Open Access

Optimization of candidate-gene SNP-genotyping by flexible oligonucleotide microarrays; analyzing variations in immune regulator genes of hay-fever samples

  • Janne Pullat1, 3, 5,
  • Robert Fleischer1,
  • Nikolaus Becker2,
  • Markus Beier4,
  • Andres Metspalu3, 5, 6, 7 and
  • Jörg D Hoheisel1Email author
BMC Genomics20078:282

DOI: 10.1186/1471-2164-8-282

Received: 20 November 2006

Accepted: 17 August 2007

Published: 17 August 2007

Abstract

Background

Genetic variants in immune regulator genes have been associated with numerous diseases, including allergies and cancer. Increasing evidence suggests a substantially elevated disease risk in individuals who carry a combination of disease-relevant single nucleotide polymorphisms (SNPs). For the genotyping of immune regulator genes, such as cytokines, chemokines and transcription factors, an oligonucleotide microarray for the analysis of 99 relevant SNPs was established. Since the microarray design was based on a platform that permits flexible in situ oligonucleotide synthesis, a set of optimally performing probes could be defined by a selection approach that combined computational and experimental aspects.

Results

While the in silico process eliminated 9% of the initial probe set, which had been picked purely on the basis of potential association with disease, the subsequent experimental validation excluded more than twice as many. The performance of the optimized microarray was demonstrated in a pilot study. The genotypes of 19 hay-fever patients (aged 40–44) with high IgE levels against inhalant antigens were compared to the results obtained with 19 age- and sex-matched controls. For several variants, allele-frequency differences of more than 10% were identified.

Conclusion

Based on the ability to improve empirically a chip design, the application of candidate-SNP typing represents a viable approach in the context of molecular epidemiological studies.

Background

Array-based technologies are revolutionizing genomics, especially the analysis of DNA variation. Array technologies are not without limitations, however, and one major drawback is the poor flexibility of typical array formats. It is cumbersome to create one's own tailored arrays by spotting DNA. Commercially available microarrays, on the other hand, either contain a fixed and usually broadly applicable content or are expensive to purchase with customized features. The fixed-content arrays are useful for taking advantage of the high resolution genetic map of the human genome that is based on single nucleotide polymorphisms [1, 1], which define DNA blocks (haplotypes) [1]. Since SNPs are the most common type of genetic variation between individuals, it makes sense to utilize them for the localization of disease genes by identifying haplotypes that are associated with phenotypic traits, especially in the case of multifactorial diseases [15]. As a consequence of such a study, however, further analysis is required for improving the resolution of the mapping process or trying to identify the polymorphisms that are actually responsible for the phenotypic variation. Alternatively to the process described above, one can immediately focus on the analysis of particular polymorphisms in candidate genes, if circumstantial evidence indicates their possible relevance to the occurrence of a disease. In either approach the production of a customized microarray is required. Also, experience has demonstrated the need for a careful design of the experimental set-up in order to avoid unacceptable error [6].

Irrespective of the algorithm used for the sequence selection of the probe set, the final functional test of the suitability of an oligonucleotide array for genotyping results from an empirical analysis of the hybridization performance of the oligonucleotide probes. In consequence, it is likely that the initial chip design will be changed by replacing ill-performing oligonucleotides with alternative sequences. For this process, the ability to easily change the chip layout is essential. Light-induced in situ synthesis controlled by a micro-mirror device [7, 8] combines high synthesis yields of more than 99.5% per condensation [9] – and therefore good oligonucleotide quality – with the power of producing oligomer arrays of high density, reproducible characteristics and flexible layout.

In this study, we present the process of establishing an oligonucleotide microarray based on an on-site in situ synthesis technology for typing DNA samples in immune regulator genes including cytokines, chemokines and transcription factors. Genetic variants in immune regulator genes have been associated with numerous diseases, including allergies and cancer, with apparently an elevated disease risks in individuals that carry a combination of disease-relevant SNPs. For the array design, we exploited the flexibility of the GeniomOne device [8]. It employs a digital projector to synthesize oligonucleotide array features within channels of a three-dimensional micro-fluidic reaction carrier. The system allows the synthesis of a probe set of up to 64,000 oligonucleotides on a single chip, which subsequently can be hybridized with up to eight samples. For this analysis a microarray that assays 99 relevant SNPs was established by an iterative cycle of probe design and experimental evaluation. Subsequently, the performance of this microarray was investigated in a pilot study. Hay-fever patients aged 40–44 that exhibited high IgE levels against inhalant antigens and an age and sex-matched control group were analyzed.

Results

From a case-control study on hay-fever [10], 19 cases with the most extreme plasma IgE levels against inhalant antigens and 19 age- and sex-matched non-atopic controls were selected for the project. Originally, 141 SNPs in cytokine genes and other immune regulatory factors were selected from published studies and SNP-databases [1113]. If possible, SNPs with known or potential functional relevance and allele frequency information were selected. Also, sequence complexity between the probes was meant to be similar, since it is well established that the rate of reassociation depends on sequence complexity [14]. In addition, the initial compilation was based on theoretical calculations of interactions between all oligonucleotide probes and PCR fragments. The program "SNP CrossChecker" by Febit GmbH was used to check the cross reactivity between oligoprobes and template sequences reducing the number of PCR-products by 13 to 128. The threshold of maximally possible homology between 23 mere oligoprobe and template sequences was set to 85%. It takes into account that if within the 23 nucleotides of a probe, 20 nucleotides will basepairing with a template, this will produce sufficiently stable complex to produce false positive signals in the genotyping analysis.

