我们以多种方式评估了关联的证据（有关详细信息，请参见方法） ，借鉴了古典和贝叶斯统计方法。对于Affymetrix芯片上的多态性SNP，我们进行了趋势测试（16度）和一般的基因型测试（2度自由度16度
，称为基因型，称为基因型） ，并计算出类似的贝叶斯因子 。从动物模型中有一些例子
，遗传效应在男性和女性中的作用不同，并且为了在数据中评估这一点 ，我们应用了性别差异测试
，该测试对两个性别中不同幅度和/或方向的关联敏感。 　　我们的研究还使我们可以寻找可能对多种疾病产生影响的基因座
。为了评估这一点，我们比较了三种自然疾病中的每一个中的所有病例：CAD+HT+T2D（代谢和心血管表型具有潜在的病态学重叠 ，例如
，涉及胰岛素作用中的缺陷）；RA+T1D（已经知道共享共同基因座）；和CD+RA+T1D（所有自身免疫性疾病）。 　　为了帮助捕获未在Affymetrix芯片上的假定疾病基因座，我们使用了一种新的多焦点方法 ，其中将种群遗传学模型应用于我们的基因型数据和HAPMAP参考样品
，以模拟或估算为2,193,483 HAPMAP SNP的基因型数据，而不是在Affymetrix芯片上 。这些归纳的基因型随后以与项目中的SNP基因分型相同的方式进行了测试
。 　　在详细介绍每种疾病的主要结果之前 ，我们首先总结了我们的主要观察结果。表2详细介绍了WTCCC扫描的发现对15种变种的发现，基于广泛的复制研究
，就有有力的先前证据证明了与研究的一种或多种疾病相关的证据 。除这些证据的大小与先前研究所估计的效果大小相一致，但所有这些证据的大小通常都与证据相一致
。我们未能获得复制证据的信号之一（CAD中的APOE）被Affymetrix 500K芯片标记不佳。另一个（T1D中的INS）由单个SNP表示 ，该SNP略微失败了我们的学习质量控制过滤器（总体缺失5.2％）
，但在检查时与T1D密切相关 。七种疾病中每种趋势测试的分位数量式图仅显示出与无效分布非常小的偏差，除非在极端的尾巴中 ，这与下面报告的关联相对应（图3）
。分位数量式图和阳性对照的结果（表2）使我们的数据质量和分析的鲁棒性具有信心。 　　在图4中说明了我们全基因组测试的全基因组结果 。芯片上SNP的单疾病趋势和基因型测试在7种超过5×10-7阈值的7个疾病中鉴定出的21个信号（表3）
。对于这些SNP中的每一个（MHC中的SNP除外）
，群集图在图10的补充图中显示，图5中显示了“信号图”。这些信号图估计了命中区域的可能性 ，并显示了基因分型和估算的SNP的信号以及局部基因组上下文 。又四个强（p< 5 × 10-7) associations were revealed by the other primary analyses described (Table 3). One locus (in RA) was revealed by the sex-differentiated analysis, two through multilocus approaches (both for T1D) and one through an analysis which combined cases from more than one autoimmune disease (signal plots in Supplementary Figs 11, 12 and 13, respectively). 　　All of these signals were subjected to visual inspection of cluster plots, and in all cases (with one exception noted below) nearby correlated SNPs also showed a strong signal (see signal plots). Thus, genotyping artefacts are unlikely to be responsible for these associations. Indeed, at the time of writing, 12 of these 25 strong signals represent replications of previously reported findings (only those with extensive prior replication are reported in Table 2). Of the remainder, follow-up studies (reported elsewhere) have confirmed all but one of the loci (ten in total) for which replication has been attempted10,19,20,21,22,23,24. The other replication study gave equivocal results. Of the 18 loci implicated in autoimmune diseases, 5 show associations (P < 0.001) to more than 1 condition, leading to a number of further potential new associations, at least one of which has also been replicated10. 　　It is likely that further susceptibility genes will be identified through follow-up of other signals for which the evidence from our scan is less conclusive (see below for some specific examples). For example, there are 58 further signals with single-point P values between 10-5 and 5 × 10-7 for which inspection of cluster plots verifies CHIAMO calls (Table 4). As described below, analyses which make use of selected case samples to expand the reference group should also provide a useful route to the prioritization of such putative signals for further analysis. For convenience, the strongest association results are presented separately for each disease in Supplementary Table 7. 　　Several general points are relevant to interpretation of these disease-association data. First, replication studies are required to confirm associations from GWAs. For the reasons given in the box, we regard very low P values (say P < 5 × 10-7) in our comparatively large sample size as strong evidence for association, and indeed all or most of the loci we find at this level are either already known or have now been confirmed by subsequent replication. Such replication studies are also the substrate for efforts to determine the range of associated phenotypes and to identify and characterize pathologically relevant variation. 　　Second, failure to detect a prominent association signal in the present study cannot provide conclusive exclusion of any given gene. This is the consequence of several factors including: less-than-complete coverage of common variation genome-wide on the Affymetrix chip; poor coverage (by design) of rare variants, including many structural variants (thereby reducing power to detect rare, penetrant, alleles)25; difficulties with defining the full genomic extent of the gene of interest; and, despite the sample size, relatively low power to detect, at levels of significance appropriate for genome-wide analysis, variants with modest effect sizes (odds ratio (OR) < 1.2). 　　