Conotruncal and related heart defects (CTDs) are a group of serious and relatively common birth defects. Although both maternal and inherited genotypes are thought to play a role in the etiology of CTDs, few specific genetic risk factors have been identified. To determine whether common variants acting through the genotype of the mother (e.g. via an in utero effect) or the case are associated with CTDs, we conducted a genome-wide association study of 750 CTD case-parent triads, with follow-up analyses in 358 independent triads. Log-linear analyses were used to assess the association of CTDs with the genotypes of both the mother and case. No association achieved genomewide significance in either the discovery or combined (discovery+follow-up) samples. However, three loci with p-values suggestive of association (p<10−5) in the discovery sample had p-values <0.05 in the follow-up sample and p-values in the combined data that were lower than in the discovery sample. These included suggestive association with an inherited intergenic variant at 20p12.3 (rs6140038, combined p = 1.0×10−5) and an inherited intronic variant in KCNJ4 at 22q13.1 (rs2267386, combined p = 9.8×10−6), as well as with a maternal variant in SLC22A24 at 11q12.3 (rs11231379, combined p = 4.2×10−6). These observations suggest novel candidate loci for CTDs, including loci that appear to be associated with the risk of CTDs via the maternal genotype, but further studies are needed to confirm these associations.
Citation: Agopian AJ, Mitchell LE, Glessner J, Bhalla AD, Sewda A, Hakonarson H, et al. (2014) Genome-Wide Association Study of Maternal and Inherited Loci for Conotruncal Heart Defects. PLoS ONE9(5): e96057. https://doi.org/10.1371/journal.pone.0096057
Editor: Jorn Bullerdiek, Center for Human Genetics, Germany
Received: November 22, 2013; Accepted: April 2, 2014; Published: May 6, 2014
Copyright: © 2014 Agopian et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by grants from the National Institutes of Health (NIH) and National Heart, Lung, and Blood Institute [HL74731], Eunice Kennedy Shriver National Institute of Child Health and Human Development [5P01HD070454], and the National Center for Research Resources [UL1RR024134]. GWAS genotyping was funded by an Institutional Development Fund to The Center for Applied Genomics from The Children's Hospital of Philadelphia. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. 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 declared that no competing interests exist.
In the United States, birth defects are the leading cause of infant mortality , . The most common birth defects are congenital heart defects, which occur in approximately 1% of live births and account for 40% of birth defect related deaths , . Because heart defects include a wide range of conditions that may be etiologically heterogeneous, epidemiological studies generally focus on subgroups of these conditions for which there is evidence of a shared etiology . Conotruncal and related malformations (CTDs) form one of the most common subgroups, accounting for approximately one-third of all congenital heart defects , .
Several lines of evidence suggest that the various CTD phenotypes (e.g. tetralogy of Fallot, conoventricular septal defects, d-transposition of the great arteries, double outlet right ventricle) share common genetic underpinnings , , , , . For example, several different CTD phenotypes are observed among individuals with specific genetic syndromes (e.g., 22q11 deletion syndrome) , , . In addition, family studies indicate that CTDs are highly heritable , , and that affected relatives of individuals with a CTD are more likely to have a CTD than other types of heart defects , , . However, the genetic contribution to CTD risk is believed to be complex, perhaps involving both the maternal and inherited (i.e. case) genotypes , , , , , , and few specific genetic risk factors have been identified.
To identify genes that influence susceptibility to CTDs through the maternal and inherited (i.e. case) genotype, we conducted a family-based genome-wide association study (GWAS) and analyzed suggestive associations in an independent, family-based follow-up sample.
Materials and Methods
Study subjects provided consent under a protocol approved by the Children's Hospital of Philadelphia (CHOP) Institutional Review Board for the Protection of Human Subjects. Specifically, adult subjects provided written consent and parents or guardians provided written consent for minors.
