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Integrating Genetics and Omics to Understand Chronic Obstructive Pulmonary Disease

The availability of comprehensive assessments of specific types of biologically active molecules, referred to as Omics, in large study populations provides unique opportunities for the discovery of new pathobiological mechanisms for complex diseases like chronic obstructive pulmonary disease (COPD). Omics data can assist in identifying key genes that are driving genetic associations to COPD. However, despite substantial progress in delineat-ing the genetic determinants of COPD, the biological networks influencing COPD remain largely undefined. Multiple methods have recently been developed to integrate multiple Omics data and identify key biological networks. These methods are already beginning to provide new insights into COPD pathogenesis and heterogeneity.


I. IntrODuctIOn
Like most diseases that are major public health problems, chronic obstructive pulmonary disease (COPD) is a complex syndrome influenced by multiple genetic and environmental determinants acting within a development context 1 . Traditionally, investigators trying to understand the biological mechanisms for a complex disease have studied selected molecules that they hypothesized would influence disease pathogenesis based on scientific intuition and/or analogies from other disease mechanisms. The development of large-scale biological assessments, referred to as "Omics", allows for comprehensive investigation of particular biological entities rather than selection of a small number of potentially important candidate molecules for study 2 . For example, instead of selecting a candidate gene for a genetic association study, all of the genes in the human genome can be assessed for association to a complex disease. The potential to gain additional pathobiological insights by integrating the information provided by multiple Omics data types is substantial and will be the focus of this review. Our goal is to provide an overview of Omics data types and analytical approaches, using applications to COPD as examples, but not to provide an exhaustive review of every multiple Omics integration method that has been proposed.

II. OvervIew Of OmIcs DAtA tyPes
The major molecular Omics data types are shown in figure 1. Although most of the three billion nucleotide base pairs of DNA sequence in the human genome are identical in all people, there are about 10 million common genetic variants, referred to as single nucleotide polymorphisms (SNPs), and many more rare genetic variants. These genetic variants can be readily assessed using specific assays for single genetic variants, genome-wide SNP panels that provide reasonable coverage of common genetic variation, and whole genome sequencing for comprehensive genetic variation assessment. Epigenetic alterations to the DNA sequence, such as methylation and histone acetylation, can influence gene regulation. Commercially available panels of DNA methylation marks can be assessed for hundreds of thousands of genomic locations; DNA sequencing before and after bisulfite conversion can provide comprehensive assessment of DNA methylation marks. High throughput assays for histone acetylation have been more challenging to develop.
Messenger RNA (mRNA) previously was assessed with microarrays but currently is analyzed with RNA-sequencing. Proteins can be measured as single analytes (e.g., enzyme-linked immunosorbent assays, ELISA), panels of selected proteins (e.g., Luminex, OLINK, SomaLogic), or using large-scale mass spectrometry approaches. Metabolites of various classes can be measured with targeted panels or untargeted assays; the identification of novel metabolites from untargeted assays can be quite challenging. Other Omics data types, which we will not focus on in this review, include microRNAs and other small RNAs as well as the microbiome.
The quality control and analytical approaches differ for each of these Omics data types, but there are four fundamental study design components for all Omics studies. First, a study population must be selected, which can be cases and controls, general population samples, or family-based units. Second, the phenotype of interest needs to be determined; both categorical phenotypes (e.g., disease affection status) and quantitative phenotypes (e.g., quantitative computed tomography [CT] emphysema) can be used. Third, the laboratory assessment method for the Omics data type needs to be selected. Finally, the analytical approaches to adjust for quality control issues (e.g., batch effects, outlier removal, normalization, imputation of missing values) and test for relationships of disease phenotypes to Omics analytes need to be performed.
Key decisions in Omics studies include the biological sample to be analyzed. Increasingly, single cell assays are utilized, although single cell Omics data suffer from sparsity due to both biologic and technical reasons, making data analysis challenging 3,4 . Single cell types (many cells of the same type, rather than individual cells) or tissue samples have been widely used for Omics analysis. Important study population-related issues include the stage of disease (mild versus severe) of included subjects and the sample size for analysis.
Although it is beyond the scope of this review, disease-related phenotypes are often considered as an additional key data type in Omics analysis, particularly given the high dimensionality of CT and other imaging data in COPD  and other complex diseases. Phenotype specificity has implications for discovery of Omics associations with COPD. Due to the misclassification bias inherent to COPD defined by International Classification of Disease (ICD) codes or self-report, these COPD definitions resulted in reduced discovery of genetic risk loci compared to a spirometric definition of COPD in the UK Biobank 5 . High quality "deep" phenotyping is germane to assuring molecular Omics studies give useful biologic insights 6,7 , particularly with complex diseases that are as phenotypically heterogeneous as COPD. Two of the integrative Omics methods that we will discuss in this review -"integrative phenotyping framework" and sparse multiple canonical correlation network analysis (SmCCNet) -were designed to incorporate phenotype information into the analysis of multiple Omics data.

