to enhance explainability and decision transparency while solving bioinformatics problems. GitHub: https://github.com/rezacsedu/XAI-for-bioinformatics.

A comparison between similarity matrices for principal component analysis to assess population stratification in sequenced genetic data sets.

Genetic similarity matrices are commonly used to assess population substructure (PS) in genetic studies. Through simulation studies and by the application to whole-genome sequencing (WGS) data, we evaluate the performance of three genetic similarity matrices: the unweighted and weighted Jaccard similarity matrices and the genetic relationship matrix. We describe different scenarios that can create numerical pitfalls and lead to incorrect conclusions in some instances. We consider scenarios in which PS is assessed based on loci that are located across the genome ('globally') and based on loci from a specific genomic region ('locally'). We also compare scenarios in which PS is evaluated based on loci from different minor allele frequency bins: common (>5%), low-frequency (5-0.5%) and rare (<0.5%) single-nucleotide variations (SNVs). Overall, we observe that all approaches provide the best clustering performance when computed based on rare SNVs. The performance of the similarity matrices is very similar for common and low-frequency variants, but for rare variants, the unweighted Jaccard matrix provides preferable clustering features. Based on visual inspection and in terms of standard clustering metrics, its clusters are the densest and the best separated in the principal component analysis of variants with rare SNVs compared with the other methods and different allele frequency cutoffs. In an application, we assessed the role of rare variants on local and global PS, using WGS data from multiethnic Alzheimer's disease data sets and European or East Asian populations from the 1000 Genome Project.

A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables.

The identification and understanding of gene-environment interactions can provide insights into the pathways and mechanisms underlying complex diseases. However, testing for gene-environment interaction remains a challenge since a.) statistical power is often limited and b.) modeling of environmental effects is nontrivial and such model misspecifications can lead to false positive interaction findings. To address the lack of statistical power, recent methods aim to identify interactions on an aggregated level using, for example, polygenic risk scores. While this strategy can increase the power to detect interactions, identifying contributing genes and pathways is difficult based on these relatively global results. Here, we propose RITSS (Robust Interaction Testing using Sample Splitting), a gene-environment interaction testing framework for quantitative traits that is based on sample splitting and robust test statistics. RITSS can incorporate sets of genetic variants and/or multiple environmental factors. Based on the user's choice of statistical/machine learning approaches, a screening step selects and combines potential interactions into scores with improved interpretability. In the testing step, the application of robust statistics minimizes the susceptibility to main effect misspecifications. Using extensive simulation studies, we demonstrate that RITSS controls the type 1 error rate in a wide range of scenarios, and we show how the screening strategy influences statistical power. In an application to lung function phenotypes and human height in the UK Biobank, RITSS identified highly significant interactions based on subcomponents of genetic risk scores. While the contributing single variant interaction signals are weak, our results indicate interaction patterns that result in strong aggregated effects, providing potential insights into underlying gene-environment interaction mechanisms.

Whispers in the wind: Face mask sampling for Mycobacterium tuberculosis detection in children with pulmonary tuberculosis.

BACKGROUND: Recently, face mask sampling (FMS) confirmed detection of Mycobacterium tuberculosis DNA from exhaled breath in adults with TB. To date, no study has evaluated the use of FMS to detect pulmonary Tuberculosis (TB) in children. We developed a method for FMS of M. tuberculosis-specific DNA in children and performed a clinical exploration to assess feasibility in children. METHODS: Face masks were spiked, analysed on GeneXpert-Ultra, qPCR, and tNGS. Children with pulmonary TB were asked to wear three modified FFP2 masks for 30 minutes as part of an exploratory clinical study. RESULTS: Experiments with H37Ra M. tuberculosis strain showed a limit of 95% detection of 3.75 CFU (4.85-3.11; 95%CI) on GeneXpert-Ultra. Ten children with pulmonary TB participated in the clinical study. M. tuberculosis-specific DNA was detected on none of the face masks. CONCLUSIONS: Paediatric FMS has a low limit of detection for M. tuberculosis-specific DNA in vitro. However, M. tuberculosis DNA was not detected in any of thirty masks worn by children with pulmonary TB. This suggests that FMS in this form may not be more effective for detecting M. tuberculosis in children with TB than existing methods.

