Skip to content

Advertisement

BMC Medical Genetics

Research article
Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Evaluation of common genetic variants in 82 candidate genes as risk factors for neural tube defects

  • Faith Pangilinan1,
  • Anne M Molloy2,
  • James L Mills3,
  • James F Troendle4,
  • Anne Parle-McDermott5,
  • Caroline Signore6,
  • Valerie B O’Leary7,
  • Peter Chines8,
  • Jessica M Seay1,
  • Kerry Geiler-Samerotte1,
  • Adam Mitchell1,
  • Julia E VanderMeer1,
  • Kristine M Krebs1,
  • Angelica Sanchez1,
  • Joshua Cornman-Homonoff1,
  • Nicole Stone1,
  • Mary Conley3,
  • Peadar N Kirke9,
  • Barry Shane10,
  • John M Scott11 and
  • Lawrence C Brody1, 12Email author
BMC Medical Genetics201213:62

https://doi.org/10.1186/1471-2350-13-62

©  Pangilinan et al.; licensee BioMed Central Ltd. 2012

Received: 13 January 2012

Accepted: 13 July 2012

Published: 2 August 2012

Open Peer Review reports

Abstract

Background

Neural tube defects (NTDs) are common birth defects (~1 in 1000 pregnancies in the US and Europe) that have complex origins, including environmental and genetic factors. A low level of maternal folate is one well-established risk factor, with maternal periconceptional folic acid supplementation reducing the occurrence of NTD pregnancies by 50-70%. Gene variants in the folate metabolic pathway (e.g., MTHFR rs1801133 (677 C > T) and MTHFD1 rs2236225 (R653Q)) have been found to increase NTD risk. We hypothesized that variants in additional folate/B12 pathway genes contribute to NTD risk.

Methods

A tagSNP approach was used to screen common variation in 82 candidate genes selected from the folate/B12 pathway and NTD mouse models. We initially genotyped polymorphisms in 320 Irish triads (NTD cases and their parents), including 301 cases and 341 Irish controls to perform case–control and family based association tests. Significantly associated polymorphisms were genotyped in a secondary set of 250 families that included 229 cases and 658 controls. The combined results for 1441 SNPs were used in a joint analysis to test for case and maternal effects.

Results

Nearly 70 SNPs in 30 genes were found to be associated with NTDs at the p < 0.01 level. The ten strongest association signals (p-value range: 0.0003–0.0023) were found in nine genes (MFTC, CDKN2A, ADA, PEMT, CUBN, GART, DNMT3A, MTHFD1 and T (Brachyury)) and included the known NTD risk factor MTHFD1 R653Q (rs2236225). The single strongest signal was observed in a new candidate, MFTC rs17803441 (OR = 1.61 [1.23-2.08], p = 0.0003 for the minor allele). Though nominally significant, these associations did not remain significant after correction for multiple hypothesis testing.

Conclusions

To our knowledge, with respect to sample size and scope of evaluation of candidate polymorphisms, this is the largest NTD genetic association study reported to date. The scale of the study and the stringency of correction are likely to have contributed to real associations failing to survive correction. We have produced a ranked list of variants with the strongest association signals. Variants in the highest rank of associations are likely to include true associations and should be high priority candidates for further study of NTD risk.

Keywords

Neural tube defectsSpina bifidaFolic acidOne-carbon metabolismCandidate gene

Background

Neural tube defects (NTDs) are one of the most common birth defects, with a historical prevalence of ~1 in 1000 in the US [1, 2]. The NTD rate is now closer to ~5 in 10,000 in areas with folic acid fortification, such as the US [3] and many European countries [4]. Between 21 and 28 days after conception, the neural plate folds and closes to form the neural tube; this structure later develops into the brain and spinal cord. Failure of the neural tube to close most commonly leads to spina bifida or anencephaly, although encephalocele, craniorachischisis and iniencephaly can also occur [5].

It is known that both environmental and genetic factors contribute to the development of NTDs. The most established environmental factor is dietary folate; significantly lower levels of folate are observed in mothers with an NTD pregnancy [6], and periconceptional folate supplementation can reduce the risk of an NTD pregnancy by up to 75% [7][8][9]. There is also growing evidence of the importance of cobalamin (vitamin B12) in the etiology of NTDs. Like folate, lower vitamin B12 levels have been reported in mothers with an NTD pregnancy [6, 10–17].

Genetic factors also contribute to NTDs. Compared to the general population, there is a 10–20 fold higher recurrence risk to siblings in families with an NTD child [18–20]. This, combined with the recognition of the importance of maternal folate, has led many groups to evaluate genetic polymorphisms related to the folate metabolic pathway as risk factors for NTDs. The best studied genetic risk factor is a single nucleotide polymorphism (SNP) in 5, 10-methylene-tetrahydrofolate reductase (MTHFR). The 677 C > T polymorphism results in the substitution of a valine for an alanine at codon 222 (A222V), leading to a thermolabile isoform of the protein [21]. A significantly higher frequency of the MTHFR 677 TT genotype has been observed in NTD cases in many populations (reviewed in [22]). Genetic variants associated with NTDs have been reported in other genes encoding folate- and vitamin B12-related proteins, such as methylenetetrahydrofolate dehydrogenase (NADP + dependent) 1, methenyltetrahydrofolate cyclohydrolase, formyltetrahydrofolate synthetase (MTHFD1) [23–26], methylenetetrahydrofolate dehydrogenase (NADP + dependent) 1-like (MTHFD1L) [27], dihydrofolate reductase (DHFR) [28, 29], methionine synthase reductase (MTRR) [16, 26, 30–33], and the transcobalamin II receptor (TCblR) [34].

As genotyping technology has advanced, the scale of studies attempting to identify genetic NTD risk factors has grown from single SNP analyses to simultaneous evaluation of dozens of variants. Several studies have evaluated specific candidate polymorphisms with evidence of functional changes and/or disease risk (87 variants in 45 genes, [35]; 48 SNPs in 11 genes [36]; 64 SNPs in 34 genes [37]). In contrast, other studies have examined all common variation in candidate genes via tagging SNPs (tagSNPs) in specific genes of interest (118 tagSNPs in 14 folate-related genes [26]; 37 tagSNPs in 6 transcriptional activator genes [38]). In the current study, we also took the tagSNP approach to evaluate 1441 SNPs in 82 candidate genes for NTD risk.

Results

Common genetic variation in 82 candidate genes (Figure 1) was tested for association with NTDs. Results were generated in two stages. In the first stage, four broad tests of association were performed on all SNPs using a subset of samples. In the second stage, SNPs of interest identified in the initial analysis were then typed in the complete cohort to maximize the power to detect an effect, and a wider range of genetic models were applied to the combined dataset to evaluate the potential contribution of all SNPs to case or maternal risk of NTDs.
Figure 1

Classification Distribution of 82 Selected Candidate Genes. Categories of selected candidate genes (legend) and their distribution (pie chart) are shown. Over half of the genes selected for study are known to encode a protein with enzymatic function related to the folate/vitamin B12 pathway.

Initial analyses

The primary sample set (320 NTD case families and 341 controls) was genotyped for 1517 tagging SNPs intended to capture common genetic variation in 82 candidate genes related to folate/vitamin B12 metabolism, transport of folate or vitamin B12, or transcriptional or developmental processes implicated in NTD mouse models (Table 1). Genotype data was successfully obtained for 1320 SNPs. Four tests of association were performed; two tests were to detect NTD case risk and two tests were to detect maternal risk for an NTD pregnancy. There were 203 SNPs in 54 genes that were significant (p < 0.05) by at least one test of association. A gene-based approach was used to select SNPs from fifteen genes to be genotyped in the secondary sample set. Five genes (mitochondrial folate transporter/carrier (MFTC), megalin (LRP2, low density lipoprotein receptor-related protein 2), DNA (cytosine-5-)-methyltransferase 3 beta (DNMT3B), phosphatidylethanolamine N-methyltransferase (PEMT), and euchromatic histone-lysine N-methyltransferase (EHMT1)) contained at least one SNP that was positive for both tests of a case effect or both tests of a maternal effect. An additional four genes (5-methyltetrahydrofolate-homocysteine methyltransferase 1 (MTR), cubilin (CUBN, intrinsic factor-cobalamin receptor), T (Brachyury homolog (mouse)), and AT rich interactive domain 1A (ARID1A)) contained more than five SNPs significant for any of the four tests of association. The remaining six genes (adenosine deaminase (ADA), ferritin, heavy polypeptide 1 (FTH1), cystathionase (CTH), peptidyl arginine deiminase, type IV (PADI4), low density lipoprotein receptor-related protein 6 (LRP6), and serine hydroxymethyltransferase 1 (SHMT1)) were selected based on a combination of factors, including the number of positive SNPs, their level of significance, and biological plausibility. Any SNP in these genes significant by any of the four tests of association (Table 2) was selected for genotyping in the secondary sample set (258 additional case families and 658 additional controls).
Table 1

Candidate Genes Selected for Evaluation of Common Genetic Variation as NTD Risk Factors

Gene

Description

No. SNPs

Category*

ADA

Adenosine deaminase

20

1 C

AHCY

S-Adenosylhomocysteine hydrolase

4

1 C

ALDH1A2

Aldehyde dehydrogenase 1 family, member A2

24

1A

ALDH1L1

Aldehyde dehydrogenase 1 family, member L1

35

1A

AMN

Amnionless homolog (mouse)

5

4

ARID1A

AT rich interactive domain 1A (SWI- like)

20

3

ATIC

5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/IMP cyclohydrolase

9

1A

BAT8

HLA-B associated transcript 8

8

5

BHMT

Betaine-homocysteine methyltransferase1

12

1 C

BHMT2

Betaine-homocysteine methyltransferase2

2

1 C

CCL2

Chemokine (C-C motif) ligand 2 (Monocyte chemoattractant protein 1)

6

5

CDKN2A

Cyclin-dep. kinase inhibitor 2A (melanoma, p16, inhibitor CDK4, P14arf)

10

3

CITED2

Cbp/p300-interacting transactivator, w. Glu/Asp-rich C-teminal domain, 2

6

3

COMT

Catechol-O-methyltransferase

16

1 C

CREBBP

CREB binding protein (Rubinstein-Taybi syndrome)

22

3

CTH

Cystathionase (cystathionine gamma-lyase)

16

1 C

CUBN

Cubilin (intrinsic factor-cobalamin receptor)

141

2B

CYB5R2

Cytochrome b5 reductase2

12

1A

DHFR

Dihydrofolate reductase

10

1A

DMGDH

Dimethylglycine dehydrogenase

24

1A

DNMT1

DNA cytosine 5 methyltransferase1

7

1 C

DNMT3A

DNA cytosine 5 methyltransferase3a

26

1 C

DNMT3B

DNA cytosine 5 methyltransferase3b

19

1 C

EHMT1

euchromatic histone methyltransferase 1

20

1 C

ENOSF1

Enolase superfamily member 1

22

1A

EP300

E1A binding protein p300

10

3

EPHA3

EPH (ephrin) receptor A3

45

4

FOLH1

Folylpolyglutamate hydrolase (gamma-glutamyl hydrolase, Glutamate carboxypeptidase II)