III. GenetIcs Of cOPD
As we have recently reviewed, there is strong evidence for genetic influences on COPD susceptibility and on COPD-related traits 8 . Rare Mendelian syndromes, such as alpha-1 antitrypsin (AAT) deficiency and cutis laxa, can include COPD as part of their phenotypic manifestations 9,10 . Although COPD unrelated to AAT deficiency is strongly influenced by cigarette smoking, genome-wide association studies (GWAS) in large numbers of COPD cases and control subjects have identified 82 genomic regions with strong statistical evidence for association to COPD (Fig. 2) 11 .
Despite this substantial progress in COPD genetics, there are a number of challenges involved in translating these genetic associations to new pathobiological insights. First, the associations highlight genomic regions of interest, but they do not provide adequate resolution to determine the key functional variants driving the statistical association between phenotype and genotype 12 . Second, even if the key genetic variant or variants in a GWAS region can be found, additional work is needed to determine which gene is influenced by those functional variants-it is often not the closest gene 13 . Third, the effect sizes of the many individual COPD GWAS loci are quite modest, suggesting that COPD is a polygenic condition. Utilizing genome-wide genetic variation in a polygenic risk score improves predictive ability for COPD 14 . However, to provide pathobiological insights and to dissect COPD heterogeneity, the specific genes involved in genetic association regions need to be identified. Some progress has been made, including the discovery of functional genetic variation influencing HHIP 15 , FAM13A 16 , and TGFB2 17 , but much more work is needed. Omics data can assist in this challenging process.

Iv. InteGrAtInG GenetIcs
AnD A sInGle OmIcs tyPe Different Omics data types may be more relevant for different subtypes (subsets of COPD subjects with a shared pathobiological mechanism) of the heterogeneous COPD syndrome. A major limitation is that despite extensive efforts with unsupervised clustering analysis 18 , visual and quantitative CT analysis 19 , and transcriptomic-based clustering efforts 20 , there is still not a consensus regarding COPD subtypes. One generally accepted distinction in COPD is that subjects with AAT deficiency have unique clinical and biological features consistent with a COPD subtype. As shown in table 1, the key

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Collaborative research 108 BRN Rev. 2020;6 (2) biological mechanism for AATD is a missense variant in the SERPINA1 gene sequence, which leads to a single amino acid change in the protein sequence and reduced circulating AAT protein levels 9 ; thus, genetics and proteomics are key Omics data types for AATD. For non-AATD COPD, the roles of different Omics data types are unclear. Despite some evidence for associations of epigenetic marks, transcriptomics, proteomics, and metabolomics with COPD, consistently replicated associations have been challenging to find, and it is

