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. Author manuscript; available in PMC: 2018 Feb 13.
Published in final edited form as: J Invest Dermatol. 2017 May;137(5):e113–e118. doi: 10.1016/j.jid.2016.03.047

The Molecular Revolution in Cutaneous Biology: The Era of Genome-Wide Association Studies and Statistical, Big Data, and Computational Topics

Hima Anbunathan 1, Anne M Bowcock 1
PMCID: PMC5810129  NIHMSID: NIHMS920685  PMID: 28411841

Abstract

The investigation of biological systems involving all organs of the body including the skin is in an era of big data. This requires heavy-duty computational tools, and novel statistical methods. Microarrays have allowed the interrogation of thousands of common genetic markers in thousands of individuals from the same population (termed genome wide association studies or GWAS) to reveal common variation associated with disease or phenotype. These markers are usually single nucleotide polymorphisms (SNPs) that are relatively common in the population. In the case of dermatological diseases such as alopecia areata, vitiligo, psoriasis and atopic dermatitis, common variants have been identified that are associated with disease, and these provide insights into biological pathways and reveal possible novel drug targets. Other skin phenotypes such as acne, color and skin cancers are also being investigated with GWAS. Analyses of such large GWAS datasets require a consideration of a number of statistical issues including the testing of multiple markers, population substructure, and ultimately a requirement for replication. There are also issues regarding the missing heritability of disease that cannot be entirely explained with current GWAS approaches. Next generation sequencing technologies such as exome and genome sequencing of similar patient cohorts will reveal additional variants contributing to disease susceptibility. However, the data generated with these approaches will be orders of magnitude greater than that those generated with arrays, with concomitant challenges in the identification of disease causing variants.

THE GENOME-WIDE ASSOCIATION STUDY ERA

Studies on Mendelian diseases in the 1980s and 1990s were performed with traditional family and linkage studies and showed genetic regions segregating with disease. Positional cloning then led to the identification of the mutated genes (Collins, 1995). However, this approach offered low power and poor resolution when dealing with polygenic disorders, in which it is less likely for all the genetic factors to aggregate in every family under study and the penetrance of risk alleles can be low. In the case of common diseases, there has been a shift to genetic association studies, where parameters such as mode of inheritance and penetrance do not need to be known. Although association studies between common traits and human phenotypes had been used for many years, it was only with the advent of genomic technologies that a global approach was possible. This is usually performed with arrays through which single nucleotide polymorphisms (SNPs) are interrogated by hundreds of individual DNA samples. Termed genome-wide association studies (GWASs), this approach has provided important insights into many common skin diseases, phenotypes, and skin cancer by providing genetic risk factors for disease (Bowcock, 2007).

GWASs are justified on the basis of the “common disease, common variant” hypothesis, where allelic variants present in more than 1e5% of the population can contribute to susceptibility to common disease. Although SNPs have usually been the polymorphic marker of choice because they are frequent in the genome and alleles are common and easy to type, there have also been association tests for other types of variation in the genome such as copy number variation (Craddock et al., 2010). The most popular study design used in GWAS is the case-control study in which the frequencies of markers are tested for association between the genotyped groups with and without disease. One of the most critical aspects of a GWAS is to use a sample size with sufficient statistical power to detect significant association with markers of low effect. The size depends in part on the study design (case-control vs. case-parents), effect sizes of genetic variants, linkage disequilibrium between markers and number of markers used. Although early GWASs were performed on hundreds of patients versus control subjects, cohort sizes in later studies increased by over 10-fold. For most diseases and phenotypes, this has led to the identification of ±10–100 genetic risk factors of low effect.

