Variants associated with HHIP expression have sex-differential effects on lung function

Breathing new life into the study of COPD with genes identified from genome-wide association studies

COPD is a major cause of morbidity and mortality globally. While the significance of environmental exposures in disease pathogenesis is well established, the functional contribution of genetic factors has only in recent years drawn attention. Notably, many genes associated with COPD risk are also linked with lung function. Because reduced lung function precedes COPD onset, this association is consistent with the possibility that derangements leading to COPD could arise during lung development. In this review, we summarise the role of leading genes (HHIP, FAM13A, DSP, AGER and TGFB2) identified by genome-wide association studies in lung development and COPD. Because many COPD genome-wide association study genes are enriched in lung epithelial cells, we focus on the role of these genes in the lung epithelium in development, homeostasis and injury.

Hedgehog pathway and its inhibitors in chronic obstructive pulmonary disease (COPD)

COPD affects millions of people and is now ranked as the third leading cause of death worldwide. This largely untreatable chronic airway disease results in irreversible destruction of lung architecture. The small lung hypothesis is now supported by epidemiological, physiological and clinical studies. Accordingly, the early and severe COPD phenotype carries the most dreadful prognosis and finds its roots during lung growth. Pathophysiological mechanisms remain poorly understood and implicate individual susceptibility (genetics), a large part of environmental factors (viral infections, tobacco consumption, air pollution) and the combined effects of those triggers on gene expression. Genetic susceptibility is most likely involved as the disease is severe and starts early in life. The latter observation led to the identification of Mendelian inheritance via disease-causing variants of SERPINA1 - known as the basis for alpha-1 anti-trypsin deficiency, and TERT. In the last two decades multiple genome wide association studies (GWAS) identified many single nucleotide polymorphisms (SNPs) associated with COPD. High significance SNPs are located in 4q31 near HHIP which encodes an evolutionarily highly conserved physiological inhibitor of the Hedgehog signaling pathway (HH). HHIP is critical to several in utero developmental lung processes. It is also implicated in homeostasis, injury response, epithelial-mesenchymal transition and tumor resistance to apoptosis. A few studies have reported decreased HHIP RNA and protein levels in human adult COPD lungs. HHIP^+/- murine models led to emphysema. HH pathway inhibitors, such as vismodegib and sonidegib, are already validated in oncology, whereas other drugs have evidenced in vitro effects. Targeting the Hedgehog pathway could lead to a new therapeutic avenue in COPD. In this review, we focused on the early and severe COPD phenotype and the small lung hypothesis by exploring genetic susceptibility traits that are potentially treatable, thus summarizing promising therapeutics for the future.

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.