Third, whereas the association signals detected can help to define regions of interest, they cannot provide unambiguous identification of the causal genes. Nevertheless, assessments on the basis of positional candidacy carry considerable weight, and, as we show, these already allow us, for selected diseases, to highlight pathways and mechanisms of particular interest. Naturally, extensive resequencing and fine-mapping work, followed by functional studies will be required before such inferences can be translated into robust statements about the molecular and physiological mechanisms involved. 　　We turn now to a discussion of the main findings for each disease, focusing here only on the most significant and interesting results from the analyses described above, and consideration of an expanded reference group, described below. 　　Bipolar disorder (BD; manic depressive illness26) refers to an episodic recurrent pathological disturbance in mood (affect) ranging from extreme elation or mania to severe depression and usually accompanied by disturbances in thinking and behaviour: psychotic features (delusions and hallucinations) often occur. Pathogenesis is poorly understood but there is robust evidence for a substantial genetic contribution to risk27,28. The estimated sibling recurrence risk (λs) is 7–10 and heritability 80–90%27,28. The definition of BD phenotype is based solely on clinical features because, as yet, psychiatry lacks validating diagnostic tests such as those available for many physical illnesses. Indeed, a major goal of molecular genetics approaches to psychiatric illness is an improvement in diagnostic classification that will follow identification of the biological systems that underpin the clinical syndromes. The phenotype definition that we have used includes individuals that have suffered one or more episodes of pathologically elevated mood (see Methods), a criterion that captures the clinical spectrum of bipolar mood variation that shows familial aggregation29. 　　Several genomic regions have been implicated in linkage studies30 and, recently, replicated evidence implicating specific genes has been reported. Increasing evidence suggests an overlap in genetic susceptibility with schizophrenia, a psychotic disorder with many similarities to BD. In particular association findings have been reported with both disorders at DAOA (-amino acid oxidase activator), DISC1 (disrupted in schizophrenia 1), NRG1 (neuregulin1) and DTNBP1 (dystrobrevin binding protein 1)31. 　　The strongest signal in BD was with rs420259 at chromosome 16p12 (genotypic test P = 6.3 × 10-8; Table 3) and the best-fitting genetic model was recessive (Supplementary Table 8). Although recognizing that this signal was not additionally supported by the expanded reference group analysis (see below and Supplementary Table 9) and that independent replication is essential, we note that several genes at this locus could have pathological relevance to BD, (Fig. 5). These include PALB2 (partner and localizer of BRCA2), which is involved in stability of key nuclear structures including chromatin and the nuclear matrix; NDUFAB1 (NADH dehydrogenase (ubiquinone) 1, alpha/beta subcomplex, 1), which encodes a subunit of complex I of the mitochondrial respiratory chain; and DCTN5 (dynactin 5), which encodes a protein involved in intracellular transport that is known to interact with the gene ‘disrupted in schizophrenia 1’ (DISC1)32, the latter having been implicated in susceptibility to bipolar disorder as well as schizophrenia33. 　　