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Collaborative research uncertain which Omics differences are causes or effects of the pathological changes in COPD without AATD. The issue of whether these non-genetic Omics measurements are markers of pathologic change or the cause of observed changes is not unique to COPD and is a problem across many complex diseases.
For each of these Omics data types, genetic determinants can be identified: expression quantitative trait loci (eQTLs) for transcriptomics 21 , protein QTLs (pQTLs) for proteomics 22 , methylation QTLs (mQTLs) for epigenetics 23 , and metabolite QTLs for metabolomics 24 . When the genetic determinants of these Omics data types are located near a coding gene, the term "cis" QTLs is used. Almost every gene will have cis-QTLs for related Omics data types (detectable if a large enough sample size is used) 25,26 , so finding a QTL near a GWAS functional variant is not proof that variant influences the gene related to that QTL. Statistical co-localization methods have been developed to determine statistically whether a genomic region associated with both a disease phenotype and an Omics QTL is likely to be driven by the same association signal 27 ; however, multiple independent QTLs exist in many genetic regions 28 ; which violates the assumptions of some colocalization methods.
Long-range genetic effects on Omics data types, known as trans-QTLs, have been more challenging to discover. Larger sample sizes are typically required to find trans than cis-QTL effects, but trans relationships can help to identify long-range biological influences. For example, Sun and colleagues 29 built a Plasma Proteome Genomic Atlas based on SomaLogic assays for 3622 proteins in 3301 participants; they found 1104 trans-pQTL associations, some of which led to hypotheses about biological mechanisms, by leveraging molecular pathways, protein-protein interactions (PPI), variant annotation, eQTLs, and chromatin interactions.
Lamontagne and colleagues 30 integrated GWAS results from the International COPD Genetics Consortium (ICGC) with the Laval/UBC/Groningen lung tissue eQTL database to interrogate likely causal genes within COPD and lung function GWAS loci. They utilized four approaches: 1) Bayesian colocalization, to determine if there was statistical evidence that the GWAS and lung eQTL associations were driven by the same signal; 2) transcriptome-wide association study (TWAS), to test for association of COPD to the estimated lung gene expression levels in the ICGC based on the genetic component of gene expression determined in the lung gene expression dataset; 3) Mendelian Randomization, to determine if using lung eQTL SNPs as instrumental variables can predict COPD; and 4) S-PrediXcan, an alternative approach to estimate gene expression from its genetic components by using an ElasticNet Model. For three COPD/lung function GWAS loci, all four methods implicated the same gene (DSP, CIGALT1, and THSD4). In 60 of the 129 GWAS loci examined, a top candidate gene for the GWAS signal was identified. Sakornsakolpat and colleagues 31 also used S-PrediXcan to estimate gene expression based on genome-wide association data for severe COPD and quantitative emphysema; they implicated key genes within several GWAS loci and found novel associations as well. These predictive approaches are valuable, but they have limitations. For example, correlation between adjacent SNPs (known as linkage disequilibrium) can affect the estimated BARCELONA RESPIRATORY NETWORK genetic influences on gene expression using approaches like TWAS and S-PrediXcan. Thus, investigations that directly assess molecular interactions of putative functional variants with GWAS genes (e.g., chromatin conformation capture 15 ) and that measure the impact of the genetic variant on gene expression (e.g., luciferase reporter assays) remain essential to confirm key genes within GWAS loci.
QTL analysis of plasma from COPD cases and controls by Sun and colleagues 22 led to the identification of many pQTLs. This work clarified the previously confusing relationship of the COPD GWAS SNP near AGER, which encodes the sRAGE protein biomarker-likely the most robust protein biomarker for COPD identified thus far 32 . Since the directions of association for that top AGER SNP, rs2070600, with COPD and with sRAGE protein levels were opposite, including the pQTL SNP genotype and sRAGE levels together in a predictive model provided stronger association with COPD.
Morrow and colleagues 23 performed methylation QTL analysis in lung tissue samples from COPD cases and controls, and they found that COPD GWAS loci were enriched with mQTLs. Using statistical colocalization, they identified several genomic regions, including EEFSEC and IL27, in which the mQTL and COPD GWAS signal appeared to colocalize.

