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 Table of Contents  
REVIEW ARTICLE
Year : 2013  |  Volume : 1  |  Issue : 1  |  Page : 4-10

A concise history of genome-wide association studies


1 Department of Medical Genetics, University Medical Center, Utrecht, The Netherlands
2 Prince Mohammed Center for Research and Consultation Studies, University of Dammam, Kingdom of Saudi Arabia
3 Department of Experimental Cardiology, and Division Heart and Lungs, University Medical Center, Utrecht, The Netherlands
4 Department of Cardiology, Division Heart and Lungs, University Medical Center, Utrecht, The Netherlands

Date of Web Publication3-Jun-2013

Correspondence Address:
Amein Al-Ali
Prince Mohammed Center for Research and Consultation Studies, University of Dammam, 31441
Kingdom of Saudi Arabia
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DOI: 10.4103/1658-631X.112902

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  Abstract 

Genome-wide association studies (GWASs) have had a tremendous impact on the pace of genomic research of common diseases. The number of identified genetic variants associated has grown exponentially. For some diseases, such as coronary heart disease (CHD), the number of known susceptibility genes has grown from a handful to more than 45. A substantial number of genes point to unexpected mechanism involved, and functional data from the "Encyclopedia of Deoxyribonucleic Acid Elements" (ENCODE) project is helpful in uncovering the functional relevance to diseases. The rapidly evolving techniques have made the shift from family-based linkage studies to GWASs possible. Advanced single nucleotide polymorphism (SNP) arrays containing hundreds of thousands of variants efficiently assess the extent of genome-wide disease-associated genetic variation. Along with SNP arrays came breakthroughs in statistical analyses and study designs leading to the exponential growth of the GWAS catalog. Pathway analyses of GWASs results with manually curated software programs have been insightful. Next-generation sequencing (NGS) of the exome or even the whole genome will undoubtedly shift the balance in focus from common variants to more rare variations impacting common diseases. Moreover, the combined power of GWASs, sequencing, pathway analysis, and functional data to study common disease shall only be limited by our ability to comprehend.

Keywords: Association, genome, genome-wide association studies


How to cite this article:
Koeleman BP, Al-Ali A, van der Laan SW, Asselbergs FW. A concise history of genome-wide association studies. Saudi J Med Med Sci 2013;1:4-10

How to cite this URL:
Koeleman BP, Al-Ali A, van der Laan SW, Asselbergs FW. A concise history of genome-wide association studies. Saudi J Med Med Sci [serial online] 2013 [cited 2017 Mar 29];1:4-10. Available from: http://www.sjmms.net/text.asp?2013/1/1/4/112902


  Introduction Top


Since the first large genome-wide association study (GWAS) of seven common diseases performed by the Wellcome Trust Case-Control Consortium (WTCCC, www.wtccc.org.uk ), a myriad of follow-up studies and independent GWASs on different diseases have fast-forwarded the genetics of common diseases. [1] As a result, our increased understanding of the genetic background of common diseases is unprecedented, and ongoing exponentially. Currently the GWAS catalog holds 4892 independent reports of loci associated with different traits and in different populations with genome-wide association level. [2] Since on average more than 450 GWAS papers are published each month, the number of associated loci will continue to increase.

Needless to say, GWASs are considered a huge success for human genetics, even though skepticism has been raised since all these loci only seem to explain a minority of the heritability of disease. For most diseases, the number of known susceptibility genes has gone from none or a handful to more than 10, to even dozens. For example, prior to the first GWAS, few genes were known to be associated with coronary heart disease (CHD), whereas currently, this number is more than 45 for CHD, and even more genes are now implicated for related phenotypes like hypertension and lipid levels. [3],[4],[5]

Along with the discovery of many disease genes came surprising and often unexpected insights on the disease mechanisms. Nevertheless, complete elucidation of the functional effects of genetic risk variants and the exact role in the disease process is lacking for the majority of discovered risk variants. Such functional studies are, however, slowly increasing and expected to become more successful. Now the "Encyclopedia of Deoxyribonucleic Acid Elements" (ENCODE) project ( www.nature.com/encode ) has started to accumulate a wealth of functional data on non-coding genomic regions that are expected to regulate gene expression. [6] Indeed, first reports show that GWAS-associated variants do accumulate, as speculated before, in non-coding genomic regions that affect gene expression regulation. [7]

Finally, the detection of disease genes has in very rare cases been translated to disease intervention. A recent example is anti-IL17 mab (ixekizumab) therapy in psoriasis that is currently in phase I clinical trials. [8],[9] Part of the evidence that led to this therapy came from the GWAS associations that clearly point to Th17 T cells that are critically under control by IL17. [10]

Here, we give a general overview of the past decade and describe the historic development of the techniques and statistical approaches of gene mapping in which GWAS takes a major place, and reflect on the future directions of genetic research.


