Quantitative Trait Loci Modifying Cardiac Atrial Septal Morphology and Risk of Patent Foramen Ovale in the Mouse
http://www.100md.com
Edwin P. Kirk, Changbaig Hyun, Peter C.
参见附件。
the Victor Chang Cardiac Research Institute (E.P.K., C.H., D.L., M.L.C., C.B., R.P.H.), St. Vincent’s Hospital, Darlinghurst
Sydney Children’s Hospital (E.P.K.), Randwick
School of Women’s and Children’s Health (E.P.K.), Faculty of Medicine, University of New South Wales, Kensington
Centre for Advanced Technologies in Animal Genetics and Reproduction (P.C.T., I.C.A.M., C.M.), University of Sydney
Molecular and Cytogenetics Unit (M.F.B.), Prince of Wales Hospital, Randwick
Faculties of Life Science and Medicine (R.P.H.), University of New South Wales, Kensington. Present address for C.H.: Section of Small Animal Internal Medicine, Department of Veterinary Medicine, Kangwon National University, Chuncheon, Korea.
Abstract
Atrial septal defect (ASD) is a common congenital heart disease (CHD) occurring in 5 to 7 per 10 000 live births. Mutations in 5 human genes (NKX2.5, TBX5, GATA4, MYHC, ACTC) are known to cause dominant ASD, but these account for a minority of cases. Human and mouse data suggest that ASD exists in an anatomical continuum with milder septal variants patent foramen ovale (PFO) and atrial septal aneurysm, strongly associated with ischemic stroke and migraine. We have previously shown in inbred mice that the incidence of PFO strongly correlates with length of the interatrial septum primum, defining a quantitative trait underlying PFO risk. To better understand genetic causation of atrial septal abnormalities, we mapped quantitative trait loci (QTL) influencing septal morphology using mouse strains (QSi5 and 129T2/SvEms) maximally informative for PFO incidence and 3 quantitative septal anatomical traits including septum primum length. [QSi5x129T2/SvEms]F2 intercross animals (n=1437) were phenotyped and a whole genome scan performed at an average 17-cM interval. Statistical methodology scoring PFO as a binary phenotype was developed as a confirmatory mapping technique. We mapped 7 significant and 6 suggestive QTL modifying quantitative phenotypes, with 4 supported by binary analysis. Quantitative traits, although strongly associated with PFO (P<0.001), correlated poorly with each other and in all but 1 case QTL for different traits were nonoverlapping. Thus, multiple anatomical processes under separate genetic control contribute to risk of PFO. Our findings demonstrate the feasibility of modeling the genetic basis of common CHD using animal genetic and genomic technologies.
Key Words: atrial septal defect patent foramen ovale NKX2.5 quantitative trait locus
Introduction
Congenital heart disease (CHD) is the most common form of congenital malformation, with approximately 0.7% of live-born children affected.1 Dominantly inherited syndromic and nonsyndromic CHD is well documented and, in some cases, causative mutations have been identified.2 However, although estimates of the genetic contribution to causation of CHD overall are high,1,3 dominantly inherited CHD represent the minority of cases, suggesting a polygenic origin for the majority.
Atrial septal defect (ASD) refers to the presence of a frank hole in the interatrial septum, the wall that separates the right and left atria. ASD is the third most common form of CHD, with an incidence of &7 in 10 000 live births.4–6 During normal heart development in mammals, the interatrial septum acts as a valved communication between the atrial chambers, allowing a right-to-left atrial blood shunt that bypasses circulation to the lungs, which are nonfunctional until after birth. Two distinct septal walls, the septum primum and septum secundum, contribute to its final structure.7 Each maintains a natural but offset opening between the atrial chambers, creating a 1-way flap valve. In the most common ostium secundum form of ASD, the septum primum is fenestrated or of insufficient length to cover the hole in the septum secundum (foramen ovale).
Expansion of the lungs at birth is accompanied by an increase in left atrial pressure, which forces the septum primum against the septum secundum. In humans, the 2 septa seal permanently by adhesion in the first year of life in &75% of individuals. However, the valve remains unsealed in &25% of the adult population, a condition termed patent foramen ovale (PFO).8–10 PFO is generally hemodynamically benign. However, there is a strong association between PFO and ischemic stroke of unknown cause (cryptogenic stroke), with the most likely mechanism being paradoxical embolism, in which a venous thrombus crosses the PFO into the systemic circulation.10 There is also emerging evidence for a relationship between PFO and migraine, particularly migraine with aura.11
PFO vary from small pinhole-sized openings to large corridors. Large PFO are often associated with other structural anomalies such as aneurysm of the septum primum10 and persistence of embryonic features of right atrial morphology9 and can border on frank ASD. Indeed, our studies of the prevalence of ASD and PFO in mice, including analysis of a genetic model of human ASD conferred by mutation of the Nkx2–5 gene, suggest that ASD, atrial septal aneurysm, and PFO are part of the same anatomical and pathological continuum and may have a common genetic basis.12 This suggestion is supported by human family studies in which relatives of individuals with either ASD or PFO were shown to have PFO or ASD, respectively, at a greater-than-population prevalence.13,14 Thus, investigation of the causes of PFO may contribute importantly to our understanding of the genetic bases of ASD.
To date, genetic studies of ASD have concentrated on the rare dominant families. Mutations have been identified in genes encoding cardiac transcription factors acting in development, including T-box factor TBX5,15 homeodomain factor NKX2.5,16–18 as well as zinc finger factor GATA419 and its downstream myofilament gene targets MYH620 and ACTC21.
We have previously demonstrated that there is substantial variation in the incidence of PFO between different strains of inbred laboratory mice, ranging from 0% to 75%.12 Certain quantitative measures of atrial septal anatomy (see Figure 1) are highly correlated with PFO prevalence between strains. Most strikingly, the mean length of the septum primum or flap valve length (FVL) is strongly inversely correlated with PFO incidence (r=–0.97).12 The width of the foramen ovale (FOW) and width of the corridor between left and right atria in cases of PFO (crescent width [CRW]) are positively correlated with PFO incidence.