Study Subjects and Analysis
Case-parent triads were collected for a discovery sample between 1992–2010 at the Cardiac Center at the CHOP. Eligible diagnoses included: tetralogy of Fallot, D-transposition of the great arteries, ventricular septal defects (conoventricular, posterior malalignment and conoseptal hypoplasia), double outlet right ventricle, aortic arch anomalies, truncus arteriosus, and interrupted aortic arch. Diagnostic criteria have been previously described . In particular, a conoventricular septal defect was defined as a defect in the interventricular septum that was located between a normally situated (i.e., not-malaligned) conal/infundibular septum and the muscular/trabecular septum, typically beneath part of the septal leaflet of the tricuspid valve. The diagnosis of a CTD in the case was confirmed by review of medical records. We performed fluorescence in situ hybridization (FISH) and/or multiplex ligation-dependent probe amplification using standard techniques to screen for 22q11 deletion syndrome when clinically suspected. Triads in which the case had a known chromosomal, genetic, or teratogenic syndrome, or in which the mother had type 1 or 2 diabetes, used insulin, or used an anticonvulsant during pregnancy were excluded since these conditions/exposures are known CTD risk factors.
Blood or saliva samples were collected from all CTD cases and their parents, and DNA extraction was performed using standard techniques (Puregene DNA isolation kit by Gentra Systems, Inc., Minneapolis, MN for blood samples, and Oragene DNA isolation kit by DNA Genotek Inc., Ontario, Canada for saliva samples). Genome-wide genotyping was performed at two time points using the Illumina HumanHap550 (v1, v3) and 610 BeadChip platforms, respectively, due to updates in the laboratory. Single nucleotide polymorphisms (SNPs) that were not represented on all BeadChips were excluded. Data for SNPs that met any of the following criteria were also excluded: (1) non-autosomal, (2) minor allele frequency <1%, (3) genotype distribution in parents deviated from Hardy-Weinberg equilibrium (p<1×10−5), (4) Mendelian error rate >1%, (5) call rate <95%. Data were also excluded for triads with a Mendelian error rate >1%, and for individuals with a genotype call rate <95%. Quality control analyses and exclusions were performed using PLINK v1.06 .
In the subset of triads in which both parents were non-Hispanic white by self-report, additional autosomal SNPs were imputed using MACH  version 1.0.16 and the phased HapMap II (release 22) CEU reference haplotypes (N = 60 founders). Imputed SNPs with imputation r2 (i.e., estimated squared correlation between the imputed and actual genotypes) <0.3 were excluded, as were all imputed SNPs with a MAF <1% or a Mendelian error rate >1%. To assess the accuracy of self-reported white race, we determined race using ancestry informative markers as described by Shaikh et al .
The associations between the maternal and inherited genotype for each variant and risk for CTDs were assessed using log-linear analyses , , , as implemented under the MI-GWAS platform . Briefly, log-linear analysis has been widely used in genetic association studies of birth defects (e.g. , , , , ), and involves comparing the observed distribution of genotypes in the triads to the expected genotypes under the assumptions of both Mendelian inheritance and symmetry of maternal and parental genotypes , , . Log-linear analysis has the advantage over the transmission disequilibrium test (TDT) of allowing for the evaluation of maternal as well as inherited genetic effects , , .
For each SNP, statistical significance was evaluated using a one-degree of freedom likelihood ratio test to compare a full model (including terms for both maternal and inherited genotypes) to a reduced model (excluding the parameter being tested). Using the default MI-GWAS parameters , an additive model was used for the genotype being tested (e.g., maternal genotype) and an unrestricted model was used for the other genotype (e.g., inherited genotype). We analyzed genotyped SNPs in the full GWAS dataset and both genotyped and imputed SNPs in the subset of non-Hispanic whites. Manhattan plots and q-q plots were constructed and lambda values were calculated using R version 2.15 (http://www.r-project.org/) for the full discovery cohort as well as the subset of non-Hispanic whites.