Theoretically the probe properties could be assessed basis their sequence similarity and hybridization properties. Experimentally "bad" probe has low specificity, sensitivity and uniformity under given reaction conditions (temperature, base composition, salt concentration, hybridization time). Specificity and stability of DNA duplex formation strongly depend on sequence and base composition [15, 16]. Also, the target sequence on either side of the SNP position plays an important role since secondary structures may strongly affect the hybridization behavior of a sample [17]. Therefore, it is frequently insufficient to predict hybridization performance merely on the basis of theoretical calculations. Consequently, we analyzed and optimized the experimental parameters of SNP position in the oligonucleotide and the overall length of the probes as well as hybridization temperature and duration. For each SNP, all four possible sequence variations were applied to the chip. One of the probes is designed to be perfectly complementary to a short stretch of the reference sequence (perfect match – PM) and the other three are identical to the first, except at the interrogation position, where one of the other three bases is substituted (mismatches – MM). PM/MM scheme enables in addition subtract directly both the background level and cross-hybridization signals providing thus with redundancy required for the reliable microarray analysis. The perfect match probe (PM) is designed complementary to the target sequence and the so-called mismatch probe (MM) is identical with the PM, except the base in the middle of the sequence. Ideally, there is 30-fold difference in the signal intensities of PM vs. MM oligo. In hybridization the oligo signal intensity depends directly of its sequence GC content. Depending on sequence content (high G/C content) the MM oligo can result sufficiently high signal and interfere discrimination between PM and MM signals. In such cases the entire set of 24 oligoprimers, specially designed for detection of one SNP from sense and antisense strands, is underperforming and has to be left out of array design. In addition, we tested positional effects by moving the polymorphic nucleotide from the center to positions +2 and -2 as well as +1 and -1 of the oligonucleotide probes (Fig. 1). This shift resulted in differences in signal intensities but did not add to the overall amount of information that could be gathered from an experiment. In consequence, we decided to use only probes that contained the respective SNP in a central position but placed three copies of the same oligosequence at different locations of the microarray.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-8-282/MediaObjects/12864_2006_Article_995_Fig1_HTML.jpg
Figure 1

Design a 23 mer oligonucleotide for SNP detection. In (a) the relevant PCR-product of 166 bp is shown. (b) exhibits the set of oligonucleotides (12 for sense and 12 for antisense strand; at n = 0 the allele is located in the middle of the oligomer, at n = -2 and n = +2 the SNP is shifted by 2 nucleotides to the left and right, respectively.

Furthermore, different temperatures for hybridisation (40°C, 45°C, 50°C, and 55°C) and changes in hybridisation time from one to four hours were compared. The time of hybridisation in this experiment had little influence on number of correct and false signals. However, increased hybridisation temperature at 50°C or 55°C reduced cross hybridisation at least 5% and lowered general amount of positive signal to 60% and 40% (respectively). Reduced stringency by decreased hybridisation temperature maximized the overall number and intensity of signals, but this was accompanied with 30% increase of unspecific hybridisation signals.

We also varied probe length, synthesizing on the same chip oligonucleotides of 19, 21, 23, 25 and 27 nucleotides. While longer sequences usually produce higher signal intensities, shorter oligonucleotides permit better discrimination of single base differences due to the more pronounced destabilizing effect of a mismatch. As expected, the signal intensities of both the fully matched (I1) and the mismatch probes (I2) increased with length while discrimination (I1/I2) improved the shorter the oligonucleotides were (Fig. 2). Measured signal intensity (I1) increases clearly with higher nucleotides number in the sequence of oligonucleotide-probe: I1 (27 bp) > I1 (19 bp) (Fig. 3). Same effect is obtained for MM (I2) oligo-probes as well. Though the discrimination between PM/MM according to the calculated relation of measured intensities (I1/I2) is higher for shortest set of oligo-probes as 19 bp (5,3...3,6) and the lowest with 27 bp (1,7...1,6) ones. Variation of I1/I2 among probes within the same number of nucleotides comes mainly from GC content differences/variations of probe-sequence itself. Why longer MM sequences give higher signal comparing to shorter ones comes mainly from weaker destabilizing effect of noncomplimentary nucleotide on formation of double-stranded complex between probe and target DNA.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-8-282/MediaObjects/12864_2006_Article_995_Fig2_HTML.jpg
Figure 2

The dependence of signal intensity on oligonucleotide length. Hybridization was done at 45°C. I1/I2 labels the ratio of the signal at the full-match oligonucleotide and the signals at the mismatched oligonucleotides. 27, 25, 23, 21 and 19 indicates the length of oligomers. The SNP was located either at the center of the oligonucleotides (0) or shifted by two bases in either direction (+1, -1).

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-8-282/MediaObjects/12864_2006_Article_995_Fig3_HTML.jpg
Figure 3

Differences in the performance of oligonucleotides. Set 1 to 6 label the oligonucleotides designed for detecting a SNP. Each column indicates the signal intensity at the oligomers that represent (left to right) the A, G, C or T variant of a sequence. Sets 1 to 3 are the data produced on replicate microarray positions that represent one strand, while sets 4 to 6 indicate the signal intensities produced by the complementary DNA strand. Panel (1) shows the result obtained for a heterozygous sample, panel (2) a homozygous sample. Panel (3) exhibits data obtained with an oligonucleotide that was predicted in silico to perform well but failed in the experiment. In panel (4) a result of an oligomer with a high degree of cross-hybridization is presented.

Tests at different hybridization temperatures (40–55°C) produced the overall best results for the majority of SNPs with 23-mer probes and 3 to 4 hours of hybridization at 45°C. Finally, the selected set of oligoprobes, as well as the hybridization conditions, were tested in addition with 4 genomic DNA samples of control individuals. These control experiments had 5-fold redundancy. Concordance of analyzed genotypes were compared individually.