In this study, we have exploited this variation between strains of mice to map quantitative trait loci (QTL) affecting variations in cardiac anatomy that are related to incidence of PFO. We used an F2 design, selecting as parental strains mice with extremes of septal anatomy, specifically based on frequency of PFO and large differences in the correlated continuously distributed atrial septal morphology traits. The strains used, QSi5 and 129T2/SvEms, have the most extreme difference in FVL of any pair of strains studied. The extent of these differences enhances the chance of detecting QTL affecting these traits and ultimately of discovering loci influencing the causation of PFO and perhaps ASD in humans.
Although PFO can vary in size and complexity, it is essentially a binary trait (either present or absent in an individual) and as such is intrinsically less informative and more difficult to analyze from a quantitative genetic perspective than a continuously distributed trait. Falconer22 developed the liability model for binary traits with multifactorial inheritance. This model assumes an underlying continuously distributed but unobservable scale of liability with a threshold above which the observable binary trait is expressed. The availability of much more informative, continuously distributed traits such as FVL, FOW, and CRW, which might be considered as proxies for liability to PFO or ASD, greatly increases the power of this study to detect underlying QTL. These traits were analyzed using standard QTL mapping procedures for normally distributed traits and the results compared with a more complex binary logistic regression model fitted to PFO presence or absence.
Materials and Methods
Study Design
We performed an F2 study using inbred strains of mice. We selected 2 strains with extreme values for FVL and PFO12: 129T2/SvEms (short FVL, high incidence of PFO) and QSi5 (long FVL, low incidence of PFO). QSi5 mice were derived from an outbred Quackenbush-Swiss line23 and have the additional advantage of very high fecundity (average litter size 13.4). Seventy-five 129T2/SvEms mice, 66 QSi5 mice, and 85 F1 mice were dissected to establish baseline characteristics (Table 1). For the QTL study, a total of 1437 F2 mice (680 female, 757 male) were dissected on 63 sampling days over a 9-month period. Ethical approval for the study was obtained from the University of Sydney Animal Ethics Committee (N02/2-2001/2/3336).
Dissection and Measurements
The heart, lungs, and mediastinum were removed en bloc by dissection and were stored in PBS pending further dissection on the same day. Spleens were dissected and snap-frozen in liquid nitrogen for DNA extraction. Further dissection using a dissecting microscope (Leica MZ8) involved removal of the lungs, thymus, great vessels, and mediastinal fat to expose the atria. The atrial septum was exposed through the left atrium. PFO was identified by the passage of blood from right to left across the intratrial septum after pressurization of the intact right atrium or of Orange G dye after injection of dye solution into the left superior vena cava using a glass pipette. Dimensions of septal features (Figure 1) were measured using an eyepiece graticule. The rationale for including measurements of FVL and FOW (see Figure 1) were as described.12 In this previous study, CRW referred to the width of the communication (blood corridor) between the left and right atria in mice with PFO, taken at the crescent-shaped edge of the septum primum, where the PFO corridor empties into the left atrium. Because this measurement was contingent on the presence of PFO, it is inappropriate for QTL analysis examining risk of PFO. Therefore, CRW is now defined as the maximum diameter of the prominent crescent-shaped ridge formed (with or without PFO) at the edge of the septum primum (Figure 1). However, for economic reasons and because the association between CRW and incidence of PFO was the weakest for all quantitative traits studied, we did not use CRW as a basis for selecting mice with extremes of phenotype for genotyping.
Molecular Genetic Methods
Genomic DNA was extracted from spleens of selected animals using a modified salt precipitation protocol.24,25 Genotyping was performed by the Australian Genome Research Facility in Melbourne, Australia. Candidate microsatellite markers were selected from the Whitehead Institute database26 (http://www.broad.mit.edu/cgi-bin/mouse/sts_infodatabase=mouserelease) and local resources, and their informative status was confirmed by screening DNA of the parental mouse strains. Eighty-nine markers were selected to span the mouse genome, yielding an average intermarker distance of &17 cM (list of markers used available on request). Microsatellite PCR primers were conjugated with the fluorescent dyes FAM, HEX, or TET. PCR products (1 mL) were multiplexed and analyzed on an Applied Biosystems 377 DNA Sequencer, with the size standard TAMRA 500 (Red) applied to each gel. Genescan software Version 3.1.2 (Applied Biosystems) was used to assign tracking for each gel lane. Files were imported into Genotyper Version 2.1 software (Applied Biosystems) for interpretation of traces and assignment of genotypes.
Analytical Methods
Minitab v14.1 (Minitab Inc, 2003) was used for all basic statistics, including ANOVA using the general linear model. A strategy of selective genotyping was used. Because animals in the phenotypic extremes contain most of the genetic information, those samples within the top and bottom 10% of the residual distributions for either FVL or FOW were chosen for genotyping. Thus, 466 mice were selected from 1328 F2 mice with complete records, after adjustment for significant effects (sex and week of dissection). Sex (P=0.019), coat color (P=0.04), and heart weight (P=0.003) all significantly affected FVL. For FOW, age (P<0.001), coat color (P=0.03), heart weight (P<0.001), and week of dissection (P<0.001) were significant. For CRW, sex (P=0.002), age (P<0.001), week of dissection (P<0.001), and weight (P=0.038) were significant, but adjusting for sex removed the effect of weight (P=0.09).
Although statistically significant, these effects were of small size. For example, mean male FVL was 1.01 mm, whereas mean female FVL was 0.98 mm, a difference of 0.03 mm, compared with an SD for FVL of 0.19. We did not adjust for coat color, as this risked concealing the presence of a QTL that may be linked to coat color genes. We also did not adjust for heart weight, as it is possible that QTL relevant to chamber morphology may also have an effect on heart weight. Importantly, although there were substantial differences between heart weight and body weight in the parental strains, in the F2 mice there was no apparent relationship between PFO status and body weight or heart weight. Mice with PFO had a body weight of 26.85±3.32 g (mean±SD) and heart weight of 0.208±0.034 g, and mice without PFO had a body weight of 26.61±3.28 g and heart weight of 0.205±0.033 g. Moreover, in F2 mice, there were only weak correlations between each of body weight and heart weight and FVL, FOW, and CRW (correlation coefficients, all <0.16).