  Gene Mapping Disease Architecture: Mendelian Versus Complex Top


Before GWAS, disease gene mapping was mainly focused on family-based approaches, such as linkage analysis of larger pedigrees or "genome-wide" linkage disequilibrium mapping of modest sized affected sib pair cohorts. [11],[12] This focus on families was set for different reasons. At that time, most disease genes had been mapped in families suffering from a Mendelian disease, and it was believed that families segregating a common disease would have a similar single disease gene segregating with disease. In a family, the chromosome region that would segregate with disease is expected to be large enough to allow traditional linkage analysis with "only" a few hundred markers tested in the single family, after which follow-up in multiple families of linked markers could confirm disease locus. Evidently, this single gene hypothesis does not seem to hold for most common disease families collected, and most likely, such families are the results of random accumulation of multiple low-risk genetic variants. As larger families are rare, yet affected sib pairs are more common, sib pair linkage studies were more feasible. Again, the chromosomal segment shared between affected sibs is still expected to be conveniently larger such that up to 500 markers would be sufficient for a genome-wide screen. [13]


  Genotyping Technologies Top


The human genome, or human Deoxyribonucleic Acid (DNA), consists of around 3.2 billion nucleotides chained together and organized into 23 different chromosome pairs. Four different nucleotides make up the sequence of this genome, and for a large part are identical between any two human individuals, but variation of nucleotide sequence exists. The most abundant variations are single nucleotide variations, i.e., single nucleotide polymorphisms (SNPs). The presence or absence of variable nucleotide sequence is called a genotype, and marks its specific position on the genome (marker). An SNP is in general a single nucleotide sequence variation at a given nucleotide in the genome. Determination of this variation in subjects is the process of genotyping. It is safe to say that the development of automated genotyping technologies is the single most important factor that has spawned the current GWAS. [14] The genotyping of thousands of SNPs from more than thousand individuals lies at the heart of every GWAS.

SNPs are the most abundant form of variation of the human genome and are truly spread "genome-wide" in sufficient density to allow complete coverage of the genome. Since the beginning of gene mapping through DNA markers, maps of the genome have been generated that show physical location of the polymorphic markers. Mapping of SNPs has been done within a large international consortium, HapMap. [15] The HapMap consortium is a catalog of common genetic variants of the human genome, and describes the type and location of variation with its distribution among different populations. Importantly, this information has been used by the scientific community and sequencing companies to select the set of variations (SNPs) that most effectively capture all variations of the genome. During the development of the HapMap project, it became evident that the human genome was inherited in selective "chunks" defined by hotspots of recombination. These chunks, originally named haplotype blocks, represent groups of SNPs that seem not to be broken by recombination. [16],[17] This unique feature of the variation in our genome allowed for selection of a minimal set of SNPs that effectively capture all variations in the haplotype blocks. [15],[18] These minimal set of SNPs were called tagging SNPs (tSNPs) as they tag other unobserved variation.

Another important aspect of the HapMap project is the information on the allele frequency distribution of SNPs in different populations. HapMap holds this information for standard sets of DNA derived from four groups of people with African, Asian, or European ancestry. It is evident that SNPs from these different ancestries can vary significantly in allele frequencies. This is very important when disease association studies are performed, since spurious results could be expected when patients are not matched with controls for their ancestry. Using the population-specific information of HapMap, researchers are able to match cases and controls for their ancestry using genome-wide SNP data as genetic marker for ancestry. [19],[20],[21]

The success of GWASs also depends on the ability to automate SNP genotyping. A few disease-specific arrays are commercially available, such as the human cancer-, immune-, and metabo-beadchips. [22],[23],[24] These chips contain all relevant SNPs for cancer, immunological, or metabolic syndrome diseases, and allow cost-effective large-scale follow-up studies of previously detected disease associations.


