A genetic signature of spina bifida risk from pathway-informed comprehensive gene-variant analysis.
ABSTRACT Despite compelling epidemiological evidence that folic acid supplements reduce the frequency of neural tube defects (NTDs) in newborns, common variant association studies with folate metabolism genes have failed to explain the majority of NTD risk. The contribution of rare alleles as well as genetic interactions within the folate pathway have not been extensively studied in the context of NTDs. Thus, we sequenced the exons in 31 folate-related genes in a 480-member NTD case-control population to identify the full spectrum of allelic variation and determine whether rare alleles or obvious genetic interactions within this pathway affect NTD risk. We constructed a pathway model, predetermined independent of the data, which grouped genes into coherent sets reflecting the distinct metabolic compartments in the folate/one-carbon pathway (purine synthesis, pyrimidine synthesis, and homocysteine recycling to methionine). By integrating multiple variants based on these groupings, we uncovered two provocative, complex genetic risk signatures. Interestingly, these signatures differed by race/ethnicity: a Hispanic risk profile pointed to alterations in purine biosynthesis, whereas that in non-Hispanic whites implicated homocysteine metabolism. In contrast, parallel analyses that focused on individual alleles, or individual genes, as the units by which to assign risk revealed no compelling associations. These results suggest that the ability to layer pathway relationships onto clinical variant data can be uniquely informative for identifying genetic risk as well as for generating mechanistic hypotheses. Furthermore, the identification of ethnic-specific risk signatures for spina bifida resonated with epidemiological data suggesting that the underlying pathogenesis may differ between Hispanic and non-Hispanic groups.
- SourceAvailable from: Allison E Ashley-Koch[show abstract] [hide abstract]
ABSTRACT: The worldwide incidence of neural tube defects (NTDs) ranges from 1.0 to 10.0 per 1,000 births with almost equal frequencies between two major categories: anencephaly and spina bifida (SB). Epidemiological studies have provided valuable insight for (a) researchers to identify nongenetic and genetic factors contributing to etiology, (b) public health officials to design and implement policies to prevent NTD pregnancies, and (c) individuals to take precautions to reduce the chance of having an NTD-affected pregnancy. Despite extensive research, our knowledge of the genetic etiology of human NTDs is limited. Although more than 200 small animal models with NTDs exist, most of these models do not replicate the human disease phenotype. Over a hundred candidate genes have been examined for risk association to human SB. The candidate genes studied include those important in folic acid metabolism, glucose metabolism, retinoid metabolism, and apoptosis. Many genes that regulate transcription in early embryogenesis and maintain planar cell polarity have also been tested as candidates. Additionally, genes identified through mouse models of NTDs have been explored as candidates. We do not know how many genes in the human genome may confer risk for NTDs in human. Less than 20% of the studied candidate genes have been determined to confer even a minor effect on risk association. Many studies have provided conflicting conclusions due to limitations in study design that potentially affect the power of statistical analysis. Future directions such as genomewide association studies (GWAS) and whole exome or even whole genome sequencing are discussed as possible avenues to identify genes that affect risk for human NTDs.Developmental Disabilities Research Reviews 01/2010; 16(1):6-15. · 2.79 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: More than 80 mutant mouse genes disrupt neurulation and allow an in-depth analysis of the underlying developmental mechanisms. Although many of the genetic mutants have been studied in only rudimentary detail, several molecular pathways can already be identified as crucial for normal neurulation. These include the planar cell-polarity pathway, which is required for the initiation of neural tube closure, and the sonic hedgehog signalling pathway that regulates neural plate bending. Mutant mice also offer an opportunity to unravel the mechanisms by which folic acid prevents neural tube defects, and to develop new therapies for folate-resistant defects.Nature Reviews Genetics 11/2003; 4(10):784-93. · 41.06 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: Almost 30 years after the initial study by Richard W. Smithells and coworkers, it is still unknown how maternal periconceptional folic acid supplementation prevents human neural tube defects (NTDs). In this article, questions about human NTD prevention are considered in relation to three groups of mouse models: NTD mutants that respond to folate, NTD mutants and strains that do not respond to folate, and mutants involving folate-pathway genes. Of the 200 mouse NTD mutants, only a few have been tested with folate; half respond and half do not. Among responsive mutants, folic acid supplementation reduces exencephaly and/or spina bifida aperta frequency in the Sp(2H), Sp, Cd, Cited2, Cart1, and Gcn5 mutants. Prevention ranges from 35 to 85%. The responsive Sp(2H) (Pax3) mutant has abnormal folate metabolism, but the responsive Cited2 mutant does not. Neither folic nor folinic acid reduces NTD frequency in Axd, Grhl3, Fkbp8, Map3k4, or Nog mutants or in the curly tail or SELH/Bc strains. Spina bifida frequency is reduced in Axd by methionine and in curly tail by inositol. Exencephaly frequency is reduced in SELH/Bc by an alternative commercial ration. Mutations in folate-pathway genes do not cause NTDs, except for 30% exencephaly in folate-treated Folr1. Among folate-pathway mutants, neural tube closure is normal in Cbs, Folr2, Mthfd1, Mthfd2, Mthfr, and Shmt1 mutants. Embryos die by midgestation in Folr1, Mtr, Mtrr, and RFC1 mutants. The mouse models point to genetic heterogeneity in the ability to respond to folic acid and also to heterogeneity in genetic cause of NTDs that can be prevented by folic acid.Birth Defects Research Part A Clinical and Molecular Teratology 01/2009; 85(4):331-9. · 2.27 Impact Factor
A Genetic Signature of Spina Bifida Risk from Pathway-
Informed Comprehensive Gene-Variant Analysis
Nicholas J. Marini1*, Thomas J. Hoffmann2, Edward J. Lammer3, Jill Hardin4, Katherine Lazaruk4, Jason B.
Stein4, Dennis A. Gilbert4, Crystal Wright5, Anna Lipzen5, Len A. Pennacchio5, Suzan L. Carmichael6,
John S. Witte2, Gary M. Shaw6, Jasper Rine1*
1Department of Molecular and Cellular Biology, California Institute for Quantitative Biosciences, University of California, Berkeley, California, United States of America,
2Department of Epidemiology and Biostatistics and Institute of Human Genetics, University of California San Francisco, San Francisco, California, United States of America,
3Children’s Hospital Oakland Research Institute, Oakland, California, United States of America, 4VitaPath Genetics, Inc., Foster City, California, United States of America,
5Department of Energy, Joint Genome Institute, Walnut Creek, California, United States of America, 6Department of Pediatrics, Stanford University School of Medicine,
Stanford, California, United States of America
Despite compelling epidemiological evidence that folic acid supplements reduce the frequency of neural tube defects
(NTDs) in newborns, common variant association studies with folate metabolism genes have failed to explain the majority of
NTD risk. The contribution of rare alleles as well as genetic interactions within the folate pathway have not been extensively
studied in the context of NTDs. Thus, we sequenced the exons in 31 folate-related genes in a 480-member NTD case-control
population to identify the full spectrum of allelic variation and determine whether rare alleles or obvious genetic
interactions within this pathway affect NTD risk. We constructed a pathway model, predetermined independent of the data,
which grouped genes into coherent sets reflecting the distinct metabolic compartments in the folate/one-carbon pathway
(purine synthesis, pyrimidine synthesis, and homocysteine recycling to methionine). By integrating multiple variants based
on these groupings, we uncovered two provocative, complex genetic risk signatures. Interestingly, these signatures differed
by race/ethnicity: a Hispanic risk profile pointed to alterations in purine biosynthesis, whereas that in non-Hispanic whites
implicated homocysteine metabolism. In contrast, parallel analyses that focused on individual alleles, or individual genes, as
the units by which to assign risk revealed no compelling associations. These results suggest that the ability to layer pathway
relationships onto clinical variant data can be uniquely informative for identifying genetic risk as well as for generating
mechanistic hypotheses. Furthermore, the identification of ethnic-specific risk signatures for spina bifida resonated with
epidemiological data suggesting that the underlying pathogenesis may differ between Hispanic and non-Hispanic groups.