SNPs with p<10−5 were considered to have suggestive evidence of association with CTDs . However, due to limitations on the number of variants that could be genotyped in the follow-up sample, we imposed additional criteria to select a subset of these SNPs for inclusion in the follow-up analysis. Specifically, each SNP for which the maternal or inherited genotype was associated with CTDs at p<10−6 was included in the follow-up study. In addition, select SNPs (described below) with association p-values 10−6<p<10−5 in either the full analytic group or the subgroup of non-Hispanic white triads were also included. The selected SNPs included those: with p<10−5 in both analytic groups; in regions with multiple associations at p<10−5; and in biologically plausible candidate genes (e.g, involved in pathways potentially related to heart development).
Additional, independent, predominantly white case-parent triad samples were collected for the follow-up sample, using the same criteria and methods as in the discovery sample. Genotyping of the follow-up sample was performed using a custom Illumina GoldenGate panel. A subset of samples from the discovery cohort was also genotyped using this platform, for comparisons with genotypes that were imputed in the discovery sample.
Quality control filters for SNPs genotyped in the follow-up sample were identical to those used for genotyped SNPs in the discovery sample. Data from the follow-up sample were analyzed using log linear analyses as described for the discovery sample. For SNPs with p<0.50 in the follow-up sample (and consistent directions of magnitudes of association between the discovery and follow-up samples), analyses were repeated in the combined (discovery + follow-up) sample.
For SNPs with combined p<10−5, we analyzed the predicted functional impact. We used Golden Helix SNP & Variation Suite v7.6 (Golden Helix, Inc., Bozeman, MT, www.goldenhelix.com) to annotate protein function scores (e.g., PolyPhen2) and the UCSC genome browser (hg19: genome.ucsc.edu)  to identify genes, transcription factor binding sites, and regions of open chromatin.
We recruited 852 case-parent triads for the discovery sample. After making exclusions based on the quality control criteria, there were 750 case-parent triads (1,868 individuals) in the discovery sample. The majority of the triads were Non-Hispanic white (n = 537 triads, 72%) (Table 1) and there was 99% concordance between self-reported white race and white classification by ancestry informative markers. The most frequent diagnoses among the cases were tetralogy of Fallot (39.2%), D-transposition of the great arteries (20.7%), and conoventricular septal defects (20.3%) (Table 1).
Log-linear analyses of the 530,347 genotyped SNPs that passed quality control criteria, in the full discovery cohort, identified nine maternal and eight inherited SNPs with suggestive (i.e., p<10−5) evidence of association  with CTDs, but none reached genome-wide significance (p<5×10−8) (Table S1). Analyses of the 530,347 genotyped and 1,890,943 imputed SNPs that passed quality control criteria (i.e., 2,421,290 total SNPs analyzed), in the non-Hispanic white triads, identified an additional 23 maternal and 80 inherited SNPs with suggestive evidence of association, but none reached genome-wide significance (Table S1, Figure S1). The q-q plots (Figure S2) suggested little deviation from expectation for maternal SNPs (lambda = 1.02 in the full analytic group and 1.00 in the non-Hispanic white subgroup) and minimal deviation from expectation for inherited SNPs (lambda = 1.08 for the full analytic group and 1.06 for the non-Hispanic white subgroup). Because tests involving the inherited genotype are not subject to bias due to population stratification in analyses of triad data , we did not attempt to reduce the genomic inflation factor.
Of the 32 maternal and 88 inherited genotypes with suggestive evidence of association with CTDs, 61 (see Materials and Methods for details of SNP selection) were assessed in the follow-up sample. Six of these 61 SNPs did not pass the genotyping quality control filters in the follow-up sample. Genotype data for the remaining 55 SNPs were available for 358, predominantly non-Hispanic white (97.2%, Table 1) triads in the follow-up sample. Log linear analyses of these data identified one maternal (rs11231379) and two inherited SNPs (rs6140038 and rs2267386) with p<0.05 in the follow-up sample (and consistent directions of magnitudes of association between the discovery and follow-up samples). In the combined analyses (discovery + follow-up samples), there was suggestive evidence of association (p<10−5) with each of these three variants and the combined p-values were less than the corresponding discovery p-values (Table 2). Several other maternal SNPs in the same region as rs11231379 were also nominally associated with CTDs in the discovery and follow-up samples (Table 2).
