Probably, never before in the history of medical research, a research method has been applied as broad to nearly all diseases, ranging from heart, lung, orthopaedic to psychiatric diseases as the genome-wide association study (GWAS). The asthma and allergy community had been on the forefront with at least 30 out of 1500 GWAS so far. Exact numbers are difficult to report as single-centre studies contributed to different consortia with overlapping traits and samples.
During the reporting period of 2011, asthma GWAS were still being published. Examples of original research papers include new asthma risk loci from Australia , Japan [2,3] England , Korea  and Russia . Furthermore, a negative report on an assumed gene–environment interaction  and refined asthma/HLA associations has been published . Innovative approaches include the screening of epigenetic changes in B lymphocytes, where CYP26A1, an enzyme regulating the cellular level of retinoic acid, was found to be associated with house dust mite allergic asthma . Another important pharmacogenetic study showed an association of the GLCCI1 gene and response to glucocorticoid therapy in asthmatics  along with a GWAS of serum vitamin D levels in asthmatics .
The number of reviews summarizing the GWAS results [12–21,22▪,23–25,26▪,27,28] even exceeded the number of original research papers. It may therefore be worthwhile developing here a perspective that goes beyond listing gene names.
GENOME-WIDE ASSOCIATION STUDY SHORTCOMINGS
The use of commercial chip systems for GWAS has been under intense debate . Past reviews describe this as ‘missing’ heritability [30▪▪], while ‘unexplained’ heritability maybe a better description of this phenomenon. Although major genetic journals ignore the unexplained heritability (‘We welcome the reassuring message from the modellers that the sky is not falling, heritability is not missing and the models currently being used provide quantitative guidance’ ), there is a widespread concern that the bulk of GWAS still have not provided any useful functional disease explanation [30▪▪].
The basis of the GWAS strategy – picking up frequent point mutations by a company, managing to get a miniaturized assay on a chip and letting researchers find out in large cross-sectional studies what are the most significant disease association – has been more fuelled by commercial interests than by a rational strategy. At least, this has been the conclusion of the most cited papers in the GWAS period [30▪▪,32]. Although the single-nucleotide polymorphism (SNP) selection by the chip manufacturers was mainly gene-based, 88% of all associated SNPs are now in intronic or intergenic regions .
Imprecise phenotyping and phenotypic heterogeneity may have additionally reduced effect estimates, whilst the main problem seems to be the biased selection of SNP markers. To get a countable outcome, SNPs have been selected for being common in the Hapmap samples. The shortcomings of this approach have been discussed on multiple blog posts as well in many scientific papers [30▪▪,34–36].
Structural variation (repeats, duplications, deletions and inversions) were only partially included in GWAS  or could be analysed only indirectly. The genomic coverage of SNPs varied along the chromosomes with insufficient coverage of sex chromosomes . Multiple assays also probed sequences that included unanticipated SNPs  leading to distorted allele frequencies. Unfortunately, also the linkage disequilibrium tagging characteristics of common variants did not work in many genomic regions as the linkage disequilibrium in the human genome is much weaker than anticipated from the Hapmap samples . The major analytical approach was also rather primitive – ranking only the most significant differences between cases and controls, whilst ignoring the complex genetic interaction.
The conclusions therefore may not be unexpected that ‘many questions remain, … as most identified genetic variants contribute only nominally to overall disease risk, genetic disease mechanisms remain uncertain, and disease-associated variants are not consistent across studies’ . It looks naïve to expect GWAS to be ‘hypothesis-free’ as assumed by some researchers, as there are numerous a priori hypotheses by population sampling and phenotyping strategy, SNP selection, genotyping platform and analysis strategy .
WHAT DID WE LEARN SO FAR?
Asthma genetics research can be divided into a period of early family studies starting with the heredity studies of Cooke 1914. It followed the period of genetic linkage studies since 1986 (summarized in [40▪▪]) that identified a couple of asthma genes by positional cloning, whilst the current, third period since 2007 was characterized by large GWAS (summarized by [30▪▪]).