9

2A

FOLR1

Folate receptor alpha

7

2A

FOLR2

Folate receptor 2 (fetal)

5

2A

FOLR3

Folate binding protein gamma and g

8

2A

FPGS

Folylpolyglutamate synthase

4

2A

FTCD

Formiminoglutamate formiminotransferase-cyclodeaminase

17

1A

FTH1

Ferritin heavy chain

11

1A

GART

Phosphoribosylglycinamide formyltransferase/synthetase, phosphoribosylaminoimidazole synthetase

10

1A

GGH

Lysosomal gamma-glutamylhydrolase

11

1A

GIF

Intrinsic factor (gastric IF)

4

2A

GNMT

Glycine N-methyltransferase

5

1 C

GRIK5

Glutamate receptor, ionotropic, kainate 5

4

4

HIF1A

Hypoxia-inducible factor 1, alpha subunit (basic helix-loop-helix trx factor)

11

3

HRMT1L2

PRMT1 (Protein arginine methyltransferase 1) HMT1 hnRNA Mtase-like2

8

1 C

HRMT1L3

PRMT3, HMT1 hnRNA Mtase-like3

30

1 C

ICMT

Isoprenylcysteine carboxyl methyltranferase

7

1 C

IRF6

Interferon regulatory factor 6

11

3

LRP2

Megalin, low density lipoprotein-related protein 2

75

2B

LRP6

Low density lipoprotein receptor-related protein 6

24

2B

MAT1A

Methionine adenosyltransferase 1

19

1 C

MAT2A

Methionine adenosyltransferase II alpha

4

1 C

MAT2B

Methionine adenosyltransferase II beta

9

1 C

MFTC

Mitochondrial folate transporter/carrier

11

2A

MMAA

Methylmalonic aciduria (cobalamin deficiency) type A

14

1B

MMAB

AdoB12 adenosyltransferase, methylmalonic aciduria type B

7

1B

MMACHC

Methylmalonic aciduria cblC type with homocystinuria (CblC compl group)

8

1B

MTHFD1

Methylenetetrahydrofolate dehydrogenase (NADP + dependent) 1, methenyltetrahydrofolate cyclohydrolase, formyltetrahydrofolate synthetase (cytosolic)

20

1A

MTHFD1L

Methylenetetrahydrofolate dehydrogenase (NADP + dependent) 1-like (Formyltetrahydrofolate synthetase domain containing 1) (mitochondrial)

115

1A

MTHFD2

Methylenetetrahydrofolate dehydrogenase (NADP + dependent) 2, methenyltetrahydrofolate cyclohydrolase (mitochondrial)

6

1A

MTHFR

Methylenetetrahydrofolate reductase

19

1A

MTHFS

5,10-Methenyltetrahydrofolate synthetase

18

1A

MTR

Methionine synthase

20

1B

MTRR

Methionine synthase reductase

23

1B

NDOR1

NADPH dependent diflavin oxidoreductase 1

2

1A

NNMT

Nicotinamide N-methyltransferase

11

1 C

NOS3

Nitric oxide synthase 3 (endothelial cell)

13

5

PADI4

peptidyl arginine deiminase, type IV

28

1 C

PAX3

Paired box gene 3 (Waardenburg syndrome 1)

51

3

PCMT1

Protein-L-isoaspartate (D-aspartate) O-methyltransferase

7

1 C

PDGFRA

Platelet-derived growth factor receptor alpha

17

4

PEMT

phosphatidylethanolamine N-methyltransferase

14

1 C

PGM2

pgm2 methionine synthase interacting protein

19

1B

PRMT8

Protein arginine methyltransferase 8

54

1 C

RAI1

Retinoic acid induced 1

13

4

SHMT1

Serine hydroxymethyl transferase (C)

12

1A

SHMT2

Serine hydroxymethyl transferase (M)

4

1A

SLC19A1

Reduced folate carrier

4

2A

T (Brachyury)

T, brachyury homolog (mouse)

20

4

TCblR

Transcobalamin II receptor

5

2B

TCN1

Transcobalamin I

2

2B

TCN2

Transcobalamin II

20

2B

TFAP2A

Transcription factor AP-2 alpha (activating enhancer binding protein 2 alpha)

16

3

TK1

Thymidine kinase 1, soluble

12

1A

TP53

Tumor protein 53 (Li-Fraumeni syndrombe)

8

3

TYMS

Thymidylate synthase

4

1A

*Category Key: 1A, Enzymatic (folate); 1B, Enzymatic (B12); 1 C, Enzymatic (methylation); 2A, Transport (folate); 2B, Transport (B12); 3, Transcription; 4, Development; 5, Other.

Table 2

SNPs Selected for Study in the Combined NTD Cohort

RSID

Gene

Case–control Logistic Regression p-value

TDT p-value

Mother-Control Logistic Regression p-value

Log-Linear Maternal Genotype p-value

rs2428735

PADI4

0.0006

0.2801

0.0229

0.4043

rs942459

PADI4

0.0089

0.3841

0.1002

.*

rs12743862

ARID1A

0.1030

0.0314

0.9124

.

rs12729444

ARID1A

0.1068

0.0314

0.9124

.

rs12726081

ARID1A

0.1030

0.0314

0.9124

.

rs12726287

ARID1A

0.1030

0.0314

0.9276

.

rs11247593

ARID1A

0.0208

0.1294

0.1815

0.1383

rs11247594

ARID1A

0.0257

0.1580

0.2126

0.1331

rs12752833

ARID1A

0.1030

0.0314

0.9124

.

rs12735646

ARID1A

0.1030

0.0290

0.9124

.

rs12737946

ARID1A

0.1030

0.0314

0.9124

.

rs12731749

ARID1A

0.0862

0.0201

0.8813

.

rs11247596

ARID1A

0.7022

0.0311

0.9123

0.9708

rs12733999

CTH

0.0277

1.0000

0.0077

.

rs10889869

CTH

0.9408

0.0436

0.2582

0.9918

rs12723350

CTH

0.0472

0.6803

0.0120

0.0572

rs16834388

MTR

0.1070

0.0225

0.9177

0.8117

rs10733117

MTR

0.1604

0.0207

0.5363

0.1956

rs10925238

MTR

0.0503

0.0139

0.9512

0.5724

rs4077829

MTR

0.0921

0.0170

0.7931

0.6494

rs12060570

MTR

0.0602

0.0201

0.9373

0.6843

rs2789352

MTR

0.2535

0.0348

0.7505

0.1245

rs7367859

MTR

0.2355

0.0444

0.5437

0.0571

rs10925260

MTR

0.1262

0.0328

0.8485

0.1713

rs2853522

MTR

0.1080

0.0449

0.8826

0.1600

rs2853523

MTR

0.2127

0.0418

0.7770

0.0594

rs3944004

LRP2

0.3963

0.0048

0.5667

0.2170

rs4667593

LRP2

0.7913

0.0087

0.3028

0.7203

rs16856530

LRP2

0.1458

0.4352

0.0396

0.7874

rs2024481

LRP2

0.9568

0.0087

0.2889

0.7601

rs10490131

LRP2

0.4348

0.0140

0.8093

0.8201

rs4668123

LRP2

0.1564

0.9379

0.0487

0.9494

rs2268373

LRP2

0.0338

0.0229

0.0718

0.4172

rs11886219

LRP2

0.0491

0.3924

0.0034

.

rs2268365

LRP2

0.6858

0.0312

0.6914

0.8866

rs2673164

LRP2

0.5967

0.7656

0.4009

0.0231

rs700550

LRP2

0.4767

0.2507

0.7529

0.0383

rs853988

LRP2

0.8386

0.6872

0.2807

0.0048

rs2673177

LRP2

0.0095

0.5791

0.0015

0.0279

rs10199676

LRP2

0.0075

0.7290

0.0213

0.9036

rs2389557

LRP2

0.0336

0.6507

0.1005

0.4636

rs16856843

LRP2

0.0075

0.9055

0.0133

0.9035

rs10806845

T (Brachyury)

0.0399

0.6331

0.0010

0.3100

rs3127441

T (Brachyury)

0.0485

0.6215

0.0043

0.8873

rs12200529

T (Brachyury)

0.5867

0.7758

0.0278

0.2407

rs16898752

T (Brachyury)

0.1443

1.0000

0.0412

0.2063

rs7753771

T (Brachyury)

0.5378

1.0000

0.0384

.

rs1001978

T (Brachyury)

0.5378

1.0000

0.0384

.

rs3099280

T (Brachyury)

0.9972

0.5149

0.0199

0.1406

rs4512347

MFTC

0.0344

0.6473

0.2789

.

rs10112450

MFTC

0.1575

0.0196

0.4726

0.3474

rs17803441

MFTC

0.0016

0.0115

0.2911

0.2838

rs3134260

MFTC

0.0016

0.0086

0.2513

0.2919

rs4876902

EHMT1

0.4872

0.0595

0.6710

0.9049

rs4526432

EHMT1

0.0433

0.0251

0.5323

0.4040

rs10752062

CUBN

0.2564

0.8216

0.0254

0.1471

rs17139378

CUBN

0.2113

0.6780

0.0086

0.1051

rs7100290

CUBN

0.4039

0.0269

0.6692

.

rs1276720

CUBN

0.2147

0.0422

0.5445

0.0714

rs1276721

CUBN

0.1737

0.0206

0.5381

0.2471

rs11254284

CUBN

0.4198

0.7175

0.0474

0.0548

rs11254313

CUBN

0.4354

0.4206

0.0277

0.3041

rs12258009

CUBN

0.7700

0.1582

0.6214

0.7119

rs17431655

CUBN

0.1453

0.8474

0.0459

.

rs17139663

CUBN

0.0407

0.8864

0.0579

0.5552

rs11254339

CUBN

0.0490

0.7928

0.0838

0.8621

rs1801228

CUBN

0.1118

0.8474

0.0356

.

rs12254816

CUBN

0.5285

0.6670

0.0407

0.1765

rs7899751

CUBN

0.4747

0.8386

0.0433

0.1183

rs11254375

CUBN

0.7142

0.6682

0.5854

0.0332

rs7070148

CUBN

0.1447

0.9013

0.0029

0.2028

rs2273737

CUBN

0.1136

0.9042

0.0064

0.3337

rs7096079

CUBN

0.0202

0.8897

0.0432

0.3447

rs2073588

FTH1

0.2129

0.0046

0.9390

.

rs17156616

FTH1

0.5455

0.0127

0.8668

.

rs195445

FTH1

0.9840

0.3738

0.8436

0.7908

rs7957531

LRP6

0.4835

0.9068

0.0407

0.2288

rs10845493

LRP6

0.1614

0.0149

0.9106

0.7491

rs17302049

LRP6

0.4464

0.0242

0.8904

0.9593

rs1181334

LRP6

0.2935

0.0211

0.9466

0.9000

rs12309338

LRP6

0.7156

0.6056

0.0329

.

rs3760183

PEMT

0.0146

0.0111

0.2960

.

rs16961845

PEMT

0.2442

0.8788

0.0043

.

rs4646342

PEMT

0.4687

0.6714

0.7126

0.0351

rs2350631

PEMT

0.0641

0.6803

0.0181

0.3410

rs9910090

SHMT1

0.0342

0.1508

0.1370

0.7042

rs9901160

SHMT1

0.5153

0.0153

0.9278

0.4380

rs4911263

DNMT3B

0.0686

0.8332

0.0145

0.0475

rs6058896

DNMT3B

0.0460

0.0016

0.5438

.

rs447833

ADA

0.0832

0.0090

0.3777

0.6497

rs2299686

ADA

0.4258

0.7269

0.0391

0.2794

rs427483

ADA

0.3645

0.5276

0.0275

0.5681

rs6094017

ADA

0.1869

0.5169

0.0088

0.6004

* (.) = Failure to converge (no result).