  Statistical Developments/Study Design Top


Next to these important technical developments, a single theoretical paper on the genetic architecture of common disease and statistical power calculations has marked the swift toward the current large-scale case-control studies. [25] At the start of the new millennium, it became clear that the moderate-sized sib pair genome-wide linkage studies at best had identified only a handful of true disease loci, which was contrasted by a large number of conflicting study results. This observation made clear that the relative risk of most genetic risk factors for common diseases is modest and lower than 1.5. In a seminal paper by Risch, the sample sizes needed to detect genetic risk factors with such low relative risk for the sib pair and case-control study design were calculated. [25] This calculation made apparent that unfeasibly large sib pair sample sizes were needed to have similar power as case-control studies for 80% power to detect modest risk factors. This paper marked the shift toward case-control GWASs. [26],[27] With the observation that the effect size of genetic risk factors is lower than expected came the need to have a theoretical framework of the genetic architecture of common diseases. Several authors have built on this framework that started with theoretical evidence for the "common disease common variant" hypothesis. [28],[29],[30] This hypothesis simply predicts that the genetic background of common diseases is polygenic and consists of multiple common disease-causing variants that are present in all human populations in which a given disease is present. The genetic risk for disease in a single individual would be determined by the presence of each variant at each gene influencing a complex disease that contributes small additive or multiplicative effect on the disease phenotype. Several studies have provided evidence for the plausibility of this theory, and current GWAS results have indeed proven that for most common diseases, multiple low risk factors do exist. However, with the increasing number of common disease variants detected by GWASs, the amount of the heritability explained by these common variants remained disappointing. This case of the missing heritability led to some adjustment of the theory of the genetic architecture of common diseases, and it is now believed and, to a certain extent, demonstrated, that disease variation fits a continuum of different allele frequencies and genetic risks [Figure 1]. [31],[32],[33] Standard-sized GWASs are able to detect the common disease variants with good power, yet disease variation with intermediate or low allele frequencies needs much larger sample sizes and different tagging SNPs to be detected. Finally, relatively rare genetic variation with high risk for common disease exists and it is generally accepted that the rare low risk variation may remain undetectable by GWAS. [34] These theoretical developments have resulted in adaptation of GWASs for detection of the full spectrum of underlying genetic risk variation. [35]
Figure 1: Expected distribution of disease variants. Theory on common disease variation predicts a continuum of the frequency and effect size of disease variants present in the population. Rare high‑risk variations are mainly expected for Mendelian disease. Less frequent disease variants with moderate effects are the subject of contemporary GWAS. The common low‑risk variants have been largely detected by GWAS

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  Current State of the Art Top