In the first reported GWAS of CTDs that included the evaluation of both inherited and maternal genetic effects, we identified several potentially interesting candidate regions for CTDs. Although no association achieved genome-wide significance (p<10−8), we report on several promising candidate regions, including loci associated with CTDs via the maternal genotype, that warrant further investigation.
There were seven maternal variants located in SLC22A24 at 11q12.3 with suggestive evidence for association with CTDs (i.e. p<10−5) in the combined data (rs11231379, rs11231379, rs7948969, rs1939748, rs1939747, rs4393318, and rs4366490) (Table 2, Table S1). This gene encodes a transmembrane protein involved in organic ion transport across cellular membranes . These SNPs are in strong linkage disequilibrium (r2>0.8), and include a missense mutation (rs1939748, Thr->Ser) that is fairly well-conserved [GERP++  score: 2.3 and PhyloP  score: 1.3] and predicted to be “probably damaging” by PolyPhen2 . An additional 30 maternal SNPs in this region, most of which are in tight linkage disequilibrium with these seven SLC22A24 variants (r2>0.8), were also nominally associated with CTDs in the discovery sample (Figure 1a).
A) SNPs in SLC22A24 B) SNPs near FHIT C) rs2267386 D) rs6140038. Each pane shows the log-linear model association statistic (−log10 p) on the left y axis for the discovery sample variant with the highest regional value that was confirmed in our follow-up sample (purple diamond) and nearby markers (circles). Linkage disequilibrium (r2) between this variant and nearby markers is indicated by red shading and recombination rates across each region in 1000 Genomes CEU data are indicated by blue lines on the right y axis. The position on the chromosome (hg18) and the position of nearby genes is shown on the x-axis.
We also identified two relatively rare (MAF<5%) SNPs with suggestive evidence of association with CTDs via the inherited genotype. One of these SNPs (rs2267386) at 22q13.1 falls within an intron in KCNJ4, which encodes the inward rectifier potassium channel 4 protein (IRK4), a protein that is expressed in the fetal human heart and plays an important role in cardiac repolarization , , .
The other SNP with a suggestive inherited genetic effect, rs6140038, is intergenic and is located between BMP2 (166 kb downstream) and FERMT1 (477 kb upstream) at 20p12.3. BMP2 is involved in differentiation of the secondary heart field progenitors into myocardium . In animal models, BMP2 is expressed by the primary outflow myocardium during the stages that the secondary myocardium is incorporated and induces expression of the contractile proteins in cells being incorporated into the outflow myocardium , . The variant rs6140038 is flanked by two regions of open chromatin with corresponding CTCF sites (at 18 kb upstream, validated in GM12878 cells and K562 cells, and 164 kb downstream, validated in GM12878 cells), suggesting that it falls within a region of regulatory activity. FERMT1 is involved in integrin signaling .
In the follow-up sample, there were 13 additional SNPs with p-values <0.50 for either the maternal or inherited genotype, consistent directions of association between the discovery and follow-up samples, and combined p-values that were suggestive of association (Table S1). These included maternal genotypes for two intergenic SNPs at 3p14.2 (rs6763159, rs1447807, Figure 1b, Table S1) that are in strong linkage disequilibrium (r2 = 1.0) and located approximately 86 kb downstream from FHIT, which encodes a tumor suppressor protein involved in cell cycle regulation and is expressed in fetal human cardiac tissue , , . There are several validated regions of open chromatin upstream of these SNPs (approximately 613 kb, 451 kb, 350 kb, and 88 kb upstream of rs6763159), many of which coincide with validated transcription factor binding sites (e.g., PolII site at 613 kb; CTCF sites at 610 kb, 451 kb, 350 kb, and 88 kb; an NFKB site at 352 kb; and FOXA1, FOXA2, GATA3, and CEBPB sites at 9 kb). These findings suggest that the upstream regions of open chromatin may have regulatory activity.