Fast computation of the eigensystem of genomic similarity matrices.

The computation of a similarity measure for genomic data is a standard tool in computational genetics. The principal components of such matrices are routinely used to correct for biases due to confounding by population stratification, for instance in linear regressions. However, the calculation of both a similarity matrix and its singular value decomposition (SVD) are computationally intensive. The contribution of this article is threefold. First, we demonstrate that the calculation of three matrices (called the covariance matrix, the weighted Jaccard matrix, and the genomic relationship matrix) can be reformulated in a unified way which allows for the application of a randomized SVD algorithm, which is faster than the traditional computation. The fast SVD algorithm we present is adapted from an existing randomized SVD algorithm and ensures that all computations are carried out in sparse matrix algebra. The algorithm only assumes that row-wise and column-wise subtraction and multiplication of a vector with a sparse matrix is available, an operation that is efficiently implemented in common sparse matrix packages. An exception is the so-called Jaccard matrix, which does not have a structure applicable for the fast SVD algorithm. Second, an approximate Jaccard matrix is introduced to which the fast SVD computation is applicable. Third, we establish guaranteed theoretical bounds on the accuracy (in [Formula: see text] norm and angle) between the principal components of the Jaccard matrix and the ones of our proposed approximation, thus putting the proposed Jaccard approximation on a solid mathematical foundation, and derive the theoretical runtime of our algorithm. We illustrate that the approximation error is low in practice and empirically verify the theoretical runtime scalings on both simulated data and data of the 1000 Genome Project.

Assessing the contribution of rare genetic variants to phenotypes of chronic obstructive pulmonary disease using whole-genome sequence data.

RATIONALE: Genetic variation has a substantial contribution to chronic obstructive pulmonary disease (COPD) and lung function measurements. Heritability estimates using genome-wide genotyping data can be biased if analyses do not appropriately account for the nonuniform distribution of genetic effects across the allele frequency and linkage disequilibrium (LD) spectrum. In addition, the contribution of rare variants has been unclear. OBJECTIVES: We sought to assess the heritability of COPD and lung function using whole-genome sequence data from the Trans-Omics for Precision Medicine program. METHODS: Using the genome-based restricted maximum likelihood method, we partitioned the genome into bins based on minor allele frequency and LD scores and estimated heritability of COPD, FEV1% predicted and FEV1/FVC ratio in 11 051 European ancestry and 5853 African-American participants. MEASUREMENTS AND MAIN RESULTS: In European ancestry participants, the estimated heritability of COPD, FEV1% predicted and FEV1/FVC ratio were 35.5%, 55.6% and 32.5%, of which 18.8%, 19.7%, 17.8% were from common variants, and 16.6%, 35.8%, and 14.6% were from rare variants. These estimates had wide confidence intervals, with common variants and some sets of rare variants showing a statistically significant contribution (P-value < 0.05). In African-Americans, common variant heritability was similar to European ancestry participants, but lower sample size precluded calculation of rare variant heritability. CONCLUSIONS: Our study provides updated and unbiased estimates of heritability for COPD and lung function, and suggests an important contribution of rare variants. Larger studies of more diverse ancestry will improve accuracy of these estimates.

A stool based qPCR for the diagnosis of TB in children and people living with HIV in Uganda, Eswatini and Mozambique (Stool4TB): a protocol for a multicenter diagnostic evaluation.

