Genome-wide association study identifies multiple HLA loci for sarcoidosis susceptibility.

Sarcoidosis is a complex systemic disease. Our study aimed to (1) identify novel alleles associated with sarcoidosis susceptibility; (2) provide an in-depth evaluation of HLA alleles and sarcoidosis susceptibility and (3) integrate genetic and transcription data to identify risk loci that may more directly impact disease pathogenesis. We report a genome-wide association study of 1335 sarcoidosis cases and 1264 controls of European descent (EA) and investigate associated alleles in a study of African Americans (AA: 1487 cases and 1504 controls). The EA and AA cohort was recruited from multiple United States sites. HLA alleles were imputed and tested for association with sarcoidosis susceptibility. Expression quantitative locus and colocalization analysis were performed using a subset of subjects with transcriptome data. Forty-nine SNPs in the HLA region in HLA-DRA, -DRB9, -DRB5, -DQA1 and BRD2 genes were significantly associated with sarcoidosis susceptibility in EA, rs3129888 was also a risk variant for sarcoidosis in AA. Classical HLA alleles DRB1*0101, DQA1*0101 and DQB1*0501, which are highly correlated, were also associated with sarcoidosis. rs3135287 near HLA-DRA was associated with HLA-DRA expression in peripheral blood mononuclear cells and bronchoalveolar lavage from subjects and lung tissue and whole blood from GTEx. We identified six novel SNPs (out of the seven SNPs representing the 49 significant SNPs) and nine HLA alleles associated with sarcoidosis susceptibility in the largest EA population. We also replicated our findings in an AA population. Our study reiterates the potential role of antigen recognition and/or presentation HLA class II genes in sarcoidosis pathogenesis.

Incidence and Progression of Fibrotic Lung Disease in an At-Risk Cohort.

Rationale: Relatives of patients with familial interstitial pneumonia (FIP) are at increased risk for pulmonary fibrosis and develop preclinical pulmonary fibrosis (PrePF). Objectives: We defined the incidence and progression of new-onset PrePF and its relationship to survival among first-degree relatives of families with FIP. Methods: This is a cohort study of family members with FIP who were initially screened with a health questionnaire and chest high-resolution computed tomography (HRCT) scan, and approximately 4 years later, the evaluation was repeated. A total of 493 asymptomatic first-degree relatives of patients with FIP were evaluated at baseline, and 296 (60%) of the original subjects participated in the subsequent evaluation. Measurements and Main Results: The median interval between HRCTs was 3.9 years (interquartile range, 3.5-4.4 yr). A total of 252 subjects who agreed to repeat evaluation were originally determined not to have PrePF at baseline; 16 developed PrePF. A conservative estimate of the annual incidence of PrePF is 1,023 per 100,000 person-years (95% confidence interval, 511-1,831 per 100,000 person-years). Of 44 subjects with PrePF at baseline, 38.4% subjects had worsening dyspnea compared with 15.4% of those without PrePF (P = 0.002). Usual interstitial pneumonia by HRCT (P < 0.0002) and baseline quantitative fibrosis score (P < 0.001) are also associated with worsening dyspnea. PrePF at the initial screen is associated with decreased survival (P < 0.001). Conclusions: The incidence of PrePF in this at-risk population is at least 100-fold higher than that reported for sporadic idiopathic pulmonary fibrosis (IPF). Although PrePF and IPF represent distinct entities, our study demonstrates that PrePF, like IPF, is progressive and associated with decreased survival.

A Polygenic Risk Score for Idiopathic Pulmonary Fibrosis and Interstitial Lung Abnormalities.