Combined analyses

In the combined analyses each SNP was evaluated for contributing to NTD risk by twelve association tests: case and maternal effects were each evaluated using three case–control (or mother-control) tests and three family-based tests (see Methods). There were 68 SNPs in 30 genes that showed an association at the p < 0.01 level by any of the twelve tests (Table 3). Of these, twelve genes contained a single associated SNP. The remaining 56 SNPs were found in 18 genes. Not all associations were independent. Areas of interest were covered by tagSNPs as well as additional SNPs for physical coverage; as a result there were seven SNP pairs in this set with a linkage disequilibrium (LD) relationship above the threshold (r2 ≥ 0.8) selected for tagging in this study (Figure 2, ARID1A_rs11247593 and ARID1A_rs11247594, CUBN_rs7070148 and CUBN_rs2273737, GART_rs2070388 and GART_rs4817580, MFTC_rs17803441 and MFTC_rs3134260, MTHFR_rs17367504 and MTHFR_rs17037425, MTR_rs10733117 and MTR_rs10925260, and PEMT_rs4646402 and PEMT rs1108579). Notably, for many genes, the associated SNPs occur in the same haplotype block (solid spine of LD based on D’ relationships), implying a single association signal for that gene (Figure 2, GART, COMT, MFTC, MTHFR, ARID1A, MTR, FOLH1, MTHFD1, PEMT, RAI1, ENOSF1). A single signal probably also accounts for the significant SNP pairs seen in PDGFRA (D’ = 0.65) and CDKN2A (D’ = 0.79). Genes exhibiting more than one independent SNP association include FTCD (D’ = 0.10), MTHFD1L (two separate haplotype blocks and two SNPs, D’ ≤ 0.57), CUBN (one haplotype block and a SNP, D’ ≤ 0.20), ALDH1A2 (D’ = 0.13) and ADA (a weak haplotype block and a SNP, D’ ≤ 0.19).
Table 3

p-values for Evaluated SNPs Showing Association (p < 0.01) with NTDs

Gene/SNP

TDT

LL CC Rec

LL CC Dom

LR CC Cont

LR CC Rec

LR CC Dom

LL Mat 2DOF

LL Mat Rec

LL Mat Dom

LR MC Cont

LR MC Rec

LR MC Dom

*MFTC rs17803441

0.0300

0.1456

0.0098

0.0003

0.0013

0.0146

0.4634

0.6352

0.2195

0.1928

0.2373

0.3535

CDKN2A rs3218009

0.1284

0.9996

0.1786

0.0426

0.9775

0.1009

.**

0.2734

0.0004

0.3032

0.5724

0.3296

*ADA rs2299686

0.8288

0.7819

0.5289

0.2994

0.3991

0.3958

0.2165

0.1923

0.1183

0.0010

0.0479

0.0005

*PEMT rs4646402

0.0009

0.1861

0.0005

0.1374

0.3728

0.1348

0.7219

0.5709

0.7630

0.7482

0.5319

0.9693

*MFTC rs3134260

0.0225

0.1301

0.0059

0.0006

0.0021

0.0141

0.4213

0.4460

0.2195

0.2102

0.2594

0.3484

*PEMT rs1108579

0.0006

0.0463

0.0014

0.0462

0.0482

0.1846

0.7076

0.4098

0.8059

0.9490

0.9274

0.9873

*PEMT rs11656215

0.0053

0.0008

0.3818

0.2070

0.1461

0.5412

0.7689

0.5639

0.8703

0.8211

0.3273

0.5520

*CUBN rs7070148

0.4864

0.4012

0.6033

0.0909

0.8065

0.0746

0.4458

0.4235

0.2551

0.0010

0.1735

0.0013

*ADA rs406383

0.0590

0.0311

0.3102

0.8420

0.7244

0.6858

0.1205

0.4468

0.0398

0.0012

0.0021

0.0136

*GART rs2070388

0.0012

0.5668

0.0012

0.0724

0.1415

0.1209

0.4526

0.2734

0.4332

0.5831

0.1515

0.8478

*ADA rs6031682

0.5166

0.6123

0.6101

0.0052

0.9578

0.0016

0.8837

0.8085

0.6321

0.0048

0.8196

0.0019

*ADA rs427483

0.6597

0.8842

0.3286

0.2509

0.1948

0.7039

0.4568

0.3370

0.2699

0.0018

0.0137

0.0063

DNMT3A rs7560488

0.1538

0.0021

0.6359

0.8325

0.8348

0.8823

0.5544

0.3633

0.3848

0.7779

0.9764

0.6509

*CUBN rs2273737

0.5067

0.7062

0.5487

0.0780

0.3584

0.0936

0.4034

0.4843

0.1971

0.0021

0.2034

0.0026

MTHFD1 rs2236225

0.1505

0.9554

0.0257

0.2570

0.3372

0.3869

0.8915

0.6549

1.0000

0.0139

0.2593

0.0022

*T rs10806845

0.2894

0.3889

0.4518

0.0830

0.0370

0.4302

0.2438

0.1204

0.2378

0.0033

0.0023

0.0701

FOLH1 rs383028

0.2073

0.4975

0.2542

0.0050

0.3617

0.0051

0.1033

1.0000

0.0359

0.0054

0.9171

0.0024

*CUBN rs1801222

1.0000

0.5013

0.4017

0.1514

0.7600

0.0181

0.2822

0.1682

0.2448

0.0091

0.1439

0.0024

*ADA rs6094017

0.2199

0.6211

0.2009

0.3960

0.8060

0.3126

0.7449

0.5105

0.5466

0.0026

0.0479

0.0046

*ARID1A rs11247593

0.0154

0.0502

0.0747

0.0027

0.0090

0.0265

0.1455

0.4060

0.1491

0.0615

0.0458

0.5659

COMT rs174675

0.0795

0.7444

0.0028

0.8722

0.5266

0.4898

0.9321

0.7778

0.7457

0.3665

0.2494

0.9574

*ENOSF1 rs1059384

0.6886

0.3493

0.9141

0.0333

0.0028

0.2734

0.9279

0.8084

0.8226

0.0848

0.0063

0.4655

*GART rs4817580

0.0029

0.9996

0.0042

0.0849

0.9745

0.1174

.

0.9996

0.9223

0.4818

0.9747

0.6024

*DNMT3B rs6058896

0.0029

0.0052

.

0.0167

0.0244

0.9744

.

0.2022

.

0.0905

0.1265

0.9744

MTHFD1L rs12199063

0.2857

0.3198

0.5830

0.1248

0.3647

0.0575

0.0855

0.0437

0.7457

0.0035

0.0029

0.1898

MTHFD1L rs6923486

0.1738

0.3458

0.1249

0.1348

0.1283

0.5396

0.2973

0.2651

0.1785

0.0030

0.0090

0.0348

COMT rs737865

0.0112

0.0149

0.1106

0.5284

0.3462

0.2354

0.1983

0.1326

0.1587

0.0852

0.0031

0.5815

*MTR rs10925260

0.0290

0.0031

0.9246

0.6329

0.4355

0.8813

0.4513

0.7071

0.2073

0.1865

0.2408

0.3423

*ADA rs452159

0.0577

0.5282

0.0047

0.7260

0.9583

0.4298

0.1675

0.0627

0.3633

0.0059

0.0578

0.0032

TFAP2A rs17635655

0.8108

0.6367

0.7196

0.0116

0.0236

0.0813

0.1688

0.0876

0.8527

0.0037

0.0075

0.0645

CUBN rs11591606

0.1808

0.2426

0.4253

0.4459

0.6193

0.2639

0.8403

0.6321

0.6380

0.3280

0.9827

0.0058

MTHFD1L rs2295083

0.6044

0.2928

0.2330

0.0168

0.0038

0.7292

0.2734

0.1752

0.5943

0.0558

0.0342

0.7136

*PEMT rs16961845

0.5691

0.4101

0.2719

0.4982

0.4864

0.9363

.

0.0082

.

0.0072

0.0048

0.9255

*ALDH1A2 rs7169289

0.1063

0.6803

0.0967

0.0214

0.5178

0.0043

0.4346

0.2067

0.6266

0.0633

0.6737

0.0513

*MTR rs10733117

0.0099

0.4520

0.0043

0.6020

0.7559

0.6099

0.2998

0.1791

0.2388

0.1436

0.3788

0.1542

MTHFD1L rs17080476

0.0233

0.9996

0.0949

0.0070

0.0244

0.0320

.

0.5299

0.9136

0.1357

0.0994

0.2676

MAT2B rs17535909

0.0102

0.0045

0.5083

0.2745

0.1949

0.7860

0.2061

0.3321

0.2406

0.8030

0.6685

0.8863

GNMT rs9462856

0.0601

0.9187

0.0048

0.7463

0.7773

0.3914

0.8419

0.5788

1.0000

0.6497

0.8424

0.5744

*CTH rs12733999

0.1546

0.1603

0.7406

0.0051

0.0048

0.6384

.

0.1477

0.3414

0.0167

0.0285

0.1264

CDKN2A rs7041637

0.4033

0.0881

0.3011

0.0048

0.0067

0.1269

0.3323

0.2793

0.1978

0.1369

0.3445

0.0716

*RAI1 rs9914733

0.2215

0.1984

0.4266

0.8834

0.4610

0.6300

0.0980

0.6952

0.0320

0.0133

0.8111

0.0049

*FTCD rs2839127

0.2296

0.0301

0.7043

0.8551

0.2725

0.8462

0.0195

0.2266

0.0053

0.1576

0.0483

0.3997

PDGFRA rs9993187

0.3615

0.0802

0.7374

0.0639

0.1576

0.0123

0.4850

0.4235

0.2999

0.0083

0.8603

0.0054

MTHFD1L rs4869987

0.1603

0.6351

0.0458

0.1693

0.2368

0.2939

0.0284

0.3579

0.0511

0.0167

0.1767

0.0061

*ARID1A rs11247594

0.0376

0.0989

0.1141

0.0059

0.0159

0.0438

0.2152

0.2972

0.3060

0.0758

0.0601

0.5558

*ARID1A rs11247596

0.0059

0.1178

0.0151

0.0570

0.2900

0.0682

0.6110

0.3713

0.8781

0.5223

0.0978

0.9777

FOLH1 rs16906205

0.2509

0.5003

0.3078

0.0993

0.9856

0.0730

.