For selecting the best performing oligoprobes in the initial optimization experiments one test-DNA with good quality was used. All hybridization reactions from chip design step were repeated 3 times. During the optimization process, we identified several oligonucleotide probes that did not perform irrespective of the chosen hybridization conditions (e.g., Fig. 2). Apparently, the previously described selection basis of cross-reactivity could be even more stringent e.g. we should allow less base pairing. Following experimental tests revealed additional oligoprobe sets falling out from final chip design because of the same reason. Herewith, basis on experimental results, the threshold for software based oligo probe selection could be set on 80% allowing less base pairing (and less false signals due to nonspecific oligo hybridization) than 85%. In total, 29 out of the 128 SNPs (22%) could not be analyzed adequately. The respective oligomer probes that had been defined as good by the in silico selection process were empirically found to be ill-performing in real hybridizations. Either the absolute signal intensity was too low to permit a statistically solid analysis or the discriminative effect was insufficient. The ratio between PM and MM oligo signal intensities is supposed to be at least 1/3 (Fig. 3). The high number of failing oligonucleotides illustrates the need for a careful experimental validation of in silico designed microarrays.

Using the optimized microarray, we performed genotyping analyses at 99 SNPs in 68 genes that have a putative functional significance for the occurrence of hay-fever. From a case-control study on hay-fever [10], 19 cases with the most extreme plasma IgE levels against inhalant antigens and 19 age- and sex-matched non-atopic controls were selected. Informed consent of the participants was given in writing and the local ethics committee approved the study. PCR-amplifications of the relevant DNA-regions were performed either individually or in pools of 5 or 10 samples. While all pentaplex reactions yielded a product for each individual band, two decaplex amplifications failed to produce 1 out of the expected 10 amplicons (Fig. 4). The 99 products were pooled prior to labelling and hybridized concomitantly (Fig. 5). For each sample, analysis was repeated up to four times. The observed allele frequencies are presented in Table 1. To assess the accuracy of the genotyping, ten PCR-products of heterozygote calls obtained from the microarray analyses were subjected to gel-based DNA sequencing for confirmation. In all cases, the results were in full agreement.
Table 1

Relative allele frequences of SNPs genotyped in 19 hay fever patients with extreme IgE phenotype and 19 non-atopic controls.

SNP Nr.