Rationale: In addition to rare genetic variants and the MUC5B locus, common genetic variants contribute to idiopathic pulmonary fibrosis (IPF) risk. The predictive power of common variants outside the MUC5B locus for IPF and interstitial lung abnormalities (ILAs) is unknown. Objectives: We tested the predictive value of IPF polygenic risk scores (PRSs) with and without the MUC5B region on IPF, ILA, and ILA progression. Methods: We developed PRSs that included (PRS-M5B) and excluded (PRS-NO-M5B) the MUC5B region (500-kb window around rs35705950-T) using an IPF genome-wide association study. We assessed PRS associations with area under the receiver operating characteristic curve (AUC) metrics for IPF, ILA, and ILA progression. Measurements and Main Results: We included 14,650 participants (1,970 IPF; 1,068 ILA) from six multi-ancestry population-based and case-control cohorts. In cases excluded from genome-wide association study, the PRS-M5B (odds ratio [OR] per SD of the score, 3.1; P = 7.1 × 10-95) and PRS-NO-M5B (OR per SD, 2.8; P = 2.5 × 10-87) were associated with IPF. Participants in the top PRS-NO-M5B quintile had ∼sevenfold odds for IPF compared with those in the first quintile. A clinical model predicted IPF (AUC, 0.61); rs35705950-T and PRS-NO-M5B demonstrated higher AUCs (0.73 and 0.7, respectively), and adding both genetic predictors to a clinical model yielded the highest performance (AUC, 0.81). The PRS-NO-M5B was associated with ILA (OR, 1.25) and ILA progression (OR, 1.16) in European ancestry participants. Conclusions: A common genetic variant risk score complements the MUC5B variant to identify individuals at high risk of interstitial lung abnormalities and pulmonary fibrosis.

Idiopathic Pulmonary Fibrosis: A Genetic Disease That Involves Mucociliary Dysfunction of the Peripheral Airways.

Idiopathic pulmonary fibrosis (IPF) is an incurable complex genetic disorder that is associated with sequence changes in 7 genes (MUC5B, TERT, TERC, RTEL1, PARN, SFTPC, and SFTPA2) and with variants in at least 11 novel loci. We have previously found that 1) a common gain-of-function promoter variant in MUC5B rs35705950 is the strongest risk factor (genetic and otherwise), accounting for 30-35% of the risk of developing IPF, a disease that was previously considered idiopathic; 2) the MUC5B promoter variant can potentially be used to identify individuals with preclinical pulmonary fibrosis and is predictive of radiologic progression of preclinical pulmonary fibrosis; and 3) MUC5B may be involved in the pathogenesis of pulmonary fibrosis with MUC5B message and protein expressed in bronchiolo-alveolar epithelia of IPF and the characteristic IPF honeycomb cysts. Based on these considerations, we hypothesize that excessive production of MUC5B either enhances injury due to reduced mucociliary clearance or impedes repair consequent to disruption of normal regenerative mechanisms in the distal lung. In aggregate, these novel considerations should have broad impact, resulting in specific etiologic targets, early detection of disease, and novel biologic pathways for use in the design of future intervention, prevention, and mechanistic studies of IPF.

Identification of influential rare variants in aggregate testing using random forest importance measures.

Aggregate tests of rare variants are often employed to identify associated regions compared to sequentially testing each individual variant. When an aggregate test is significant, it is of interest to identify which rare variants are "driving" the association. We recently developed the rare variant influential filtering tool (RIFT) to identify influential rare variants and showed RIFT had higher true positive rates compared to other published methods. Here we use importance measures from the standard random forest (RF) and variable importance weighted RF (vi-RF) to identify influential variants. For very rare variants (minor allele frequency [MAF] < 0.001), the vi-RF:Accuracy method had the highest median true positive rate (TPR = 0.24; interquartile range [IQR]: 0.13, 0.42) followed by the RF:Accuracy method (TPR = 0.16; IQR: 0.07, 0.33) and both were superior to RIFT (TPR = 0.05; IQR: 0.02, 0.15). Among uncommon variants (0.001 < MAF < 0.03), the RF methods had higher true positive rates than RIFT while observing comparable false positive rates. Finally, we applied the RF methods to a targeted resequencing study in idiopathic pulmonary fibrosis (IPF), in which the vi-RF approach identified eight and seven variants in TERT and FAM13A, respectively. In summary, the vi-RF provides an improved, objective approach to identifying influential variants following a significant aggregate test. We have expanded our previously developed R package RIFT to include the random forest methods.

Genetic predisposition to sarcoidosis.

Sarcoidosis is a complex systemic disease with clinical heterogeneity based on varying phenotypes and natural history. The detailed etiology of sarcoidosis remains unknown, but genetic predisposition as well as environmental exposures play a significant role in disease pathogenesis. We performed a comprehensive review of germline genetic (DNA) and transcriptomic (RNA) studies of sarcoidosis, including both previous studies and more recent findings. In this review, we provide an assessment of the following: genetic variants in sarcoidosis susceptibility and phenotypes, ancestry- and sex-specific genetic variants in sarcoidosis, shared genetic architecture between sarcoidosis and other diseases, and gene-environment interactions in sarcoidosis. We also highlight the unmet needs in sarcoidosis genetic studies, including the pressing requirement to include diverse populations and have consistent definitions of phenotypes in the sarcoidosis research community to help advance the application of genetic predisposition to sarcoidosis disease risk and manifestations.

