Explore the vast range of titles available.

Long-read sequencing (LRS) technologies have enhanced the ability to resolve complex genomic architecture and determine the ‘phase’ relationships of genetic variants over long distances. Although genome-wide association studies (GWAS) identify individual variants associated with complex traits, they do not typically account for whether multiple associated signals at a locus may act in cis or trans , or whether they reflect allelic heterogeneity. As a result, effects that arise specifically from phase relationships may remain hidden in analyses using short-read and microarray data. While the advent of LRS has enabled accurate measurement of phase in population cohorts, statistical methods that leverage phase in genetic association analysis remain underdeveloped. Here, we introduce the Regression on Phase (RoP) method, which directly models cis and trans phase effects between variants under a regression framework. In simulations, RoP outperforms genotype interaction tests that detect phase effects indirectly, and distinguishes in- cis from in- trans phase effects. We implemented RoP at two cystic fibrosis (CF) modifier loci discovered by GWAS. At the chromosome 7q35 trypsinogen locus, RoP confirmed that two variants contributed independently (allelic heterogeneity). At the SLC6A14 locus on chromosome X, phase analysis uncovered a coordinated regulatory mechanism in which a promoter variant modulates lung phenotypes in individuals with CF when acting in cis with a lung-specific enhancer (E2765449/enhD). This coordinated regulation was confirmed in functional studies. These findings highlight the potential of leveraging phase information from LRS in genetic association studies. Analyzing phase effects with RoP can provide deeper insights into the complex genetic architectures underlying disease phenotypes, ultimately guiding more informed functional investigations and potentially revealing new therapeutic targets.

Genome-wide association studies (GWAS) have primarily identified trait-associated loci in the non-coding genome. Colocalization analyses of SNP associations from GWAS with expression quantitative trait loci (eQTL) evidence enable the generation of hypotheses about responsible mechanism, genes and tissues of origin to guide functional characterization. Here, we present a web-based colocalization browsing and testing tool named LocusFocus (https://locusfocus.research.sickkids.ca). LocusFocus formally tests colocalization using our established Simple Sum method to identify the most relevant genes and tissues for a particular GWAS locus in the presence of high linkage disequilibrium and/or allelic heterogeneity. We demonstrate the utility of LocusFocus, following up on a genome-wide significant locus from a GWAS of meconium ileus (an intestinal obstruction in cystic fibrosis). Using LocusFocus for colocalization analysis with eQTL data suggests variation in ATP12A gene expression in the pancreas rather than intestine is responsible for the GWAS locus. LocusFocus has no operating system dependencies and may be installed in a local web server. LocusFocus is available under the MIT license, with full documentation and source code accessible on GitHub at https://github.com/naim-panjwani/LocusFocus.

Cystic Fibrosis (CF) exhibits morbidity in several organs, including progressive lung disease in all patients and intestinal obstruction at birth (meconium ileus) in ~15%. Individuals with the same causal CFTR mutations show variable disease presentation which is partly attributed to modifier genes. With >6,500 participants from the International CF Gene Modifier Consortium, genome-wide association investigation identified a new modifier locus for meconium ileus encompassing ATP12A on chromosome 13 (min p = 3.83x10(-10)); replicated loci encompassing SLC6A14 on chromosome X and SLC26A9 on chromosome 1, (min p<2.2x10(-16), 2.81x10(-11), respectively); and replicated a suggestive locus on chromosome 7 near PRSS1 (min p = 2.55x10(-7)). PRSS1 is exclusively expressed in the exocrine pancreas and was previously associated with non-CF pancreatitis with functional characterization demonstrating impact on PRSS1 gene expression. We thus asked whether the other meconium ileus modifier loci impact gene expression and in which organ. We developed and applied a colocalization framework called the Simple Sum (SS) that integrates regulatory and genetic association information, and also contrasts colocalization evidence across tissues or genes. The associated modifier loci colocalized with expression quantitative trait loci (eQTLs) for ATP12A (p = 3.35x10(-8)), SLC6A14 (p = 1.12x10(-10)) and SLC26A9 (p = 4.48x10(-5)) in the pancreas, even though meconium ileus manifests in the intestine. The meconium ileus susceptibility locus on chromosome X appeared shifted in location from a previously identified locus for CF lung disease severity. Using the SS we integrated the lung disease association locus with eQTLs from nasal epithelia of 63 CF participants and demonstrated evidence of colocalization with airway-specific regulation of SLC6A14 (p = 2.3x10(-4)). Cystic Fibrosis is realizing the promise of personalized medicine, and identification of the contributing organ and understanding of tissue specificity for a gene modifier is essential for the next phase of personalizing therapeutic strategies.

