Research Techniques Made Simple: Using Genome-Wide Association Studies to Understand Complex Cutaneous Disorders

  • Lam C. Tsoi
    Correspondence
    Correspondence: Lam C. Tsoi or James T. Elder, Department of Dermatology, University of Michigan, 7421 Medical Sciences I, 1301 East Catherine, Ann Arbor, MI 48109-5609, USA.
    Affiliations
    Department of Dermatology, University of Michigan Medical School, Ann Arbor, Michigan, USA

    Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, USA

    Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
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  • Matthew T. Patrick
    Affiliations
    Department of Dermatology, University of Michigan Medical School, Ann Arbor, Michigan, USA
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  • James T. Elder
    Correspondence
    Correspondence: Lam C. Tsoi or James T. Elder, Department of Dermatology, University of Michigan, 7421 Medical Sciences I, 1301 East Catherine, Ann Arbor, MI 48109-5609, USA.
    Affiliations
    Department of Dermatology, University of Michigan Medical School, Ann Arbor, Michigan, USA

    Ann Arbor Veterans Affairs Hospital, Ann Arbor, Michigan, USA
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      Complex cutaneous disorders result from the combined effect of many different genes and environmental factors, with individual genetic variants often having only a modest effect on disease risk. The ability to examine large numbers of samples is required for correlating genetic variants with diseases/traits. Technological advances in high-throughput genotyping, along with mapping of the human genome and its associated inter-individual variation, have allowed genetic variants to be analyzed at high density in large case-control cohorts for many diseases, including several major skin diseases. These genome-wide association studies focus on showing differences in the frequencies of variants between case and control groups, rather than co-transmission of a variant and disease through a family, as is done in linkage studies. In this review, we provide overall guidance for genome-wide association study analysis and interpreting the results. Additionally, we discuss challenges and future directions for genome-wide association studies, focusing on translation of findings to provide biological and clinical implications for dermatology.

      Abbreviation:

      GWAS (genome-wide association study)
      CME Activity Dates: 21 February 2018
      Expiration Date: 20 February 2019
      Estimated Time to Complete: 1 hour
      Planning Committee/Speaker Disclosure: James T. Elder, MD is a consultant/advisor for Johnson & Johnson and Novartis AG. All other authors, planning committee members, CME committee members and staff involved with this activity as content validation reviewers have no financial relationships with commercial interests to disclose relative to the content of this CME activity.
      Commercial Support Acknowledgment: This CME activity is supported by an educational grant from Lilly USA, LLC.
      Description: This article, designed for dermatologists, residents, fellows, and related healthcare providers, seeks to reduce the growing divide between dermatology clinical practice and the basic science/current research methodologies on which many diagnostic and therapeutic advances are built.
      Objectives: At the conclusion of this activity, learners should be better able to:
      • Recognize the newest techniques in biomedical research.
      • Describe how these techniques can be utilized and their limitations.
      • Describe the potential impact of these techniques.
      CME Accreditation and Credit Designation: This activity has been planned and implemented in accordance with the accreditation requirements and policies of the Accreditation Council for Continuing Medical Education through the joint providership of Beaumont Health and the Society for Investigative Dermatology. Beaumont Health is accredited by the ACCME to provide continuing medical education for physicians. Beaumont Health designates this enduring material for a maximum of 1.0 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
      Method of Physician Participation in Learning Process: The content can be read from the Journal of Investigative Dermatology website: http://www.jidonline.org/current. Tests for CME credits may only be submitted online at https://beaumont.cloud-cme.com/RTMS-Mar18 – click ‘CME on Demand’ and locate the article to complete the test. Fax or other copies will not be accepted. To receive credits, learners must review the CME accreditation information; view the entire article, complete the post-test with a minimum performance level of 60%; and complete the online evaluation form in order to claim CME credit. The CME credit code for this activity is: 21310. For questions about CME credit email [email protected] .

      Advantages and Limitations of GWAS

      Advantages
      • GWASs can identify new susceptibility regions without the need to know which variants may be relevant in advance (“hypothesis-free” approach).
      • Knowledge obtained from GWASs can be used to guide other types of experiments.
      • GWAS is a well-developed approach with many tools available for data analysis and interpretation of results.
      • GWAS is suitable for complex polygenic diseases, with many genes contributing only modestly to disease risk.
      • GWAS has the potential to guide development of precision (personalized) medicine and health care, especially when combined with other biomarkers.
      Limitations
      • GWAS needs a large sample size to achieve sufficient power (i.e., the multiple testing problem).
      • It is often not trivial to identify how variants affect biology.