Of all the genes published during the linkage period, a ‘top 100’ has been nominated with a small group of genes even standing out: ‘The elite group of genes that have been associated with an asthma or atopy phenotype [… were …] IL4, IL13, ADRB2, TNF, HLA-DRB1, FCER1B and IL4RA, as well as the CD14, HLA-DQB1 and ADAM33 genes’ [41▪]. It may be interesting to note that with a few exceptions most of these were never replicated in the four major asthma GWAS (Decode [42▪▪], Gabriel [43▪▪], EVE [44▪▪] and APCAT [45▪▪]). This may be because of an insufficient coverage of SNP marker at the regions of interest or – equally likely – replication is not possible as most disease-causing variants have been confined to single families and individuals.
Even the results between the major meta-analysis asthma GWAS do not fully match. Although the Gabriel study, for example, included more than 10 000 asthmatics from 23 studies (and should therefore be immune against bias introduced by regional population characteristics), the most significant association with a SNP in HLA-DQ (7.0 × 10−14) was not replicated in the ethnically diverse EVE study. Another variant at 17q21 (promoted as ‘ORMDL3’ association ) seems to be associated with childhood onset asthma only. Although it may be formally correct to assume an underpowered sample size in replication studies, this argument does not make so much sense from a biological viewpoint as already thousands of asthmatics have been included. It seems that there is simply no HLA genotype that is shared by all asthmatics.
A different situation comes with SNPs in IL33 and IL1RL1/ST2 that were associated in all EVE subgroups, and also in the three other meta-analyses as the second and third most prominent SNP in GABRIEL (reporting at least SNP in IL18R1 in high linkage disequilibrium with IL1RL1 SNPs), the first and third in Decode and the first and second most significant association in APCAT.
Which of the numerous pitfalls affecting GWAS had the main impact is difficult to assess. The bias in selecting common SNPs at least has been severe as low-frequency (1–5%) and rare variant (<1% minor allele frequency)  is much more abundant in the human genome  than previously anticipated with every 17th base being exchanged .
Our own reanalysis of the supplemental data file deposited at the http://www.cng.fr/gabriel of the Gabriel study provides further insight as it includes also those SNPs with a minor allele frequency less than 1% that had been removed from the analysis. Such an exclusion of rare variants may have been a necessary quality control measure in the early days of genotyping but is now considered as being inadequate as the error rate of chip genotyping system is reported to be less than 0.01%. Upon reanalysis (Fig. 1), rare variants are more than seven-fold enriched in the group of highly significant SNPs, making up 45% of the 109 SNPs with P < 7.2 × 10–8. Rare variants are distributed amongst more than 50 unique genes with odds ratios up to 50. Calling of these rare variants certainly needs to be verified, statistics adjusted for rare events and residual stratification excluded before making any further conclusions. Most of these high-risk alleles are not even seen in the 1000 genomes CEU panel and are contributed by just one population. As outlined above, the analysis of rare variants is not an easy task [50,51] as calling of these variants is more difficult than with common variants. In addition, correct phasing of variants is necessary .
The importance of rare variants is further confirmed by the EVE consortium , in which the coding exons and flanking regions of nine asthma candidate-genes were sequenced showing an excess of P-values of rare variants in four genes including IL12RB1 amongst African-Americans. Rare variants in IL12RB1 were also associated with asthma amongst European-Americans, although by a different set of rare variants.
PROMISING ASSOCIATIONS: HLA DQ, IL33/ST2, AND TCF3
Unfortunately, the long-known HLA association with asthma  could not be turned into any functional readout so far. The MHC II class proteins present peptides broken down to about 10 amino acid length, stimulating the expansion of T cells. According to their commitment, the MHC locus is one of the most genetically variable regions in the human genome, whilst a detailed knowledge of the HLA antigenic recognition sequence should allow some predictions about the antigen target. Indeed, a haplotype analysis of rheumatoid arthritis GWAS data identified recently three amino acid positions (11, 71 and 74) in HLA-DRβ1 with two other single-amino-acid polymorphisms that could almost completely explain the MHC association . Such an analysis is still missing from the asthma GWAS.