Linkage analyses were performed using the Mapmaker/QTL package.27 For the X chromosome, male and female mice were analyzed separately because of the differences in expected genotype ratios (males expected 1:1 hemizygous for each parental X chromosome; females expected 1:1 homozygous for QSi5 X chromosome and heterozygous for QSi5 and 129T2/SvEms X chromosomes). All analyses were performed in parallel using Map Manager QTXb13 software,28 and the results were concordant.
For binary analysis of PFO, the model took the form equation
or equivalently, equation
where pi is the probability that an animal has PFO, sexi is a 0 or 1 indicator variable for sex (male=1, female=0) and xi(QQ), xi(Qq), and xi(qq) are unobserved 0 or 1 indicator variables indexing the QTL genotype (QQ, Qq, or qq), with xi(QQ)+xi(Qq)+xi(qq)=1. Note that the Q allele refers to the 129T2/SvEms line, whereas the q allele refers to the QSi5 line. The parameters a and d refer to additive and dominance effects of the 129T2/SvEms allele, on the logit scale.
Because the QTL genotypes indicator variables are unobserved, the model is fitted as a 3-component mixture, with mixing probabilities i(QQ)=P{xi(QQ)=1|mi}, i(Qq)=P{xi(Qq)=1|mi}, and i(qq)=P{xi(qq)=1|mi}, with i(QQ)+i(Qq)+i(qq)=1, where these are the conditional probabilities of the QTL genotype, given the flanking marker genotypes, mi. These are calculated in a standard way for an inbred F2 design.
As a protection against spurious results, QTL "peaks" with LOD >2 were checked by selecting the nearest marker and performing a standard (nonmixture) logistic regression, equation
where the xi(MM), xi(Mm), and xi(mm) are the (observed) 0 or 1 indicator variables for the marker genotypes.
Results
Phenotypes
Phenotypic data for the parental strains and F1 and F2 mice are summarized in Table 1. Of note, the mean FVL in 129T2/SvEms mice was 0.60±0.11 mm, compared with a mean of 1.13± 0.11 mm in QSi5 mice, a difference of 4.8 SDs. The interstrain differences for other phenotypes were less pronounced. ANOVA of the F2 data confirmed a very strong statistical effect (P<0.001) of PFO on each of the 3 continuous traits (FVL, FOW, and CRW), which persisted after normalization for other factors including sex, day of dissection, body weight, heart weight, age, and coat color (note that biological causation is not implied by the use of the statistical term "effect" in this context). The correlations between the different traits are shown in Table I in the online data supplement available at http://circres.ahajournals.org. In contrast to the strong association (positive or negative) between PFO and the individual continuous traits, there was only weak correlation between each pair of FVL, FOW, and CRW, suggesting that the genetic basis for variation in each trait is likely to be largely independent. Consistent with this, there was little overlap between the mice selected for genotyping on the basis of having extreme values for FVL or FOW (see Materials and Methods): a total of 466 mice were genotyped on this basis, and only 66 met selection criteria for both traits.
Linkage Results
The test statistics for every chromosome are plotted in Figure 2, and all suggestive and significant results are summarized in Tables 2 through 4. Using the stringent criteria proposed by Lander and Kruglyak29 for this study design, with LOD score cutoffs of 2.8 for suggestive linkage and 4.3 for significant linkage, we have identified 7 significant and 6 suggestive loci. However, it should be noted that the cutoff for suggestive linkage is based on a LOD score, which might be expected to be observed by chance once per full genome scan.29 It is therefore likely that some or all of the 6 loci identified as showing "suggestive" evidence of linkage do in fact represent true QTL. Chromosomes with particularly noteworthy findings are discussed below.
MMU1
There was significant evidence for linkage for FOW. The results for the binary analysis of the PFO data produced a strikingly similar pattern to that seen for FOW. There was no significant evidence for linkage with the other 2 traits on this chromosome.
MMU2
There was significant evidence for linkage for FOW. The mouse ortholog of ACTC, a human gene implicated in autosomal dominant ASD,21 lies within the 1-LOD drop-off interval for the position of this QTL. This is the only 1 of the 5 reported human ASD genes to have an ortholog within 1 of the regions identified by this study (supplemental Table II).
MMU4
There was highly significant evidence for linkage for FOW. There was evidence for a QTL affecting PFO from the binary analysis, and although the shapes of the curves for FOW and PFO are not as similar as for MMU1, some correspondence between the 2 is apparent.
MMU6
There was suggestive evidence, only a little below the threshold for significant evidence, of a QTL for FVL. Again, the curve for PFO paralleled that for FVL over the last 20 cM of the chromosome.
MMU15
There was significant evidence for a QTL for FVL. There was also suggestive evidence of a QTL for FOW, with the curves having similar shapes. This is the only chromosome for which there was evidence of a QTL which may affect 2 of the 3 continuous traits.
MMU19
There was significant evidence for a QTL for FVL. In addition, there was suggestive evidence of a QTL from the PFO analysis, also with a similarly shaped curve for PFO and FVL.
Discussion
We used an F2 design to map 7 QTL with significant and 6 with suggestive evidence of linkage, affecting 3 distinct atrial septal anatomical phenotypes relevant to septal dysmorphogenesis in the mouse. For 4 of these loci (on MMU1, MMU4, MMU6, and MMU19), there is strong supportive evidence for the presence of a QTL from a binary analysis of the data relating to presence or absence of PFO. It should be noted that we have used the stringent criteria for evidence of linkage proposed by Lander and Kruglyak.29 The cutoff for suggestive linkage at a LOD score of 2.8 is based on the level at which 1 locus per full genome scan might be identified by chance. It is therefore likely that most or all of the "suggestive" loci identified here will be confirmed.