Cordell et al. recently published a case-control GWAS of tetralogy of Fallot, the most common CTD among our cases . Associations between inherited genotypes and tetralogy of Fallot were reported for a region on chromosome 12q24 (six SNPs) and 13q32 (two SNPs) ; however, the inherited genotypes for these eight SNPs were not associated with CTDs in our data (range of p-values for these eight SNPs among our non-Hispanic white triads analyses: 0.54–0.94). Cordell et al. did not evaluate any of the SNPs that were associated with CTDs via the inherited genotype in our study (i.e., those listed in Table 2) or SNPs in tight linkage disequilibrium with these SNPs. Further, they did not evaluate association with the maternal genotype. However, they did evaluate the inherited genotype for the two SNPs near FHIT for which we found suggestive evidence of an association via the maternal genotype; they reported p-values for these SNPs that were even lower than those in our follow-up sample (rs6763159 p = 0.0006, odds ratio = 0.83; rs1447807 p = 0.0008, odds ratio = 0.83). Since the inherited genotype is confounded with the maternal genotype in case-control studies , this provides some limited additional support for an association, via the maternal genotype, between this region and the risk of CTDs. There was no overlap between the regions with suggestive evidence of association with CTDs in our study and top loci from other recent GWAS or genome-wide linkage analyses of heart defects , , .
Although our analyses were limited by a relatively small sample, given the rarity of CTDs, our sample represents one of the largest. However, it was not possible to analyze specific types of defects, and we cannot rule out the possibility that our analyses of all CTDs could have missed loci associated with specific defects. This study is one of the first GWAS of any disease to identify suggestive associations with maternal genetic regions, and our results emphasize that accounting for maternal genetic effects in GWAS may broaden our understanding of the genetics of complex traits, particularly traits with a young age of onset.
Manhattan plot of log-linear model likelihood ratio test p-values (on logarithmic scale) in the discovery cohort for A) inherited genotyped SNPs in the full analytic group B) maternal genotyped SNPs in the full analytic group C) inherited genotyped and imputed SNPs in the non-Hispanic white subgroup D) maternal genotyped and imputed SNPs in the Non-Hispanic white subgroup.
Quantile-quantile plot showing observed (black dots) and expected p-values (orange line) for the log-linear model likelihood ratio tests in the discovery cohort for A) inherited genotyped SNPs in the full analytic group (lambda = 1.08) B) maternal genotyped SNPs in the full analytic group (lambda = 1.02) C) inherited genotyped and imputed SNPs in the non-Hispanic white subgroup (lambda = 1.06) D) maternal genotyped and imputed SNPs in the non-Hispanic white subgroup (lambda = 1.00).
We thank Sharon Edman, Jennifer Garbarini, Stacy Woyciechowski, and Brande Latney for technical assistance, as well as the families who consented to participate in this study.
Conceived and designed the experiments: LEM HH EG. Performed the experiments: JG HH. Analyzed the data: AJA ADB AS. Wrote the paper: AJA LEM JG ADB AS HH EG.
- 1. Yoon PW, Olney RS, Khoury MJ, Sappenfield WM, Chavez GF, et al. (1997) Contribution of birth defects and genetic diseases to pediatric hospitalizations. A population-based study. Arch Pediatr Adolesc Med 151: 1096–1103.
- 2. Martin JA, Kochanek KD, Strobino DM, Guyer B, MacDorman MF (2005) Annual summary of vital statistics—2003. Pediatrics 115: 619–634.
- 3. Hoffman JI, Kaplan S (2002) The incidence of congenital heart disease. J Am Coll Cardiol 39: 1890–1900.