SNP name

SNP identifier

Allele 1

Relative frequence of allele 1 in case sample

Relative frequence of allele 1 in control sample

1

IL-2_1

rs2069772

T

0.58

0.83

2

IL-2_2

rs2069763

G

0.67

0.61

3

IL-10_4

rs1800894

G

1.00

1.00

4

IL-10_5

rs1800871

C

0.77

0.83

5

IL-10_6

rs1800872

C

0.77

0.83

6

TNFA_7

rs11565

C

0.85

0.92

7

TNFA_8

rs673

A

0.03

0.03

8

TNFA_9

rs1800629

A

0.16

0.05

9

TNFA_10

rs361525

A

0.11

0.03

10

IL4_11

rs2243246

T

0.95

0.86

11

IL4_12

rs2243250

C

1.00

1.00

12

IL4_13

rs34185442

C

1.00

1.00

13

IL4_14

rs2970874

C

1.00

0.97

14

IL6_16

rs1800797

G

0.63

0.60

15

IL6_16

rs1800796

G

1.00

1.00

16

IL6_17

rs1800795

G

0.63

0.47

17

IL4R_18

rs1801275

A

0.87

0.68

18

IL4R_19

rs1805011

C

0.05

0.13

19

IL4R_20

rs8832

G

0.75

0.62

20

IL4R_21

rs1805015

T

1.00

0.89

21

IL4R_22

rs1805010

A

0.70

0.56

22

IL12p40_23

rs3124

C

1.00

1.00

23

STAT6_24

rs167769

A

0.32

0.20

24

STAT6_25

rs324015

G

0.72

0.82

25

STAT6_26

rs703817

G

0.50

0.38

26*

STAT6_27

rs4559

A

0.27

0.25

27

IFNG_28

rs2234685

A

1.00

1.00

28

IFNG_29

rs1861493

T

0.69

0.69

29

IFNG_30

rs2234687

C

1.00

1.00

30*

IFNG_31

rs2430561

T

0.50

0.38

31

IFNGR2_34

rs1802585

C

1.00

1.00

32

IFNGR2_35

rs1059293

T

0.39

0.50

33

IFNGR2_36

rs9808753

A

0.86

0.94

34

IRF1_37

rs839

G

0.74

0.89

35*

IRF1_38

rs9282762

A

0.42

0.60

36*

IRF2_40

rs1131553

G

0.32

0.50

37

IL8_41

rs1175

A

0.41

0.47

38

IL8_42

rs2227307

G

0.42

0.50

39

IL13_43

rs20541

G

0.89

0.79

40

IL13_44

rs1800925

C

0.88

0.82

41*

IL18_47

rs1946518

G

0.63

0.67

42*

IL18_48

rs1946519

C

0.87

0.71

43*

IL1B_49

rs16944

T

0.16

0.15

44

IL1B_50

rs1143627

C

0.36

0.38

45

IL1B_51

rs1799916

T

1.00

1.00

46

IL1A_52

rs17561

G

0.64

0.74

47

IL1A_53

rs1800587

T

0.36

0.31

48

IL9_56

rs1799962

A

1.00

1.00

49

TNFR1_60

rs1800692

C

0.63

0.53

50

TNFR1_61

rs1800693

A

0.53

0.72

51*

TNFRSF6_62

rs2234768

T

0.00

1.00

52

LTA_65

rs1800683

A

1.00

1.00

53

LTA_66

rs1041981

A

0.29

0.19

54

LTA_67

rs909253

G

0.25

0.19

55

IL1RN_68

rs2234676

G

0.86

0.74

56

IL1RN_69

rs419598

T

0.87

0.88

57

CTLA4_70

rs2384137

G

0.11

0.06

58

NFKBIA_72

rs1800439

G

0.53

0.56

59

IL8RB_77

rs2230054

T

0.06

0.17

60

ICAM1_78

rs1799969

A

1.00

0.97

61*

ICAM1_79

rs5498

G

0.61

0.64

62

IL3_81

rs40401

G

0.95

0.94

63

IL3_82

rs31480

G

0.84

0.82

64

MCP1_87

rs4611511

A

0.89

0.92

65

MCP1_88

rs34020694

A

0.87

0.87

66

RANTES_89

rs2107538

G

0.88

0.91

67

RANTES_90

rs2280788

C

0.95

0.95

68

CCR5_91

rs1799863

A

0.03

0.00

69

CCR2_94

rs1799865

T

0.50

0.56

70

C5_95

rs17611

G

0.66

0.50

71

C5_96

rs17612

C

0.11

0.06

72

P2X7_97

rs3751143

C

0.06

0.03

73

IL7R_106

rs1494555

G

0.34

0.35

74

PRF1_107

rs885822

T

0.83

0.94

75

TLR2_108

rs1804965

G

1.00

1.00

76

TCL1B_109

rs1064017

G

0.44

0.56

77

CCR5_110

rs1800452

G

1.00

1.00

78

IL11_111

rs1126757

A

0.38

0.44

79

IL11_112

rs2298885

G

0.61

0.85

80

IL8RA_117

rs2234671

G

0.40

0.44

81*

IL1L1_118

rs1800930

A

0.78

0.83

82*

CD36_119

rs1334512

G

0.96

0.85

83

VDR_121

rs1544410

G

0.31

0.44

84*

VDR_122

rs7975232

T

0.73

0.54

85

IL5RA_123

rs2290610

A

0.83

0.61

86

IL5R_124

rs2069812

C

0.79

0.78

87

IL5R_125

rs2069818

C

1.00

1.00

88

CX3CR1_126

rs3732379

G

0.68

0.67

89

CX3CR1_127

rs3732378

C

0.78

0.74

90*

TNFRSF1B_128

rs1061622

T

0.88

0.65

91

TNFRSF1B_129

rs1061624

A

0.31

0.47

92

TNFRSF1B_130

rs3397

T

0.88

0.79

93*

TNFRSF1A_131

rs887477

G

0.54

0.35

94

TNFRSF1A_132

rs4149570

G

0.76

0.53

95

IL4R_135

rs1805016

T

1.00

1.00

96

IL6_137

rs20069860

A

0.97

1.00

97*

IL9_138

rs20069885

C

1.00

0.96

98*

NKFB_139

rs1020759

C

1.00

1.00

99

GATA3_141

rs57013

A

0.72

0.58

* The SNP detection reproducibility <80%

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-8-282/MediaObjects/12864_2006_Article_995_Fig4_HTML.jpg
Figure 4

Gel-electrophoretic separation of the products of multiplex-PCR. Two decaplex amplifications are shown in comparison to the respective individual reactions. In both cases presented here, one product was not amplified in the multiplex reaction while the reaction worked fine in the individual amplification.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-8-282/MediaObjects/12864_2006_Article_995_Fig5_HTML.jpg
Figure 5

Image of a simultaneous hybridization of 99 PCR-products to an in situ synthesized oligonucleotide microarray. Usually, the features were scrambled across the array. For illustrative purposes, they were placed next to each other in this particular experiment.

Hybridization experiments for all studied 38 individuals were repeated twice.

16% of SNPs presented only one allele in the 38 studied samples. For 14 samples (7 cases and 7 controls) the call rate for all variants was above 90%. And in one case it was below 80% due to the low quality of this particular DNA sample. For 17 SNPs the amplification step basically failed due to low quality of clinical genomic DNA samples. After exclusion of these particular 17 SNPs (indicated with an asterisk in Tab. 1) that performed poorly in hybridizations the average concordance was 93%. From the variants with high quality data, five SNPs in the genes IL2 (rs2069772), TCL1B (rs1064017), IL11 (rs2298885), IL5RA (rs2290610) and TNFRSF1A (rs4149570) had p-values smaller than 0.05 for the association of carrying the mutant allele with the high IgE phenotype. The homozygous genotype A for the IL5 receptor alpha (IL5RA Ile129 Val) was associated with a 6.8-fold risk (95% confidence interval, 1.6–29.1) of a high IgE phenotype.

Discussion

An oligonucleotide microarray was produced using GeniomOne device to facilitate the screening of single nucleotide polymorphisms in several genes that are associated with hay-fever as a pilot project. Based on an in silico design, the selected set of oligonucleotides was optimized by a subsequent experimental analysis. While the in silico process eliminated 9% of the initially 141 SNPs that had been picked purely on the basis of a potential association with the occurrence of hay-fever, the subsequent experimental validation eliminated another 20% of these oligomers, more than twice as many. This result illustrates the importance of experimental validation of the microarray designs. Even in analyses that are based on a continuous detection of the hybridization and dissociation process (dynamic allele-specific hybridization) [18] the selection is critical, although an analysis of the association and dissociation curves of the duplexes permit a more discriminative and accurate SNP detection.