As described above, IL33 is the most interesting finding from the GWAS. IL33 has been described only in 2005 and together with IL1, IL18, TGFβ and TSLP a major action site may be seen at the epithelial innate-immunity response to microbial antigens in the gut . Dendritic cells induce the development of Treg cells through a TGFβ and retinoic-acid-dependent process. IL18 and IL33 are expressed also in other epithelial tissues , but when released extracellularly, IL18 is more associated with a prototypic Th1 response than IL33 that favours a Th2-type reaction . IL33 binds with high affinity to IL33R/ST2 and involves MyD88 and TRAF6-dependent pathways. IL33 is regarded as a danger signal of damaged tissue regulating not only macrophage response to endotoxin , but also nematode infection . IL33 promotes eosinophil survival, adhesion, superoxide production and degranulation. A recent review  concludes that IL33 action is similar to IL5 signalling but acting more on a local level. IL33 is synergistic with TSLP, IgE and SCF for mast cell survival, as well as IgE receptor signalling leading to the release of histamine . Which functional variants in IL33 and its receptor are responsible for the observed association is unclear, with the IL33/ST2 association being primarily restricted to allergic asthma [42▪▪]. Experimentally induced allergic asthma confirms the dendritic-cell-mediated mechanism of IL33 .
Using the supplemental data file deposited at http://www.cng.fr/gabriel of the Gabriel study 2010 [43▪▪], we calculated the association results for childhood-onset disease under a random effects model. We took all genome-wide significant markers (P < 10−7) and extended the SNPs via linkage disequilibrium (r2 ≥ 0.9) based SNPs of the 1000 genomes project CEU panel . This marker set was annotated using the variant effect predictor tool and all SNPs located in regulatory regions were extracted . To infer functionality, we used these SNPs as input for intersection with eQTL information from two large-scale studies [63–65] accessible through GENEVAR. This resulted in 36 genes which are significantly (P < 0.01) associated with allele-specific differential expression in at least one of three tissues.
Analysis of conserved transcription factor binding sites around these genes returned significant (P < 0.01) enrichment of the TAL1α/E47 motif which is bound by TCF3, a binding partner of β-catenin and thus downstream effector in the Wnt signalling pathway . Wnt signalling genes have been implicated in asthma pathogenesis before , whilst, just recently, TCF3-binding sites have been described as being enriched in asthma-associated gene sets . TCF3 is also a negative regulator of CD5 expression during thymocyte development , whilst CD5 mitigates B cell receptor signalling so that cells can only be activated by very strong stimuli as bacterial proteins . Finally, the purification of a complex associated with TCF3 identified DNA methyltransferases  that may be involved in some epigenetic changes implied earlier in asthma pathogenesis .
The clinical relevance of GWAS results has been discussed before . Although some new therapeutic targets have been identified like VCAM1 in multiple sclerosis, the number of these examples is limited.
What did we really learn so far? Asthma is a complex disease entity with a huge physiologic range from healthy airways, exaggerated bronchial hyperreactivity to lethal asthma attacks. It is unlikely that one or at least a few functional gene variants will ever explain all known pathophysiological events of innate and adaptive immune response . Attempts are now underway to separate the asthma phenotype by latent class clustering of GWAS SNPs (Siroux, personal communication, 2012). Possibly, there might also be monogenic asthma cases that cannot be discriminated so far from other sporadic asthma cases. And of course, there are numerous false-positive associations, most of them unwittingly published, whilst many causative associations are still resting in the ‘long tail of the distribution’ not passing the arbitrary significance thresholds .