      Introduction

      Large-scale efforts, such as the Human Genome Project and the 1000 Genomes Project (
      • Auton A.
      • Brooks L.D.
      • Durbin R.M.
      • Garrison E.P.
      • Kang H.M.
      • et al.
      1000 Genomes Project Consortium
      A global reference for human genetic variation.
      ), have allowed common genetic variations (e.g., genetic differences between individuals in which the rare variant is present in >5% of individuals) to be mapped across multiple populations. This has facilitated development of new techniques to study the genetics and genomics of human diseases, including statistical tools for correlating genetic variants with diseases/traits of interest in genome-wide association studies (GWASs). GWASs have significantly advanced the identification of susceptibility regions (i.e., disease-associated regions in the human genome) for cutaneous disorders in different populations, including psoriasis (
      • Tsoi L.C.
      • Stuart P.E.
      • Tian C.
      • Gudjonsson J.E.
      • Das S.
      • Zawistowski M.
      • et al.
      Large scale meta-analysis characterizes genetic architecture for common psoriasis associated variants.
      ,
      • Yin X.
      • Low H.Q.
      • Wang L.
      • Li Y.
      • Ellinghaus E.
      • Han J.
      • et al.
      Genome-wide meta-analysis identifies multiple novel associations and ethnic heterogeneity of psoriasis susceptibility.
      ), atopic dermatitis (
      • Hirota T.
      • Takahashi A.
      • Kubo M.
      • Tsunoda T.
      • Tomita K.
      • Sakashita M.
      • et al.
      Genome-wide association study identifies eight new susceptibility loci for atopic dermatitis in the Japanese population.
      ,
      • Paternoster L.
      • Standl M.
      • Waage J.
      • Baurecht H.
      • Hotze M.
      • Strachan D.P.
      • et al.
      Multi-ancestry genome-wide association study of 21,000 cases and 95,000 controls identifies new risk loci for atopic dermatitis.
      ), alopecia areata (
      • Betz R.C.
      • Petukhova L.
      • Ripke S.
      • Huang H.
      • Menelaou A.
      • Redler S.
      • et al.
      Genome-wide meta-analysis in alopecia areata resolves HLA associations and reveals two new susceptibility loci.
      ), acne vulgaris (
      • He L.
      • Wu W.J.
      • Yang J.K.
      • Cheng H.
      • Zuo X.B.
      • Lai W.
      • et al.
      Two new susceptibility loci 1q24.2 and 11p11.2 confer risk to severe acne.
      ,
      • Navarini A.A.
      • Simpson M.A.
      • Weale M.
      • Knight J.
      • Carlavan I.
      • Reiniche P.
      • et al.
      Genome-wide association study identifies three novel susceptibility loci for severe acne vulgaris.
      ), vitiligo (
      • Jin Y.
      • Andersen G.
      • Yorgov D.
      • Ferrara T.M.
      • Ben S.
      • Brownson K.M.
      • et al.
      Genome-wide association studies of autoimmune vitiligo identify 23 new risk loci and highlight key pathways and regulatory variants.
      ), and lupus (
      • Morris D.L.
      • Sheng Y.
      • Zhang Y.
      • Wang Y.F.
      • Zhu Z.
      • Tombleson P.
      • et al.
      Genome-wide association meta-analysis in Chinese and European individuals identifies ten new loci associated with systemic lupus erythematosus.
      ) (see Supplementary Table S1 online). These discoveries have led to the uncovering of disease pathways and thus have potential to facilitate novel drug development, including the notable example of PCSK9 as a therapeutic target to reduce low-density lipoprotein (LDL) cholesterol levels in hypercholesterolemia (
      • Price A.L.
      • Spencer C.C.
      • Donnelly P.
      Progress and promise in understanding the genetic basis of common diseases.
      ).
      Results from GWASs are also shaping our understanding of biological effects. Far from the early expectations that GWAS would uncover “nonsynonymous” disease-associated mutations (i.e., genetic changes that alter protein structure), interpretation of recent GWAS results has led to an appreciation that disease-associated genetic differences commonly affect the efficiency of regulatory elements in a cell type-specific manner (
      • Farh K.K.
      • Marson A.
      • Zhu J.
      • Kleinewietfeld M.
      • Housley W.J.
      • Beik S.
      • et al.
      Genetic and epigenetic fine mapping of causal autoimmune disease variants.
      ), rather than altering proteins. Coupled with the sheer numbers of variants correlating with disease (for instance, more than 60 distinct loci in psoriasis alone [
      • Tsoi L.C.
      • Stuart P.E.
      • Tian C.
      • Gudjonsson J.E.
      • Das S.
      • Zawistowski M.
      • et al.
      Large scale meta-analysis characterizes genetic architecture for common psoriasis associated variants.
      ]), it becomes apparent why most variants, when considered individually, have only modest effect on disease risk. It is important to understand that this modest risk does not mean that these variants are unimportant, only that further experiments are needed to (i) identify which genes are actually affected by these variations and (ii) understand how the affected genes participate in the disease process.
      This review aims to provide an overview of GWAS and its associated techniques. Specifically, we illustrate how GWAS data, methods, and results can be interpreted, and we discuss the benefits and limitations of GWAS. Although we focus on genotyping arrays, some topics discussed can also be applied to genetic data generated from DNA sequencing experiments.