- 4. Griebsch I, Knowles RL, Brown J, Bull C, Wren C, et al. (2007) Comparing the clinical and economic effects of clinical examination, pulse oximetry, and echocardiography in newborn screening for congenital heart defects: a probabilistic cost-effectiveness model and value of information analysis. Int J Technol Assess Health Care 23: 192–204.
- 5. Botto LD, Lin AE, Riehle-Colarusso T, Malik S, Correa A (2007) Seeking causes: Classifying and evaluating congenital heart defects in etiologic studies. Birth Defects Res A Clin Mol Teratol 79: 714–727.
- 6. Ferencz C, Correa-Villasenor A, Loffredo CA, Wilson PD (1997) Ventricular septal defects. Genetic and Environmental Risk Factors of Major Cardiovascular Malformations: The Baltimore-Washington Infant Study: 1981-1989. Armonk: Futura Publishing Company, Inc. pp. 124–165.
- 7. Perry LW, Neill CA, Ferencz C, Rubin JD, Loffredo CA (1993) Infants with congenital heart disease: the cases. In: Ferencz C, Rubin JD, Loffredo CA, Magee CA, editors. Epidemiology of Congenital Heart Disease: The Baltimore-Washington Infant Study 1981–1989. Mount Kisco: Futura Publishing Company, Inc. pp. 33–62.
- 8. Shaw GM, Iovannisci DM, Yang W, Finnell RH, Carmichael SL, et al. (2005) Risks of human conotruncal heart defects associated with 32 single nucleotide polymorphisms of selected cardiovascular disease-related genes. Am J Med Genet A 138: 21–26.
- 9. Shaw GM, Lu W, Zhu H, Yang W, Briggs FB, et al. (2009) 118 SNPs of folate-related genes and risks of spina bifida and conotruncal heart defects. BMC Med Genet 10: 49.
- 10. Mitchell LE, Long J, Garbarini J, Paluru P, Goldmuntz E (2010) Variants of folate metabolism genes and risk of left-sided cardiac defects. Birth Defects Res A Clin Mol Teratol 88: 48–53.
- 11. Goldmuntz E, Woyciechowski S, Renstrom D, Lupo PJ, Mitchell LE (2008) Variants of folate metabolism genes and the risk of conotruncal cardiac defects. Circ Cardiovasc Genet 1: 126–132.
- 12. Lupo PJ, Mitchell LE, Goldmuntz E (2011) NAT1, NOS3, and TYMS genotypes and the risk of conotruncal cardiac defects. Birth Defects Res A Clin Mol Teratol 91: 61–65.
- 13. Goldmuntz E, Clark BJ, Mitchell LE, Jawad AF, Cuneo BF, et al. (1998) Frequency of 22q11 deletions in patients with conotruncal defects. J Am Coll Cardiol 32: 492–498.
- 14. McElhinney DB, Clark BJ 3rd, Weinberg PM, Kenton ML, McDonald-McGinn D, et al. (2001) Association of chromosome 22q11 deletion with isolated anomalies of aortic arch laterality and branching. J Am Coll Cardiol 37: 2114–2119.
- 15. McElhinney DB, Driscoll DA, Levin ER, Jawad AF, Emanuel BS, et al. (2003) Chromosome 22q11 deletion in patients with ventricular septal defect: frequency and associated cardiovascular anomalies. Pediatrics 112: e472.
- 16. Nora JJ, Nora AH (1983) Genetic epidemiology of congenital heart diseases. Prog Med Genet 5: 91–137.
- 17. Kwiatkowska J, Wierzba J, Aleszewicz-Baranowska J, Erecinski J (2007) Genetic background of congenital conotruncal heart defects—a study of 45 families. Kardiol Pol 65: 32–37 discussion 38–39.
- 18. Oyen N, Poulsen G, Boyd HA, Wohlfahrt J, Jensen PK, et al. (2009) Recurrence of congenital heart defects in families. Circulation 120: 295–301.