GWAS HIGHLIGHTS IN DERMATOLOGY

Alopecia areata, vitiligo, psoriasis, and atopic dermatitis are common skin diseases with complex etiologies involving genetic and environmental risk factors. In many cases, GWASs have shown the involvement of unexpected pathways or cell types. Alopecia areata is an autoimmune disease resulting from damage of the hair follicle by T cells. Associations with the major histocompatibility complex (MHC), including HLA-DR, have been described. A region of strong association also resides within the ULBP gene cluster on chromosome band 6q25.1, which encodes activating ligands of the natural killer cell receptor NKG2D. This had not previously been implicated in an autoimmune disease. ULBP3 is up-regulated in lesional scalp from patients with alopecia areata in the hair follicle dermal sheath during active disease (Petukhova et al., 2010). The latter observation is consistent with the observation that cytotoxic CD8+NKG2D+ T cells are both necessary and sufficient for the induction of alopecia areata in mouse models of disease. Other pathways implicated from GWAS include autophagy/apoptosis, transforming growth factor-β/regulatory T cells, Jak signaling, and activation and proliferation of regulatory T cells (Betz et al., 2015; Petukhova et al., 2010).

Vitiligo GWASs have shown associations with genes encoding immunoregulatory components such as TYR, CD80, CLNK, BACH2, SLA, CASP7, CD44, IKZF4, and SH2B3, as well as genes encoding melanocyte components (OCA2-HERC2 and TYR) (Jin et al., 2012).

GWASs for psoriasis have shown over 50 associations to date. This includes an early association with the IL12B 3′-untranslated-region SNP (Cargill et al., 2007), confirming the results of a small Japanese study (Tsunemi et al., 2002). Subsequently, many GWASs for psoriasis have been performed with overwhelming evidence for association with the class I region of the MHC being obtained in all studies. Other implicated pathways include T-cell development and T-cell polarization, innate immunity, and negative regulators of immune responses (Jordan et al., 2012; Nair et al., 2009; Strange et al., 2010; Stuart et al., 2010; Sun et al., 2010; Tsoi et al., 2012; Yin et al., 2014). There are also associations with a select set of genes involved in barrier formation (e.g. deletion of a region of the late cornified envelope gene cluster) (de Cid et al., 2009).

Heterozygotes with loss-of-function mutations in the FLG gene are at increased risk of AD (Palmer et al., 2006). This important finding implicated altered epidermal differentiation in the genesis of this disease. Subsequent GWASs of large European and Asian cohorts have shown over 20 associated loci that also implicate the innate-adaptive immune response, IL-1 family signaling, regulatory T cells, the vitamin D pathway, and the nerve growth factor pathway. Some loci are shared by both populations (e.g., that harboring the gene for OVOL1), and others are unique (e.g., the MHC and CARD11 in Asia) (Ellinghaus et al., 2013; Esparza-Gordillo et al., 2009; Hirota et al., 2012; Sun et al., 2011; Tamari and Hirota, 2014; Weidinger et al., 2013).

Systemic lupus erythematosus is a heterogeneous disease with a diverse spectrum of clinical symptoms, ranging from skin rash to end-organ damage. GWASs for systemic lupus erythematosus have confirmed earlier known HLA associations and have shown additional associations at IRF5 and genes encoding immune system components (Gateva et al., 2009; Graham et al., 2008; Han et al., 2009; International Consortium for Systemic Lupus Erythematosus et al., 2008).

Individuals with Behçet’s disease suffer from recurrent inflammatory attacks leading to recurrent ocular symptoms, oral and genital ulcers, and skin lesions. Behçet’s disease is associated with HLA-B*51, and GWAS of a Turkish cohort has also shown association of a variant within IL-10 conferring decreased mRNA expression (Remmers et al., 2010) and IL23R-IL12RB2. There are also associations with genes encoding other immune system components. Three risk loci are shared with ankylosing spondylitis and psoriasis and include the MHC class I region, ERAP1 and IL-23R, and the MHC class I-ERAP1 interaction), as well as two loci encoding genes shared with inflammatory bowel disease (IL-23R and IL-10). This implicates shared pathogenic pathways in the spondyloarthritides and Behçet’s disease (Kirino et al., 2013).