Spatially distinct molecular patterns of gene expression in idiopathic pulmonary fibrosis.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a heterogeneous disease that is pathologically characterized by areas of normal-appearing lung parenchyma, active fibrosis (transition zones including fibroblastic foci) and dense fibrosis. Defining transcriptional differences between these pathologically heterogeneous regions of the IPF lung is critical to understanding the distribution and extent of fibrotic lung disease and identifying potential therapeutic targets. Application of a spatial transcriptomics platform would provide more detailed spatial resolution of transcriptional signals compared to previous single cell or bulk RNA-Seq studies. METHODS: We performed spatial transcriptomics using GeoMx Nanostring Digital Spatial Profiling on formalin-fixed paraffin-embedded (FFPE) tissue from 32 IPF and 12 control subjects and identified 231 regions of interest (ROIs). We compared normal-appearing lung parenchyma and airways between IPF and controls with histologically normal lung tissue, as well as histologically distinct regions within IPF (normal-appearing lung parenchyma, transition zones containing fibroblastic foci, areas of dense fibrosis, and honeycomb epithelium metaplasia). RESULTS: We identified 254 differentially expressed genes (DEGs) between IPF and controls in histologically normal-appearing regions of lung parenchyma; pathway analysis identified disease processes such as EIF2 signaling (important for cap-dependent mRNA translation), epithelial adherens junction signaling, HIF1α signaling, and integrin signaling. Within IPF, we identified 173 DEGs between transition and normal-appearing lung parenchyma and 198 DEGs between dense fibrosis and normal lung parenchyma; pathways dysregulated in both transition and dense fibrotic areas include EIF2 signaling pathway activation (upstream of endoplasmic reticulum (ER) stress proteins ATF4 and CHOP) and wound healing signaling pathway deactivation. Through cell deconvolution of transcriptome data and immunofluorescence staining, we confirmed loss of alveolar parenchymal signals (AGER, SFTPB, SFTPC), gain of secretory cell markers (SCGB3A2, MUC5B) as well as dysregulation of the upstream regulator ATF4, in histologically normal-appearing tissue in IPF. CONCLUSIONS: Our findings demonstrate that histologically normal-appearing regions from the IPF lung are transcriptionally distinct when compared to similar lung tissue from controls with histologically normal lung tissue, and that transition zones and areas of dense fibrosis within the IPF lung demonstrate activation of ER stress and deactivation of wound healing pathways.

Colocalization of Gene Expression and DNA Methylation with Genetic Risk Variants Supports Functional Roles of MUC5B and DSP in Idiopathic Pulmonary Fibrosis.

Rationale: Common genetic variants have been associated with idiopathic pulmonary fibrosis (IPF). Objectives: To determine functional relevance of the 10 IPF-associated common genetic variants we previously identified. Methods: We performed expression quantitative trait loci (eQTL) and methylation quantitative trait loci (mQTL) mapping, followed by co-localization of eQTL and mQTL with genetic association signals and functional validation by luciferase reporter assays. Illumina multi-ethnic genotyping arrays, mRNA sequencing, and Illumina 850k methylation arrays were performed on lung tissue of participants with IPF (234 RNA and 345 DNA samples) and non-diseased controls (188 RNA and 202 DNA samples). Measurements and Main Results: Focusing on genetic variants within 10 IPF-associated genetic loci, we identified 27 eQTLs in controls and 24 eQTLs in cases (false-discovery-rate-adjusted P < 0.05). Among these signals, we identified associations of lead variants rs35705950 with expression of MUC5B and rs2076295 with expression of DSP in both cases and controls. mQTL analysis identified CpGs in gene bodies of MUC5B (cg17589883) and DSP (cg08964675) associated with the lead variants in these two loci. We also demonstrated strong co-localization of eQTL/mQTL and genetic signal in MUC5B (rs35705950) and DSP (rs2076295). Functional validation of the mQTL in MUC5B using luciferase reporter assays demonstrates that the CpG resides within a putative internal repressor element. Conclusions: We have established a relationship of the common IPF genetic risk variants rs35705950 and rs2076295 with respective changes in MUC5B and DSP expression and methylation. These results provide additional evidence that both MUC5B and DSP are involved in the etiology of IPF.