The general outline of the steps taken in a GWAS is presented in [Figure 2]. The first large GWAS was conducted by the WTCCC and consisted of simultaneous effort involving seven disease cohorts of ±2000 patients each and two central control groups of ±1500 individuals each. [1] This first study virtually doubled the known genetic risk factors for most of the targeted diseases. For the autoimmune diseases, Type 1 Diabetes (T1D), Crohn's disease, and rheumatoid arthritis (RA), this first and the following GWASs have been particularly successful, and so far, more than 20 common genetic risk variants have been detected. These discoveries have led to an increase in our understanding of the disease mechanism. For example, for T1D, GWAS hits confirmed the importance of T-cell functionality for disease evident from significant associations of CTLA4, PTPN22, IL2, and IL2RA, and the central role of the HLA class II antigen presentation mechanism of the adaptive immune system. [36] Not surprisingly, these immune-related associations are largely shared with other autoimmune diseases, yet notable exceptions are still not clarified "telltales" of disease-specific different mechanisms within autoimmune diseases. [37] However, unexpected associations were also detected. For example, association was also detected between SH2B3 on chromosome 12q24 and T1D. [38] Surprisingly, this association is not only shared with other autoimmune diseases such as celiac disease (CeD), systemic lupus erythematous, RA, and multiple sclerosis (MS), but also with platelet count and cardiovascular disease, hypertension, myeloproliferative diseases, erythrocytosis, suggesting a shared disease mechanism between these diseases mediated through SH2B3. [39],[40],[41],[42],[43],[44],[45],[46],[47] SH2B3 encodes for LNK, a member of the SH2B family of adaptor proteins that is a key molecule for several signaling pathways mediated by Janus kinase and receptor tyrosine kinases. The most relevant function of LNK is its regulatory function in hematopoeitic and non-hematopo eitic cells such as endothelial cells (EC) and its key inhibitory role is growth factor and cytokine receptor-mediated signaling, yet it may also serve as a protective factor against bacterial infection. [48],[49] These findings suggest that LNK could be a useful target for modulation therapies. Another example of how GWAS has confirmed or in part elucidated the underlying disease mechanism is MS. [50] A recent MS consortium GWAS of close to 10,000 cases identified and confirmed more than 57 susceptibility loci, 21 of which are overlapping with CeD, Crohn's disease, T1D, or RA. [51] For more common diseases, such as CHD, Type 2 Diabetes (T2D), and other traits such as height and body mass index (BMI), GWASs have expanded in size to more than 10,000 cases and controls. For CHD, the first major advance came in 2007 with the WTCCC and three independent studies reporting a strong association between common variants on chromosome 9p211. [52],[53],[54] Furthermore, this locus has subsequently been found to be associated to T2D, and aneurysms, but is not associated with cardiovascular risk factors such as lipids, blood pressure, or obesity. [55],[56],[57],[58] These observations are suggestive that the biological consequence of the risk variation at 9p21 affects an unexpected disease mechanism for CHD.
Figure 2: Workflow genome‑wide association study. (a) The initial prerequisite for a GWAS is the collections of DNA samples for genotyping from nonrelated subjects. For disease associations, the group of patients is compared with representative subjects from the general population. Controls should be confirmed not to have the disease if this disease is relatively common (>1% in the general population). (b) DNA of all subjects is isolated and genotyped using SNP microarrays containing several thousands to more than a million. (c) With HapMap reference data, ancestry can be calculated using the so‑called principle component analysis (PCA). Two PCA values can be plotted for each individual (dots in the graph), which graphically represents the genetic signature that represents ancestry. Clusters of dots represent groups of individuals with shared ancestry. (d) A QQ‑plot shows the observed P values against the expected P values for all the SNPs tested. Given the large number of independent tests, low P values are expected by change at a rate following normal distribution (diagonal line). Any deviation can be taken as an evidence for bias. Deviation of the diagonal at the extreme end of the distribution is a cases% controls% A% B% C% D% E% reflection of an enrichment of likely true disease loci. (e) The so‑called Manhattan plot visualizes GWAS results. For each SNP (colored dots) association, P values (y‑axis) are plotted against genomic position in basepairs for each chromosome in numerical order (x‑axis). SNPs on different chromosomes are plotted in different colors

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Much research has been conducted since to elucidate this mechanism. Two genes are at some distance of the most associated SNPs, CDKN2A and CDKN2B. These two genes encode cyclin-dependent kinase (CDK) inhibitors. CDKN2A is frequently found mutated in tumors and is a known tumor suppressor gene. CDKN2B is an inhibitor of CDKs and functions as an important regulator of cell growth. A mouse model in which the 9p21 region had been deleted showed increased expression of CDKN2A and CDKN2B and abnormal aortic smooth muscle cells. [59] Finally, the 9p21 risk variants have been associated to the non-coding RNA (ncRNA), ANRIL (antisense ncRNA in the INK4 locus), that can alter expression of other genes through silencing. [60],[61] Apart from the 9p21 locus, more than 12 GWASs of CHD and meta-analyses of independent studies combined have validated previously reported loci and identified more than 45 genetic risk loci significantly associated with CHD. [3],[62],[63]

From the examples given above, it is clear that GWASs have an enormous impact on our genetic understanding of common diseases. Finally, significant efforts are only recently underway to translate the genetic finding to disease-related function and possible application in clinical practice.