- 19. Wulfsberg EA, Zintz EJ, Moore JW (1991) The inheritance of conotruncal malformations: a review and report of two siblings with tetralogy of Fallot with pulmonary atresia. Clin Genet 40: 12–16.
- 20. Long J, Lupo PJ, Goldmuntz E, Mitchell LE (2011) Evaluation of heterogeneity in the association between congenital heart defects and variants of folate metabolism genes: conotruncal and left-sided cardiac defects. Birth Defects Res A Clin Mol Teratol 91: 879–884.
- 21. Lupo PJ, Goldmuntz E, Mitchell LE (2010) Gene-gene interactions in the folate metabolic pathway and the risk of conotruncal heart defects. J Biomed Biotechnol 2010: 630940.
- 22. Zhu H, Yang W, Lu W, Etheredge AJ, Lammer EJ, et al. (2012) Gene variants in the folate-mediated one-carbon metabolism (FOCM) pathway as risk factors for conotruncal heart defects. Am J Med Genet A 158A: 1124–1134.
- 23. Hobbs CA, Cleves MA, Karim MA, Zhao W, MacLeod SL (2010) Maternal folate-related gene environment interactions and congenital heart defects. Obstet Gynecol 116: 316–322.
- 24. Peyvandi S, Lupo PJ, Garbarini J, Woyciechowski S, Edman S, et al. (2013) 22q11.2 deletions in patients with conotruncal defects: data from 1,610 consecutive cases. Pediatric Cardiology 34: 1687–1694.
- 25. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81: 559–575.
- 26. Li Y, Abecasis G (2006) MACH 1.0: rapid haplotype reconstruction and missing genotype inference. Am J Hum Genet S79: 2290.
- 27. Shaikh TH, Gai X, Perin JC, Glessner JT, Xie H, et al. (2009) High-resolution mapping and analysis of copy number variations in the human genome: a data resource for clinical and research applications. Genome Res 19: 1682–1690.
- 28. Weinberg CR, Wilcox AJ, Lie RT (1998) A log-linear approach to case-parent-triad data: assessing effects of disease genes that act either directly or through maternal effects and that may be subject to parental imprinting. Am J Hum Genet 62: 969–978.
- 29. Wilcox AJ, Weinberg CR, Lie RT (1998) Distinguishing the effects of maternal and offspring genes through studies of “case-parent triads”. Am J Epidemiol 148: 893–901.
- 30. Weinberg CR (1999) Allowing for missing parents in genetic studies of case-parent triads. Am J Hum Genet 64: 1186–1193.
- 31. Agopian AJ, Mitchell LE (2011) MI-GWAS: a SAS platform for the analysis of inherited and maternal genetic effects in genome-wide association studies using log-linear models. BMC Bioinformatics 12: 117.
- 32. van der Zanden LF, Galesloot TE, Feitz WF, Brouwers MM, Shi M, et al. (2012) Exploration of gene-environment interactions, maternal effects and parent of origin effects in the etiology of hypospadias. Journal of Urology 188: 2354–2360.
- 33. Doolin MT, Barbaux S, McDonnell M, Hoess K, Whitehead AS, et al. (2002) Maternal genetic effects, exerted by genes involved in homocysteine remethylation, influence the risk of spina bifida. American Journal of Human Genetics 71: 1222–1226.
- 34. Jensen LE, Wall AM, Cook M, Hoess K, Thorn CF, et al. (2004) A common ABCC2 promoter polymorphism is not a determinant of the risk of spina bifida. Birth Defects Research Part A, Clinical and Molecular Teratology 70: 396–399.
- 35. Martinelli M, Masiero E, Carinci F, Morselli PG, Pezzetti F, et al. (2011) New evidence for the role of cystathionine beta-synthase in non-syndromic cleft lip with or without cleft palate. European Journal of Oral Sciences 119: 193–197.