Refractory neutrophilic asthma and ciliary genes.

BACKGROUND: Refractory asthma (RA) remains poorly controlled, resulting in high health care utilization despite guideline-based therapies. Patients with RA manifest higher neutrophilia as a result of increased airway inflammation and subclinical infection, the underlying mechanisms of which remain unclear. OBJECTIVE: We sought to characterize and clinically correlate gene expression differences between refractory and nonrefractory (NR) asthma to uncover molecular mechanisms driving group distinctions. METHODS: Microarray gene expression of paired airway epithelial brush and endobronchial biopsy samples was compared between 60 RA and 30 NR subjects. Subjects were hierarchically clustered to identify subgroups of RA, and biochemical and clinical traits (airway inflammatory molecules, respiratory pathogens, chest imaging) were compared between groups. Weighted gene correlation network analysis was used to identify coexpressed gene modules. Module expression scores were compared between groups using linear regression, controlling for age, sex, and body mass index. RESULTS: Differential gene expression analysis showed upregulation of proneutrophilic and downregulation of ciliary function genes/pathways in RA compared to NR. A subgroup of RA with downregulated ciliary gene expression had increased levels of subclinical infections, airway neutrophilia, and eosinophilia as well as higher chest imaging mucus burden compared to other RA, the dominant differences between RA and NR. Weighted gene correlation network analysis identified gene modules related to ciliary function, which were downregulated in RA and were associated with lower pulmonary function and higher airway wall thickness/inflammation, markers of poorer asthma control. CONCLUSIONS: Identification of a novel ciliary-deficient subgroup of RA suggests that diminished mucociliary clearance may underlie repeated asthma exacerbations despite adequate treatment, necessitating further exploration of function, mechanism, and therapeutics.

Multiomic Signatures of Chronic Beryllium Disease Bronchoalveolar Lavage Cells Relate to T-Cell Function and Innate Immunity.

Chronic beryllium disease (CBD) is a Th1 granulomatous lung disease preceded by sensitization to beryllium (BeS). We profiled the methylome, transcriptome, and selected proteins in the lung to identify molecular signatures and networks associated with BeS and CBD. BAL cell DNA and RNA were profiled using microarrays from CBD (n = 30), BeS (n = 30), and control subjects (n = 12). BAL fluid proteins were measured using Olink Immune Response Panel proteins from CBD (n = 22) and BeS (n = 22) subjects. Linear models identified features associated with CBD, adjusting for covariation and batch effects. Multiomic integration methods identified correlated features between datasets. We identified 1,546 differentially expressed genes in CBD versus control subjects and 204 in CBD versus BeS. Of the 101 shared transcripts, 24 have significant cis relationships between gene expression and DNA methylation, assessed using expression quantitative trait methylation analysis, including genes not previously identified in CBD. A multiomic model of top DNA methylation and gene expression features demonstrated that the first component separated CBD from other samples and the second component separated control subjects from remaining samples. The top features on component one were enriched for T-lymphocyte function, and the top features on component two were enriched for innate immune signaling. We identified six differentially abundant proteins in CBD versus BeS, with two (SIT1 and SH2D1A) selected as important RNA features in the multiomic model. Our integrated analysis of DNA methylation, gene expression, and proteins in the lung identified multiomic signatures of CBD that differentiated it from BeS and control subjects.

Clinical phenotyping in sarcoidosis using cluster analysis.