The reasons for the failing probes could be manifold [19]. Although only short fragments were hybridized, secondary structures formed either within one sample molecule or between different targets could cause inefficient binding to the array-bound probe molecules. Also, it is well known that dangling ends of the target molecules may have a profound effect on the hybridization [20]. Documentation of the effectiveness of the genotyping ability of particular sets of oligonucleotide probes is essential for a study of high accuracy. Use of flexible in situ synthesized oligonucleotide microarrays to such ends appears to be an efficient and attractive method for fast and cost-efficient pre-screening of candidate SNPs for an eventual high-throughput genotyping.

GeniomOne allows synthesizing 8 × 8.000 probes per array overnight and test them right after in hybridization experiments. In this way many combinations can be tested in parallel without additional cost, which allows selecting an optimal set of oligoprobes for the following experiments. This is a big advantage of GeniomOne technology.

In the analysis of the 38 DNA samples of hay-fever cases and controls, we were able to identify at least five polymorphisms in immune regulator genes that contribute to the extreme IgE phenotype and deserve further testing. For 22% of the selected SNPs, only one genotype was seen in 38 individuals. For several variants, allele-frequency differences between cases and controls exceeded 10%. These include non-synonymous variants in the IL5 receptor alpha (IL5RA Ile129 Val) and TCL1B (Gly93Arg), promoter polymorphisms in IL2 (-330 T/G) and TNFRSF1A (-609 G/T), and a polymorphism in the 3' UTR of IL11. IL5RA is a crucial factor in IL5 signalling and a contributor to the genetics of atopy in mice [21]. The extreme phenotype design of the study performed here may be an efficient alternative for the identification of disease-relevant sequence variants.

Conclusion

Based on a platform that permits flexible in situ oligonucleotide synthesis, a set of optimally performing probes could be defined by a selection approach that combined computational and experimental aspects. The final design achieved by this process permitted an effective discrimination of both homo- and heterozygote polymorphisms in hay-fever patients. Allele-frequencies of more than 10% could be identified.

Methods

Microarray synthesis

All analysis steps, (i) in situ synthesis of the oligonucleotide microarray, (ii) hybridization of the labeled PCR-product mixture and (iii) detection of the signal intensities were performed with the GeniomOne device of febit biotech (Heidelberg, Germany) according to the manufacturer's instructions. The reaction carrier (DNA-processor) represents a microstructured disposable system that consists of four or eight individual arrays, respectively, which can be used individually or in any combination [8]. Controlled by a mask-free, light-controlled process, oligonucleotide probes were synthesized in situ in 3' to 5' direction [9]. For each selected SNP, 24 oligonucleotide probes were synthesized, 12 for either DNA strand (Fig. 1), all designed to exhibit similar hybridization characteristics. The arrays used in this study consisted of 7,448 distinct oligonucleotides (594 perfect match probes and 6,534 mismatch probes, plus 320 copies of a control oligonucleotide). A complete list can be obtained from the authors.

PCR-amplification

For each SNP, PCR-primers were designed for the amplification of the relevant DNA-fragment using the Primer3 program [22]. All primers have a Tm value of 60°C. The oligonucleotides were obtained from Thermo Hybaid (Ulm, Germany). Their sequences are presented in the Table 2. The length of the PCR-products varies between 100 bp and 270 bp.
Table 2

Primer sequences used for PCR-amplification of the SNP-regions.

No.

SNP Name

SNP ID

Forward Primer (5'-3')

Reverse Primer (5'-3')