      Strategies for GWAS

      Genotyping

      To understand the GWAS strategy, it is important to understand the concept of linkage disequilibrium. Figure 1a shows that by crossover during meiosis recombination over many generations, our ancestors’ chromosomes formed small “chunks” of genetic materials (i.e., haplotypes) in which their underlying variations have been preserved (
      • Ott J.
      Analysis of human genetic linkage.
      ). GWAS takes advantage of linkage disequilibrium structure to genotype only one or a few of the correlated variants in the haplotypes and offers clues about causal disease-associated variants.
      Figure 1
      Figure 1Basic illustrations for GWAS. (a) Chromosomes are “sliced and diced” by meiosis over thousands of generations, such that only small chunks of the ancestral chromosomes persist intact in the present-day chromosome (linkage disequilibrium). Each haplotype is represented by a different color, with the crossing point of the blue and orange haplotypes indicating crossover (i.e., the exchange of haplotypes during meiosis). The square brackets indicate a mutation from A to G, which occurs within a small chunk of the blue haplotype. (b) Hybridization and fluorescence technologies define genotypes for each marker across different samples (here represented as dots). The x-axis corresponds to the contrast between the two fluorescent intensities for the two alleles; the y-axis is the average intensity. Samples with homozygous genotypes are colored in red or blue, and the heterozygous genotype is colored purple. Classification of genotypes does not work well if the intensities of the samples do not fall in any one of the three clusters (black). (c) GWASs identify genetic signals (i.e., where there is a statistically-significant difference in allele frequencies) for a particular trait using a statistical model. These markers may lead to a specific phenotype through changes to proteins or regulatory mechanisms. GWAS, genome-wide association study; SNP, single-nucleotide polymorphism.
      Genotyping is the most commonly used approach to profile genetic data for GWAS (
      • Bush W.S.
      • Moore J.H.
      Chapter 11: genome-wide association studies.
      ). Genotyping arrays exploit DNA hybridization and fluorescence technologies (Figure 1b). To detect a single-nucleotide polymorphism, several probes are placed on the array in such a way that for any given probe, the hybridization efficiency of one single-nucleotide polymorphism allele is substantially different from the other allele(s).
      Various genotyping arrays have been developed for association studies (Table 1). Traditional GWAS arrays cover the entire genome and focus on genotyping common variants. Custom arrays, such as Metabochip or Immunochip (Illumina, San Diego, CA), provide high density genotyping in specific regions of interest identified by earlier GWAS studies (
      • Cortes A.
      • Brown M.A.
      Promise and pitfalls of the Immunochip.
      ). For example, the exome array (Exomechip; Illumina) focuses on the approximately 2% of the genome transcribed and translated into proteins. There is general agreement that even if most disease-associated variation relates to gene regulation, finding associations that influence protein structure is of high importance, even if this is uncommon (
      • Rivas M.A.
      • Beaudoin M.
      • Gardet A.
      • Stevens C.
      • Sharma Y.
      • Zhang C.K.
      • et al.
      Deep resequencing of GWAS loci identifies independent rare variants associated with inflammatory bowel disease.
      ,
      • Tang H.
      • Jin X.
      • Li Y.
      • Jiang H.
      • Tang X.
      • Yang X.
      • et al.
      A large-scale screen for coding variants predisposing to psoriasis.
      ). Large-scale genotyping and sequencing projects (
      • Auton A.
      • Brooks L.D.
      • Durbin R.M.
      • Garrison E.P.
      • Kang H.M.
      • et al.
      1000 Genomes Project Consortium
      A global reference for human genetic variation.
      ) have advanced the development of genotyping platforms and efficient strategies in tagging common variants in GWAS arrays. These arrays can be used to study small insertions/deletions in addition to single-nucleotide polymorphisms. Genetic data from genotyping arrays can have many different formats, but the file format used by the PLINK software (a publicly available whole-genome data analysis toolset [
      • Purcell S.
      • Neale B.
      • Todd-Brown K.
      • Thomas L.
      • Ferreira M.A.
      • Bender D.
      • et al.
      PLINK: a tool set for whole-genome association and population-based linkage analyses.
      ], with PLINK 2.0 being the latest version) is commonly supported.
      Table 1Commonly used array platforms for GWAS
      PropertiesGWAS ArrayExome ArrayTargeted Array
      Number of markers500,000 to 5,000,000∼200,000 (can add GWAS content)Vary (e.g., Immunochip and Metabochip, ∼200,000)
      Regions of interestWhole genomeExomeTargeted
      Allow imputationYesNo, if without GWAS contentYes, but well-imputed markers are limited
      Requires prior knowledgeFor taggingExonic regionsCandidate regions
      Variants to studyCommonRareCommon/low allele frequency variants
      Abbreviations: GWAS, genome-wide association study.