Although improved SNP panels are already available, the interest in improving on GWAS is rather modest. Some researchers already fear that the unexplained heritability is such a serious problem that it will remain intractable also by genome sequencing (see Fig. 2), as each individual variant tested alone has zero penetrance even when tested in a large sample.
The negligible outcome of translational research [72,75] may not be disencouraging. We clearly do not need to catalogue all asthma-associated variants but find only those that explain the main pathology.
GWAS aiming at disease-causing genes should also be separated from the studies on variants associated with medication-related outcomes. A possible impact of genetic variants on β-agonist therapy (ADRB2, ARG1 and GSNOR), inhaled steroid use (CRHR1, GLCC11, TBX21, FCER2 and STIP1) or leukotriene signalling (ALOX5, LTC4S and SCCo2B1) may already be tested now in further clinical trials [26▪].
The next wave of asthma studies will probably not so much improve on SNP chips, but already use full-genome sequencing analysis . For that purpose, we need to go back to family studies in which phased chromosomes can be used as reference genomes [77▪]. In addition, parental origin of genes may be a critical factor . Lastly, as a pure genomic approach is at risk of being lost amongst many irrelevant genetic associations, cell-specific gene-expression pattern obtained from patients with various asthma types and stages may be equally important.
This may be the last step of a century-long search for asthma genes – satisfying scientific curiosity and hopefully also providing data applicable in translational medicine.
Conflicts of interest
The authors declare no particular conflict of interest with the data and conclusions presented here. Matthias Wjst received funding during the past 3 years from the European Union and the Helmholtz Center Munich and lecture fees from various medical associations as well as from Novartis.
REFERENCES AND RECOMMENDED READING
Papers of particular interest, published within the annual period of review, have been highlighted as:
▪ of special interest
▪▪ of outstanding interest
Additional references related to this topic can also be found in the Current World Literature section in this issue (p. 124).
1. Ferreira MA, Matheson MC, Duffy DL, et al. Identification of IL6R and chromosome 11q13.5 as risk loci for asthma. Lancet 2011; 378:1006–1014.
2. Hirota T, Takahashi A, Kubo M, et al. Genome-wide association study identifies three new susceptibility loci for adult asthma in the Japanese population. Nat Genet 2011; 43:893–896.
3. Noguchi E, Sakamoto H, Hirota T, et al. Genome-wide association study identifies HLA-DP as a susceptibility gene for pediatric asthma in Asian populations. PLoS Genet 2011; 7:e1002170.
4. Marenholz I, Bauerfeind A, Esparza-Gordillo J, et al. The eczema risk variant on chromosome 11q13 (rs7927894) in the population-based ALSPAC cohort: a novel susceptibility factor for asthma and hay fever. Hum Mol Genet 2011; 20:2443–2449.
5. Yoon D, Ban HJ, Kim YJ, et al. Replication of genome-wide association studies on asthma and allergic diseases in Korean adult population. BMB Rep 2012; 45:305–310.
6. Karunas AS, Iunusbaev BB, Fedorova I, et al. Genome-wide association study of bronchial asthma in the Volga-Ural region of Russia. Mol Biol (Mosk) 2011; 45:992–1003.
7. Ege MJ, Strachan DP, Cookson WO, et al. Gene–environment interaction for childhood asthma and exposure to farming in Central Europe. J Allergy Clin Immunol 2011; 127:138–144.144 e131–e134.
8. Granada M, Wilk JB, Tuzova M, et al. A genome-wide association study of plasma total IgE concentrations in the Framingham Heart Study. J Allergy Clin Immunol 2012; 129:840.e21–845.e21.
9. Pascual M, Suzuki M, Isidoro-Garcia M, et al. Epigenetic changes in B lymphocytes associated with house dust mite allergic asthma. Epigenetics 2011; 6:1131–1137.