Citation: Marini NJ, Hoffmann TJ, Lammer EJ, Hardin J, Lazaruk K, et al. (2011) A Genetic Signature of Spina Bifida Risk from Pathway-Informed Comprehensive
Gene-Variant Analysis. PLoS ONE 6(11): e28408. doi:10.1371/journal.pone.0028408
Editor: Osman El-Maarri, University of Bonn, Institut of Experimental Hematology and Transfusion Medicine, Germany
Received August 16, 2011; Accepted November 7, 2011; Published November 30, 2011
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for
any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Funding: This work was supported by grants R01 GM072859 and RC1 DE020640 from the National Institutes of Health (NJM, SLC, EJL, GMS, JR). Partial support
for SLC, EJL, and GMS was also provided by R01 NS05249. TJH was supported by National Institutes of Health Training Grant (R25 CA112355). Research was
conducted at the E.O. Lawrence Berkeley National Laboratory and performed under Department of Energy contract DE-AC02-05CH11231, University of California.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have read the journal’s policy and have the following conflicts. Nicholas J. Marini and Jasper Rine are founders of VitaPath
Genetics, and Jasper Rine is a member of its scientific advisory board. Edward J. Lammer and John S. Witte are consultants for VitaPath Genetics. Authors Hardin,
Lazaruk, Stein and Gilbert are employed by VitaPath Genetics, Inc. This does not alter the authors’ adherence to all the PLoS ONE policies on sharing data and
* E-mail: email@example.com (NJM); firstname.lastname@example.org (JR)
Neural tube defects (NTDs) are common, costly, and deadly
human congenital anomalies whose causes remain largely
unknown. The birth prevalence of NTDs varies from approx-
imately 0.8/1,000 births in most areas of the US to 3.5/1,000 in
Mexico . Anencephaly and spina bifida are the most common
forms of NTDs and result from failure of the neural tube to close
properly in the developing brain or lower spine, respectively.
Infants with anencephaly are stillborn or die shortly after birth,
whereas many infants with spina bifida survive, but typically have
severe, life-long disabilities.
Over 20 years of clinical investigation and studies with mouse
NTD models indicate that these disorders arise from a
combination of factors including complex genetic and gene-
environment interactions [2,3]. The most promising clue to the
etiologies of NTDs, however, is that women who use vitamins
containing folic acid periconceptionally (prior to and early in
pregnancy) are at reduced risk for NTD-affected pregnancies
[4,5]. In addition, maternal use of anticonvulsants or other folic
acid antagonist medications increases the occurrence of NTDs in
offspring . Taken together, these observations suggest that folic
acid supplementation prevents NTDs by compensating for
susceptibilities in folate transport, metabolism, or utilization.
However, the underlying mechanisms by which folic acid
contributes to these reduced risks are still unknown. Also unknown
is why some women who take folic acid supplements in the
periconceptional period still have offspring with NTDs.
The folate metabolic pathway plays critical roles in processes
ranging from nucleotide biosynthesis, needed for cell proliferation,
PLoS ONE | www.plosone.org1 November 2011 | Volume 6 | Issue 11 | e28408
to generation of pterin cofactors impacting biochemical reactions,
to generation of the principal methyl donor, S-adenosyl methio-
nine (AdoMet), needed for methylation of DNA, proteins and
lipids [7,8]. Alterations in any of these processes may lead to
folate-related pathologies. For example, decreased thymidylate
synthesis results in increased uracil misincorporation into DNA
and genomic instability . Decreased AdoMet synthesis alters
DNA and histone methylation, which can affect gene expression
Because of this, multiple studies have explored possible
associations between common single nucleotide polymorphisms
(SNPs) in folate pathway genes and risk of NTDs . Many of
the known pathway SNPs have been evaluated, yet the results
have shown either no or little association and many of the
associations have not been consistently observed across studies.
For example, as of 2009 there were 32 published studies of the
association between the common 677CRT (A222V) variant of
MTHFR and NTDs across many populations [1,10]; half of these
studies concluded that the 677T allele increased risk (usually when
homozygous) whereas half found no statistically significant
associations. A recent meta-analysis found association only in
non-Latin populations, principally the Irish .
Other approaches have focused on identifying mouse genes
that, when mutated, result in NTDs, hoping that human orthologs
of such genes would be good candidates to harbor mutations that
contribute to human NTDs. The potential complexity of NTD
genetics is underscored by the more than 150 mouse genes
implicated in NTDs which, for the most part, do not overlap with
the folate metabolic pathway [3,12]. Instead, these genes are
centered around signaling pathways in development (such as non-
canonical WNT), involved in cell morphology and differentiation
[3,12,13]. Many of the mouse NTD models do not respond to folic
acid supplementation , so it is unclear how well these models
mimic human NTDs. Moreover, mouse studies tend to focus on
null alleles, which could result in early prenatal lethality in
humans. In any event, human homologs of some mouse NTD
genes have been examined in association studies or directly
sequenced in mutation screens, with few significant findings to
date (with the possible exception of a functionally impaired
mutation in VANGL1 in one NTD patient;). However, a
recent study with mice harboring mutations in the folate-related
gene SHMT1 offer a breakthrough, establishing a folate-remedial
NTD phenotype that interacts with NTD-disposing mutations in
Pax3 (Pax3sp), a transcription factor involved in cell differentiation
Thus, the multitude of genetic studies indicates that identifying
specific NTD risk alleles has proven far from straightforward. The
inconsistent results between different cohorts and populations for
many common SNPs indicate that few, if any, of these SNPs have
a major effect. These studies are complicated by several factors
including: 1) the intricate interplay and cross-regulation between
components of folate metabolism, 2) the potential number of genes
participating in neurulation, and 3) the potential heterogeneity of
the underlying mutation spectrum. To better unravel NTD
genetics, it may be essential to evaluate multiple genes in the
same individual to detect possible synergistic effects of combina-
tions of risk alleles that, individually, would not be statistically or
biologically significant . For example, there are several
examples in human  and mouse  suggesting that multiple
genetic interactions underlie genetic susceptibilities that create
NTD risk. Moreover, it may be important to consider gene
variants in the context of metabolic pathway function, and how
combinations of alleles impact pathway outputs. In addition,
expanding consideration to rare or private mutations may be more
effective than the historic focus on known, common polymor-
phisms as etiological determinants. Indeed, there is growing
appreciation that common variants do not account for most of the
heritability of many common diseases [17,18].
This study used this more comprehensive SNP discovery and
analysis approach. We sequenced the exons of 31 genes encoding
enzymes central to folate metabolism in a population-based case-
control study (N=480). Our goal was to identify the full spectrum
of allelic variation in folate pathway genes and determine whether
rare alleles, combinations of alleles, or obvious genetic interactions
within this pathway conferred NTD risk, specifically spina bifida.
We found that analytical approaches that focused on individual
alleles, or individual genes, as the units by which to assign risk did
not show convincing disease associations. However, analyses based
on simple pathway modeling that allowed us to infer metabolic
consequences from groups of variants, and subsequently draw
associations between the inferred metabolic impact and the NTD
phenotype, revealed significant case-control differences. Further-
more, such ‘‘pathway level analysis’’ has indicated that the genetic
contribution from folate pathway variation is both heterogeneous
and mechanistically distinct in different races/ethnicities.
Folate Pathway Sequencing in NTD Cases and Controls
We sequenced the coding regions of 31 genes in the folate-
homocysteine metabolic pathway, comprising 430 coding exons,
in 239 newborns with spina bifida and 241 non-malformed
controls. We focused on coding regions because mutations in these
regions are more likely to have functional impact, yet exhibit
folate-remedial enzyme deficiencies , a characteristic that is
consistent with and may underlie the folate-responsiveness of the
402 of the 430 target exons (.93%) gave robust sequence data
with an average sample coverage of 90.6%. Of the 28 failed exons,
11 were from FOLH1, which has a nearby pseudogene. The
number of variants by category for the 1,441 variants identified is
summarized in Table 1. The exon resequencing strategy also
produced a considerable amount of flanking non-coding sequence
variation, which has proven quite informative (see below). The
allelic variant calls were validated from a second, independent
WGA of the bloodspot genomic DNA in two ways: 1) targeted re-
sequencing confirmed over 90% of the original singleton calls (82
of 90 tested); 5 of the 8 that did not validate were either minor
allele homozygotes or indels within homopolymeric tracts, and
thus were already flagged; 2) Genotypes for 270 variants were
determined in all 480 samples by TaqMan allelic discrimination
assays and showed 99.8% concordance with the sequencing
The complete list of 1,441 variants is shown in Dataset S1 along
with allele frequencies and case/control counts. We calculated
univariate P-values for all variants seen multiple times (adjusted for
race/ethnicity; see Methods). There were only 18 alleles that
differed in frequency between cases and controls that had a P-
value,0.05, and several of these were tightly linked to each other
(Dataset S1). Only one of these 18 SNPs had been previously
tested in another study (ATIC_14329/rs2372536;) and in that
study no association was found. Although these SNPs have
suggestive P-values, it should be noted that because of the
relatively large number of variants interrogated, even a liberal
multiple comparison correction would have pushed all SNPs
below a cutoff deemed significant. Thus, until these data are
Folate Pathway Variants and Spina Bifida Risk
PLoS ONE | www.plosone.org2 November 2011 | Volume 6 | Issue 11 | e28408
replicated, it is difficult to draw conclusions from such equivocal
associations of individual variants. However, in this study we
aimed to move beyond traditional univariate analysis, which has
proven inconsistent in NTD genetics, by evaluating the potential
role of the complete ensemble of gene variants in the folate
pathway. Specifically, we investigated in parallel whether the
aggregate burden of variants (particularly rare alleles) was
associated with the NTD phenotype and whether there was
evidence for folate pathway component interactions.