BACKGROUND: Most phenotyping paradigms in sarcoidosis are based on expert opinion; however, no paradigm has been widely adopted because of the subjectivity in classification. We hypothesized that cluster analysis could be performed on common clinical variables to define more objective sarcoidosis phenotypes. METHODS: We performed a retrospective cohort study of 554 sarcoidosis cases to identify distinct phenotypes of sarcoidosis based on 29 clinical features. Model-based clustering was performed using the VarSelLCM R package and the Integrated Completed Likelihood (ICL) criteria were used to estimate number of clusters. To identify features associated with cluster membership, features were ranked based on variable importance scores from the VarSelLCM model, and additional univariate tests (Fisher's exact test and one-way ANOVA) were performed using q-values correcting for multiple testing. The Wasfi severity score was also compared between clusters. RESULTS: Cluster analysis resulted in 6 sarcoidosis phenotypes. Salient characteristics for each cluster are as follows: Phenotype (1) supranormal lung function and majority Scadding stage 2/3; phenotype (2) supranormal lung function and majority Scadding stage 0/1; phenotype (3) normal lung function and split Scadding stages between 0/1 and 2/3; phenotype (4) obstructive lung function and majority Scadding stage 2/3; phenotype (5) restrictive lung function and majority Scadding stage 2/3; phenotype (6) mixed obstructive and restrictive lung function and mostly Scadding stage 4. Although there were differences in the percentages, all Scadding stages were encompassed by all of the phenotypes, except for phenotype 1, in which none were Scadding stage 4. Clusters 4, 5, 6 were significantly more likely to have ever been on immunosuppressive treatment and had higher Wasfi disease severity scores. CONCLUSIONS: Cluster analysis produced 6 sarcoidosis phenotypes that demonstrated less severe and severe phenotypes. Phenotypes 1, 2, 3 have less lung function abnormalities, a lower percentage on immunosuppressive treatment and lower Wasfi severity scores. Phenotypes 4, 5, 6 were characterized by lung function abnormalities, more parenchymal abnormalities, an increased percentage on immunosuppressive treatment and higher Wasfi severity scores. These data support using cluster analysis as an objective and clinically useful way to phenotype sarcoidosis subjects and to empower clinicians to identify those with more severe disease versus those who have less severe disease, independent of Scadding stage.

A comparison of methods for multiple degree of freedom testing in repeated measures RNA-sequencing experiments.

BACKGROUND: As the cost of RNA-sequencing decreases, complex study designs, including paired, longitudinal, and other correlated designs, become increasingly feasible. These studies often include multiple hypotheses and thus multiple degree of freedom tests, or tests that evaluate multiple hypotheses jointly, are often useful for filtering the gene list to a set of interesting features for further exploration while controlling the false discovery rate. Though there are several methods which have been proposed for analyzing correlated RNA-sequencing data, there has been little research evaluating and comparing the performance of multiple degree of freedom tests across methods. METHODS: We evaluated 11 different methods for modelling correlated RNA-sequencing data by performing a simulation study to compare the false discovery rate, power, and model convergence rate across several hypothesis tests and sample size scenarios. We also applied each method to a real longitudinal RNA-sequencing dataset. RESULTS: Linear mixed modelling using transformed data had the best false discovery rate control while maintaining relatively high power. However, this method had high model non-convergence, particularly at small sample sizes. No method had high power at the lowest sample size. We found a mix of conservative and anti-conservative behavior across the other methods, which was influenced by the sample size and the hypothesis being evaluated. The patterns observed in the simulation study were largely replicated in the analysis of a longitudinal study including data from intensive care unit patients experiencing cardiogenic or septic shock. CONCLUSIONS: Multiple degree of freedom testing is a valuable tool in longitudinal and other correlated RNA-sequencing experiments. Of the methods that we investigated, linear mixed modelling had the best overall combination of power and false discovery rate control. Other methods may also be appropriate in some scenarios.

HLA-DPB1 E69 genotype and exposure in beryllium sensitisation and disease.

OBJECTIVES: Human leukocyte antigen-DP beta 1 (HLA-DPB1) with a glutamic acid at the 69th position of the ß chain (E69) genotype and inhalational beryllium exposure individually contribute to risk of chronic beryllium disease (CBD) and beryllium sensitisation (BeS) in exposed individuals. This retrospective nested case-control study assessed the contribution of genetics and exposure in the development of BeS and CBD. METHODS: Workers with BeS (n=444), CBD (n=449) and beryllium-exposed controls (n=890) were enrolled from studies conducted at nuclear weapons and primary beryllium manufacturing facilities. Lifetime-average beryllium exposure estimates were based on workers' job questionnaires and historical and industrial hygienist exposure estimates, blinded to genotype and case status. Genotyping was performed using sequence-specific primer-PCR. Logistic regression models were developed allowing for over-dispersion, adjusting for workforce, race, sex and ethnicity. RESULTS: Having no E69 alleles was associated with lower odds of both CBD and BeS; every additional E69 allele increased odds for CBD and BeS. Increasing exposure was associated with lower odds of BeS. CBD was not associated with exposure as compared to controls, yet the per cent of individuals with CBD versus BeS increased with increasing exposure. No evidence of a gene-by-exposure interaction was found for CBD or BeS. CONCLUSIONS: Risk of CBD increases with E69 allele frequency and increasing exposure, although no gene by environment interaction was found. A decreased risk of BeS with increasing exposure and lack of exposure response in CBD cases may be due to the limitations of reconstructed exposure estimates. Although reducing exposure may not prevent BeS, it may reduce CBD and the associated health effects, especially in those carrying E69 alleles.