10. Tantisira KG, Lasky-Su J, Harada M, et al. Genomewide association between GLCCI1 and response to glucocorticoid therapy in asthma. N Engl J Med 2011; 365:1173–1183.
11. Lasky-Su J, Lange N, Brehm JM, et al. Genomewide association analysis of circulating vitamin D levels in children with asthma. Hum Genet 2012;131:1495–1505.
12. Akhabir L, Sandford AJ. Genome-wide association studies for discovery of genes involved in asthma. Respirology 2011; 16:396–406.
13. Barnes KC. Genetic studies of the etiology of asthma. Proc Am Thorac Soc 2011; 8:143–148.
14. Bernstein DI. Genetics of occupational asthma. Curr Opin Allergy Clin Immunol 2011; 11:86–89.
15. Cookson W, Moffatt M, Strachan DP. Genetic risks and childhood-onset asthma. J Allergy Clin Immunol 2011; 128:266–270.quiz 271–272.
16. Cookson WO, Moffatt MF. Genetics of complex airway disease. Proc Am Thorac Soc 2011; 8:149–153.
17. Gu ML, Zhao J. Mapping and localization of susceptible genes in asthma. Chin Med J 2011; 124:132–143.
18. March ME, Sleiman PM, Hakonarson H. The genetics of asthma and allergic disorders. Discov Med 2011; 11:35–45.
19. North ML, Ellis AK. The role of epigenetics in the developmental origins of allergic disease. Ann Allergy Asthma Immunol 2011; 106:355–361.quiz 362.
20. Obeidat M, Hall IP. Genetics of complex respiratory diseases: implications for pathophysiology and pharmacology studies. Br J Pharmacol 2011; 163:96–105.
21. Ober C, Vercelli D. Gene–environment interactions in human disease: nuisance or opportunity? Trends Genet 2011; 27:107–115.
22▪. Ober C, Yao TC. The genetics of asthma and allergic disease: a 21st century perspective. Immunol Rev 2011; 242:10–30.
The most recent and comprehensive review on the genetics of asthma.
23. Rigoli L, Briuglia S, Caimmi S, et al. Gene–environment interaction in childhood asthma. Int J Immunopathol Pharmacol 2011; 24:41–47.
24. Tamari M, Tomita K, Hirota T. Genome-wide association studies of asthma. Allergol Int 2011; 60:247–252.
25. Todd JL, Goldstein DB, Ge D, et al. The state of genome-wide association studies in pulmonary disease: a new perspective. Am J Respir Crit Care Med 2011; 184:873–880.
26▪. Weiss ST. New approaches to personalized medicine for asthma: where are we? J Allergy Clin Immunol 2012; 129:327–334.
A good summary of pharmacogenetic aspects.
27. Wenzel SE. Asthma phenotypes: the evolution from clinical to molecular approaches. Nat Med 2012; 18:716–725.
28. Zhang Y, Moffatt MF, Cookson WO. Genetic and genomic approaches to asthma: new insights for the origins. Curr Opin Pulmon Med 2012; 18:6–13.
29. Adcock IM, Barnes PJ. Con: Genome-wide association studies have not been useful in understanding asthma. Am J Respir Crit Care Med 2011; 184:633–636.
30▪▪. Manolio TA, Collins FS, Cox NJ, et al. Finding the missing heritability of complex diseases. Nature 2009; 461:747–753.
An outstanding study indicating the end of the GWAS period.
31. On beyond GWAS. Nat Genet 2010; 42:551.
32. Maher B. Personal genomes: the case of the missing heritability. Nature 2008; 456:18–21.
33. An integrated encyclopedia of DNA elements in the human genome. Nature 2012; 489:57–74.
34. Clarke AJ, Cooper DN. GWAS: heritability missing in action? Eur J Hum Genet 2010; 18:859–861.
35. Van der Sluis S, Verhage M, Posthuma D, Dolan CV. Phenotypic complexity, measurement bias, and poor phenotypic resolution contribute to the missing heritability problem in genetic association studies. PLoS One 2010; 5:e13929.