      Association

      Single-variant association is performed to associate the alleles/genotypes of each variant with the trait of interest, typically through a generalized linear model (
      • Bush W.S.
      • Moore J.H.
      Chapter 11: genome-wide association studies.
      ). To determine which variants are associated with the trait, a genome-wide significance threshold (P < 5 × 10–8) is normally used (
      • Fadista J.
      • Manning A.K.
      • Florez J.C.
      • Groop L.
      The (in)famous GWAS P-value threshold revisited and updated for low-frequency variants.
      ). This value was chosen to account for P-values being significant by random chance (a frequent problem in multiple testing) by controlling the family-wise error rate, under the assumption of one million independent haplotypes (0.05/106 = 5 × 10–8). The criterion is sufficiently robust for common variants in European populations; however, more stringent criteria might be needed for less common variants or association studies in other populations (
      • Fadista J.
      • Manning A.K.
      • Florez J.C.
      • Groop L.
      The (in)famous GWAS P-value threshold revisited and updated for low-frequency variants.
      ). In case/control studies, odds ratios are often reported as effect sizes for the associated variants identified. Findings from earlier GWAS studies tend to have higher odds ratios (
      • Tsoi L.C.
      • Spain S.L.
      • Knight J.
      • Ellinghaus E.
      • Stuart P.E.
      • Capon F.
      • et al.
      Identification of 15 new psoriasis susceptibility loci highlights the role of innate immunity.
      ) because of the limitations in showing modest signals with a small sample size, whereas the newer loci revealed by later association studies (revealed by larger sample size) tend to have smaller odds ratios.

      Quality control

      Quality control is a critical data processing procedure for ensuring robustness of any downstream analysis for genetic data (
      • Winkler T.W.
      • Day F.R.
      • Croteau-Chonka D.C.
      • Wood A.R.
      • Locke A.E.
      • Magi R.
      • et al.
      Quality control and conduct of genome-wide association meta-analyses.
      ), as with other omics data. In Table 2, we lay out some typical quality control procedures (and commonly used software) for genetic studies, and here we use the quality control metrics used by a genetic study on psoriasis for illustration (
      • Tsoi L.C.
      • Spain S.L.
      • Knight J.
      • Ellinghaus E.
      • Stuart P.E.
      • Capon F.
      • et al.
      Identification of 15 new psoriasis susceptibility loci highlights the role of innate immunity.
      ). The quality of genotyped data may be evaluated using different metrics, and only samples/markers with high quality are considered (e.g., only including samples and markers with ≥95% of genotyping rate; using Hardy-Weinberg equilibrium, P < 1 × 10–6 as a cutoff to filter out markers with observed genotype frequencies deviating from expected) (
      • Bush W.S.
      • Moore J.H.
      Chapter 11: genome-wide association studies.
      ). Most often, raw intensity files are provided, which provide valuable quantitative information regarding hybridization intensity. Cluster plots (Figure 1b) can be produced from intensity files to validate the genotypes that have been generated from the genotype calling algorithm. Such plots are particularly important for rare genetic variants, for which genotype calling is often not trivial (
      • Goldstein J.I.
      • Crenshaw A.
      • Carey J.
      • Grant G.B.
      • Maguire J.
      • Fromer M.
      • et al.
      zCall: a rare variant caller for array-based genotyping: genetics and population analysis.
      ).
      Table 2Common procedures in performing genome-wide association analysis and result interpretation
      StepDescriptionExample Software Programs
      Quality controlArrayPLINK