v. InteGrAtInG multIPle OmIcs: rAtIOnAle
There are many reasons why integrating different Omics data types should be pursued (Table 2). First, Omics data have substantial variability unrelated to the disease process of interest. This Omics "noise" can be technical -related to batch effects and measurement error, and biological -related to sex, age, environmental exposures, etc. Even if two different Omics data types are capturing the same disease-related biological signals, their technical and biological variability may be unrelated to each other; thus, true disease-related biological signals may be identified by implicating shared molecules and pathways in different Omics data types. Second, integrating Omics data types could provide insights into biological mechanisms for genetic variation. As discussed previously, quantitative trait loci between functional genetic variants and Omics data can provide insight into gene regulatory mechanisms. Third, different Omics data types can reflect different time scales for biological processes. Except for rare somatic mutation events, the DNA sequence of an individual is invariant over their lifetime. Precise measurements of the half-lives of Omics data types have been limited. Gene expression levels can change rapidly in response to environmental changes 33 , since the half-lives of mRNAs are typically less than 20 minutes. The proteins that they encode can have widely variable lifespans; lung elastin likely lasts an entire lifetime 34 , but other proteins have half-lives less than one day 35 . The metabolites produced by those proteins have relatively short life spans, and they are often assessed by flux balance analysis 36 . Epigenetic marks are typically longer lasting but can change in response to environmental exposures 37 . By reflecting different time scales, different Omics data types have the potential to give alternate perspectives

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Collaborative research on disease pathobiology. Fourth, assessment of multiple Omics data can allow identification of interactions between biological levels. For example, DNA methylation marks can regulate gene expression levels, and proteins (e.g., enzymes) can control metabolite production. Finally, and perhaps most importantly, biological systems operate on multiple biological levels. Integrating multiple Omics data types across these levels could provide more accurate biological models, with regulatory motifs such as feed-back and feed-forward regulation.
The recognition that biological systems function as molecular networks has helped to crystallize thinking about combining multiple Omics data in the field of Network Medicine 2,38 . The various methods utilized to build molecular networks have been recently reviewed by members of the International Network Medicine Consortium 39 . Although genetic studies of complex diseases have traditionally focused on analyses of single variants or clustered groups of variants, the role of biological networks has received increased attention through the Omnigenic Model of complex diseases 40 . As shown in figure 3, central goals of Network Medicine approaches to complex diseases include understanding gene regulation (since most complex disease genetic variants are regulatory and do not alter the protein-coding sequence), determining biological function (since most complex disease genetic variants have previously unknown biological effects), and defining disease pathobiology (which is necessary for improved diagnosis and treatment). "Bottom-up" approaches begin with the identification of functional genetic variants and their relationship to key disease genes, often with evidence provided from Omics data. "Top-down" approaches begin with large-scale Omics data sets and then use various types of network models, including correlation-based networks, gene regulatory networks, and protein-protein interaction networks. Both Bottom-up and Top-down approaches aspire to address one or more of the central goals of Network Medicine.  [41][42][43][44][45] . Key goals of multiple Omics integration include dissecting disease heterogeneity, understanding disease pathogenesis, and predicting disease outcomes ( Table 3).
Many of these integrative methods attempt to combine Omics data agnostically in an effort to reduce biological noise and to use multiple Omics to reclassify a complex disease into subtypes. For example, similarity network

Figure 3. Comparison of Top-Down and Bottom-Up Approaches to Build Biological Networks.
Biological networks can be built from the bottom-up, starting with GWAS regions and identifying the key genes and the functional variants that impact those genes. Networks can be extended from those genes using tools that assess binding with other proteins (e.g., tandem affinity purification, co-immunoprecipitation) as well as hypothesis-based molecular biology experiments. Top-down approaches begin with Big Data assessments of key biological molecules like DNA (with SNP genotyping based on commercial panels or sequencing), RNA (with RNA-seq), proteins (with single analytes, commercial panels, or affinity purification/mass spectrometry [AP-MS]), and metabolites (with targeted or untargeted assays). Various types of networks can be built, including correlation-based networks, gene regulatory networks, and protein-protein interaction networks. Ultimately, bottom-up and top-down approaches may converge to give insights into gene regulation, biological function, and disease pathobiology relevant to COPD (from EK Silverman 8 , Annual Review of Physiology 2020, with permission) . COPD: chronic obstructive pulmonary disease; GWAS: genome-wide association studies; SNP: single nucleotide polymorphism; WGCNA: weighted gene correlation network analysis.