Minor Allele Summing: All Genes/Individual Genes
We speculated that folate/one-carbon pathway function might
be particularly susceptible to the aggregate mutation burden
throughout the pathway, since there are many co-dependent and
interacting components. In this way, the contribution of any one
risk allele (or possibly even one gene) to pathway function might
not be very significant in isolation, and thus explain the lack of
heritability found in many common SNP-NTD association studies
. This limitation might be especially true for rare variants
which are difficult to statistically analyze individually, but may be
relevant when considered in aggregate [17,21].
We performed various minor allele summing/collapsing
analyses in which we simply tallied the number of minor alleles
(counting 1 for a heterozygote and 2 for a homozygote) in the 31
sequenced genes as a straightforward measure of mutation burden
in the pathway. However, such analysis for nonsynonymous SNPs
did not reveal statistically significant differences between cases and
controls (Figure 1). This lack of a signal was true whether we
considered only common variants (defined here as MAF $ 2.5%;
Figure 1A), or only rare variants (MAF,2.5%; Figure 1B). The
significance levels (permuted P-values) for these and other case-
control comparisons using different variant subsets are described
in Table 2. Also shown are comparisons for the two most frequent
race/ethnicities: Hispanics and non-Hispanic whites (65% and
21% of the samples, respectively). We found it more informative to
perform these analyses following race-ethnic stratification which
enabled us to query whether any NTD-associated genetic
signatures were common to both groups. All comparisons failed
to reveal a statistically significant difference (permuted P-
value#0.05, not adjusted for multiple comparisons) irrespective
of the variant subset considered (including variants not shown such
as synonymous changes). Testing these distributions via Kolmo-
gorov-Smirnov goodness-of-fit (rather than means) did not change
these results . Thus, the hypothesis that a cumulative mutation
burden across the entire pathway was responsible for NTD risk
was not supported by the data when all genes were aggregated and
weighted equally (Table 2).
To determine whether there might exist subsets of this 31-gene
set that may harbor more significant case-control differences, we
then used these same variant collapsing approaches to look at the
contribution of individual genes, rather than the entire group, to
the NTD phenotype. Again, the collection of common and/or
rare nonsynonymous SNPs similarly analyzed in each gene did not
convincingly distinguish cases from controls for most genes
(Figure 2). The most suggestive result came from collapsing
common nonsynonymous SNPs in MTHFD1 in the Hispanic sub-
population (permuted P-value=0.04, not adjusted for multiple
comparisons). Interestingly, a common nonsynonymous SNP in
MTHFD1 (R653Q) has been previously identified as a NTD risk
factor in Irish and Italian populations [23,24]. Changing the MAF
cut-off used to differentiate common and rare alleles to 1% or 5%
did not materially change the results (data not shown), nor did the
use of more complex analytical methods that weight variants
[25,26] or group variants by statistical criteria . Furthermore,
the case-control distributions for other variant subsets (e.g. non-
coding) displayed no significant differences for individual genes.
Thus, this gene-level analysis did not strongly indict any of these
genes in NTD risk.
Minor Allele Summing: Pathway Model
Many proteins play important roles in folate metabolism and
the pathway is characterized by multiple interconnected cycles
Table 1. Numbers of Variants Indentified by Category.
IdentifiedMAF# #2.5% Singletons
Insertion/Deletion 53 4129
Figure 1. Total Nonsynonymous Changes in NTD Cases and
Controls. A. All common nonsynonymous alleles (MAF.2.5%)
identified from the 31 folate pathway genes sequenced were summed
in each individual and the case/control distributions of these sums are
shown. Minor allele homozygotes were counted as two alleles whereas
heterozygotes were counted as one. B. Case/control distributions of
rare (MAF#2.5%) nonsynonymous allele sums. Population means and
permuted P-values are shown in Table 2.
Folate Pathway Variants and Spina Bifida Risk
PLoS ONE | www.plosone.org3 November 2011 | Volume 6 | Issue 11 | e28408
with multiple metabolic branch points and feedback regulatory
mechanisms . At the outset of the study, we therefore reasoned
that simply measuring the pathway-wide accumulation of
mutations, or considering genes or alleles in isolation, might not
adequately take into account this interplay. Indeed, mutations in
certain genes might be expected to be deleterious to one set of
reactions, but advantageous to others, because that enzyme
competes for substrates with other enzymes, or drives a reaction
in an opposite direction. If the NTD phenotype were affected by
pathway function, then a more relevant measure for disease risk
would be metabolic output, which would be due to the integrated
effect of numerous variants across the pathway.
To this end, we developed a simple model to investigate the
integrated effect of multiple genetic changes on three key
metabolites relevant to folate metabolism: thymidylate synthesis,
purine synthesis, and homocysteine recycling. In this model, we
incorporated mostly genetic and cell-based observational studies
from the literature to derive relationships between enzymes and
their effects on pathway flux for the various metabolites. With this
information, we identified subsets of the pathway relevant to each
metabolite and further inferred whether gene function would
contribute positively or negatively to that process. We then signed
each variant allele in each gene accordingly, with a ‘‘+’’ or ‘‘2’’, to
reflect its potential impact. In this method, for example, minor
allele homozygotes in a gene whose function is beneficial to a
process are assigned +2, whereas heterozygotes in a gene whose
function may compete with the process are assigned 21. The
resulting sum of alleles in a particular metabolic compartment was
a single number (referred to as the Metabolic Index Score or MIS)
that reflected the mutational load on that process in that
individual. It should be emphasized that these designations were
all made prior to data analysis and, thus, were not biased by any
Table 2. Mean Number of Minor Alleles from 31 Folate Pathway Genes in Cases vs. Controls for Different Variant Subsets.
All116.8613.2 119.1615.30.1 115.3612.9117.7614.1 0.12125.7610.8125.5615.4 0.95
Nonsynonymous 1162.8 11.363 0.2 1162.6 11.563 0.12 12.263.212.562.90.65
Non-Coding 91.261193.4613.2 0.06 90.1610.5 92.3612.1 0.13 95.8610.297612.9 0.62
All 7.8 (1–54) 7.8 (0–42)0.54 6.1 (0–20)6.1 (0–22)0.82 5 (0–34)4.4 (0–13)0.73
Nonsynonymous1 (0–9) 1 (0–6)0.96 0.9 (0–5)0.9 (0–5)0.93 0.7 (0–4)0.5 (0–2)0.6
Non-Coding 6.2 (0–43)6.2 (0–38) 0.474.7 (0–18) 4.7 (0–19)0.81 3.8 (0–29)3.5 (0–11) 0.48
aCommon alleles are defined as MAF$2.5% within the specific race-ethnic group analyzed, whereas rare alleles are MAF,2.5%. Means are shown6standard deviation
for common variant analyses where the distributions were near normal. For rare variants, the distributions were tailed (see Figure 1B) and the range of values is
provided in parentheses. Permuted P-values were calculated as in Materials and Methods, and were not adjusted for multiple comparisons.
Figure 2. Allele Sums by Individual Gene. Common and rare nonsynonymous changes were summed as in Figure 1 except here on a gene-by-
gene basis rather than considering the pathway as a whole. –log (P-values) were calculated by permutation (but not adjusted for multiple
comparisons) based on the case/control distributions within each gene in Hispanics and non-Hispanic whites. On the y-axis, 1.3 corresponds to P-
Folate Pathway Variants and Spina Bifida Risk
PLoS ONE | www.plosone.org4 November 2011 | Volume 6 | Issue 11 | e28408
observed trends in the variant data. Analysis based on pathway
relationships was carried out independently and in parallel with
the allele- and gene-level analyses described above.
This was a relatively simple model because folate levels were not
explicitly accounted for. In addition, each gene (whether positive
or negative), and each mutation within a gene, were given
identical weight. This strategy was in contrast with previously
described, more mathematically-intensive models that take into
account enzyme-specific parameters and metabolite levels [28–
30]. Nevertheless, there was a good deal of qualitative agreement
between these two methods of designation.