Of the four regions showing association at P < 5 × 10-7 in the expanded reference group analysis (Supplementary Table 9), it is of interest that the closest gene to the signal at rs1526805 (P = 2.2 × 10-7) is KCNC2 which encodes the Shaw-related voltage-gated potassium channel. Ion channelopathies are well-recognized as causes of episodic central nervous system disease, including seizures, ataxias and paralyses34. It is possible that this may extend to episodic disturbances of mood and behaviour. 　　Amongst the other higher ranked signals in the BD data set (Supplementary Table 7), there is support for the previously suggested importance of GABA neurotransmission (rs7680321 (P = 6.2 × 10-5) in GABRB1 encoding a ligand-gated ion channel (GABA A receptor, beta 1))35, glutamate neurotransmission (rs1485171 (P = 9.7 × 10-5) in GRM7 (glutamate receptor, metabotropic 7))35 and synaptic function (rs11089599 (P = 7.2 × 10-5) in SYN3 (synapsin III)36). 　　We note that a broad range of genetic and non-genetic data point to the importance of analyses that use alternative approaches to phenotype definition, including symptom dimensions31. Although beyond the scope of the current paper, such analyses will be required to maximize the potential of the current BD data set. 　　Coronary artery disease (coronary atherosclerosis) is a chronic degenerative condition in which lipid and fibrous matrix is deposited in the walls of the coronary arteries to form atheromatous plaques37. It may be clinically silent or present with angina pectoris or acute myocardial infarction. Pathogenesis is complex, with endothelial dysfunction, oxidative stress and inflammation contributing to development and instability of the atherosclerotic plaque37. 　　In addition to lifestyle and environmental factors, genes are important in the aetiology of CAD38. For early myocardial infarction, estimates of λs range from 2 to 7 (ref. 39). Genetic variation is thought likely to influence risk of CAD both directly and through effects on known CAD risk factors including hypertension, diabetes and hypercholesterolaemia. Genome-wide linkage studies have mapped several loci that may affect susceptibility to CAD/myocardial infarction40 although for only two of these has the likely gene been identified (ALOX5AP (arachidonate 5-lipoxygenase-activating protein) and LTA4H (leukotriene A4 hydrolase))41,42. Association studies have identified several plausible genetic variants affecting lipids, thrombosis, inflammation or vascular biology but for most the evidence is not yet conclusive40. We did not find evidence for strong association at any of these genes within our study (Table 2 and Supplementary Table 10). 　　The most notable new finding for CAD is the powerful association on chromosome 9p21.3 (Table 3; Fig. 5). Although the strongest signal is seen at rs1333049 (P = 1.8 × 10-14), associations are seen for SNPs across > 100 kilobases. This region has not been highlighted in previous studies of CAD or myocardial infarction40,43. The region of interest contains the coding sequences of genes for two cyclin dependent kinase inhibitors, CDKN2A (encoding p16INK4a) and CDKN2B (p15INK4b), although the most closely associated SNP is some distance removed. Both genes have multiple isoforms, have an important role in the regulation of the cell cycle and are widely expressed44, with CDKN2B known to be expressed in the macrophages but not the smooth muscle cells of fibrofatty lesions45,46. It is of interest that expression of CDKN2B is induced by transforming growth factor beta (TGF-β) and that the TGF-β signalling system is implicated in the pathogenesis of human atherosclerosis45,46. Besides CDKN2A and CDKN2B, the only other known gene nearby is MTAP which encodes methylthioadenosine phosphorylase, an enzyme that contributes to polyamine metabolism and is important for the salvage of both adenine and methionine. MTAP is ubiquitously expressed, including in the cardiovascular system47. Further work is required to determine whether the CAD association at this locus is mediated through CDKN2A/B, MTAP or some other mechanism. The same region also shows replicated evidence of association to T2D in the WTCCC and other data sets19,21,22, though different SNPs seem to be involved. 　　