0.9996

0.0060

0.0160

0.9856

0.0102

*FTCD rs7280485

0.5455

1.0000

0.2928

0.9719

0.9612

1.0000

0.0402

0.2205

0.0796

0.1847

0.9315

0.0062

ENOSF1 rs10502289

0.4050

0.0606

0.9176

0.2608

0.0084

0.8743

0.9012

0.6553

0.8231

0.7398

0.4196

0.9254

*MFTC rs10112450

0.0065

0.0154

0.1380

0.1077

0.0765

0.7454

0.4967

0.2680

0.4937

0.5459

0.7307

0.3836

*MFTC rs1865855

0.0339

0.2226

0.0596

0.0066

0.2347

0.0091

0.4858

0.3270

0.3615

0.3489

0.3248

0.2103

MTHFD1 rs11627525

0.7010

0.9468

.

0.7285

0.5723

0.9802

.

0.0067

1.0000

0.5750

0.9194

0.1216

GGH rs11988534

0.0409

0.0067

0.6955

0.2427

0.1156

0.8060

0.6044

0.6257

0.4665

0.9112

0.8775

0.5796

MTHFR rs17037425

1.0000

0.8419

0.6233

0.4158

0.2803

0.6769

0.0149

0.0068

0.7817

0.1828

0.0796

0.4756

MTHFD1L rs487637

0.1946

0.4121

0.0364

0.0162

0.5831

0.0069

0.8861

0.7152

0.8488

0.2125

0.5912

0.2003

DMGDH rs663646

0.2749

0.0070

0.8907

0.7786

0.1838

0.7429

0.8537

0.5782

0.9263

0.5080

0.1877

0.8789

NNMT rs10400393

0.0074

0.1090

0.0203

0.8395

0.6189

0.9926

0.9369

0.7317

1.0000

0.5062

0.4840

0.6355

*CUBN rs11254375

0.3772

0.5709

0.4612

0.6008

0.1449

0.7839

0.0331

0.6321

0.0097

0.0274

0.0074

0.2404

MTHFD1L rs11155772

0.1876

0.1492

0.8084

0.5140

0.8021

0.2358

0.2306

0.0932

1.0000

0.0126

0.0075

0.5404

ALDH1A2 rs6493978

0.5450

0.0984

0.5323

0.1136

0.6939

0.0308

0.9785

0.9093

0.8399

0.0333

0.4523

0.0076

RAI1 rs11658846

0.2504

1.0000

0.2329

0.1382

0.6534

0.1409

0.3954

0.9994

0.1351

0.0104

0.6431

0.0088

LRP2 rs3914468

0.8840

0.1061

0.1878

0.2044

0.0181

0.7696

0.1386

0.7577

0.0734

0.2555

0.0080

0.9562

PDGFRA rs2114039

0.0081

0.1297

0.0185

0.9479

1.0000

0.9317

0.7483

0.4513

0.9146

0.5652

0.7360

0.5786

MTHFR rs17367504

0.7868

0.3302

0.9193

0.7251

0.6814

0.5857

0.0174

0.7817

0.0081

0.2908

0.4835

0.1536

*EHMT1 rs7039441

0.4612

0.3106

0.8211

0.9611

0.8037

0.9150

0.0319

0.2588

0.0090

0.0981

0.8608

0.0282

*MFTC rs750606

0.0378

0.7460

0.0220

0.0091

0.3426

0.0090

0.2301

0.0994

0.3619

0.3230

0.3719

0.1578

MTHFD1L rs12524884

0.6841

0.7842

0.6630

0.2910

0.5126

0.1146

0.2320

0.0876

0.7633

0.0094

0.0142

0.1789

*RAI1 rs11654526

0.1373

0.0904

0.3348

0.9082

0.6677

0.7709

0.1313

0.4417

0.0450

0.0099

0.2692

0.0106

* Indicates SNP was genotyped in the full cohort (570 triads, 999 controls). Otherwise SNP was genotyped in the primary sample set (320 triads, 341 controls).

** (.) = Failure to converge (no result).

Note: MTHFD1 rs2236225 and all MTHFD1L SNPs in this table were previously examined for NTD association in this dataset [23, 25, 27].

Figure 2

Linkage Disequilibrium Relationships Between SNPs Significantly Associated with NTDs. Sixty-eight SNPs were associated with case or maternal effects (p < 0.01, uncorrected). Of these, 56 SNPs occur in the same gene as at least one other significantly associated SNP. Their D’ (red scale) and r2 (gray scale) relationships are shown. These SNPs are plotted by relative position. Haplotype blocks defined by a Solid Spine of LD (D’) are outlined in black. Each SNP is numbered and the gene and dbSNP accession number for each are: 1) MTHFR_rs17367504; 2) MTHFR_rs17037425; 3) ARID1A_rs11247593; 4) ARID1A_rs11247594; 5) ARID1A_rs11247596; 6) MTR_rs10733117; 7) MTR_rs10925260; 8) CUBN_rs11591606; 9) CUBN_rs1801222; 10) CUBN_rs11254375; 11) CUBN_rs7070148; 12) CUBN_rs2273737; 13) FOLH1_rs383028; 14) FOLH1_rs16906205; 15) MTHFD1_rs11627525; 16) MTHFD1_rs2236225; 17) ALDH1A2_rs7169289; 18) ALDH1A2_rs6493978; 19) PEMT_rs4646402; 20) PEMT_rs1108579; 21) PEMT_rs16961845; 22) PEMT_rs11656215; 23) RAI1_rs11654526; 24) RAI1_rs9914733; 25) RAI1_rs11658846; 26) ENOSF1_rs1059384; 27) ENOSF1_rs10502289; 28) ADA_rs6031682; 29) ADA_rs452159; 30) ADA_rs406383; 31) ADA_rs2299686; 32) ADA_rs427483; 33) ADA_rs6094017; 34) GART_rs2070388; 35) GART_rs4817580; 36) FTCD_rs7280485; 37) FTCD_rs2839127; 38) COMT_rs737865; 39) COMT_rs174675; 40) PDGFRA_rs2114039; 41) PDGFRA_rs9993187; 42) MTHFD1L_rs2295083; 43) MTHFD1L_rs17080476; 44) MTHFD1L_rs487637; 45) MTHFD1L_rs6923486; 46) MTHFD1L_rs12524884; 47) MTHFD1L_rs11155772; 48) MTHFD1L_rs12199063; 49) MTHFD1L_rs4869987; 50) MFTC_rs10112450; 51) MFTC_rs1865855; 52) MFTC_rs17803441; 53) MFTC_rs750606; 54) MFTC_rs3134260; 55) CDKN2A_rs7041637; 56) CDKN2A_rs3218009

We ranked SNPs by the lowest p-value for any test, accounting for relatedness to other highly significant SNPs. The nine genes exhibiting the 10 strongest association signals were: MFTC, CDKN2A, ADA, PEMT, CUBN, GART, ADA, DNMT3A, MTHFD1 and T (Brachyury) (Table 4). MFTC, ADA, PEMT and CUBN contained more than one significant SNP, and ADA showed evidence of two independent association signals.
Table 4

The Ten Independent * Association Signals Exhibiting the Lowest Uncorrected p-values for Any Test of NTD Association

 

Gene/SNP

Risk Allele Frequency**

Cohort***

Model

OR

95% CI (LL)

95% CI (UL)

P

1 MFTC

        

rs17803441

 

0.07

Combined

LR CC Continuous

1.61

1.23

2.08

0.0003

MFTC

        

rs3134260

 

0.07

Combined

LR CC Continuous

1.56

1.22

2.04

0.0006

2 CDKN2A

   

LL Maternal effect,

    

rs3218009

 

0.13

Primary

Dominant

2.32

1.45

3.71

0.0004

3 ADA

        

rs2299686

 

0.45

Combined

LR MC Dominant

1.56

1.22

2.04

0.0005

ADA

        

rs406383

 

0.25

Combined

LR MC Continuous

 

1.12

1.58

 

ADA

        

rs427483

 

0.33

Combined

LR MC Continuous

1.28

1.10

1.52

0.0018

4 PEMT

        

rs1108579

 

0.52

Combined

TDT

1.47

1.18

1.82

0.0006

PEMT

        

rs11656215

 

0.49

Combined

LL Case effect, recessive

1.68

1.24

2.28

0.0008

PEMT

        

rs4646402

 

0.57

Combined

LL Case effect, dominant

1.67

1.25

2.17

0.0005

5 CUBN

        

rs7070148

 

0.90

Combined

LR MC Continuous

1.64

1.22

2.17

0.0010

CUBN

        

rs2273737

 

0.89

Combined

LR MC Continuous

1.54

1.18

2.04

0.0021

6 GART

        

rs2070388

 

0.91

Combined

TDT

1.89

1.28

2.78

0.0012

GART

        

rs2070388

 

0.91

Combined

LL Case effect, dominant

1.96

1.30

2.94

0.0012

7 ADA rs6031682

 

0.16

Combined

LR CC Dominant

1.49

1.16

1.92

0.0016

8 DNMT3A rs7560488

 

0.42

Primary

LL Case effect, recessive

2.10

1.31

3.38

0.0021

9 MTHFD1 rs2236225

 

0.40

Primary

LR MC Dominant

1.96

1.28

3.03

0.0022

10 T (Brachyury) rs10806845

 

0.51

Combined

LR MC Recessive

1.54

1.16

2.00

0.0023

* LD between SNPs within an association signal are as follows: MFTC (D’ = 1, r2 = 0.993), ADA (D’ > 0.822, r2 > 0.394), PEMT (D’ > 0.693, r2 > 0.428) and CUBN (D’ = 0.935, r2 = 0.823). ADA rs6031682 was designated as an independent association signal because it shares very little LD (D’ < 0.206, r2 < 0.016) with the other ADA SNPs (rs2299686, rs406383, rs427483).

**Frequency of the risk allele was calculated using control sample genotypes.

*** Primary: 320 triads, 341 controls. Combined: 570 triads, 999 controls.

SNPs in MFTC, PEMT and ADA account for seven of the top ten SNPs (Table 4). In MFTC, these two SNPs (rs17803441 and rs3134260) are essentially in perfect LD (D’ = 1.0, r2 = 0.99). As expected, they yielded very similar evidence of NTD risk in a continuous model of logistic regression (rs17803441: OR = 1.61 [1.23-2.08], p = 0.0003, Risk Allele Frequency (RAF) = 0.07; rs3134260: OR = 1.56 [1.22-2.04], p = 0.0006, RAF = 0.07), as well as a recessive model of logistic regression (rs17803441: OR = 1.59 [1.19-2.08], p = 0.0013; rs3134260: OR = 1.54 [1.18-2.04], p = 0.0021). There are a total of five highly significant (p < 0.01) MFTC SNPs and they are all consistent with a case effect. MFTC rs10112450 is significant by the transmission disequilibrium test (TDT, GRR = 1.42, p = 0.0065, RAF = 0.79), and the remaining MFTC SNPs (rs1865855 and rs750606) show evidence of NTD risk to the case by logistic regression with a continuous or dominant model (p < 0.0091). Because all five of these SNPs fall in the same haplotype block (D’ > 0.96), it is likely there is a single variant in this region responsible for the association signals.