Systemic sclerosis is an autoimmune disease characterized by fibrosis of the skin and internal organs that leads to profound disability and premature death. Associated loci include CD247, the MHC, IRF5, and the gene encoding STAT4 (Radstake et al., 2010). Acute urticaria and angioedema (nonsteroidal anti-inflammatory drug-induced) have also shown associations through a small GWASs (Cornejo-Garcia et al., 2013).

Risk of acne vulgaris, a common inflammatory disorder of the cutaneous pilosebaceous unit is associated with a locus at chromosome band 11q13.1 in Asians (candidates are DDB2 and SELL) (He et al., 2014). These are both genes encoding proteins that are involved in androgen metabolism, inflammation processes, and scar formation. In Europeans, three associated loci contain genes linked to the TGF-βcell signaling pathway (OVOL1, FST, and TGFB2) (Navarini et al., 2014). Although there is little overlap between the studies, these studies illustrate the power of GWAS in helping understand the causes of some common, poorly understood skin diseases.

Other skin phenotypes that have also been examined through GWAS include skin color. Nine SNPs can predict skin color in Europeans and neighboring populations and could be relevant in future forensic and anthropological investigations (Han et al., 2008; Liu et al., 2015). Several SNPs located in or adjacent to pigmentation genes play a role in tanning response after exposure to sunlight (Nan et al., 2009). There is also a locus on chromosome 3, harboring skin-expressed genes STXBP5L and FBXO40, that is associated with facial photoaging (Le Clerc et al., 2013).

GWASs have also shown associations with basal cell and melanoma skin cancer risk (Barrett et al., 2011; Bishop et al., 2009; Brown et al., 2008; Falchi et al., 2009; Gudbjartsson et al., 2008; Law et al., 2015; Macgregor et al., 2011; Stacey et al., 2008, 2009, 2011, 2015). In the case of melanoma these include genes involved in melanocyte development and telomere maintenance. In the case of basal cell carcinoma, these include MYCN, CASP8-ALS2CR12, ZFHX4, and GATA3 in European populations.

STATISTICAL CONSIDERATIONS IN GWAS

Analysis methods

For binary traits such as presence or absence of disease or a particular phenotype, contingency tables are used to test deviation from independence expected under the null hypothesis of no association. Commonly used statistical tests include Fisher exact test, genotypic tests, and the Cochran-Armitage trend test, which tests if a given genetic marker is associated with the disease, in which case the allele frequencies or genotype frequencies will be different between the two groups. For complex traits for which the models need to include additional covariates such as epidemiological or clinical risk factors, logistic regression models are used. These predict the probability of having the disease for a given genotype. Analysis of variance is used for quantitative traits that are similar to linear regression with a categorical outcome variable. This tests the null hypothesis of no difference between the phenotypic means of any genotype group and can incorporate covariates with main effects and interactions.

For family-based association studies, the transmission disequilibrium test (TDT) (Spielman et al., 1994) can be used to evaluate transmission of one allele more often by chance among families with at least one affected child. PLINK is a popular tool that is used to perform these standard genome-wide association analyses and to provide a framework for modeling quantitative data, allowing incorporation of covariates and family-based genome-wide association analyses like the transmission disequilibrium, DFAM (family-based association for disease traits), and QFAM (family-based association tests for quantitative traits) tests (Purcell et al., 2007).

Multiple testing

The enormous number of statistical tests of association performed for each SNP against the phenotype is an essential part of the GWAS. However, because many tests are done (e.g., by using thousands of SNPs), P-values must be adjusted for multiple testing. In general, this has been somewhat arbitrarily set at 5 × 10−8 (on the basis of the number of markers tested and a P-value of association of .025 per marker). However, this restrictive value means that many bona fide associations cannot be identified because they lie within a region harboring multiple associations that are due to statistical noise. There are also issues of the population being screened. Although 500,000 SNPs is sufficient for detecting a reasonable number of associated loci in European populations, older populations such as those from Africa, where regions of linkage disequilibrium are smaller (for example ±5–30 kilobase pairs for African genomes vs. 30–100 kilo base pairs for European genomes, on average), a GWAS in African populations could require a larger number of markers (probably > 1.5 million) and a concomitantly more significant P-value for detecting and reporting associations.