As an example, the subset of folate pathway genes that we
defined as particularly relevant for purine synthesis and their
inferred relationships governing metabolite flow for this process
are illustrated in Figure 3. Our inferences were based on the
following considerations. In general, the conversion between
tetrahydrofolate (THF) and 5,10 methylene-THF (CH2-THF)
operates in the oxidative direction in the mitochondria (CH2-THF
R THF via MTHFD2 and MTHFD1L reactions), but in the
reductive direction in the cytoplasm via MTHFD1 [31,32].
Though MTHFD1 is a tri-functional enzyme in this conversion,
only the formyl-THF synthase activity is shown in Figure 3, which
utilizes mitochondrially-produced formate (CHOOH) in the
synthesis of 10-formyl-THF (10f-THF) in the cytoplasm. This
intermediate is an essential carbon donor at two distinct steps in
purine biosynthesis (catalyzed by ATIC and GART), thus
MTHFD1 inactivation is lethal in mice and results in purine
auxotrophy in cultured cells [9,33].
Under most conditions, the majority of one-carbon units for
cytoplasmic production of purines and methionine are derived
from mitochondrial formate, which is produced by the formyl-
THF-dehydrogenase MTHFD1L [9,31]. Thus all reactions that
feed into the MTHFD1L reaction (MTHFD2, SHMT2, AMT,
SARDH, DMGDH) were considered beneficial for purine
synthesis, whereas reactions that may compete with MTHFD1L
in formate generation (MtFMT, ALDH1L2) were considered
Similarly, cytoplasmic ALDH1L1 was considered deleterious to
purine synthesis because it would compete with GART/ATIC for
10f-THF. Although high levels of ALDH1L1 do not appear to
specifically deplete 10f-THF, there is a general depletion of
cellular folates (especially 5-methyl-THF) indicating perturbations
to flux in the reductive direction . SHMT1 has been
designated as negatively affecting flux to purines because mice
with decreased MTHFD1 activity show enhanced de novo
thymidylate synthesis, suggesting that SHMT1 and MTHFD1
compete for a limiting pool of THF in the cytoplasm .
We have also designated coherent, signed gene sets for
thymidylate synthesis and homocysteine levels (Table S3) in a
similar way. Homocysteine-related genes overlap the purine group
(but contain more genes) because they were designated by much of
the same reasoning. For example, several studies link mitochon-
drial formate to the remethylation of homocysteine as it enters the
methyl cycle [9,31,35]. The additional genes in the homocysteine
group are mostly methionine cycle genes (BHMT,BHMT2,
MTHFR,MTR,MTRR) and trans-sulfuration pathway genes
(CBS,CTH) whose collective activity can drive homocysteine
utilization into either of these processes (and thus negatively
impact homocysteine levels). MAT1A and MAT2A (which
synthesize AdoMet directly from methionine) are excluded from
the homocysteine gene set since AdoMet levels do not have a
major effect on homocysteine levels, though AdoMet can influence
whether homocysteine is metabolized via the methionine cycle or
trans-sulfuration (through allosteric inhibition of MTHFR and
activation of CBS;). For most genes present in both the purine
and homocysteine gene sets, the signing to derive the Metabolic
Index Score is opposite because a reduced pathway flux would
have a negative effect on purine synthesis, but a positive effect on
homocysteine levels (less homocysteine would be utilized for
For thymidylate, our relevant gene set was centered around the
module consisting of SHMT1-TYMS-DHFR because of several
lines of compelling evidence: First, these 3 enzymes exhibit
regulated translocation from the cytoplasm into the nucleus during
S-phase to compartmentalize dTMP synthesis [37,38]. Second,
SHMT1 loss results in decreased thymidylate synthesis ,
whereas MTHFD1 defects may increase thymidylate synthesis by
allowing more THF into the SHMT1 reaction . In addition, we
factored in the observation that MTHFR can compete with
TYMS for CH2-THF and thus have a negative impact on dTMP
synthesis , whereas the downstream enzymes in the methio-
nine cycle responsible for AdoMet production (MTR,MTRR,MA-
T1A,MAT2A) might have a positive effect on dTMP synthesis due
to the AdoMet-mediated inhibition of MTHFR. This integrated
approach, which accounts for relevant pathway interactions,
revealed statistically significant NTD-associated signals following
stratification of the study subjects by race/ethnicity (Figure 4). For
each pathway compartment, we drew case-control comparisons of
Figure 3. Purine Metabolic Cassette. The subset of reactions within the folate metabolic pathway that have been inferred to be relevant to
purine biosynthesis based on observations in the literature (see text for details). Genes (balloons) and their cognate enzymatic steps (arrows) are
colored either green (beneficial to purine synthesis) or red (detrimental to purine synthesis). The MTHFD1L reaction, responsible for generating
mitochondrial formate  and beneficial to purine synthesis, is shown in blue because it was not one of the 31 sequenced genes.
Folate Pathway Variants and Spina Bifida Risk
PLoS ONE | www.plosone.org5 November 2011 | Volume 6 | Issue 11 | e28408
the MIS using 4 different variant subsets, signed and summed as
described above. For the Hispanic sub-population, a strong
discriminatory signal emanated from common, nonsynonymous
SNPs (15 variants; unadjusted P-value=0.0009) in genes binned
as relevant to purine synthesis. For non-Hispanic whites, this signal
was absent, but a suggestive signal unique to this group
(unadjusted P-value=.008) was derived from the collection of
rare, non-coding variants (n=195) in homocysteine-related genes.
Adjusting the P-values for multiple comparisons (most of which are
shown in Table 2 and Figure 4) by max(T) permutation 
resulted in a P-value of 0.024 for the purine-related signal in
Hispanics and a P-value of 0.13 for the homocysteine-related
signal in non-Hispanic whites. These results suggested that these
metabolic processes may be altered in NTD cases and,
intriguingly, that the underlying mechanisms of NTD risk may
be different in these two groups.
Hispanic Signature in Purine-Related Genes
The folate-mediated enzymatic steps necessary for purine
synthesis occur in both the cytoplasm and mitochondria
(Figure 3). To determine if the metabolic contribution from both
compartments was represented in the Hispanic signature, we
subsequently analyzed the subsets of purine-related genes specific
to each compartment. This analysis clearly indicated that the
signal principally derived from 4 genes encoding cytoplasmic
enzymes: ALDH1L1, MTHFD1, ATIC, and GART (Figure 5).
These genes contained a total of 9 common, nonsynonymous
SNPs. Interestingly, some of this signal was also hinted at from
individual gene analysis (Figure 2), since these 4 genes displayed
the greatest case-control differences in Hispanics, though only
MTHFD1 exhibited a P-value,0.05. Significantly, these 4 gene
products converge on cytoplasmic 10f-THF, a metabolite critical
for purine synthesis.
Comparing those high in the purine MIS derived from these 9
variants to those low in the score was highly associated with case
status (P-value=0.00005; P-value=0.001 when adjusted for
multiple comparisons; Figure 5A). Indeed, there was a strong
correlation between the MIS and the probability of case status
(Figure 5B). The odds ratio based on the Hispanic control
population median (MIS=2) was 0.26 (95% CI 0.15 – 0.47),
whereas an odds ratio calculated on only the 17% of the
population at the extremes of the distributions (Metabolic Index
Scores#21 or $ 4; n=44) was 0.09 (95% CI 0.02 – 0.41). This is
an unusually large magnitude of effect compared to previous
reports for NTD association studies. The odds ratios are less than
1 because this risk profile is one where the mutation load, as
evidenced by the Metabolic Index Score, confers a reduced risk of
case status. The implications of a lower MIS in cases is discussed
The 9 SNPs in this signature and their attributes are listed in
Figure 5C. It is worth noting that the combination of genotypes
from this group was very heterogeneous in the population. For
example, in 173 Hispanic cases, there were 110 distinct genotype
combinations at these 9 sites. Furthermore, only one of these 9
SNPs was individually mildly suggestive with respect to NTD
association in this Hispanic population (GART V42I, P-
value=0.028; Figure 5C). Thus, it appeared that rationally
integrating this set of variants in a biological context was required
to reveal a statistically significant trend. For example, note that all
nonsynonymous variants in ALDH1L1, which was designated as
deleterious to purine synthesis (signed ‘‘-’’), were over-represented
in cases (ORs . 1), whereas the variants in genes designated as
beneficial to purine synthesis were over-represented in controls
(ORs,1). The allele frequencies for these variants in the Hispanic
controls were in good agreement with HapMap 3 data for
individuals with Mexican ancestry. Thus our population was not
skewed in this respect from the frequencies expected from this
Though these associations have not yet been formally validated
in a second population, the signal is quite strong in Hispanics and
was present in all subsets of the Hispanic population tested. For
example, case-control differences in the 4-gene purine MIS were
significant in children from Hispanic mothers born outside the
United States (n=224; unadjusted P-value=0.0005) and sugges-
tive in children from U.S.–born Hispanics (n=87; unadjusted P-
value=0.01). In addition, significant differences were seen in
Hispanic samples collected from 1984 to 1986, prior to mandatory
folic acid fortification of grain products (; n=183; unadjusted
P-value=0.002) as well as in Hispanic samples collected post-
fortification (1999-2003; n=128; unadjusted P-value=0.009).