      GTOOL
       Check genomic build
       Sample genotyping rate
       Check sex inconsistencies
      Marker
       Marker genotyping rate
       Mapping probe to genome (to ensure unique mapping)
       Remove monomorphic markers
       Hardy-Weinberg equilibrium
      Genotype clusteringZ-call

      optiCall
      Principal component analysisEIGENSTRAT

      LASER
      Relationship inferenceKING
      ImputationPhasingMaCH

      ShapeIT

      Beagle
      ImputationMinimac

      IMPUTE2

      Beagle
      HLA imputationSNP2HLA
      AssociationSingle variant association/burden test for rare variantsPLINK-1.9

      EPACTS
      Meta-analysisMETAL

      rareMETAL
      AnnotationAnnotate variantsANNOVAR
      Pathway analysisIdentify enriched functionsINRICH

      ALIGATOR

      MAGENTA

      MEAGA
      Candidate gene prioritizationProvide inference for the best candidate genes from associated lociGRAIL

      OntoFing

      DEPICT
      False positive results can creep in through dependencies between related individuals (including cryptic relatedness) and differences in the underlying genetic structures of the case and control populations (population stratification). Under the latter scenario, the difference in allele frequencies will reflect only the systematic ancestry differences between the two groups (
      • Price A.L.
      • Zaitlen N.A.
      • Reich D.
      • Patterson N.
      New approaches to population stratification in genome-wide association studies.
      ). This can be particularly challenging if either the cases or the control groups are enriched in outliers for the population being studied and can be problematic even for studies that use shared controls. Kinship coefficient (e.g., KING) (
      • Manichaikul A.
      • Mychaleckyj J.C.
      • Rich S.S.
      • Daly K.
      • Sale M.
      • Chen W.M.
      Robust relationship inference in genome-wide association studies.
      ) can be used together with a mixed model (
      • Kang H.M.
      • Sul J.H.
      • Service S.K.
      • Zaitlen N.A.
      • Kong S.Y.
      • Freimer N.B.
      • et al.
      Variance component model to account for sample structure in genome-wide association studies.
      ) to address these issues effectively. Principal component analysis or multidimensional scaling, dimension reduction techniques that project genetic data to lower-dimension space, can also be used to generate covariates for association (
      • Price A.L.
      • Zaitlen N.A.
      • Reich D.
      • Patterson N.
      New approaches to population stratification in genome-wide association studies.
      ) to address population stratification. By performing principal component analysis/multidimensional scaling analysis together with different populations (e.g., 1000 Genomes [
      1000 Genomes Project Consotium
      An integrated map of genetic variation from 1,092 human genomes.
      ]), outliers can be shown by comparing their principal component analysis/multidimensional scaling coordinates with those from the population of interest. Although these are promising approaches for common variants, more advanced techniques may be needed to control for population stratification in the context of rare variant analysis (
      • Lee S.
      • Abecasis G.R.
      • Boehnke M.
      • Lin X.
      Rare-variant association analysis: study designs and statistical tests.
      ) and targeted/exome platforms (
      • Wang C.
      • Zhan X.
      • Liang L.
      • Abecasis G.R.
      • Lin X.
      Improved ancestry estimation for both genotyping and sequencing data using projection procrustes analysis and genotype imputation.
      ). Finally, genomic control (λGC) is a metric of population stratification (
      • Devlin B.
      • Roeder K.
      Genomic control for association studies.
      ) that may be applied to evaluate association results after principal component analysis adjustment or mixed model correction (
      • Devlin B.
      • Roeder K.
      Genomic control for association studies.
      ). Under the null hypothesis that genetic variants are not associated with the trait of interest and the population stratification is adequately corrected, the λGC value would be equal to 1.