The genetic architecture of type 2 diabetes.

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.

Identification of Influential Variants in Significant Aggregate Rare Variant Tests.

INTRODUCTION: Studies that examine the role of rare variants in both simple and complex disease are increasingly common. Though the usual approach of testing rare variants in aggregate sets is more powerful than testing individual variants, it is of interest to identify the variants that are plausible drivers of the association. We present a novel method for prioritization of rare variants after a significant aggregate test by quantifying the influence of the variant on the aggregate test of association. METHODS: In addition to providing a measure used to rank variants, we use outlier detection methods to present the computationally efficient Rare Variant Influential Filtering Tool (RIFT) to identify a subset of variants that influence the disease association. We evaluated several outlier detection methods that vary based on the underlying variance measure: interquartile range (Tukey fences), median absolute deviation, and SD. We performed 1,000 simulations for 50 regions of size 3 kb and compared the true and false positive rates. We compared RIFT using the Inner Tukey to 2 existing methods: adaptive combination of p values (ADA) and a Bayesian hierarchical model (BeviMed). Finally, we applied this method to data from our targeted resequencing study in idiopathic pulmonary fibrosis (IPF). RESULTS: All outlier detection methods observed higher sensitivity to detect uncommon variants (0.001 < minor allele frequency, MAF > 0.03) compared to very rare variants (MAF <0.001). For uncommon variants, RIFT had a lower median false positive rate compared to the ADA. ADA and RIFT had significantly higher true positive rates than that observed for BeviMed. When applied to 2 regions found previously associated with IPF including 100 rare variants, we identified 6 polymorphisms with the greatest evidence for influencing the association with IPF. DISCUSSION: In summary, RIFT has a high true positive rate while maintaining a low false positive rate for identifying polymorphisms influencing rare variant association tests. This work provides an approach to obtain greater resolution of the rare variant signals within significant aggregate sets; this information can provide an objective measure to prioritize variants for follow-up experimental studies and insight into the biological pathways involved.

Molecular Signatures of Idiopathic Pulmonary Fibrosis.

Molecular patterns and pathways in idiopathic pulmonary fibrosis (IPF) have been extensively investigated, but few studies have assimilated multiomic platforms to provide an integrative understanding of molecular patterns that are relevant in IPF. Herein, we combine the coding and noncoding transcriptomes, DNA methylomes, and proteomes from IPF and healthy lung tissue to identify molecules and pathways associated with this disease. RNA sequencing, Illumina MethylationEPIC array, and liquid chromatography-mass spectrometry proteomic data were collected on lung tissue from 24 subjects with IPF and 14 control subjects. Significant differential features were identified by using linear models adjusting for age and sex, inflation, and bias when appropriate. Data Integration Analysis for Biomarker Discovery Using a Latent Component Method for Omics Studies was used for integrative multiomic analysis. We identified 4,643 differentially expressed transcripts aligning to 3,439 genes, 998 differentially abundant proteins, 2,500 differentially methylated regions, and 1,269 differentially expressed long noncoding RNAs (lncRNAs) that were significant after correcting for multiple tests (false discovery rate < 0.05). Unsupervised hierarchical clustering using 20 coding mRNA, protein, methylation, and lncRNA features with the highest loadings on the top latent variable from the four data sets demonstrates perfect separation of IPF and control lungs. Our analysis confirmed previously validated molecules and pathways known to be dysregulated in disease and implicated novel molecular features as potential drivers and modifiers of disease. For example, 4 proteins, 18 differentially methylated regions, and 10 lncRNAs were found to have strong correlations (|r| > 0.8) with MMP7 (matrix metalloproteinase 7). Therefore, by using a system biology approach, we have identified novel molecular relationships in IPF.

Genome-Wide Association Study of Susceptibility to Idiopathic Pulmonary Fibrosis.