Non-Hispanic White Signature in Homocysteine-Related
From our pathway-level analysis, a homocysteine metabolism
genetic signature that significantly distinguished cases from
controls was observed only in the non-Hispanic, white population
(Figure 4), suggesting that homocysteine recycling may be
implicated in NTD risk for this group, a link that has been drawn
previously [42,43]. Unlike the purine synthesis signature, this
homocysteine signal emanated from a large number of rare, non-
coding variants signed and summed as described above. Upon
further dissection of this signal, we found that, similar to the purine
signature above, genes encoding mitochondrial enzymes did not
appreciably contribute to the case-control differences. A Metabolic
Index Score comparison built only on the 15 cytoplasmic genes
Figure 4. Inferred Metabolic Impact in NTD Cases and Controls.
Metabolic Index Scores were calculated for 3 key folate-regulated
metabolites (Purines, Thymidylate, and Homocysteine) and differences
in the case/control distributions are represented by –log(P-values).
Index Scores were derived from alleles summed according to pathway
designations (gene sets and directionality; see Figure 3 and Table S3)
and P-values following permutation were calculated as described in
Methods. The P-values shown are not adjusted for multiple comparison
testing. For each metabolite, 4 different subsets of variants were
considered to derive Index Scores and are indicated by the following
abbreviations on the x-axis: C, common alleles (MAF.2.5%); R, rare
alleles (MAF#2.5%); Ns, nonsynonymous variants; NC, non-coding
variants. Results are shown for Hispanic and non-Hispanic white subsets
of the study population.
Folate Pathway Variants and Spina Bifida Risk
PLoS ONE | www.plosone.org6 November 2011 | Volume 6 | Issue 11 | e28408
(which harbor 149 rare, non-coding variants) designated as
relevant to homocysteine metabolism (see Table S3) is shown in
Figure 6. The signal was suggestive of an association (P-
value=0.004; P-value=0.076 when adjusted for multiple com-
parisons) and was the most noteworthy trend seen in the non-
Hispanic white sub-population. The odds ratio comparing
individuals with MIS below 0 to those above was 3.4 (95% CI
1.2 – 9.8).
This report described a large and comprehensive variant
discovery effort that identified the full spectrum of allelic variation
in nearly the entire folate metabolic pathway in spina bifida cases
and non-malformed controls to evaluate the contributions of rare
variants and pathway interactions to disease risk. We interpreted
the resulting variant data at several levels from traditional single-
allele associations, to rare-variant summing methods, to defining
relevant genetic interactions based on folate pathway relationships
to infer metabolic consequences. As described above, all analyses
were performed independently in parallel following completion of
the variant dataset.
Analytical approaches that focused on individual alleles, or
individual genes, as the units by which to assign risk did not show
convincing disease associations. However, analyses that accounted
for pathway function through a simple model revealed two
provocative, complex genetic signatures that showed compelling
statistical evidence for distinguishing cases from controls. In one, a
genetic signature that strikingly differentiated between Hispanic
cases and controls derived from the combined effect of folate
pathway genes related to purine biosynthesis, whereas a risk
signature in non-Hispanic whites emanated from genes related to
These genetic risk profiles appeared to be ethnically specific,
indicating the underlying disease mechanisms may also be
different. These findings, which emphasized genetic heterogeneity
and interactions among multiple genes, may explain why results
from previous association studies have been inconsistent. In
addition, these findings may provide some mechanistic insights
Figure 5. Nonsynonymous Variants in the Hispanic Signature. A. Hispanic case/control distributions of purine Metabolic Index Scores
calculated from common synonymous variants (n=9) in 4 genes (ALDH1L1, ATIC, GART, MTHFD1). The P-value shown is not adjusted for multiple
comparisons (see text). B. The data in panel A transformed to demonstrate the fraction of cases as a function of purine Metabolic Index Score. C.
Attributes of the 9 SNPs that went into the purine MIS calculations of panel A. ‘‘Inferred Effect on Purine Synthesis’’ is the inferred effect of that
particular gene product on purine biosynthesis (see text for details). Case and control counts are given as individual genotype distributions: # minor
allele homozygotes/# heterozygotes/# major allele homozygotes). Log additive P-values and odds ratios were calculated as in Methods using only
Hispanic samples and not multiple-testing adjusted. *Minor Q653 MTHFD1 enzyme variant is the major allele in Mexican-American populations
Figure 6. Homocysteine-Related Signature in Non-Hispanic
Whites. Non-Hispanic white case/control distributions of homocyste-
ine Metabolic Index Scores calculated from rare, non-coding variants
(n=149) in 15 homocysteine-related genes, signed and summed
according to Table S3. See text for details.
Folate Pathway Variants and Spina Bifida Risk
PLoS ONE | www.plosone.org7 November 2011 | Volume 6 | Issue 11 | e28408
into the well-described (but poorly understood) differences in NTD
links with nutritional status, genetic susceptibilities and Hispanic
Purine Synthesis and NTDs
Evidence implicating purine synthesis in NTD risk has come
from the repeated identification of a common, nonsynonymous
risk allele in MTHFD1 (1958 GRA; R653Q) [23,24]. This amino
acid substitution mildly impairs enzyme thermostability in vitro,
but markedly impairs de novo purine synthesis in cells . This
variant was not an independent risk factor in our study, but was
part of the Hispanic purine signature. Interestingly, the Q variant
is the minor allele in all race-ethnic groups tested except those of
Mexican ancestry (http://hapmap.ncbi.nlm.nih.gov/; Figure 5C),
in which the NTD risk is significantly greater than in non-Hispanic
whites or African Americans [45,46]. Furthermore, in one mouse
model of NTDs, homozygous Splotch mutant (Pax3sp/sp) embryos
exhibit fully penetrant, yet folate-remedial, spina bifida .
Metabolic experiments with embryonic fibroblasts from Pax3
mutant mice suggest that the major defect is de novo purine
In developing embryos, the concerted efforts of MTHFD2 and
MTHFD1L (to generate formate in the mitochondria) and
MTHFD1 (to incorporate formate into 10f-THF) are essential
for purine synthesis [31,32]. This dependence on de novo purine
synthesis is apparently restricted to developing embryos, since
adult mitochondria lack MTHFD2 and MTHFD1L activity and
produce significantly less formate than embryonic mitochondria
. Interestingly, MTHFD2, MTHFD1L and MTHFD1 share a
similar spatial pattern of expression in developing mouse embryos
with the highest levels of expression in the developing brain,
craniofacial structures, limbs/digits, neural tube, and tail bud,
which are all undergoing high levels of cell division . While no
variants in MTHFD2 have been associated with NTD risk, a
common, short-repeat variant in MTHFD1L that affects mRNA
splicing has been associated with risk in an Irish population .