36. Kitsios GD, Zintzaras E. Genome-wide association studies: hypothesis-‘free’ or ‘engaged’? Transl Res 2009; 154:161–164.
37. Lee SH, Park JS, Park CS. The search for genetic variants and epigenetics related to asthma. Allergy Asthma Immunol Res 2011; 3:236–244.
38. Hickey PF, Bahlo M. X chromosome association testing in genome wide association studies. Genet Epidemiol 2011; 35:664–670.
39. Rosenfeld JA, Mason CE, Smith TM. Limitations of the human reference genome for personalized genomics. PLoS One 2012; 7:e40294.
40▪▪. Altmüller J, Palmer LJ, Fischer G, et al. Genomewide scans of complex human diseases: true linkage is hard to find. Am J Hum Genet 2001; 69:936–950.
A key study indicating the end of the linkage era.
41▪. Ober C, Hoffjan S. Asthma genetics 2006: the long and winding road to gene discovery. Genes Immun 2006; 7:95–100.
A nearly complete review summarizing known associations from the linkage era.
42▪▪. Gudbjartsson DF, Bjornsdottir US, Halapi E, et al. Sequence variants affecting eosinophil numbers associate with asthma and myocardial infarction. Nat Genet 2009; 41:342–347.
A first large-scale asthma GWAS meta-analysis.
43▪▪. Moffatt MF, Gut IG, Demenais F, et al. A large-scale, consortium-based genomewide association study of asthma. N Engl J Med 2010; 363:1211–1221.
A second large-scale asthma GWAS meta-analysis.
44▪▪. Torgerson DG, Ampleford EJ, Chiu GY, et al. Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations. Nat Genet 2011; 43:887–892.
A third large-scale asthma GWAS meta-analysis.
45▪▪. Ramasamy A, Kuokkanen M, Vedantam S, et al. Genome-wide association studies of asthma in population-based cohorts confirm known and suggested loci and identify an additional association near HLA. PLoS One 2012; 7:e44008.
A fourth large-scale asthma GWAS meta-analysis.
46. Wjst M. ORMDL3 – guilt by association? Clin Exp Allergy 2008; 38:1579–1581.
47. Anderson CA, Soranzo N, Zeggini E, Barrett JC. Synthetic associations are unlikely to account for many common disease genome-wide association signals. PLoS Biol 2011; 9:e1000580.
48. A map of human genome variation from population-scale sequencing. Nature 2010; 467:1061–1073.
49. Nelson MR, Wegmann D, Ehm MG, et al. An abundance of rare functional variants in 202 drug target genes sequenced in 14 002 people. Science 2012; 337:100–104.
50. Li G, Gelernter J, Kranzler HR, Zhao H. M(3): an improved SNP calling algorithm for Illumina BeadArray data. Bioinformatics 2012; 28:358–365.
51. Li B, Leal SM. Discovery of rare variants via sequencing: implications for the design of complex trait association studies. PLoS Genet 2009; 5:e1000481.
52. Suk EK, McEwen GK, Duitama J, et al. A comprehensively molecular haplotype-resolved genome of a European individual. Genome Res 2011; 21:1672–1685.
53. Torgerson DG, Capurso D, Mathias RA, et al. Resequencing candidate genes implicates rare variants in asthma susceptibility. Am J Hum Genet 2012; 90:273–281.
54. Geerts SJ, Pottgens H, Limburg M, van Rood JJ. Letter: predisposition for atopy or allergy linked to HL-A. Lancet 1975; 1:461.
55. Raychaudhuri S, Sandor C, Stahl EA, et al. Five amino acids in three HLA proteins explain most of the association between MHC and seropositive rheumatoid arthritis. Nat Genet 2012; 44:291–296.
56. Maynard CL, Elson CO, Hatton RD, Weaver CT. Reciprocal interactions of the intestinal microbiota and immune system. Nature 2012; 489:231–241.