Several of the QTL identified in this study are "cryptic," ie, the effect of the QTL is in the opposite direction to that which would have been predicted from the phenotypes of the 2 parental strains. These include 3 of the 4 for FOW (2 with significant and 1 with suggestive evidence for linkage) and both for CRW (1 with significant and 1 with suggestive evidence for linkage). For example, mean FOW in QSi5 mice is 0.21 mm, compared with 0.24 mm in 129T2/SvEms mice. For the FOW QTL with strongest statistical support (a LOD score of 9.05), homozygosity for the 129T2/SvEms allele is associated with a decreasing effect on FOW compared with the QSi5 allele. This phenomenon, known as transgressive segregation, can result in more extreme phenotypes in F2 individuals than in either parental strain. Cryptic QTL are particularly commonly reported in plant studies,30,31 but the phenomenon is by no means restricted to plants. In a review of 171 studies conducted in a variety of plant and animal species, 44% of 1229 traits studied were transgressive.32
It is likely that the liability model for binary traits,22 which we have applied here, is applicable to most forms of CHD. The identification of continuous traits that act as proxies for the phenotype of interest provides considerably more power than would be available using a binary analysis alone. This is illustrated by our results; even where the binary analysis closely conforms to the results for 1 of the 3 continuous traits, the strength of evidence for linkage is always substantially lower for the binary than for the continuous trait. However, we show here that binary analysis can provide important independent information in support of standard QTL analysis.
We were able to confirm the strong relationship between various anatomical features of the atrial septal wall and the presence of PFO. Short FVL, large FOW, and short CRW are all strongly associated with an increased likelihood of PFO. One interpretation of this is that a short flap valve, large foramen ovale width, and short crescent result in (or are reflective of) less adhesive contact between the tissues of the septum primum and septum secundum and thus less chance for a seal to form in postnatal life. However, it is possible that this is an overly simplistic interpretation. It is noteworthy that the atrial septum primum incorporates mediastinal mesenchyme during its formation, potentially part of a progenitor field called the secondary heart field.7 QTL may fundamentally alter the properties of these progenitors, predisposing the forming heart to PFO.
It was striking that there was a very low (albeit statistically significant) correlation between each pair of FVL, FOW, and CRW. This suggests that these 3 traits, although all contributing to the likelihood of PFO, were largely independent and thus should be modified by variation at different genetic loci. The results of the linkage studies support this idea. Of all of the QTL identified, in only 1 instance (on MMU15) was there suggestive evidence of linkage for a second trait (FOW) at a locus where a QTL for another trait (FVL) had been identified. We cannot exclude the possibility that other identified QTL do in fact contribute to more than 1 trait and that we have simply been unable to detect this effect. Nonetheless, given the power of this analysis, we think it likely that these traits are indeed largely under separate genetic control.
Assuming that none of the "suggestive" loci are chance findings and taking the conservative position that QTL are additive, the QTL identified account for 18%, 14%, and 13% of the phenotypic variance of FVL, FOW, and CRW, respectively. However, phenotypic variance includes nongenetic variance attributable to measurement error (which may be significant for this type of study), environmental effects, and biological noise. Another way of considering the magnitude of the genetic effect on phenotype of these QTL is to consider their contribution to observed differences in mean phenotypic values between the parental strains. For example, for FVL, each of the 7 QTL identified accounts for 13% to 18% of the difference between parental means. Assuming additivity, this accounts for the complete difference between those strains. Strikingly, a single QTL for FOW (on MMU1) accounts for 111% of the difference between parental means. This overrepresentation almost certainly reflects the presence of cryptic QTL for this and other traits contributing to the parental means. Cryptic QTL detected for FOW and CRW individually impact on these phenotypes by 73% to 143% relative to the difference between parental means. The seemingly exaggerated effects testify to the fact that QTL mapping can reveal genetic information that individually can contribute to trait variation to an extent far beyond that seen as the difference between parental strains. Thus, although it is difficult to precisely quantify the effects of individual QTL on the genetic component of variation, we conclude that QTL detected in this study are all of relatively strong effect.
Of the QTL regions mapped in this study, only the 1 for FOW on MMU2 contains a mouse ortholog (Actc) of 1 of the 5 known human dominant ASD genes (supplemental Table I). We sequenced exons of the Actc gene from the QSi5 and 129T2/SvEms strains and compared levels of Actc mRNA in the atria and remaining portions of dissected E9.5 embryonic hearts by quantitative RT-PCR. We found no nonsynonymous polymorphisms in coding regions of Actc or significant difference in mRNA levels that would implicate Actc as the gene underlying the MMU2 QTL.
The mouse orthologs of the other 4 known ASD genes, Tbx5, Myh6, Gata4, and Nkx2–5, all fall within regions where we have found little evidence for linkage. For each QTL, we tabulated genes spanning a chromosomal interval including the 1 LOD drop-off interval on either side of the LOD score peak, using the mouse genome informatics program of The Jackson Laboratory (http://www.informatics.jax.org). Of 4964 genes listed within these regions, 185 were annotated as "heart" (supplemental Table II). Among them are many plausible candidate QTL genes, including members of the bmp and fgf growth factor pathways involved in cardiac induction and proliferation, as well as a host of others governing transcriptional regulation, signaling, cell cycle, cell death, and extracellular matrix biology. We have generated and are now phenotyping and mapping a more extensively intercrossed mouse resource (advanced intercross line) for the purpose of refining QTL intervals and thus reducing the number of positional candidate genes.
The human orthologs for the underlying genetic elements responsible for these QTL, once characterized, will be important candidates for the study of human CHD. Although this study’s most obvious relevance is to human atrial septal abnormalities, there are likely to be broader implications. Studies of the role of NKX2.5 and other ASD genes in human CHD show that mutations can contribute to a variety of cardiac phenotypes, not exclusively ASD.7,33 Historically, the discovery of genes underlying QTL has been time consuming, expensive and risky. However, modern genetic techniques are poised to dramatically accelerate the gene discovery process.34 QTL analysis in animal models is therefore likely to gain in prominence as a tool for dissecting the complex genetic basis of common human disease.
Acknowledgments
We gratefully acknowledge the support of the National Heart Foundation of Australia, the RT Hall Trust (Australia), and the National Heart, Lung, and Blood Institute, NIH (R01HL68885-01). E.P.K. was a recipient of a National Heart Foundation of Australia scholarship. We thank Frank Nicholas and Carol Cheung for helpful discussions regarding study design and data analysis, respectively.