Respiratory infection caused by P. aeruginosa is one of the most critical health burdens worldwide. People affected by P. aeruginosa infection include patients with a weakened immune system, such as those with cystic fibrosis (CF) genetic disease or non-CF bronchiectasis. Disease outcomes range from fatal pneumonia to chronic life-threatening infection and inflammation leading to the progressive deterioration of pulmonary function. The development of these respiratory infections is mediated by multiple causes. However, the genetic factors underlying infection susceptibility are poorly known and difficult to predict. Our study employed novel approaches and improved mouse disease models to identify genetic modifiers that affect the severity of P. aeruginosa lung infection. We identified candidate genes to enhance our understanding of P. aeruginosa infection in humans and provide a proof of concept that could be exploited for other human pathologies mediated by bacterial infection. Human genetics influence a range of pathological and clinical phenotypes in respiratory infections; however, the contributions of disease modifiers remain underappreciated. We exploited the Collaborative Cross (CC) mouse genetic-reference population to map genetic modifiers that affect the severity of Pseudomonas aeruginosa lung infection. Screening for P. aeruginosa respiratory infection in a cohort of 39 CC lines exhibits distinct disease phenotypes ranging from complete resistance to lethal disease. Based on major changes in the survival times, a quantitative-trait locus (QTL) was mapped on murine chromosome 3 to the genomic interval of Mb 110.4 to 120.5. Within this locus, composed of 31 protein-coding genes, two candidate genes, namely, dihydropyrimidine dehydrogenase ( Dpyd ) and sphingosine-1-phosphate receptor 1 ( S1pr1 ), were identified according to the level of genome-wide significance and disease gene prioritization. Functional validation of the S1pr1 gene by pharmacological targeting in C57BL/6NCrl mice confirmed its relevance in P. aeruginosa pathophysiology. However, in a cohort of Canadian patients with cystic fibrosis (CF) disease, regional genetic-association analysis of the syntenic human locus on chromosome 1 (Mb 97.0 to 105.0) identified two single-nucleotide polymorphisms (rs10875080 and rs11582736) annotated to the Dpyd gene that were significantly associated with age at first P. aeruginosa infection. Thus, there is evidence that both genes might be implicated in this disease. Our results demonstrate that the discovery of murine modifier loci may generate information that is relevant to human disease progression. IMPORTANCE Respiratory infection caused by P. aeruginosa is one of the most critical health burdens worldwide. People affected by P. aeruginosa infection include patients with a weakened immune system, such as those with cystic fibrosis (CF) genetic disease or non-CF bronchiectasis. Disease outcomes range from fatal pneumonia to chronic life-threatening infection and inflammation leading to the progressive deterioration of pulmonary function. The development of these respiratory infections is mediated by multiple causes. However, the genetic factors underlying infection susceptibility are poorly known and difficult to predict. Our study employed novel approaches and improved mouse disease models to identify genetic modifiers that affect the severity of P. aeruginosa lung infection. We identified candidate genes to enhance our understanding of P. aeruginosa infection in humans and provide a proof of concept that could be exploited for other human pathologies mediated by bacterial infection.

Over 400 variants in the cystic fibrosis (CF) transmembrane conductance regulator (CFTR) are CF-causing. CFTR modulators target variants to improve lung function, but marked variability in response exists and current therapies do not address all CF-causing variants highlighting unmet needs. Alternative epithelial ion channel/transporters such as SLC26A9 could compensate for CFTR dysfunction, providing therapeutic targets that may benefit all individuals with CF. We investigate the relationship between rs7512462, a marker of SLC26A9 activity, and lung function pre- and post-treatment with CFTR modulators in Canadian and US CF cohorts, in the general population, and in those with chronic obstructive pulmonary disease (COPD). Rs7512462 CC genotype is associated with greater lung function in CF individuals with minimal function variants (for which there are currently no approved therapies; p = 0.008); and for gating (p = 0.033) and p.Phe508del/ p.Phe508del (p = 0.006) genotypes upon treatment with CFTR modulators. In parallel, human nasal epithelia with CC and p.Phe508del/p.Phe508del after Ussing chamber analysis of a combination of approved and experimental modulator treatments show greater CFTR function (p = 0.0022). Beyond CF, rs7512462 is associated with peak expiratory flow in a meta-analysis of the UK Biobank and Spirometa Consortium (p = 2.74 × 10−44) and provides p = 0.0891 in an analysis of COPD case-control status in the UK Biobank defined by spirometry. These findings support SLC26A9 as a therapeutic target to improve lung function for all people with CF and in individuals with other obstructive lung diseases.

Endoplasmic reticulum (ER) stress has been recognized to play an important role in chronic inflammatory diseases such as cystic fibrosis (CF), and targeting ER stress may be useful for alleviating damaging neutrophilic inflammation in CF airways. Cellular models were used in conjunction with data from a recent CF genome-wide association study (GWAS) meta-analysis to determine modulators of ER stress-mediated inflammation. Surprisingly, cells undergoing ER stress during inflammatory stimulation showed reduced interleukin 8 (IL-8) and CXCL1 secretion (P < .001). Neutralization of CXCL1 and IL-8 reduced neutrophil chemotaxis >50% to supernatants from IL-1 β-stimulated CF airway epithelial cells (P < .01). The clinical importance of these chemokines was validated by association of CXCL1 and IL8 polymorphisms with changes in lung disease severity in patients with CF (n = 6365; IL8, P = .001; CXCL1, P = .001), confirming that targeting these chemokine pathways could help improve lung disease. We determined that production of these chemokines was partially controlled by ER stress in a signal transducer and activator of transcription 3 (STAT3)–dependent manner, whereby ER stress inhibited STAT3 activation. Our findings support a role for CXCL1 and IL-8 in CF lung disease severity and identify STAT3 as a modulating pathway. Targeting these pathways may help improve health outcomes in CF.

The Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene variant, c.3453G > C (D1152H), is associated with mild Cystic Fibrosis (CF) disease, though there is considerable clinical variability ranging from no detectable symptoms to lung disease with early acquisition of Pseudomonas aeruginosa. The approval extension of ivacaftor, the first CFTR modulator drug approved, to include D1152H was based on a positive drug response of defective CFTR-D1152H chloride channel function when expressed in FRT cells. Functional analyses of primary human nasal epithelial cells (HNE) from an individual homozygous for D1152H now revealed that while CFTR-D1152H demonstrated normal, wild-type level chloride conductance, its bicarbonate-selective conductance was impaired. Treatment with ivacaftor increased this bicarbonate-selective conductance. Extensive genetic, protein and functional analysis of the nasal cells of this D1152H/D1152H patient revealed a 90% reduction of CFTR transcripts due to the homozygous presence of the 5T polymorphism in the poly-T tract forming a complex allele with D1152H. Thus, we confirm previous observation in patient-derived tissue that 10% normal CFTR transcripts confer normal, wild-type level chloride channel activity. Together, this study highlights the benefit of patient-derived tissues to study the functional expression and pharmacological modulation of CF-causing mutations, in order to understand pathogenesis and therapeutic responses.

Does genotype imputation with public reference panels identify variants contributing to disease? Genotype imputation using the 1000 Genomes Project (1KG; 2504 individuals) displayed poor coverage at the causal cystic fibrosis (CF) transmembrane conductance regulator (CFTR) locus for the International CF Gene Modifier Consortium. Imputation with the larger Haplotype Reference Consortium (HRC; 32,470 individuals) displayed improved coverage but low sensitivity of variants clinically relevant for CF. A hybrid reference that combined whole genome sequencing (WGS) from 101 CF individuals with the 1KG imputed a greater number of single-nucleotide variants (SNVs) that would be analyzed in a genetic association study (r2 ≥ 0.3 and MAF ≥ 0.5%) than imputation with the HRC, while the HRC excelled in the lower frequency spectrum. Using the 1KG or HRC as reference panels missed the most common CF-causing variants or displayed low imputation accuracy. Designs that incorporate population-specific WGS can improve imputation accuracy at disease-specific loci, while imputation using public data sets can omit disease-relevant genotypes.

Long-read sequencing (LRS) technologies have enhanced the ability to resolve complex genomic architecture and determine the ‘phase’ relationships of genetic variants over long distances. Although genome-wide association studies (GWAS) identify individual variants associated with complex traits, they do not typically account for whether multiple associated signals at a locus may act in cis or trans, or whether they reflect allelic heterogeneity. As a result, effects that arise specifically from phase relationships may remain hidden in analyses using short-read and microarray data. While the advent of LRS has enabled accurate measurement of phase in population cohorts, statistical methods that leverage phase in genetic association analysis remain underdeveloped. Here, we introduce the Regression on Phase (RoP) method, which directly models cis and trans phase effects between variants under a regression framework. In simulations, RoP outperforms genotype interaction tests that detect phase effects indirectly, and distinguishes in-cis from in-trans phase effects. We implemented RoP at two cystic fibrosis (CF) modifier loci discovered by GWAS. At the chromosome 7q35 trypsinogen locus, RoP confirmed that two variants contributed independently (allelic heterogeneity). At the SLC6A14 locus on chromosome X, phase analysis uncovered a coordinated regulatory mechanism in which a promoter variant modulates lung phenotypes in individuals with CF when acting in cis with a lung-specific enhancer (E2765449/enhD). This coordinated regulation was confirmed in functional studies. These findings highlight the potential of leveraging phase information from LRS in genetic association studies. Analyzing phase effects with RoP can provide deeper insights into the complex genetic architectures underlying disease phenotypes, ultimately guiding more informed functional investigations and potentially revealing new therapeutic targets. Traditional genetic association studies typically link individual genetic variants to diseases but often neglect how variants may jointly affect outcomes based on their arrangement across maternal and paternal chromosomes, known as phase relationships. Understanding phase effects is essential for uncovering the mechanisms underlying complex diseases. Recent advances in long-read sequencing technology allow precise measurement of phase relationships over extensive chromosome regions; however, statistical methods for analyzing these effects remain limited. We developed a novel statistical approach called Regression on Phase (RoP) to directly assess these complex genetic interactions. Our simulation studies demonstrated that RoP effectively identifies effects dependent on specific phase arrangements. Applying RoP to genetic variants contributing to cystic fibrosis (CF) revealed phase-dependent mechanisms affecting CF-related lung disease, which were missed by traditional methods. Analyzing phase effects with RoP can advance our understanding of disease mechanisms, guide future functional studies, and ultimately support the development of personalized medicine.