None of the loci showing more modest associations with CAD (Table 4) includes genes hitherto strongly implicated in the pathogenesis of CAD. A potentially interesting association is at rs6922269 (P = 6.3 × 10-6), an intronic SNP in MTHFD1L, which encodes methylenetetrahydrofolate dehydrogenase (NADP+-dependent) 1-like, the mitochondrial isozyme of C1-tetrahydrofolate (THF) synthase48,49. C1-THF synthases interconvert the one carbon units carried by the biologically active form of folic acid, C1-tetrahydrofolate. These are used in a variety of cellular processes including purine and methionine synthesis48. Another enzyme in the same pathway, methylene THF reductase (encoded by MTHFR) is subject to a common mutation which influences plasma homocysteine level50 and has been associated with increased risk of coronary and other atherosclerotic disease51. The possibility of a link between variants in MTHFD1L and CAD risk is supported by evidence that MTHFD1L activity also contributes to plasma homocysteine52 and that defects in the MTHFD1L pathway may increase plasma homocysteine level48,53. 　　An intronic SNP in ADAMTS17 (a disintegrin and metalloproteinase with thrombospondin motifs 17), which showed modest association (rs1994016; P = 1.1 × 10-4) in our primary analysis, showed a much stronger association in the expanded reference group analysis (see below and Supplementary Table 9). Although the specific function of ADAMTS17 has not been determined, other members of the ADAMTS family have been implicated in vascular extracellular matrix degradation, vascular remodelling and atherosclerosis54,55. 　　Crohn’s disease is a common form of chronic inflammatory bowel disease56. The pathogenic mechanisms are poorly understood, but probably involve a dysregulated immune response to commensal intestinal bacteria and possibly defects in mucosal barrier function or bacterial clearance57. Genetic predisposition to CD is suggested by a λs of 17–35 and by twin studies that contrast monozygotic concordance rates of 50% with only 10% in dizygotic pairs58,59. 　　A number of CD-susceptibility loci have previously been defined, and all of these generate strong signals in our data (Table 2). In 2001, positional cloning identified CARD15 (caspase recruitment domain family, member 15; NOD2) as the first confirmed CD-susceptibility gene60,61. In the present study, this locus is represented by rs17221417 (P = 9.4 × 10-12). A second association, on chromosome 5q31 (ref. 62) has been widely replicated, although the identity of the causative gene is disputed owing to extensive regional linkage disequilibrium63. Here, the previously described risk haplotype is tagged by rs6596075 (P = 5.4 × 10-7). 　　More recent studies have identified four further CD-susceptibility loci, all of which are strongly replicated in the present study. The association between CD and SNPs within IL23R (interleukin 23 receptor)63 is here represented by a cluster of associated SNPs, including rs11805303 (P = 6.5 × 10-13). The strongest signal for CD in the present scan (at rs10210302; P = 7.1 × 10-14) maps to the ATG16L1 (ATG16 autophagy related 16-like 1) gene and is in strong linkage disequilibrium (r2 = 0.97) with a non-synonymous SNP (T300A, rs2241880) associated with CD in a German non-synonymous SNP scan64. The third is a locus at chromosome 10q21 around rs10761659 (P = 2.7 × 10-7) and represents a non-coding intergenic SNP mapping 14-kb telomeric to gene ZNF365 and 55-kb centromeric to the pseudogene antiquitin-like 4—a recently detected signal65. Finally, strong association with a cluster of SNPs around rs17234657 (P = 2.1 × 10-13) within a 1.2 Mb gene desert on chromosome 5p13.1, recapitulates the finding of a recent GWA study66. 　　The current study identifies four further new strong association signals in CD, located on chromosomes 3p21, 5q33, 10q24 and 18p11 (Table 3; Fig. 5). Successful replication for all four loci is reported elsewhere23. 　　