Three SNPs in PEMT were in the top ten SNPs, and two of these are in high linkage disequilibrium (rs1108579 and rs4646402; D’ = 1.0, r2 = 0.809). These SNPs also yield very similar evidence of NTD risk by TDT (rs1108579: GRR = 1.47, p = 0.0006, RAF = 0.52; rs4646402: GRR = 1.43, p = 0.0009, RAF = 0.57). PEMT rs11656215 is less strongly linked to these SNPs (r2 < 0.6), and shows association with NTD risk by TDT (GRR = 1.35, p = 0.0053, RAF = 0.49) and by log-linear analysis of a recessive model (GRR = 1.68 [1.24-2.28], p = 0.0008). A fourth highly significant SNP was found in the maternal analysis (rs16961845). It shows evidence of maternal risk in a recessive model of log-linear analysis (GRR = 1.92 [1.18-3.11], p = 0.0082), as well as recessive (p < 0.0048) and continuous (p < 0.0072) models of logistic regression. By r2 measures of LD, this SNP is the least related to the other three SNPs (r2 < 0.08), although all four of these SNPs fall in the same haplotype block (Figure 2).

Lastly, three SNPs in ADA were among the ten SNPs with the lowest p values. They show evidence of LD by D’ (≥0.82) but less so by r2 (≤0.54). These three SNPs are all significantly associated with a maternal effect by logistic regression of a continuous model (rs2299686: OR = 1.30 [1.11-1.52], p = 0.0010, RAF = 0.45; rs427483: OR = 1.28 [1.10-1.52], p = 0.0018, RAF = 0.33; rs406383: OR = 1.33 [1.12-1.58], p = 0.0012, RAF = 0.25). Three other SNPs in ADA (rs6031682, rs452159 and rs6094017) also showed a highly significant association with a maternal effect in a continuous model of logistic regression (p < 0.0059). All six SNPs were highly significant (p < 0.01) for association with maternal risk in either a recessive model (rs406383, p = 0.0021) or dominant model (the other five SNPs, p ≤ 0.0063) of logistic regression. Additionally, ADA rs6031682 shows association with case risk in a continuous model of logistic regression (1.38 [1.10-1.73], p = 0.0052, RAF = 0.84). These six SNPs do not appear to be strongly linked; no haplotype blocks (solid spine of LD) larger than two SNPs were identified. It appears that rs6031682 is clearly outside of a degraded haplotype block consisting of the other five SNPs.

CUBN was the only other gene with more than one SNP found in the ten most strongly associated signals. CUBN rs7070148 and CUBN rs2273737 were both significant for association with maternal risk in a continuous model of logistic regression (OR = 1.64 [1.22-2.17], p = 0.0010, RAF = 0.90 and OR = 1.54 [1.18-2.04], p = 0.0021, RAF = 0.89, respectively). These SNPs are highly linked (D’ = 0.935, r2 = 0.823). There are three other highly significant (p < 0.01) SNPs in CUBN. Two other SNPs in the same haplotype block with CUBN rs7070148 and CUBN rs2273737 (rs1801222 and rs11254375, D’ < 0.76) also showed an association with maternal effects. A third SNP, CUBN rs11591606, is outside this block (D’ < 0.20) and is associated with maternal risk in a dominant model of logistic regression (OR = 4.15 [1.51-11.39], p = 0.0058, RAF = 0.17).

Correction for multiple testing was performed for three of the twelve tests of association. No adjusted p-value was found to remain significant, and no further correction was performed.

Discussion

This study represents a new scale of evaluation of genetic contribution to NTD risk. Common variants in 82 biologically plausible candidate genes were tested for association with NTDs in a large Irish population. Seventeen variants in nine genes account for the ten most significant associations observed. CDKN2A, GART, DNMT3A, MTHFD1 and T (Brachyury) contained a single SNP among the ten lowest p-values observed for all tests. In contrast, MFTC, ADA, PEMT, and CUBN each contained more than one such SNP. This seems to be due to strong LD relationships between the associated SNPs. The only exception is in ADA, which shows evidence of two strong, unrelated association signals.

ADA (adenosine deaminase) converts adenosine to inosine by removal of an amino group. Deficiency in this enzyme causes severe combined immunodeficiency disease (SCID), which is characterized by compromise of both T cells and B cells. Interestingly, ADA activity was significantly elevated in a study of 68 pregnant women carrying a fetus with a central nervous system malformation [39]; of these women, 17 had a spina bifida pregnancy. Consistent with this, six unrelated (r2 < 0.70), noncoding ADA SNPs were found in the current study to be associated with maternal risk of carrying an NTD pregnancy (p ≤ 0.006, uncorrected). Genetic variation in ADA may contribute to maternal risk of NTDs. In addition, this gene was the only one to exhibit two independent association signals among the top ten signals observed. This may indicate that there is more than one allele associated with risk or the same allele has recurred on more than one haplotype. ADA rs6031682 shows evidence of case effects (p = 0.0016) as well as maternal effects (p = 0.0019) in log-linear analyses of a dominant model, and it is clearly independent of the other significant ADA SNPs (D’ < 0.19). It would be of interest to test the associated ADA SNPs in an independent study, especially since the scale of correction would be much smaller in a focused study.

PEMT (phosphatidylethanolamine N-methyltransferase) plays a role in choline metabolism. It converts phosphatidylethanolamine to phosphatidylcholine in the liver; phosphatidylcholine is a major component of cell membranes. This role for choline can compete with its role as a methyl donor. Choline can be converted to betaine, which acts as a methyl donor in an alternate, folate-independent conversion of homocysteine to methionine. This link between folate and choline metabolism makes PEMT an interesting candidate gene, and interactions between PEMT SNPs have been reported to be associated with NTDs. In a case–control study, single SNP effects were not observed, although some compound genotypes for PEMT rs7946 and PEMT rs897453 were associated with decreased NTD risk [40]. The latter variant was not directly tested in the current study, and no association for PEMT rs7946 was observed in this Irish sample [37]. However, one related SNP pair (r2 = 0.80) and two other SNPs (r2 < 0.60) in PEMT falling in the same haplotype block (D’ ≥ 0.69) showed NTD association (p < 0.0053, uncorrected) in the current study, suggesting a role for this gene in NTD risk. Unlike the other three SNPs that exhibited case effects, the least related SNP in this block (PEMT rs16961845, 0.69 ≤ D’ ≤ 0.89) was positive for maternal effects by three tests of association. This intronic SNP is in strong r2 LD with 6 other intronic SNPs, making it difficult to speculate about its function. It is also difficult to discern whether the associated SNPs in this block represent independent signals for case risk and for maternal risk, or whether a single signal for a case effect is being detected. Therefore, further studies on the variation of this gene and NTDs are warranted.

MFTC (mitochondrial folate transporter/carrier, SLC23A32) transports folate from the cytoplasm into the mitochondria. Some folate metabolic reactions occur in both the cytoplasm and in mitochondria via compartment-specific enzymes. The mitochondrion produces the majority of the one carbon units used by the cell (reviewed in [41]). As the genes coding for these mitochondrial enzymes have been identified, they have been shown to be intriguing and relevant candidates for NTD studies. For example, we previously reported that the gene encoding 5, 10-methylene-tetrahydrofolate dehydrogenase 1-like (MTHFD1L) contains a polymorphism associated with NTDs [27]. Genetic variation affecting mitochondrial folate transport may also contribute to NTDs, as seen by our finding that 5 of 11 tested MFTC SNPs showed association (p < 0.01) with NTD risk in cases. This gene falls in a region of very high D’ LD; the haplotype block containing these five SNPs extends ~92 kb and contains two other genes: DCAF13 (DDB1 and CUL4 associated factor 13) and CTHRC1 (collagen triple helix repeat containing 1). Any SNP in this large haplotype block could be the causative variant driving the observed associations. As the only coding SNP in MFTC, the best candidate is rs17803441 (R117H). However, as arginine and histidine are both polar, basic amino acids, this is a fairly conservative change. We observed a minor allele frequency of 0.07 in this study. Conservation of the more common arginine residue is observed in chimp, wolf, cow, mouse, rat and zebrafish, but not in chicken or invertebrates. All of the SNPs in high LD (r2 > 0.7) with MFTC rs17803441 (R117H) in this block are intronic or intergenic. This SNP also had the lowest p-value for any test of association of all SNPs tested in this study. It would be of great interest to determine in an independent population whether it contributes to NTD risk.

CUBN encodes the intestinal receptor responsible for the uptake of the vitamin B12-intrinsic factor complex. It is also expressed in the kidney, where it is involved in reabsorption of many proteins and vitamins, including vitamin B12. This gene spans more than 300 kb of DNA. The only reported SNP association in CUBN is for rs1907362, which was associated with case risk in a Dutch population [35]. In contrast, we observed two highly significant SNPs in CUBN (rs7070148 and rs2273737) associated with maternal NTD risk. Due to their high LD these SNPs represent a single association signal. There were three other highly associated (p < 0.01) SNPs in this gene. CUBN rs11591606 was associated with maternal risk, and is in a smaller haplotype block at the 3’ end of the gene. Two other CUBN SNPs (rs1801222 [S253F] and rs11254375) were also highly associated (p < 0.01) with maternal risk and are in the same ~30 kb haplotype block with rs7070148 and rs2273737 at the 5’ end of the gene. While there are many SNPs in this block that could be the causal risk SNP, rs1801222 is of interest since it is a coding SNP (S253F) that was significantly associated with lower serum vitamin B12 levels in a meta-analysis of three genome wide association studies of three Caucasian populations [42]. This does not prove that CUBN rs1801222 is the causal SNP in either study, but it is consistent with the hypothesis that this SNP or another CUBN polymorphism linked to it within this haplotype block lowers vitamin B12 levels and thereby increases risk of an NTD pregnancy.

Multiple highly significant SNPs in ADA, PEMT, MFTC and CUBN account for half of the ten strongest association signals observed. The remaining five association signals are equally compelling. MTHFD1 rs2236225 (R653Q) was previously reported as a maternal NTD risk factor in the current study population [23, 25] and others [22, 24], while the other four signals represent new associations. First, CDKN2A rs3218009 was associated with maternal risk for NTDs. CDKN2a is a tumor suppressor gene that codes for several isoforms, including ARF (alternate open reading frame), a protein that stabilizes p53. A subset of mice carrying p53 null alleles exhibit overgrowth of neural tissue, supporting the importance of this pathway in normal neural tube development. Second, the same highly significant p-value was obtained for GART rs2070388 by two tests for case effect: TDT and log-linear analysis of a dominant model of case effect. GART is a trifunctional enzyme (phosphoribosylglycinamide formyltransferase, phosphoribosylglycinamide synthetase, phosphoribosylaminoimidazole synthetase) involved in de novo purine synthesis. For its phosphoribosylglycinamide activity, GART uses N10-formyl tetrahydrofolate as a one-carbon donor in the synthesis pathway of inosine monophosphate (IMP), a purine precursor. Interestingly, GART rs4817579 in intron 2 has been associated with cleft lip and/or palate plus dental anomalies [43]. This variant was not tested in the current study, and the absence of GART rs2070388 from the HapMap data prevents us from evaluating the relatedness of these markers.