BACKGROUND: Tuberculosis (TB) is a major cause of mortality worldwide. Children and people living with HIV (PLHIV) have an increased risk of mortality, particularly in the absence of rapid diagnosis. The main challenges of diagnosing TB in these populations are due to the unspecific and paucibacillary disease presentation and the difficulty of obtaining respiratory samples. Thus, novel diagnostic strategies, based on non-respiratory specimens could improve clinical decision making and TB outcomes in high burden TB settings. We propose a multi-country, prospective diagnostic evaluation study with a nested longitudinal cohort evaluation to assess the performance of a new stool-based qPCR, developed by researchers at Baylor College of Medicine (Houston, Texas, USA) for TB bacteriological confirmation with promising results in pilot studies. METHODS: The study will take place in high TB/HIV burden countries (Mozambique, Eswatini and Uganda) where we will enroll, over a period of 30 months, 650 PLHIV (> 15) and 1295 children under 8 years of age (irrespective of HIV status) presenting pressumptive TB. At baseline, all participants will provide clinical history, complete a physical assessment, and undergo thoracic chest X-ray imaging. To obtain bacteriological confirmation, participants will provide respiratory samples (1 for adults, 2 in children) and 1 stool sample for Xpert Ultra MTB/RIF (Cepheid, Sunnyvale, CA, USA). Mycobacterium tuberculosis (M.tb) liquid culture will only be performed in respiratory samples and lateral flow lipoarabinomannan (LF-LAM) in urine following WHO recommendations. Participants will complete 2 months follow-up if they are not diagnosed with TB, and 6 months if they are. For analytical purposes, the participants in the pediatric cohort will be classified into "confirmed tuberculosis", "unconfirmed tuberculosis" and "unlikely tuberculosis". Participants of the adult cohort will be classified as "bacteriologically confirmed TB", "clinically diagnosed TB" or "not TB". We will assess accuracy of the novel qPCR test compared to bacteriological confirmation and Tb diagnosis irrespective of laboratory results. Longitudinal qPCR results will be analyzed to assess its use as treatment response monitoring. DISCUSSION: The proposed stool-based qPCR is an innovation because both the strategy of using a non-sputum based sample and a technique specially designed to detect M.tb DNA in stool. PROTOCOL REGISTRATION DETAILS: ClinicalTrials.gov Identifier: NCT05047315.

Chronic Pulmonary Aspergillosis as a Considerable Complication in Post-Tuberculosis Lung Disease.

Post-tuberculosis lung disease (PTLD) has only recently been put in the spotlight as a medical entity. Recent data suggest that up to 50% of tuberculosis (TB) patients are left with PTLD-related impairment after completion of TB treatment. The presence of residual cavities in the lung is the largest risk factor for the development of chronic pulmonary aspergillosis (CPA) globally. Diagnosis of CPA is based on four criteria including a typical radiological pattern, evidence of Aspergillus species, exclusion of alternative diagnosis, and a chronic course of disease. In this manuscript, we provide a narrative review on CPA as a serious complication for patients with PTLD.

Treatment of MDR, Pre-XDR, XDR, and Rifampicin-Resistant Tuberculosis or in Case of Intolerance to at Least Rifampicin in Austria, Germany, and Switzerland.

Based on the assessment of new evidence, the World Health Organization (WHO) updated its guidelines for the treatment of drug-resistant tuberculosis (TB) in December 2022. The new recommendations and the latest study data made it necessary to update the existing guideline on the treatment of at least rifampicin-resistant TB (RR-TB) for the German-speaking countries, replacing the respective chapters of the treatment guidelines published in 2022. A shortened treatment of proven RR-TB and multidrug-resistant TB for at least 6 months using the fixed and non-modifiable drug combination of bedaquiline, pretomanid, linezolid, and moxifloxacin (BPaLM) is now also recommended for Austria, Germany, and Switzerland under certain conditions considering the existing barriers for the implementation of the new treatment regimen. For the treatment of pre-extensively drug-resistant (pre-XDR-) TB, an individualized treatment for 18 months continues to be the primary recommendation. The non-modifiable drug combination of bedaquiline, pretomanid, and linezolid (BPaL) may be used alternatively in selected pre-XDR-TB cases, provided that all prerequisites are met. The necessary requirements for using BPaLM and BPaL are presented in detail in this amendment to the consensus-based TB treatment guideline for adult patients.

Tuberculosis in people of Ukrainian origin in the European Union and the European Economic Area, 2019 to 2022.