  Future Directions Top


It is clear that current GWASs can only detect common variants with population frequencies of at least 5% or more, and therefore, are unable to give a complete picture of all the genetic variations underlying a disease. Following the indirect association method of GWASs, this is now being adapted by changing contents of genotyping arrays that include more rare, non-synonymous, and previously associated disease variations. [64] Furthermore, the 1000 Genome Project ( www.1000genomes.org ) is filling in the gaps of our knowledge of rare variations in the general population that can be used in a similar manner to HapMap to calculate association for rare unobserved variations. [65]

Genotyping arrays as a technology are being replaced by novel sequencing applications, next-generation sequencing (NGS). For example, several large whole exome sequencing (WES) studies have been performed in which all exons of protein coding genes are sequenced by NGS. [66] Such studies have suggested that many rare variations may underlie common diseases, and excitingly implicated de novo mutations as a potential disease mechanism, in particular, in autism. [67],[68],[69],[70]

Finally, the biology behind the detected GWAS association is slowly emerging. As stated above, the ENCODE project has paved the way for more insight in gene regulation effects of associated variants, and the first general studies have been published recently. [6],[7],[71] Studies of disease-specific cell types have given exciting preliminary results of the cell-specific effects and phenotypes that are conferred by some GWAS-associated SNPs. [72] Such advances are important and give researchers the correct cell models to design and test future effective therapies. [73]


  Acknowledgments Top


Sander W. van der Laan is funded through a grant from CVON (GENIUS), the Netherlands. Folkert W. Asselbergs is supported by a clinical fellowship from the Netherlands Organisation for Health Research and Development (ZonMw grant 90700342).

 
  References Top

1.Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 2007;447:661-78.  Back to cited text no. 1
    
2.Hindorff LA, MacArthur J, Morales J, Junkins HA, Hall PN, Klemm AK, et al. A Catalog of Published Genome-Wide Association Studies. Available from: http://www.genome.gov/gwastudies [Last accessed on 2012 Dec 15].  Back to cited text no. 2
    
3.CARDIoGRAMplusC4D Consortium; Deloukas P, Kanoni S, Willenborg C, Farrall M, Assimes TL, et al. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet 2013;45:25-33.  Back to cited text no. 3
    
4.International Consortium for Blood Pressure Genome-Wide Association Studies, Ehret GB, Munroe PB, Rice KM, Bochud M, Johnson AD, et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 2011;478:103-9.  Back to cited text no. 4
    
5.Asselbergs FW, Guo Y, van Iperen EP, Sivapalaratnam S, Tragante V, Lanktree MB, et al. Large-scale gene-centric meta-analysis across 32 studies identifies multiple lipid loci. Am J Hum Genet 2012;91:823-38.  Back to cited text no. 5
    
6.ENCODE Project Consortium, Dunham I, Kundaje A, Aldred SF, Collins PJ, Davis CA, et al. The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 2012;489:57-74.  Back to cited text no. 6
    
7.Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H, et al. Systematic localization of common disease associated variation in regulatory DNA. Science 2012;337:1190-5.  Back to cited text no. 7
    
8.Krueger JG, Fretzin S, Suárez-Fariñas M, Haslett PA, Phipps KM, Cameron GS, et al. IL-17A is essential for cell activation and inflammatory gene circuits in subjects with psoriasis. J Allergy Clin Immunol 2012;130:145-54.  Back to cited text no. 8
    
9.Leonardi C, Matheson R, Zachariae C, Cameron G, Li L, Edson-Heredia E, et al. Anti-interleukin-17 monoclonal antibody ixekizumab in chronic plaque psoriasis. N Engl J Med 2012;366:1190-9.  Back to cited text no. 9
    
10.Tsoi LC, Spain SL, Knight J, Ellinghaus E, Stuart PE, Capon F, et al. Identification of 15 new psoriasis susceptibility loci highlights the role of innate immunity. Nat Genet 2012;44:1341-8.  Back to cited text no. 10
    
11.Weeks DE, Lathrop GM. Polygenic disease: methods for mapping complex disease traits. Trends Genet 1995;11:513-9.  Back to cited text no. 11
    
12.Rich SS, Concannon P. Challenges and strategies for investigating the genetic complexity of common human diseases. Diabetes 2002;51(Suppl 3):S288-94.  Back to cited text no. 12
    
13.Freimer N, Sabatti C. The use of pedigree, sib-pair and association studies of common diseases for genetic mapping and epidemiology. Nat Genet 2004;36:1045-51.  Back to cited text no. 13
    
14.Fan JB, Chee MS, Gunderson KL. Highly parallel genomic assays. Nat Rev Genet 2006;7:632-44.  Back to cited text no. 14
    
15.The International HapMap Consortium. The International HapMap Project. Nature 2003;426:789-96.  Back to cited text no. 15
    