Footnotes
Original received October 23, 2005; revision received January 18, 2006; accepted February 2, 2006.
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the Victor Chang Cardiac Research Institute (E.P.K., C.H., D.L., M.L.C., C.B., R.P.H.), St. Vincent’s Hospital, Darlinghurst
Sydney Children’s Hospital (E.P.K.), Randwick
School of Women’s and Children’s Health (E.P.K.), Faculty of Medicine, University of New South Wales, Kensington
Centre for Advanced Technologies in Animal Genetics and Reproduction (P.C.T., I.C.A.M., C.M.), University of Sydney
Molecular and Cytogenetics Unit (M.F.B.), Prince of Wales Hospital, Randwick
Faculties of Life Science and Medicine (R.P.H.), University of New South Wales, Kensington. Present address for C.H.: Section of Small Animal Internal Medicine, Department of Veterinary Medicine, Kangwon National University, Chuncheon, Korea.
Abstract
Atrial septal defect (ASD) is a common congenital heart disease (CHD) occurring in 5 to 7 per 10 000 live births. Mutations in 5 human genes (NKX2.5, TBX5, GATA4, MYHC, ACTC) are known to cause dominant ASD, but these account for a minority of cases. Human and mouse data suggest that ASD exists in an anatomical continuum with milder septal variants patent foramen ovale (PFO) and atrial septal aneurysm, strongly associated with ischemic stroke and migraine. We have previously shown in inbred mice that the incidence of PFO strongly correlates with length of the interatrial septum primum, defining a quantitative trait underlying PFO risk. To better understand genetic causation of atrial septal abnormalities, we mapped quantitative trait loci (QTL) influencing septal morphology using mouse strains (QSi5 and 129T2/SvEms) maximally informative for PFO incidence and 3 quantitative septal anatomical traits including septum primum length. [QSi5x129T2/SvEms]F2 intercross animals (n=1437) were phenotyped and a whole genome scan performed at an average 17-cM interval. Statistical methodology scoring PFO as a binary phenotype was developed as a confirmatory mapping technique. We mapped 7 significant and 6 suggestive QTL modifying quantitative phenotypes, with 4 supported by binary analysis. Quantitative traits, although strongly associated with PFO (P<0.001), correlated poorly with each other and in all but 1 case QTL for different traits were nonoverlapping. Thus, multiple anatomical processes under separate genetic control contribute to risk of PFO. Our findings demonstrate the feasibility of modeling the genetic basis of common CHD using animal genetic and genomic technologies.
Key Words: atrial septal defect patent foramen ovale NKX2.5 quantitative trait locus
Introduction
Congenital heart disease (CHD) is the most common form of congenital malformation, with approximately 0.7% of live-born children affected.1 Dominantly inherited syndromic and nonsyndromic CHD is well documented and, in some cases, causative mutations have been identified.2 However, although estimates of the genetic contribution to causation of CHD overall are high,1,3 dominantly inherited CHD represent the minority of cases, suggesting a polygenic origin for the majority.
Atrial septal defect (ASD) refers to the presence of a frank hole in the interatrial septum, the wall that separates the right and left atria. ASD is the third most common form of CHD, with an incidence of &7 in 10 000 live births.4–6 During normal heart development in mammals, the interatrial septum acts as a valved communication between the atrial chambers, allowing a right-to-left atrial blood shunt that bypasses circulation to the lungs, which are nonfunctional until after birth. Two distinct septal walls, the septum primum and septum secundum, contribute to its final structure.7 Each maintains a natural but offset opening between the atrial chambers, creating a 1-way flap valve. In the most common ostium secundum form of ASD, the septum primum is fenestrated or of insufficient length to cover the hole in the septum secundum (foramen ovale).
Expansion of the lungs at birth is accompanied by an increase in left atrial pressure, which forces the septum primum against the septum secundum. In humans, the 2 septa seal permanently by adhesion in the first year of life in &75% of individuals. However, the valve remains unsealed in &25% of the adult population, a condition termed patent foramen ovale (PFO).8–10 PFO is generally hemodynamically benign. However, there is a strong association between PFO and ischemic stroke of unknown cause (cryptogenic stroke), with the most likely mechanism being paradoxical embolism, in which a venous thrombus crosses the PFO into the systemic circulation.10 There is also emerging evidence for a relationship between PFO and migraine, particularly migraine with aura.11
PFO vary from small pinhole-sized openings to large corridors. Large PFO are often associated with other structural anomalies such as aneurysm of the septum primum10 and persistence of embryonic features of right atrial morphology9 and can border on frank ASD. Indeed, our studies of the prevalence of ASD and PFO in mice, including analysis of a genetic model of human ASD conferred by mutation of the Nkx2–5 gene, suggest that ASD, atrial septal aneurysm, and PFO are part of the same anatomical and pathological continuum and may have a common genetic basis.12 This suggestion is supported by human family studies in which relatives of individuals with either ASD or PFO were shown to have PFO or ASD, respectively, at a greater-than-population prevalence.13,14 Thus, investigation of the causes of PFO may contribute importantly to our understanding of the genetic bases of ASD.
To date, genetic studies of ASD have concentrated on the rare dominant families. Mutations have been identified in genes encoding cardiac transcription factors acting in development, including T-box factor TBX5,15 homeodomain factor NKX2.5,16–18 as well as zinc finger factor GATA419 and its downstream myofilament gene targets MYH620 and ACTC21.
We have previously demonstrated that there is substantial variation in the incidence of PFO between different strains of inbred laboratory mice, ranging from 0% to 75%.12 Certain quantitative measures of atrial septal anatomy (see Figure 1) are highly correlated with PFO prevalence between strains. Most strikingly, the mean length of the septum primum or flap valve length (FVL) is strongly inversely correlated with PFO incidence (r=–0.97).12 The width of the foramen ovale (FOW) and width of the corridor between left and right atria in cases of PFO (crescent width [CRW]) are positively correlated with PFO incidence.