The first of these includes several SNPs around IRGM (immunity-related guanosine triphosphatase; the human homologue of the mouse Irgm/Lrg47), the strongest signal being at rs1000113 (P = 5.1 × 10-8). IRGM encodes a GTP-binding protein which induces autophagy and is involved in elimination of intracellular bacteria, including Mycobacterium tuberculosis67. Reduced function and/or activity of this gene would be expected to lead to persistence of intracellular bacteria, consistent with existing models of CD pathogenesis57 and the recent ATG16L1 association64 (see above). 　　The second novel CD association is seen at rs9858542 (P = 7.7 × 10-7), a synonymous coding SNP within the BSN (bassoon) gene on chromosome 3p21. BSN is thought to encode a scaffold protein expressed in brain and involved in neurotransmitter release; a more plausible regional candidate is MST1 (macrophage stimulating 1), which encodes a protein influencing motile activity and phagocytosis by resident peritoneal macrophages68. 　　The third novel association involves a cluster of SNPs around rs10883365 (P = 1.4 × 10-8) on chromosome 10q24.2. The most credible candidate here is the NKX2-3 (NK2 transcription factor related, locus 3) gene, a member of the NKX family of homeodomain-containing transcription factors. Targeted disruption of the murine homologue of NKX2-3 results in defective development of the intestine and secondary lymphoid organs69. Abnormal expression of NKX2-3 may alter gut migration of antigen-responsive lymphocytes and influence the intestinal inflammatory response. 　　The final novel association, at rs2542151 (P = 4.6 × 10-8) maps 5.5-kb upstream of PTPN2 (protein tyrosine phosphatase, non-receptor type 2) on chromosome 18p11. PTPN2 encodes the T cell protein tyrosine phosphatase TCPTP, a key negative regulator of inflammatory responses. The same locus also shows strong association with T1D susceptibility (trend test P = 1.9 × 10-6) and a consistent, though weaker, association with RA (P = 1.9 × 10-2), supporting the existence of overlapping pathways in the pathogenesis of very distinct inflammatory phenotypes (combined trend test P value for all three diseases = 9 × 10-8) (Table 3; ref. 10). 　　Several further loci generating less strong evidence for association are of interest on the basis of their biological candidacy (Table 4). For example, rs9469220 (P = 8.7 × 10-7) mapping to the human leukocyte antigen (HLA) system class II region was detected in the ‘second tier’ of associations (Table 4). This suggests a significant contribution of HLA to CD-susceptibility, though less marked than seen in classical autoimmune conditions such as RA and T1D. Another interesting candidate flagged in Table 4 is TNFAIP3 (TNFα induced protein 3), the closest gene to rs7753394 on chromosome 6q23. The protein product inhibits TNFα-induced NFκB-dependent gene expression by interfering with RIP- or TRAF-2-mediated transactivation signals—hence interacting with the same pathway as CARD15 (NOD2). Markers with lower levels of significance include rs6478108 (P = 9.0 × 10-5) within TNFSF15 (tumour necrosis factor super family, member 15), previously reported associated with CD70; and rs3816769 (P = 3.1 × 10-5) which maps within STAT3 (signal transducers and activator of transcription, member 3). On the X chromosome rs2807261 (P = 1.3 × 10-7) maps 50-kb from the gene CD40LG (CD40 ligand—previously known as TNF superfamily, member 5), implicated in the regulation of B-cell proliferation, adhesion and immunoglobulin class switching71. As described in the section on T1D, a modest association between CD and SNPs in the vicinity of the PTPN11 gene on chromosome 12q24 (P = 1.5 × 10-3) probably reflects a locus influencing general autoimmune predisposition. 　　An emerging theme from molecular genetic studies of CD is the importance of defects in autophagy and the processing of phagocytosed bacteria. A number of other specific components within innate and adaptive immune pathways are also highlighted. 　　