Homocysteine Metabolism and NTDs
A homocysteine metabolism genetic signature that significantly
distinguished cases from controls was observed only in non-
Hispanic whites (Figures 4,6]. This risk signature emanated from
variants in non-coding regions, suggesting that effects on gene
expression underlie this signal. This finding resonates with earlier
data in which homocysteine was already implicated as a risk factor
for NTDs [42,43], presumably due to the relationship between
elevated homocysteine levels and perturbations of the methylation
cycle [49–52]. Furthermore, this signature was found in the racial
group for which the strongest evidence of its contribution to risk
exists (see above). It should be noted, however, that in most
association studies homocysteine levels are typically assessed in
mothers after delivery, whereas the genetic signature described
here is fetal. Whether this is a function of transmission of maternal
genetic defects (in which case the signature would presumably be
stronger in mothers) or indicates fetal metabolic deficiencies is
unknown. In either case, this complex signature was identified
from a relatively small white population (44 cases/56 controls)
and, although statistically meaningful, needs to be replicated in a
It should be emphasized that we surveyed only the non-coding
regions that were immediately adjacent to the target exons of these
genes and, therefore, represented only a small fraction of the non-
coding DNA within these genes. Whether we increased our
chances for finding meaningful variants by focusing on regions
close to exons is unknown, but this finding warrants further
exploration into non-coding DNA. Non-coding mutations have
also been implicated in murine models of NTDs, such as the ct/ct
The Metabolic Index Score
The MIS was a simple metric designed to incorporate
biologically relevant interactions among alleles to infer a
physiological outcome. Relationships were designated based
mostly on phenotypes from genetic and cell-based studies and
thus it is a somewhat qualitative tool. Nevertheless, there was good
agreement between relevant gene sets defined here and those from
a more mathematical model of folate metabolism that is based on
intracellular folate concentrations, enzymatic kinetics, allosteric
inhibition, and known polymorphism-biomarker relationships
One aspect of the Metabolic Index Scores reported here merits
further discussion. Since the MIS is indicative of the mutational
burden expected to have a negative impact on a given process, it
was somewhat surprising that the Hispanic cases are set apart from
controls in the purine synthesis signature because cases have a
lower average score. Thus, the inferred metabolic impact would be
that of increased purine synthesis in cases relative to controls. A
similarly surprising inference was drawn from the homocysteine
risk signature in non-Hispanic whites. In this population, cases
have a higher average MIS (reflecting the mutational burden on
homocysteine abundance; Figure 6), suggesting a potentially lower
level of homocysteine than in controls. However, one might have
expected the opposite, given the previously observed relationship
between elevated homocysteine and NTD risk (see above). Thus,
in both instances, the MIS suggested an unexpected metabolic
Metabolic inferences from these observations should be drawn
with caution for several reasons: In a pathway with many
feedback regulatory mechanisms, response to certain perturba-
tions can have unexpected consequences. In other words, a
reduction (or gain) of activity at certain pathway reactions may
result in a metabolic profile that was not predicted by that
change. For example, overexpression of ALDH1L1 (which
converts 10f-THF to THF by deformylation) results in a higher
ratio of 10f-THF:THF when the opposite would be expected
. Furthermore, we do not yet know the functional impact for
most variants and which, if any, alleles may be gain-of-function
variants. Although, the MTHFD1 R653Q variant has a slight
thermostability defect , other missense changes in the purine
set have not been empirically tested. The functional impact of
non-coding changes is more difficult to assess. Moreover, we do
not yet know whether alleles may exhibit stronger phenotypes in
combination. Because of these caveats, there may not be a
straightforward relationship between a higher Metabolic Index
Score and reduced metabolic flux.
Alternatively, there are examples in which major alleles can be
risk alleles, while minor alleles may be protective. For example, in
individuals with nonsyndromic orofacial clefts, the major allele for
common polymorphisms in IRF6 and FOXE1 are over-represented
[53–55]. Likewise in MTHFD1L, the major non-coding allele in
Northern European populations (which is thought to retain correct
mRNA splicing) may increase risk for NTDs in the Irish, whereas
a minor defective allele may reduce risk . These observations
may indicate that some metabolic ‘‘inefficiencies’’ are beneficial.
For example, purine synthesis may compete with homocysteine
remethylation for one-carbon units [31,34]. Therefore, an increase
in flux into the purine compartment (which may be inferred from
the lower MIS in Hispanic cases) may compromise homocysteine
remethylation and, consequently DNA methylation.
Folate Pathway Variants and Spina Bifida Risk
PLoS ONE | www.plosone.org8 November 2011 | Volume 6 | Issue 11 | e28408
Ultimately, the mechanisms behind this or any disease-risk
genetic signature will have to be addressed empirically by direct
metabolic measurements rather than relying solely on inferences
and models. The approach described here, whereby biological
principles are employed to focus on relevant allelic interactions,
can catalyze specific tests of the impact of risk signatures on
measurable metabolites and, thus, provides multiple avenues for
supporting or rejecting mechanistic hypotheses.
Is it Surprising that NTD Genetics may Differ between
Hispanics and non-Hispanics?
A race-ethnic difference in underlying NTD genetics would be
consistent with several epidemiological studies. For example, folate
supplementation confers less risk reduction among Hispanic
(primarily Mexican) populations [56,57]. Because Mexican-
Americans have 2 – 3 times higher risk for NTDs than those of
non-Hispanic whites and African Americans [45,46], these studies
suggested that conventional levels of folate intake (via diet or
supplements) may not adequately protect this population. We
speculate that Mexican Americans may require higher intakes of
folate to prevent NTDs to compensate for a unique signature of
underlying susceptibilities in the folate pathway.
Furthermore, our observation that a homocysteine-relevant
signal is seen in non-Hispanic whites, but not Hispanics, is
consistent with studies surrounding the common 677 CRT
(A222V) variant of MTHFR . This variant has received
considerable attention (with mixed results) because it results in an
impaired enzyme that can be remediated by folate supplementa-
tion . Furthermore, reductions in MTHFR activity result in
accumulation of homocysteine and a subsequent perturbation of
the methylation cycle [49,50]. The most recent meta-analysis of
this allele , did not find a positive association in Hispanic
groups when stratified by ethnicity, and suggested that non-
Hispanic descent could be a requirement for the association of
NTDs and MTHFR 677CRT.
Strengths/Weaknesses of Study
The strengths of this study are two-fold. First, we have
completed a sizeable and unique effort to catalogue both common
and rare variation in nearly the entire folate pathway as it relates
to the NTD phenotype. Furthermore, we have presented a unique
way in which to analyze allelic interactions by integrating the
variant data in the context of a folate pathway model to infer
metabolic outcomes from multiple alleles in a single individual.
Such an exploratory genetic analysis revealed significant case-
control differences in our population. One limitation in our
analysis, however, is that the pathway relationships that we
designated to guide whether variants may synergize or compensate
involved two sets of assumptions, both of which were broadly
reasonable, though in detail may not accurately reflect pathway
function for every individual. First, we assigned subsets of the
folate pathway as more relevant to certain metabolites based on
observations in the literature. Almost certainly, all of the metabolic
processes discussed will be affected by more genes than we
considered (including those encoding proteins outside of folate-
related enzymes). Thus, the extent to which our model re-created
all relevant interactions accurately is unknown. In addition, to
simplify analysis, all genes (and all variants within the genes) were
equally weighted. Therefore all are assumed to have the same,
negative impact on gene function and contribute equally overall to
the particular process. However, we do not know the functional
impact of most variants and thus it is possible that we are scoring
benign alleles incorrectly. Formally it is also possible that a small
subset of mutations could be activating rather than inactivating as
some enzymes have regulatory domains. This issue may also be
particularly relevant for the homocysteine signature because it
emanated from non-coding alleles whose functions are difficult to
infer. Indeed, it is unlikely that all of the rare alleles included to
derive the homocysteine Metabolic Index Score have functional
consequence. In fact, a knowledge of which alleles are functionally
altered and which are benign may sharpen trends in these
associations. Nevertheless, a successful implementation of this
approach does not demand that all assumptions be correct, but
rather that we have captured enough biological relevance to detect
a signal above the noise. In this regard it will be imperative to
replicate these findings in additional populations; particularly in
non-Hispanic whites were our current sample size is small.
Materials and Methods
This case-control study included data on deliveries that had
estimated due dates from 1984–86 or 1999–2003. The study
included liveborn infants with spina bifida (cases; N=241) or
without any malformation (controls; N=239) identified by the
California Birth Defects Monitoring Program. At the time of
collection, parents/guardians were given the opportunity to have
any residual sample (remaining after newborn genetic screening)
removed from future state-approved health research. Only
individuals whose parents or guardians did not elect to have
their samples removed from such future studies were included.
The race-ethnic breakdown of the study samples is in Table S1.
Case information was abstracted from hospital reports and
medicalrecords following established
California Birth Defects Monitoring Program . Each medical
record was further reviewed by a medical geneticist (E.J.L.).
Infants with trisomies were ineligible. Controls were selected
randomly to represent the population from which the cases were
derived in selected counties and birth periods. This study,
including the collection and use of archived newborn bloodspots,
was approved by the California State Committee for the
Protection of Human Subjects as well as Institutional Review
Boards at Stanford University and the University of California,
Sample Prep/Bloodspot workup
Genomic DNA from each individual was isolated from a single
surgical bloodspot punch (2 mm dia.) using the QIAamp DNA
Micro Kit (Qiagen) according to the manufacturer, with a final
column elution volume of 25 ul. Average yield was 40 ng gDNA/
punch. 5 ul of the gDNA prep was whole-genome amplified
(Repli-G Midi, Qiagen) and subsequently purified on QIAamp
DNA mini columns (Qiagen) according to the manufacturer. The
average yield of purified whole-genome amplified (WGA) DNA
was 15 ug per individual.
The coding regions of 31 folate-related genes (Table S2) were
sequenced in each individual by PCR/sequencing using exon-
specific primers as described previously . Because primer
design captured a substantial amount of non-coding DNA from
adjacent introns with potential regulatory sites, variants in these
regions were also catalogued and incorporated into analyses.