1

IL-2_1

rs2069772

CCATTCTGAAACAGGAAACCA

CTTTAAGGGGGTGGGGATAC

2

IL-2_2

rs2069763

TGCAACTCCTGTCTTGCATT

ACTTACATTAATTCCATTCAAAATCA

3

IL-10_4

rs1800894

TCCAGCCACAGAAGCTTACA

GTGCTCACCATGACCCCTAC

4

IL-10_5

rs1800871

TCCAGCCACAGAAGCTTACA

GTGCTCACCATGACCCCTAC

5

IL-10_6

rs1800872

TCCAGCCACAGAAGCTTACA

GTGCTCACCATGACCCCTAC

6

TNFA_7

rs11565

ACCACAGCAATGGGTAGGAG

CATGCCCCTCAAAACCTAT

7

TNFA_8

rs673

ACCACAGCAATGGGTAGGAG

CGTCCCCTGTATTCCATACCT

8

TNFA_9

rs1800629

GCCCCTCCCAGTTCTAGTTC

GCATCAAGGATACCCCTCA

9

TNFA_10

rs361525

GCCCCTCCCAGTTCTAGTTC

GCATCAAGGATACCCCTCA

10

IL4_11

rs2243246

GCCCCTCCCAGTTCTAGTTC

GCATCAAGGATACCCCTCA

11

IL4_12

rs2243250

AGTGAGTGGTGGGGTCCTTA

AATGCCCACTTTTTGAATGG

12

IL4_13

rs34185442

ACCCAAACTAGGCCTCACCT

GGTGGCATCTTGGAAACTGT

13

IL4_14

rs2970874

GGAAGAGAGGTGCTGATTGG

CGATTTGCAGTGACAATGTG

14

IL6_16

rs1800797

GGAAGAGAGGTGCTGATTGG

CGATTTGCAGTGACAATGTG

15

IL6_16

rs1800796

TGGCAAAAAGGAGTCACACA

CCCAAGCCTGGGATTATGAAG

16

IL6_17

rs1800795

TGGCAAAAAGGAGTCACACA

CCCAAGCCTGGGATTATGAAG

17

IL4R_18

rs1801275

GCTAGCCTCAATGACGACCT

TCATGGGAAAATCCCACATT

18

IL4R_19

rs1805011

GAAACCTGGGAGCAGATCCTC

GGCCTTGTAACCAGCCTCTC

19

IL4R_20

rs8832

AAAGGGAGCTTCTGTGCATC

TCTCCGAGCTGGTCCAG

20

IL4R_21

rs1805015

TTCCTTAGGTTGATGCTGGAG

GGTTCCATGCATACGAGGAG

21

IL4R_22

rs1805010

ACCTGACTTGCACAGAGACG

AGGGCATGTGGGTTCTACT

22

IL12p40_23

rs3124

GCCTACAGGTGACCAGCCTA

AGCCCACGGTCCAGTGTAT

23

STAT6_24

rs167769

CACAACGGAATAGACCCAAAA

ATGGCAACTTGAGAGCTGGA

24

STAT6_25

rs324015

GCACTGACTGGAAGGGAAGT

CCCTAACCTGTGCTCTTACCC

25

STAT6_26

rs703817

GTCTCAGCCCTAGGGGAATG

CTCCACCTGGCTAACAGGAA

26

STAT6_27

rs4559

CAAAAGTACAAGGGCTGA

CCCAAATTTGTGTTGTCACG

27

IFNG_28

rs2234685

GGAAGTAGGTGAGGAAGAAGCG

TGGAGCAAAGAAGGTCATCA

28

IFNG_29

rs1861493

GGAAGTAGGTGAGGAAGAAGCG

TGGAGCAAAGAAGGTCATCA

29

IFNG_30

rs2234687

TCCCATGGGTTGTGTGTTTA

GGGTCACCTGACACATTCAA

30

IFNG_31

rs2430561

TTCAGACATTCACAATTGATTTTATTC

CCCCAATGGTACAGGTTTCT

31

IFNGR2_34

rs1802585

CCCAACTCAGCCCATCTTAG

ATCTCTTCCAGGGAGCCAGT

32

IFNGR2_35

rs1059293

GGGCTGAGCAGTCAGAA

CATTTTAAGCCAGCACACCA

33

IFNGR2_36

rs9808753

CAGAGCAGGTCCTGAGTTGGGAGC

GTTTCCCACGGGTTTGATAA

34

IRF1_37

rs839

GGACTGTTCCAAAGCCAGTG

CAGAAATGTGGCAAGATCCA

35

IRF1_38

rs9282762

TTGCAAACTAAGAAAGCACACAA

ATGGGTGACACCTGGAAGTT

36

IRF2_40

rs1131553

CTCCCAAAGTGCTGGGATTA

CTGTTGTAAGGCACCGGATT

37

IL8_41

rs1175

CTTCACCATCATGATAGCATCTGT

GGAGTATGACGAAAGTTTTCTTTG

38

IL8_42

rs2227307

TGCTTTGGTAACAAACATCCTTT

GGTAACCGTCCTTCTCAATAGG

39

IL13_43

rs20541

CTTCCGTGAGGACTGAATGAGAC

CTGCAAATAATGATGCTTTCGAAGTTTCAG

40

IL13_44

rs1800925

TGACATCAACACCCAACAGG

GCAGAATGAGTGCTGCTGTGGAG

41

IL18_47

rs1946518

GGTCAGTCTTTGCTATCATTCCAG

AGCCACACGGATACCATCAT

42

IL18_48

rs1946519

GGTCAGTCTTTGCTATCATTCCAG

AGCCACACGGATACCATCAT

43

IL1B_49

rs16944

AGCCTGAACCCTGCATACC

CAATAGCCCATCCCTGTCTGT

44

IL1B_50

rs1143627

TCTCAGCCTCCTACTTCTGCTT

CAGAGAGACTCCCTTAGCACCT

45

IL1B_51

rs1799916

TCTCAGCCTCCTACTTCTGCTT

CAGAGAGACTCCCTTAGCACCT

46

IL1A_52

rs17561

CCCCCTCCAGAACTATTTTCC

ACTTTGATTGAGGGCGTCAT

47

IL1A_53

rs1800587

TAGGCTGGCCACAGGAATTA

AGCCAGAACCAGTGGCTAAG

48

IL9_56

rs1799962

CCTTCGTTAGAACACCCATGA

AGACAGGGATTCTGGTGTGA

49

TNFR1_60

rs1800692

TCCCCCTCCTGTATTCTGTG

GTGCACACGGTGTTCTGTTT

50

TNFR1_61

rs1800693

CCTGGAGTGCACGAAGTTGT

ATAGATGGATGGGTGGGATG

51

TNFRSF6_62

rs2234768

CATCCTCCTTATCCCACTTCTTT

CACCCTGTGTTTTGCATCTA

52

LTA_65

rs1800683

CACTGCCGCTTCCTCTATAA

GGTAGTCCAAAGCACGAAGC

53

LTA_66

rs1041981

CCCCCTCAACTCTGTTCTCC

GGGAGGTCAGGTGGATGTTT

54

LTA_67

rs909253

GGGTTTGGTTTTGGTTTCCT

CAGAGAAACCCCAAGGTGAG

55

IL1RN_68

rs2234676

GCCCATCTCCTCATGCTG

GCTGCTGCCCATAAAGTAG

56

IL1RN_69