Hypertension refers to a clinically significant increase in blood pressure and constitutes an important risk factor for cardiovascular disease (http://www.who.int/whr/2002/en/; ref. 72). Lifestyle exposures that elevate blood pressure, including sodium intake, alcohol and excess weight73 are well-described risk factors. Genetic factors are also important74,75. Estimates of λs are approximately 2.5–3.5. 　　Experimental models have highlighted a number of quantitative trait loci but these have yet to translate into insights into human hypertension76. Linkage studies are consistent with susceptibility genes of modest effect size77 and well-replicated findings have yet to emerge from association approaches. 　　None of the variants previously associated with HT showed evidence for association in our study although we note that some, such as promoter of the WNK1 (WNK lysine deficient protein kinase 1) gene78,79, are not well tagged by the Affymetrix chip. 　　For HT there were no SNPs with significance below 5 × 10-7 (Table 3) but the number and distribution of association signals in the range 10-4 to 10-7 was similar to that of the other diseases studied (Table 4 and Supplementary Table 7). There are several possible explanations. First, HT may have fewer common risk alleles of larger effect sizes than some of the other complex phenotypes. If so, then identification of susceptibility variants for HT is likely to be reliant on the synthesis of findings from multiple large-scale studies. Second, the present study may have failed to detect genuine common susceptibility variants of large effect size because they happened to be poorly tagged by the set of SNPs genotyped in the current study. If so, further rounds of genotyping using resources that offer increased density (or complementary SNP sets), and/or improved analytical methods (for example, imputation-based) should facilitate their discovery. Third, study of HT may be more susceptible than other phenotypes to the diluting effects of misclassification bias due to the presence of hypertensive individuals within the control samples. If so, power can be improved in future studies by use of controls specifically screened to exclude individuals with elevated blood pressure. 　　The most strongly associated SNPs (Table 4) do not identify genes from physiological systems previously implicated by clinical or genetic studies in hypertension. The strongest signal overall is with rs2820037 on 1q43 (genotypic test, P = 7.7 × 10-7). The closest genes are RYR2 (encoding the ryanodine receptor 2), mutations in which are associated with stress-induced polymorphic ventricular tachycardia and arrhythmogenic right ventricular dysplasia80,81; CHRM3, encoding the cholinergic receptor muscarinic 3, a member of the G protein-coupled receptor family32; and ZP4, the product of which is zona pellucida glycoprotein 481. The strong association signals on the X chromosome using an expanded reference group (see below and Supplementary Table 9) are of substantial interest but they do not identify known genes of obvious relevance to HT. 　　Rheumatoid arthritis is a chronic inflammatory disease characterized by destruction of the synovial joints resulting in severe disability, particularly in patients who remain refractory to available therapies82. Susceptibility to, and severity of, RA are determined by both genetic and environmental factors, with λs estimates ranging from 5–10 (ref. 83). 　　An association between RA and alleles of the HLA-DRB1 locus has long been established84. Despite extensive linkage85,86,87 and association studies, only one other RA susceptibility locus has been convincingly identified in Caucasians. In common with several autoimmune diseases including T1D, carriage of the T allele of the rs2476601 SNP in the PTPN22 (protein tyrosine phosphatase, non-receptor type 22) gene has been reproducibly associated with RA, conferring a genetic relative risk of approximately 1.8 (refs 88, 89). These known associations with HLA-DRB1 and PTPN22 explain around 50% of the familial aggregation of RA. 　　