Rationale: Idiopathic pulmonary fibrosis (IPF) is a complex lung disease characterized by scarring of the lung that is believed to result from an atypical response to injury of the epithelium. Genome-wide association studies have reported signals of association implicating multiple pathways including host defense, telomere maintenance, signaling, and cell-cell adhesion.Objectives: To improve our understanding of factors that increase IPF susceptibility by identifying previously unreported genetic associations.Methods: We conducted genome-wide analyses across three independent studies and meta-analyzed these results to generate the largest genome-wide association study of IPF to date (2,668 IPF cases and 8,591 controls). We performed replication in two independent studies (1,456 IPF cases and 11,874 controls) and functional analyses (including statistical fine-mapping, investigations into gene expression, and testing for enrichment of IPF susceptibility signals in regulatory regions) to determine putatively causal genes. Polygenic risk scores were used to assess the collective effect of variants not reported as associated with IPF.Measurements and Main Results: We identified and replicated three new genome-wide significant (P < 5 × 10-8) signals of association with IPF susceptibility (associated with altered gene expression of KIF15, MAD1L1, and DEPTOR) and confirmed associations at 11 previously reported loci. Polygenic risk score analyses showed that the combined effect of many thousands of as yet unreported IPF susceptibility variants contribute to IPF susceptibility.Conclusions: The observation that decreased DEPTOR expression associates with increased susceptibility to IPF supports recent studies demonstrating the importance of mTOR signaling in lung fibrosis. New signals of association implicating KIF15 and MAD1L1 suggest a possible role of mitotic spindle-assembly genes in IPF susceptibility.

Evaluating the contribution of rare variants to type 2 diabetes and related traits using pedigrees.

A major challenge in evaluating the contribution of rare variants to complex disease is identifying enough copies of the rare alleles to permit informative statistical analysis. To investigate the contribution of rare variants to the risk of type 2 diabetes (T2D) and related traits, we performed deep whole-genome analysis of 1,034 members of 20 large Mexican-American families with high prevalence of T2D. If rare variants of large effect accounted for much of the diabetes risk in these families, our experiment was powered to detect association. Using gene expression data on 21,677 transcripts for 643 pedigree members, we identified evidence for large-effect rare-variant cis-expression quantitative trait loci that could not be detected in population studies, validating our approach. However, we did not identify any rare variants of large effect associated with T2D, or the related traits of fasting glucose and insulin, suggesting that large-effect rare variants account for only a modest fraction of the genetic risk of these traits in this sample of families. Reliable identification of large-effect rare variants will require larger samples of extended pedigrees or different study designs that further enrich for such variants.

MCMSeq: Bayesian hierarchical modeling of clustered and repeated measures RNA sequencing experiments.

BACKGROUND: As the barriers to incorporating RNA sequencing (RNA-Seq) into biomedical studies continue to decrease, the complexity and size of RNA-Seq experiments are rapidly growing. Paired, longitudinal, and other correlated designs are becoming commonplace, and these studies offer immense potential for understanding how transcriptional changes within an individual over time differ depending on treatment or environmental conditions. While several methods have been proposed for dealing with repeated measures within RNA-Seq analyses, they are either restricted to handling only paired measurements, can only test for differences between two groups, and/or have issues with maintaining nominal false positive and false discovery rates. In this work, we propose a Bayesian hierarchical negative binomial generalized linear mixed model framework that can flexibly model RNA-Seq counts from studies with arbitrarily many repeated observations, can include covariates, and also maintains nominal false positive and false discovery rates in its posterior inference. RESULTS: In simulation studies, we showed that our proposed method (MCMSeq) best combines high statistical power (i.e. sensitivity or recall) with maintenance of nominal false positive and false discovery rates compared the other available strategies, especially at the smaller sample sizes investigated. This behavior was then replicated in an application to real RNA-Seq data where MCMSeq was able to find previously reported genes associated with tuberculosis infection in a cohort with longitudinal measurements. CONCLUSIONS: Failing to account for repeated measurements when analyzing RNA-Seq experiments can result in significantly inflated false positive and false discovery rates. Of the methods we investigated, whether they model RNA-Seq counts directly or worked on transformed values, the Bayesian hierarchical model implemented in the mcmseq R package (available at https://github.com/stop-pre16/mcmseq ) best combined sensitivity and nominal error rate control.