In this study, we have exploited this variation between strains of mice to map quantitative trait loci (QTL) affecting variations in cardiac anatomy that are related to incidence of PFO. We used an F2 design, selecting as parental strains mice with extremes of septal anatomy, specifically based on frequency of PFO and large differences in the correlated continuously distributed atrial septal morphology traits. The strains used, QSi5 and 129T2/SvEms, have the most extreme difference in FVL of any pair of strains studied. The extent of these differences enhances the chance of detecting QTL affecting these traits and ultimately of discovering loci influencing the causation of PFO and perhaps ASD in humans.
Although PFO can vary in size and complexity, it is essentially a binary trait (either present or absent in an individual) and as such is intrinsically less informative and more difficult to analyze from a quantitative genetic perspective than a continuously distributed trait. Falconer22 developed the liability model for binary traits with multifactorial inheritance. This model assumes an underlying continuously distributed but unobservable scale of liability with a threshold above which the observable binary trait is expressed. The availability of much more informative, continuously distributed traits such as FVL, FOW, and CRW, which might be considered as proxies for liability to PFO or ASD, greatly increases the power of this study to detect underlying QTL. These traits were analyzed using standard QTL mapping procedures for normally distributed traits and the results compared with a more complex binary logistic regression model fitted to PFO presence or absence.
Materials and Methods
Study Design
We performed an F2 study using inbred strains of mice. We selected 2 strains with extreme values for FVL and PFO12: 129T2/SvEms (short FVL, high incidence of PFO) and QSi5 (long FVL, low incidence of PFO). QSi5 mice were derived from an outbred Quackenbush-Swiss line23 and have the additional advantage of very high fecundity (average litter size 13.4). Seventy-five 129T2/SvEms mice, 66 QSi5 mice, and 85 F1 mice were dissected to establish baseline characteristics (Table 1). For the QTL study, a total of 1437 F2 mice (680 female, 757 male) were dissected on 63 sampling days over a 9-month period. Ethical approval for the study was obtained from the University of Sydney Animal Ethics Committee (N02/2-2001/2/3336).
Dissection and Measurements
The heart, lungs, and mediastinum were removed en bloc by dissection and were stored in PBS pending further dissection on the same day. Spleens were dissected and snap-frozen in liquid nitrogen for DNA extraction. Further dissection using a dissecting microscope (Leica MZ8) involved removal of the lungs, thymus, great vessels, and mediastinal fat to expose the atria. The atrial septum was exposed through the left atrium. PFO was identified by the passage of blood from right to left across the intratrial septum after pressurization of the intact right atrium or of Orange G dye after injection of dye solution into the left superior vena cava using a glass pipette. Dimensions of septal features (Figure 1) were measured using an eyepiece graticule. The rationale for including measurements of FVL and FOW (see Figure 1) were as described.12 In this previous study, CRW referred to the width of the communication (blood corridor) between the left and right atria in mice with PFO, taken at the crescent-shaped edge of the septum primum, where the PFO corridor empties into the left atrium. Because this measurement was contingent on the presence of PFO, it is inappropriate for QTL analysis examining risk of PFO. Therefore, CRW is now defined as the maximum diameter of the prominent crescent-shaped ridge formed (with or without PFO) at the edge of the septum primum (Figure 1). However, for economic reasons and because the association between CRW and incidence of PFO was the weakest for all quantitative traits studied, we did not use CRW as a basis for selecting mice with extremes of phenotype for genotyping.
Molecular Genetic Methods
Genomic DNA was extracted from spleens of selected animals using a modified salt precipitation protocol.24,25 Genotyping was performed by the Australian Genome Research Facility in Melbourne, Australia. Candidate microsatellite markers were selected from the Whitehead Institute database26 (http://www.broad.mit.edu/cgi-bin/mouse/sts_infodatabase=mouserelease) and local resources, and their informative status was confirmed by screening DNA of the parental mouse strains. Eighty-nine markers were selected to span the mouse genome, yielding an average intermarker distance of &17 cM (list of markers used available on request). Microsatellite PCR primers were conjugated with the fluorescent dyes FAM, HEX, or TET. PCR products (1 mL) were multiplexed and analyzed on an Applied Biosystems 377 DNA Sequencer, with the size standard TAMRA 500 (Red) applied to each gel. Genescan software Version 3.1.2 (Applied Biosystems) was used to assign tracking for each gel lane. Files were imported into Genotyper Version 2.1 software (Applied Biosystems) for interpretation of traces and assignment of genotypes.
Analytical Methods
Minitab v14.1 (Minitab Inc, 2003) was used for all basic statistics, including ANOVA using the general linear model. A strategy of selective genotyping was used. Because animals in the phenotypic extremes contain most of the genetic information, those samples within the top and bottom 10% of the residual distributions for either FVL or FOW were chosen for genotyping. Thus, 466 mice were selected from 1328 F2 mice with complete records, after adjustment for significant effects (sex and week of dissection). Sex (P=0.019), coat color (P=0.04), and heart weight (P=0.003) all significantly affected FVL. For FOW, age (P<0.001), coat color (P=0.03), heart weight (P<0.001), and week of dissection (P<0.001) were significant. For CRW, sex (P=0.002), age (P<0.001), week of dissection (P<0.001), and weight (P=0.038) were significant, but adjusting for sex removed the effect of weight (P=0.09).
Although statistically significant, these effects were of small size. For example, mean male FVL was 1.01 mm, whereas mean female FVL was 0.98 mm, a difference of 0.03 mm, compared with an SD for FVL of 0.19. We did not adjust for coat color, as this risked concealing the presence of a QTL that may be linked to coat color genes. We also did not adjust for heart weight, as it is possible that QTL relevant to chamber morphology may also have an effect on heart weight. Importantly, although there were substantial differences between heart weight and body weight in the parental strains, in the F2 mice there was no apparent relationship between PFO status and body weight or heart weight. Mice with PFO had a body weight of 26.85±3.32 g (mean±SD) and heart weight of 0.208±0.034 g, and mice without PFO had a body weight of 26.61±3.28 g and heart weight of 0.205±0.033 g. Moreover, in F2 mice, there were only weak correlations between each of body weight and heart weight and FVL, FOW, and CRW (correlation coefficients, all <0.16).