Both these previous associations emerge strongly here (Table 2). The most associated marker within PTPN22 (rs6679677: chromosome 1p13) is perfectly correlated (HapMap CEU data r2 = 1) with the functionally relevant SNP (rs2476601) described previously, and the effect size is consistent with previous estimates89. Amongst other putative RA susceptibility genes, two SNPs mapping to CTLA-4 (cytotoxic T-lymphocyte associated 4) rs3087243 and rs11571300 were only nominally significant (P = 0.085 and P = 0.034, respectively) (Supplementary Table 10). 　　RA was the sole disease for which the sex-differentiated analysis generated a strong signal due to different genetic effects in males and females. The SNP rs11761231 (chromosome 7) generates a P value of 3.9 × 10-7 for the 2-degrees of freedom (d.f.) sex-differentiated test which combines trend tests in males and females (Table 3). (The trend test ignoring the sex of the individuals has a P value of 1.7 × 10-6.) This genotype has no effect on disease status in males, but a strong apparently additive effect in females (P value in a logistic regression model with additive log-odds is 0.68 in males and 6.8 × 10-8 in females, additive OR for females 1.32), and may represent one of the first sex-differentiated effects in human diseases. Cluster plots for this SNP seem good, but it is surrounded by recombination hotspots and has no other SNPs on the Affymetrix chip with r2 >0.1（补充图11）。因此
，需要一些谨慎，但这代表了一个潜在的有趣发现 ，需要进一步研究
，尤其是考虑到与性别有关的患病率差异的特征
。 　　在10-5至5×10-7范围内具有名义P值的9个SNP（表4）映射到先前与RA相关的基因座。特别令人感兴趣的是SNP映射的关联，接近IL2受体的Alpha和Beta链（在IL2RA的情况下为RS2104286； RS743777和IL2RB） 。IL2受体介导了T淋巴细胞的IL2刺激 ，因此被认为在预防自身免疫性方面具有重要作用
。IL2RA的罕见的4基底缺失与严重的自身免疫性疾病的发展有关，并且有证据（从以前的Data91
，以及这项研究及其随访）表明IL2RA基因区域内的SNP与T1D有关（另请参见T1D部分）。 　　在10-4至10-5范围内具有名义意义的几个SNP（补充表7）映射到具有合理生物学相关性的基因 。Examples include SNPs within genes implicated in the TNF pathway (for example, rs2771369 in TNFAIP2 (tumour necrosis factor, alpha-induced protein 2)) or in the regulation of T-cell function (rs854350 in GZMB (granzyme B) and rs4750316 in PRKCQ (protein kinase C, theta)).Kazald1（Kazal型丝氨酸蛋白酶抑制剂结构蛋白1前体）与RS10786617的关联，该基因已知其产物在损伤后在骨骼再生中起作用 ，可能与RA中骨腐蚀的发展有关
。 　　RA和T1D已知有两个共同的疾病易感基因：在MHC和PTPN22。如其他地方所详述
，我们的研究提供了数据，表明该列表可以扩展到包括IL2RA（染色体10p15染色体） ，PTPN2（染色体18P11）和12q24染色体区域（补充表11）周围的变体
，这在RA中显然是新颖的 。 　　1型糖尿病是一种慢性自身免疫性疾病，通常在儿童期92
。T1D的λs为15 ，双数据数据表明
，超过85％的表型方差是由于遗传因子93引起的。There are six genes/regions for which there is strong pre-existing statistical support for a role in T1D-susceptibility: these are the major histocompatibility complex (MHC), the genes encoding insulin, CTLA-4 (cytotoxic T-lymphocyte associated 4) and PTPN22 (protein tyrosine phosphatase, non-receptor type 22), and the regions around the白介素2受体α（IL2RA/CD25）和干扰素诱导的解旋酶1基因（IFIH1/MDA5）94 。但是，这些信号只能解释T1D家族聚集的一部分
。在此扫描中检测到这些先前鉴定的关联中的五个（p≤0.001）（表2和补充表10） ，例外是上面讨论的INS基因。 　　在这项研究中
，单点分析揭示了三个新的区域（关于染色体12q13、12q24和16p13），显示了有力的关联证据（P <5×10-7；表3） 。通过多焦点分析（染色体4q27和12p13：表3 ，补充图12）或通过自身免疫性病例（染色体18P11和10P15 CD25区域：表3，表3
，补充图13），其他四个区域具有相似的显着性水平。染色体的T1D的关联在2013年第12季度 ，12q24 、16p13和18p11的关联已在独立和多个种群中得到证实
。 　　12染色体上的两个信号（在2013年第12季度和12q24）映射到覆盖十多个基因的广泛连接不平衡区域（图5）。其中几个代表了功能性候选物
，因为它们在免疫信号传导中的作用，被认为是T1D敏感性的主要特征 。其中包括在12q13和SH2B3/LNK（SH2B适配器蛋白3） ，TRAFD1（TRAF-type锌指域
，包含1）和PTPN11（PTPN11（PTPN11）（Protein protein protein protosine Proseine phosepeptor型，非对比型11）11）的ERBB3（受体酪氨酸 - 蛋白激酶ERBB-3前体）和SH2B3/LNK（SH2B适配蛋白3） ，TRAFD1（traf-type锌指域）
。特别是对于这些信号区域
，广泛的重新纠正，进一步的基因分型和靶向功能研究将是识别哪种基因或基因为因果关系的重要步骤。在列出的那些人中 ，PTPN11是一个特别有吸引力的候选者
，鉴于胰岛素和免疫信号96 。它也是与PTPN22相同的调节性磷酸酶家族的成员，该家族已被确定为T1D和其他自身免疫性疾病的重要易感基因94,97
。实际上 ，与T1D最相关的12q24变体在CD和RA扫描中也具有特征，为所有9.3×10-10的自身免疫性病例产生了组合信号（补充表11）。 　　相比之下
，可用的注释表明16p13区域仅包含两个功能未知的基因，kiaa0350和地塞米松诱导的转录本（图5） 。Also, the region of association identified on 18p11 (Supplementary Fig. 14), which seems to confer susceptibility to all three autoimmune conditions studied (combined trend test P = 9 × 10-8, P = 4.6 × 10-8 for CD, 1.9 × 10-2 for RA, and 1.9 × 10-6 for T1D: Supplementary Table 11), maps to a single gene, PTPN2 (protein tyrosine磷酸酶 ，非受体2）
，与PTPN22和PTPN11同一家族的成员，并参与免疫调节96
。 　　我们的扫描发现在包含CD25的10P15区域内的SNP相关联 ，编码了IL-2的高亲和力受体。这与先前关于该区域与T1D91关联的报告一致 。CD25区域先前已显示与Graves的疾病98有关
，本研究还提供了与RA相关的证据（RA的趋势测试p = 5×10-8，P = 7×10-6 ，RA和T1D分别提供了补充表11）