      Genotype imputation

      Genotype imputation is a powerful statistical genetic technique (
      • Marchini J.
      • Howie B.
      Genotype imputation for genome-wide association studies.
      ) that allows combining multiple cohorts (through meta-analysis) by providing a common framework to analyze genotypes derived from different platforms. Meta-analysis can significantly enhance power to show more subtle signals associated with the traits of interest. Variants that are not genotyped in a cohort can be imputed (
      • Das S.
      • Forer L.
      • Schonherr S.
      • Sidore C.
      • Locke A.E.
      • Kwong A.
      • et al.
      Next-generation genotype imputation service and methods.
      ) using reference haplotypes from panels with high variant density (e.g., 1000 Genomes, Haplotype Reference Consortium [
      • McCarthy S.
      • Das S.
      • Kretzschmar W.
      • Delaneau O.
      • Wood A.R.
      • Teumer A.
      • et al.
      A reference panel of 64,976 haplotypes for genotype imputation.
      ]). First, the haplotypes of genotyped variants in the cohort are inferred (i.e., phased), with alleles assigned to either the maternal or paternal chromosomes (
      • Delaneau O.
      • Marchini J.
      • Zagury J.F.
      A linear complexity phasing method for thousands of genomes.
      ). Then, the haplotype structure and frequencies (as well as the markers present in both the cohort and reference panel) are used to impute genotypes for the missing variants. In addition to single-nucleotide polymorphisms and insertions/deletions, one can also impute HLA alleles and their amino acid sequences for different classical alleles in the major histocompatibility complex region (
      • Jia X.
      • Han B.
      • Onengut-Gumuscu S.
      • Chen W.M.
      • Concannon P.J.
      • Rich S.S.
      • et al.
      Imputing amino acid polymorphisms in human leukocyte antigens.
      ). This is particularly useful in fine-mapping (i.e., high-resolution mapping for disease-associated variants) major histocompatibility complex associations for immune-mediated diseases, such as psoriasis (
      • Okada Y.
      • Han B.
      • Tsoi L.C.
      • Stuart P.E.
      • Ellinghaus E.
      • Tejasvi T.
      • et al.
      Fine mapping major histocompatibility complex associations in psoriasis and its clinical subtypes.
      ).
      Because a statistical model is used to infer genotypes for the unobserved markers, they must be represented using a continuous “dosage” value (
      • Howie B.
      • Marchini J.
      • Stephens M.
      Genotype imputation with thousands of genomes.
      ). Typically, this value is set to between 0 and 2, indicating the expected number of times the alternative allele occurs. So that dosage values may be used in regression models (i.e., as part of association analysis), genotyped markers are represented in the same way (with 0 indicating that both copies have the reference allele, 2 indicating that both copies have the alternate allele, and 1 being the heterozygous case). By comparing the allele frequency of the marker in the reference samples with that inferred from the cohort, imputation quality metrics measure the accuracy of imputed markers (e.g., r2 for MiniMac [
      • Das S.
      • Forer L.
      • Schonherr S.
      • Sidore C.
      • Locke A.E.
      • Kwong A.
      • et al.
      Next-generation genotype imputation service and methods.
      ], “info score” for IMPUTE2 [
      • Howie B.
      • Marchini J.
      • Stephens M.
      Genotype imputation with thousands of genomes.
      ]). Markers with low imputation quality (e.g., r2 < 0.7 [
      • Tsoi L.C.
      • Stuart P.E.
      • Tian C.
      • Gudjonsson J.E.
      • Das S.
      • Zawistowski M.
      • et al.
      Large scale meta-analysis characterizes genetic architecture for common psoriasis associated variants.
      ]) are removed from the downstream analysis. Imputation works very well for common variants (e.g., a recent study used imputation to evaluate more than 6 times the number of genotyped markers and thus identified five novel disease susceptibility regions for psoriasis [
      • Tsoi L.C.
      • Spain S.L.
      • Ellinghaus E.
      • Stuart P.E.
      • Capon F.
      • Knight J.
      • et al.
      Enhanced meta-analysis and replication studies identify five new psoriasis susceptibility loci.
      ]) but is not as effective for variants with low allele frequencies. Large reference haplotypes (e.g., 1000G via public access [
      • Auton A.
      • Brooks L.D.
      • Durbin R.M.
      • Garrison E.P.
      • Kang H.M.
      • et al.
      1000 Genomes Project Consortium
      A global reference for human genetic variation.
      ]) or Haplotype Reference Consortium [HRC] via Imputation Server [
      • Das S.
      • Forer L.
      • Schonherr S.
      • Sidore C.
      • Locke A.E.
      • Kwong A.
      • et al.
      Next-generation genotype imputation service and methods.
      ]) can help enhance imputation quality for less common variants.