16.Reich DE, Cargill M, Bolk S, Ireland J, Sabeti PC, Richter DJ, et al. Linkage disequilibrium in the human genome. Nature 2001;411:199-204.  Back to cited text no. 16
    
17.Wall JD, Pritchard JK. Haplotype blocks and linkage disequilibrium in the human genome. Nat Rev Genet 2003;4:587-97.  Back to cited text no. 17
    
18.de Bakker PI, Yelensky R, Pe′er I, Gabriel SB, Daly MJ, Altshuler D. Efficiency and power in genetic association studies. Nat Genet 2005;37:1217-23.  Back to cited text no. 18
    
19.Tian C, Gregersen PK, Seldin MF. Accounting for ancestry: population substructure and genome-wide association studies. Hum Mol Genet 2008;15:R143-50.  Back to cited text no. 19
    
20.Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nature Genet 2006;38:904-9.  Back to cited text no. 20
    
21.Zheng G, Freidlin B, Gastwirth JL. Robust genomic control for association studies. Am J Hum Genet 2006;78:350-6.  Back to cited text no. 21
    
22.Voight BF, Kang HM, Ding J, Palmer CD, Sidore C, Chines PS, et al. The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet 2012;8:e1002793.  Back to cited text no. 22
    
23.Polychronakos C. Fine points in mapping autoimmunity. Nat Genet 2011;43:1173-4.  Back to cited text no. 23
    
24.Packer BR, Yeager M, Burdett L, Welch R, Beerman M, Qi L, et al. SNP500Cancer: A public resource for sequence validation, assay development, and frequency analysis for genetic variation in candidate genes. Nucleic Acids Res 2006;34:D617-21.  Back to cited text no. 24
    
25.Risch NJ. Searching for genetic determinants in the new millennium. Nature 2000;405:847-56.  Back to cited text no. 25
    
26.Wang WY, Barratt BJ, Clayton DG, Todd JA. Genome-wide association studies: theoretical and practical concerns. Nat Rev Genet 2005;6:109-18.  Back to cited text no. 26
    
27.Hirschhorn JN, Daly MJ. Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 2005;6:95-108.  Back to cited text no. 27
    
28.Pritchard JK. Are rare variants responsible for susceptibility to complex diseases? Am J Hum Genet 2001;69:124-37.  Back to cited text no. 28
    
29.Reich DE, Lander ES. On the allelic spectrum of human disease. Trends Genet 2001;17:502-10.  Back to cited text no. 29
    
30.Bodmer W, Bonilla C. Common and rare variants in Multifactorial susceptibility to common diseases. Nat Genet 2008;40:695-701.  Back to cited text no. 30
    
31.McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JP, et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 2008;9:356-69.  Back to cited text no. 31
    
32.Wang WY, Pike N. The allelic spectra of common diseases may resemble the allelic spectrum of the full genome. Med Hypotheses 2004;63:748-51.  Back to cited text no. 32
    
33.Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. Finding the missing heritability of complex diseases. Nature 2009;461:747-53.  Back to cited text no. 33
    
34.Cirulli ET, Goldstein DB. Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nat Rev Genet 2010;11:415-25.  Back to cited text no. 34
    
35.Eichler EE, Flint J, Gibson G, Kong A, Leal SM, Moore JH, et al. Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet 2010;11:446-50.  Back to cited text no. 35
    
36.Barrett JC, Clayton DG, Concannon P, Akolkar B, Cooper JD, Erlich HA, et al. Type 1 Diabetes Genetics Consortium. Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nat Genet 2009;41:703-7.  Back to cited text no. 36
    
37.Cotsapas C, Voight BF, Rossin E, Lage K, Neale BM, Wallace C, et al. FOCiS Network of Consortia. Pervasive sharing of genetic effects in autoimmune disease. PLoS Genet 2011;7:e1002254.  Back to cited text no. 37
    
38.Todd JA, Walker NM, Cooper JD, Smyth DJ, Downes K, Plagnol V, et al. Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes. Nat Genet 2007;39:857-64.  Back to cited text no. 38
    
39.Romanos J, Barisani D, Trynka G, Zhernakova A, Bardella MT, Wijmenga C. Six new coeliac disease loci replicated in an Italian population confirm association with coeliac disease. J Med Genet 2009;46:60-3.  Back to cited text no. 39
    