Third, DNMT3A rs7560488 was associated with NTD risk in cases. This gene encodes a DNA methyltransferase involved in de novo methylation during development. The folate pathway generates S-adenosyl methionine, which is used by DNMT3A as a methyl donor. Fourth, T (Brachyury) rs10806845 was associated with maternal NTD risk. The T (Brachyury) gene encodes a transcription factor involved in mesoderm formation and differentiation, and mice null for T (Brachyury) do not survive to term due to a number of developmental abnormalities, including fusion of the neural tube to the gut. Although previous studies differ in whether genetic variation in T (Brachyury) contributes to NTD risk in cases [44–48], our observation may be the first indication of its contribution to maternal risk of carrying an affected fetus.

Although no associations remained significant after conservative adjustment for multiple tests, it remains very possible that some of the evaluated candidates do in fact contribute to NTDs. The scale of our study design (using twelve tests of association to evaluate 1441 candidate SNPs) could contribute to Type II errors. This possibility is supported by the fact that of three SNPs previously reported to be associated with NTDs in this cohort (MTHFR 677 C > T [49, 50], MTHFD1 R653Q [23, 25], TCblR G220R [34]) only one was observed to be associated in the current study design (Table 5). Only MTHFD1 R653Q was found to be significantly associated in the primary phase of the analysis, which was performed on approximately half the samples. MTHFR 677 C > T and TCblR G220R were only found to be associated (p < 0.05, uncorrected) when the full cohort of samples were used. This suggests the possibility that the stringency of correction may be too high. Additionally, it is important to note that MTHFD1 R653Q was ninth among the top ten association signals in this study (Table 4). This suggests that a number of the ten strongest association signals observed in this study play a role in NTD risk, and they should be high priority candidates for further study.
Table 5

Significant (Uncorrected) Analyses of SNPs Previously Reported as NTD Risk Factors in this Population

Gene/SNP

MAF*

Cohort**

Model

OR

95% CI (LL)

95% CI (UL)

P

MTHFR rs1801133 677 C > T

0.32

Combined

LR CC Continuous

1.23

1.05

1.45

0.011

  

Combined

LR CC Recessive

1.26

1.01

1.57

0.041

  

Combined

LR CC Dominant

1.44

1.04

2.00

0.029

  

Combined

LR MC Continuous

1.22

1.04

1.43

0.017

  

Combined

LR MC Recessive

1.25

1.01

1.57

0.045

MTHFD1 rs2236225 R653Q

0.46

Primary

LR MC Continuous

1.35

1.06

1.71

0.014

  

Primary

LR MC Dominant

1.97

1.28

3.04

0.002

  

Combined

LR MC Dominant

1.48

1.14

1.92

0.003

  

Primary

LL Case effect, dominant

0.53

0.30

0.93

0.026

  

Combined

LL TDT

0.80

0.65

0.99

0.039

  

Combined

LL Case effect, dominant

0.60

0.42

0.86

0.005

TCblR rs2336573 G220R

0.04

Combined

LR CC Dominant

.***

  

0.013

*For each SNP, the minor allele was evaluated as the risk allele.

** Primary: 320 triads, 341 controls. Combined: 570 triads, 999 controls.

*** (.) = Failure to converge. Pexact is shown.

Conclusions

In summary, this study involves the largest evaluation of common genetic variation for NTD risk yet reported: 1441 SNPs in 82 candidate genes. While no SNP associations remained significant after correction for multiple tests, there is a strong possibility that the study design and/or stringency of correction has resulted in obscuring true associations. At least one established risk factor, MTHFD1 R653Q, was corrected away, suggesting our approach was extremely conservative. Therefore, variation in the top genes identified in this study should be examined in independent populations for NTD risk, especially since many of these genes (MFTC, CDKN2A, ADA, CUBN, DNMT3A, and T (Brachyury)) represent new avenues of investigation.

Methods

Study population

The recruitment of the Irish NTD families (cases and parents) and controls has been described [23, 34, 51, 52]. Briefly, the cohort includes 586 families with an NTD case; 442 of these families are full family triads (DNA from case, mother and father). For this study, 570 of the NTD families had sufficient DNA and were divided into two sets, one for primary analysis and one for secondary (combined) analysis. The primary and secondary sample sets were matched as closely as possible for the following parameters: the number of complete NTD triads, the proportion of NTD cases with spina bifida vs. other NTDs, and NTD case gender (Table 6).
Table 6

Characteristics of the Irish NTD Sets Used in this Study

 

No. Families (%)

No. Cases (%)

% Complete Triads

% Spina Bifida

% Female Cases

Primary set

320 (56.1%)

301 (56.8%)

75.2%

94.9%

51.8%

Secondary set

250 (43.9%)

229 (43.2%)

73.6%

95.7%

55.3%

The control population (n = 999) is a random sample drawn from 56,049 blood samples donated by women at their first prenatal visit to the three major maternity hospitals in Dublin (1986–1990). A subset of controls (n = 341) was randomly selected for the primary screen, and the remaining controls were used in the secondary set.

Written, informed consent was obtained from study subjects, their parents or their guardians. Archived control samples were anonymized prior to analysis. The study was approved by the Ethics Research Committee of the Health Research Board (Dublin, Ireland) and the Institutional Review Board at the National Human Genome Research Institute (Bethesda, MD, USA). Extraction of genomic DNA from blood samples and buccal swabs was performed using the QIAamp DNA Blood Mini Kit (Qiagen, Valencia, CA, USA).

SNP selection

Genes were chosen for study because they are involved in folate and vitamin B12 metabolism or other metabolic and signaling pathways implicated in the etiology of NTDs (Figure 1). Although previously published, MTHFR[52–54], MTHFD1[23, 25], MTHFD1L[27], TCblR[34], and TP53[51] were reanalyzed in this study in order to compare the differing research strategy, which includes new genetic models of risk assessment. For each gene chosen, we evaluated the transcribed region of the gene and 10 kilobases (kb) upstream and downstream of the gene in an effort to include polymorphisms with potential proximal effects, such as promoter variants. In order to capture the common variation in each gene, SNPs genotyped by HapMap (Data Release 21, Phase II, Anon, 2003 [55]) were considered. A set of tagSNPs was identified using an algorithm based on optimizing for tagSNPs with maximal minor allele frequencies (MAFs) and an r2 threshold of 0.8, while maximizing the MAF of the selected tagSNP. In addition to this set of tagSNPs, validated variants from dbSNP [26] were also selected for physical coverage so that spacing between SNPs would be less than 20 kb within D’ haplotype blocks and less than 5 kb between haplotype blocks. A total of 1517 SNPs were selected.

Genotyping

The selected 1517 SNPs were genotyped on the primary sample sets (320 NTD case families and 341 controls). Genotypes were generated by the Johns Hopkins University SNP Center (Baltimore, MD) using the Illumina GoldenGate assay (Illumina, San Diego, CA). Of the 1517 SNPs attempted, 1320 SNPs (87.2%) remained after filtering out low quality data (re-attempted on another platform, see below). The overall call rate for these 1320 SNPs was 98%. All but 150 of these SNPs had a call rate of >95%; the rest had an average call rate of 87.2% (±7.9%) and were re-genotyped on another platform (see below). Both the overall duplicate concordance rates and the Mendelian consistency rates were 99.99% for the 1320 accepted SNPs.

Based on analyses of the primary sample sets, 93 SNPs were genotyped in the secondary sample set (250 NTD case families and 658 controls). Genotypes were generated by KASPar chemistry at Kbiosciences (Herts, UK). Three SNPs failed on this platform: rs127317149, rs7367859 and rs7096079. For the 90 successfully genotyped SNPs, the overall duplicate concordance rate was 99.81% and the overall Mendelian consistency rate was 99.94%.

SNPs that could not be assayed (n = 197) or returned low call rates (<95%, n = 150) via Illumina GoldenGate chemistry were re-genotyped in the entire sample set (570 NTD families and 999 controls) by detection of allele-specific primer extension using matrix-assisted laser desorption/ionization – time of flight (MALDI-TOF) mass spectrometry (Sequenom, San Diego, CA, USA). Genotyping data from SNPs with call rates above 95% were added to the final analyses (121/197 SNPs that failed and 106/150 SNPs that yielded low call rates). The overall duplicate concordance rate was 99.10% and the overall Mendelian consistency rate was 99.32% for these 227 SNPs.

To summarize, our final data set consisted of 1441 SNPs; 1320 high quality SNPs from the Illumina platform (93 of these SNPS were also typed in the secondary samples using the KASPar platform, and 106 of these SNPs were re-typed in the entire sample set using the Sequenom platform) plus 121 SNPs from the Sequenom platform.

Linkage disequilibrium

D’ and r2 measures of LD for SNPs of interest were estimated based on control genotypes using Haploview [56]. Haplotype blocks were based on D’ values using the Solid Spine of LD option in Haploview.

Statistical methods

The design for this study involved two stages of genotyping. Rather than use the secondary samples for a replication study, joint analysis of the combined dataset was performed since it generally provides greater power to detect a genetic effect [57]. In the initial analysis, all SNPs successfully typed with the Illumina GoldenGate assay (n = 1320, including the 150 high quality SNPs with call rates of <95%) were analyzed in the primary sample set by four tests of association. Two tests were performed to evaluate case effects: 1) Logistic regression, a 1-degree of freedom (DOF) test of association between the affected status and number of risk alleles; and 2) the Spielman transmission disequilibrium test (TDT, [58]). Similarly, two tests were performed to evaluate maternal effects: 1) Logistic regression, a 1-DOF test of association between the maternal status and number of risk alleles; and 2) log-linear modeling with 2-DOF to test for effect of the maternal genotype.

SNPs were selected to be genotyped on all samples (570 NTD case families and 999 controls) if they met the following criteria: 1) SNPs of interest reaching a significance level in the primary analysis (n = 93, Table 2); or 2) failed SNPs (n = 121) and SNPs with low call rates (<95%, n = 106). Final analyses were performed on the entire dataset and consisted of twelve association tests. NTD case–control and NTD mother-control comparisons were performed using continuous, recessive and dominant coded models of logistic regression to generate odds ratios (ORs) and 95% confidence intervals (CI). Six family-based tests of association were also applied using log-linear models. The NTD triads (case, mother and father) were analyzed for case effects using recessive, dominant and linear (TDT) coding while direct maternal effects were analyzed using recessive, dominant and 2-DOF models.

To correct for multiple comparison, the final analysis used the complete information on the 1441 SNPs. Correction was performed by multivariate permutation (N = 9,999 random permutations) for three of the tests used in the initial analysis: case–control logistic regression, mother-control logistic regression and the TDT. This method accounts for any linkage disequilibrium between SNPs. Multivariate permuting of triads for the TDT was performed by treating the test as a one-sample test and permuting the risk allele [59]. Independent permutations were performed for the cases and controls or mothers and controls to adjust for multiple comparisons in the tests of logistic regression. The results were combined by Bonferroni adjustment to account for all comparisons (including all SNPs because of our multivariate permutation approach) while controlling the probability of any false positives at 5%. Since a SNP could only be found significant based on this combined analysis, our method provides type I error control regardless of the SNP selection process or number of SNPs selected for genotyping on all samples.