      Applications to interpret association results

      The interpretation of GWAS findings is of critical importance to understand how these genetic signals relate to biological events (
      • Foulkes A.C.
      • Watson D.S.
      • Griffiths C.E.M.
      • Warren R.B.
      • Huber W.
      • Barnes M.R.
      Research techniques made simple: bioinformatics for genome-scale biology.
      ). One of the first steps in downstream analysis is to perform functional annotations for the identified markers (Figure 1c). These annotations can be used to classify the potential role(s) of the implicated variants (e.g., coding or noncoding regions) and to identify nearby genes of interest (
      • Wang K.
      • Li M.
      • Hakonarson H.
      ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data.
      ). As noted, recent large-scale GWASs have found that disease-associated genetic variants (or signals) tend to play regulatory roles. By integrating information from recent large-scale epigenomics projects, such as ENCODE (
      ENCODE Project Consortium
      An integrated encyclopedia of DNA elements in the human genome.
      ) and the National Institutes of Health (NIH) Roadmap (
      • Romanoski C.E.
      • Glass C.K.
      • Stunnenberg H.G.
      • Wilson L.
      • Almouzni G.
      Epigenomics: Roadmap for regulation.
      ), we can provide inference for the chromatin states and corresponding cell types of the associated regions.
      Once markers have been annotated, pathway analysis can be used to identify biological functions for the genes among the disease loci. Bioinformatics approaches identify the pathways/functions that are enriched among the genes in associated loci (
      • Lee P.H.
      • O’Dushlaine C.
      • Thomas B.
      • Purcell S.M.
      INRICH: interval-based enrichment analysis for genome-wide association studies.
      ) compared with genes from the (nonsignificant) background regions. Identifying the best candidate genes from disease regions can also be important, especially when designing replication experiments (e.g., resequencing selected candidate genes or regulatory sequences). Various approaches (based on text mining [
      • Raychaudhuri S.
      • Plenge R.M.
      • Rossin E.J.
      • Ng A.C.
      • International Schizophrenia C.
      • Purcell S.M.
      • et al.
      Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions.
      ], gene expression [
      • Pers T.H.
      • Karjalainen J.M.
      • Chan Y.
      • Westra H.J.
      • Wood A.R.
      • Yang J.
      • et al.
      Biological interpretation of genome-wide association studies using predicted gene functions.
      ], or ontology [
      • Tsoi L.C.
      • Boehnke M.
      • Klein R.L.
      • Zheng W.J.
      Evaluation of genome-wide association study results through development of ontology fingerprints.
      ]) have been proposed to integrate independent information with traits/tissue types of interest to enhance the prioritization of candidate genes in each locus. Pathway analysis can also prioritize genes that are mapped to the enriched functions (
      • Tsoi L.C.
      • Elder J.T.
      • Abecasis G.R.
      Graphical algorithm for integration of genetic and biological data: proof of principle using psoriasis as a model.
      ), and network-based approaches capturing gene-gene interactions can be used to identify gene clusters with significant connectivity (
      • Rossin E.J.
      • Lage K.
      • Raychaudhuri S.
      • Xavier R.J.
      • Tatar D.
      • Benita Y.
      • et al.
      Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology.
      ) or shortest distances (
      • Tsoi L.C.
      • Elder J.T.
      • Abecasis G.R.
      Graphical algorithm for integration of genetic and biological data: proof of principle using psoriasis as a model.
      ). Statistical genetics techniques have also been developed to provide robust estimation of heritability using GWAS data. For example, genome-wide complex trait analysis (i.e., GCTA) uses variance component estimation to estimate the heritability of genetic variants captured by the genotyping platform (
      • Yang J.
      • Lee S.H.
      • Goddard M.E.
      • Visscher P.M.
      GCTA: a tool for genome-wide complex trait analysis.
      ).