Quality and accuracy of sequence data were evaluated by
performing a second WGA on gDNA isolated from each punch
and confirming, 1) a subset of common SNP genotypes with
TaqMan allelic-discrimination assays, and 2) a subset of singletons
by re-sequencing. See Results section for more details.
Folate Pathway Variants and Spina Bifida Risk
PLoS ONE | www.plosone.org9November 2011 | Volume 6 | Issue 11 | e28408
Imputation of Missing data
Prior to all analyses, we discarded individuals and genotypes
with more than 25% missing data. This excluded 3 individuals and
141 of 1441 total variants called. For the remaining 1300 variants,
missing data (5.9%) were imputed via the program Beagle v3.0.4
Analyses of Single Alleles
Associations between genotypes and spina bifida were assessed
assuming an additive model of inheritance. Associations calculated
from the entire study (those shown in Dataset S1) were adjusted for
race/ethnicity. Odds ratios and p-values were generated using
exact inference for logistic regression as implemented in the R
package elrm . All models were undertaken with R software,
Allele Summing Analyses
As a measure of mutation burden, unweighted minor allele
sums (1 for a heterozygote, 2 for a homozygote) were calculated in
each individual for each gene (or group of genes) using various
genetic subsets (e.g. rare, nonsynonymous mutations). Allele sums
for common variants (Minor Allele Frequency (MAF) $ 2.5%), or
common plus rare, were approximately normally distributed in
case and control populations and differences in the means between
these groups were evaluated using the student’s T-test. For rare
alleles (MAF,2.5%), the distributions were skewed toward fewer
numbers of alleles and differences between case and control
distributions were estimated by the Mann-Whitney U test. Final P-
values were calculated by permuting the case-control labels.
Prior to any data analysis, we constructed a simple pathway
model (see Results for details) in which individual genes were
considered as either beneficial or deleterious to a particular
pathway function (e.g. thymidylate synthesis). Then based on this,
allele sums for a group of metabolically-related genes were
obtained by adding the number of beneficial variants in some
genes while subtracting the number of deleterious variants in other
genes. These sums, from which we inferred metabolic impact on
pathway function, were approximately normally distributed in
cases and controls and, as above, were compared with the
student’s T-test permuting the case-control labels. In addition
(where indicated in the text), all pathway combinations were
corrected for multiple comparisons by the max(T) permutation
procedure . For all allele-summing analyses, variants that were
in linkage disequilibrium with a R2. 0.8 were represented by a
single allele from that LD group.
controls in the study population.
The race-ethnic breakdown of the cases and
identifiers) whose coding regions were sequenced.
The list of folate-related genes (with NCBI
sis, homocysteine levels and purine synthesis. Gene sets
inferred to be relevant for a metabolite are indicated by a ‘‘+’’ or
‘‘–’’ sign, which indicates the inferred effect that gene product
exerts on metabolite levels and, thus, whether variants in those
genes are added to or subtracted from the Metabolic Index Score
(see text for details). Those without a sign are not factored into the
pathway-level Metabolic Index Score for that metabolite.
Pathway designations for thymidylate synthe-
in the study. Variant ID is in the form ‘‘GENE_Locus
Coordinate’’ where position #1 is -1000 nucleotides from the
transcription start site according to the refseq IDs in Table S2.
The first 18 variants were the only ones in the study to display P-
values , 0.05 using a log-additive disease model, adjusted for
race-ethnicity, and for which odds ratios were calculated. Within
this group of 18, variants that are in linkage disequilibrium with R2
. 0.8 are indicated by the same superscript number (1,2,3,or4) in
the Variant ID. Following this group of 18, variants identified in
this study are listed in alpha-numeric order by Variant ID.
The list of 1441 variant positions identified
We thank the California Department of Public Health Maternal Child and
Adolescent Health Division for providing data for these analyses. The
findings and conclusions in this report are those of the authors and do not
necessarily represent the views of the California Department of Public
Conceived and designed the experiments: NJM EJL SLC GMS JR.
Performed the experiments: NJM KL JBS CW AL LAP. Analyzed the
data: NJM TJH JH DAG SLC JSW GMS JR. Contributed reagents/
materials/analysis tools: TJH JH JSW. Wrote the paper: NJM TJH EJL JH
DAG LAP SLC JSW GMS JR.
1. Au KS, Ashley-Koch A, Northrup H (2010) Epidemiologic and genetic aspects
of spina bifida and other neural tube defects. Dev Disabil Res Rev 16: 6–15.
2. Copp AJ, Greene ND, Murdoch JN (2003) The genetic basis of mammalian
neurulation. Nat Rev Genet 4: 784–793.
3. Harris MJ (2009) Insights into prevention of human neural tube defects by folic
acid arising from consideration of mouse mutants. Birth Defects Res A Clin Mol
Teratol 85: 331–339.
4. Carmichael SL, Shaw GM, Selvin S, Schaffer DM (2003) Diet quality and risk of
neural tube defects. Med Hypotheses 60: 351–355.
5. Williams LJ, Mai CT, Edmonds LD, Shaw GM, Kirby RS, et al. (2002)
Prevalence of spina bifida and anencephaly during the transition to mandatory
folic acid fortification in the United States. Teratology 66: 33–39.
6. Hernandez-Diaz S, Werler MM, Walker AM, Mitchell AA (2001) Neural tube
defects in relation to use of folic acid antagonists during pregnancy.
Am J Epidemiol 153: 961–968.
7. Blom HJ, Shaw GM, den Heijer M, Finnell RH (2006) Neural tube defects and
folate: case far from closed. Nat Rev Neurosci 7: 724–731.
8. Beaudin AE, Stover PJ (2007) Folate-mediated one-carbon metabolism and
neural tube defects: balancing genome synthesis and gene expression. Birth
Defects Res C Embryo Today 81: 183–203.
9. MacFarlane AJ, Perry CA, Girnary HH, Gao D, Allen RH, et al. (2009) Mthfd1
is an essential gene in mice and alters biomarkers of impaired one-carbon
metabolism. J Biol Chem 284: 1533–1539.
10. Greene ND, Stanier P, Copp AJ (2009) Genetics of human neural tube defects.
Hum Mol Genet 18: R113–129.
11. Amorim MR, Lima MA, Castilla EE, Orioli IM (2007) Non-Latin European
descent could be a requirement for association of NTDs and MTHFR variant
677C . T: a meta-analysis. Am J Med Genet A 143A: 1726–1732.
12. Ross ME (2010) Gene-environment interactions, folate metabolism and the
embryonic nervous system. Wiley Interdiscip Rev Syst Biol Med 2:
13. Harris MJ, Juriloff DM (2007) Mouse mutants with neural tube closure defects
and their role in understanding human neural tube defects. Birth Defects
Res A Clin Mol Teratol 79: 187–210.
Folate Pathway Variants and Spina Bifida Risk
PLoS ONE | www.plosone.org10 November 2011 | Volume 6 | Issue 11 | e28408
14. Beaudin AE, Abarinov EV, Noden DM, Perry CA, Chu S, et al. (2011) Shmt1
and de novo thymidylate biosynthesis underlie folate-responsive neural tube
defects in mice. Am J Clin Nutr 93: 789–798.
15. Carroll N, Pangilinan F, Molloy AM, Troendle J, Mills JL, et al. (2009) Analysis
of the MTHFD1 promoter and risk of neural tube defects. Hum Genet 125:
16. Neumann PE, Frankel WN, Letts VA, Coffin JM, Copp AJ, et al. (1994)
Multifactorial inheritance of neural tube defects: localization of the major gene
and recognition of modifiers in ct mutant mice. Nat Genet 6: 357–362.
17. Schork NJ, Murray SS, Frazer KA, Topol EJ (2009) Common vs rare allele
hypotheses for complex diseases. Curr Opin Genet Dev 19: 212–219.
18. Cirulli ET, Goldstein DB (2010) Uncovering the roles of rare variants in
common disease through whole-genome sequencing. Nat Rev Genet 11:
19. Marini NJ, Gin J, Ziegle J, Keho KH, Ginzinger D, et al. (2008) The prevalence
20. Franke B, Vermeulen SH, Steegers-Theunissen RP, Coenen MJ, Schijvenaars MM,
et al. (2009) 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 85: 216–226.
21. Johansen CT, Wang J, Lanktree MB, Cao H, McIntyre AD, et al. (2011) Excess
of rare variants in genes identified by genome-wide association study of
hypertriglyceridemia. Nat Genet 4: 684–687.
22. Massey FJ (1951) The Kolmogorov-Smirnov test for goodness-of-fit. J Am Stat
Assoc 46: 68–78.
23. Parle-McDermott A, Kirke PN, Mills JL, Molloy AM, Cox C, et al. (2006)
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 14: 768–772.