。这一发现与多焦点分析揭示的T1D与4Q27区域之间关联的证据具有明显的生物学联系（补充表12
，补充图12）。该区域包含编码IL-2和IL-21的基因 。T1D的点头（非肥胖糖尿病）小鼠模型的研究表明，主要的非MHC基因座（IDD3）反映了IL2 Gene99的调节变化 ，我们的结果表明
，IL-2途径在T1D和其他自身免疫性疾病中的主要重要性。 　　另一个地区值得评论
。在多焦点分析中，对包含多个候选基因的染色体12p13区域的支持增加了 ，包括CD69（CD69抗原（P60，早期T细胞活化抗原））和多个CLEC（C-Type lectin域域）基因。In contrast to the chromosome 4 region where the effect of imputation is to tip an already-strong signal (5.01 × 10-7 for typed rs17388568, trend test) over the arbitrary threshold of 5 × 10-7, the 12p13 locus involves a more marked change between imputed and actual (7.2 × 10-7 for rs11052552, general test).迄今为止
，对该估算的SNP的复制研究产生了模棱两可的结果（有关详细信息，请参见参考文献10） 。 　　2型糖尿病是一种慢性代谢疾病 ，通常在成年年级中期至后期首次诊断为100
。与肥胖密切相关
，该疾病在胰岛素101的分泌和周围作用中都具有缺陷。T2D的可观家族聚集（欧洲个体中的λs估计为3.0）73反映了共同的家庭环境和遗传倾向 。遗传力值在30至70％101之间的大多数估计值差异很大
。 　　迄今为止，在非分离人群中的稳健 ，广泛复制的关联仅限于三个基因的变异：PPARG（编码过氧化物酶体增殖激活的受体γ; p12a102）
，kcnj11，kcnj11（内心偏移的kir6.2 pancreatic beta beta beta canlel2 cyner2 cyner2 cyner 2 clindp; ecl2 cyner2 clinder tc; e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e ecl2;（转录因子7样2; rs7903146（参考文献104 ，105））。 　　此处检测到所有这三个信号
，其效应范围与以前的报告一致（表2） 。TCF7L2内染色体10q上的SNP簇，由rs4506565（趋势测试 ，或1.36
，p = 5.7×10-13）代表，产生了T2D的最强关联信号（表3 ，图5）
。RS4506565处于紧密的连锁不平衡（HAPMAP的CEU组件中的R2为0.92），其rs7903146是rs7903146
，该变体具有最强的Aetioology Sipporss104,106。实际上，我们的插补分析证实 ，卢比虽然在芯片上没有代表性
，但负责该区域中最强的关联效应（图5） 。TCF7L2在WNT信号途径内起作用，对糖尿病风险的影响似乎主要通过β细胞功能障碍107介导
。 　　正如预期的那样 ，考虑到现有效应大小的估计值
，与其他已建立的T2D易感性基因中的变异相关的信号KCNJ11（rs5215，R2为0.9 ，rs5219
，e23k），E23K）和PPARG和PPARG（rs17036328 ，r2 of 2 test and 152128
，RS1801282，prAM123 ，PRAM）分别p = 0.001）。这些示例说明了真正的疾病敏感性变体如何产生关联信号，这不会引起在全基因组环境中随访的立即关注 。 　　除TCF7L2外
，扫描还显示了T2D的两个信号，P值小于5×10-7（表3 ，图5）。这些图中的第一个图（脂肪质量和肥胖相关）基因在16q染色体上
。几个相邻的SNP（包括RS9939609
，RS7193144和RS8050136）产生的信号为特征，其特征是每位贵族或T2D为1.25 ，并且在控件中的风险等位基因频率为40％。正如该发现引起的后续研究中最近所描述的那样
，这些变体对T2D风险的影响已被复制，并且完全由它们对脂肪的显着影响24所介导 。 　　第三个关联信号（6p22染色体）具有高度相关的SNP（包括RS9465871）的簇 ，风险 - 高位等位基因频率在18％至35％之间
，映射到CDKAL1的内含子5（CDK5调节性亚基相关蛋白相关蛋白1类）基因
。尽管CDKAL1的功能尚不清楚，但它与CDK5调节亚基相关蛋白1（CDK5RAP1）在蛋白质域水平上共享同源性。已知CDK5RAP1可以抑制CDK5的激活 ，CDK5是一种依赖细胞周期蛋白的激酶
，与正常β细胞功能的维持有关 。我们自己的后续研究以及其他小组的扫描显示了这一发现的强烈复制
。19,20,21,22。该变体对T2D风险的影响显示出与添加性的显着偏离（补充表8） 。 　　在具有更少关联信号的变体中，一个值得注意的包含是染色体10上的SNP簇 ，包括RS10748582和RS7923866，它在10-4和10-5之间产生趋势测试P值
。在HHEX（同源型
，造血表达）和IDE（胰岛素降解酶）基因附近的集群图中，在最近在1363 French Origin Origin109受试者中进行的T2D进行的GWA扫描中强调的区域中。在我们数据中显示关联的SNP是法国研究中报告的SNP ，并为T2D产生相似的效应尺寸估计值 。 　　在法国Scan109突出显示的其他三个区域中
，我们的数据不能证实
。法国报告中与T2D相关的SLC30A8中的SNP（RS13266634）与Affymetrix芯片上的SNP（R2 <0.01）的SNP相关（R2 <0.01），并且区域中的广泛重组事件限制了数据输入方法的价值。LOC387761和EXT2信号的覆盖范围要好得多 ，但是
，对于这些信号，既没有基因分型也没有归为SNP ，就可以显示与T2D关联的证据 。 　　WTCCC数据有助于鉴定两个额外的鲁棒复制T2D信号
，映射到IGF2BP2基因和CDKN2A/CDKN2B区域19,21,22，尽管两者都没有在初级扫描分析上产生令人印象深刻的P值（单点P均不是<10-4）。尽管涉及不同的SNP ，但后者的信号映射到染色体9上的CAD信号相同的区域
。表4中的其他SNP不映射到先前与T2D发病机理有关的基因或区域
，迄今为止的复制工作尚未鉴定出任何确认的信号19。 　　对于固定数量的病例，可以通过扩大参考组来增加病例对照研究的功率 。我们的主要分析使用每种疾病的对照：病例比为1.5：1
。其他6个疾病数据集的可用性使我们有机会将参考组扩大到7.5：1的比率 ，并具有对每种疾病分析的潜在相互利益。对于BD和T2D，扩展的参考组包括58C和UKBS对照组
，并补充了其他6种疾病集；对于CAD和HT，该扩展的参考组分别降低了HT和CAD 。对于CD ，RA和T1D
，仅由非自动免疫性疾病的病例增加参考组
。 　　大多数基因座的关联证据证明了扩展的参考组方法的实用性，这些效用是从我们的主要分析中获得最强支持的大多数基因座 ，其中包括Loci的许多信号
，已知在T1D，T2D和CD中显示出牢固的关联（补充表9）。此外 ，该分析在初级分析中提高了几个基因座
，具有适中的统计显着性水平，达到了统计显着性的最高级别（p <5×10-7） 。 　　我们的数据表明 ，这种方法可能是常规分析的有用辅助手段
，应考虑将其确定为高度显着的基因座进行跟进
。有两个重要的警告。首先，影响测试疾病和参考组中包含的一种或多种疾病的易感基因将导致功率损失 。其次 ，可能会发生“镜像 ”效应，从而在扩展的参考样本中（例如
，自身免疫性疾病中的HLA）在测试疾病中与​​相反的等位基因引起了虚假的关联
。因此，必须在参考组中包含的疾病的关联发现的背景下解释使用扩展参考组的正相关。 　　有趣的是 ，考虑哪些统计模型最能描述与疾病状况密切相关的基因座和基因座之间的数据 。这些统计模型的生物学解释并不直接
，但它们可以帮助选择更强大的统计工具来检测关联。 　　首先，考虑19个非MHC SNP中的每一个 ，在表3中显示出关于趋势或基因型检测的相关性的有力证据
。对于这19个中的四个
，在2-D.F上的p值。基因型测试小于1-D.F的基因型测试 。趋势测试（表3）
。在比较疾病模型时，这些也是四个SNP ，有证据表明疾病的几率与风险等位基因的副本数量多样化（补充表8）。这支持了我们的观点
，即除趋势测试外，还应进行基因型测试 ，尽管也许应该更加谨慎地查看以两个原因：它更容易受到基因分型错误的影响；（根据我们的发现）经验并不支持强烈的主导作用 。 　　一个单独的问题与不同基因座结合影响疾病的易感性的方式的最佳模型有关
，因此应采用明确允许在基因座之间进行相互作用的方法来检测关联110
。此处报告的所有分析都不包括此类互动，因此我们不太适合解决一个总体问题。尽管如此 ，在每个集合中都有多个相关区域（CD，T1D和T2D）中
，我们在表3中考虑了所有非MHC SNP，并寻找与两个基因座结合以增加添加剂方式增加log-odds的模型 。我们发现了CD中从RS1000113和RS10761659之间的多焦点添加性（未经调整的P值= 0.002）和RS9465871和T2D中RS4506565之间的有暗示性证据
。似乎有必要对这个问题进行进一步调查 ，最好是通过应用单基因座和基于相互作用的方法发现的无偏见的疾病基因座。