      Challenges and Future Directions

      GWASs have facilitated both the generation and evaluation of new hypotheses in basic science and clinical research over the last decade (
      • Claussnitzer M.
      • Dankel S.N.
      • Kim K.H.
      • Quon G.
      • Meuleman W.
      • Haugen C.
      • et al.
      FTO obesity variant circuitry and adipocyte browning in humans.
      ,
      • Price A.L.
      • Spencer C.C.
      • Donnelly P.
      Progress and promise in understanding the genetic basis of common diseases.
      ,
      • Turner A.R.
      • Kader A.K.
      • Xu J.
      Utility of genome-wide association study findings: prostate cancer as a translational research paradigm.
      ). Most GWASs have been conducted in European populations, with relatively less comprehensive genetic information for other underrepresented populations (e.g., Arabic, Indian). Although GWASs with increased sample size and transethnic components are ongoing (
      • Morris D.L.
      • Sheng Y.
      • Zhang Y.
      • Wang Y.F.
      • Zhu Z.
      • Tombleson P.
      • et al.
      Genome-wide association meta-analysis in Chinese and European individuals identifies ten new loci associated with systemic lupus erythematosus.
      ,
      • Paternoster L.
      • Standl M.
      • Waage J.
      • Baurecht H.
      • Hotze M.
      • Strachan D.P.
      • et al.
      Multi-ancestry genome-wide association study of 21,000 cases and 95,000 controls identifies new risk loci for atopic dermatitis.
      ), the current challenges are to provide biological inference for each of the disease loci identified. Specifically, functional assays need to be in place to test hypotheses developed from GWAS results, providing in silico/in vitro experimental evaluations on the biological effects for the susceptibility loci. In addition, it is important that follow-up studies involve appropriate cell types, because disease-associated regulatory events are usually cell type specific (
      • Farh K.K.
      • Marson A.
      • Zhu J.
      • Kleinewietfeld M.
      • Housley W.J.
      • Beik S.
      • et al.
      Genetic and epigenetic fine mapping of causal autoimmune disease variants.
      ). Epigenetic and expression data (
      ENCODE Project Consortium
      An integrated encyclopedia of DNA elements in the human genome.
      ,
      • Lonsdale J.
      • Thomas J.
      • Salvatore M.
      • Phillips R.
      • Lo E.
      • Shad S.
      • et al.
      The genotype-tissue expression (GTEx) project.
      ) can be used to investigate whether disease-associated genetic variants alter the chromatin accessibility of specific genes and thereby gene expression. Complex cutaneous disorders are unique in that the affected tissues are readily available and relatively easy to obtain, thus making the design and implementation of downstream analysis more efficient, as illustrated in the large-scale transcriptomic studies conducted on skin tissues (
      • Johnston A.
      • Sarkar M.K.
      • Vrana A.
      • Tsoi L.C.
      • Gudjonsson J.E.
      The molecular revolution in cutaneous biology: the era of global transcriptional analysis.
      ). There is potential for a higher rate of ascertainment bias in self-reported or health record data related to skin conditions for genetic studies, but methods are being developed to address this potential challenge (
      • Tsoi L.C.
      • Stuart P.E.
      • Tian C.
      • Gudjonsson J.E.
      • Das S.
      • Zawistowski M.
      • et al.
      Large scale meta-analysis characterizes genetic architecture for common psoriasis associated variants.
      ).
      The vast amount of data and information obtained from GWAS studies may inform precision/personalized medicine for patients with cutaneous disorders. The next wave of GWASs should aim to integrate information from clinical data by associating genetic data with health records (i.e., Phenome-wide association studies [PheWAS]) (
      • Denny J.C.
      • Bastarache L.
      • Ritchie M.D.
      • Carroll R.J.
      • Zink R.
      • Mosley J.D.
      • et al.
      Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data.
      ), or drug responses (i.e., pharmacogenetics) (
      • Whirl-Carrillo M.
      • McDonagh E.M.
      • Hebert J.M.
      • Gong L.
      • Sangkuhl K.
      • Thorn C.F.
      • et al.
      Pharmacogenomics knowledge for personalized medicine.
      ). The challenge, however, is that GWAS loci alone cannot yet provide clinically relevant risk assessment for disease (such as the risk of development of psoriatic arthritis in a psoriasis patient [
      • Stuart P.E.
      • Nair R.P.
      • Tsoi L.C.
      • Tejasvi T.
      • Das S.
      • Kang H.M.
      • et al.
      Genome-wide association analysis of psoriatic arthritis and cutaneous psoriasis reveals differences in their genetic architecture.
      ]). Moving forward, efforts should focus on integrating information from GWASs with a variety of other clinical biomarkers and omics data (i.e., proteomics, metabolomics, transcriptomics, etc.) to produce useful tests to allow clinical decision making for individualized health care.

      Multiple Choice Questions

      • 1.
        Which of the following is NOT a type of array used for genotyping?
        • A.
          Exomechip
        • B.
          Immunochip
        • C.
          Metabochip
        • D.
          Compuchip
      • 2.
        What is the typical range of values for imputed genotypes?
        • A.
          0 to 1
        • B.
          0 to 2
        • C.
          –1 to 1
        • D.
          0 to 100
      • 3.
        Which of the following can be used to address population stratification?
        • A.
          Annotation
        • B.
          Genomic control
        • C.
          Multiple testing
        • D.
          Phasing
      • 4.
        What P-value threshold is commonly used for genome-wide significance?
        • A.
          5 × 10–4
        • B.
          5 × 10–6
        • C.
          5 × 10–8
        • D.
          5 × 10–10
      • 5.
        Which of the following is not a priority for GWAS research in skin disease?
        • A.
          Increased sample size and integration across ethnicities
        • B.
          Inferring the biological function of the disease loci identified
        • C.
          Integrating information from clinical data for precision medicine
        • D.
          Identifying differences in gene expression

      Conflict of Interest

      The authors state no conflict of interest.

      Acknowledgments

      This work was supported by the Arthritis National Research Foundation and the National Psoriasis Foundation (LCT and MTP) and by awards from the National Institutes of Health ( K01AR072129 to LCT and R01AR042742 , R01AR050511 , R01AR054966 , R01AR063611 , and R01AR065183 to JTE). LCT was also supported by the Dermatology Foundation. LCT and JTE were supported by the Dawn and Dudley Holmes Foundation and the Babcock Memorial Trust. JTE is supported by the Ann Arbor Veterans Affairs Hospital.

      Supplementary Material

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