rs419598

TCCTTTTCAGAATCTGGGATGT

CGTGATGCCCAGATACATTG

57

CTLA4_70

rs2384137

AACACCGCTCCCATAAAGC

CCTCCTCCATCTTCATGCTC

58

NFKBIA_72

rs1800439

CCTTGTTTTCAGCTGCCCTA

TCGTCCCCTACAAAAAGTTCA

59

IL8RB_77

rs2230054

ACATTCCAAGCCTCATGTCC

TACCAGGGCAGGCTTTCTA

60

ICAM1_78

rs1799969

CTTGAGGGCACCTACCTCTG

AGGATACAACAGGCGGTGAG

61

ICAM1_79

rs5498

CTTGAGGGCACCTACCTCTG

AGGATACAACAGGCGGTGAG

62

IL3_81

rs40401

GAGCAGTTAACCCAGCTTGTC

CACCTTGCTGCTGCACATA

63

IL3_82

rs31480

GAGCAGTTAACCCAGCTTGTC

CACCTTGCTGCTGCACATA

64

MCP1_87

rs4611511

AAAGCTGCCTCCTCAGAGTG

CACAGGGAAGGTGAAGGGTA

65

MCP1_88

rs34020694

AGAGAAAACCCGAAGCATGA

TCTTCCTAGGCCATCTCACC

66

RANTES_89

rs2107538

ATCCAGAGGACCCTCCTCAA

GGAGTGGCAGTTAGGACAGG

67

RANTES_90

rs2280788

TTCTTTTCCGTTTTGTGCAAT

CGTGCTGTCTTGATCCTCTG

68

CCR5_91

rs1799863

CTGCCTCCGCTCTACTCACT

GCCAGGTTGAGCAGGTAG

69

CCR2_94

rs1799865

AGAGGCATAGGGCAGTGAGA

GGTCCAGTTGACTGGGTGCTT

70

C5_95

rs17611

TGCAGTTTGCCCTACCTGAT

TGCTACCATTTAAGTCCTGGGTA

71

C5_96

rs17612

TTTTAGCTACAAGCCCAGCA

AATGAAGCATTCACAACACGA

72

P2X7_97

rs3751143

TTCCTGGACAACCAGAGGAG

ACCAGCTTCCTGAACAGCTC

73

IL7R_106

rs1494555

CACTATAGTTAAACCTGAGGCTCC

TCCTGGCGGTAAGCTACATC

74

PRF1_107

rs885822

CCCAGGTCAACATAGGCATCC

CGAACAGCAGGTCGTTAAT

75

TLR2_108

rs1804965

ATTCTTCTGGAGCCCATTGA

GGACTTTATCGCAGCTCTCA

76

TCL1B_109

rs1064017

ACAGTGCACTTGTGGCAG

CTGGCCATGGTCTGCTATTT

77

CCR5_110

rs1800452

TAGTCATCTTGGGGCTGGTC

TGTAGGGAGCCCAGAAGAGA

78

IL11_111

rs1126757

GGGACCACAACCTGGATTC

ATCAGAGAACACCCGACCAG

79

IL11_112

rs2298885

GGCTGTGTTCACCATAGCAA

ATCCCAAGCAAGCCTCTCTC

80

IL8RA_117

rs2234671

CATCTTTGCTGTCGTCCTCA

CCAGAATCTCAGTGGCATCC

81

IL1L1_118

rs1800930

GATGGTGCTACTGCTGTGGA

GGGCTCAGGGTAACACTG

82

CD36_119

rs1334512

CTGGCAACAAACCACACACT

TCCTACACTGCAGTCC

83

VDR_121

rs1544410

CCTCACTGCCCTTAGCTCTG

CAGGAATGTTGAGCCCAGTT

84

VDR_122

rs7975232

CTGCCGTTGAGTGTCTGTGT

ACGTCTGCAGTGTGTTGGAC

85

IL5RA_123

rs2290610

CCATGGCAATGTTTTGTCCT

CAGGTGCAGTGAAGGGAAAC

86

IL5R_124

rs2069812

CTTGCTTTTTCCTGCTGCTC

AGTCCAGGAATGGAGGCTCT

87

IL5R_125

rs2069818

TGTGGAGAAGAAAGACGGAGA

CAAAATCTTTGGCTGCAACAAACCA

88

CX3CR1_126

rs3732379

GGTGGTCATCGTGTTTTTCC

AGGCAACAATGGCTAATGC

89

CX3CR1_127

rs3732378

GGTGGTCATCGTGTTTTTCC

AGGCAACAATGGCTAATGC

90

TNFRSF1B_128

rs1061622

CTCCTGACCAAGCCTCCTC

GTCACTGGCTGGGGTAAGTG

91

TNFRSF1B_129

rs1061624

TCCTCTAGTGCCCTCCACAG

CACAGAGAGTCAGGGACTTGC

92

TNFRSF1B_130

rs3397

TCCTCTAGTGCCCTCCACAG

CACAGAGAGTCAGGGACTTGC

93

TNFRSF1A_131

rs887477

CAGCACAACTGGTCAGAACC

CCTCCTCCCAGTTCAACAAG

94

TNFRSF1A_132

rs4149570

TACAGGAACCCCAGGAGACA

TGGGTTCCAATTCAGAATGCTT

95

IL4R_135

rs1805016

GTGTCATGGCCAGGAGGAT

AGACTGGCCTCCAGTGGAAC

96

IL6_137

rs20069860

TCCCTCCACTGCAAAGGATT

CTGCAGCCACTGGTTCTGT

97

IL9_138

rs20069885

ACTTTCATCC CCACAGT

TTGCCTCTCATCCCTCTCAT

98

NKFB_139

rs1020759

TGCTTCCCTCTTGTGTTTCA

GGGGATGACCTTTAAGTGGA

99

GATA3_141

rs57013

TCCATCCATTGCACTGAGTC

CCAGAGCAGCTGGTTTAAGTG

Genomic DNA from lymphocytes was extracted using the QiaAmp Blood kit according to the manufacturer's instructions (Qiagen, Hilden, Germany). PCR-amplifications of individual loci were carried out in a Mastercycler gradient thermocycler (Eppendorf-Netheler-Hinz, Hamburg, Germany) in 25 μl of 1× Thermoprime polymerase puffer (AB Gene, Epson, UK), 200 μmol/l of each deoxynucleotide triphosphate (Qiagen), 1.5 mmol/L MgCl2 (AB Gene), 0.25 units Thermoprime DNA polymerase (AB Gene), 1.0 to 2.5 μmol/l of both forward and reverse primer, and 20 ng DNA. The initial annealing occurred at 94°C for 2 min, followed by 30 cycles of 94°C for 40 sec, 57°C for 40 sec and 72°C for 30 sec. Subsequently, a final extension step was performed at 72°C for 1 min.