Abbreviations

DOF: 

Degree(s) of freedom

LD: 

Linkage disequilibrium

NTDs: 

Neural tube defects

RAF: 

Risk allele frequency

SNP: 

Single nucleotide polymorphism

TDT: 

Transmission disequilibrium test.

Declarations

Acknowledgements

These studies would not be possible without the participation of the affected families, and their recruitment by the Irish Association of Spina Bifida and Hydrocephalus and the Irish Public Health Nurses in Ireland. This study was supported by the Intramural Research Programs of the National Institutes of Health, Eunice Kennedy Shriver National Institute of Child Health and Human Development, and the National Human Genome Research Institute.

Authors’ Affiliations

(1)
Molecular Pathogenesis Section, Genome Technology Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, USA
(2)
Department of Clinical Medicine, Trinity College Dublin, Dublin, Ireland
(3)
Division of Epidemiology, Statistics and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services, Bethesda, USA
(4)
Department of Health and Human Services, Office of Biostatistics Research, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, USA
(5)
School of Biotechnology, Dublin City University, Dublin, Ireland
(6)
Department of Health and Human Services, Pregnancy and Perinatology Branch, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, USA
(7)
International Centre for Neurotherapeutics, Dublin City University, Dublin, Ireland
(8)
Department of Health and Human Services, Molecular Genetics Section, Genome Technology Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, USA
(9)
Child Health Epidemiology Unit, Health Research Board, Dublin, Ireland
(10)
Department of Nutritional Sciences and Toxicology, University of California, Berkeley, USA
(11)
School of Biochemistry and Immunology, Trinity College Dublin, Dublin, Ireland
(12)
Molecular Pathogenesis Section, Genome Technology Branch, National Human Genome Research Institute, Bethesda, USA