Linkage analyses were performed using the Mapmaker/QTL package.27 For the X chromosome, male and female mice were analyzed separately because of the differences in expected genotype ratios (males expected 1:1 hemizygous for each parental X chromosome; females expected 1:1 homozygous for QSi5 X chromosome and heterozygous for QSi5 and 129T2/SvEms X chromosomes). All analyses were performed in parallel using Map Manager QTXb13 software,28 and the results were concordant.
For binary analysis of PFO, the model took the form equation
or equivalently, equation
where pi is the probability that an animal has PFO, sexi is a 0 or 1 indicator variable for sex (male=1, female=0) and xi(QQ), xi(Qq), and xi(qq) are unobserved 0 or 1 indicator variables indexing the QTL genotype (QQ, Qq, or qq), with xi(QQ)+xi(Qq)+xi(qq)=1. Note that the Q allele refers to the 129T2/SvEms line, whereas the q allele refers to the QSi5 line. The parameters a and d refer to additive and dominance effects of the 129T2/SvEms allele, on the logit scale.
Because the QTL genotypes indicator variables are unobserved, the model is fitted as a 3-component mixture, with mixing probabilities i(QQ)=P{xi(QQ)=1|mi}, i(Qq)=P{xi(Qq)=1|mi}, and i(qq)=P{xi(qq)=1|mi}, with i(QQ)+i(Qq)+i(qq)=1, where these are the conditional probabilities of the QTL genotype, given the flanking marker genotypes, mi. These are calculated in a standard way for an inbred F2 design.
As a protection against spurious results, QTL "peaks" with LOD >2 were checked by selecting the nearest marker and performing a standard (nonmixture) logistic regression, equation
where the xi(MM), xi(Mm), and xi(mm) are the (observed) 0 or 1 indicator variables for the marker genotypes.
Results
Phenotypes
Phenotypic data for the parental strains and F1 and F2 mice are summarized in Table 1. Of note, the mean FVL in 129T2/SvEms mice was 0.60±0.11 mm, compared with a mean of 1.13± 0.11 mm in QSi5 mice, a difference of 4.8 SDs. The interstrain differences for other phenotypes were less pronounced. ANOVA of the F2 data confirmed a very strong statistical effect (P<0.001) of PFO on each of the 3 continuous traits (FVL, FOW, and CRW), which persisted after normalization for other factors including sex, day of dissection, body weight, heart weight, age, and coat color (note that biological causation is not implied by the use of the statistical term "effect" in this context). The correlations between the different traits are shown in Table I in the online data supplement available at http://circres.ahajournals.org. In contrast to the strong association (positive or negative) between PFO and the individual continuous traits, there was only weak correlation between each pair of FVL, FOW, and CRW, suggesting that the genetic basis for variation in each trait is likely to be largely independent. Consistent with this, there was little overlap between the mice selected for genotyping on the basis of having extreme values for FVL or FOW (see Materials and Methods): a total of 466 mice were genotyped on this basis, and only 66 met selection criteria for both traits.
Linkage Results
The test statistics for every chromosome are plotted in Figure 2, and all suggestive and significant results are summarized in Tables 2 through 4. Using the stringent criteria proposed by Lander and Kruglyak29 for this study design, with LOD score cutoffs of 2.8 for suggestive linkage and 4.3 for significant linkage, we have identified 7 significant and 6 suggestive loci. However, it should be noted that the cutoff for suggestive linkage is based on a LOD score, which might be expected to be observed by chance once per full genome scan.29 It is therefore likely that some or all of the 6 loci identified as showing "suggestive" evidence of linkage do in fact represent true QTL. Chromosomes with particularly noteworthy findings are discussed below.
MMU1
There was significant evidence for linkage for FOW. The results for the binary analysis of the PFO data produced a strikingly similar pattern to that seen for FOW. There was no significant evidence for linkage with the other 2 traits on this chromosome.
MMU2
There was significant evidence for linkage for FOW. The mouse ortholog of ACTC, a human gene implicated in autosomal dominant ASD,21 lies within the 1-LOD drop-off interval for the position of this QTL. This is the only 1 of the 5 reported human ASD genes to have an ortholog within 1 of the regions identified by this study (supplemental Table II).
MMU4
There was highly significant evidence for linkage for FOW. There was evidence for a QTL affecting PFO from the binary analysis, and although the shapes of the curves for FOW and PFO are not as similar as for MMU1, some correspondence between the 2 is apparent.
MMU6
There was suggestive evidence, only a little below the threshold for significant evidence, of a QTL for FVL. Again, the curve for PFO paralleled that for FVL over the last 20 cM of the chromosome.
MMU15
There was significant evidence for a QTL for FVL. There was also suggestive evidence of a QTL for FOW, with the curves having similar shapes. This is the only chromosome for which there was evidence of a QTL which may affect 2 of the 3 continuous traits.
MMU19
There was significant evidence for a QTL for FVL. In addition, there was suggestive evidence of a QTL from the PFO analysis, also with a similarly shaped curve for PFO and FVL.
Discussion
We used an F2 design to map 7 QTL with significant and 6 with suggestive evidence of linkage, affecting 3 distinct atrial septal anatomical phenotypes relevant to septal dysmorphogenesis in the mouse. For 4 of these loci (on MMU1, MMU4, MMU6, and MMU19), there is strong supportive evidence for the presence of a QTL from a binary analysis of the data relating to presence or absence of PFO. It should be noted that we have used the stringent criteria for evidence of linkage proposed by Lander and Kruglyak.29 The cutoff for suggestive linkage at a LOD score of 2.8 is based on the level at which 1 locus per full genome scan might be identified by chance. It is therefore likely that most or all of the "suggestive" loci identified here will be confirmed.