Population substructure

One of the important considerations in the GWAS design is to control for population structures between the groups. SNP allele frequencies often vary in different populations and can cause spurious associations. Population stratification can be detected with algorithms such as EIGENSTRAT, which uses a principal components analysis approach (Li and Yu, 2008). Data with subpopulations can then be used in genome-wide association analysis by excluding samples with allele frequencies dissimilar to the target population or by adding this as a covariate in the statistical model. Both principal component analysis and multidimensional scaling have been applied to correct population stratifications to reduce spurious associations (Price et al., 2010).

Replication

After detection of association, it is recommended that the association be replicated in another cohort of samples to distinguish true signals from spurious ones and to make a reliable estimate of the effect sizes. It has been a challenge to replicate many GWAS findings, and some guidelines have been outlined by National Human Genome Research Institute working group (Chanock et al., 2007). Some of these include use of sufficient sample sizes, independent datasets from the population in which the initial GWAS finding was made, and identical phenotype criteria in both the GWAS and replication studies.

Meta-analyses

To increase statistical power, multiple studies can be combined to perform a meta-analysis. This helps in the discovery of new variants or in replication of known significant variant, but this approach requires many stages of organization (Evangelou and Ioannidis, 2013). Imputation is used to predict the genotypes of SNPs that have not been typed in the study samples or are missing. A number of algorithms have been developed for this and include IMPUTE, version 1 (hidden Markov model) (Marchini et al., 2007), fastPHASE (Scheet and Stephens, 2006), BIMBAM (expectatione—maximization algorithm) (Servin and Stephens, 2007), MACH (imputes unobserved genotypes fusing reference panel of haplotypes) (Li et al., 2010), and BEAGLE (uses graphical model) (Browning and Browning, 2011). Other imputing methods include an SNP-tagging approach (Johnson et al., 2001), which is used by PLINK (Purcell et al., 2007), SNPMSTAT (Lin et al., 2008), and TUNA (Nicolae, 2006). Approaches for imputation are rapidly evolving, and the next-generation sequencing methods can complement the array-based methods for SNP detection, to help increase the phenotypic prediction accuracy.

Conditional analysis

There are a number of instances in which the top GWA SNP does not account for all the variation at that locus. To detect this additional variation, conditional analyses are performed where secondary association signals are detected after conditioning on the primary associated SNP. Examples of this in skin diseases are the MHC in psoriasis, where there are at least two other secondary signals and secondary hits at IFIH1 and IL12B (Cargill et al., 2007; Yin et al., 2015), and Behçet’s disease, where there is an MHC class II association and a second, independent association within the MHC class I region (Remmers et al., 2010).

Missing heritability

One major issue with GWASs is the missing heritability. Even with increased sample sizes it is not possible to account for a considerable fraction of the genetic contribution of common disease. Detection of missing heritability can include meta-analyses to increase the power of GWASs, resequencing to search for rare variants that cannot be detected with GWAS, and a search for epistasis (gene-gene interactions) and heritable epigenetic effects. An analysis of rare variants identified by resequencing over 1,000 genomes (Abecasis et al., 2012) and typed on an “exome chip” has shown additional variants associated with psoriasis that account for an additional 1.9% of psoriasis heritability (Zuo et al., 2015).

Modeling gene-gene interactions presents a challenge because of the small sample size and large number of SNPs interrogated. Although most computers can handle statistical analysis of few hundred SNPs, higher-order interactions, for which the search space grows with the number of SNPs, require much greater computational resources, along with robust statistical methods. A number of methods have been used for modeling epistatic interactions, including PLINK using standard regression, Random forest (trademark of Leo Breiman and Adele Cutler), and bayesian epistasis association mapping using Markov chain Monte Carlo sampling (Zhang and Liu 2007). In the case of psoriasis, interaction between HLA-C and ERAP1 (Strange et al., 2010) and between HLA-C and a locus within the epidermal differentiation complex (the LCE deletion described earlier) (Bergboer et al., 2012) have been described. Interaction between HLA-B*51 and ERAP1 is described in Behçet’s disease (Remmers et al., 2010). However, there are few examples of gene-gene interactions in any common human disease. This could be due to lack of power to detect such epistatic interactions, even with large cohorts. The interactions with the MHC that have been described might have achieved statistical significance because of the strong initial associations of this locus with the diseases in question.