Multiplex-PCR was performed in a total volume of 25 μl solution containing 80 ng human genomic DNA, 1.2 μmol/l of each primer, 1 mmol/l deoxynucleotide triphosphates (dNTPs), 5 mmol/l MgCl2 and 2 units of Thermoprime Plus DNA polymerase (AB Gene). All primer pairs had been checked in silico for possible primer dimers using the program "Primer Premier 5" (Premier Biosoft International, Palo Alto, USA).

DNA amplification for individuals, studied in present work, was done as described in single PCR cycling reactions. All PCR-products were checked by electrophoresis on 2% agarose gels.

Sample processing

About 200 ng of each PCR-product were pooled and purified with the QIAquick PCR purification kit (Qiagen) according to the manufacturer's instructions. Biotin 3'-end labeling was performed as described [8]. In brief, the eluate resulting from the purification step was dried in a vacuum concentrator and the pellet was dissolved in 5 μl of water. Labeling was performed in a total volume of 10 μl with 2.5 U terminal transferase (Roche, Mannheim, Germany), 0.1 mmol/L Biotin-N6-ddATP (PerkinElmer, Rodgau, Germany), 2.5 nmol/l CoCl2 and 1× reaction buffer (Roche). After 1 h incubation at 37°C, the enzyme was inactivated at 99°C for 15 min and the mixture cooled on ice.

The final sample hybridization cocktail was made of 10 μl of this labeling reaction, 4 μl herring sperm DNA (0.1 mg/mL with 0.5 mg/ml BSA), 9 μl 2 × 2-[N-morpholino]ethanesulfonic (MES) acid buffer [54.8 mmol/l MES (free acid monohydrate), 147.7 mmol/l MES sodium salt, 1.8 mol/l NaCl, 40 mmol/l Na2EDTA, 0.02% (v/v) Tween20] and 6 μl water. As an internal hybridization control, 1 μl of a mixture of biotin-(50 nmol/l) and Cy5-labeled (250 nmol/l) control oligonucleotides (GCAGTGCTGCCATAACCATGAGTGA, CGCAAACTATTAACTGGCGAACTAC, GAACTGGATCTCAACAGCGGTAAGA, AAGATCAGTTGGGTGCACGAGTGGG, CGCAACAATTAATAGACTGGATGG, GCAGTGCTGCCAAAACCATGAGTGA), supplied by febit biotech, were added. The total volume of the hybridization mixture was 30 μl, which was stored frozen until use at -20°C.

Hybridization and detection

The DNA-fragments of all SNPs of one individual person were analyzed simultaneously in a single hybridization. The biotin-labeled PCR-products in the hybridization mixture were denatured at 99°C for 5 min and quickly cooled on ice for 2 min. The probe arrays were incubated with 1 × MES solution (containing 1% BSA) at room temperature for 15 min. Then the hybridization mixture was loaded to the array. Hybridization was performed at 45°C for 4 h. Subsequently, the used sample was recovered and the array was washed with 0.5 × SSPE buffer (diluted from 6 × SSPE stock-solution consisting of 0.9 mol/l NaCl, 60 mmol/l NaH2PO4 (pH 7.4) and 6 mmol/l Na2EDTA) at 45°C. Staining was performed with 4 ml of 2.5 μg/ml streptavidin R-phycoerythrin conjugate (Molecular Probes, Cologne, Germany) in 6 × SSPE at room temperature for 10 minutes. All these steps were carried out automatically by the GeniomOne instrument.

Data analysis

Image analysis was done automatically with the GeniomOne system-embedded CCD imaging system. All steps such as configuration of detection parameters, acquisition of array image, detection of feature position, calculation of signal intensity and data export to a database were performed automatically. The pattern recognition rules are digitally encoded in the analysis software, simplifying and shortening the result reading. Raw data were further processed with the integrated analysis software with the default settings.

Statistical analysis of epidemiological data

The analysis was performed with SAS software PHREG version 9 (SAS Institute, Cary, USA). Relative risk of the elevated IgE phenotype associated with genetic variants was estimated by odds ratios (OR) and associated 95 percent confidence limits using the procedure for conditional logistic regression. The gene variants were computed as simultaneous limits of the parameters of a multinomial distribution according to Nieters et al. [23].

Declarations

Acknowledgements

We thank Ina Koegel, Benjamin Heinzerling, Sandra Widder, and Jochen Rudolph for technical support. This study was partially supported by the Estonian Ministry of Education core grant no. 0182582s03 and the MolTools project funded by the European Commission.

Authors’ Affiliations

(1)
Division of Functional Genome Analysis, Deutsches Krebsforschungszentrum
(2)
Division of Clinical Epidemiology, Deutsches Krebsforschungszentrum
(3)
Institute of Molecular and Cell Biology
(4)
Febit biotech,
(5)
The Estonian Biocentre
(6)
Molecular Diagnostics Centre, United Laboratories of the Tartu University Hospital
(7)
Estonian Genome Project of University of Tartu

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© Pullat et al; licensee BioMed Central Ltd. 2007

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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