Approximately five million Ukrainians were displaced to the EU/EEA following the Russian invasion of Ukraine. While tuberculosis (TB) notification rates per 100,000 Ukrainians in the EU/EEA remained stable, the number of notified TB cases in Ukrainians increased almost fourfold (mean 2019-2021: 201; 2022: 780). In 2022, 71% cases were notified in three countries, and almost 20% of drug-resistant TB cases were of Ukrainian origin. Targeted healthcare services for Ukrainians are vital for early diagnosis and treatment, and preventing transmission.

On the effect heterogeneity of established disease susceptibility loci for Alzheimer's disease across different genetic ancestries.

INTRODUCTION: Genome-wide association studies have identified numerous disease susceptibility loci (DSLs) for Alzheimer's disease (AD). However, only a limited number of studies have investigated the dependence of the genetic effect size of established DSLs on genetic ancestry. METHODS: We utilized the whole genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP) including 35,569 participants. A total of 25,459 subjects in four distinct populations (African ancestry, non-Hispanic White, admixed Hispanic, and Asian) were analyzed. RESULTS: We found that nine DSLs showed significant heterogeneity across populations. Single nucleotide polymorphism (SNP) rs2075650 in translocase of outer mitochondrial membrane 40 (TOMM40) showed the largest heterogeneity (Cochran's Q = 0.00, I2 = 90.08), followed by other SNPs in apolipoprotein C1 (APOC1) and apolipoprotein E (APOE). Two additional loci, signal-induced proliferation-associated 1 like 2 (SIPA1L2) and solute carrier 24 member 4 (SLC24A4), showed significant heterogeneity across populations. DISCUSSION: We observed substantial heterogeneity for the APOE-harboring 19q13.32 region with TOMM40/APOE/APOC1 genes. The largest risk effect was seen among African Americans, while Asians showed a surprisingly small risk effect.

[Everything under control?] / Alles unter Kontrolle?

A 43-year-old quarry worker, after being exposed to fine quartz dust for 16 years in a German quarry, is on the waiting list for a lung transplant. The inhalation of the fine dust irreversibly damaged his lungs and facilitated the occurrence of fulminant mycobacterial and fungal infections, which have already led to a unilateral pneumonectomy and increasing respiratory failure. Despite regular monitoring by the occupational health and safety board, this dramatic development of silicosis could not be prevented.

[Treatment of MDR, pre-XDR, XDR and rifampicin resistant tuberculosis or in case of intolerance to at least rifampicin in Austria, Germany and Switzerland - Amendment dated 19.09.2023 to the Sk2-Guideline: Tuberculosis in adulthood of the German Central Committee against Tuberculosis (DZK) on behalf of the German Respiratory Society (DGP)]. / Therapie bei MDR-, prä-XDR-, XDR-Tuberkulose und Rifampicin-Resistenz oder bei Medikamentenunverträglichkeit gegenüber mindestens Rifampicin.

In December 2022, based on the assessment of new evidence, the World Health Organization (WHO) updated its guidelines for the treatment of drug-resistant tuberculosis (TB). The evaluation of both, these recommendations, and the latest study data, makes it necessary to update the existing guidelines on the treatment of at least rifampicin-resistant tuberculosis for the German-speaking region, hereby replacing the respective chapters. A shortened MDR-TB treatment of at least 6 month using the fixed and non-modifiable drug combination of bedaquiline, pretomanid, linezolid, and moxifloxacin (BPaLM) is now also recommended for Germany, Austria, and Switzerland under certain conditions. This recommendation applies to TB cases with proven rifampicin resistance, including rifampicin monoresistance. For treatment of pre-extensively drug resistant TB (pre-XDR-TB), an individualized treatment for 18 months adjusted to resistance data continues to be the primary recommendation. The non-modifiable drug combination of bedaquiline, pretomanid, and linezolid (BPaL) may be used alternatively in pre-XDR TB if all prerequisites are met. The necessary prerequisites for the use of BPaLM and BPaL are presented in this amendment to the S2k guideline for 'Tuberculosis in adulthood'.

Limited Nosocomial Transmission of Drug-Resistant Tuberculosis, Moldova.