40.Hunt KA, Zhernakova A, Turner G, Heap GA, Franke L, Bruinenberg M, et al. Newly identified genetic risk variants for celiac disease related to the immune response. Nat Genet 2008;40:395-402.  Back to cited text no. 40
    
41.Gateva V, Sandling JK, Hom G, Taylor KE, Chung SA, Sun X, et al. A large scale replication study identifies TNIP1, PRDM1, JAZF1, UHRF1BP1 and IL10 as risk loci for systemic lupus erythematosus. Nat Genet 2009;41:1228-33.  Back to cited text no. 41
    
42.Coenen MJ, Trynka G, Heskamp S, Franke B, van Diemen CC, Smolonska J, et al. Common and different genetic background for rheumatoid arthritis and coeliac disease. Hum Mol Genet 2009;18:4195-203.  Back to cited text no. 42
    
43.Alcina A, Vandenbroeck K, Otaegui D, Saiz A, Gonzalez JR, Fernandez O, et al. The autoimmune disease-associated KIF5A, CD226 and SH2B3 gene variants confer susceptibility for multiple sclerosis. Genes Immun 2010;11:439-45.  Back to cited text no. 43
    
44.Levy D, Ehret GB, Rice K, Verwoert GC, Launer LJ, Dehghan A, et al. Genomewide association study of blood pressure and hypertension. Nat Genet 2009;41:677-87.  Back to cited text no. 44
    
45.Oh ST, Simonds EF, Jones C, Hale MB, Goltsev Y, Gibbs Jr KD, et al. Novel mutations in the inhibitory adaptor protein LNK drive JAK-STAT signaling in patients with myeloproliferative neoplasms. Blood 2010;116:988-92.  Back to cited text no. 45
    
46.Lasho TL, Pardanani A, Tefferi A. LNK mutations in JAK2 mutation-negative erythrocytosis. N Engl J Med 2010;363:1189-90.  Back to cited text no. 46
    
47.Soranzo N, Spector TD, Mangino M, Kühnel B, Rendon A, Teumer A et al. A genome-wide meta-analysis identifies 22 loci associated with eight hematological parameters in the HaemGen consortium. Nat Genet 2009;41:1182-90.  Back to cited text no. 47
    
48.Zhernakova A, Elbers CC, Ferwerda B, Romanos J, Trynka G, Dubois PC, et al. Evolutionary and functional analysis of celiac risk loci reveals SH2B3 as a protective factor against bacterial infection. Am J Hum Genet 2010;86:970-7.  Back to cited text no. 48
    
49.Devallière J, Charreau B. The adaptor Lnk (SH2B3): An emerging regulator in vascular cells and a link between immune and inflammatory signaling. Biochem Pharmacol 2011;82:1391-402.  Back to cited text no. 49
    
50.Compston A, Coles A. Multiple sclerosis. Lancet 2008;372:1502-17.  Back to cited text no. 50
    
51.International Multiple Sclerosis Genetics Consortium; Wellcome Trust Case Control Consortium 2, Sawcer S, Hellenthal G, Pirinen M, Spencer CC, Patsopoulos NA, et al. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 2011;476:214-9.  Back to cited text no. 51
    
52.Samani NJ, Erdmann J, Hall AS, Hengstenberg C, Mangino M, Mayer B, et al. Genomewide association analysis of coronary artery disease. N Engl J Med 2007;357:443-53.  Back to cited text no. 52
    
53.McPherson R, Pertsemlidis A, Kavaslar N, Stewart A, Roberts R, Cox DR, et al. A common allele on chromosome 9 associated with coronary heart disease. Science 2007;316:1488-91.  Back to cited text no. 53
    
54.Helgadottir A, Thorleifsson G, Manolescu A, Gretarsdottir S, Blondal T, Jonasdottir A, et al. A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science 2007;316:1491-3.  Back to cited text no. 54
    
55.Shea J, Agarwala V, Philippakis AA, Maguire J, Banks E, Depristo M, et al. Comparing strategies to fine-map the association of common SNPs at chromosome 9p21 with type 2 diabetes and myocardial infarction. Nat Genet 2011;43:801-5.  Back to cited text no. 55
    