24. De Marco P, Merello E, Calevo MG, Mascelli S, Raso A, et al. (2006)
Evaluation of a methylenetetrahydrofolate-dehydrogenase 1958G.A polymor-
phism for neural tube defect risk. J Hum Genet 51: 98–103.
25. Morgenthaler S, Thilly WG (2007) A strategy to discover genes that carry multi-
allelic or mono-allelic risk for common diseases: a cohort allelic sums test
(CAST). Mutat Res 615: 28–56.
26. Madsen BE, Browning SR (2009) A groupwise association test for rare mutations
using a weighted sum statistic. PLoS Genet 5: e100038.
27. Hoffmann TJ, Marini NJ, Witte JS (2010) Comprehensive approach to
analyzing rare genetic variants. PLoS One 5: e13584.
28. Nijhout HF, Reed MC, Budu P, Ulrich CM (2004) A mathematical model of the
folate cycle: new insights into folate homeostasis. J Biol Chem 279:
29. Reed MC, Nijhout HF, Sparks R, Ulrich CM (2004) A mathematical model of
the methionine cycle. J Theor Biol 226: 33–43.
30. Ulrich CM, Neuhouser M, Liu AY, Boynton A, Gregory JF 3rd, et al. (2008)
Mathematical modeling of folate metabolism: predicted effects of genetic
polymorphisms on mechanisms and biomarkers relevant to carcinogenesis.
Cancer Epidemiol Biomarkers Prev 17: 1822–1831.
31. Pike ST, Rajendra R, Artzt K, Appling DR (2010) Mitochondrial C1-
tetrahydrofolate synthase (MTHFD1L) supports the flow of mitochondrial
one-carbon units into the methyl cycle in embryos. J Biol Chem 285:
32. Christensen KE, MacKenzie RE (2006) Mitochondrial one-carbon metabolism
is adapted to the specific needs of yeast, plants and mammals. Bioessays 28:
33. Christensen KE, Patel H, Kuzmanov U, Mejia NR, MacKenzie RE (2005)
Disruption of the mthfd1 gene reveals a monofunctional 10-formyltetrahydro-
folate synthetase in mammalian mitochondria. J Biol Chem 280: 7597–7602.
34. Anguera MC, Field MS, Perry C, Ghandour H, Chiang EP, et al. (2006)
Regulation of folate-mediated one-carbon metabolism by 10-formyltetrahydro-
folate dehydrogenase. J Biol Chem 281: 18335–18342.
35. Herbig K, Chiang EP, Lee LR, Hills J, Shane B, et al. (2002) Cytoplasmic serine
hydroxymethyltransferase mediates competition between folate-dependent
deoxyribonucleotide and S-adenosylmethionine biosyntheses. J Biol Chem
36. Nijhout HF, Reed MC, Ulrich CM (2008) Mathematical models of folate-
mediated one-carbon metabolism. Vitam Horm 79: 45–82.
37. Woeller CF, Anderson DD, Szebenyi DM, Stover PJ (2007) Evidence for small
ubiquitin-like modifier-dependent nuclear import of the thymidylate biosynthesis
pathway. J Biol Chem 282: 17623–17631.
38. Anderson DD, Woeller CF, Stover PJ (2007) Small ubiquitin-like modifier-1
(SUMO-1) modification of thymidylate synthase and dihydrofolate reductase.
Clin Chem Lab Med 45: 1760–1763.
39. MacFarlane AJ, Liu X, Perry CA, Flodby P, Allen RH, et al. (2008) Cytoplasmic
serine hydroxymethyltransferase regulates the metabolic partitioning of
methylenetetrahydrofolate but is not essential in mice. J Biol Chem 283:
40. Dudoit S, Shaffer JP, Boldrick JC (2003) Multiple hypothesis testing in
microarray experiments. Stat Sci 18: 71–103.
41. Food and Drug Administration (1996) Food Standards. Federal Register 61:
42. Steegers-Theunissen RP, Boers GH, Trijbels FJ, Finkelstein JD, Blom HJ, et al.
(1994) Maternal hyperhomocysteinemia: a risk factor for neural-tube defects?
Metabolism 43: 1475–1480.
43. Mills JL, McPartlin JM, Kirke PN, Lee YJ, Conley MR, et al. (1995)
Homocysteine metabolism in pregnancies complicated by neural-tube defects.
Lancet 345: 149–151.
44. Christensen KE, Rohlicek CV, Andelfinger GU, Michaud J, Bigras JL, et al.
(2009) The MTHFD1 pArg653Gln variant alters enzyme function and increases
risk for congenital heart defects. Hum Mutat 30: 212–220.
45. Harris JA, Shaw GM (1995) Neural tube defects--why are rates high among
populations of Mexican descent? Environ Health Perspect 103 Suppl 6:
46. Hendricks KA, Simpson JS, Larsen RD (1999) Neural tube defects along the
Texas-Mexico border, 1993-1995. Am J Epidemiol 149: 1119–1127.
47. Fleming A, Copp AJ (1998) Embryonic folate metabolism and mouse neural
tube defects. Science 280: 2107–2109.
48. Parle-McDermott A, Pangilinan F, O’Brien KK, Mills JL, Magee AM, et al.
(2009) A common variant in MTHFD1L is associated with neural tube defects
and mRNA splicing efficiency. Hum Mutat 30: 1650–1656.
49. Friso S, Choi SW, Girelli D, Mason JB, Dolnikowski GG, et al. (2002) A
common mutation in the 5,10-methylenetetrahydrofolate reductase gene affects
genomic DNA methylation through an interaction with folate status. Proc Natl
Acad Sci U S A 99: 5606–5611.
50. Shelnutt KP, Kauwell GP, Gregory JF 3rd, Maneval DR, Quinlivan EP, et al.
(2004) Methylenetetrahydrofolate reductase 677C--.T polymorphism affects
DNA methylation in response to controlled folate intake in young women. J Nutr
Biochem 15: 554–560.
51. Afman LA, Blom HJ, Van der Put NM, Van Straaten HW (2003) Homocysteine
interference in neurulation: a chick embryo model. Birth Defects Res A Clin Mol
Teratol 67: 421–428.
52. Dunlevy LP, Burren KA, Mills K, Chitty LS, Copp AJ, et al. (2006) Integrity of
the methylation cycle is essential for mammalian neural tube closure. Birth
Defects Res A Clin Mol Teratol 76: 544–552.
53. Zucchero TM, Cooper ME, Maher BS, Daack-Hirsch S, Nepomuceno B, et al.
(2004) Interferon regulatory factor 6 (IRF6) gene variants and the risk of isolated
cleft lip or palate. N Engl J Med 351: 769–780.
54. Blanton SH, Cortez A, Stal S, Mulliken JB, Finnell RH, et al. (2005) Variation in
IRF6 contributes to nonsyndromic cleft lip and palate. Am J Med Genet A
55. Moreno LM, Mansilla MA, Bullard SA, Cooper ME, Busch TD, et al. (2009)
FOXE1 association with both isolated cleft lip with or without cleft palate, and
isolated cleft palate. Hum Mol Genet 18: 4879–4896.
56. Suarez L, Hendricks KA, Cooper SP, Sweeney AM, Hardy RJ, et al. (2000)
Neural tube defects among Mexican Americans living on the US-Mexico
border: effects of folic acid and dietary folate. Am J Epidemiol 152: 1017–1023.
57. Shaw GM, Schaffer D, Velie EM, Morland K, Harris JA (1995) Periconcep-
tional vitamin use, dietary folate, and the occurrence of neural tube defects.
Epidemiology 6: 219–226.
58. Yamada K, Chen Z, Rozen R, Matthews RG (2001) Effects of common
polymorphisms on the properties of recombinant human methylenetetrahydro-
folate reductase. Proc Natl Acad Sci U S A 98: 14853–14858.
59. Croen LA, Shaw GM, Jensvold NG, Harris JA (1991) Birth defects monitoring
in California: a resource for epidemiological research. Paediatr Perinat
Epidemiol 5: 423–427.
60. Ahituv N, Kavaslar N, Schackwitz W, Ustaszewska A, Martin J, et al. (2007)
Medical sequencing at the extremes of human body mass. Am J Hum Genet 80:
61. Browning BL, Browning SR (2009) A unified approach to genotype imputation
and haplotype-phase inference for large data sets of trios and unrelated
individuals. Am J Hum Genet 84: 210–223.
62. Zamar D, McNeney B, Graham J (2007) elrm: Software implementing exact-like
inference for logistic regression models. J Stat Softw 21(3).
Folate Pathway Variants and Spina Bifida Risk
PLoS ONE | www.plosone.org11 November 2011 | Volume 6 | Issue 11 | e28408