Applying whole-genome-sequencing, we aimed to detect transmission events of multidrug-resistant/rifampin-resistant strains of Mycobacterium tuberculosis complex at a tuberculosis hospital in Chisinau, Moldova. We recorded ward, room, and bed information for each patient and monitored in-hospital transfers over 1 year. Detailed molecular and patient surveillance revealed only 2 nosocomial transmission events.

Zinc finger protein 33B demonstrates sex interaction with atopy-related markers in childhood asthma.

BACKGROUND: Sex differences related to immune responses can influence atopic manifestations in childhood asthma. While genome-wide association studies have investigated a sex-specific genetic architecture of the immune response, gene-by-sex interactions have not been extensively analysed for atopy-related markers including allergy skin tests, IgE and eosinophils in asthmatic children. METHODS: We performed a genome-wide gene-by-sex interaction analysis for atopy-related markers using whole-genome sequencing data based on 889 trios from the Genetic Epidemiology of Asthma in Costa Rica Study (GACRS) and 284 trios from the Childhood Asthma Management Program (CAMP). We also tested the findings in UK Biobank participants with self-reported childhood asthma. Furthermore, downstream analyses in GACRS integrated gene expression to disentangle observed associations. RESULTS: Single nucleotide polymorphism (SNP) rs1255383 at 10q11.21 demonstrated a genome-wide significant gene-by-sex interaction (pinteraction=9.08×10-10) for atopy (positive skin test) with opposite direction of effects between females and males. In the UK Biobank participants with a history of childhood asthma, the signal was consistently observed with the same sex-specific effect directions for high eosinophil count (pinteraction=0.0058). Gene expression of ZNF33B (zinc finger protein 33B), located at 10q11.21, was moderately associated with atopy in girls, but not in boys. CONCLUSIONS: We report SNPs in/near a zinc finger gene as novel sex-differential loci for atopy-related markers with opposite effect directions in females and males. A potential role for ZNF33B should be studied further as an important driver of sex-divergent features of atopy in childhood asthma.

X chromosome associations with chronic obstructive pulmonary disease and related phenotypes: an X chromosome-wide association study.

BACKGROUND: The association between genetic variants on the X chromosome to risk of COPD has not been fully explored. We hypothesize that the X chromosome harbors variants important in determining risk of COPD related phenotypes and may drive sex differences in COPD manifestations. METHODS: Using X chromosome data from three COPD-enriched cohorts of adult smokers, we performed X chromosome specific quality control, imputation, and testing for association with COPD case-control status, lung function, and quantitative emphysema. Analyses were performed among all subjects, then stratified by sex, and subsequently combined in meta-analyses. RESULTS: Among 10,193 subjects of non-Hispanic white or European ancestry, a variant near TMSB4X, rs5979771, reached genome-wide significance for association with lung function measured by FEV1/FVC ([Formula: see text] 0.020, SE 0.004, p 4.97 × 10-08), with suggestive evidence of association with FEV1 ([Formula: see text] 0.092, SE 0.018, p 3.40 × 10-07). Sex-stratified analyses revealed X chromosome variants that were differentially trending in one sex, with significantly different effect sizes or directions. CONCLUSIONS: This investigation identified loci influencing lung function, COPD, and emphysema in a comprehensive genetic association meta-analysis of X chromosome genetic markers from multiple COPD-related datasets. Sex differences play an important role in the pathobiology of complex lung disease, including X chromosome variants that demonstrate differential effects by sex and variants that may be relevant through escape from X chromosome inactivation. Comprehensive interrogation of the X chromosome to better understand genetic control of COPD and lung function is important to further understanding of disease pathology. Trial registration Genetic Epidemiology of COPD Study (COPDGene) is registered at ClinicalTrials.gov, NCT00608764 (Active since January 28, 2008). Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints Study (ECLIPSE), GlaxoSmithKline study code SCO104960, is registered at ClinicalTrials.gov, NCT00292552 (Active since February 16, 2006). Genetics of COPD in Norway Study (GenKOLS) holds GlaxoSmithKline study code RES11080, Genetics of Chronic Obstructive Lung Disease.