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fusion (SNF) generates similarity networks among individuals for a particular Omics data type, and then "fuses" the similarity networks for each Omics data type into an overall network model that leverages shared and unique information from each data type 46 . By creating a separate subject-to-subject similarity network for each Omics data type, the impact of each Omics type on the overall similarity matrix is not influenced by the number of features within an Omics data type. Subjects are clustered into subtypes based on their relationship to other subjects within this fused similarity network. Entropy-based consensus clustering (ECC) is another related method for agnostically combining Omics data 47 ; multiple Omics data can be utilized to provide optimal molecular partitioning of a study population into discrete clusters, which potentially represent distinct molecular subtypes of a complex disease.
Network models have also been utilized to provide insights into disease pathobiology. Correlation-based networks, such as weighted gene correlation network analysis (WGCNA), have been widely used for analyses of single Omics data 48 . WGCNA network modules based on gene expression analysis have been compared between lung tissue, induced sputum, and peripheral blood in COPD 49 . Shi and Kechris 50 developed another unsupervised correlation-based network approach, parse Multiple Canonical Correlation Network Analysis (SmCCNet), which analyzes multiple Omics data to identify networks related to a quantitative disease-related phenotype of interest. They applied their approach to two Omics data types measured in blood (miRNA and mRNA) in small numbers of COPD cases and controls, with quantitative phenotypes of forced expiratory volume in one second (FEV 1 ) and CT quantitative emphysema. Other Network Medicine approaches leverage the known biological relationships between different Omics data types to provide insight into disease pathogenesis. For example, the Passing Attributes between Networks for Data Assimilation (PANDA) approach creates gene regulatory networks related to a disease of interest by using gene expression data, protein-protein interaction networks, and transcription factor binding site information in a message passing approach 51 . Although PANDA applications thus far have focused on transcriptomic data, other Omics data could be incorporated within this framework in the future. Multiple Omics data can also be used to predict disease-related outcomes. Supervised machine learning approaches, including both traditional machine learning (e.g., random forests) and deep learning (e.g., convolutional neural networks) can be applied to multiple Omics data in order to make disease-related predictions 41 . Relevant COPD-related outcomes could include COPD exacerbations, disease progression, and mortality. The challenge of having more biological features than clinical observations increases the risk of overfitting machine learning models based on multiple Omics data; use of separate training and test data sets, optimally with inclusion of a completely separate validation dataset, should be strongly considered.

vII. InteGrAtInG multIPle OmIcs: APPlIcAtIOns In cOPD
Although we are at the beginning of the multiple Omics era in complex diseases, there have already been several scientific efforts to combine multiple Omics data to understand COPD heterogeneity and COPD pathogenesis. Machine learning approaches have been applied to clinical and imaging data in COPD subjects to predict outcomes such as mortality 52 and to single Omics data, including blood miRNA to predict lung cancer risk in COPD patients 53 . However, multiple Omics data have not yet been reported for machine learning disease prediction in COPD.
Kim, Kaminski and colleagues 54 developed a model-free integrative Omics approach, which they named an "integrative phenotyping framework." After the data are pre-processed to remove batch effects, features are combined across Omics data types, dimension reduction is performed, and feature intensities are smoothed. Subsequently, clustering is done to identify disease subtypes. This approach was applied to a data set that included clinical features, miRNA, and mRNA from lung tissue samples of 319 COPD and interstitial lung disease (ILD) subjects-which was divided into a training and testing data set. In addition to finding clusters of subjects that were predominantly COPD or ILD, a mixed cluster of subjects with COPD-like molecular features who had been diagnosed as ILD was found.
The integrative phenotyping framework has many advantages; one of its limitations is that Omics data can only be combined in a pairwise fashion.
Kusko, Kaminski and colleagues 55 utilized lung tissue samples from COPD (both with and without emphysema) and idiopathic pulmonary fibrosis (IPF) patients for transcriptomics analysis. They generated miRNA and mRNA transcriptomic data from lung tissue, and they used miRNA binding predictions and functionally validated miRNA/mRNA regulatory relationships to build miRNA/mRNA gene regulatory networks using the MirConnX software 56 . Networks were built using transcripts that were differentially expressed: 1) in the same direction in both IPF and emphysema, 2) when comparing emphysema versus control, and 3) when comparing IPF versus control lung tissue samples. All three approaches implicated miR96 as a potentially key miRNA.
Li and Wheelock 57 used similarity network fusion to classify individuals with mild-moderate COPD from nonsmoking controls and smokers with normal spirometry in a sample of 52 female subjects from the COSMIC study. They