Several of the QTL identified in this study are "cryptic," ie, the effect of the QTL is in the opposite direction to that which would have been predicted from the phenotypes of the 2 parental strains. These include 3 of the 4 for FOW (2 with significant and 1 with suggestive evidence for linkage) and both for CRW (1 with significant and 1 with suggestive evidence for linkage). For example, mean FOW in QSi5 mice is 0.21 mm, compared with 0.24 mm in 129T2/SvEms mice. For the FOW QTL with strongest statistical support (a LOD score of 9.05), homozygosity for the 129T2/SvEms allele is associated with a decreasing effect on FOW compared with the QSi5 allele. This phenomenon, known as transgressive segregation, can result in more extreme phenotypes in F2 individuals than in either parental strain. Cryptic QTL are particularly commonly reported in plant studies,30,31 but the phenomenon is by no means restricted to plants. In a review of 171 studies conducted in a variety of plant and animal species, 44% of 1229 traits studied were transgressive.32
It is likely that the liability model for binary traits,22 which we have applied here, is applicable to most forms of CHD. The identification of continuous traits that act as proxies for the phenotype of interest provides considerably more power than would be available using a binary analysis alone. This is illustrated by our results; even where the binary analysis closely conforms to the results for 1 of the 3 continuous traits, the strength of evidence for linkage is always substantially lower for the binary than for the continuous trait. However, we show here that binary analysis can provide important independent information in support of standard QTL analysis.
We were able to confirm the strong relationship between various anatomical features of the atrial septal wall and the presence of PFO. Short FVL, large FOW, and short CRW are all strongly associated with an increased likelihood of PFO. One interpretation of this is that a short flap valve, large foramen ovale width, and short crescent result in (or are reflective of) less adhesive contact between the tissues of the septum primum and septum secundum and thus less chance for a seal to form in postnatal life. However, it is possible that this is an overly simplistic interpretation. It is noteworthy that the atrial septum primum incorporates mediastinal mesenchyme during its formation, potentially part of a progenitor field called the secondary heart field.7 QTL may fundamentally alter the properties of these progenitors, predisposing the forming heart to PFO.
It was striking that there was a very low (albeit statistically significant) correlation between each pair of FVL, FOW, and CRW. This suggests that these 3 traits, although all contributing to the likelihood of PFO, were largely independent and thus should be modified by variation at different genetic loci. The results of the linkage studies support this idea. Of all of the QTL identified, in only 1 instance (on MMU15) was there suggestive evidence of linkage for a second trait (FOW) at a locus where a QTL for another trait (FVL) had been identified. We cannot exclude the possibility that other identified QTL do in fact contribute to more than 1 trait and that we have simply been unable to detect this effect. Nonetheless, given the power of this analysis, we think it likely that these traits are indeed largely under separate genetic control.
Assuming that none of the "suggestive" loci are chance findings and taking the conservative position that QTL are additive, the QTL identified account for 18%, 14%, and 13% of the phenotypic variance of FVL, FOW, and CRW, respectively. However, phenotypic variance includes nongenetic variance attributable to measurement error (which may be significant for this type of study), environmental effects, and biological noise. Another way of considering the magnitude of the genetic effect on phenotype of these QTL is to consider their contribution to observed differences in mean phenotypic values between the parental strains. For example, for FVL, each of the 7 QTL identified accounts for 13% to 18% of the difference between parental means. Assuming additivity, this accounts for the complete difference between those strains. Strikingly, a single QTL for FOW (on MMU1) accounts for 111% of the difference between parental means. This overrepresentation almost certainly reflects the presence of cryptic QTL for this and other traits contributing to the parental means. Cryptic QTL detected for FOW and CRW individually impact on these phenotypes by 73% to 143% relative to the difference between parental means. The seemingly exaggerated effects testify to the fact that QTL mapping can reveal genetic information that individually can contribute to trait variation to an extent far beyond that seen as the difference between parental strains. Thus, although it is difficult to precisely quantify the effects of individual QTL on the genetic component of variation, we conclude that QTL detected in this study are all of relatively strong effect.
Of the QTL regions mapped in this study, only the 1 for FOW on MMU2 contains a mouse ortholog (Actc) of 1 of the 5 known human dominant ASD genes (supplemental Table I). We sequenced exons of the Actc gene from the QSi5 and 129T2/SvEms strains and compared levels of Actc mRNA in the atria and remaining portions of dissected E9.5 embryonic hearts by quantitative RT-PCR. We found no nonsynonymous polymorphisms in coding regions of Actc or significant difference in mRNA levels that would implicate Actc as the gene underlying the MMU2 QTL.
The mouse orthologs of the other 4 known ASD genes, Tbx5, Myh6, Gata4, and Nkx2–5, all fall within regions where we have found little evidence for linkage. For each QTL, we tabulated genes spanning a chromosomal interval including the 1 LOD drop-off interval on either side of the LOD score peak, using the mouse genome informatics program of The Jackson Laboratory (http://www.informatics.jax.org). Of 4964 genes listed within these regions, 185 were annotated as "heart" (supplemental Table II). Among them are many plausible candidate QTL genes, including members of the bmp and fgf growth factor pathways involved in cardiac induction and proliferation, as well as a host of others governing transcriptional regulation, signaling, cell cycle, cell death, and extracellular matrix biology. We have generated and are now phenotyping and mapping a more extensively intercrossed mouse resource (advanced intercross line) for the purpose of refining QTL intervals and thus reducing the number of positional candidate genes.
The human orthologs for the underlying genetic elements responsible for these QTL, once characterized, will be important candidates for the study of human CHD. Although this study’s most obvious relevance is to human atrial septal abnormalities, there are likely to be broader implications. Studies of the role of NKX2.5 and other ASD genes in human CHD show that mutations can contribute to a variety of cardiac phenotypes, not exclusively ASD.7,33 Historically, the discovery of genes underlying QTL has been time consuming, expensive and risky. However, modern genetic techniques are poised to dramatically accelerate the gene discovery process.34 QTL analysis in animal models is therefore likely to gain in prominence as a tool for dissecting the complex genetic basis of common human disease.
Acknowledgments
We gratefully acknowledge the support of the National Heart Foundation of Australia, the RT Hall Trust (Australia), and the National Heart, Lung, and Blood Institute, NIH (R01HL68885-01). E.P.K. was a recipient of a National Heart Foundation of Australia scholarship. We thank Frank Nicholas and Carol Cheung for helpful discussions regarding study design and data analysis, respectively.
Footnotes
Original received October 23, 2005; revision received January 18, 2006; accepted February 2, 2006.
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