- 36. Shi M, Murray JC, Marazita ML, Munger RG, Ruczinski I, et al. (2012) Genome wide study of maternal and parent-of-origin effects on the etiology of orofacial clefts. American Journal of Medical Genetics Part A 158A: 784–794.
- 37. Duggal P, Gillanders EM, Holmes TN, Bailey-Wilson JE (2008) Establishing an adjusted p-value threshold to control the family-wide type 1 error in genome wide association studies. BMC Genomics 9: 516.
- 38. Meyer LR, Zweig AS, Hinrichs AS, Karolchik D, Kuhn RM, et al. (2013) The UCSC Genome Browser database: extensions and updates 2013. Nucleic Acids Res 41: D64–69.
- 39. Jacobsson JA, Haitina T, Lindblom J, Fredriksson R (2007) Identification of six putative human transporters with structural similarity to the drug transporter SLC22 family. Genomics 90: 595–609.
- 40. Davydov EV, Goode DL, Sirota M, Cooper GM, Sidow A, et al. (2010) Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLoS Comput Biol 6: e1001025.
- 41. Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A (2010) Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res 20: 110–121.
- 42. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, et al. (2010) A method and server for predicting damaging missense mutations. Nat Methods 7: 248–249.
- 43. Perier F, Radeke CM, Vandenberg CA (1994) Primary structure and characterization of a small-conductance inwardly rectifying potassium channel from human hippocampus. Proc Natl Acad Sci U S A 91: 6240–6244.
- 44. Yan X, Zhou H, Zhang J, Shi C, Xie X, et al. (2009) Molecular mechanism of inward rectifier potassium channel 2.3 regulation by tax-interacting protein-1. J Mol Biol 392: 967–976.
- 45. Conti A, Fabbrini F, D'Agostino P, Negri R, Greco D, et al. (2007) Altered expression of mitochondrial and extracellular matrix genes in the heart of human fetuses with chromosome 21 trisomy. BMC Genomics 8: 268.
- 46. Dyer LA, Kirby ML (2009) The role of secondary heart field in cardiac development. Dev Biol 336: 137–144.
- 47. Waldo KL, Kumiski DH, Wallis KT, Stadt HA, Hutson MR, et al. (2001) Conotruncal myocardium arises from a secondary heart field. Development 128: 3179–3188.
- 48. Yamada M, Szendro PI, Prokscha A, Schwartz RJ, Eichele G (1999) Evidence for a role of Smad6 in chick cardiac development. Dev Biol 215: 48–61.
- 49. Karakose E, Schiller HB, Fassler R (2010) The kindlins at a glance. J Cell Sci 123: 2353–2356.
- 50. Su AI, Wiltshire T, Batalov S, Lapp H, Ching KA, et al. (2004) A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci U S A 101: 6062–6067.
- 51. Pecherzewska R, Nawrot B (2009) [FHIT—tumor suppressor protein involved in induction of apoptosis and cell cycle regulation]. Postepy Biochem 55: 66–75.
- 52. Cordell HJ, Topf A, Mamasoula C, Postma AV, Bentham J, et al.. (2013) Genome-wide association study identifies loci on 12q24 and 13q32 associated with Tetralogy of Fallot. Hum Mol Genet: doi: 10.1093/hmg/dds1552.
- 53. Hu Z, Shi Y, Mo X, Xu J, Zhao B, et al. (2013) A genome-wide association study identifies two risk loci for congenital heart malformations in Han Chinese populations. Nature Genetics 45: 818–821.
- 54. Cordell HJ, Bentham J, Topf A, Zelenika D, Heath S, et al. (2013) Genome-wide association study of multiple congenital heart disease phenotypes identifies a susceptibility locus for atrial septal defect at chromosome 4p16. Nature Genetics 45: 822–824.
- 55. Flaquer A, Baumbach C, Pinero E, Garcia Algas F, de la Fuente Sanchez MA, et al. (2013) Genome-wide linkage analysis of congenital heart defects using MOD score analysis identifies two novel loci. BMC Genet 14: 44.