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used mRNA, miRNA, proteomics, and metabolomics data from different biospecimens (bronchoalveolar lavage [BAL] fluid, BAL cells, bronchial epithelial cells, and serum). A total of nine Omics/biospecimen combinations were included. Permutation testing was used to determine whether improvements in COPD classification accuracy were statistically significant. Although the specific Omics/sample sets included impacted the classification rate, prediction accuracy generally increased with larger numbers of Omics data sets from one to seven that were included. Replication of these findings in additional cohorts will be necessary, along with further assessments to ensure that over-fitting the large amount of Omics data with a small number of subjects was not problematic.
Multiple Omics data can also be used to understand COPD pathogenesis. Recently, Mastej and Kechris 58 used SmCCNet with two Omics data types (metabolomics and proteomics of plasma) and quantitative COPD-related phenotypes (FEV 1 and CT quantitative emphysema) in 1008 COPDGene study participants. A network of seven metabolites and thirteen proteins was significantly correlated to lung function, and a network of ten metabolites and thirteen proteins was significantly correlated to emphysema. Interestingly, only two proteins (Troponin T and Hemojuvelin) and none of the metabolites overlapped between these two networks.

vIII. Key chAllenGes fOr APPlyInG multIPle OmIcs In cOPD
Leveraging Omics data effectively will require both larger data sets in which multiple Omics data are measured in relevant biospecimens within the same subjects and new analytical methods to integrate these data types more effectively. The integration of common genetic variants with single Omics data types has become standard; additional methodological research will be required for rare genetic determinants, since those analyses will be underpowered with standard genetic association approaches. Rare variant association analysis approaches like linear mixed models in SAIGE hold promise 59 . Larger data sets are needed from biologically relevant specimen types; this limitation is being overcome with large projects like the NHLBI Trans-Omics for Precision Medicine program (TOPMed).
Further research is needed to validate molecular network models developed with multiple Omics data using both cell-based and animal models. Clarification is needed regarding what constitutes appropriate validation of a network. Is seeing a consistent signal after CRISPR-Cas9 manipulation of a genetic variant in a transformed cell line adequate? Are primary cells required? Where do animal models fit in? Further consideration of appropriate biological read-outs is required. Moreover, validation of an entire, complex molecular network is unrealistic; typically, a subset of key network relationships is chosen for investigation.
Finally, new network analysis methods are needed to integrate Omics data types across multiple biological layers in ways that are pathobiologically informative. Multi-level network models are easy to draw schematically but challenging to analyze rigorously. The development of models that incorporate the dynamics of biological systems and their application to longitudinal multiple Omics data, as

IX. cOnclusIOn
The creation of large-scale Omics data sets in well-characterized human populations has the potential to transform studies of COPD pathobiology. Although assessment of single Omics associations with COPD is important, integration of these Omics data types presents opportunities to understand the complex sets of interactions that lead to COPD pathology. However, for the effective application of integrative Omics analysis in COPD (and all complex diseases), several important challenges need to be addressed. As these challenges are met, exciting opportunities will follow for new pathobiological insights into COPD that may lead to improved diagnostic classification, prognostic accuracy, and therapeutic development in COPD.