References

  1. Congenital malformations worldwide: A report from the International Clearinghouse for Birth Defects Monitoring Systems. 1991, Amsterdam, Elsevier Science PublishersGoogle Scholar
  2. Busby A, Abramsky L, Dolk H, Armstrong B: Preventing neural tube defects in Europe: population based study. BMJ. 2005, 330 (7491): 574-575. 10.1136/bmj.330.7491.574.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Canfield MA, Honein MA, Yuskiv N, Xing J, Mai CT, Collins JS, Devine O, Petrini J, Ramadhani TA, Hobbs CA, et al: National estimates and race/ethnic-specific variation of selected birth defects in the United States, 1999–2001. Birth Defects Res A Clin Mol Teratol. 2006, 76 (11): 747-756. 10.1002/bdra.20294.View ArticlePubMedGoogle Scholar
  4. Botto LD, Lisi A, Robert-Gnansia E, Erickson JD, Vollset SE, Mastroiacovo P, Botting B, Cocchi G, de Vigan C, de Walle H, et al: International retrospective cohort study of neural tube defects in relation to folic acid recommendations: are the recommendations working?. BMJ. 2005, 330 (7491): 571-10.1136/bmj.38336.664352.82.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Botto LD, Moore CA, Khoury MJ, Erickson JD: Neural-tube defects. N Engl J Med. 1999, 341 (20): 1509-1519. 10.1056/NEJM199911113412006.View ArticlePubMedGoogle Scholar
  6. Kirke PN, Molloy AM, Daly LE, Burke H, Weir DG, Scott JM: Maternal plasma folate and vitamin B12 are independent risk factors for neural tube defects. Q J Med. 1993, 86 (11): 703-708.PubMedGoogle Scholar
  7. MRC Vitamin Study Research Group: Prevention of neural tube defects: results of the Medical Research Council Vitamin Study. Lancet. 1991, 338 (8760): 131-137.View ArticleGoogle Scholar
  8. Czeizel AE, Dudas I: Prevention of the first occurrence of neural-tube defects by periconceptional vitamin supplementation. N Engl J Med. 1992, 327 (26): 1832-1835. 10.1056/NEJM199212243272602.View ArticlePubMedGoogle Scholar
  9. Berry RJ, Li Z, Erickson JD, Li S, Moore CA, Wang H, Mulinare J, Zhao P, Wong LY, Gindler J, et al: Prevention of neural-tube defects with folic acid in China. China-U.S. Collaborative Project for Neural Tube Defect Prevention. N Engl J Med. 1999, 341 (20): 1485-1490. 10.1056/NEJM199911113412001.View ArticlePubMedGoogle Scholar
  10. Wright ME: A case–control study of maternal nutrition and neural tube defects in Northern Ireland. Midwifery. 1995, 11 (3): 146-152. 10.1016/0266-6138(95)90029-2.View ArticlePubMedGoogle Scholar
  11. Zhang T, Xin R, Gu X, Wang F, Pei L, Lin L, Chen G, Wu J, Zheng X: Maternal serum vitamin B12, folate and homocysteine and the risk of neural tube defects in the offspring in a high-risk area of China. Public Health Nutr. 2009, 12 (5): 680-686. 10.1017/S1368980008002735.View ArticlePubMedGoogle Scholar
  12. Gaber KR, Farag MK, Soliman SE, El-Bassyouni HT, El-Kamah G: Maternal vitamin B12 and the risk of fetal neural tube defects in Egyptian patients. Clin Lab. 2007, 53 (1–2): 69-75.PubMedGoogle Scholar
  13. Hao L, Liu M, Liu X, Chen X, Tang Y, Li Z: Relationship between folate, vitamin B12, total plasma homocysteine and mutation of reductase. Zhonghua Yu Fang Yi Xue Za Zhi. 2000, 34 (1): 22-24.PubMedGoogle Scholar
  14. Suarez L, Hendricks K, Felkner M, Gunter E: Maternal serum B12 levels and risk for neural tube defects in a Texas-Mexico border population. Ann Epidemiol. 2003, 13 (2): 81-88. 10.1016/S1047-2797(02)00267-3.View ArticlePubMedGoogle Scholar
  15. Groenen PM, van Rooij IA, Peer PG, Gooskens RH, Zielhuis GA, Steegers-Theunissen RP: Marginal maternal vitamin B12 status increases the risk of offspring with spina bifida. Am J Obstet Gynecol. 2004, 191 (1): 11-17. 10.1016/j.ajog.2003.12.032.View ArticlePubMedGoogle Scholar
  16. Candito M, Rivet R, Herbeth B, Boisson C, Rudigoz RC, Luton D, Journel H, Oury JF, Roux F, Saura R, et al: Nutritional and genetic determinants of vitamin B and homocysteine metabolisms in neural tube defects: a multicenter case–control study. Am J Med Genet A. 2008, 146A (9): 1128-1133. 10.1002/ajmg.a.32199.View ArticlePubMedGoogle Scholar
  17. Molloy AM, Kirke PN, Troendle JF, Burke H, Sutton M, Brody LC, Scott JM, Mills JL: Maternal vitamin B12 status and risk of neural tube defects in a population with high neural tube defect prevalence and no folic Acid fortification. Pediatrics. 2009, 123 (3): 917-923. 10.1542/peds.2008-1173.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Deak KL, Siegel DG, George TM, Gregory S, Ashley-Koch A, Speer MC: Further evidence for a maternal genetic effect and a sex-influenced effect contributing to risk for human neural tube defects. Birth Defects Res A Clin Mol Teratol. 2008, 82 (10): 662-669. 10.1002/bdra.20511.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Epidemiology and control of neural tube defects: Epidemiology and control of neural tube defects. 1992, Oxford University Press, Oxford, EnglandGoogle Scholar
  20. Joo JG, Beke A, Papp C, Toth-Pal E, Csaba A, Szigeti Z, Papp Z: Neural tube defects in the sample of genetic counselling. Prenat Diagn. 2007, 27 (10): 912-921. 10.1002/pd.1801.View ArticlePubMedGoogle Scholar
  21. Frosst P, Blom HJ, Milos R, Goyette P, Sheppard CA, Matthews RG, Boers GJ, den Heijer M, Kluijtmans LA, van den Heuvel LP, et al: A candidate genetic risk factor for vascular disease: a common mutation in methylenetetrahydrofolate reductase. Nat Genet. 1995, 10 (1): 111-113. 10.1038/ng0595-111.View ArticlePubMedGoogle Scholar
  22. Botto LD, Yang Q: 5,10-Methylenetetrahydrofolate reductase gene variants and congenital anomalies: a HuGE review. Am J Epidemiol. 2000, 151 (9): 862-877. 10.1093/oxfordjournals.aje.a010290.View ArticlePubMedGoogle Scholar
  23. Brody LC, Conley M, Cox C, Kirke PN, McKeever MP, Mills JL, Molloy AM, O'Leary VB, Parle-McDermott A, Scott JM, et al: A polymorphism, R653Q, in the trifunctional enzyme methylenetetrahydrofolate dehydrogenase/methenyltetrahydrofolate cyclohydrolase/formyltetrahydrofolate synthetase is a maternal genetic risk factor for neural tube defects: report of the Birth Defects Research Group. Am J Hum Genet. 2002, 71 (5): 1207-1215. 10.1086/344213.View ArticlePubMedPubMed CentralGoogle Scholar
  24. De Marco P, Merello E, Calevo MG, Mascelli S, Raso A, Cama A, Capra V: Evaluation of a methylenetetrahydrofolate-dehydrogenase 1958 G > A polymorphism for neural tube defect risk. J Hum Genet. 2006, 51 (2): 98-103. 10.1007/s10038-005-0329-6.View ArticlePubMedGoogle Scholar
  25. Parle-McDermott A, Kirke PN, Mills JL, Molloy AM, Cox C, O'Leary VB, Pangilinan F, Conley M, Cleary L, Brody LC, et al: Confirmation of the R653Q polymorphism of the trifunctional C1-synthase enzyme as a maternal risk for neural tube defects in the Irish population. Eur J Hum Genet. 2006, 14 (6): 768-772. 10.1038/sj.ejhg.5201603.View ArticlePubMedGoogle Scholar
  26. Shaw GM, Lu W, Zhu H, Yang W, Briggs FB, Carmichael SL, Barcellos LF, Lammer EJ, Finnell RH: 118 SNPs of folate-related genes and risks of spina bifida and conotruncal heart defects. BMC Med Genet. 2009, 10: 49-View ArticlePubMedPubMed CentralGoogle Scholar
  27. Parle-McDermott A, Pangilinan F, O'Brien KK, Mills JL, Magee AM, Troendle J, Sutton M, Scott JM, Kirke PN, Molloy AM, et al: A common variant in MTHFD1L is associated with neural tube defects and mRNA splicing efficiency. Hum Mutat. 2009, 30 (12): 1650-1656. 10.1002/humu.21109.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Parle-McDermott A, Pangilinan F, Mills JL, Kirke PN, Gibney ER, Troendle J, O'Leary VB, Molloy AM, Conley M, Scott JM, et al: The 19-bp deletion polymorphism in intron-1 of dihydrofolate reductase (DHFR) may decrease rather than increase risk for spina bifida in the Irish population. Am J Med Genet A. 2007, 143A (11): 1174-1180. 10.1002/ajmg.a.31725.View ArticlePubMedGoogle Scholar
  29. Johnson WG, Stenroos ES, Spychala JR, Chatkupt S, Ming SX, Buyske S: New 19 bp deletion polymorphism in intron-1 of dihydrofolate reductase (DHFR): a risk factor for spina bifida acting in mothers during pregnancy?. Am J Med Genet A. 2004, 124A (4): 339-345. 10.1002/ajmg.a.20505.View ArticlePubMedGoogle Scholar
  30. Doudney K, Grinham J, Whittaker J, Lynch SA, Thompson D, Moore GE, Copp AJ, Greene ND, Stanier P: Evaluation of folate metabolism gene polymorphisms as risk factors for open and closed neural tube defects. Am J Med Genet A. 2009, 149A (7): 1585-1589. 10.1002/ajmg.a.32937.View ArticlePubMedGoogle Scholar
  31. Relton CL, Wilding CS, Pearce MS, Laffling AJ, Jonas PA, Lynch SA, Tawn EJ, Burn J: Gene-gene interaction in folate-related genes and risk of neural tube defects in a UK population. J Med Genet. 2004, 41 (4): 256-260. 10.1136/jmg.2003.010694.View ArticlePubMedPubMed CentralGoogle Scholar
  32. van der Linden IJ, den Heijer M, Afman LA, Gellekink H, Vermeulen SH, Kluijtmans LA, Blom HJ: The methionine synthase reductase 66A > G polymorphism is a maternal risk factor for spina bifida. J Mol Med (Berl). 2006, 84 (12): 1047-1054. 10.1007/s00109-006-0093-x.View ArticleGoogle Scholar
  33. Zhu H, Wicker NJ, Shaw GM, Lammer EJ, Hendricks K, Suarez L, Canfield M, Finnell RH: Homocysteine remethylation enzyme polymorphisms and increased risks for neural tube defects. Mol Genet Metab. 2003, 78 (3): 216-221. 10.1016/S1096-7192(03)00008-8.View ArticlePubMedGoogle Scholar
  34. Pangilinan F, Mitchell A, VanderMeer J, Molloy AM, Troendle J, Conley M, Kirke PN, Sutton M, Sequeira JM, Quadros EV, et al: Transcobalamin II receptor polymorphisms are associated with increased risk for neural tube defects. J Med Genet. 2010, 47 (10): 677-685. 10.1136/jmg.2009.073775.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Franke B, Vermeulen SH, Steegers-Theunissen RP, Coenen MJ, Schijvenaars MM, Scheffer H, den Heijer M, Blom HJ: An association study of 45 folate-related genes in spina bifida: Involvement of cubilin (CUBN) and tRNA aspartic acid methyltransferase 1 (TRDMT1). Birth Defects Res A Clin Mol Teratol. 2009, 85 (3): 216-226. 10.1002/bdra.20556.View ArticlePubMedGoogle Scholar
  36. Martinez CA, Northrup H, Lin JI, Morrison AC, Fletcher JM, Tyerman GH, Au KS: Genetic association study of putative functional single nucleotide polymorphisms of genes in folate metabolism and spina bifida. Am J Obstet Gynecol. 2009, 201 (4): 394 e391-311.View ArticleGoogle Scholar
  37. Carter TC, Pangilinan F, Troendle JF, Molloy AM, VanderMeer J, Mitchell A, Kirke PN, Conley MR, Shane B, Scott JM, et al: Evaluation of 64 candidate single nucleotide polymorphisms as risk factors for neural tube defects in a large Irish study population. Am J Med Genet A. 2011, 155A (1): 14-21.View ArticlePubMedGoogle Scholar
  38. Lu W, Guzman AR, Yang W, Chapa CJ, Shaw GM, Greene RM, Pisano MM, Lammer EJ, Finnell RH, Zhu H: Genes encoding critical transcriptional activators for murine neural tube development and human spina bifida: a case–control study. BMC Med Genet. 2010, 11: 141-10.1186/1471-2350-11-141.View ArticlePubMedPubMed CentralGoogle Scholar
  39. Engin-Ustun Y, Ustun Y, Sezgin B, Karabulut AB, Kiran TR, Kafkasli A: Activity of adenosine deaminase in mothers who have conceived a fetus with central nervous system malformations. J Perinat Med. 2007, 35 (5): 399-402.View ArticlePubMedGoogle Scholar
  40. Zhang J, Zhu H, Yang W, Shaw GM, Lammer EJ, Finnell RH: Phosphatidylethanolamine N-methyltransferase (PEMT) gene polymorphisms and risk of spina bifida. Am J Med Genet A. 2006, 140 (7): 785-789.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Tibbetts AS, Appling DR: Compartmentalization of Mammalian folate-mediated one-carbon metabolism. Annu Rev Nutr. 2010, 30: 57-81. 10.1146/annurev.nutr.012809.104810.View ArticlePubMedGoogle Scholar
  42. Hazra A, Kraft P, Lazarus R, Chen C, Chanock SJ, Jacques P, Selhub J, Hunter DJ: Genome-wide significant predictors of metabolites in the one-carbon metabolism pathway. Hum Mol Genet. 2009, 18 (23): 4677-4687. 10.1093/hmg/ddp428.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Vieira AR, McHenry TG, Daack-Hirsch S, Murray JC, Marazita ML: Candidate gene/loci studies in cleft lip/palate and dental anomalies finds novel susceptibility genes for clefts. Genet Med. 2008, 10 (9): 668-674. 10.1097/GIM.0b013e3181833793.View ArticlePubMedPubMed CentralGoogle Scholar
  44. Morrison K, Papapetrou C, Attwood J, Hol F, Lynch SA, Sampath A, Hamel B, Burn J, Sowden J, Stott D, et al: Genetic mapping of the human homologue (T) of mouse T(Brachyury) and a search for allele association between human T and spina bifida. Hum Mol Genet. 1996, 5 (5): 669-674. 10.1093/hmg/5.5.669.View ArticlePubMedGoogle Scholar
  45. Trembath D, Sherbondy AL, Vandyke DC, Shaw GM, Todoroff K, Lammer EJ, Finnell RH, Marker S, Lerner G, Murray JC: Analysis of select folate pathway genes, PAX3, and human T in a Midwestern neural tube defect population. Teratology. 1999, 59 (5): 331-341. 10.1002/(SICI)1096-9926(199905)59:5<331::AID-TERA4>3.0.CO;2-L.View ArticlePubMedGoogle Scholar
  46. Shields DC, Ramsbottom D, Donoghue C, Pinjon E, Kirke PN, Molloy AM, Edwards YH, Mills JL, Mynett-Johnson L, Weir DG, et al: Association between historically high frequencies of neural tube defects and the human T homologue of mouse T (Brachyury). Am J Med Genet. 2000, 92 (3): 206-211. 10.1002/(SICI)1096-8628(20000529)92:3<206::AID-AJMG9>3.0.CO;2-W.View ArticlePubMedGoogle Scholar
  47. Speer MC, Melvin EC, Viles KD, Bauer KA, Rampersaud E, Drake C, George TM, Enterline DS, Mackey JF, Worley G, et al: T locus shows no evidence for linkage disequilibrium or mutation in American Caucasian neural tube defect families. Am J Med Genet. 2002, 110 (3): 215-218. 10.1002/ajmg.10436.View ArticlePubMedGoogle Scholar
  48. Jensen LE, Barbaux S, Hoess K, Fraterman S, Whitehead AS, Mitchell LE: The human T locus and spina bifida risk. Hum Genet. 2004, 115 (6): 475-482. 10.1007/s00439-004-1185-8.View ArticlePubMedGoogle Scholar
  49. Kirke PN, Mills JL, Whitehead AS, Molloy A, Scott JM: Methylenetetrahydrofolate reductase mutation and neural tube defects. Lancet. 1996, 348 (9033): 1037-1038.View ArticlePubMedGoogle Scholar
  50. Parle-McDermott A, Mills JL, Kirke PN, O'Leary VB, Swanson DA, Pangilinan F, Conley M, Molloy AM, Cox C, Scott JM, et al: Analysis of the MTHFR 1298A– > C and 677 C– > T polymorphisms as risk factors for neural tube defects. J Hum Genet. 2003, 48 (4): 190-193. 10.1007/s10038-003-0008-4.View ArticlePubMedGoogle Scholar
  51. Pangilinan F, Geiler K, Dolle J, Troendle J, Swanson DA, Molloy AM, Sutton M, Conley M, Kirke PN, Scott JM, et al: Construction of a high resolution linkage disequilibrium map to evaluate common genetic variation in TP53 and neural tube defect risk in an Irish population. Am J Med Genet A. 2008, 146A (20): 2617-2625. 10.1002/ajmg.a.32504.View ArticlePubMedPubMed CentralGoogle Scholar
  52. Shields DC, Kirke PN, Mills JL, Ramsbottom D, Molloy AM, Burke H, Weir DG, Scott JM, Whitehead AS: The "thermolabile" variant of methylenetetrahydrofolate reductase and neural tube defects: An evaluation of genetic risk and the relative importance of the genotypes of the embryo and the mother. Am J Hum Genet. 1999, 64 (4): 1045-1055. 10.1086/302310.View ArticlePubMedPubMed CentralGoogle Scholar
  53. Whitehead AS, Gallagher P, Mills JL, Kirke PN, Burke H, Molloy AM, Weir DG, Shields DC, Scott JM: A genetic defect in 5,10 methylenetetrahydrofolate reductase in neural tube defects. QJM. 1995, 88 (11): 763-766.PubMedGoogle Scholar
  54. Kirke PN, Mills JL, Molloy AM, Brody LC, O'Leary VB, Daly L, Murray S, Conley M, Mayne PD, Smith O, et al: Impact of the MTHFR C677T polymorphism on risk of neural tube defects: case–control study. BMJ. 2004, 328 (7455): 1535-1536. 10.1136/bmj.38036.646030.EE.View ArticlePubMedPubMed CentralGoogle Scholar
  55. ,: The International HapMap Project. Nature. 2003, 426 (6968): 789-796. 10.1038/nature02168.Google Scholar
  56. Barrett JC, Fry B, Maller J, Daly MJ: Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005, 21 (2): 263-265. 10.1093/bioinformatics/bth457.View ArticlePubMedGoogle Scholar
  57. Skol AD, Scott LJ, Abecasis GR, Boehnke M: Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet. 2006, 38 (2): 209-213. 10.1038/ng1706.View ArticlePubMedGoogle Scholar
  58. Spielman RS, McGinnis RE, Ewens WJ: Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM). Am J Hum Genet. 1993, 52 (3): 506-516.PubMedPubMed CentralGoogle Scholar
  59. Troendle JF, Mills JL: Correction for multiplicity in genetic association studies of triads: the permutational TDT. Ann Hum Genet. 2011, 75 (2): 284-291.PubMedGoogle Scholar

    60. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2350/13/62/prepub

Copyright

© Pangilinan et al.; licensee BioMed Central Ltd. 2012

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.

Advertisement

BMC Medical Genetics

ISSN: 1471-2350

Contact us