Pathway analysis

Once a GWAS has shown its top hits and provided a preliminary framework for identifying the genetic component of a disease or phenotype of interest, additional analyses are required to accumulate evidence for association (particularly in the case of less significant findings). Taking a pathway-based approach can help prioritize GWAS results to identify disease-specific pathways, classify clinical outcomes, and find drug targets. These analyses can be loosely classified into three categories (Kraft and Raychaudhuri, 2009): user-defined gene set analysis (over-representational analysis and gene set enrichment analysis), data mining methods (Chen et al., 2010; Wu et al., 2010), and network-based approaches. The last include GRAIL (Raychaudhuri et al., 2009), DAPPLE (Rossin et al., 2011), and GeneMANIA (Warde-Farley et al., 2010). Network-based approaches interrogate a variety of sources including the literature, gene expression, known protein-protein interactions, protein domain similarity, and protein co-localization. Besides pinpointing plausible genes in regions of association, they can help identify additional susceptibility that might be missed because of the genome-wide significance threshold. This was used for osteoporosis, and additional novel genes were identified using connectivity to known bone genes (Farber, 2013).

Transcriptome analyses of psoriatic versus normal skin have been performed with both array-based and next-generation sequencing methods (Li et al., 2014; Suarez-Farinas et al., 2010), and integration of the altered transcriptome with genes identified through GWAS can also be useful for creating altered disease networks. Although currently challenging because of limited knowledge, such approaches have shown transcription factor/DNA-binding proteins that also connect transcriptomics to drug development (Swindell et al., 2015b). Recently, proteomic studies have also been performed in psoriasis (Swindell et al., 2015a), reflecting a future direction of “-omic” studies.

Computational challenges with big data

With the technological advances in the capture of biological data (including next-generation sequencing), the volume of data for common diseases and phenotypes is rapidly growing. Studies involving subjects from large populations with phenotypic and health information require a pipeline that uses advanced informatics methods to convert raw data into useful information. However, these approaches impose a heavy computational burden, and tools used for these computations need to be developed and optimized for the large amounts of data and iterative processes.

PERSONALIZED MEDICINE

With the advances in next-generation sequencing it is possible to identify highly penetrant mutations that could have a direct impact on a patient. However, unlike Mendelian traits, for which genetic risk factors (mutations) are rare and of high penetrance, risk factors identified through GWAS are common and of low penetrance. Hence, although factors identified through GWAS might help assess risk susceptibility for a disease or drug response, the genetic prediction for a complex disease using information from SNPs with alleles with small effect sizes may add little value in clinical decision making until it is possible to explain most of the genetic heritability of any common disease. In spite of this limitation, multiple risk factors from GWASs often cluster into specific biological pathways, and in this way provide novel drug targets (Price et al., 2010). One excellent example of this is the association of psoriasis with the IL-23 receptor gene (IL23R) (Cargill et al., 2007) and the efficacy of biologics targeting components of the IL-23 heterodimer (IL-12B/p40 by ustekinumab) and its downstream pathway (e.g., the anti-IL-17 receptor A by brodalumab) (Lebwohl et al., 2015). In the case of common diseases of the skin, the integration of many layers of biological data (clinical, genetics/genomics, proteomics, epigenomics, metabolomics etc.) will ultimately show the basis for their complex and heterogeneous natures.

Acknowledgments

This study was supported in part by NIH grant AR050266 to AMB.

Abbreviations

GWAS

genome-wide association study

MHC

major histocompatibility complex

SNP

single nucleotide polymorphism

Footnotes

CONFLICT OF INTEREST

The authors state no conflict of interest.

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