56.Helgadottir A, Thorleifsson G, Magnusson KP, Grétarsdottir S, Steinthorsdottir V, Manolescu A, et al. The same sequence variant on 9p21 associates with myocardial infarction, abdominal aortic aneurysm and intracranial aneurysm. Nat Genet 2008;40:217-24.  Back to cited text no. 56
    
57.Samani NJ, Raitakari OT, Sipilä K, Tobin MD, Schunkert H, Juonala M, et al. Coronary artery disease-associated locus on chromosome 9p21 and early markers of atherosclerosis. Arterioscler Thromb Vasc Biol 2008;28:1679-83.  Back to cited text no. 57
    
58.Angelakopoulou A, Shah T, Sofat R, Shah S, Berry DJ, Cooper J, et al. Comparative analysis of genomewide-association studies signals for lipids, diabetes, and coronary heart disease: Cardiovascular Biomarker Genetics Collaboration. Eur Heart J 2012;33:393-407.  Back to cited text no. 58
    
59.Visel A, Zhu Y, May D, Afzal V, Gong E, Attanasio C, et al. Targeted deletion of the 9p21 non-coding coronary artery disease risk interval in mice. Nature 2010;464:409-12.  Back to cited text no. 59
    
60.Cunnington MS, Santibanez Koref M, Mayosi BM, Burn J, Keavney B. Chromosome 9p21SNPs associated with multiple disease phenotypes correlate with ANRIL expression. PLoS Genet 2010;6:e1000899.  Back to cited text no. 60
    
61.Zhuang J, Peng W, Li H, Wang W, Wei Y, Li W, et al. Methylation of p15INK4b and expression of ANRIL on chromosome 9p21 are associated with coronary artery disease. PLoS One 2012;7:e47193.  Back to cited text no. 61
    
62.Schunkert H, König IR, Kathiresan S, Reilly MP, Assimes TL, Holm H, et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat Genet 2011;43:333-8.  Back to cited text no. 62
    
63.Coronary Artery Disease (CAD) Genetics Consortium. A genome-wide association study in Europeans and South Asians identifies five new loci for coronary artery disease. Nat Genet 2011;43:339-44.  Back to cited text no. 63
    
64.Huyghe JR, Jackson AU, Fogarty MP, Buchkovich ML, Stančáková A, Stringham HM, et al. Exome array analysis identifies new loci and low-frequency variants influencing insulin processing and secretion. Nat Genet 2013;45:197-201.  Back to cited text no. 64
    
65.The 1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 2012;491:56-65.  Back to cited text no. 65
    
66.Ng SB, Turner EH, Robertson PD, Flygare SD, Bigham AW, Lee C, et al. Targeted capture and massively parallel sequencing of 12 human exomes. Nature 2009;461:272-6.  Back to cited text no. 66
    
67.Yu TW, Chahrour MH, Coulter ME, Jiralerspong S, Okamura-Ikeda K, Ataman B, et al. Using Whole-Exome Sequencing to Identify Inherited Causes of Autism. Neuron 2013;77:259-73.  Back to cited text no. 67
    
68.Neale BM, Kou Y, Liu L, Ma′ayan A, Samocha KE, Sabo A, et al. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 2012;485:242-5.  Back to cited text no. 68
    
69.O′Roak BJ, Vives L, Girirajan S, Karakoc E, Krumm N, Coe BP, et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 2012;485:246-50.  Back to cited text no. 69
    
70.Sanders SJ, Murtha MT, Gupta AR, Murdoch JD, Raubeson MJ, Willsey AJ, et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 2012;485:237-41.  Back to cited text no. 70
    
71.Trynka G, Sandor C, Han B, Xu H, Stranger BE, Liu XS, et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat Genet 2013;45:124-30.  Back to cited text no. 71
    
72.Simpfendorfer KR, Olsson LM, Manjarrez Orduño N, Khalili H, Simeone AM, Katz MS, et al. The autoimmunity-associated BLK haplotype exhibits cis-regulatory effects on mRNA and protein expression that are prominently observed in B cells early in development. Hum Mol Genet 2012;21:3918-25.  Back to cited text no. 72
    
73.Gregersen PK, Diamond B, Plenge RM. GWAS implicates a role for quantitative immune traits and threshold effects in risk for human autoimmune disorders. Curr Opin Immunol 2012;24:538-43.  Back to cited text no. 73
    


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