57. Smith DE. The biological paths of IL-1 family members IL-18 and IL-33. J Leukoc Biol 2011; 89:383–392.
58. Espinassous Q, Garcia-de-Paco E, Garcia-Verdugo I, et al. IL-33 enhances lipopolysaccharide-induced inflammatory cytokine production from mouse macrophages by regulating lipopolysaccharide receptor complex. J Immunol 2009; 183:1446–1455.
59. Humphreys NE, Xu D, Hepworth MR, et al. IL-33, a potent inducer of adaptive immunity to intestinal nematodes. J Immunol 2008; 180:2443–2449.
60. Pecaric-Petkovic T, Didichenko SA, Kaempfer S, et al. Human basophils and eosinophils are the direct target leukocytes of the novel IL-1 family member IL-33. Blood 2009; 113:1526–1534.
61. Chu DK, Llop-Guevara A, Walker TD, et al. IL-33, but not thymic stromal lymphopoietin or IL-25, is central to mite and peanut allergic sensitization. J Allergy Clin Immunol 2012. [Epub ahead of print]
62. McLaren W, Pritchard B, Rios D, et al. Deriving the consequences of genomic variants with the Ensembl API and SNP effect predictor. Bioinformatics 2010; 26:2069–2070.
63. Yang TP, Beazley C, Montgomery SB, et al. Genevar: a database and Java application for the analysis and visualization of SNP-gene associations in eQTL studies. Bioinformatics 2010; 26:2474–2476.
64. Grundberg E, Small KS, Hedman AK, et al. Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nat Genet 2012. [Epub ahead of print]
65. Stranger BE, Montgomery SB, Dimas AS, et al. Patterns of cis regulatory variation in diverse human populations. PLoS Genet 2012; 8:e1002639.
66. Katoh M, Katoh M. WNT signaling pathway and stem cell signaling network. Clin Cancer Res 2007; 13:4042–4045.
67. Sharma S, Tantisira K, Carey V, et al. A role for Wnt signaling genes in the pathogenesis of impaired lung function in asthma. Am J Respir Crit Care Med 2010; 181:328–336.
68. Chowdhary R, Tan SL, Pavesi G, et al. A database of annotated promoters of genes associated with common respiratory and related diseases. Am J Respir Cell Mol Biol 2012; 47:112–119.
69. Yang Y, Contag CH, Felsher D, et al. The E47 transcription factor negatively regulates CD5 expression during thymocyte development. Proc Natl Acad Sci USA 2004; 101:3898–3902.
70. Berland R, Wortis HH. Origins and functions of B-1 cells with notes on the role of CD5. Annu Rev Immunol 2002; 20:253–300.
71. Kim MS, Kondo T, Takada I, et al. DNA demethylation in hormone-induced transcriptional derepression. Nature 2009; 461:1007–1012.
72. Visscher PM, Brown MA, McCarthy MI, Yang J. Five years of GWAS discovery. Am J Hum Genet 2012; 90:7–24.
73. Holgate ST. Innate and adaptive immune responses in asthma. Nat Med 2012; 18:673–683.
74. Cardon LR, Bell JI. Association study designs for complex diseases. Nat Rev Genet 2001; 2:91–99.
75. Contopoulos-Ioannidis DG, Alexiou GA, Gouvias TC, Ioannidis JP. Medicine. Life cycle of translational research for medical interventions. Science 2008; 321:1298–1299.
76. Yang IV, Schwartz DA. The next-generation of complex lung genetic studies. Am J Respir Crit Care Med 2012. [Epub ahead of print]
77▪. Dewey FE, Chen R, Cordero SP, et al. Phased whole-genome genetic risk in a family quartet using a major allele reference sequence. PLoS Genet 2011; 7:e1002280.
An important methodology study.
© 2013 Lippincott Williams & Wilkins, Inc.